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Wang J, Deng Q, Qi L. Integrated bioinformatics, machine learning, and molecular docking reveal crosstalk genes and potential drugs between periodontitis and systemic lupus erythematosus. Sci Rep 2025; 15:15787. [PMID: 40328806 PMCID: PMC12055969 DOI: 10.1038/s41598-025-00620-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2025] [Accepted: 04/29/2025] [Indexed: 05/08/2025] Open
Abstract
Evidence indicates a connection between periodontitis (PD) and systemic lupus erythematosus (SLE), though the underlying co-morbid mechanisms remain unclear. This study sought to identify the genetic factors and potential therapeutic agents involved in the interaction between PD and SLE. We employed multi-omics methodologies, encompassing differential expression analysis, weighted gene co-expression network analysis (WGCNA), functional enrichment (GO/KEGG), LASSO regression, diagnostic model construction, protein-protein interaction (PPI) networks, immune infiltration profiling, computational drug prediction, molecular docking, and disease subtyping, to analyze PD and SLE expression datasets from the Gene Expression Omnibus (GEO) database (GSE10334, GSE16134, GSE50772, and GSE81622). Cross-analysis identified 32 crosstalk genes (CGs) common to both PD and SLE. LASSO analysis pinpointed three key diagnostic genes (TAGLN, MMP9, TNFAIP6) for both conditions. The resulting diagnostic models demonstrated robust efficacy in both training and validation datasets. Four topological algorithms in Cytoscape highlighted four central crosstalk genes (TAGLN, MMP9, TNFAIP6, IL1B). Additionally, hesperidin, doxycycline, and cytochalasin D emerged as potential therapeutic agents. Two subtypes (C1 and C2) of PD and SLE were delineated based on CG expression profiles. The development of diagnostic models, potential drug identification, and disease subtype classification are poised to enhance diagnosis and treatment. These findings aim to deepen the understanding of PD and SLE complexities.
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Affiliation(s)
- Junjie Wang
- The Second Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang Uygur Autonomous Region, China
| | - Qingao Deng
- The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang Uygur Autonomous Region, China
| | - Lu Qi
- The Second Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang Uygur Autonomous Region, China.
- Department of Stomatology, The Second Affiliated Hospital of Xinjiang Medical University, Xinjiang Uygur Autonomous Region, No. 38, North Second Lane, Nanhu East Road, Urumqi, 830000, China.
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Zhang D, Zhao T, Gao L, Zhu H, Jin H, Liu G, Wang D. Development and validation of a nomogram for predicting low Kt/V urea in peritoneal dialysis patients. BMC Nephrol 2025; 26:223. [PMID: 40316989 PMCID: PMC12046862 DOI: 10.1186/s12882-025-04124-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2024] [Accepted: 04/14/2025] [Indexed: 05/04/2025] Open
Abstract
BACKGROUND This study aimed to develop a nomogram to predict peritoneal dialysis (PD) adequacy in incident PD patients and identify those at high risk for low Kt/Vurea PD function. METHODS We retrospectively analyzed 141 incident PD patients from January 2021 to January 2024. Baseline characteristics, including BMI, hemoglobin levels, and high transport PD membrane, were compared between patients with and without adequate PD function. Univariate logistic regression, LASSO analysis, and Random Forest (RF) algorithms were employed to identify potential biomarkers. Significant predictors were integrated into a multivariable logistic regression model to construct a predictive nomogram. RESULTS The study found that 32.1% of patients had low total Kt/Vurea. Significant predictors of low Kt/Vurea included smoking (OR 2.23, CI 1.47-5.85), BMI (OR 1.35, CI 1.17-1.59), hemoglobin levels (OR 0.98, CI 0.95-0.99), and High transport (OR 0.2., CI 0.04-0.72). These factors were incorporated into a nomogram, which demonstrated strong predictive accuracy, with a C-Index of 0.802 in the main study group. The model's AUC was 0.778 (95% CI: 0.686-0.870), and Decision Curve Analysis (DCA) confirmed its clinical utility across a wide range of threshold probabilities. CONCLUSIONS We developed a nomogram that accurately predicts PD total Kt/Vurea in incident PD patients. This model can be a valuable tool for identifying patients at risk of low PD total Kt/Vurea, facilitating timely interventions to improve patient outcomes.
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Affiliation(s)
- Danfeng Zhang
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Kidney Disease, Inflammation & Immunity-mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, The Key Laboratory of Anti-inflammatory of Immune Medicines, Ministry of Education, Anhui Institute of Innovative Drugs, School of Pharmacy, Anhui Medical University, Hefei, China
| | - Tian Zhao
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, The Key Laboratory of Anti-inflammatory of Immune Medicines, Ministry of Education, Anhui Institute of Innovative Drugs, School of Pharmacy, Anhui Medical University, Hefei, China
| | - Liting Gao
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Kidney Disease, Inflammation & Immunity-mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Huan Zhu
- Second School of Clinical Medicine, Anhui Medical University, Hefei, China
| | - Haowei Jin
- Second School of Clinical Medicine, Anhui Medical University, Hefei, China
| | - Guiling Liu
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
- Institute of Kidney Disease, Inflammation & Immunity-mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
| | - Deguang Wang
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
- Institute of Kidney Disease, Inflammation & Immunity-mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
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Wang Y, Li Q. Integrated multiomics analysis identifies potential biomarkers and therapeutic targets for autophagy associated AKI to CKD transition. Sci Rep 2025; 15:13687. [PMID: 40258914 PMCID: PMC12012120 DOI: 10.1038/s41598-025-97269-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2024] [Accepted: 04/03/2025] [Indexed: 04/23/2025] Open
Abstract
This study explored the relationship between acute kidney injury (AKI) and chronic kidney disease (CKD), focusing on autophagy-related genes and their immune infiltration during the transition from AKI to CKD. We performed weighted correlation network analysis (WGCNA) using two microarray datasets (GSE139061 and GSE66494) in the GEO database and identified autophagy signatures by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), and GSEA enrichment analysis. Machine learning algorithms such as LASSO, random forest, and XGBoost were used to construct the diagnostic model, and the diagnostic performance of GSE30718 (AKI) and GSE37171 (CKD) was used as validation cohorts to evaluate its diagnostic performance. The study identified 14 autophagy candidate genes, among which ATP6V1C1 and COPA were identified as key biomarkers that were able to effectively distinguish between AKI and CKD. Immune cell infiltration and GSEA analysis revealed immune dysregulation in AKI, and these genes were associated with inflammation and immune pathways. Single-cell analysis showed that ATP6V1C1 and COPA were specifically expressed in AKI and CKD, which may be related to renal fibrosis. In addition, drug prediction and molecular docking analysis proposed SZ(+)-(S)-202-791 and PDE4 inhibitor 16 as potential therapeutic agents. In summary, this study provides new insights into the relationship between AKI and CKD and lays a foundation for the development of new treatment strategies.
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Affiliation(s)
- Yaojun Wang
- Clinical Medical College, Affiliated Hospital, Hebei University, Baoding, 071000, Hebei, China
| | - Qiang Li
- Department of Dermatology, Air Force Medical Center, PLA, Beijing, 100142, China.
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Xia Z, Rong X, Chen Q, Fang M, Xiao J. A nomogram to predict lung cancer in pulmonary lesions for tuberculosis infection patients. Monaldi Arch Chest Dis 2025; 95. [PMID: 38497197 DOI: 10.4081/monaldi.2024.2847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 03/01/2024] [Indexed: 03/19/2024] Open
Abstract
Similar clinical features make the differential diagnosis difficult, particularly between lung cancer and pulmonary tuberculosis (TB), without pathological evidence for patients with concomitant TB infection. Our study aimed to build a nomogram to predict malignant pulmonary lesions applicable to clinical practice. We retrospectively analyzed clinical characteristics, imaging features, and laboratory indicators of TB infection of patients diagnosed with lung cancer or active pulmonary TB at Xiangya Hospital of Central South University. A total of 158 cases from January 1, 2018, to May 30, 2019, were included in the training cohort. Predictive factors for lung cancer were screened by a multiple-stepwise logistic regression analysis. A nomogram model was established, and the discrimination, stability, and prediction performance of the model were analyzed. A total of 79 cases from June 1, 2019, to December 30, 2019, were used as the validation cohort to verify the predictive value of the model. Eight predictor variables, including age, pleural effusion, mediastinal lymph node, the number of positive tumor markers, the T cell spot test for TB, pulmonary lesion morphology, location, and distribution, were selected to construct the model. The corrected C-statistics and the Brier scores were 0.854 and 0.130 in the training cohort and 0.823 and 0.163 in the validation cohort. Calibration plots showed good performance, and decision curve analysis indicated a high net benefit. In conclusion, the nomogram model provides an effective method to calculate the probability of lung cancer in TB infection patients, and it has excellent discrimination, stability, and prediction performance in detecting a malignant diagnosis of undiagnosed pulmonary lesions.
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Affiliation(s)
- Zhi Xia
- Department of Oncology, Hunan Provincial People's Hospital, Changsha; Key Laboratory of Small Molecule Targeted Drug Research and Creation in Hunan Province, Changsha; Hunan Provincial Clinical Medical Research Center for Hepatobiliary Pancreatic Tumors, Changsha
| | - Xueyao Rong
- Department of Geriatric Respiratory and Critical Care Medicine, Xiangya Hospital, Central South University, Changsha; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha
| | - Qiong Chen
- Department of Geriatric Respiratory and Critical Care Medicine, Xiangya Hospital, Central South University, Changsha; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha
| | - Min Fang
- Hunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, the "Double-First Class" Application Characteristic Discipline of Hunan Province (Pharmaceutical Science), Changsha Medical University; School of Pharmacy, Changsha Medical University
| | - Jian Xiao
- Department of Geriatric Respiratory and Critical Care Medicine, Xiangya Hospital, Central South University, Changsha; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha
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Jiang Y, Meng H, Zhang X, Yang J, Sun C, Wang X. Identification of subtypes and biomarkers associated with disulfidptosis-related ferroptosis in ulcerative colitis. Hereditas 2025; 162:27. [PMID: 39987439 PMCID: PMC11846262 DOI: 10.1186/s41065-025-00390-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2024] [Accepted: 02/14/2025] [Indexed: 02/24/2025] Open
Abstract
BACKGROUND Disulfidptosis and ferroptosis are different programmed cell death modes, which are closely related to the development of a variety of diseases, but the relationship between them and ulcerative colitis (UC) is still unclear. Therefore, our study aimed to explore the molecular subtypes and biomarkers associated with disulfidptosis-related ferroptosis (DRF) in UC. METHODS We used Pearson analysis to identify DRF genes. Then, we classified 140 UC samples into different subtypes based on the DRF genes and explored the biological and clinical characteristics between them. Next, the hub genes were identified by differential analysis and WGCNA algorithms, and three machine learning algorithms were used to screen biomarkers for UC from hub genes. In addition, we analyzed the relationship between biomarkers of immune cells and transcription factors and predicted natural compounds that might be used to treat UC. Finally, we further verified the reliability of the markers by RT-qPCR experiments. RESULTS 118 DRF genes were identified using Pearson analysis. Based on the expression level of the DRF genes, we classified UC patients into C1 and C2 subtypes, with significant differences in the abundance of immune infiltration and disease activity between the two subtypes. The machine learning algorithms identified three biomarkers, including XBP1, FH, and MAP3K5. Further analyses revealed that the three biomarkers were closely associated with a variety of immune cells and transcription factors. In addition, six natural compounds corresponding to the biomarkers were predicted, which may contribute to the effective treatment of UC. Finally, the expression trends of XBP1, FH, and MAP3K5 in animal experiments were consistent with the results of bioinformatics analysis. CONCLUSION In this study, we systematically elucidated the role of DRF genes in the development of UC, and identified three potential biomarkers, providing a new idea for the diagnosis and treatment of UC.
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Affiliation(s)
- Yinghao Jiang
- The First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Hongyan Meng
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Xin Zhang
- Department of Gastroenterology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Jinguang Yang
- Staff Hospital of JIER MACHINE-TOOL GROUP CO.,LTD, Jinan, China
| | - Chengxin Sun
- The First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Xiaoyan Wang
- College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
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Zheng S, He Y, Chen Y, Chen M, Xian H, Ming W, Jiang Y, Shan WH, Hang T, Tan X, Lyu J, Deng L. A population-based study using nomograms to predict overall and cancer-specific survival in HPV-associated CSCC. Cancer Sci 2025; 116:470-487. [PMID: 39528226 PMCID: PMC11786314 DOI: 10.1111/cas.16392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Revised: 10/09/2024] [Accepted: 10/25/2024] [Indexed: 11/16/2024] Open
Abstract
Constructing and validating two nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) in cutaneous squamous cell carcinoma (CSCC) correlated with human papillomavirus (HPV) infection was the main goal of this study. We constructed predictive models for OS and CSS incidence in HPV infection-associated CSCC using information from 2238 patients in the Surveillance, Epidemiology, and End Results (SEER) database and screened the variables by LASSO regression, Cox univariate regression, and Cox multifactorial regression models, which were calibrated and validated by internal and external cohorts. Finally, all patients were categorized into intermediate-risk, low-risk, and high-risk groups based on the optimal threshold calculated from the total score. Multivariate analysis showed that HPV infection status, marital status, tumor metastatic stage, surgical status, radiotherapy status, lymph node biopsy, local lymph node dissection, primary tumor status, and bone metastasis were risk factors for OS and CSS. The C index, the time-dependent area under the receiver-operating characteristic curve, and the column-line diagrams of the calibration plot were among the excellent-performance metrics that were effectively displayed. Moreover, the decision curve analysis of the two nomograms consistently revealed their favorable net benefits spanning 1, 2, and 3 years. In addition, the survival curves indicate that each of the two risk classification systems clearly differentiates high, medium, and low risk groups. These meticulously crafted nomograms stand poised to serve as indispensable instruments in clinical practice, empowering clinicians to adeptly communicate with patients regarding their prognostic outlook over the forthcoming 1, 2, and 3 years.
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Affiliation(s)
- Suzheng Zheng
- Department of DermatologyThe First Affiliated Hospital of Jinan University and Jinan University Institute of DermatologyGuangzhouChina
| | - Yong He
- Department of DermatologyThe First Affiliated Hospital of Jinan University and Jinan University Institute of DermatologyGuangzhouChina
| | - Yanan Chen
- Department of DermatologyMarine Corps Hospital of PLAChaozhouChina
| | - Ming Chen
- Department of DermatologyThe First Affiliated Hospital of Jinan University and Jinan University Institute of DermatologyGuangzhouChina
| | - Hua Xian
- Department of plastic surgeryThe Dermetolgy Hospital of Southern Medical UniversityGuangzhouChina
| | - Wai‐kit Ming
- Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life SciencesCity University of Hong KongHong KongChina
| | - Yuzhen Jiang
- Royal Free Hospital & University College LondonLondonUK
| | - Wong Hoi Shan
- Department of DermatologyKiang wu hospitalMacauChina
| | - Tie Hang
- Chinese Academy of Inspection and Quarantine GREATER BAY AREAZhongshanChina
| | - Xiaoqi Tan
- Department of Dermatology, the Affiliated HospitalSouthwest Medical UniversityLuzhouChina
| | - Jun Lyu
- Department of Clinical ResearchThe First Affiliated Hospital of Jinan UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine InformatizationGuangzhouChina
| | - Liehua Deng
- Department of DermatologyThe First Affiliated Hospital of Jinan University and Jinan University Institute of DermatologyGuangzhouChina
- Department of DermatologyThe Fifth Affiliated Hospital of Jinan UniversityHeyuanChina
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Lin T, Wan H, Ming J, Liang Y, Ran L, Lu J. The role of CTGF and MFG-E8 in the prognosis assessment of SCAP: a study combining machine learning and nomogram analysis. Front Immunol 2025; 16:1446415. [PMID: 39917305 PMCID: PMC11799283 DOI: 10.3389/fimmu.2025.1446415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2024] [Accepted: 01/02/2025] [Indexed: 02/09/2025] Open
Abstract
Background Severe Community-Acquired Pneumonia (SCAP) is a serious global health issue with high incidence and mortality rates. In recent years, the role of biomarkers such as Connective Tissue Growth Factor (CTGF) and Milk Fat Globule-Epidermal Growth Factor 8 (MFG-E8) in disease diagnosis and prognosis has increasingly gained attention. However, their specific functions in SCAP have still remained unclear. By conducting a prospective analysis, this study has explored the relationship between these two proteins and the diagnosis and mortality of SCAP patients. Additionally, founded on comparing the applications of machine learning and nomograms as predictive models in forecasting the 28-day mortality risk of SCAP patients, this paper has discussed their performance in different medical scenarios to provide more accurate treatment options and improve prognosis. Methods 198 patients diagnosed with SCAP, 80 patients with CAP and 80 healthy individuals were encompassed in the study. Demographic characteristics, clinical features and biomarkers were extracted. The ELISA method was employed to measure the levels of MFG-E8 and CTGF in the three groups. The 28-day mortality of SCAP patients was tracked. Eleven models, including XGBoost and CatBoost, were used as prediction models and compared with a nomogram. And 14 scoring methods, like F1 Score and AUC Score, were used to evaluate the prediction models. Results Compared to healthy controls, SCAP patients had higher serum levels of CTGF and MFG-E8, suggesting that these biomarkers are associated with poor prognosis. Compared to CAP patients, SCAP patients had lower levels of MFG-E8 and higher levels of CTGF. In the deceased group of SCAP patients, their CTGF levels were higher and MFG-E8 levels were lower. Using the CatBoost model for prediction, it performed the best, with key predictive features including Oxygenation Index, cTnT, MFG-E8, Dyspnea, CTGF and PaCO2. Conclusion This study has highlighted the critical role of clinical and biochemical markers such as CTGF and MFG-E8 in assessing the severity and prognosis of SCAP. The CatBoost model has shown the significant potential in predicting mortality risk by virtue of its unique algorithmic advantages and efficiency.
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Affiliation(s)
- Tingting Lin
- Department of Respiratory Medicine, Xiamen Humanity Hospital, Fujian Medical University, Xiamen, China
- Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Huimin Wan
- Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jie Ming
- Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yifei Liang
- Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Linxin Ran
- Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jingjing Lu
- Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
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Xu C, Zhu J, Tu K, Tang H, Zhou X, Li Q, Chen K, Yang X, Huang Y. The clinical features and risk factors of coagulopathy associated with cefoperazone/sulbactam: a nomogram prediction model. Front Pharmacol 2025; 15:1505653. [PMID: 39830359 PMCID: PMC11742127 DOI: 10.3389/fphar.2024.1505653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2024] [Accepted: 11/26/2024] [Indexed: 01/22/2025] Open
Abstract
Background Cefoperazone/sulbactam (CPZ/SAM) is an important treatment option for infections caused by multidrug-resistant gram-negative bacteria. However, it is associated with an increased risk of coagulation disorders (CD) and causes severe bleeding in some instances. Early identification of risk factors and prediction of CD related to CPZ/SAM are crucial for prevention and treatment. This study aimed to explore the risk factors and developed a nomogram model for predicting the risk of coagulopathy in patients undergoing CPZ/SAM treatment. Methods A total of 1719 patients who underwent CPZ/SAM in the Affiliated Hospital of Southwest Medical University from August 2018 to August 2022, were recruited as the training cohort. For validation, 1,059 patients treated with CPZ/SAM from September 2022 to August 2024 were enrolled. Patients were divided into the CD and the N-CD groups. The occurrence of CD was designated as the dependent variable. The univariate and multivariate logistic regression analysis was performed to identify the risk factors of CD. A nomogram model was constructed from the multivariate logistic regression analysis and internally validated for model discrimination and calibration. The performance of the nomogram was estimated using the concordance index (C-index) and calibration curve. Results The multivariate logistic regression analysis resulted in the following independent risk factors for CD: baseline INR level (OR: 5.768, 95% CI: 0.484∼11.372, p = 0.036), nutritional risk (OR:2.711, 95%CI: 1.495∼4.125, p < 0.001), comorbidity of digestive system (OR:1.287, 95%CI: 0.434∼2.215, p = 0.004), poor food intake (OR:1.261, 95%CI: 0.145∼2.473, p = 0.032), ALB level (OR: -0.132, 95%CI: -0.229∼-0.044, p = 0.005) and GFR< 30 mL/min (OR: 1.925, 95%CI: 0.704∼3.337, p = 0.004). The internal validation confirmed the model's good performance (C-index, 0.905 [95% CI: 0.864∼0.945]). The calibration plots in the nomogram model were of high quality. Validation further confirmed the reliability of the nomogram, with a C-index of 0.886 (95% CI: 0.832-0.940). Conclusion The nomogram model facilitated accurate prediction of CD in patients undergoing CPZ/SAM. And this could potentially contribute to reducing the incidence of CPZ/SAM-associated CD and consequently improving patients' outcomes.
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Affiliation(s)
- Changjing Xu
- Department of Pharmacy, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Junlong Zhu
- Department of Vascular Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Kun Tu
- School of pharmacy, Southwest Medical University, Luzhou, China
| | - Hui Tang
- Department of Pharmacy, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xinxin Zhou
- Department of Pharmacy, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Qiuyu Li
- School of pharmacy, Southwest Medical University, Luzhou, China
| | - Kun Chen
- Department of Pharmacy, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xuping Yang
- Department of Pharmacy, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yilan Huang
- Department of Pharmacy, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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Li P, Huo D, Li D, Si M, Xu R, Ma X, Wang X, Wang K. Impact of Treatment Strategies on Survival and Within Multivariate Predictive Model for Renal Cell Carcinoma Based on the SEER Database: A Retrospective Cohort Study. J INVEST SURG 2024; 37:2435045. [PMID: 39668775 DOI: 10.1080/08941939.2024.2435045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2024] [Revised: 10/25/2024] [Accepted: 10/31/2024] [Indexed: 12/14/2024]
Abstract
BACKGROUND This project aims to shed light on how various treatment approaches affect RCC patients' chances of survival and create a prediction model for them. METHODS Data from the Surveillance, Epidemiology, and End Results database were used in this investigation. OS and RCSS after radiation, chemotherapy, and surgery were investigated using the Kaplan-Meier approach. Fourteen factors, including gender, age, race, and others, were subjected to univariate and multivariate COX analyses. Predicting RCSS at three, five, or ten years is the main goal. Predicting OS at three, five, or ten years is the secondary endpoint. Cox analyses, both univariate and multivariate, were used to identify prognostic factors. Furthermore, a nomogram was developed to precisely forecast patient survival rates at 3-, 5-, and 10-year intervals. DCA, calibration curves, and ROC were used to assess the nomogram's efficacy. RESULTS Kaplan-Meier analysis revealed that PN was associated with better survival compared to RN for tumors ≤10 cm. Cox analysis identified 10 independent prognostic factors. These variables included gender, age, race, histological type, histological grade, AJCC stage, N stage, T stage, M stage, and surgical type. Based on these variables, a nomogram for OS and RCSS prediction was created. CONCLUSION PN is advised over RN for RCC patients whose tumors are less than 10 cm in diameter since it offers more advantages. The combined nomogram model, which is based on clinicopathological characteristics, therapy data, and demographic variables, may be used to predict the survival of RCC patients and perform prognostic and survival analysis with accuracy.
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Affiliation(s)
- Pengbo Li
- Department of Urology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Diwei Huo
- Department of Urology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Donglong Li
- Department of Urology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Minggui Si
- Department of Urology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Ruicong Xu
- Department of Urology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xuebin Ma
- Department of Urology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xunwei Wang
- Department of Urology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Keliang Wang
- Department of Urology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
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Yan Q, Liu G, Wang R, Li D, Chen X, Wang D. Development and validation of a nomogram for predicting refractory peritoneal dialysis related peritonitis. Ren Fail 2024; 46:2368083. [PMID: 38958248 PMCID: PMC467101 DOI: 10.1080/0886022x.2024.2368083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 06/10/2024] [Indexed: 07/04/2024] Open
Abstract
OBJECTIVE To identify the risk factors of refractory peritoneal dialysis related peritonitis (PDRP) and construct a nomogram to predict the occurrence of refractory PDRP. METHODS Refractory peritonitis was defined as the peritonitis episode with persistently cloudy bags or persistent dialysis effluent leukocyte count >100 × 109/L after 5 days of appropriate antibiotic therapy. The study dataset was randomly divided into a 70% training set and a 30% validation set. Univariate logistic analysis, LASSO regression analysis, and random forest algorithms were utilized to identify the potential risk factors for refractory peritonitis. Independent risk factors identified using multivariate logistic analysis were used to construct a nomogram. The discriminative ability, calibrating ability, and clinical practicality of the nomogram were evaluated using the receiver operating characteristic curve, Hosmer-Lemeshow test, calibration curve, and decision curve analysis. RESULTS A total of 294 peritonitis episodes in 178 patients treated with peritoneal dialysis (PD) were enrolled, of which 93 were refractory peritonitis. C-reactive protein, serum albumin, diabetes mellitus, PD duration, and type of causative organisms were independent risk factors for refractory peritonitis. The nomogram model exhibited excellent discrimination with an area under the curve (AUC) of 0.781 (95% CI: 0.716-0.847) in the training set and 0.741 (95% CI: 0.627-0.855) in the validation set. The Hosmer-Lemeshow test and calibration curve indicated satisfactory calibration ability of the predictive model. Decision curve analysis revealed that the nomogram model had good clinical utility in predicting refractory peritonitis. CONCLUSION This nomogram can accurately predict refractory peritonitis in patients treated with PD.
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Affiliation(s)
- Qiqi Yan
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Guiling Liu
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Ruifeng Wang
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Dandan Li
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Xiaoli Chen
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Deguang Wang
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
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11
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Li Q, Wei X, Wu F, Qin C, Dong J, Chen C, Lin Y. Development and validation of preeclampsia predictive models using key genes from bioinformatics and machine learning approaches. Front Immunol 2024; 15:1416297. [PMID: 39544937 PMCID: PMC11560445 DOI: 10.3389/fimmu.2024.1416297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Accepted: 09/27/2024] [Indexed: 11/17/2024] Open
Abstract
Background Preeclampsia (PE) poses significant diagnostic and therapeutic challenges. This study aims to identify novel genes for potential diagnostic and therapeutic targets, illuminating the immune mechanisms involved. Methods Three GEO datasets were analyzed, merging two for training set, and using the third for external validation. Intersection analysis of differentially expressed genes (DEGs) and WGCNA highlighted candidate genes. These were further refined through LASSO, SVM-RFE, and RF algorithms to identify diagnostic hub genes. Diagnostic efficacy was assessed using ROC curves. A predictive nomogram and fully Connected Neural Network (FCNN) were developed for PE prediction. ssGSEA and correlation analysis were employed to investigate the immune landscape. Further validation was provided by qRT-PCR on human placental samples. Result Five biomarkers were identified with validation AUCs: CGB5 (0.663, 95% CI: 0.577-0.750), LEP (0.850, 95% CI: 0.792-0.908), LRRC1 (0.797, 95% CI: 0.728-0.867), PAPPA2 (0.839, 95% CI: 0.775-0.902), and SLC20A1 (0.811, 95% CI: 0.742-0.880), all of which are involved in key biological processes. The nomogram showed strong predictive power (C-index 0.873), while FCNN achieved an optimal AUC of 0.911 (95% CI: 0.732-1.000) in five-fold cross-validation. Immune infiltration analysis revealed the importance of T cell subsets, neutrophils, and NK cells in PE, linking these genes to immune mechanisms underlying PE pathogenesis. Conclusion CGB5, LEP, LRRC1, PAPPA2, and SLC20A1 are validated as key diagnostic biomarkers for PE. Nomogram and FCNN could credibly predict PE. Their association with immune infiltration underscores the crucial role of immune responses in PE pathogenesis.
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Affiliation(s)
- Qian Li
- Reproductive Medicine Center, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaowei Wei
- Reproductive Medicine Center, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fan Wu
- The International Peace Maternity and Child Health Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chuanmei Qin
- Reproductive Medicine Center, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Junpeng Dong
- Reproductive Medicine Center, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Cailian Chen
- Department of Automation, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China
| | - Yi Lin
- Reproductive Medicine Center, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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12
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Liang Q, Li M, Huang G, Li R, Qin L, Zhong P, Xing X, Yu X. Genetic Susceptibility, Mendelian Randomization, and Nomogram Model Construction of Gestational Diabetes Mellitus. J Clin Endocrinol Metab 2024; 109:2802-2814. [PMID: 38625888 DOI: 10.1210/clinem/dgae200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 03/13/2024] [Accepted: 03/25/2024] [Indexed: 04/18/2024]
Abstract
CONTEXT Gestational diabetes mellitus (GDM) is a pregnancy-complicated disease that poses a risk to maternal and infant health. However, the etiology of the disease has been not yet elucidated. OBJECTIVE To detect the genetic susceptibility and construct a nomogram model with significantly associated polymorphisms and key clinical indicators for early prediction of GDM. METHODS Eleven functional single nucleotide polymorphisms (SNPs) screened by genome-wide association study were genotyped in 554 GDM cases and 641 healthy controls. Functional analysis of GDM positively associated SNPs, multivariate mendelian randomization (MVMR), and a GDM early predictive nomogram model construction were performed. RESULT rs1965211, rs3760675, and rs7814359 were significantly associated with genetic susceptibility to GDM after adjusting age and prepregnancy body mass index (pre-BMI). It seems that GDM-associated SNPs have effects on regulating target gene transcription factor binding, posttranscriptional splicing, and translation product structure. Besides, rs3760675 can be expression quantitative trait loci and increase the XAB2 mRNA expression level (P = .047). The MVMR analysis showed that the increase of clinical variables of BMI, hemoglobin A1c (HbA1c), and fasting plasma glucose (FPG) had significant causal effects on GDM (BMI-ORMVMR = 1.52, HbA1c-ORMVMR = 1.32, FPG-ORMVMR = 1.78), P < .05. A nomogram model constructed with pre-BMI, FPG, HbA1c, and genotypes of rs1965211, rs3760675, and rs7814359 showed an area under the receiver operating characteristic curve of 0.824. CONCLUSION Functional polymorphisms can change women's susceptibility to GDM and the predictive nomogram model based on genetic and environmental factors can effectively distinguish individuals with different GDM risks in early stages of pregnancy.
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Affiliation(s)
- Qiulian Liang
- The Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Health, The School of Public Health, Guilin Medical University, Guilin 541000, China
| | - Ming Li
- Department of Histology and Embryology, School of Basic Medicine, Hunan University of Medicine, Hunan 418000, China
| | - Gongchen Huang
- The Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Health, The School of Public Health, Guilin Medical University, Guilin 541000, China
| | - Ruiqi Li
- The Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Health, The School of Public Health, Guilin Medical University, Guilin 541000, China
| | - Linyuan Qin
- The Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Health, The School of Public Health, Guilin Medical University, Guilin 541000, China
| | - Ping Zhong
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Guilin Medical University, Guilin 541000, China
| | - Xuekun Xing
- The Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Health, The School of Public Health, Guilin Medical University, Guilin 541000, China
| | - Xiangyuan Yu
- The Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Health, The School of Public Health, Guilin Medical University, Guilin 541000, China
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Cui G, Zhang S, Zhang X, Li S. Development and validation of a nomogram for predicting anorexia of aging in older people. Appetite 2024; 201:107606. [PMID: 39029530 DOI: 10.1016/j.appet.2024.107606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 06/24/2024] [Accepted: 07/16/2024] [Indexed: 07/21/2024]
Abstract
BACKGROUND Anorexia of aging (AA) is a common geriatric syndrome that seriously endangers the health of older adults. Early identification of populations at risk of AAand the implementation of appropriate intervention measures hold significant public health importance. This study aimed to develop a nomogram for predicting the risk of AA among older people. METHODS We conducted a cross-sectional study involving 2144 community-dwelling older adults to evaluate the AA using the Simplified Nutritional Appetite Questionnaire. We utilized the Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression analysis to select variables and develop a nomogram prediction model. The predictive performance of the nomogram was evaluated using the Receiver Operating Characteristic (ROC) curves, calibration curves, Decision Curve Analysis (DCA), and internal validation. RESULTS The prevalence of AA among Chinese older adults was 21.7% (95%CI: 20.0%-23.5%). Age, sex, family economic level, smoking status, dysphagia, loneliness, depressive symptoms, living alone, health literacy, life satisfaction, and body mass index have been identified as predictive factors for AA among older people. The nomogram constructed based on these predictive factors showed an area under the curve (AUC) of 0.766 (95%CI: 0.742-0.791), indicating good calibration and discrimination ability. Additionally, the results obtained from the 10-fold cross-validation process confirmed the nomogram's good predictive capabilities. Furthermore, the DCA results showed that the nomogram has clinical utility. CONCLUSION The nomogram constructed in this study serves as an effective tool for predicting anorexia of aging among community-dwelling older adults. Its implementation can help community healthcare workers evaluate the risk of AA in this population and identify high-risk groups.
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Affiliation(s)
- Guanghui Cui
- Department of Integrated Traditional Chinese and Western Medicine, Peking University First Hospital, Institute of Integrated Traditional Chinese and Western Medicine, Peking University, Beijing, China
| | - Shengkai Zhang
- Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Xiaochen Zhang
- Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Shaojie Li
- School of Public Health, Peking University, Beijing, China; China Center for Health Development Studies, Peking University, Beijing, China.
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Yu ZH, Hong YT, Chou CP. Enhancing Breast Cancer Diagnosis: A Nomogram Model Integrating AI Ultrasound and Clinical Factors. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:1372-1380. [PMID: 38897841 DOI: 10.1016/j.ultrasmedbio.2024.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/16/2024] [Accepted: 05/12/2024] [Indexed: 06/21/2024]
Abstract
PURPOSE A novel nomogram incorporating artificial intelligence (AI) and clinical features for enhanced ultrasound prediction of benign and malignant breast masses. MATERIALS AND METHODS This study analyzed 340 breast masses identified through ultrasound in 308 patients. The masses were divided into training (n = 260) and validation (n = 80) groups. The AI-based analysis employed the Samsung Ultrasound AI system (S-detect). Univariate and multivariate analyses were conducted to construct nomograms using logistic regression. The AI-Nomogram was based solely on AI results, while the ClinAI- Nomogram incorporated additional clinical factors. Both nomograms underwent internal validation with 1000 bootstrap resamples and external validation using the independent validation group. Performance was evaluated by analyzing the area under the receiver operating characteristic (ROC) curve (AUC) and calibration curves. RESULTS The ClinAI-Nomogram, which incorporates patient age, AI-based mass size, and AI-based diagnosis, outperformed an existing AI-Nomogram in differentiating benign from malignant breast masses. The ClinAI-Nomogram surpassed the AI-Nomogram in predicting malignancy with significantly higher AUC scores in both training (0.873, 95% CI: 0.830-0.917 vs. 0.792, 95% CI: 0.748-0.836; p = 0.016) and validation phases (0.847, 95% CI: 0.763-0.932 vs. 0.770, 95% CI: 0.709-0.833; p < 0.001). Calibration curves further revealed excellent agreement between the ClinAI-Nomogram's predicted probabilities and actual observed risks of malignancy. CONCLUSION The ClinAI- Nomogram, combining AI alongside clinical data, significantly enhanced the differentiation of benign and malignant breast masses in clinical AI-facilitated ultrasound examinations.
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Affiliation(s)
- Zi-Han Yu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; Department of Radiology, Jiannren Hospital, Kaohsiung, Taiwan
| | - Yu-Ting Hong
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Chen-Pin Chou
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; Department of Medical Laboratory Science and Biotechnology, Fooyin University, Kaohsiung, Taiwan; Department of Pharmacy, College of Pharmacy, Tajen University, Pingtung, Taiwan.
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15
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Huang RH, Yang L, Yang Y, Xu QB, Xie LL, Cao LF. Development and application of a nomogram model for predicting the risk of central precocious puberty in obese girls. Front Pediatr 2024; 12:1421775. [PMID: 39281189 PMCID: PMC11393738 DOI: 10.3389/fped.2024.1421775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 08/12/2024] [Indexed: 09/18/2024] Open
Abstract
Objective The purpose of this study is to develop and assess a nomogram risk prediction model for central precocious puberty (CPP) in obese girls. Methods We selected 154 cases of obese girls and 765 cases of non-obese girls with precocious puberty (PP) who underwent the gonadotropin-releasing hormone stimulation test at the Jiangxi Provincial Children's Hospital. Univariate analysis and multivariate analysis were conducted to identify predictors of progression to CPP in girls with PP. A predictive model was developed and its predictive ability was preliminarily evaluated. The nomogram was used to represent the risk prediction model for CPP in girls with obesity. The model was validated internally using the Bootstrap method, and its efficacy was assessed using calibration curves and clinical decision analysis curves. Results In obese girls with PP, basal luteinizing hormone (LH) and follicular stimulating hormone (FSH) levels, as well as uterine volume, were identified as independent risk factors for progression to CPP. In non-obese girls, the basal LH level, bone age, and uterine volume were identified as independent risk factors for progression to CPP. With an AUC of 0.896, the risk prediction model for obese girls, was found to be superior to that for non-obese girls, which had an AUC of 0.810. The model displayed strong predictive accuracy. Additionally, a nomogram was used to illustrate the CPP risk prediction model for obese girls. This model performs well in internal validation and is well calibrated, providing a substantial net benefit for clinical use. Conclusion A medical nomogram model of CPP risk in obese girls comprised of basal LH value, basal FSH value, and uterine volume, which can be used to identify those at high risk for progression of CPP in obese girls and develop individualized prevention programs.
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Affiliation(s)
- Ren-Hao Huang
- Department of Endocrinology, Jiangxi Provincial Children's Hosptial/Jiangxi Provincial Clinical Research Center for Children's Genetic Metabolic Diseases, Nanchang, China
- Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Li Yang
- Department of Endocrinology, Jiangxi Provincial Children's Hosptial/Jiangxi Provincial Clinical Research Center for Children's Genetic Metabolic Diseases, Nanchang, China
| | - Yu Yang
- Department of Endocrinology, Jiangxi Provincial Children's Hosptial/Jiangxi Provincial Clinical Research Center for Children's Genetic Metabolic Diseases, Nanchang, China
| | - Qing-Bo Xu
- Department of Endocrinology, Jiangxi Provincial Children's Hosptial/Jiangxi Provincial Clinical Research Center for Children's Genetic Metabolic Diseases, Nanchang, China
| | - Li-Ling Xie
- Department of Endocrinology, Jiangxi Provincial Children's Hosptial/Jiangxi Provincial Clinical Research Center for Children's Genetic Metabolic Diseases, Nanchang, China
| | - Lan-Fang Cao
- Department of Endocrinology, Jiangxi Provincial Children's Hosptial/Jiangxi Provincial Clinical Research Center for Children's Genetic Metabolic Diseases, Nanchang, China
- Jiangxi Medical College, Nanchang University, Nanchang, China
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An W, Zhou J, Qiu Z, Wang P, Han X, Cheng Y, He Z, An Y, Li S. Identification of crosstalk genes and immune characteristics between Alzheimer's disease and atherosclerosis. Front Immunol 2024; 15:1443464. [PMID: 39188714 PMCID: PMC11345154 DOI: 10.3389/fimmu.2024.1443464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Accepted: 07/25/2024] [Indexed: 08/28/2024] Open
Abstract
Background Advancements in modern medicine have extended human lifespan, but they have also led to an increase in age-related diseases such as Alzheimer's disease (AD) and atherosclerosis (AS). Growing research evidence indicates a close connection between these two conditions. Methods We downloaded four gene expression datasets related to AD and AS from the Gene Expression Omnibus (GEO) database (GSE33000, GSE100927, GSE44770, and GSE43292) and performed differential gene expression (DEGs) analysis using the R package "limma". Through Weighted gene correlation network analysis (WGCNA), we selected the gene modules most relevant to the diseases and intersected them with the DEGs to identify crosstalk genes (CGs) between AD and AS. Subsequently, we conducted functional enrichment analysis of the CGs using DAVID. To screen for potential diagnostic genes, we applied the least absolute shrinkage and selection operator (LASSO) regression and constructed a logistic regression model for disease prediction. We established a protein-protein interaction (PPI) network using STRING (https://cn.string-db.org/) and Cytoscape and analyzed immune cell infiltration using the CIBERSORT algorithm. Additionally, NetworkAnalyst (http://www.networkanalyst.ca) was utilized for gene regulation and interaction analysis, and consensus clustering was employed to determine disease subtypes. All statistical analyses and visualizations were performed using various R packages, with a significance level set at p<0.05. Results Through intersection analysis of disease-associated gene modules identified by DEGs and WGCNA, we identified a total of 31 CGs co-existing between AD and AS, with their biological functions primarily associated with immune pathways. LASSO analysis helped us identify three genes (C1QA, MT1M, and RAMP1) as optimal diagnostic CGs for AD and AS. Based on this, we constructed predictive models for both diseases, whose accuracy was validated by external databases. By establishing a PPI network and employing four topological algorithms, we identified four hub genes (C1QB, CSF1R, TYROBP, and FCER1G) within the CGs, closely related to immune cell infiltration. NetworkAnalyst further revealed the regulatory networks of these hub genes. Finally, defining C1 and C2 subtypes for AD and AS respectively based on the expression profiles of CGs, we found the C2 subtype exhibited immune overactivation. Conclusion This study utilized gene expression matrices and various algorithms to explore the potential links between AD and AS. The identification of CGs revealed interactions between these two diseases, with immune and inflammatory imbalances playing crucial roles in their onset and progression. We hope these findings will provide valuable insights for future research on AD and AS.
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Affiliation(s)
- Wenhao An
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Jiajun Zhou
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Zhiqiang Qiu
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Peishen Wang
- Department of Research and Development, Beijing Yihua Biotechnology Co., Ltd, Beijing, China
| | - Xinye Han
- Department of Research and Development, Beijing Yihua Biotechnology Co., Ltd, Beijing, China
| | - Yanwen Cheng
- Department of Research and Development, Beijing Yihua Biotechnology Co., Ltd, Beijing, China
| | - Zi He
- Department of Research and Development, Beijing Yihua Biotechnology Co., Ltd, Beijing, China
| | - Yihua An
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Shouwei Li
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, China
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Huang JX, Chen YJ, Wang XY, Huang JH, Gan KH, Tang LN, Pei XQ. Nomogram Based on US and Clinicopathologic Characteristics: Axillary Nodal Evaluation Following Neoadjuvant Chemotherapy in Patients With Node-Positive Breast Cancer. Clin Breast Cancer 2024; 24:e452-e463.e4. [PMID: 38580573 DOI: 10.1016/j.clbc.2024.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 04/07/2024]
Abstract
BACKGROUND To develop a convenient modality to predict axillary response to neoadjuvant chemotherapy (NAC) in breast cancer patients. MATERIALS AND METHODS In this multi-center study, a total of 1019 breast cancer patients with biopsy-proven positive lymph node (LN) receiving NAC were randomly assigned to the training and validation groups at a ratio of 7:3. Clinicopathologic and ultrasound (US) characteristics of both primary tumors and LNs were used to develop corresponding prediction models, and a nomogram integrating clinicopathologic and US predictors was generated to predict the axillary response to NAC. RESULTS Axillary pathological complete response (pCR) was achieved in 47.79% of the patients. The expression of estrogen receptor, human epidermal growth factor receptor -2, Ki-67 score, and clinical nodal stage were independent predictors for nodal response to NAC. Location and radiological response of primary tumors, cortical thickness and shape of LNs on US were also significantly associated with nodal pCR. In the validation cohort, the discrimination of US model (area under the curve [AUC], 0.76) was superior to clinicopathologic model (AUC, 0.68); the combined model (AUC, 0.85) demonstrates strong discriminatory power in predicting nodal pCR. Calibration curves of the nomogram based on the combined model demonstrated that substantial agreement can be observed between the predictions and observations. This nomogram showed a false-negative rates of 16.67% in all patients and 10.53% in patients with triple negative breast cancer. CONCLUSION Nomogram incorporating routine clinicopathologic and US characteristics can predict nodal pCR and represents a tool to aid in treatment decisions for the axilla after NAC in breast cancer patients.
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Affiliation(s)
- Jia-Xin Huang
- Department of Medical Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Yi-Jie Chen
- Department of Medical Ultrasound, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, PR China
| | - Xue-Yan Wang
- Department of Medical Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Jia-Hui Huang
- Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, PR China
| | - Ke-Hong Gan
- Department of Medical Ultrasound, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, PR China
| | - Li-Na Tang
- Department of Medical Ultrasound, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, PR China
| | - Xiao-Qing Pei
- Department of Medical Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, PR China.
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Huang XW, Li Y, Jiang LN, Zhao BK, Liu YS, Chen C, Zhao D, Zhang XL, Li ML, Jiang YY, Liu SH, Zhu L, Zhao JM. Nomogram for preoperative estimation of microvascular invasion risk in hepatocellular carcinoma. Transl Oncol 2024; 45:101986. [PMID: 38723299 PMCID: PMC11101742 DOI: 10.1016/j.tranon.2024.101986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 04/22/2024] [Accepted: 05/05/2024] [Indexed: 05/21/2024] Open
Abstract
Microvascular invasion (MVI) is an adverse prognostic indicator of tumor recurrence after surgery for hepatocellular carcinoma (HCC). Therefore, developing a nomogram for estimating the presence of MVI before liver resection is necessary. We retrospectively included 260 patients with pathologically confirmed HCC at the Fifth Medical Center of Chinese PLA General Hospital between January 2021 and April 2024. The patients were randomly divided into a training cohort (n = 182) for nomogram development, and a validation cohort (n = 78) to confirm the performance of the model (7:3 ratio). Significant clinical variables associated with MVI were then incorporated into the predictive nomogram using both univariate and multivariate logistic analyses. The predictive performance of the nomogram was assessed based on its discrimination, calibration, and clinical utility. Serum carnosine dipeptidase 1 ([CNDP1] OR 2.973; 95 % CI 1.167-7.575; p = 0.022), cirrhosis (OR 8.911; 95 % CI 1.922-41.318; p = 0.005), multiple tumors (OR 4.095; 95 % CI 1.374-12.205; p = 0.011), and tumor diameter ≥3 cm (OR 4.408; 95 % CI 1.780-10.919; p = 0.001) were independent predictors of MVI. Performance of the nomogram based on serum CNDP1, cirrhosis, number of tumors and tumor diameter was achieved with a concordance index of 0.833 (95 % CI 0.771-0.894) and 0.821 (95 % CI 0.720-0.922) in the training and validation cohorts, respectively. It fitted well in the calibration curves, and the decision curve analysis further confirmed its clinical usefulness. The nomogram, incorporating significant clinical variables and imaging features, successfully predicted the personalized risk of MVI in HCC preoperatively.
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Affiliation(s)
- Xiao-Wen Huang
- Medical School of Chinese PLA, Beijing, China; Department of Pathology and Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yan Li
- Department of Pathology and Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Li-Na Jiang
- Department of Pathology and Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Bo-Kang Zhao
- Department of Hepatology, Center of Infectious Diseases and Pathogen Biology, The First Hospital of Jilin University, Changchun, China
| | - Yi-Si Liu
- First Department of Liver Disease Center, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Chun Chen
- Senior Department of Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Dan Zhao
- Department of Pathology and Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xue-Li Zhang
- Department of Pathology and Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Mei-Ling Li
- Department of Pathology and Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yi-Yun Jiang
- Department of Pathology and Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Shu-Hong Liu
- Department of Pathology and Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Li Zhu
- Department of Pathology and Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jing-Min Zhao
- Medical School of Chinese PLA, Beijing, China; Department of Pathology and Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China.
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Cai Z, Lin H, Li Z, Zhou J, Chen W, Wu J, Zhang W, Wu H, Guo Z, Xu Y. A clinicopathologic feature-based nomogram for preoperative estimation of splenic hilar lymph node metastasis in advanced proximal gastric cancer without invasion of the greater curvature. Surgery 2024; 176:100-107. [PMID: 38584073 DOI: 10.1016/j.surg.2024.02.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 08/06/2023] [Accepted: 02/29/2024] [Indexed: 04/09/2024]
Abstract
BACKGROUND The indications for splenic hilar lymph node dissection in advanced proximal gastric cancer without invasion of the greater curvature are controversial. We aimed to develop a preoperative nomogram for individualized prediction of splenic hilar lymph node metastasis in non-greater curvature advanced proximal gastric cancer. METHODS From January 2014 to December 2021, 558 patients with non-greater curvature advanced proximal gastric cancer who underwent D2 lymphadenectomy (including splenic hilar lymph node) were retrospectively analyzed and divided into a training cohort (n = 361) and validation cohort (n = 197), depending on the admission time. A preoperative predictive nomogram of splenic hilar lymph node metastasis was established based on independent predictors identified by multivariate analysis, and the performance and prognostic value were confirmed. RESULTS In the training and validation cohorts, 48 (13.3%) and 24 patients (12.2%) had pathologically confirmed splenic hilar lymph node metastasis, respectively. Tumor located in the posterior wall, tumor size ≥5 cm, Borrmann type IV, and splenic hilar lymph node lymphadenectasis on computed tomography were preoperative factors independently associated with splenic hilar lymph node metastasis. The nomogram developed based on these four parameters had a high concordance index of 0.850 (95% confidence interval, 0.793-0.907) and 0.825 (95% confidence interval, 0.743-0.908) in the training and validation cohorts, respectively, with well-fitting calibration plots and better net benefits in the decision curve analysis. In addition, disease-free survival and overall survival were significantly shorter in the high-risk group, with hazard ratios of 3.660 (95% confidence interval, 2.228-6.011; log-rank P < .0001) and 3.769 (95% confidence interval, 2.279-6.231; log-rank P < .0001), respectively. CONCLUSION The nomogram has good performance in predicting the risk of splenic hilar lymph node metastasis in non-greater curvature advanced proximal gastric cancer preoperatively, which can help surgeons make rational clinical decisions.
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Affiliation(s)
- Zhiming Cai
- School of Clinical Medicine, Fujian Medical University, Fuzhou, China; Gastrointestinal Surgery Unit 1, The First Hospital of Putian City, Putian, China; Putian University, Putian, China
| | - Huimei Lin
- Department of Anorectal Surgery, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, China
| | - Zhixiong Li
- Gastrointestinal Surgery Unit 1, The First Hospital of Putian City, Putian, China
| | - Jinfeng Zhou
- School of Clinical Medicine, Fujian Medical University, Fuzhou, China; Gastrointestinal Surgery Unit 1, The First Hospital of Putian City, Putian, China; Putian University, Putian, China
| | - Weixiang Chen
- School of Clinical Medicine, Fujian Medical University, Fuzhou, China; Gastrointestinal Surgery Unit 1, The First Hospital of Putian City, Putian, China; Putian University, Putian, China
| | - Jihuang Wu
- Gastrointestinal Surgery Unit 1, The First Hospital of Putian City, Putian, China
| | - Weihong Zhang
- Gastrointestinal Surgery Unit 1, The First Hospital of Putian City, Putian, China
| | - Haiyan Wu
- Department of Pathology, The First Hospital of Putian City, Putian, China
| | - Zipei Guo
- School of Clinical Medicine, Fujian Medical University, Fuzhou, China; Gastrointestinal Surgery Unit 1, The First Hospital of Putian City, Putian, China; Putian University, Putian, China
| | - Yanchang Xu
- Gastrointestinal Surgery Unit 1, The First Hospital of Putian City, Putian, China.
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Li X, Li X, Zhao W, Wang D. Development and validation of a nomogram for predicting in-hospital death in cirrhotic patients with acute kidney injury. BMC Nephrol 2024; 25:175. [PMID: 38773418 PMCID: PMC11110328 DOI: 10.1186/s12882-024-03609-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 05/13/2024] [Indexed: 05/23/2024] Open
Abstract
BACKGROUND The purpose of this study was to develop a nomogram for predicting in-hospital mortality in cirrhotic patients with acute kidney injury (AKI) in order to identify patients with a high risk of in-hospital death early. METHODS This study collected data on cirrhotic patients with AKI from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. Multivariate logistic regression was used to identify confounding factors related to in-hospital mortality, which were then integrated into the nomogram. The concordance index (C-Index) was used to evaluate the accuracy of the model predictions. The area under the curve (AUC) and decision curve analysis (DCA) was used to assess the predictive performance and clinical utility of the nomogram. RESULTS The final study population included 886 cirrhotic patients with AKI, and 264 (29.8%) died in the hospital. After multivariate logistic regression, age, gender, cerebrovascular disease, heart rate, respiration rate, temperature, oxygen saturation, hemoglobin, blood urea nitrogen, serum creatinine, international normalized ratio, bilirubin, urine volume, and sequential organ failure assessment score were predictive factors of in-hospital mortality. In addition, the nomogram showed good accuracy in estimating the in-hospital mortality of patients. The calibration plots showed the best agreement with the actual presence of in-hospital mortality in patients. In addition, the AUC and DCA curves showed that the nomogram has good prediction accuracy and clinical value. CONCLUSIONS We have created a prognostic nomogram for predicting in-hospital death in cirrhotic patients with AKI, which may facilitate timely intervention to improve prognosis in these patients.
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Affiliation(s)
- Xiang Li
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Nephrology, Affiliated Hospital of Jining Medical University, Jining, China
| | - Xunliang Li
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Wenman Zhao
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Deguang Wang
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
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Luo P, Li YY, Huang C, Guo J, Yao X. A novel conditional survival nomogram for monitoring real-time prognosis of non-metastatic colorectal cancer. Discov Oncol 2024; 15:179. [PMID: 38772985 PMCID: PMC11109079 DOI: 10.1007/s12672-024-01042-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 05/17/2024] [Indexed: 05/23/2024] Open
Abstract
AIMS The aim of this study is to enhance the accuracy of monitoring and treatment information for patients diagnosed with colorectal cancer (CRC). METHODS Utilizing the Surveillance, Epidemiology, and End Results (SEER) database, a cohort of 335,948 eligible CRC patients was included in this investigation. Conditional survival probability and actuarial overall survival were employed as methodologies to investigate the association between clinicopathological characteristics and cancer prognosis. RESULTS Among CRC patients, the 5-year survival rate was 59%, while the 10-year survival rate was 42%. Over time, conditional survival showed a consistent increase, with rates reaching 45% and 48% for individuals surviving 1 and 2 years, respectively. Notably, patients with unfavorable tumor stages exhibited substantial improvements in conditional survival, thereby narrowing the disparity with actuarial overall survival over time. CONCLUSION This study underscores the significance of time-dependent conditional survival probability, particularly for patients with a poorer prognosis. The findings suggest that long-term CRC survivors may experience improved cancer prognosis over time.
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Affiliation(s)
- Pei Luo
- Department of Gastroenterology, People's Hospital of Qianxinan Prefecture, Xingyi, Guizhou, 562400, China.
| | - Ying-Ying Li
- Department of Gerontology, People's Hospital of Qianxinan Prefecture, Xingyi, Guizhou, 562400, China
| | - Can Huang
- Department of Gastroenterology, People's Hospital of Qianxinan Prefecture, Xingyi, Guizhou, 562400, China
| | - Jun Guo
- Department of Gastroenterology, People's Hospital of Qianxinan Prefecture, Xingyi, Guizhou, 562400, China
| | - Xin Yao
- Department of Gastroenterology, People's Hospital of Qianxinan Prefecture, Xingyi, Guizhou, 562400, China
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Wang D, Pu Y, Tan S, Wang X, Zeng L, Lei J, Gao X, Li H. Identification of immune-related biomarkers for glaucoma using gene expression profiling. Front Genet 2024; 15:1366453. [PMID: 38694874 PMCID: PMC11062407 DOI: 10.3389/fgene.2024.1366453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 04/03/2024] [Indexed: 05/04/2024] Open
Abstract
Introduction: Glaucoma, a principal cause of irreversible vision loss, is characterized by intricate optic neuropathy involving significant immune mechanisms. This study seeks to elucidate the molecular and immune complexities of glaucoma, aiming to improve our understanding of its pathogenesis. Methods: Gene expression profiles from glaucoma patients were analyzed to identify immune-related differentially expressed genes (DEGs). Techniques used were weighted gene co-expression network analysis (WGCNA) for network building, machine learning algorithms for biomarker identification, establishment of subclusters related to immune reactions, and single-sample gene set enrichment analysis (ssGSEA) to explore hub genes' relationships with immune cell infiltration and immune pathway activation. Validation was performed using an NMDA-induced excitotoxicity model and RT-qPCR for hub gene expression measurement. Results: The study identified 409 DEGs differentiating healthy individuals from glaucoma patients, highlighting the immune response's significance in disease progression. Immune cell infiltration analysis revealed elevated levels of activated dendritic cells, natural killer cells, monocytes, and immature dendritic cells in glaucoma samples. Three hub genes, CD40LG, TEK, and MDK, were validated as potential diagnostic biomarkers for high-risk glaucoma patients, showing increased expression in the NMDA-induced excitotoxicity model. Discussion: The findings propose the three identified immune-related genes (IRGs) as novel diagnostic markers for glaucoma, offering new insights into the disease's pathogenesis and potential therapeutic targets. The strong correlation between these IRGs and immune responses underscores the intricate role of immunity in glaucoma, suggesting a shift in the approach to its diagnosis and treatment.
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Affiliation(s)
- Dangdang Wang
- Department of Ophthalmology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory for the Prevention and Treatment of Major Blinding Eye Diseases, Chongqing Eye Institute, Chongqing Branch of National Clinical Research Center for Ocular Diseases, Chongqing, China
| | - Yanyu Pu
- Department of Ophthalmology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory for the Prevention and Treatment of Major Blinding Eye Diseases, Chongqing Eye Institute, Chongqing Branch of National Clinical Research Center for Ocular Diseases, Chongqing, China
| | - Sisi Tan
- Department of Ophthalmology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory for the Prevention and Treatment of Major Blinding Eye Diseases, Chongqing Eye Institute, Chongqing Branch of National Clinical Research Center for Ocular Diseases, Chongqing, China
| | - Xiaochen Wang
- Department of Ophthalmology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory for the Prevention and Treatment of Major Blinding Eye Diseases, Chongqing Eye Institute, Chongqing Branch of National Clinical Research Center for Ocular Diseases, Chongqing, China
| | - Lihong Zeng
- Department of Ophthalmology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory for the Prevention and Treatment of Major Blinding Eye Diseases, Chongqing Eye Institute, Chongqing Branch of National Clinical Research Center for Ocular Diseases, Chongqing, China
| | - Junqin Lei
- Department of Ophthalmology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory for the Prevention and Treatment of Major Blinding Eye Diseases, Chongqing Eye Institute, Chongqing Branch of National Clinical Research Center for Ocular Diseases, Chongqing, China
| | - Xi Gao
- Department of Ophthalmology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory for the Prevention and Treatment of Major Blinding Eye Diseases, Chongqing Eye Institute, Chongqing Branch of National Clinical Research Center for Ocular Diseases, Chongqing, China
| | - Hong Li
- Department of Ophthalmology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory for the Prevention and Treatment of Major Blinding Eye Diseases, Chongqing Eye Institute, Chongqing Branch of National Clinical Research Center for Ocular Diseases, Chongqing, China
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Miché M, Strippoli MPF, Preisig M, Lieb R. Evaluating the clinical utility of an easily applicable prediction model of suicide attempts, newly developed and validated with a general community sample of adults. BMC Psychiatry 2024; 24:217. [PMID: 38509477 PMCID: PMC10953234 DOI: 10.1186/s12888-024-05647-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 02/28/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND A suicide attempt (SA) is a clinically serious action. Researchers have argued that reducing long-term SA risk may be possible, provided that at-risk individuals are identified and receive adequate treatment. Algorithms may accurately identify at-risk individuals. However, the clinical utility of algorithmically estimated long-term SA risk has never been the predominant focus of any study. METHODS The data of this report stem from CoLaus|PsyCoLaus, a prospective longitudinal study of general community adults from Lausanne, Switzerland. Participants (N = 4,097; Mage = 54 years, range: 36-86; 54% female) were assessed up to four times, starting in 2003, approximately every 4-5 years. Long-term individual SA risk was prospectively predicted, using logistic regression. This algorithm's clinical utility was assessed by net benefit (NB). Clinical utility expresses a tool's benefit after having taken this tool's potential harm into account. Net benefit is obtained, first, by weighing the false positives, e.g., 400 individuals, at the risk threshold, e.g., 1%, using its odds (odds of 1% yields 1/(100-1) = 1/99), then by subtracting the result (400*1/99 = 4.04) from the true positives, e.g., 5 individuals (5-4.04), and by dividing the result (0.96) by the sample size, e.g., 800 (0.96/800). All results are based on 100 internal cross-validations. The predictors used in this study were: lifetime SA, any lifetime mental disorder, sex, and age. RESULTS SA at any of the three follow-up study assessments was reported by 1.2%. For a range of seven a priori selected threshold probabilities, ranging between 0.5% and 2%, logistic regression showed highest overall NB in 97.4% of all 700 internal cross-validations (100 for each selected threshold probability). CONCLUSION Despite the strong class imbalance of the outcome (98.8% no, 1.2% yes) and only four predictors, clinical utility was observed. That is, using the logistic regression model for clinical decision making provided the most true positives, without an increase of false positives, compared to all competing decision strategies. Clinical utility is one among several important prerequisites of implementing an algorithm in routine practice, and may possibly guide a clinicians' treatment decision making to reduce long-term individual SA risk. The novel metric NB may become a standard performance measure, because the a priori invested clinical considerations enable clinicians to interpret the results directly.
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Affiliation(s)
- Marcel Miché
- Department of Psychology, Division of Clinical Psychology and Epidemiology, University of Basel, Missionsstrasse 60-62, 4055, Basel, Switzerland.
| | - Marie-Pierre F Strippoli
- Psychiatric Epidemiology and Psychopathology Research Center, Lausanne University Hospital, University of Lausanne, Prilly, Switzerland
| | - Martin Preisig
- Psychiatric Epidemiology and Psychopathology Research Center, Lausanne University Hospital, University of Lausanne, Prilly, Switzerland
| | - Roselind Lieb
- Department of Psychology, Division of Clinical Psychology and Epidemiology, University of Basel, Missionsstrasse 60-62, 4055, Basel, Switzerland
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Suartz CV, Cordeiro MD, Botelho LAA, Gallucci FP, Cho DH, de Arruda Pessoa F, da Silva FR, Costa MSS, Cardili L, Audenet F, Mota JM, Toren P, Nahas WC, Ribeiro-Filho LA. Predicting individual outcomes after radical cystectomy in urothelial variants with Cancer of the Bladder Risk Assessment (COBRA) score. World J Urol 2024; 42:155. [PMID: 38483580 DOI: 10.1007/s00345-024-04798-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 01/14/2024] [Indexed: 03/19/2024] Open
Abstract
OBJECTIVE To validate the Cancer of the Bladder Risk Assessment (COBRA) score in patients with urothelial variants. METHODS Epidemiological, clinical, radiological, and anatomopathological data were collected from patients with urothelial carcinoma who underwent radical cystectomy at the Institute of Cancer of São Paulo between May 2008 and December 2022. Patients with the presence of at least 10% of any urothelial variants in the radical cystectomy specimens' anatomopathological exam were included in the study. The COBRA score and derivatives were applied and correlated with oncological outcomes. RESULTS A total of 680 patients [482 men (70.9%) and 198 women (29.1%)]; 66 years (IQR 59-73) underwent radical cystectomy for bladder tumor, and of these patients, a total of 167 patients presented any type of urothelial variant. The median follow-up time was 28.77 months (IQR 12-85). The three most prevalent UV were squamous differentiation (50.8%), glandular differentiation (31.3%), and micropapillary differentiation (11.3%). The subtypes with the worst prognosis were sarcomatoid with a median survival of 8 months (HR 1.161; 95% CI 0.555-2.432) and plasmacytoid with 14 months (HR 1.466; 95% CI 0.528-4.070). The COBRA score for patients with micropapillary variants demonstrated good predictive accuracy for OS (log-rank P = 0.009; 95% IC 6.78-29.21) and CSS (log-rank P = 0.002; 95% IC 13.06-26.93). CONCLUSIONS In our study, the COBRA score proved an effective risk stratification tool for urothelial histological variants, especially for the micropapillary urothelial variant. It may be helpful in the prognosis evaluation of UV patients after radical cystectomy.
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Affiliation(s)
- Caio Vinícius Suartz
- Division of Urology, Institute of Cancer of São Paulo State, University of São Paulo, Avenida Dr. Éneas de Carvalho Aguiar, 255 Sala 710F, 7º Andar, São Paulo, SP, CEP 05403-000, Brazil.
| | - Maurício Dener Cordeiro
- Division of Urology, Institute of Cancer of São Paulo State, University of São Paulo, Avenida Dr. Éneas de Carvalho Aguiar, 255 Sala 710F, 7º Andar, São Paulo, SP, CEP 05403-000, Brazil
| | - Luiz Antonio Assan Botelho
- Division of Urology, Institute of Cancer of São Paulo State, University of São Paulo, Avenida Dr. Éneas de Carvalho Aguiar, 255 Sala 710F, 7º Andar, São Paulo, SP, CEP 05403-000, Brazil
| | - Fábio Pescarmona Gallucci
- Division of Urology, Institute of Cancer of São Paulo State, University of São Paulo, Avenida Dr. Éneas de Carvalho Aguiar, 255 Sala 710F, 7º Andar, São Paulo, SP, CEP 05403-000, Brazil
| | - David Hamilton Cho
- Division of Urology, Institute of Cancer of São Paulo State, University of São Paulo, Avenida Dr. Éneas de Carvalho Aguiar, 255 Sala 710F, 7º Andar, São Paulo, SP, CEP 05403-000, Brazil
| | - Filipe de Arruda Pessoa
- Division of Urology, Institute of Cancer of São Paulo State, University of São Paulo, Avenida Dr. Éneas de Carvalho Aguiar, 255 Sala 710F, 7º Andar, São Paulo, SP, CEP 05403-000, Brazil
| | - Flávio Rossi da Silva
- Division of Urology, Institute of Cancer of São Paulo State, University of São Paulo, Avenida Dr. Éneas de Carvalho Aguiar, 255 Sala 710F, 7º Andar, São Paulo, SP, CEP 05403-000, Brazil
| | - Mateus Silva Santos Costa
- Division of Urology, Institute of Cancer of São Paulo State, University of São Paulo, Avenida Dr. Éneas de Carvalho Aguiar, 255 Sala 710F, 7º Andar, São Paulo, SP, CEP 05403-000, Brazil
| | - Leonardo Cardili
- Division of Urology, Institute of Cancer of São Paulo State, University of São Paulo, Avenida Dr. Éneas de Carvalho Aguiar, 255 Sala 710F, 7º Andar, São Paulo, SP, CEP 05403-000, Brazil
| | - François Audenet
- Division of Urology, Université Paris Cité Faculté de Santé, Paris, France
| | - José Maurício Mota
- Genitourinary Medical Oncology Service, Institute of Cancer of São Paulo State, University of São Paulo, São Paulo, Brazil
| | - Paul Toren
- Division of Urology, Université Laval Faculté de Médecine, Quebec City, Canada
| | - William Carlos Nahas
- Division of Urology, Institute of Cancer of São Paulo State, University of São Paulo, Avenida Dr. Éneas de Carvalho Aguiar, 255 Sala 710F, 7º Andar, São Paulo, SP, CEP 05403-000, Brazil
| | - Leopoldo Alves Ribeiro-Filho
- Division of Urology, Institute of Cancer of São Paulo State, University of São Paulo, Avenida Dr. Éneas de Carvalho Aguiar, 255 Sala 710F, 7º Andar, São Paulo, SP, CEP 05403-000, Brazil
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He Y, Shao S, Qiao Y, Zhang N, Gong X, Hua Y, Zhou K, Li Y, Liu X, Wang C. Using nomogram scores to predict the early regression of coronary artery aneurysms of Kawasaki disease. Cardiol Young 2024; 34:348-355. [PMID: 37424509 DOI: 10.1017/s1047951123001610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
BACKGROUND Coronary artery aneurysms have been considered the most serious complication of Kawasaki disease. However, some coronary artery aneurysms do regress. Therefore, the ability to predict the expected time of coronary artery aneurysm regression is critical. Herein, we have created a nomogram prediction system to determine the early regression (<1 month) among patients with small to medium coronary artery aneurysms. METHODS Seventy-six Kawasaki disease patients identified with coronary artery aneurysms during the acute or subacute phase were included. All the patients who met inclusion criteria demonstrated regression of coronary artery aneurysms within the first-year post Kawasaki disease diagnosis. The clinical and laboratory parameters were compared between the groups of coronary artery aneurysms regression duration within and beyond 1 month. Multivariate logistic regression analysis was used to identify the independent parameters for early regression based on the results from the univariable analysis. Then nomogram prediction systems were established with associated receiver operating characteristic curves. RESULTS Among the 76 included patients, 40 cases recovered within 1 month. Haemoglobin, globulin, activated partial thromboplastin time, the number of lesions, location of the aneurysm, and coronary artery aneurysm size were identified as independent factors for early regression of coronary artery aneurysms in Kawasaki disease patients. The predictive nomogram models revealed a high efficacy in predicting early regression of coronary artery aneurysms. CONCLUSION The size of coronary artery aneurysms, the number of lesions, and the location of aneurysms presented better predictive value for predicting coronary artery aneurysms regression. The nomogram system created from the identified risk factors successfully predicted early coronary artery aneurysm regression.
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Affiliation(s)
- Yunru He
- Department of Pediatrics, Ministry of Education Key Laboratory of Women and Children's Diseases and Birth Defects, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Shuran Shao
- Department of Pediatrics, Ministry of Education Key Laboratory of Women and Children's Diseases and Birth Defects, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yanni Qiao
- Department of Pediatrics, Ministry of Education Key Laboratory of Women and Children's Diseases and Birth Defects, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Pediatrics, Affiliated People's Hospital of Chongqing Three Gorges Medical College, Wanzhou, Chongqing, China
| | - Nanjun Zhang
- Department of Pediatrics, Ministry of Education Key Laboratory of Women and Children's Diseases and Birth Defects, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xue Gong
- Department of Pediatrics, Ministry of Education Key Laboratory of Women and Children's Diseases and Birth Defects, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yimin Hua
- Department of Pediatrics, Ministry of Education Key Laboratory of Women and Children's Diseases and Birth Defects, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Kaiyu Zhou
- Department of Pediatrics, Ministry of Education Key Laboratory of Women and Children's Diseases and Birth Defects, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yifei Li
- Department of Pediatrics, Ministry of Education Key Laboratory of Women and Children's Diseases and Birth Defects, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xiaoliang Liu
- Department of Pediatrics, Ministry of Education Key Laboratory of Women and Children's Diseases and Birth Defects, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Chuan Wang
- Department of Pediatrics, Ministry of Education Key Laboratory of Women and Children's Diseases and Birth Defects, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
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Klemm J, Bekku K, Abufaraj M, Laukhtina E, Matsukawa A, Parizi MK, Karakiewicz PI, Shariat SF. Upper Tract Urothelial Carcinoma: A Narrative Review of Current Surveillance Strategies for Non-Metastatic Disease. Cancers (Basel) 2023; 16:44. [PMID: 38201472 PMCID: PMC10777993 DOI: 10.3390/cancers16010044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 12/11/2023] [Accepted: 12/19/2023] [Indexed: 01/12/2024] Open
Abstract
Non-metastatic upper urinary tract carcinoma (UTUC) is a comparatively rare condition, typically managed with either kidney-sparing surgery (KSS) or radical nephroureterectomy (RNU). Irrespective of the chosen therapeutic modality, patients with UTUC remain at risk of recurrence in the bladder; in patients treated with KSS, the risk of recurrence is high in the remnant ipsilateral upper tract system but there is a low but existent risk in the contralateral system as well as in the chest and in the abdomen/pelvis. For patients treated with RNU for high-risk UTUC, the risk of recurrence in the chest, abdomen, and pelvis, as well as the contralateral UT, depends on the tumor stage, grade, and nodal status. Hence, implementing a risk-stratified, location-specific follow-up is indicated to ensure timely detection of cancer recurrence. However, there are no data on the type and frequency/schedule of follow-up or on the impact of the recurrence type and site on outcomes; indeed, it is not well known whether imaging-detected asymptomatic recurrences confer a better outcome than recurrences detected due to symptoms/signs. Novel imaging techniques and more precise risk stratification methods based on time-dependent probabilistic events hold significant promise for making a cost-efficient individualized, patient-centered, outcomes-oriented follow-up strategy possible. We show and discuss the follow-up protocols of the major urologic societies.
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Affiliation(s)
- Jakob Klemm
- Department of Urology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
- Department of Urology, Comprehensive Cancer Center, Medical University of Vienna, 1090 Vienna, Austria; (K.B.); (M.A.); (E.L.); (A.M.); (M.K.P.); (S.F.S.)
| | - Kensuke Bekku
- Department of Urology, Comprehensive Cancer Center, Medical University of Vienna, 1090 Vienna, Austria; (K.B.); (M.A.); (E.L.); (A.M.); (M.K.P.); (S.F.S.)
- Department of Urology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama 700-8525, Japan
| | - Mohammad Abufaraj
- Department of Urology, Comprehensive Cancer Center, Medical University of Vienna, 1090 Vienna, Austria; (K.B.); (M.A.); (E.L.); (A.M.); (M.K.P.); (S.F.S.)
- Division of Urology, Department of Special Surgery, Jordan University Hospital, The University of Jordan, Amman 11733, Jordan
| | - Ekaterina Laukhtina
- Department of Urology, Comprehensive Cancer Center, Medical University of Vienna, 1090 Vienna, Austria; (K.B.); (M.A.); (E.L.); (A.M.); (M.K.P.); (S.F.S.)
- Institute for Urology and Reproductive Health, Sechenov University, 119991 Moscow, Russia
| | - Akihiro Matsukawa
- Department of Urology, Comprehensive Cancer Center, Medical University of Vienna, 1090 Vienna, Austria; (K.B.); (M.A.); (E.L.); (A.M.); (M.K.P.); (S.F.S.)
- Department of Urology, Jikei University School of Medicine, Tokyo 105-8461, Japan
| | - Mehdi Kardoust Parizi
- Department of Urology, Comprehensive Cancer Center, Medical University of Vienna, 1090 Vienna, Austria; (K.B.); (M.A.); (E.L.); (A.M.); (M.K.P.); (S.F.S.)
| | - Pierre I. Karakiewicz
- Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Centre, Montreal, QC H2X 3E4, Canada;
| | - Shahrokh F. Shariat
- Department of Urology, Comprehensive Cancer Center, Medical University of Vienna, 1090 Vienna, Austria; (K.B.); (M.A.); (E.L.); (A.M.); (M.K.P.); (S.F.S.)
- Institute for Urology and Reproductive Health, Sechenov University, 119991 Moscow, Russia
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 11942, Jordan
- Karl Landsteiner Institute of Urology and Andrology, 1090 Vienna, Austria
- Department of Urology, Weill Cornell Medical College, New York, NY 10065, USA
- Department of Urology, University of Texas Southwestern, Dallas, TX 75390, USA
- Department of Urology, Second Faculty of Medicine, Charles University, 252 50 Prague, Czech Republic
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Liu L, Wang W. Developing and Validating a New Model to Predict the Risk of Poor Neurological Status of Acute Ischemic Stroke After Intravenous Thrombolysis. Neurologist 2023; 28:391-401. [PMID: 37639528 PMCID: PMC10627548 DOI: 10.1097/nrl.0000000000000506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
OBJECTIVES The objective of this study was to develop and validate a predictive model for the risk of poor neurological status in in-hospital patients with acute ischemic stroke (AIS) after intravenous thrombolysis. METHODS This 2-center retrospective study included patients with AIS treated at the Advanced Stroke Center of the Second Hospital of Hebei Medical University and Baoding No.1 Central Hospital between January 2018 and January 2020). The neurological function status at day 7 of AIS onset was used as the endpoint of the study, which was evaluated using the National Institute of Health Stroke Scale (NIHSS) score. RESULTS A total of 878 patients were included in the study and divided into training (n=652) and validation (n=226) sets. Seven variables were selected as predictors to establish the risk model: age, NIHSS before thrombolysis (NIHSS1), NIHSS 24 hours after thrombolysis (NIHSS3), high-density lipoprotein, antiplatelet, cerebral computed tomography after thrombolysis (CT2), and lower extremity venous color Doppler ultrasound. The risk prediction model achieved good discrimination (the areas under the Receiver Operating Characteristic curve in the training and validation sets were 0.9626 and 0.9413, respectively) and calibration (in the training set Emax=0.072, Eavg=0.01, P =0.528, and in the validation set Emax=0.123, Eavg=0.019, P =0.594, respectively). The decision curve analysis showed that the model could achieve a good net benefit. CONCLUSIONS The prediction model obtained in this study showed good discrimination, calibration, and clinical efficacy. This new nomogram can provide a reference for predicting the risk of poor neurological status in patients with acute ischemic stroke after intravenous thrombolysis.
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Affiliation(s)
- Lu Liu
- Department of Neurology, The Baoding Central Hospital, Baoding, Hebei, China
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Liu G, Li X, Zhao W, Shi R, Zhu Y, Wang Z, Pan H, Wang D. Development and validation of a nomogram for predicting gram-negative bacterial infections in patients with peritoneal dialysis-associated peritonitis. Heliyon 2023; 9:e18551. [PMID: 37520948 PMCID: PMC10382673 DOI: 10.1016/j.heliyon.2023.e18551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 07/14/2023] [Accepted: 07/20/2023] [Indexed: 08/01/2023] Open
Abstract
Background This study aimed to develop a nomogram for predicting gram-negative bacterial (GNB) infections in patients with peritoneal dialysis-associated peritonitis (PDAP) to identify patients at high risk for GNB infections. Methods In this investigation, hospitalization information was gathered retrospectively for patients with PDAP from January 2016 to December 2021. The concatenation of potential biomarkers obtained by univariate logistic regression, LASSO analysis, and RF algorithms into multivariate logistic regression was used to identify confounding factors related to GNB infections, which were then integrated into the nomogram. The concordance index (C-Index) was utilized to assess the precision of the model's predictions. The area under the curve (AUC) and decision curve analysis (DCA) was used to assess the predictive performance and clinical utility of the nomogram. Results The final study population included 217 patients with PDAP, and 37 (17.1%) patients had gram-negative bacteria due to dialysate effluent culture. After multivariate logistic regression, age, procalcitonin, and hemoglobin were predictive factors of GNB infections. The C-index and bootstrap-corrected index of the nomogram for estimating GNB infections in patients were 0.821 and 0.814, respectively. The calibration plots showed good agreement between the predictions of the nomogram and the actual observation of GNB infections. The AUC of the receiver operating characteristic curve was 0.821, 95% CI: 0.747-0.896, which indicates that the model has good predictive accuracy. In addition, the DCA curve showed that the nomogram had a high clinical value in the range of 1%-94%, which further demonstrated that the nomogram could accurately predict GNB infection in patients with PDAP. Conclusions We have created a new nomogram for predicting GNB infections in patients with PDAP. The nomogram model may improve the identification of GNB infections in patients with PDAP and contribute to timely intervention to improve patient prognosis.
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Affiliation(s)
- Guiling Liu
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xunliang Li
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Wenman Zhao
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Rui Shi
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yuyu Zhu
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Zhijuan Wang
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Haifeng Pan
- Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Deguang Wang
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
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Zheng X, He X. Development of a nomogram for the prediction of complicated appendicitis during pregnancy. BMC Surg 2023; 23:188. [PMID: 37393302 DOI: 10.1186/s12893-023-02064-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 05/31/2023] [Indexed: 07/03/2023] Open
Abstract
BACKGROUND Complicated appendicitis during pregnancy directly affects the clinical prognosis of both mother and fetus. However, accurate identification of complicated appendicitis in pregnancy is fraught with various challenges. The purpose of this study was to identify the risk factors and to develop a useful nomogram to predict complicated appendicitis during pregnancy. METHODS This retrospective study involved pregnant women who underwent appendectomy at the Maternal and Child Health Hospital of Hubei Provincial from May 2016 to May 2022 and who ultimately had histopathological confirmed acute appendicitis. Univariate and multivariate logistic regression were applied to analyze clinical parameters and imaging features as a way to identify risk factors. Then, nomogram and scoring systems predicting complicated appendicitis in pregnancy were constructed and evaluated. Finally, the potential non-linear association between risk factors and complicated appendicitis was analyzed using restricted cubic splines. RESULTS Three indicators were finally identified for the construction of the nomogram: gestational weeks, C-reactive protein (CRP), and neutrophil percentage (NEUT%). To improve the clinical utility, the gestational weeks were divided into three periods (first trimesters, second trimesters, and third trimesters), while the optimal cut-offs for CRP level and NEUT% were found to be 34.82 mg/L and 85.35%, respectively. Multivariate regression analysis showed that third trimesters (P = 0.013, OR = 16.81), CRP level ≥ 34.82 mg/L (P = 0.007, OR = 6.24) and NEUT% ≥85.35% (P = 0.011, OR = 18.05) were independent risk factors for complicated appendicitis. The area under the ROC curve (AUC) of the nomogram predicting complicated appendicitis in pregnancy was 0.872 (95% CI: 0.803-0.942). In addition, the model was shown to have excellent predictive performance by plotting calibration plots, Decision Curve Analysis (DCA), and clinical impact curves. When the optimal cut-off point of the scoring system was set at 12, the corresponding AUC, sensitivity, specificity, Positive Likelihood Ratio (PLR), Negative Likelihood Ratio (NLR), Positive Predictive Value (PPV), and Negative Predictive Value (NPV) values were AUC: 0.869(95% CI: 0.799-0.939),100%, 58.60%, 2.41, 0, 42%, and 100%, respectively. The restricted cubic splines revealed a linear relationship between these predictors and complicated appendicitis during pregnancy. CONCLUSIONS The nomogram utilizes a minimum number of variables to develop an optimal predictive model. Using this model, the risk of developing complicated appendicitis in individual patients can be determined so that reasonable treatment choices can be made.
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Affiliation(s)
- Xiaosong Zheng
- Department of General Surgery, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, NO.745 Wuluo Road, Hongshan District, Wuhan City, Hubei Province, 430070, P.R. China
| | - Xiaojun He
- Department of General Surgery, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, NO.745 Wuluo Road, Hongshan District, Wuhan City, Hubei Province, 430070, P.R. China.
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Han R, Huang L, Zhou S, Shen J, Li P, Li M, Wu X, Wang R. Novel clinical radiomic nomogram method for differentiating malignant from non-malignant pleural effusions. Heliyon 2023; 9:e18056. [PMID: 37539225 PMCID: PMC10395353 DOI: 10.1016/j.heliyon.2023.e18056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 06/29/2023] [Accepted: 07/05/2023] [Indexed: 08/05/2023] Open
Abstract
Objectives To establish a clinical radiomics nomogram that differentiates malignant and non-malignant pleural effusions. Methods A total of 146 patients with malignant pleural effusion (MPE) and 93 patients with non-MPE (NMPE) were included. The ROI image features of chest lesions were extracted using CT. Univariate analysis was performed, and least absolute shrinkage selection operator and multivariate logistic analysis were used to screen radiomics features and calculate the radiomics score. A nomogram was constructed by combining clinical factors with radiomics scores. ROC curve and decision curve analysis (DCA) were used to evaluate the prediction effect. Results After screening, 19 radiomics features and 2 clinical factors were selected as optimal predictors to establish a combined model and construct a nomogram. The AUC of the combined model was 0.968 (95% confidence interval [CI] = 0.944-0.986) in the training cohort and 0.873 (95% CI = 0.796-0.940) in the validation cohort. The AUC value of the combined model was significantly higher than those of the clinical and radiomics models (0.968 vs. 0.874 vs. 0.878, respectively). This was similar in the validation cohort (0.873, 0.764, and 0.808, respectively). DCA confirmed the clinical utility of the radiomics nomogram. Conclusion CT-based radiomics showed better diagnostic accuracy and model fit than clinical and radiological features in distinguishing MPE from NMPE. The combination of both achieved better diagnostic performance. These findings support the clinical application of the nomogram in diagnosing MPE using chest CT.
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Affiliation(s)
- Rui Han
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
| | - Ling Huang
- Department of Infectious Disease, Hefei Second People's Hospital, Hefei, 230001, China
| | - Sijing Zhou
- Department of Occupational Disease, Hefei Third Clinical College of Anhui Medical University, Hefei, 230022, China
| | - Jiran Shen
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
| | - Pulin Li
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
| | - Min Li
- Department of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
| | - Xingwang Wu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
| | - Ran Wang
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
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Rajakannu M, Cherqui D, Cunha AS, Castaing D, Adam R, Vibert E. Predictive nomograms for postoperative 90-day morbidity and mortality in patients undergoing liver resection for various hepatobiliary diseases. Surgery 2023; 173:993-1000. [PMID: 36669938 DOI: 10.1016/j.surg.2022.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 11/10/2022] [Accepted: 11/13/2022] [Indexed: 01/20/2023]
Abstract
BACKGROUND Postoperative complications affect the long-term survival and quality of life in patients undergoing liver resection. No model has yet been validated to predict 90-day severe morbidity and mortality. METHODS The prospective recruitment of patients undergoing liver resection for various indications was performed. Preoperative clinical and laboratory data, including liver stiffness, indocyanine green retention, and intraoperative parameters, were analyzed to develop predictive nomograms for postoperative severe morbidity and mortality. Calibration plots were used to perform external validation. RESULTS The most common indications in 418 liver resections performed were colorectal metastases (N = 149 [35.6%]), hepatocellular carcinoma (N = 106 [25.4%]), and benign liver tumors (N = 60 [14.3%]). Major liver resections were performed in 164 (39.2%) patients. Severe morbidity and mortality were observed in 87 (20.8%) and 9 (2.2%) of patients, respectively, during the 90-day postoperative period. Post-hepatectomy liver failure was observed in 19 (4.5%) patients, resulting in the death of 4. The independent predictors of 90-day severe morbidity were age (odds ratio:1.02, P = .06), liver stiffness (odds ratio: 1.23, P = .04], number of resected segments (odds ratio: 1.28, P = .004), and operative time (odds ratio: 1.01, P = .01). Independent predictors of 90-day mortality were diabetes mellitus (odds ratio: 6.6, P = .04), tumor size >50 mm (odds ratio:4.8, P = .08), liver stiffness ≥22 kPa (odds ratio:7.0, P = .04), and operative time ≥6 hours (odds ratio: 6.1, P = .05). Nomograms were developed using these independent predictors and validated by testing the Goodness of fit in calibration plots (P = .64 for severe morbidity; P = .8 for mortality). CONCLUSION Proposed nomograms would enable a personalized approach to identifying patients at risk of complications and adapting surgical treatment according to their clinical profile and the center's expertise.
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Affiliation(s)
- Muthukumarassamy Rajakannu
- Centre Hépato-Biliaire, AH-HP Hôpital Paul Brousse, Villejuif, France; Inserm, Unité UMR-S 1193, Villejuif, France; Université Paris-Sud, Faculté de Médecine, Le Kremlin-Bicêtre, France
| | - Daniel Cherqui
- Centre Hépato-Biliaire, AH-HP Hôpital Paul Brousse, Villejuif, France; Université Paris-Sud, Faculté de Médecine, Le Kremlin-Bicêtre, France
| | - Antonio Sa Cunha
- Centre Hépato-Biliaire, AH-HP Hôpital Paul Brousse, Villejuif, France; Université Paris-Sud, Faculté de Médecine, Le Kremlin-Bicêtre, France
| | - Denis Castaing
- Centre Hépato-Biliaire, AH-HP Hôpital Paul Brousse, Villejuif, France; Inserm, Unité UMR-S 1193, Villejuif, France; Université Paris-Sud, Faculté de Médecine, Le Kremlin-Bicêtre, France
| | - René Adam
- Centre Hépato-Biliaire, AH-HP Hôpital Paul Brousse, Villejuif, France; Inserm, Unité UMR-S 1193, Villejuif, France; Inserm, Unité UMR-S 776, Villejuif, France
| | - Eric Vibert
- Centre Hépato-Biliaire, AH-HP Hôpital Paul Brousse, Villejuif, France; Inserm, Unité UMR-S 1193, Villejuif, France; Université Paris-Sud, Faculté de Médecine, Le Kremlin-Bicêtre, France. https://twitter.com/Eric_Vibert
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Gong X, Tang L, Wu M, Shao S, Zhou K, Hua Y, Wang C, Li Y. Development of a nomogram prediction model for early identification of persistent coronary artery aneurysms in kawasaki disease. BMC Pediatr 2023; 23:79. [PMID: 36797697 PMCID: PMC9933279 DOI: 10.1186/s12887-023-03876-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 01/27/2023] [Indexed: 02/18/2023] Open
Abstract
BACKGROUND Coronary artery aneurysms (CAA) persistence prediction is critical in evaluating Kawasaki disease (KD). This study established a nomogram prediction system based on potential risk factors for assessing the risk of CAA persistence in a contemporary cohort of patients with KD. METHODS This cohort comprised 105 patients with KD who had been diagnosed with CAA during the acute or subacute phase by echocardiography. The follow-up duration was at least 1 year. The clinical and laboratory parameters were compared between the CAA regression and persistence groups. Multivariable logistic regression analysis was used to identify the independent risk factors for CAA persistence, which were subsequently used to build the nomogram predictive model. Decision curve analysis was used to assess the net benefits of different nomogram scores. RESULTS Of these patients with CAA, 27.6% of patients presented with persistent lesions. The incidences of CAA persistence were 14.1%, 81.3%, and 100.0% in patients with small, medium, and large aneurysms, respectively. The ratio of neutrophils to lymphocytes, γ-GT, and CAA size at diagnosis were considered as the independent risk factors for CAA persistence in patients with KD. The nomogram predictive models yielded a high capability in predicting CAA persistence, based on either univariable or multivariable analyses-identified parameters, compared with using CAA size as a single predictor. CONCLUSION The initial ratio of neutrophils to lymphocytes, γ-GT, and CAA size were the independent risk factors for CAA persistence in patients with KD. Nomogram scores could help elevate predictive efficacy in detecting CAA persistence.
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Affiliation(s)
- Xue Gong
- grid.461863.e0000 0004 1757 9397Department of Pediatrics, Ministry of Education Key Laboratory of Women and Children’s Diseases and Birth Defects, West China Second University Hospital, Sichuan University, Sichuan Chengdu, China
| | - Liting Tang
- grid.461863.e0000 0004 1757 9397Department of Pediatrics, Ministry of Education Key Laboratory of Women and Children’s Diseases and Birth Defects, West China Second University Hospital, Sichuan University, Sichuan Chengdu, China
| | - Mei Wu
- grid.461863.e0000 0004 1757 9397Department of Pediatrics, Ministry of Education Key Laboratory of Women and Children’s Diseases and Birth Defects, West China Second University Hospital, Sichuan University, Sichuan Chengdu, China
| | - Shuran Shao
- grid.461863.e0000 0004 1757 9397Department of Pediatrics, Ministry of Education Key Laboratory of Women and Children’s Diseases and Birth Defects, West China Second University Hospital, Sichuan University, Sichuan Chengdu, China
| | - Kaiyu Zhou
- grid.461863.e0000 0004 1757 9397Department of Pediatrics, Ministry of Education Key Laboratory of Women and Children’s Diseases and Birth Defects, West China Second University Hospital, Sichuan University, Sichuan Chengdu, China
| | - Yimin Hua
- grid.461863.e0000 0004 1757 9397Department of Pediatrics, Ministry of Education Key Laboratory of Women and Children’s Diseases and Birth Defects, West China Second University Hospital, Sichuan University, Sichuan Chengdu, China
| | - Chuan Wang
- Department of Pediatrics, Ministry of Education Key Laboratory of Women and Children's Diseases and Birth Defects, West China Second University Hospital, Sichuan University, Sichuan, Chengdu, China. .,Department of Pediatrics, West China Second University Hospital, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, Sichuan University, 20 3rd Section, Renmin S.Rd, Sichuan, 610041, Chengdu, China.
| | - Yifei Li
- Department of Pediatrics, Ministry of Education Key Laboratory of Women and Children's Diseases and Birth Defects, West China Second University Hospital, Sichuan University, Sichuan, Chengdu, China. .,Department of Pediatrics, West China Second University Hospital, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, Sichuan University, 20 3rd Section, Renmin S.Rd, Sichuan, 610041, Chengdu, China.
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Identification of Key Biomarkers Associated with Immunogenic Cell Death and Their Regulatory Mechanisms in Severe Acute Pancreatitis Based on WGCNA and Machine Learning. Int J Mol Sci 2023; 24:ijms24033033. [PMID: 36769358 PMCID: PMC9918120 DOI: 10.3390/ijms24033033] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 01/20/2023] [Accepted: 01/30/2023] [Indexed: 02/09/2023] Open
Abstract
Immunogenic cell death (ICD) is a form of programmed cell death with a strong sense of inflammatory detection, whose powerful situational awareness can cause the reactivation of aberrant immunity. However, the role of ICD in the pathogenesis of severe acute pancreatitis (SAP) has yet to be investigated. This study aims to explore the pivotal genes associated with ICD in SAP and how they relate to immune infiltration and short-chain fatty acids (SCFAs), in order to provide a theoretical foundation for further, in-depth mechanistic studies. We downloaded GSE194331 datasets from the Gene Expression Omnibus (GEO). The use of differentially expressed gene (DEG) analysis; weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression analysis allowed us to identify a total of three ICD-related hub genes (LY96, BCL2, IFNGR1) in SAP. Furthermore, single sample gene set enrichment analysis (ssGSEA) demonstrated that hub genes are closely associated with the infiltration of specific immune cells, the activation of immune pathways and the metabolism of SCFAs (especially butyrate). These findings were validated through the analysis of gene expression patterns in both clinical patients and rat animal models of SAP. In conclusion, the first concept of ICD in the pathogenesis of SAP was proposed in our study. This has important implications for future investigations into the pro-inflammatory immune mechanisms mediated by damage-associated molecular patterns (DAMPs) in the late stages of SAP.
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A nomogram for the preoperative estimation of neuroblastoma risk despite inadequate biopsy information. Pediatr Surg Int 2023; 39:98. [PMID: 36725741 DOI: 10.1007/s00383-023-05370-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/26/2022] [Indexed: 02/03/2023]
Abstract
BACKGROUND AND PURPOSE If the preoperative pathological information is inadequate, a risk classification may not be able to be determined for some patients with neuroblastoma. Our objectives were to include imaging factors, serum biomarkers, and demographic factors in a nomogram to distinguish high-risk patients before surgical resection based on the COG classification. METHOD A total of 106 patients were included in the study. Of these, patients with clinicopathologically confirmed neuroblastoma at Tianjin Children's Hospital from January 2013 to November 2021 formed the training cohort (n = 82) for nomogram development, and those patients from January 2010 to December 2013 formed the validation cohort (n = 24) to confirm the model's performance. RESULT On multivariate analysis of the primary cohort, independent factors for high risk were the presence of distant metastasis (p = 0.004), lactate dehydrogenase (LDH) (p = 0.009), and tumor volume (p = 0.033), which were all selected into the nomogram. The calibration curve for probability showed good agreement between prediction by nomogram and actual observation. The C-index of the nomogram was 0.95 95% [0.916-0.99]. Application of the nomogram in the validation cohort still gave good discrimination and good calibration. CONCLUSION Three independent factors including the presence of distant metastasis, lactate dehydrogenase (LDH), and tumor volume are associated with high-risk neuroblastoma and selected into the nomogram. The novel nomogram has the flexibility to apply a clinically suitable cutoff to identify high-risk neuroblastoma patients despite inadequate preoperative pathological information. The nomogram can allow these patients to be offered suitable induction chemotherapy regimens and surgical plans. LEVELS OF EVIDENCE Level III.
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Gu Z, Zheng Z, Zhang W, Mao S, Wang S, Geng J, Yao X. The development and assessment of a predicting nomogram for the recovery of immediate urinary continence following laparoscopic radical prostatectomy. Front Surg 2023; 9:1071093. [PMID: 36684134 PMCID: PMC9852533 DOI: 10.3389/fsurg.2022.1071093] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 11/11/2022] [Indexed: 01/09/2023] Open
Abstract
Purpose This study aimed to develop a nomogram to predict the recovery of immediate urinary continence in laparoscopic radical prostatectomy (LRP) patients. Methods A prediction model was developed based on a dataset of 154 LRP patients. Immediate urinary continence was defined as free from using pads within 7 days after the removal of the urinary catheter. The least absolute shrinkage and selection operator regression (LASSO) model was applied to screen the features. Multivariate logistic regression analysis was used to establish prediction model integrating the features selected from the LASSO regression analysis. Receiver operating curve (ROC), calibration and decision curve analysis (DCA) were used to assess the model's discrimination, calibration and clinical utility. Results The identified features of the prediction model included age, body mass index (BMI) and three pelvic anatomic parameters measured by MRI: membranous urethral length (MUL), intravesical prostatic protrusion length (IPPL) and puborectalis muscle width (PMW). The nomogram showed good discrimination with an are under the curve(AUC) of 0.914 (95% CI, 0.865-0.959, p < 0.001). Moreover, good calibration was showed in the model. Lastly, DCA showed that the nomogram was clinically useful. Conclusion The developed novel nomogram that can predict the possibility for post-prostatectomy patients to recover immediate urinary continence could be used as a counseling tool to explain urinary incontinence to patients after LRP.
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Affiliation(s)
- Zhuoran Gu
- Department of Urology, Shanghai Tenth People's Hospital; Institute of Urinary Oncology, Tongji University School of Medicine, Shanghai, China
| | - Zongtai Zheng
- Department of Urology, Shanghai Tenth People's Hospital; Institute of Urinary Oncology, Tongji University School of Medicine, Shanghai, China
| | - Wentao Zhang
- Department of Urology, Shanghai Tenth People's Hospital; Institute of Urinary Oncology, Tongji University School of Medicine, Shanghai, China
| | - Shiyu Mao
- Department of Urology, Shanghai Tenth People's Hospital; Institute of Urinary Oncology, Tongji University School of Medicine, Shanghai, China
| | - Shuai Wang
- Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Jiang Geng
- Department of Urology, Shanghai Tenth People's Hospital; Institute of Urinary Oncology, Tongji University School of Medicine, Shanghai, China
| | - Xudong Yao
- Department of Urology, Shanghai Tenth People's Hospital; Institute of Urinary Oncology, Tongji University School of Medicine, Shanghai, China
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Kubo N, Cho H, Lee D, Yang H, Kim Y, Khalayleh H, Yoon HM, Ryu KW, Hanna GB, Coit DG, Hakamada K, Kim YW. Risk prediction model of peritoneal seeding in advanced gastric cancer: A decision tool for diagnostic laparoscopy. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2022; 49:853-861. [PMID: 36586786 DOI: 10.1016/j.ejso.2022.12.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 12/07/2022] [Accepted: 12/23/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND Selective diagnostic laparoscopy in gastric cancer patients at high risk of peritoneal metastasis is essential for optimal treatment planning. In this study available clinicopathologic factors predictive of peritoneal seeding in advanced gastric cancer (AGC) were identified, and this information was translated into a clinically useful tool. METHODS Totally 2833 patients underwent surgery for AGC between 2003 and 2013. The study identified clinicopathologic factors associated with the risk of peritoneal seeding for constructing nomograms using a multivariate logistic regression model with backward elimination. A nomogram was constructed to generate a numerical value indicating risk. Accuracy was validated using bootstrapping and cross-validation. RESULTS The proportion of seeding positive was 12.7% in females and 9.6% in males. Of 2833 patients who underwent surgery for AGC, 300 (10.6%) were intraoperatively identified with peritoneal seeding. Multivariate analysis revealed the following factors associated with peritoneal seeding: high American Society of Anesthesiologists score, fibrinogen, Borrmann type 3 or 4 tumors, the involvement of the middle, anterior, and greater curvature, cT3 or cT4cN1 or cN2 or cN3, cM1, and the presence of ascites or peritoneal thickening or plaque or a nodule on the peritoneal wall on computed tomography. The bootstrap analysis revealed a robust concordance between mean and final parameter estimates. The area under the ROC curve for the final model was 0.856 (95% CI, 0.835-0.877), which implies good performance. CONCLUSIONS This nomogram provides effective risk estimates of peritoneal seeding from gastric cancer and can facilitate individualized decision-making regarding the selective use of diagnostic laparoscopy.
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Affiliation(s)
- Norihito Kubo
- Center for Gastric Cancer, National Cancer Center, Korea; Department of Gastroenterological Surgery, Hirosaki University Graduate School of Medicine, Japan
| | - Hyunsoon Cho
- Department of Cancer Control and Population Science, Graduate School of Cancer Science and Policy, National Cancer Center, Korea
| | - Dahhay Lee
- Department of Cancer Control and Population Science, Graduate School of Cancer Science and Policy, National Cancer Center, Korea
| | - Hannah Yang
- Center for Gastric Cancer, National Cancer Center, Korea; Division of Biology and Biological Engineering, California Institute of Technology Pasadena, California, 91125, USA
| | - Youngsook Kim
- Center for Gastric Cancer, National Cancer Center, Korea
| | - Harbi Khalayleh
- Center for Gastric Cancer, National Cancer Center, Korea; Faculty of Medicine, Hebrew University of Jerusalem, Israel; The Department of Surgery, Kaplan Medical Center, Israel
| | - Hong Man Yoon
- Center for Gastric Cancer, National Cancer Center, Korea
| | - Keun Won Ryu
- Center for Gastric Cancer, National Cancer Center, Korea
| | - George B Hanna
- Department of Surgery and Cancer, Imperial College of London, United Kingdom
| | - Daniel G Coit
- Gastric and Mixed Tumor Service, Memorial Sloan Kettering Cancer Center, USA
| | - Kenichi Hakamada
- Department of Gastroenterological Surgery, Hirosaki University Graduate School of Medicine, Japan
| | - Young-Woo Kim
- Center for Gastric Cancer, National Cancer Center, Korea; Department of Cancer Control and Population Science, Graduate School of Cancer Science and Policy, National Cancer Center, Korea.
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Lin C, Mi J, Zhang Y, Duan S, Wu J, Li Y. A nomogram prediction model for early death in patients with persistent pulmonary hypertension of the newborn. Front Cardiovasc Med 2022; 9:1077339. [PMID: 36620618 PMCID: PMC9813219 DOI: 10.3389/fcvm.2022.1077339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 12/07/2022] [Indexed: 12/24/2022] Open
Abstract
Background Persistent pulmonary hypertension of the newborn (PPHN) is a major lethal disorder in neonates that leads to an extremely high mortality rate. Thus, the early identification of adverse outcomes in PPHN is critical for clinical practice. This research attempted to develop a nomogram prediction system for assessing the mortality of newborns with PPHN. Methods Two hundred and three newborns with PPHN diagnosed from January 2015 to March 2022 were involved in the study. The clinical features of these newborns and pregnancy details were compared between newborns in the survival and lethal groups. Univariable and multivariate analyses were established in sequence to demonstrate the essential risk factors. The nomogram prediction model was built. Results A total of 203 newborns were included in the analysis. 136 (67.0%) newborns represented the hospital survival group. Plasma pH value (OR = 0.606, p = 0.000, 95% CI 0.45715-0.80315), septicemia (OR = 3.544, p = 0.000, 95% CI 1.85160-6.78300), and abnormal pregnancy history (OR = 3.331, p = 0.008, 95% CI 1.37550-8.06680) were identified as independent risk factors for neonatal death in newborns associated with PPHN. Finally, the nomogram predictive model was established based on multivariate analysis results, indicating the efficacies of prediction and calibration. Conclusion This study generated an applicable risk score formula using the plasma pH value, septicemia, and abnormal pregnancy history to recognize neonatal death in newborns with PPHN, presenting a sufficient predictive value and calibration.
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Affiliation(s)
- Chuyang Lin
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jiao Mi
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yinyue Zhang
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Sichen Duan
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jinlin Wu
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yifei Li
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
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Tao Y, Chen S, Yu J, Shen Q, Ruan R, Wang S. Risk factors of lymph node metastasis or lymphovascular invasion for superficial esophageal squamous cell carcinoma: A practical and effective predictive nomogram based on a cancer hospital data. Front Med (Lausanne) 2022; 9:1038097. [DOI: 10.3389/fmed.2022.1038097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 11/02/2022] [Indexed: 11/18/2022] Open
Abstract
BackgroundLymphovascular invasion (LVI) is mostly used as a preoperative predictor to establish lymph node metastasis (LNM) prediction models for superficial esophageal squamous cell carcinoma (SESCC). However, LVI still needs to be confirmed by postoperative pathology. In this study, we combined LNM and LVI as a unified outcome and named it LNM/LVI, and aimed to develop an LNM/LVI prediction model in SESCC using preoperative factors.MethodsA total of 512 patients who underwent radical resection of SESCC were retrospectively collected. Logistic regression and least absolute shrinkage and selection operator (LASSO) regression were adopted to identify the predictive factors of LNM/LVI. Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were calculated to select the potential predictive factors from the results of LASSO and logistic regression. A nomogram for predicting LNM/LVI was established by incorporating these factors. The efficacy, accuracy, and clinical utility of the nomogram were, respectively, assessed with the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). Finally, the random forest (RF) algorithm was used to further evaluate the impact of these factors included in the nomogram on LNM/LVI.ResultsTumor size, tumor location, tumor invasion depth, tumor differentiation, and macroscopic type were confirmed as independent risk factors for LNM/LVI according to the results of logistic regression, LASSO regression, IDI, and NRI analyses. A nomogram including these five variables showed a good performance in LNM/LVI prediction (AUC = 0.776). The calibration curve revealed that the predictive results of this nomogram were nearly consistent with actual observations. Significant clinical utility of our nomogram was demonstrated by DCA. The RF model with the same five variables also had similar predictive efficacy with the nomogram (AUC = 0.775).ConclusionThe nomogram was adopted as a final tool for predicting LNM/LVI because its risk score system made it more user-friendly and clinically useful than the random forest model, which can help clinicians make optimal treatment decisions for patients with SESCC.
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Li H, Sheng W, Cai M, Chen Q, Lin B, Zhang W, Li W. A predictive nomogram for a failed trial of labor after cesarean: A retrospective cohort study. J Obstet Gynaecol Res 2022; 48:2798-2806. [PMID: 36055678 PMCID: PMC9825937 DOI: 10.1111/jog.15398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 07/29/2022] [Accepted: 08/01/2022] [Indexed: 01/11/2023]
Abstract
AIM To validate risk factors and a nomogram prediction model for the failure of a trial of labor after cesarean section (TOLAC) in a Chinese population. METHODS We included women who tried TOLAC between January 2017 and May 2019, grouped according to the success/failure of TOLAC. The patients were randomized 3:1 into the development and validation sets. Multivariable logistic regression analyses were used to develop a nomogram prediction model for TOLAC failure. RESULTS In total, 535 (86.3%) of the women (n = 620) aged 29-34 years had a successful vaginal birth after cesarean (VBAC). All women had a fully healed previous uterine incision. The univariable analyses showed that the cephalopelvic score (p < 0.001), BMI (p = 0.001), full engagement into the pelvis (p < 0.001), Bishop cervical maturity score (p < 0.001), and estimated fetal weight at admission (p < 0.001) could enter the multivariable model. Furthermore, the multivariable analysis showed that the cephalopelvic score (OR = 0.42, 95%CI: 0.23-0.77, p = 0.005), full engagement in the pelvis (OR = 0.16, 95%CI: 0.08-0.33, p < 0.001), and Bishop cervical maturity score (OR = 0.46, 95%CI: 0.35-0.59, p < 0.001) were independent predictors of the failure of TOLAC. CONCLUSION This study proposes a nomogram that can assess the risk of failure of TOLAC in Chinese pregnant women. The statistical model could help clinicians know the likelihood of successful TOLAC in the clinical setting.
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Affiliation(s)
- Hua Li
- Department of ObstetricsChangsha Hospital for Maternal & Child Health CareChangshaHunan ProvinceChina
| | - Wen Sheng
- Department of ObstetricsChangsha Hospital for Maternal & Child Health CareChangshaHunan ProvinceChina
| | - Min Cai
- Department of ObstetricsChangsha Hospital for Maternal & Child Health CareChangshaHunan ProvinceChina
| | - Qiuling Chen
- Department of ObstetricsChangsha Hospital for Maternal & Child Health CareChangshaHunan ProvinceChina
| | - Beibei Lin
- Department of ObstetricsChangsha Hospital for Maternal & Child Health CareChangshaHunan ProvinceChina
| | - Weishe Zhang
- Department of ObstetricsXiangya Hospital Central South UniversityHunanChina
| | - Wenxia Li
- Department of ObstetricsChangsha Hospital for Maternal & Child Health CareChangshaHunan ProvinceChina
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A novel nomogram based on log odds of positive lymph nodes to predict survival for non-metastatic gallbladder adenocarcinoma after surgery. Sci Rep 2022; 12:16466. [PMID: 36183006 PMCID: PMC9526724 DOI: 10.1038/s41598-022-20933-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 09/21/2022] [Indexed: 11/16/2022] Open
Abstract
The prognosis of non-metastatic gallbladder adenocarcinoma (NM-GBA) patients is affected by the status of metastatic lymph nodes. The purpose of this study was to explore the prognostic value of the log odds of positive lymph nodes (LODDS) and develop a novel nomogram to predict the overall survival in NM-GBA patients. A total of 1035 patients confirmed to have NM-GBA were selected from the Surveillance, Epidemiology, and End Results (SEER) database and further divided into training and validation cohorts. The discrimination and calibration of the nomogram were evaluated using the concordance index (C-index), the area under the time-dependent receiver operating characteristic curve (time-dependent AUC), and calibration plots. The net benefits and clinical utility of the nomogram were quantified and compared with those of the 8th edition American Joint Committee on Cancer (AJCC) Tumor-Node-Metastasis (TNM) staging system using decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination improvement (IDI). The risk stratifications of the nomogram and the TNM-staging system were compared. LODDS showed the highest accuracy in predicting OS for NM-GBA. The C-index (0.730 for the training cohort and 0.746 for the validation cohort) and the time-dependent AUC (> 0.7) indicated the satisfactory discriminative ability of the nomogram. The calibration plots showed a high degree of consistency. The DCA, NRI, and IDI indicated that the nomogram performed significantly better than the TNM-staging (P < 0.05). A novel LODDS-included nomogram was developed and validated to assist clinicians in evaluating the prognosis of NM-GBA patients.
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Development and Validation of a Novel Nomogram to Predict the Risk of Intervertebral Disc Degeneration. Mediators Inflamm 2022; 2022:3665934. [PMID: 36123994 PMCID: PMC9482533 DOI: 10.1155/2022/3665934] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 08/08/2022] [Accepted: 08/26/2022] [Indexed: 11/18/2022] Open
Abstract
Intervertebral disc degeneration (IVDD) has been a complex disorder resulted from genetic and environmental risk factors. The aim of this study was to identify the risk factors associated with IVDD in orthopaedic patients and develop a prediction model for predicting the risk of IVDD. A total of 309 patients were retrospectively included in the study and randomly divided into the training group and the validation group. The least absolute shrinkage and selection operator regression (LASSO) and the univariate logistic regression analysis were used to optimize factors selection for the IVDD risk model. Multivariable logistic regression analysis was used to establish a predicting nomogram model incorporating the factors. In addition, discrimination, calibration, and clinical usefulness of the nomogram model were evaluated via the C-index, receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA). Then, based on the results above, the relationship between IVDD and angiotensin II (AngII) level in peripheral blood was examined prospectively. The predictors of the nomogram include age, sex, hypertension, diabetes, gout, working posture, and exercising hours per week. The C-index values of the training and validation groups were 0.916 (95% CI, 0.876-0.956) and 0.949 (95% CI, 0.909-0.989), respectively, which indicated that the model displayed good discrimination. In addition, the area under the curve (AUC) values of the ROC curve of the training and the validation group were 0.815 (95% CI, 0.759-0.870) and 0.805 (95% CI, 0.718-0.892), respectively, revealing the satisfactory discrimination performance of the model. The prospective investigation showed that the average AngII level in the degenerated group (97.62 ± 44.02 pg/mL) was significantly higher than that in the nondegenerated group (52.91 ± 9.01 pg/mL) (p < 0.001). This present study explored the risk factors for IVDD and established a prediction model, which would effectively predict the risk of IVDD. In addition, based on the prediction model, AngII was revealed to be a potentially auxiliary clinical diagnostic marker for IVDD.
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Liu Q, Tang L, Chen M. Ultrasound Strain Elastography and Contrast-Enhanced Ultrasound in Predicting the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer: A Nomogram Integrating Ki-67 and Ultrasound Features. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2022; 41:2191-2201. [PMID: 34888900 DOI: 10.1002/jum.15900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 09/27/2021] [Accepted: 11/19/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVES To explore whether conventional elastography and contrast-enhanced ultrasound (CEUS) combined with histopathology can monitor the efficacy of neoadjuvant chemotherapy (NAC) for breast cancer (BC), and develop a Nomogram prediction model monitoring response to NAC. METHODS From February 2010 to November 2015, 91 BC patients who received NAC were recruited. The maximum diameter, stiffness, and CEUS features were assessed. Core biopsy, surgical pathology immunophenotype, and Miller-Payne (MP) evaluation were documented. Univariate and multivariate analysis was performed using receiver operating characteristic (ROC) analysis and logistic regression analysis. RESULTS There were 37 cases showing pathological complete response (pCR) and 54 of non-pCR. The changes of maximal diameter were correlated with MP (P < .05). The sensitivity (SEN), specificity (SPE), and area under the ROC curve (AUC) of baseline size predicting pCR were 57.40%, 70.30%, and 0.64 (P = .024). Baseline Ki-67 index of pCR group is significantly higher than that of non-pCR group (P = .029), and the ROC analysis of baseline Ki-67 indicates the SEN, SPE, and AUC of 51.70%, 78.00%, and 0.638 (P = .050). When combined with size, CEUS features, stiffness, and Ki-67 of baseline, the ROC curve shows good performance with SEN, SPE, and AUC of 70.00%, 76.19%, 0.821 (P = .004). Incorporating the change of characteristics into multivariate regression analysis, the results demonstrate excellent performance (SEN 100.00%, SPE 95.24%, AUC 0.986, P = .000). CONCLUSIONS The change of the maximum size was correlated with MP score, which can provide reference to predict efficacy of NAC and evaluate residual lesions. When combining with elastography, CEUS, and Ki-67, better performance in predicting pathological response was shown.
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Affiliation(s)
- Qi Liu
- Department of Ultrasound Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lei Tang
- Department of Ultrasound Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Man Chen
- Department of Ultrasound Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Nie K, Hu P, Zheng J, Zhang Y, Yang P, Jabbour SK, Yue N, Dong X, Xu S, Shen B, Niu T, Hu X, Cai X, Sun J. Incremental Value of Radiomics in 5-Year Overall Survival Prediction for Stage II-III Rectal Cancer. Front Oncol 2022; 12:779030. [PMID: 35847948 PMCID: PMC9279662 DOI: 10.3389/fonc.2022.779030] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 05/30/2022] [Indexed: 11/13/2022] Open
Abstract
Although rectal cancer comprises up to one-third of colorectal cancer cases and several prognosis nomograms have been established for colon cancer, statistical tools for predicting long-term survival in rectal cancer are lacking. In addition, previous prognostic studies did not include much imaging findings, qualitatively or quantitatively. Therefore, we include multiparametric MRI information from both radiologists' readings and quantitative radiomics signatures to construct a prognostic model that allows 5-year overall survival (OS) prediction for advance-staged rectal cancer patients. The result suggested that the model combined with quantitative imaging findings might outperform that of conventional TNM staging or other clinical prognostic factors. It was noteworthy that the identified radiomics signature consisted of three from dynamic contrast-enhanced (DCE)-MRI, four from anatomical MRI, and one from functional diffusion-weighted imaging (DWI). This highlighted the importance of multiparametric MRI to address the issue of long-term survival estimation in rectal cancer. Additionally, the constructed radiomics signature demonstrated value to the conventional prognostic factors in predicting 5-year OS for stage II-III rectal cancer. The presented nomogram also provides a practical example of individualized prognosis estimation and may potentially impact treatment strategies.
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Affiliation(s)
- Ke Nie
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Peng Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jianjun Zheng
- Department of Radiology, Hwa Mei Hospital, Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, University of Chinese Academy of Sciences, Ningbo, China
| | - Yang Zhang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Pengfei Yang
- Institute of Translational Medicine, Zhejiang University, Hangzhou, China
| | - Salma K. Jabbour
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Ning Yue
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Xue Dong
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shufeng Xu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Bo Shen
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tianye Niu
- Institute of Translational Medicine, Zhejiang University, Hangzhou, China
| | - Xiaotong Hu
- Biomedical Research Center and Key Laboratory of Biotherapy of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiujun Cai
- Department of General Surgery, Innovation Center for Minimally Invasive Techniques and Devices, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jihong Sun
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Innovation Center for Minimally Invasive Techniques and Devices, Zhejiang University, Hangzhou, China
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Jiang J, Zhong W, Huang W, Gao Y, He Y, Li X, Liu Z, Zhou H, Fu Y, Liu R, Zhang W. Development and Validation of a Predictive Nomogram with Age and Laboratory Findings for Severe COVID-19 in Hunan Province, China. Ther Clin Risk Manag 2022; 18:579-591. [PMID: 35607424 PMCID: PMC9123913 DOI: 10.2147/tcrm.s361936] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 05/03/2022] [Indexed: 12/13/2022] Open
Abstract
Purpose To identify more objectively predictive factors of severe outcome among patients hospitalized for coronavirus disease 2019 (COVID-19). Patients and Methods A retrospective cohort of 479 hospitalized patients diagnosed with COVID-19 in Hunan Province was selected. The prognostic effects of factors such as age and laboratory indicators were analyzed using the Kaplan–Meier method and Cox proportional hazards model. A prognostic nomogram model was established to predict the progression of patients with COVID-19. Results A total of 524 patients in Hunan province with COVID-19 from December 2019 to October 2020 were retrospectively recruited. Among them, 479 eligible patients were randomly assigned into the training cohort (n = 383) and validation cohort (n = 96), at a ratio of 8:2. Sixty-eight (17.8%) and 15 (15.6%) patients developed severe COVID-19 after admission in the training cohort and validation cohort, respectively. The differences in baseline characteristics were not statistically significant between the two cohorts with regard to age, sex, and comorbidities (P > 0.05). Multivariable analyses included age, C-reactive protein, fibrinogen, lactic dehydrogenase, neutrophil-to-lymphocyte ratio, urea, albumin-to-globulin ratio, and eosinophil count as predictive factors for patients with progression to severe COVID-19. A nomogram was constructed with sufficient discriminatory power (C index = 0.81), and proper consistency between the prediction and observation, with an area under the ROC curve of 0.81 and 0.86 in the training and validation cohort, respectively. Conclusion We proposed a simple nomogram for early detection of patients with non-severe COVID-19 but at high risk of progression to severe COVID-19, which could help optimize clinical care and personalized decision-making therapies.
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Affiliation(s)
- Junyi Jiang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, People’s Republic of China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, People’s Republic of China
- National Clinical Research Center for Geriatric Disorders, Changsha, Hunan, People’s Republic of China
- Aier Eye Institute, Changsha, Hunan, People’s Republic of China
| | - WeiJun Zhong
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, People’s Republic of China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, People’s Republic of China
- National Clinical Research Center for Geriatric Disorders, Changsha, Hunan, People’s Republic of China
| | - WeiHua Huang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, People’s Republic of China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, People’s Republic of China
- National Clinical Research Center for Geriatric Disorders, Changsha, Hunan, People’s Republic of China
| | - Yongchao Gao
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, People’s Republic of China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, People’s Republic of China
- National Clinical Research Center for Geriatric Disorders, Changsha, Hunan, People’s Republic of China
| | - Yijing He
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, People’s Republic of China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, People’s Republic of China
- National Clinical Research Center for Geriatric Disorders, Changsha, Hunan, People’s Republic of China
| | - Xi Li
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, People’s Republic of China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, People’s Republic of China
- National Clinical Research Center for Geriatric Disorders, Changsha, Hunan, People’s Republic of China
| | - Zhaoqian Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, People’s Republic of China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, People’s Republic of China
- National Clinical Research Center for Geriatric Disorders, Changsha, Hunan, People’s Republic of China
| | - Honghao Zhou
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, People’s Republic of China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, People’s Republic of China
- National Clinical Research Center for Geriatric Disorders, Changsha, Hunan, People’s Republic of China
| | - Yacheng Fu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Cofoe Medical Technology Co., Ltd, Changsha, People’s Republic of China
| | - Rong Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, People’s Republic of China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, People’s Republic of China
- National Clinical Research Center for Geriatric Disorders, Changsha, Hunan, People’s Republic of China
| | - Wei Zhang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, People’s Republic of China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, People’s Republic of China
- National Clinical Research Center for Geriatric Disorders, Changsha, Hunan, People’s Republic of China
- Correspondence: Wei Zhang; Rong Liu, Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410008, People’s Republic of China, Tel +86 731 84805380, Fax +86 731 82354476, Email ;
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Zhang DY, Ku JW, Zhao XK, Zhang HY, Song X, Wu HF, Fan ZM, Xu RH, You D, Wang R, Zhou RX, Wang LD. Increased prognostic value of clinical–reproductive model in Chinese female patients with esophageal squamous cell carcinoma. World J Gastroenterol 2022; 28:1347-1361. [PMID: 35645543 PMCID: PMC9099181 DOI: 10.3748/wjg.v28.i13.1347] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 01/21/2022] [Accepted: 02/27/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND In China, it has been well recognized that some female patients with esophageal squamous cell carcinoma (ESCC) have different overall survival (OS) time, even with the same tumor-node-metastasis (TNM) stage, challenging the prognostic value of the TNM system alone. An effective predictive model is needed to accurately evaluate the prognosis of female ESCC patients.
AIM To construct a novel prognostic model with clinical and reproductive data for Chinese female patients with ESCC, and to assess the incremental prognostic value of the full model compared with the clinical model and TNM stage.
METHODS A new prognostic nomogram incorporating clinical and reproductive features was constructed based on univariatie and Cox proportional hazards survival analysis from a training cohort (n = 175). The results were recognized using the internal (n = 111) and independent external (n = 85) validation cohorts. The capability of the clinical–reproductive model was evaluated by Harrell’s concordance index (C-index), Kaplan–Meier curve, time-dependent receiver operating characteristic (ROC), calibration curve and decision curve analysis. The correlations between estrogen response and immune-related pathways and some gene markers of immune cells were analyzed using the TIMER 2.0 database.
RESULTS A clinical–reproductive model including incidence area, age, tumor differentiation, lymph node metastasis (N) stage, estrogen receptor alpha (ESR1) and beta (ESR2) expression, menopausal age, and pregnancy number was constructed to predict OS in female ESCC patients. Compared to the clinical model and TNM stage, the time-dependent ROC and C-index of the clinical–reproductive model showed a good discriminative ability for predicting 1-, 3-, and 5-years OS in the primary training, internal and external validation sets. Based on the optimal cut-off value of total prognostic scores, patients were classified into high- and low-risk groups with significantly different OS. The estrogen response was significantly associated with p53 and apoptosis pathways in esophageal cancer.
CONCLUSION The clinical–reproductive prognostic nomogram has an incremental prognostic value compared with the clinical model and TNM stage in predicting OS in Chinese female ESCC patients.
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Affiliation(s)
- Dong-Yun Zhang
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, Henan Province, China
- Department of Pathology, Nanyang Medical College, Nanyang 473061, Henan Province, China
| | - Jian-Wei Ku
- Department of Endoscopy, The Third Affiliated Hospital, Nanyang Medical College, Nanyang 473061, Henan Province, China
| | - Xue-Ke Zhao
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, Henan Province, China
| | - Hai-Yan Zhang
- Department of Pathology, The First Affiliated Hospital, Nanyang Medical College, Nanyang 473061, Henan Province, China
| | - Xin Song
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, Henan Province, China
| | - Hong-Fang Wu
- Department of Pathology, Nanyang Medical College, Nanyang 473061, Henan Province, China
| | - Zong-Min Fan
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, Henan Province, China
| | - Rui-Hua Xu
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, Henan Province, China
| | - Duo You
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, Henan Province, China
- Department of Medical Oncology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450052, Henan Province, China
| | - Ran Wang
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, Henan Province, China
| | - Ruo-Xi Zhou
- Department of Biology, University of Richmond, Richmond, VA 23173, United States
| | - Li-Dong Wang
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, Henan Province, China
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A nomogram to predict prolonged postoperative ileus after intestinal resection for Crohn's disease. Int J Colorectal Dis 2022; 37:949-956. [PMID: 35315507 DOI: 10.1007/s00384-022-04134-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/16/2022] [Indexed: 02/04/2023]
Abstract
PURPOSE We aim to identify the risk factors of PPOI in patients with CD and create a nomogram for prediction of PPOI for CD. METHODS Data on 462 patients who underwent partial intestinal resection for CD in Jin-ling Hospital between January 2019 and June 2021 were retrospectively collected. Univariate and multivariate analyses were performed to determine the risk factors for PPOI and we used the risk factors to create a nomogram. Then we used the Bootstrap-Concordance index and calibration diagrams to evaluate the performance of the Nomogram. Decision curve analysis was performed to evaluate clinical practicability of the model. RESULTS The incidence of PPOI was 27.7% (n of N). Course of CD ≥ 10 years, operation time ≥ 154 min, the lowest mean arterial pressure ≤ 76.2 mmHg, in-out balance per body weight ≥ 22.90 ml/kg, post-op day 1 infusion ≥ 2847 ml, post-op lowest K+ ≤ 3.75 mmol/L, and post-op day 1 procalcitonin ≥ 2.445 ng/ml were identified as the independent risk factors of PPOI in patients with CD. The nomogram we created by these risk factors presented with good discriminative ability (concordance index 0.723) and was moderately calibrated (bootstrapped concordance index 0.704). The results of decision curve analysis showed that the nomogram was clinically effective within probability thresholds in the 8 to 66% range. CONCLUSION The nomogram we developed is helpful to evaluate the risk of developing PPOI after partial intestinal resection for CD. Clinicians can take more necessary measures to prevent PPOI in CD's patients or at least minimize the incidence.
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Development and validation of a clinical prediction model for post thrombotic syndrome following anticoagulant therapy for acute deep venous thrombosis. Thromb Res 2022; 214:68-75. [DOI: 10.1016/j.thromres.2022.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 04/03/2022] [Accepted: 04/11/2022] [Indexed: 11/24/2022]
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Prediction of Incontinence after Robot-Assisted Radical Prostatectomy: Development and Validation of a 24-Month Incontinence Nomogram. Cancers (Basel) 2022; 14:cancers14071644. [PMID: 35406416 PMCID: PMC8997126 DOI: 10.3390/cancers14071644] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/18/2022] [Accepted: 03/20/2022] [Indexed: 11/26/2022] Open
Abstract
Simple Summary Many men fear urinary leakage after radical surgery for prostate cancer and may even choose against operation for unrealistic fears of leakage. Many urologists are unaware of their own results, and some urologists who collect their results do so in different ways. We collected urinary leakage data from 680 men in a uniform and simple way at 6, 12, and 24 months after operation: no pads, 1–2 pads, or ≥3 pads required daily. We used many patient characteristics to identify the key factors that predict recovery of urinary control after operation: age, race, height and weight, and preoperative erectile function. Easy-to-use nomograms were constructed that should be tested by other urologists to make sure they perform equally well in their patients. Nomograms like these allow men and the urologists counseling them to share patient-specific information about the timeline for, and the chance of, recovery of urinary control after operation. Abstract Incontinence after robot-assisted radical prostatectomy (RARP) is feared by most patients with prostate cancer. Many risk factors for incontinence after RARP are known, but a paucity of data integrates them. Prospectively acquired data from 680 men who underwent RARP January 2008–December 2015 and met inclusion/exclusion criteria were queried retrospectively and then divided into model development (80%) and validation (20%) cohorts. The UCLA-PCI-Short Form-v2 Urinary Function questionnaire was used to categorize perfect continence (0 pads), social continence (1–2 pads), or incontinence (≥3 pads). The observed incontinence rates were 26% at 6 months, 7% at 12 months, and 3% at 24 months. Logistic regression was used for model development, with variables identified using a backward selection process. Variables found predictive included age, race, body mass index, and preoperative erectile function. Internal validation and calibration were performed using standard bootstrap methodology. Calibration plots and receiver operating curves were used to evaluate model performance. The initial model had 6-, 12-, and 24-month areas under the curves (AUCs) of 0.64, 0.66, and 0.80, respectively. The recalibrated model had 6-, 12-, and 24-month AUCs of 0.52, 0.52, and 0.76, respectively. The final model was superior to any single clinical variable for predicting the risk of incontinence after RARP.
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Wei Y, Gong J, He X, Liu B, Liu T, Yang S, Zhou Z, Liang L, Zhan S, Xia Z, Duan G, Lin B, Han Q, Li S, Qin W, Pickhardt PJ, Deng D. An MRI-Based Radiomic Model for Individualized Prediction of Hepatocellular Carcinoma in Patients With Hepatitis B Virus-Related Cirrhosis. Front Oncol 2022; 12:800787. [PMID: 35359425 PMCID: PMC8964115 DOI: 10.3389/fonc.2022.800787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Accepted: 02/17/2022] [Indexed: 12/01/2022] Open
Abstract
Objective To develop and validate a radiomic nomogram for individualized prediction of hepatocellular carcinoma (HCC) in HBV cirrhosis patients based on baseline magnetic resonance imaging examinations and clinical data. Methods 364 patients with HBV cirrhosis from five hospitals were assigned to the training, internal validation, external validation-1 or external validation-2 cohort. All patients underwent baseline magnetic resonance image (MRI) scans and clinical follow-up within three-year time. Clinical risk factors and MRI-based features were extracted and analyzed. The radiomic signatures were built using the radiomics-score (Rad-score) that calculated for each patient as a linear weighted combination of selected MRI-based features. Prognostic performances of the clinical and radiomic nomograms were evaluated with Cox modeling in the training and validation cohorts. Results Eighteen features were selected for inclusion in the Rad-score prognostic model. The radiomic signature from multi-sequence MRI yielded a concordance index (C-index) of 0.710, 0.681, 0.632 and 0.658 in the training, internal validation, external validation-1, external validation-2 cohorts, respectively. Sex and Child-Turcotte-Pugh (CTP) class were the most prognostic clinical risk factors in univariate Cox proportional hazards analyses. The radiomic combined nomogram that integrated the radiomic signature with the clinical factors yielded a C-index of 0.746, 0.710, and 0.641 in the training, internal validation, and external validation-1 cohorts, respectively, which was an improvement over either the clinical nomogram or radiomic signature alone. Conclusion We developed an MRI-based radiomic combined nomogram with good discrimination ability for the individualized prediction of HCC in HBV cirrhosis patients within three-year time.
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Affiliation(s)
- Yichen Wei
- Department of Radiology, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Jie Gong
- Life Science Research Center, School of Life Science and Technology, Xidian University, Xi’an, China
| | - Xin He
- Department of Radiology, First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning, China
| | - Bo Liu
- Department of Radiology, Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Tiejun Liu
- Department of Radiology, Affiliated Hospital, Guangxi Medicine University, Liuzhou People’s Hospital, Liuzhou, China
| | - Shuohui Yang
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Zhipeng Zhou
- Department of Radiology, Affiliated Hospital of Guilin Medical University, Guilin, China
| | - Lingyan Liang
- Department of Radiology, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Songhua Zhan
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ziqiang Xia
- Department of Radiology, Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Gaoxiong Duan
- Department of Radiology, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Bin Lin
- Department of Radiology, Affiliated Hospital of Guilin Medical University, Guilin, China
| | - Qiuli Han
- Department of Radiology, Affiliated Hospital, Guangxi Medicine University, Liuzhou People’s Hospital, Liuzhou, China
| | - Shasha Li
- Department of Radiology, First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning, China
| | - Wei Qin
- Life Science Research Center, School of Life Science and Technology, Xidian University, Xi’an, China
- *Correspondence: Demao Deng, ; Wei Qin,
| | - Perry J. Pickhardt
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, United States
| | - Demao Deng
- Department of Radiology, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
- *Correspondence: Demao Deng, ; Wei Qin,
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Wang W, Mu Z, Zhu G, Wang T, Lai S, Guo Y, Yin X, Wen L, Chen D. A Nomogram for Predicting Portal Hypertensive Gastropathy in Patients With Liver Cirrhosis: A Retrospective Analysis. Front Med (Lausanne) 2022; 9:834159. [PMID: 35252265 PMCID: PMC8894675 DOI: 10.3389/fmed.2022.834159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 01/19/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundThere is an urgent need for non-invasive methods for predicting portal hypertensive gastropathy (PHG). This study aims to develop and validate a non-invasive method based on clinical parameters for predicting PHG in patients with liver cirrhosis (LC).MethodsThe overall survival (OS) and hepatocellular carcinoma (HCC)-free survival were evaluated in LC patients, both with and without PHG. A prediction model for PHG was then constructed based on a training dataset that contained data on 492 LC patients. The discrimination, calibration, and clinical utility of the predicting nomogram were assessed using the C-index, calibration plot, and decision curve analysis. Internal validation was conducted using a bootstrapping method, and further external validation using data on the 208 other patients.ResultsLC patients with PHG had a worse prognosis compared with those without PHG. A nomogram was constructed using clinical parameters, such as age, hemoglobin content, platelet count and Child-Pugh class. The C-index was 0.773 (95% CI: 0.730–0.816) in the training cohort, 0.761 after bootstrapping and 0.745 (95% CI: 0.673–0.817) in the validation cohort. The AUC values were 0.767, 0.724, and 0.756 in the training, validation and total cohorts, respectively. Well-fitted calibration curves were observed in the training and validation cohorts. Decision curve analysis demonstrated that the nomogram was clinically useful at a threshold of 15%.ConclusionThe nomogram constructed to predict the risk of developing PHG was found to be clinically viable. Furthermore, PHG is an independent risk factor for OS of LC, but not for the occurrence of HCC.
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