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Xie Q, Hu J, Liu Y, Tabengwa GT, Huang J, Liu S, Chen P, Hu Q, Zhang X, Xie T. Retrospective study of leptomeningeal metastasis: unveiling the indolent and rapid progression phases. J Neurooncol 2025:10.1007/s11060-025-05059-0. [PMID: 40392515 DOI: 10.1007/s11060-025-05059-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2025] [Accepted: 04/21/2025] [Indexed: 05/22/2025]
Abstract
PURPOSE Leptomeningeal metastasis (LM) is a severe cancer complication with poor prognosis and inconsistent treatment. Most studies are from Western countries, limiting understanding of LM in the Chinese population. This study aims to explore LM characteristics in Chinese patients and develop tailored treatment strategies. METHODS We retrospectively studied 103 Chinese LM patients, all confirmed by CSF cytology, from 2015 to 2024. Data on demographics, medical history, imaging, and follow-up were gathered. Survival analysis was conducted using the Kaplan - Meier method, and univariate and multivariate analyses were performed to identify prognostic factors. A nomogram was developed, and patients were stratified into risk groups based on these factors. RESULTS The median age was 54.99 ± 11.18 years, with 53.4% being female. Lung cancer was the primary tumor in 76.7% of patients. Headache was the most common symptom. The median survival was 441 days. Primary tumor site, CSF tumor cell proportion, and asymptomatic status at diagnosis were independent prognostic factors. The nomogram's C - index was 0.81. We identified two distinct groups of LM patients with markedly different characteristics, which we designated as the indolent and rapid progression phases of LM. CONCLUSIONS The clinical characteristics of LM patients with positive CSF cytology at the center were described, with a longer median survival than previously reported. The developed nomogram demonstrated potential clinical predictive value. Two distinct LM patient groups were identified: the indolent and rapid progression phases, which hold significant clinical relevance.
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Affiliation(s)
- Qiang Xie
- Department of Neurosurgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, China
| | - Jiamin Hu
- Department of Neurosurgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, China
| | - Yi Liu
- Department of Neurosurgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, China
| | - George Takura Tabengwa
- Department of Neurosurgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, China
| | - Jinlong Huang
- Department of Neurosurgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, China
| | - Shuang Liu
- Department of Neurosurgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, China
| | - Pin Chen
- Department of Neurosurgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, China
| | - Qin Hu
- Department of Pathology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, China
| | - Xiaobiao Zhang
- Department of Neurosurgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, China
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China
- Cancer Center, Shanghai Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Neurosurgery, Shanghai Geriatric Medical Center, Shanghai, China
- The Innovation and Translation Alliance of Neuroendoscopy in the Yangtze River Delta, Shanghai, China
| | - Tao Xie
- Department of Neurosurgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, China.
- Cancer Center, Shanghai Zhongshan Hospital, Fudan University, Shanghai, China.
- Department of Neurosurgery, Shanghai Geriatric Medical Center, Shanghai, China.
- The Innovation and Translation Alliance of Neuroendoscopy in the Yangtze River Delta, Shanghai, China.
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Wakabayashi H, Terakura S, Ishigiwa K, Ohara F, Hirano S, Yokota H, Kuwano S, Furukawa K, Shimada K, Sato T, Hanajiri R, Kiyoi H. Simple and early prediction of severe CAR-T-related adverse events after Axi-cel infusion by initial high fever. Int J Hematol 2025:10.1007/s12185-025-03957-7. [PMID: 40014276 DOI: 10.1007/s12185-025-03957-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2024] [Revised: 02/14/2025] [Accepted: 02/17/2025] [Indexed: 02/28/2025]
Abstract
Chimeric antigen receptor T-cell (CAR-T)-related adverse events (CAR-AEs), such as immune effector cell-associated neurotoxicity syndrome (ICANS) and cytokine release syndrome (CRS), can be life-threatening and may require high-dose steroids. Identifying patients at high risk for severe CAR-AEs in a simplified way is crucial for early therapeutic intervention. This retrospective study analyzed 44 patients treated with axicabtagene ciloleucel (Axi-cel) to identify predictive factors for severe CAR-AEs. We found that grade ≥ 3 ICANS, hemophagocytic syndrome, and ICU admission were associated with a greater need for high-dose steroids, which we defined as events associated with high-dose steroids (EHS). The incidence of EHS was significantly higher in patients who developed an initial fever (≥ 38.6 °C) within 24 h of CAR-T infusion (p < 0.001). Progression-free survival (PFS) was significantly shorter in patients with EHS compared to those without EHS (p < 0.001). Additionally, patients who developed a fever within 24 h and those with a peak fever of ≥ 38.6 °C both tended to have higher peak CAR-T counts compared to other patients. Our findings suggest that an initial fever (≥ 38.6 °C) within 24 h of Axi-cel infusion may predict severe CAR-AEs requiring high-dose steroids, and that EHS is associated with worse PFS.
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Affiliation(s)
- Hiroya Wakabayashi
- Department of Hematology and Oncology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8560, Japan
| | - Seitaro Terakura
- Department of Hematology and Oncology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8560, Japan.
| | - Kohei Ishigiwa
- Department of Hematology and Oncology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8560, Japan
| | - Fumiya Ohara
- Department of Hematology and Oncology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8560, Japan
| | - Shiho Hirano
- Department of Hematology and Oncology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8560, Japan
| | - Hirofumi Yokota
- Department of Hematology and Oncology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8560, Japan
| | - Shihomi Kuwano
- Department of Hematology and Oncology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8560, Japan
| | - Katsuya Furukawa
- Department of Hematology and Oncology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8560, Japan
| | - Kazuyuki Shimada
- Department of Hematology and Oncology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8560, Japan
| | - Takahiko Sato
- Department of Hematology and Oncology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8560, Japan
| | - Ryo Hanajiri
- Department of Hematology and Oncology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8560, Japan.
| | - Hitoshi Kiyoi
- Department of Hematology and Oncology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8560, Japan
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Liu Y, He X, Liu J, Wu J. Is the Use of Unanchored Matching-Adjusted Indirect Comparison Always Superior to Naïve Indirect Comparison on Survival Outcomes? A Simulation Study. APPLIED HEALTH ECONOMICS AND HEALTH POLICY 2025:10.1007/s40258-025-00952-1. [PMID: 39988641 DOI: 10.1007/s40258-025-00952-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/03/2025] [Indexed: 02/25/2025]
Abstract
OBJECTIVE To compare the performance of matching-adjusted indirect comparison (MAIC) and naïve indirect comparison (NIC) under a wide range of data scenarios on survival outcome. METHODS A simulation study included 729 (36) single-arm trial data scenarios, which were created by performing a three-level full factorial arrangement of six situational variables, including individual patient data (IPD) sample size, aggregate data (AgD) sample size, covariate strength, covariate correlation, covariate overlap, and relative treatment effect. In each scenario, 1000 repetitions of simulated datasets were generated using the Monte Carlo approach. MAIC and NIC methods were used to estimate the relative treatment effect of each simulated dataset. The performance was evaluated in terms of bias, empirical standard error (ESE), mean squared error (MSE), and confidence interval coverage, respectively. RESULTS MAIC yielded relatively unbiased estimates of relative treatment effect compared with NIC in most scenarios, with better coverage and MSE but higher ESE. None of the situational variables had a significant impact on the bias and coverage of MAIC. However, increasing IPD sample size and covariate overlap significantly reduced the ESE and MSE of MAIC. In scenarios with low covariate overlap and high covariate strength, the bias of MAIC was larger and even greater than that of NIC. CONCLUSIONS The performance of MAIC consistently demonstrates advantage over NIC across various scenarios. MAIC often provides more unbiased estimates and achieves confidence interval coverage close to nominal values compared with NIC. While MAIC may exhibit higher ESE in specific scenarios, this additional uncertainty can offer a more accurate reflection of variability, enhancing the robustness of the results. Researchers should thoroughly comprehend the influencing factors and interactions affecting the performance of these methods and judiciously apply research findings.
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Affiliation(s)
- Ying Liu
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China
- Center for Social Science Survey and Data, Tianjin University, Tianjin, China
| | - Xiaoning He
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China
- Center for Social Science Survey and Data, Tianjin University, Tianjin, China
| | - Jia Liu
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China
- Center for Social Science Survey and Data, Tianjin University, Tianjin, China
| | - Jing Wu
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China.
- Center for Social Science Survey and Data, Tianjin University, Tianjin, China.
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Guo Y, Li L, Zheng K, Du J, Nie J, Wang Z, Hao Z. Development and validation of a survival prediction model for patients with advanced non-small cell lung cancer based on LASSO regression. Front Immunol 2024; 15:1431150. [PMID: 39156899 PMCID: PMC11327039 DOI: 10.3389/fimmu.2024.1431150] [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: 05/11/2024] [Accepted: 07/19/2024] [Indexed: 08/20/2024] Open
Abstract
Introduction: Lung cancer remains a significant global health burden, with non-small cell lung cancer (NSCLC) being the predominant subtype. Despite advancements in treatment, the prognosis for patients with advanced NSCLC remains unsatisfactory, underscoring the imperative for precise prognostic assessment models. This study aimed to develop and validate a survival prediction model specifically tailored for patients diagnosed with NSCLC. METHODS A total of 523 patients were randomly divided into a training dataset (n=313) and a validation dataset (n=210). We conducted initial variable selection using three analytical methods: univariate Cox regression, LASSO regression, and random survival forest (RSF) analysis. Multivariate Cox regression was then performed on the variables selected by each method to construct the final predictive models. The optimal model was selected based on the highest bootstrap C-index observed in the validation dataset. Additionally, the predictive performance of the model was evaluated using time-dependent receiver operating characteristic (Time-ROC) curves, calibration plots, and decision curve analysis (DCA). RESULTS The LASSO regression model, which included N stage, neutrophil-lymphocyte ratio (NLR), D-dimer, neuron-specific enolase (NSE), squamous cell carcinoma antigen (SCC), driver alterations, and first-line treatment, achieved a bootstrap C-index of 0.668 (95% CI: 0.626-0.722) in the validation dataset, the highest among the three models tested. The model demonstrated good discrimination in the validation dataset, with area under the ROC curve (AUC) values of 0.707 (95% CI: 0.633-0.781) for 1-year survival, 0.691 (95% CI: 0.616-0.765) for 2-year survival, and 0.696 (95% CI: 0.611-0.781) for 3-year survival predictions, respectively. Calibration plots indicated good agreement between predicted and observed survival probabilities. Decision curve analysis demonstrated that the model provides clinical benefit at a range of decision thresholds. CONCLUSION The LASSO regression model exhibited robust performance in the validation dataset, predicting survival outcomes for patients with advanced NSCLC effectively. This model can assist clinicians in making more informed treatment decisions and provide a valuable tool for patient risk stratification and personalized management.
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Affiliation(s)
- Yimeng Guo
- Department of Pharmacy, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, China
| | - Lihua Li
- Department of Pharmacy, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, China
| | - Keao Zheng
- School of Pharmacy, Shanxi Medical University, Taiyuan, China
| | - Juan Du
- Department of Pharmacy, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, China
| | - Jingxu Nie
- Department of Pharmacy, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, China
| | - Zanhong Wang
- Department of Obstetrics and Gynecology, Shanxi Bethune Hospital/Shanxi Academy of Medical Sciences/Tongji Shanxi Hospital/Third Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Zhiying Hao
- Department of Pharmacy, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, China
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Durez A, Theys T, van Loon J, Van Paesschen W. Retention rate of vagus nerve stimulation for the treatment of drug-resistant epilepsy: A single-centre, retrospective study. Epilepsy Res 2024; 203:107383. [PMID: 38795656 DOI: 10.1016/j.eplepsyres.2024.107383] [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: 03/11/2024] [Revised: 05/07/2024] [Accepted: 05/19/2024] [Indexed: 05/28/2024]
Abstract
The aim of this single-centre, retrospective, observational study was to evaluate long-term effectiveness of vagus nerve stimulation (VNS) in drug-resistant epilepsy (DRE) by using retention rate as a surrogate measure for seizure reduction. We included all patients with DRE, treated at the adult neurology department of the University Hospitals Leuven and who started VNS therapy from January 1, 1994, until May 1, 2021, with follow-up data cutoff on January 1, 2023. Retention rate of VNS was defined as the percentage of patients who maintain VNS at established time points. We estimated cumulative retention rate and battery replacement rate and correlated these with seizure reduction, using Kaplan-Meier analysis. Statistical analysis of potential predictors of VNS outcome (age, sex and epilepsy duration at implantation) was performed using mono- and multivariate analyses. VNS was started in 110 patients with DRE, with a mean follow-up of 8.7 years (SD 6.5). VNS was discontinued in 55 patients (50%), with ineffectiveness as the main reason for discontinuation (98%). The battery was replaced at least once in 42 patients (38%). Estimated retention rates were 70%, 52%, 45% and 33% after 5, 10, 15 and 20 years, respectively. Estimated first battery replacement rates were 16%, 42% and 47% after 5, 10 and 15 years, respectively. Both estimates showed a statistically significant correlation with seizure reduction. No independent predictors of long-term outcome of VNS were found. This is the first long-term study using retention rate of VNS to assess effectiveness. VNS is a well-tolerated therapy, but retention rates decline with long follow-up.
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Affiliation(s)
- Astrid Durez
- Department of Neurology, University Hospitals Leuven, Herestraat 49, Leuven 3000, Belgium
| | - Tom Theys
- Department of Neurosurgery, University Hospitals Leuven, Herestraat 49, Leuven 3000, Belgium
| | - Johannes van Loon
- Department of Neurosurgery, University Hospitals Leuven, Herestraat 49, Leuven 3000, Belgium
| | - Wim Van Paesschen
- Department of Neurology, University Hospitals Leuven, Herestraat 49, Leuven 3000, Belgium; Laboratory for Epilepsy Research, KU Leuven, Leuven, Belgium.
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Tannemann N, Erbel R, Nöthen MM, Jöckel KH, Pechlivanis S. Genetic polymorphisms affecting telomere length and their association with cardiovascular disease in the Heinz-Nixdorf-Recall study. PLoS One 2024; 19:e0303357. [PMID: 38743757 PMCID: PMC11093374 DOI: 10.1371/journal.pone.0303357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 04/23/2024] [Indexed: 05/16/2024] Open
Abstract
Short telomeres are associated with cardiovascular disease (CVD). We aimed to investigate, if genetically determined telomere-length effects CVD-risk in the Heinz-Nixdorf-Recall study (HNRS) population. We selected 14 single-nucleotide polymorphisms (SNPs) associated with telomere-length (p<10-8) from the literature and after exclusion 9 SNPs were included in the analyses. Additionally, a genetic risk score (GRS) using these 9 SNPs was calculated. Incident CVD was defined as fatal and non-fatal myocardial infarction, stroke, and coronary death. We included 3874 HNRS participants with available genetic data and had no known history of CVD at baseline. Cox proportional-hazards regression was used to test the association between the SNPs/GRS and incident CVD-risk adjusting for common CVD risk-factors. The analyses were further stratified by CVD risk-factors. During follow-up (12.1±4.31 years), 466 participants experienced CVD-events. No association between SNPs/GRS and CVD was observed in the adjusted analyses. However, the GRS, rs10936599, rs2487999 and rs8105767 increase the CVD-risk in current smoker. Few SNPs (rs10936599, rs2487999, and rs7675998) showed an increased CVD-risk, whereas rs10936599, rs677228 and rs4387287 a decreased CVD-risk, in further strata. The results of our study suggest different effects of SNPs/GRS on CVD-risk depending on the CVD risk-factor strata, highlighting the importance of stratified analyses in CVD risk-factors.
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Affiliation(s)
- Nico Tannemann
- Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, University Duisburg-Essen, Essen, Germany
| | - Raimund Erbel
- Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, University Duisburg-Essen, Essen, Germany
| | - Markus M. Nöthen
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Karl-Heinz Jöckel
- Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, University Duisburg-Essen, Essen, Germany
| | - Sonali Pechlivanis
- Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, University Duisburg-Essen, Essen, Germany
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Asthma and Allergy Prevention, Neuherberg, Germany
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Späth J, Sewald Z, Probul N, Berland M, Almeida M, Pons N, Le Chatelier E, Ginès P, Solé C, Juanola A, Pauling J, Baumbach J. Privacy-Preserving Federated Survival Support Vector Machines for Cross-Institutional Time-To-Event Analysis: Algorithm Development and Validation. JMIR AI 2024; 3:e47652. [PMID: 38875678 PMCID: PMC11041494 DOI: 10.2196/47652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 08/06/2023] [Accepted: 02/10/2024] [Indexed: 06/16/2024]
Abstract
BACKGROUND Central collection of distributed medical patient data is problematic due to strict privacy regulations. Especially in clinical environments, such as clinical time-to-event studies, large sample sizes are critical but usually not available at a single institution. It has been shown recently that federated learning, combined with privacy-enhancing technologies, is an excellent and privacy-preserving alternative to data sharing. OBJECTIVE This study aims to develop and validate a privacy-preserving, federated survival support vector machine (SVM) and make it accessible for researchers to perform cross-institutional time-to-event analyses. METHODS We extended the survival SVM algorithm to be applicable in federated environments. We further implemented it as a FeatureCloud app, enabling it to run in the federated infrastructure provided by the FeatureCloud platform. Finally, we evaluated our algorithm on 3 benchmark data sets, a large sample size synthetic data set, and a real-world microbiome data set and compared the results to the corresponding central method. RESULTS Our federated survival SVM produces highly similar results to the centralized model on all data sets. The maximal difference between the model weights of the central model and the federated model was only 0.001, and the mean difference over all data sets was 0.0002. We further show that by including more data in the analysis through federated learning, predictions are more accurate even in the presence of site-dependent batch effects. CONCLUSIONS The federated survival SVM extends the palette of federated time-to-event analysis methods by a robust machine learning approach. To our knowledge, the implemented FeatureCloud app is the first publicly available implementation of a federated survival SVM, is freely accessible for all kinds of researchers, and can be directly used within the FeatureCloud platform.
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Affiliation(s)
- Julian Späth
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Zeno Sewald
- LipiTUM, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Niklas Probul
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Magali Berland
- MetaGenoPolis, INRAE, Université Paris-Saclay, Jouy-en-Josas, France
| | - Mathieu Almeida
- MetaGenoPolis, INRAE, Université Paris-Saclay, Jouy-en-Josas, France
| | - Nicolas Pons
- MetaGenoPolis, INRAE, Université Paris-Saclay, Jouy-en-Josas, France
| | | | - Pere Ginès
- Liver Unit, Hospital Clínic de Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigacion en Red de Enfermedades hepaticas y Digestivas (CIBEReHD), Madrid, Spain
- Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - Cristina Solé
- Liver Unit, Hospital Clínic de Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigacion en Red de Enfermedades hepaticas y Digestivas (CIBEReHD), Madrid, Spain
| | - Adrià Juanola
- Liver Unit, Hospital Clínic de Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigacion en Red de Enfermedades hepaticas y Digestivas (CIBEReHD), Madrid, Spain
| | - Josch Pauling
- LipiTUM, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
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Iddrisu A, Otoo D, Kwasi A, Gumedze F. Assessing the hazard of death, cardiac tamponade, and pericardial constriction among HIV and tuberculosis pericarditis patients using the extended Cox-hazard model: Intervention study. Health Sci Rep 2024; 7:e1892. [PMID: 38361809 PMCID: PMC10867395 DOI: 10.1002/hsr2.1892] [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: 09/18/2023] [Revised: 01/02/2024] [Accepted: 01/23/2024] [Indexed: 02/17/2024] Open
Abstract
Background and Aims Tuberculous (TB) pericarditis (TBP), a TB of the heart, is linked to significant morbidity and mortality rates. Administering glucocorticoid therapy to individuals with TBP might enhance overall results and lower the likelihood of fatality. However, the actual clinical effectiveness of supplementary glucocorticoids remains uncertain. This study specifically evaluated the effects of prednisolone, prednisolone-antiretroviral therapy (ART) interaction, and other potential risk factors in reducing the hazard of the composite outcome, death, cardiac tamponade, and constriction, among TBP and human immunodeficiency virus (HIV) patients. Methods The data used in this study were obtained from the investigation of the Management of Pericarditis trial, a multicentre international randomized double-blind placebo-controlled 2 × 2 factorial study that investigated the effects of two TB treatments, prednisolone and Mycobacterium indicus pranii immunotherapy in patients with TBP in Africa. This study used a sample size of 587 TBP and HIV-positive patients randomized into prednisolone and its corresponding placebo arm. We used the extended Cox-proportional hazard model to evaluate the effects of the covariates on the hazard of the survival outcomes. Models fitting and parameter estimation were carried out using R version 4.3.1. Results Prednisolone reduces the hazard of composite outcome (hazrad ratio [HR] = 0.32, 95% confidence interval [CI] = 0.19 , 0.54 , p < 0.001), cardiac tamponade (HR = 0.14, 95% CI = 0.05, 0.42, p < 0.001) and constriction (HR = 0.81, 95% CI = 0.41, 1.61, p = 0.55). However, prednisolone increases the hazard of death (HR = 1.58, 95% CI = 1.11, 2.24, p = 0.01). Consistent usage of ART reduces the hazard of composite outcome, death, and constriction but insignificantly increased the hazard of cardiac tamponade. Conclusion The study offers valuable insights into how prednisolone impact the hazard of different outcomes in patients with TBP and HIV. The findings hold potential clinical significance, particularly in guiding treatment decisions and devising strategies to enhance outcomes in this specific patient group. However, there are concerns about prednisolone potentially increasing the risk of death due to HIV-related death.
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Affiliation(s)
- Abdul‐Karim Iddrisu
- Department of Mathematics and StatisticsUniversity of Energy and Natural ResourcesSunyaniGhana
| | - Dominic Otoo
- Department of Mathematics and StatisticsUniversity of Energy and Natural ResourcesSunyaniGhana
| | - Afa Kwasi
- Department of Mathematics and StatisticsUniversity of Energy and Natural ResourcesSunyaniGhana
| | - Freedom Gumedze
- Department of Statistical SciencesUniversity of Cape TownRondeboschSouth Africa
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Tanner KT, Keogh RH, Coupland CAC, Hippisley-Cox J, Diaz-Ordaz K. Dynamic updating of clinical survival prediction models in a changing environment. Diagn Progn Res 2023; 7:24. [PMID: 38082429 PMCID: PMC10714456 DOI: 10.1186/s41512-023-00163-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 10/17/2023] [Indexed: 01/31/2024] Open
Abstract
BACKGROUND Over time, the performance of clinical prediction models may deteriorate due to changes in clinical management, data quality, disease risk and/or patient mix. Such prediction models must be updated in order to remain useful. In this study, we investigate dynamic model updating of clinical survival prediction models. In contrast to discrete or one-time updating, dynamic updating refers to a repeated process for updating a prediction model with new data. We aim to extend previous research which focused largely on binary outcome prediction models by concentrating on time-to-event outcomes. We were motivated by the rapidly changing environment seen during the COVID-19 pandemic where mortality rates changed over time and new treatments and vaccines were introduced. METHODS We illustrate three methods for dynamic model updating: Bayesian dynamic updating, recalibration, and full refitting. We use a simulation study to compare performance in a range of scenarios including changing mortality rates, predictors with low prevalence and the introduction of a new treatment. Next, the updating strategies were applied to a model for predicting 70-day COVID-19-related mortality using patient data from QResearch, an electronic health records database from general practices in the UK. RESULTS In simulated scenarios with mortality rates changing over time, all updating methods resulted in better calibration than not updating. Moreover, dynamic updating outperformed ad hoc updating. In the simulation scenario with a new predictor and a small updating dataset, Bayesian updating improved the C-index over not updating and refitting. In the motivating example with a rare outcome, no single updating method offered the best performance. CONCLUSIONS We found that a dynamic updating process outperformed one-time discrete updating in the simulations. Bayesian updating offered good performance overall, even in scenarios with new predictors and few events. Intercept recalibration was effective in scenarios with smaller sample size and changing baseline hazard. Refitting performance depended on sample size and produced abrupt changes in hazard ratio estimates between periods.
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Affiliation(s)
- Kamaryn T Tanner
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK.
| | - Ruth H Keogh
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - Carol A C Coupland
- Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, OX2 6HT, UK
- Centre for Academic Primary Care, University of Nottingham, Nottingham, NG7 2UH, UK
| | - Julia Hippisley-Cox
- Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, OX2 6HT, UK
| | - Karla Diaz-Ordaz
- Department of Statistical Science, University College London, London, WC1E 6BT, UK
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10
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Sajedi S, Ebrahimi G, Roudi R, Mehta I, Heshmat A, Samimi H, Kazempour S, Zainulabadeen A, Docking TR, Arora SP, Cigarroa F, Seshadri S, Karsan A, Zare H. Integrating DNA methylation and gene expression data in a single gene network using the iNETgrate package. Sci Rep 2023; 13:21721. [PMID: 38066050 PMCID: PMC10709411 DOI: 10.1038/s41598-023-48237-8] [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: 08/08/2023] [Accepted: 11/23/2023] [Indexed: 12/18/2023] Open
Abstract
Analyzing different omics data types independently is often too restrictive to allow for detection of subtle, but consistent, variations that are coherently supported based upon different assays. Integrating multi-omics data in one model can increase statistical power. However, designing such a model is challenging because different omics are measured at different levels. We developed the iNETgrate package ( https://bioconductor.org/packages/iNETgrate/ ) that efficiently integrates transcriptome and DNA methylation data in a single gene network. Applying iNETgrate on five independent datasets improved prognostication compared to common clinical gold standards and a patient similarity network approach.
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Affiliation(s)
- Sogand Sajedi
- Department of Cell Systems & Anatomy, The University of Texas Health Science Center, San Antonio, TX, 78229, USA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, San Antonio, TX, 78229, USA
| | - Ghazal Ebrahimi
- Bioinformatics Program, The University of British Columbia, Vancouver, BC, Canada
| | - Raheleh Roudi
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Isha Mehta
- Department of Immunology, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Amirreza Heshmat
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Hanie Samimi
- School of Architecture, University of Utah, Salt Lake City, UT, 84112, USA
| | - Shiva Kazempour
- Department of Cell Systems & Anatomy, The University of Texas Health Science Center, San Antonio, TX, 78229, USA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, San Antonio, TX, 78229, USA
| | - Aamir Zainulabadeen
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, USA
| | - Thomas Roderick Docking
- Canada's Michael Smith Genome Sciences Centre, British Columbia Cancer Research Centre, Vancouver, BC, V5Z 1L3, Canada
| | - Sukeshi Patel Arora
- Mays Cancer Center, The University of Texas Health Science Center, San Antonio, TX, 78229, USA
| | - Francisco Cigarroa
- Malu and Carlos Alvarez Center for Transplantation, Hepatobiliary Surgery and Innovation, The University of Texas Health Science Center, San Antonio, TX, 78229, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, San Antonio, TX, 78229, USA
- Department of Neurology, University of Texas, San Antonio, TX, 78229, USA
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, 02139, USA
| | - Aly Karsan
- Canada's Michael Smith Genome Sciences Centre, British Columbia Cancer Research Centre, Vancouver, BC, V5Z 1L3, Canada
| | - Habil Zare
- Department of Cell Systems & Anatomy, The University of Texas Health Science Center, San Antonio, TX, 78229, USA.
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, San Antonio, TX, 78229, USA.
- Department of Cell Systems & Anatomy, 7703 Floyd Curl Drive, San Antonio, TX, 78229, USA.
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11
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Nyadanu SD, Tessema GA, Mullins B, Chai K, Yitshak-Sade M, Pereira G. Critical Windows of Maternal Exposure to Biothermal Stress and Birth Weight for Gestational Age in Western Australia. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:127017. [PMID: 38149876 PMCID: PMC10752220 DOI: 10.1289/ehp12660] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 10/05/2023] [Accepted: 11/29/2023] [Indexed: 12/28/2023]
Abstract
BACKGROUND There is limited and inconsistent evidence on the risk of ambient temperature on small for gestational age (SGA) and there are no known related studies for large for gestational age (LGA). In addition, previous studies used temperature rather than a biothermal metric. OBJECTIVES Our aim was to examine the associations and critical susceptible windows of maternal exposure to a biothermal metric [Universal Thermal Climate Index (UTCI)] and the hazards of SGA and LGA. METHODS We linked 385,337 singleton term births between 1 January 2000 and 31 December 2015 in Western Australia to daily spatiotemporal UTCI. Distributed lag nonlinear models with Cox regression and multiple models were used to investigate maternal exposure to UTCI from 12 weeks preconception to birth and the adjusted hazard ratios (HRs) of SGA and LGA. RESULTS Relative to the median exposure, weekly and monthly specific exposures showed potential critical windows of susceptibility for SGA and LGA at extreme exposures, especially during late gestational periods. Monthly exposure showed strong positive associations from the 6th to the 10th gestational months with the highest hazard of 13% for SGA (HR = 1.13 ; 95% CI: 1.10, 1.14) and 7% for LGA (HR = 1.07 ; 95% CI: 1.03, 1.11) at the 10th month for the 1st UTCI centile. Entire pregnancy exposures showed the strongest hazards of 11% for SGA (HR = 1.11 ; 95% CI: 1.04, 1.18) and 3% for LGA (HR = 1.03 ; 95% CI: 0.95, 1.11) at the 99th UTCI centile. By trimesters, the highest hazards were found during the second and first trimesters for SGA and LGA, respectively, at the 99th UTCI centile. Based on estimated interaction effects, male births, mothers who were non-Caucasian, smokers, ≥ 35 years of age, and rural residents were most vulnerable. CONCLUSIONS Both weekly and monthly specific extreme biothermal stress exposures showed potential critical susceptible windows of SGA and LGA during late gestational periods with disproportionate sociodemographic vulnerabilities. https://doi.org/10.1289/EHP12660.
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Affiliation(s)
- Sylvester Dodzi Nyadanu
- Curtin School of Population Health, Curtin University, Perth, Bentley, Western Australia, Australia
- Education, Culture, and Health Opportunities (ECHO) Ghana, ECHO Research Group International, Aflao, Ghana
| | - Gizachew A. Tessema
- Curtin School of Population Health, Curtin University, Perth, Bentley, Western Australia, Australia
- School of Public Health, University of Adelaide, Adelaide, South Australia, Australia
- enAble Institute, Curtin University, Perth, Bentley, Western Australia, Australia
| | - Ben Mullins
- Curtin School of Population Health, Curtin University, Perth, Bentley, Western Australia, Australia
| | - Kevin Chai
- Curtin School of Population Health, Curtin University, Perth, Bentley, Western Australia, Australia
| | - Maayan Yitshak-Sade
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Gavin Pereira
- Curtin School of Population Health, Curtin University, Perth, Bentley, Western Australia, Australia
- enAble Institute, Curtin University, Perth, Bentley, Western Australia, Australia
- World Health Organization Collaborating Centre for Environmental Health Impact Assessment, Faculty of Health Science, Curtin University, Bentley, Western Australia, Australia
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Vignoli A, Miolo G, Tenori L, Buonadonna A, Lombardi D, Steffan A, Scalone S, Luchinat C, Corona G. Novel metabolomics-biohumoral biomarkers model for predicting survival of metastatic soft-tissue sarcomas. iScience 2023; 26:107678. [PMID: 37752948 PMCID: PMC10518687 DOI: 10.1016/j.isci.2023.107678] [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: 03/22/2023] [Revised: 06/23/2023] [Accepted: 08/14/2023] [Indexed: 09/28/2023] Open
Abstract
Soft tissue sarcomas (STSs) are rare malignant tumors that are difficult to prognosticate using currently available instruments. Omics sciences could provide more accurate and individualized survival predictions for patients with metastatic STS. In this pilot, hypothesis-generating study, we integrated clinicopathological variables with proton nuclear magnetic resonance (1H NMR) plasma metabolomic and lipoproteomic profiles, capturing both tumor and host characteristics, to identify novel prognostic biomarkers of 2-year survival. Forty-five metastatic STS (mSTS) patients with prevalent leiomyosarcoma and liposarcoma histotypes receiving trabectedin treatment were enrolled. A score combining acetate, triglycerides low-density lipoprotein (LDL)-2, and red blood cell count was developed, and it predicts 2-year survival with optimal results in the present cohort (84.4% sensitivity, 84.6% specificity). This score is statistically significant and independent of other prognostic factors such as age, sex, tumor grading, tumor histotype, frailty status, and therapy administered. A nomogram based on these 3 biomarkers has been developed to inform the clinical use of the present findings.
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Affiliation(s)
- Alessia Vignoli
- Magnetic Resonance Center (CERM) and Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
| | - Gianmaria Miolo
- Medical Oncology and Cancer Prevention Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM) and Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
- Consorzio Interuniversitario Risonanze Magnetiche MetalloProteine (CIRMMP), 50019 Sesto Fiorentino, Italy
| | - Angela Buonadonna
- Medical Oncology and Cancer Prevention Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy
| | - Davide Lombardi
- Medical Oncology and Cancer Prevention Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy
| | - Agostino Steffan
- Immunopathology and Cancer Biomarkers Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy
| | - Simona Scalone
- Medical Oncology and Cancer Prevention Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy
| | - Claudio Luchinat
- Consorzio Interuniversitario Risonanze Magnetiche MetalloProteine (CIRMMP), 50019 Sesto Fiorentino, Italy
- GiottoBiotech s.r.l, Sesto Fiorentino, Italy
| | - Giuseppe Corona
- Immunopathology and Cancer Biomarkers Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy
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Michalek IM, Graff RE, Sanchez A, Choueiri TK, Cho E, Preston MA, Wilson KM. Evaluation of statin use and renal cell carcinoma risk identifies sex-specific associations with RCC subtypes. Acta Oncol 2023; 62:988-993. [PMID: 37482537 DOI: 10.1080/0284186x.2023.2238883] [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: 05/20/2023] [Accepted: 07/11/2023] [Indexed: 07/25/2023]
Abstract
Background: The association between statin use and risk of renal cell carcinoma (RCC) has been debated. We aimed to evaluate whether statin use is associated with RCC risk.Material and methods: We studied 100,195 women in the Nurses' Health Study (NHS) from 1994 to 2016; 91,427 women in the Nurses' Health Study II (NHS II) from 1999 to 2015; and 45,433 men in the Health Professionals Follow-up Study (HPFS) from 1990 to 2016. Statins and covariate data were collected at baseline and then biennially. Outcome was measured as incidence of total RCC and clinically relevant disease subgroups. Cox proportional hazards models estimated covariate-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs).Results: During follow-up, 661 participants developed RCC. There was no significant association between the use of statins and the risk of overall RCC, fatal RCC, or advanced or localized disease. Across cohorts, the adjusted HR for ever vs. never users was 0.97 (95% CI 0.81-1.16). Female ever users of statins were at increased risk of high-grade disease in the NHS only (HR 1.75, 95% CI 1.07-2.85). Among men only, ≥4 years of statin use was associated with an increased risk of clear cell RCC (HR 1.65, 95% CI 1.10-2.47).Conclusions: Statin use was not associated with the overall risk of RCC. However, it was associated with an increased risk of high-grade disease among women in the NHS cohort and an increased risk of clear cell RCC among men. The reasons for these inconsistent results by sex are unclear.
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Affiliation(s)
- Irmina Maria Michalek
- Department of Pathology, Maria Sklodowska-Curie National Research Institute of Oncology in Warsaw, Warsaw, Poland
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Rebecca E Graff
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Alejandro Sanchez
- Division of Urology, Department of Surgery, Huntsman Cancer Institute and University of Utah, Salt Lake City, UT, USA
| | - Toni K Choueiri
- Department of Medical Oncology, Dana-Farber Cancer Institute, The Lank Center for Genitourinary Oncology, Boston, MA, USA
| | - Eunyoung Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Dermatology, The Warren Alpert Medical School of Brown University, Providence, RI, USA
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
| | - Mark A Preston
- Division of Urology, Brigham and Women's Hospital, Boston, MA, USA
| | - Kathryn M Wilson
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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14
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Li MX, Tu HX, Yin MC. Meta-analysis of outcomes from drug-eluting stent implantation in infrapopliteal arteries. World J Clin Cases 2023; 11:5273-5287. [PMID: 37621588 PMCID: PMC10445070 DOI: 10.12998/wjcc.v11.i22.5273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/07/2023] [Accepted: 07/17/2023] [Indexed: 08/04/2023] Open
Abstract
BACKGROUND Percutaneous drug-eluting stent implantation (DESI) is an emerging and promising treatment modality for infrapopliteal artery diseases (IPADs). This systematic review and meta-analysis summarizes and quantitatively analyzes the outcomes of DESI in IPADs considering the hazard ratio (HR), which is a more accurate and appropriate outcome measure than the more commonly used relative risk and odds ratio. AIM To explore the superiority of drug-eluting stents (DESs) vs traditional treatment modalities for IPADs. METHODS The following postoperative indicators were the outcomes of interest: All-cause death (ACD)-free survival, major amputation (MA)-free survival, target lesion revascularization (TLR)-free survival, adverse event (AE)-free survival, and primary patency (PP) survival. The outcome measures were then compared according to their respective HRs with 95% confidence intervals (CIs). The participants were human IPAD patients who underwent treatments for infrapopliteal lesions. DESI was set as the intervention arm, and traditional percutaneous transluminal angioplasty (PTA) with or without bare metal stent implantation (BMSI) was set as the control arm. A systematic search in the Excerpta Medica Database (Embase), PubMed, Web of Science, and Cochrane Library was performed on November 29, 2022. All controlled studies published in English with sufficient data on outcomes of interest for extraction or conversion were included. When studies did not directly report the HRs but gave a corresponding survival curve, we utilized Engauge Digitizer software and standard formulas to convert the information and derive HRs. Then, meta-analyses were conducted using a random-effects model. RESULTS Five randomized controlled trials and three cohort studies involving 2639 participants were included. The ACD-free and MA-free survival HR values for DESI were not statistically significant from those of the control treatment (P > 0.05); however, the HR values for TLR-free, AE-free, and PP-survival differed significantly [2.65 (95%CI: 1.56-4.50), 1.57 (95%CI: 1.23-2.01), and 5.67 (95%CI: 3.56-9.03), respectively]. CONCLUSION Compared with traditional treatment modalities (i.e., PTA with or without BMSI), DESI for IPADs is superior in avoiding TLR and AEs and maintaining PP but shows no superiority or inferiority in avoiding ACD and MA.
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Affiliation(s)
- Ming-Xuan Li
- Department of Vascular Surgery, Beijing Fengtai You'anmen Hospital, Beijing 100069, China
| | - Hai-Xia Tu
- Department of Vascular Surgery, Beijing Fengtai You'anmen Hospital, Beijing 100069, China
| | - Meng-Chen Yin
- Department of Vascular Surgery, Beijing Fengtai You'anmen Hospital, Beijing 100069, China
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15
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Li MX, Tu HX, Yin MC. Meta-analysis of outcomes from drug-eluting stent implantation in infrapopliteal arteries. World J Clin Cases 2023; 11:5267-5281. [DOI: 10.12998/wjcc.v11.i22.5267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/07/2023] [Accepted: 07/17/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND Percutaneous drug-eluting stent implantation (DESI) is an emerging and promising treatment modality for infrapopliteal artery diseases (IPADs). This systematic review and meta-analysis summarizes and quantitatively analyzes the outcomes of DESI in IPADs considering the hazard ratio (HR), which is a more accurate and appropriate outcome measure than the more commonly used relative risk and odds ratio.
AIM To explore the superiority of drug-eluting stents (DESs) vs traditional treatment modalities for IPADs.
METHODS The following postoperative indicators were the outcomes of interest: All-cause death (ACD)-free survival, major amputation (MA)-free survival, target lesion revascularization (TLR)-free survival, adverse event (AE)-free survival, and primary patency (PP) survival. The outcome measures were then compared according to their respective HRs with 95% confidence intervals (CIs). The participants were human IPAD patients who underwent treatments for infrapopliteal lesions. DESI was set as the intervention arm, and traditional percutaneous transluminal angioplasty (PTA) with or without bare metal stent implantation (BMSI) was set as the control arm. A systematic search in the Excerpta Medica Database (Embase), PubMed, Web of Science, and Cochrane Library was performed on November 29, 2022. All controlled studies published in English with sufficient data on outcomes of interest for extraction or conversion were included. When studies did not directly report the HRs but gave a corresponding survival curve, we utilized Engauge Digitizer software and standard formulas to convert the information and derive HRs. Then, meta-analyses were conducted using a random-effects model.
RESULTS Five randomized controlled trials and three cohort studies involving 2639 participants were included. The ACD-free and MA-free survival HR values for DESI were not statistically significant from those of the control treatment (P > 0.05); however, the HR values for TLR-free, AE-free, and PP-survival differed significantly [2.65 (95%CI: 1.56-4.50), 1.57 (95%CI: 1.23-2.01), and 5.67 (95%CI: 3.56-9.03), respectively].
CONCLUSION Compared with traditional treatment modalities (i.e., PTA with or without BMSI), DESI for IPADs is superior in avoiding TLR and AEs and maintaining PP but shows no superiority or inferiority in avoiding ACD and MA.
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Affiliation(s)
- Ming-Xuan Li
- Department of Vascular Surgery, Beijing Fengtai You'anmen Hospital, Beijing 100069, China
| | - Hai-Xia Tu
- Department of Vascular Surgery, Beijing Fengtai You'anmen Hospital, Beijing 100069, China
| | - Meng-Chen Yin
- Department of Vascular Surgery, Beijing Fengtai You'anmen Hospital, Beijing 100069, China
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Qu J, Tao D, Huang W, Lu L, Fan J, Zhang S, Huang F. Assessment of prognostic role of a novel 7-lncRNA signature in HCC patients. Heliyon 2023; 9:e18493. [PMID: 37520979 PMCID: PMC10382640 DOI: 10.1016/j.heliyon.2023.e18493] [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: 01/15/2023] [Revised: 07/07/2023] [Accepted: 07/19/2023] [Indexed: 08/01/2023] Open
Abstract
Background Hepatocellular carcinoma (HCC) is characterized by extensive risk factors, high morbidity and mortality. Clinical prognostic evaluation assay assumes a nonspecific quality. Better HCC prognostics are urgently needed. Long noncoding RNAs (lncRNAs) exerts a crucial role in tumorigenesis and development. Excavating specific lncRNAs signature to ameliorate the high-risk survival prediction in HCC patients is worthwhile. Methods Differentially expressed lncRNAs (DElncRNAs) profile was acquired from The Cancer Genome Atlas database (TCGA). Then, the lncRNAs high-risk survival prognostic model was established using the least absolute shrinkage and selection operator (LASSO)-Cox regression algorithm. The lncRNAs were evaluated in clinical specimen by PCR. The receiver operating characteristic curve (ROC) analysis was further conducted to assess the potential prognostic value of the model. Moreover, a visible nomogram containing clinicopathological features and prognostic model was developed for prediction of survival property. Potential molecular mechanism was assessed by GO, KEGG, GSEA enrichment analysis and CIBERSORT immune infiltration analysis. Results A novel 7-lncRNA risk model (AL161937.2, LINC01063, AC145207.5, POLH-AS1, LNCSRLR, MKLN1-AS, AC105345.1) was constructed and validated for HCC prognosis prediction. Kaplan-Meier analysis revealed that patients in the high-risk group suffered a poor prognosis (p = 1.813 × 10-8). These genes were detected by PCR, and the expression trend was in accordance with TCGA database. Interestingly, the risk score served as an independent risk factor for HCC patients (HR: 1.166, 95% CI:1.119-1.214, p < 0.001). The nomogram was established, and the predictive accuracy in the nomogram was prior to the TNM stage according to the ROC curve analysis. Cell proliferation related pathway, decreased CD4+ T cell, CD8+ T cell, NK cell and elevated Neutrophil, Macrophage M0 were observed in high-risk group. Besides, suppression of MKLN1-AS expression inhibited cell proliferation of HCC cells by CCK8 assay in vitro. Conclusion The 7-lncRNA signature may exert a particular prognostic prediction role in HCC and provide new insight in HCC carcinogenesis.
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Affiliation(s)
- Junchi Qu
- Department of Gastroenterology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
- Department of Gastroenterology, The First People's Hospital of PingJiang, Yueyang 410400, China
| | - Di Tao
- Department of Gastroenterology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Wei Huang
- Department of Gastroenterology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
| | - Liting Lu
- Department of Gastroenterology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
| | - Junming Fan
- Department of Gastroenterology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
| | - Shineng Zhang
- Department of Gastroenterology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Fengting Huang
- Department of Gastroenterology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
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Chapfuwa P, Tao C, Li C, Khan I, Chandross KJ, Pencina MJ, Carin L, Henao R. Calibration and Uncertainty in Neural Time-to-Event Modeling. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:1666-1680. [PMID: 33119513 PMCID: PMC8439415 DOI: 10.1109/tnnls.2020.3029631] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Models for predicting the time of a future event are crucial for risk assessment, across a diverse range of applications. Existing time-to-event (survival) models have focused primarily on preserving pairwise ordering of estimated event times (i.e., relative risk). We propose neural time-to-event models that account for calibration and uncertainty while predicting accurate absolute event times. Specifically, an adversarial nonparametric model is introduced for estimating matched time-to-event distributions for probabilistically concentrated and accurate predictions. We also consider replacing the discriminator of the adversarial nonparametric model with a survival-function matching estimator that accounts for model calibration. The proposed estimator can be used as a means of estimating and comparing conditional survival distributions while accounting for the predictive uncertainty of probabilistic models. Extensive experiments show that the distribution matching methods outperform existing approaches in terms of both calibration and concentration of time-to-event distributions.
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Li X, Gao M, Chu M, Huang S, Fang Z, Chen T, Lee CY, Chiang YC. Promoting the well-being of rural elderly people for longevity among different birth generations: A healthy lifestyle perspective. Front Public Health 2023; 11:1050789. [PMID: 36908453 PMCID: PMC9995922 DOI: 10.3389/fpubh.2023.1050789] [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: 09/22/2022] [Accepted: 02/02/2023] [Indexed: 02/25/2023] Open
Abstract
Background Wellbeing may have a protective role in health maintenance. However, no specific study clarified the particular protective effect of the subjective wellbeing of rural elderly people on survival probability. Few studies have examined the effect of the lifestyle of rural elderly people on their subjective wellbeing from different perspectives. We investigated whether improving subjective wellbeing increased the probability of longevity of rural elderly people and the effects of lifestyle behaviors on the subjective wellbeing of rural elderly people in different birth generations. Materials and methods Data were derived from the China Health and Nutrition Survey (CHNS), which is an ongoing open cohort study that adopts a multistage, random clustered sampling process. We used the data of elderly people who were aged 65 or over during 2006-2015 for analysis. The Kaplan-Meier method and log-rank test found that the survival probability of rural elderly people was significantly lower than urban elderly people. Based on a sample of rural elderly people, Cox regression and generalized estimating equations were performed as further analyses. Results A total of 892 rural elderly people aged 65 or over were included in the sample in 2006. High subjective wellbeing was a protective factor against death. The subjective wellbeing of rural elderly people born in the 1940s/1930s/1908-1920s birth generations first decreased then increased. For rural elderly people born in the 1940s, there were significant positive effects of a preference for eating vegetables and walking/Tai Chi on subjective wellbeing. For rural elderly people born in the 1930s, preferences for eating vegetables, reading, and watching TV all had significant positive effects on subjective wellbeing. Rural elderly people born in the 1908-1920s who preferred watching TV had more subjective wellbeing. Conclusion Improving subjective wellbeing extended the life span and reduced mortality risk in rural elderly people and may be achieved by the shaping of a healthy lifestyle, such as preferences for eating vegetables, walking/Tai Chi, and reading.
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Affiliation(s)
- Xian Li
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Min Gao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Meijie Chu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Shiling Huang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Zhiwei Fang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Chun-Yang Lee
- School of International Business, Xiamen University Tan Kah Kee College, Zhangzhou, China
| | - Yi-Chen Chiang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
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Multi-modality data-driven analysis of diagnosis and treatment of psoriatic arthritis. NPJ Digit Med 2023; 6:13. [PMID: 36732611 PMCID: PMC9895430 DOI: 10.1038/s41746-023-00757-3] [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: 09/12/2022] [Accepted: 01/16/2023] [Indexed: 02/04/2023] Open
Abstract
Psoriatic arthritis (PsA) is associated with psoriasis, featured by its irreversible joint symptoms. Despite the significant impact on the healthcare system, it is still challenging to leverage machine learning or statistical models to predict PsA and its progression, or analyze drug efficacy. With 3961 patients' clinical records, we developed a machine learning model for PsA diagnosis and analysis of PsA progression risk, respectively. Furthermore, general additive models (GAMs) and the Kaplan-Meier (KM) method were applied to analyze the efficacy of various drugs on psoriasis treatment and inhibiting PsA progression. The independent experiment on the PsA prediction model demonstrates outstanding prediction performance with an AUC score of 0.87 and an AUPR score of 0.89, and the Jackknife validation test on the PsA progression prediction model also suggests the superior performance with an AUC score of 0.80 and an AUPR score of 0.83, respectively. We also identified that interleukin-17 inhibitors were the more effective drug for severe psoriasis compared to other drugs, and methotrexate had a lower effect in inhibiting PsA progression. The results demonstrate that machine learning and statistical approaches enable accurate early prediction of PsA and its progression, and analysis of drug efficacy.
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20
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Dauda KA, Department of Statistics and Mathematical Sciences, Kwara State University, Malete, Nigeria. Optimal Tuning of Random Survival Forest Hyperparameter with an Application to Liver Disease. Malays J Med Sci 2022; 29:67-76. [PMID: 36818901 PMCID: PMC9910370 DOI: 10.21315/mjms2022.29.6.7] [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: 11/26/2021] [Accepted: 03/11/2022] [Indexed: 12/25/2022] Open
Abstract
Background Random Forest (RF) is a technique that optimises predictive accuracy by fitting an ensemble of trees to stabilise model estimates. The RF techniques were adapted into survival analysis to model the survival of patients with liver disease in order to identify biomarkers that are highly influential in patient prognostics. Methods The methodology of this study begins by applying the classical Cox proportional hazard (Cox-PH) model and three parametric survival models (exponential, Weibull and lognormal) to the published dataset. The study further applied the supervised learning methods of Tuning Random Survival Forest (TRSF) parameters and the conditional inference Forest (Cforest) to optimally predict patient survival probabilities. Results The efficiency of these models was compared using the Akaike information criteria (AIC) and integrated Brier score (IBS). The results revealed that the Cox-PH model (AIC = 185.7233) outperforms the three classical models. We further analysed these data to observe the functional relationships that exist between the patient survival function and the covariates using TRSF. Conclusion The IBS result of the TRFS demonstrated satisfactory performance over other methods. Ultimately, it was observed from the TRSF results that some of the covariates contributed positively and negatively to patient survival prognostics.
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21
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Nateghi Haredasht F, Vens C. Predicting Survival Outcomes in the Presence of Unlabeled Data. Mach Learn 2022. [DOI: 10.1007/s10994-022-06257-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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22
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Le QC, Arimura H, Ninomiya K, Kodama T, Moriyama T. Can Persistent Homology Features Capture More Intrinsic Information about Tumors from 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Images of Head and Neck Cancer Patients? Metabolites 2022; 12:metabo12100972. [PMID: 36295874 PMCID: PMC9610853 DOI: 10.3390/metabo12100972] [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: 09/21/2022] [Revised: 10/09/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022] Open
Abstract
This study hypothesized that persistent homology (PH) features could capture more intrinsic information about the metabolism and morphology of tumors from 18F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography (CT) images of patients with head and neck (HN) cancer than other conventional features. PET/CT images and clinical variables of 207 patients were selected from the publicly available dataset of the Cancer Imaging Archive. PH images were generated from persistent diagrams obtained from PET/CT images. The PH features were derived from the PH PET/CT images. The signatures were constructed in a training cohort from features from CT, PET, PH-CT, and PH-PET images; clinical variables; and the combination of features and clinical variables. Signatures were evaluated using statistically significant differences (p-value, log-rank test) between survival curves for low- and high-risk groups and the C-index. In an independent test cohort, the signature consisting of PH-PET features and clinical variables exhibited the lowest log-rank p-value of 3.30 × 10−5 and C-index of 0.80, compared with log-rank p-values from 3.52 × 10−2 to 1.15 × 10−4 and C-indices from 0.34 to 0.79 for other signatures. This result suggests that PH features can capture the intrinsic information of tumors and predict prognosis in patients with HN cancer.
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Affiliation(s)
- Quoc Cuong Le
- Ho Chi Minh City Oncology Hospital, Ho Chi Minh City 700000, Vietnam
| | - Hidetaka Arimura
- Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka City 812-8582, Japan
- Correspondence:
| | - Kenta Ninomiya
- Sanford Burnham Prebys Medical Discovery Institute, 10901 North Torrey Pines Road, La Jolla, San Diego, CA 92037, USA
| | - Takumi Kodama
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka City 812-8582, Japan
| | - Tetsuhiro Moriyama
- Institute of Mathematics for Industry, Kyushu University, Fukuoka City 819-0395, Japan
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23
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Davis JS, Chavez JC, Kok M, San Miguel Y, Lee HY, Henderson H, Overman MJ, Morris V, Kee B, Fogelman D, Advani SM, Johnson B, Parseghian C, Shen JP, Dasari A, Shaw KR, Vilar E, Raghav KP, Shureiqi I, Wolff RA, Meric-Bernstam F, Maru D, Menter DG, Kopetz S, Chang S. Association of Prediagnosis Obesity and Postdiagnosis Aspirin With Survival From Stage IV Colorectal Cancer. JAMA Netw Open 2022; 5:e2236357. [PMID: 36239938 PMCID: PMC9568800 DOI: 10.1001/jamanetworkopen.2022.36357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
IMPORTANCE The potential relationship between obesity and colorectal cancer (CRC) outcome is poorly understood in patients with late-stage disease. Increased body mass index may negate aspirin use for cancer prevention, but its role as a factor on the effectiveness of postdiagnosis aspirin use is unclear. OBJECTIVE To evaluate how prediagnosis obesity and postdiagnosis aspirin use may be associated with overall survival in patients with late-stage colorectal cancer. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study used self-reported data from patients with metastatic or treatment-refractory disease who consented to a clinical protocol at MD Anderson Cancer Center, a large US cancer treatment center. Patients were enrolled between 2010 and 2018 and followed up for mortality through July 2020. Analyses were conducted through March 2022. EXPOSURES Body mass index in the decade prior to initial diagnosis and regular aspirin use at survey completion. MAIN OUTCOMES AND MEASURES Overall survival was measured from stage IV diagnosis until death or last follow-up. Cox proportional hazards models were constructed to estimate associations of prediagnosis obesity and postdiagnosis aspirin use with overall survival. RESULTS Of 656 patients included in this analysis, 280 (42.7%) were women, 135 (20.6%) were diagnosed with CRC before age 45 years, 414 (63.1%) were diagnosed between ages 45 and 65 years, and 107 (16.3%) were diagnosed at 65 years or older; 105 patients (16.0%) were Black or Hispanic, and 501 (76.4%) were non-Hispanic White. Controlling for age, sex, race, stage at initial diagnosis, and weight change between prediagnosis and survey date, patients with obesity in the decade prior to CRC diagnosis had significantly higher likelihood of death (hazard ratio, 1.45; 95% CI, 1.11-1.91) compared with those with normal prediagnosis body mass index. Furthermore, only patients with normal prediagnosis body mass index experienced significant survival benefit with postdiagnosis aspirin use (hazard ratio, 0.59; 95% CI, 0.39-0.90). CONCLUSIONS AND RELEVANCE In this cross-sectional study, our findings suggest potentially differential tumor development in the long-term physiologic host environment of obesity. Confirmation and further evaluation are needed to determine whether prediagnosis body mass index may be used to estimate the benefit from postdiagnosis aspirin use.
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Affiliation(s)
- Jennifer S. Davis
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston
- Now with Department of Cancer Biology, University of Kansas Medical Center, Kansas City
| | - Janelle C. Chavez
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston
- Department of Cancer Prevention Research Training Program, The University of Texas MD Anderson Cancer Center, Houston
- Now with Stanford University School of Medicine, Stanford, California
| | - Melissa Kok
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston
- Department of Cancer Prevention Research Training Program, The University of Texas MD Anderson Cancer Center, Houston
- Now with Baylor College of Medicine, Houston, Texas
| | - Yazmin San Miguel
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston
- Department of Cancer Prevention Research Training Program, The University of Texas MD Anderson Cancer Center, Houston
- Now with Abbott Laboratories, Chicago, Illinois
| | - Hwa Young Lee
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston
- Department of Cancer Prevention Research Training Program, The University of Texas MD Anderson Cancer Center, Houston
| | - Henry Henderson
- Department of Cancer Prevention Research Training Program, The University of Texas MD Anderson Cancer Center, Houston
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston
- Now with Foundation Medicine, Atlanta, Georgia
| | - Michael J. Overman
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston
| | - Van Morris
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston
| | - Bryan Kee
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston
| | - David Fogelman
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston
- Now with Merck & Co, Philadelphia, Pennsylvania
| | - Shailesh M. Advani
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston
- Now with Terasaki Institute of Biomedical Innovation, Los Angeles, California
| | - Benny Johnson
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston
| | - Christine Parseghian
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston
| | - John Paul Shen
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston
| | - Arvind Dasari
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston
| | - Kenna R. Shaw
- Department of Sheikh Khalifa Nahyan Ben Zayed Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston
| | - Eduardo Vilar
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston
| | - Kanwal P. Raghav
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston
| | - Imad Shureiqi
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston
- Now with Department of Cancer Biology, University of Michigan Medical School, Ann Arbor
| | - Robert A. Wolff
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston
| | - Funda Meric-Bernstam
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston
| | - Dipen Maru
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston
| | - David G. Menter
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston
| | - Scott Kopetz
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston
| | - Shine Chang
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston
- Department of Cancer Prevention Research Training Program, The University of Texas MD Anderson Cancer Center, Houston
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24
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Jackson WE, Malamon JS, Kaplan B, Saben JL, Schold JD, Pomposelli JJ, Pomfret EA. Survival Benefit of Living-Donor Liver Transplant. JAMA Surg 2022; 157:926-932. [PMID: 35921119 PMCID: PMC9350845 DOI: 10.1001/jamasurg.2022.3327] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Importance Despite the acceptance of living-donor liver transplant (LDLT) as a lifesaving procedure for end-stage liver disease, it remains underused in the United States. Quantification of lifetime survival benefit and the Model for End-stage Liver Disease incorporating sodium levels (MELD-Na) score range at which benefit outweighs risk in LDLT is necessary to demonstrate its safety and effectiveness. Objective To assess the survival benefit, life-years saved, and the MELD-Na score at which that survival benefit was obtained for individuals who received an LDLT compared with that for individuals who remained on the wait list. Design, Setting, and Participants This case-control study was a retrospective, secondary analysis of the Scientific Registry of Transplant Recipients database of 119 275 US liver transplant candidates and recipients from January 1, 2012, to September 2, 2021. Liver transplant candidates aged 18 years or older who were assigned to the wait list (N = 116 455) or received LDLT (N = 2820) were included. Patients listed for retransplant or multiorgan transplant and those with prior kidney or liver transplants were excluded. Exposures Living-donor liver transplant vs remaining on the wait list. Main Outcomes and Measures The primary outcome of this study was life-years saved from receiving an LDLT. Secondary outcomes included 1-year relative mortality and risk, time to equal risk, time to equal survival, and the MELD-Na score at which that survival benefit was obtained for individuals who received an LDLT compared with that for individuals who remained on the wait list. MELD-Na score ranges from 6 to 40 and is well correlated with short-term survival. Higher MELD-Na scores (>20) are associated with an increased risk of death. Results The mean (SD) age of the 119 275 study participants was 55.1 (11.2) years, 63% were male, 0.9% were American Indian or Alaska Native, 4.3% were Asian, 8.2% were Black or African American, 15.8% were Hispanic or Latino, 0.2% were Native Hawaiian or Other Pacific Islander, and 70.2% were White. Mortality risk and survival models confirmed a significant survival benefit for patients receiving an LDLT who had a MELD-Na score of 11 or higher (adjusted hazard ratio, 0.64 [95% CI, 0.47-0.88]; P = .006). Living-donor liver transplant recipients gained an additional 13 to 17 life-years compared with patients who never received an LDLT. Conclusions and Relevance An LDLT is associated with a substantial survival benefit to patients with end-stage liver disease even at MELD-Na scores as low as 11. The findings of this study suggest that the life-years gained are comparable to or greater than those conferred by any other lifesaving procedure or by a deceased-donor liver transplant. This study's findings challenge current perceptions regarding when LDLT survival benefit occurs.
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Affiliation(s)
- Whitney E Jackson
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora.,Colorado Center for Transplantation Care, Research, and Education, University of Colorado Anschutz Medical Campus, Aurora
| | - John S Malamon
- Colorado Center for Transplantation Care, Research, and Education, University of Colorado Anschutz Medical Campus, Aurora.,Department of Surgery, University of Colorado Anschutz Medical Campus, Aurora
| | - Bruce Kaplan
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora.,Colorado Center for Transplantation Care, Research, and Education, University of Colorado Anschutz Medical Campus, Aurora.,Department of Surgery, University of Colorado Anschutz Medical Campus, Aurora
| | - Jessica L Saben
- Colorado Center for Transplantation Care, Research, and Education, University of Colorado Anschutz Medical Campus, Aurora.,Department of Surgery, University of Colorado Anschutz Medical Campus, Aurora
| | - Jesse D Schold
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio
| | - James J Pomposelli
- Colorado Center for Transplantation Care, Research, and Education, University of Colorado Anschutz Medical Campus, Aurora.,Department of Surgery, University of Colorado Anschutz Medical Campus, Aurora
| | - Elizabeth A Pomfret
- Colorado Center for Transplantation Care, Research, and Education, University of Colorado Anschutz Medical Campus, Aurora.,Department of Surgery, University of Colorado Anschutz Medical Campus, Aurora
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25
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Jose A, Zhou C, Baker R, Walker J, Kurek N, O'Donnell RE, Elwing JM, Gerson M. Predictive value of incidental right ventricular abnormalities identified on SPECT for mortality and pulmonary hypertension. J Nucl Cardiol 2022; 29:1903-1914. [PMID: 33851355 PMCID: PMC8043660 DOI: 10.1007/s12350-021-02612-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 03/17/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND The clinical significance of incidentally found RV abnormalities on low-risk SPECT studies is not well-defined. The objective of this study was to determine the predictive value of incidental right ventricular (RV) abnormalities identified on single photon emission computed tomography (SPECT) scans for mortality and pulmonary hypertension (PH). METHODS We retrospectively analyzed all low-risk SPECT studies in patients without known coronary artery or pulmonary vascular disease, performed at our institution, from 2007-2020. Adjusted Cox proportional hazards models were used to evaluate the association between incidental RV abnormalities on low-risk SPECT studies and outcomes. RESULTS Of the 4761 patients included in the analysis, mortality events were present in 494, and echocardiographic PH was present in 619. Incidental RV abnormalities on low-risk SPECT studies were significantly and independently associated with all-cause mortality (HR = 1.41, CI [1.07-1.86], P = 0.0152) and echocardiographic PH (HR = 2.06, CI [1.64-2.60], P < 0.0001). CONCLUSIONS These data suggest incidental RV abnormalities found on low-risk SPECT imaging studies are significantly and independently associated with increased mortality and risk of developing echocardiographic PH, and could identify high-risk patients for closer monitoring and additional diagnostic testing.
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Affiliation(s)
- Arun Jose
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Cincinnati College of Medicine, 6352-A, Medical Sciences Building, 231 Albert Sabin Way, Cincinnati, OH, 45267, USA.
| | - Christine Zhou
- Division of Internal Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Rachel Baker
- Undergraduate Studies, University of Cincinnati College of Arts and Sciences, Cincinnati, OH, USA
| | - Jackson Walker
- Division of Internal Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Nicholas Kurek
- Division of Internal Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Robert E O'Donnell
- Division of Cardiovascular Diseases, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Jean M Elwing
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Cincinnati College of Medicine, 6352-A, Medical Sciences Building, 231 Albert Sabin Way, Cincinnati, OH, 45267, USA
| | - Myron Gerson
- Division of Cardiovascular Diseases, University of Cincinnati College of Medicine, Cincinnati, OH, USA
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Liu Y, Li A, Liu J, Meng G, Wang M. TSDLPP: A Novel Two-Stage Deep Learning Framework For Prognosis Prediction Based on Whole Slide Histopathological Images. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2523-2532. [PMID: 33989155 DOI: 10.1109/tcbb.2021.3080295] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Recently, digital pathology image-based prognosis prediction has become a hot topic in healthcare research to make early decisions on therapy and improve the treatment quality of patients. Therefore, there has been a recent surge of interest in designing deep learning method solving the problem of prognosis prediction with digital pathology images. However, whole slide histopathological images (WSIs) based prognosis prediction is still a challenge due to the large size of pathological images, the heterogeneity of tumors and the high cost of region of interests (ROIs) labeling. In this study, we design a novel two-stage deep learning framework for prognosis prediction (TSDLPP) based on WSIs. Our proposed framework consists of two-stage paradigms: 1) training tissue decomposition network (TDNet) to divide WSIs into cancerous and non-cancerous regions, 2) integrating general prognosis-related densely connected CNN (GPR-DCCNN) and morphology-specific prognosis-related densely connected CNNs (MSPR-DCCNNs) to extract different level features of pathological images. In the end, we apply TSDLPP to the prognosis prediction of breast cancer using The Cancer Genome Atlas (TCGA) datasets. Experiment results demonstrate that TSDLPP obtains superior performance of prognosis prediction compared with the existing state-of-arts methods.
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27
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Zhang K, Karanth S, Patel B, Murphy R, Jiang X. A multi-task Gaussian process self-attention neural network for real-time prediction of the need for mechanical ventilators in COVID-19 patients. J Biomed Inform 2022; 130:104079. [PMID: 35489596 PMCID: PMC9044651 DOI: 10.1016/j.jbi.2022.104079] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 04/06/2022] [Accepted: 04/18/2022] [Indexed: 02/04/2023]
Abstract
OBJECTIVE The Coronavirus Disease 2019 (COVID-19) pandemic has overwhelmed the capacity of healthcare resources and posed a challenge for worldwide hospitals. The ability to distinguish potentially deteriorating patients from the rest helps facilitate reasonable allocation of medical resources, such as ventilators, hospital beds, and human resources. The real-time accurate prediction of a patient's risk scores could also help physicians to provide earlier respiratory support for the patient and reduce the risk of mortality. METHODS We propose a robust real-time prediction model for the in-hospital COVID-19 patients' probability of requiring mechanical ventilation (MV). The end-to-end neural network model incorporates the Multi-task Gaussian Process to handle the irregular sampling rate in observational data together with a self-attention neural network for the prediction task. RESULTS We evaluate our model on a large database with 9,532 nationwide in-hospital patients with COVID-19. The model demonstrates significant robustness and consistency improvements compared to conventional machine learning models. The proposed prediction model also shows performance improvements in terms of area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) compared to various deep learning models, especially at early times after a patient's hospital admission. CONCLUSION The availability of large and real-time clinical data calls for new methods to make the best use of them for real-time patient risk prediction. It is not ideal for simplifying the data for traditional methods or for making unrealistic assumptions that deviate from observation's true dynamics. We demonstrate a pilot effort to harmonize cross-sectional and longitudinal information for mechanical ventilation needing prediction.
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Affiliation(s)
- Kai Zhang
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
| | - Siddharth Karanth
- Department of Internal Medicine, McGovern Medical School of The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Bela Patel
- Department of Internal Medicine, McGovern Medical School of The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Robert Murphy
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Xiaoqian Jiang
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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28
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Vignoli A, Fornaro A, Tenori L, Castelli G, Cecconi E, Olivotto I, Marchionni N, Alterini B, Luchinat C. Metabolomics Fingerprint Predicts Risk of Death in Dilated Cardiomyopathy and Heart Failure. Front Cardiovasc Med 2022; 9:851905. [PMID: 35463749 PMCID: PMC9021397 DOI: 10.3389/fcvm.2022.851905] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 03/01/2022] [Indexed: 11/17/2022] Open
Abstract
Background Heart failure (HF) is a leading cause of morbidity and mortality worldwide. Metabolomics may help refine risk assessment and potentially guide HF management, but dedicated studies are few. This study aims at stratifying the long-term risk of death in a cohort of patients affected by HF due to dilated cardiomyopathy (DCM) using serum metabolomics via nuclear magnetic resonance (NMR) spectroscopy. Methods A cohort of 106 patients with HF due to DCM, diagnosed and monitored between 1982 and 2011, were consecutively enrolled between 2010 and 2012, and a serum sample was collected from each participant. Each patient underwent half-yearly clinical assessments, and survival status at the last follow-up visit in 2019 was recorded. The NMR serum metabolomic profiles were retrospectively analyzed to evaluate the patient's risk of death. Overall, 26 patients died during the 8-years of the study. Results The metabolomic fingerprint at enrollment was powerful in discriminating patients who died (HR 5.71, p = 0.00002), even when adjusted for potential covariates. The outcome prediction of metabolomics surpassed that of N-terminal pro b-type natriuretic peptide (NT-proBNP) (HR 2.97, p = 0.005). Metabolomic fingerprinting was able to sub-stratify the risk of death in patients with both preserved/mid-range and reduced ejection fraction [hazard ratio (HR) 3.46, p = 0.03; HR 6.01, p = 0.004, respectively]. Metabolomics and left ventricular ejection fraction (LVEF), combined in a score, proved to be synergistic in predicting survival (HR 8.09, p = 0.0000004). Conclusions Metabolomic analysis via NMR enables fast and reproducible characterization of the serum metabolic fingerprint associated with poor prognosis in the HF setting. Our data suggest the importance of integrating several risk parameters to early identify HF patients at high-risk of poor outcomes.
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Affiliation(s)
- Alessia Vignoli
- Department of Chemistry “Ugo Schiff”, Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy
- Interuniversity Consortium for Magnetic Resonance of Metalloproteins, Sesto Fiorentino, Italy
| | | | - Leonardo Tenori
- Department of Chemistry “Ugo Schiff”, Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy
- Interuniversity Consortium for Magnetic Resonance of Metalloproteins, Sesto Fiorentino, Italy
| | | | - Elisabetta Cecconi
- Division of Cardiovascular and Perioperative Medicine, Careggi University Hospital, Florence, Italy
| | - Iacopo Olivotto
- Cardiomyopathy Unit, Careggi University Hospital, Florence, Italy
| | - Niccolò Marchionni
- Division of General Cardiology, Department of Experimental and Clinical Medicine, Careggi University Hospital, University of Florence, Florence, Italy
| | - Brunetto Alterini
- Division of Cardiovascular and Perioperative Medicine, Careggi University Hospital, Florence, Italy
- *Correspondence: Brunetto Alterini
| | - Claudio Luchinat
- Department of Chemistry “Ugo Schiff”, Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy
- Interuniversity Consortium for Magnetic Resonance of Metalloproteins, Sesto Fiorentino, Italy
- Claudio Luchinat
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Blood Count Recovery Following Induction Therapy for Acute Myeloid Leukemia in Children Does Not Predict Survival. Cancers (Basel) 2022; 14:cancers14030616. [PMID: 35158884 PMCID: PMC8833679 DOI: 10.3390/cancers14030616] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/18/2022] [Accepted: 01/21/2022] [Indexed: 12/19/2022] Open
Abstract
Simple Summary International Working Group (IWG) and European LeukemiaNet (ELN) adult response definitions are currently used to evaluate the efficacy of new agents for childhood acute myeloid leukemia (AML); however, the criteria are not consistent with consensus definitions used in pediatric trials or the common practice of intensifying treatment prior to full hematopoietic recovery of ANC ≥ 1000 cells/μL and platelets ≥ 100 cells/μL. This retrospective analysis of the two most recent Phase 3 AML trials in the Children’s Oncology Group assesses the incidence, timing, and prognostic significance of count recovery following induction chemotherapy in children with AML. These data confirm that awaiting count recovery to meet adult criteria does not reflect standard practice in pediatric AML and IWG/ELN-defined CR does not have a significant impact on survival in children. Continuing to use adult IWG/ELN count recovery definitions limits childhood AML drug development by underestimating response, and therefore, updated response criteria are needed for pediatric AML patients. Abstract International Working Group (IWG) and European LeukemiaNet (ELN) response definitions are utilized to evaluate the efficacy of new agents for childhood acute myeloid leukemia (AML) for regulatory purposes. However, these criteria are not consistent with definitions used in pediatric AML trials or with standard pediatric practice to proceed with subsequent therapy cycles prior to IWG/ELN-defined count recovery. We retrospectively analyzed data from the two most recent Phase 3 pediatric AML clinical trials conducted by the Children’s Oncology Group (COG) to assess the incidence, timing, and prognostic significance of count recovery following induction chemotherapy. Of the patients with fewer than 5% bone marrow blasts at the end of first induction, 21.5% of patients proceeded to a second induction cycle prior to achieving ANC ≥ 500 cells/μL and platelets ≥ 50,000 cells/μL, both well below the IWG/ELN thresholds of ANC > 1000 cells/μL and platelets > 100,000 cells/μL. In these two sequential childhood AML Phase 3 trials, neither ANC nor platelet recovery predicted survival. Intensification of treatment through the initiation of subsequent therapy cycles prior to attainment of IWG/ELN-defined CR is common practice in clinical trials for children with AML, suggesting that updated response definitions are needed for pediatric AML.
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30
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Sharma R, Anand H, Badr Y, Qiu RG. Time-to-event prediction using survival analysis methods for Alzheimer's disease progression. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2021; 7:e12229. [PMID: 35005207 PMCID: PMC8719343 DOI: 10.1002/trc2.12229] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 10/14/2021] [Accepted: 11/15/2021] [Indexed: 11/15/2022]
Abstract
INTRODUCTION Many research studies have well investigated Alzheimer's disease (AD) detection and progression. However, the continuous-time survival prediction of AD is not yet fully explored to support medical practitioners with predictive analytics. In this study, we develop a survival analysis approach to examine interactions between patients' inherent temporal and medical patterns and predict the probability of the AD next stage progression during a time period. The likelihood of reaching the following AD stage is unique to a patient, helping the medical practitioner analyze the patient's condition and provide personalized treatment recommendations ahead of time. METHODOLOGIES We simulate the disease progression based on patient profiles using non-linear survival methods-non-linear Cox proportional hazard model (Cox-PH) and neural multi-task logistic regression (N-MTLR). In addition, we evaluate the concordance index (C-index) and Integrated Brier Score (IBS) to describe the evolution to the next stage of AD. For personalized forecasting of disease, we also developed deep neural network models using the dataset provided by the National Alzheimer's Coordinating Center with their multiple-visit details between 2005 and 2017. RESULTS The experiment results show that our N-MTLR based survival models outperform the CoxPH models, the best of which gives Concordance-Index of 0.79 and IBS of 0.09. We obtained 50 critical features out of 92 by applying recursive feature elimination and random forest techniques on the clinical data; the top ones include normal cognition and behavior, criteria for dementia, community affairs, etc. Our study demonstrates that selecting critical features can improve the effectiveness of probabilities at each time interval. CONCLUSIONS The proposed deep learning-based survival method and model can be used by medical practitioners to predict the patients' AD shift efficiently and recommend personalized treatment to mitigate or postpone the effects of AD. More generally, our proposed survival analysis approach for predicting disease stage shift can be used for other progressive diseases such as cancer, Huntington's disease, and scleroderma, just to mention a few, using the corresponding clinical data.
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Affiliation(s)
- Rahul Sharma
- The Pennsylvania State UniversityMalvernPennsylvaniaUSA
| | - Harsh Anand
- The Pennsylvania State UniversityMalvernPennsylvaniaUSA
| | - Youakim Badr
- The Pennsylvania State UniversityMalvernPennsylvaniaUSA
| | - Robin G. Qiu
- The Pennsylvania State UniversityMalvernPennsylvaniaUSA
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31
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Garibaldi BT, Wang K, Robinson ML, Betz J, Alexander GC, Andersen KM, Joseph CS, Mehta HB, Korwek K, Sands KE, Fisher AM, Bollinger RC, Xu Y. Real-World Effectiveness Of Remdesivir In Adults Hospitalized With Covid-19: A Retrospective, Multicenter Comparative Effectiveness Study. Clin Infect Dis 2021; 75:e516-e524. [PMID: 34910128 PMCID: PMC8754724 DOI: 10.1093/cid/ciab1035] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Indexed: 12/15/2022] Open
Abstract
Background There is an urgent need to understand the real-world effectiveness of remdesivir in the treatment of SARS-CoV-2. Methods This was a retrospective comparative effectiveness study. Individuals hospitalized in a large private healthcare network in the US from February 23, 2020 through February 11, 2021 with a positive test for SARS-CoV-2 and ICD-10 diagnosis codes consistent with symptomatic COVID-19 were included. Remdesivir recipients were matched to controls using time-dependent propensity scores. The primary outcome was time to improvement with a secondary outcome of time to death. Results Of 96,859 COVID-19 patients, 42,473 (43.9%) received at least one remdesivir dose. The median age of remdesivir recipients was 65 years, 23,701 (55.8%) were male and 22,819 (53.7%) were non-white. Matches were found for 18,328 patients (43.2%). Remdesivir recipients were significantly more likely to achieve clinical improvement by 28 days (adjusted hazard ratio [1.19, 95% confidence interval (CI), 1.16-1.22]). Remdesivir patients on no oxygen (aHR 1.30, 95% CI 1.22-1.38) or low-flow oxygen (aHR 1.23, 95% CI 1.19-1.27) were significantly more likely to achieve clinical improvement by 28 days. There was no significant impact on the likelihood of mortality overall (aHR 1.02, 95% CI 0.97-1.08). Remdesivir recipients on low-flow oxygen were significantly less likely to die than controls (aHR 0.85, 95% CI 0.77-0.92; 28-day mortality 8.4% [865 deaths] for remdesivir patients, 12.5% [1,334 deaths] for controls). Conclusions These results support the use of remdesivir for hospitalized COVID-19 patients on no or low-flow oxygen. Routine initiation of remdesivir in more severely ill patients is unlikely to be beneficial.
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Affiliation(s)
- Brian T Garibaldi
- Division of Pulmonary and Critical Care, Johns Hopkins University School of Medicine, Baltimore MD, USA.,COVID-19 Consortium of HCA Healthcare and Academia for Research GEneration, USA
| | - Kunbo Wang
- COVID-19 Consortium of HCA Healthcare and Academia for Research GEneration, USA.,Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
| | - Matthew L Robinson
- COVID-19 Consortium of HCA Healthcare and Academia for Research GEneration, USA.,Division of Infectious Disease, Johns Hopkins University School of Medicine, USA
| | - Joshua Betz
- COVID-19 Consortium of HCA Healthcare and Academia for Research GEneration, USA.,Division of Biostatistics, Johns Hopkins Bloomberg School of Public Health, USA
| | - G Caleb Alexander
- COVID-19 Consortium of HCA Healthcare and Academia for Research GEneration, USA.,Center for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.,Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore MD, USA
| | - Kathleen M Andersen
- COVID-19 Consortium of HCA Healthcare and Academia for Research GEneration, USA.,Center for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.,Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore MD, USA
| | - Corey S Joseph
- COVID-19 Consortium of HCA Healthcare and Academia for Research GEneration, USA.,Center for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.,Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore MD, USA
| | - Hemalkumar B Mehta
- COVID-19 Consortium of HCA Healthcare and Academia for Research GEneration, USA.,Center for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.,Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore MD, USA
| | - Kimberly Korwek
- COVID-19 Consortium of HCA Healthcare and Academia for Research GEneration, USA.,Clinical Operations Group, HCA Healthcare, Nashville TN, USA
| | - Kenneth E Sands
- COVID-19 Consortium of HCA Healthcare and Academia for Research GEneration, USA.,Clinical Operations Group, HCA Healthcare, Nashville TN, USA
| | - Arielle M Fisher
- COVID-19 Consortium of HCA Healthcare and Academia for Research GEneration, USA.,Genospace, Sarah Cannon, Boston, MA, USA
| | - Robert C Bollinger
- COVID-19 Consortium of HCA Healthcare and Academia for Research GEneration, USA.,Division of Infectious Disease, Johns Hopkins University School of Medicine, USA
| | - Yanxun Xu
- COVID-19 Consortium of HCA Healthcare and Academia for Research GEneration, USA.,Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA.,Division of Biostatistics and Bioinformatics at The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Zhang Q, Ahn H. Subgroup analysis of censored data on cancer treatment. COMMUN STAT-SIMUL C 2021. [DOI: 10.1080/03610918.2019.1636998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Qing Zhang
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
| | - Hongshik Ahn
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
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Chen Z, Song J, Wang W, Bai J, Zhang Y, Shi J, Bai J, Zhou Y. A novel 4-mRNA signature predicts the overall survival in acute myeloid leukemia. Am J Hematol 2021; 96:1385-1395. [PMID: 34339537 DOI: 10.1002/ajh.26309] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 07/28/2021] [Accepted: 07/29/2021] [Indexed: 12/13/2022]
Abstract
Acute myeloid leukemia (AML) is an aggressive cancer of myeloid cells with high levels of heterogeneity and great variability in prognostic behaviors. Cytogenetic abnormalities and genetic mutations have been widely used in the prognostic stratification of AML to assign patients into different risk categories. Nevertheless, nearly half of AML patients assigned to intermediate risk need more precise prognostic schemes. Here, 336 differentially expressed genes (DEGs) between AML and control samples and 206 genes representing the intratumor heterogeneity of AML were identified. By applying a LASSO Cox regression model, we generated a 4-mRNA prognostic signature comprising KLF9, ENPP4, TUBA4A and CD247. Higher risk scores were significantly associated with shorter overall survival, complex karyotype, and adverse mutations. We then validated the prognostic value of this 4-mRNA signature in two independent cohorts. We also proved that incorporation of the 4-mRNA-based signature in the 2017 European LeukemiaNet (ELN) risk classification could enhance the predictive accuracy of survival in patients with AML. Univariate and multivariate analyses showed that this signature was independent of traditional prognostic factors such as age, WBC count, and unfavorable cytogenetics. Finally, the molecular mechanisms underlying disparate outcomes in high-risk and low-risk AML patients were explored. Therefore, our findings suggest that the 4-mRNA signature refines the risk stratification and prognostic prediction of AML patients.
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Affiliation(s)
- Zizhen Chen
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Tianjin China
| | - Junzhe Song
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Tianjin China
| | - Wenjun Wang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Tianjin China
| | - Jiaojiao Bai
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Tianjin China
| | - Yuhui Zhang
- Department of Hematology The Second Affiliated Hospital of Tianjin Medical University Tianjin China
| | - Jun Shi
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Tianjin China
| | - Jie Bai
- Department of Hematology The Second Affiliated Hospital of Tianjin Medical University Tianjin China
| | - Yuan Zhou
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Tianjin China
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Marateb HR, von Cube M, Sami R, Haghjooy Javanmard S, Mansourian M, Amra B, Soltaninejad F, Mortazavi M, Adibi P, Khademi N, Sadat Hosseini N, Toghyani A, Hassannejad R, Mañanas MA, Binder H, Wolkewitz M. Absolute mortality risk assessment of COVID-19 patients: the Khorshid COVID Cohort (KCC) study. BMC Med Res Methodol 2021; 21:146. [PMID: 34261439 PMCID: PMC8278186 DOI: 10.1186/s12874-021-01340-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 06/17/2021] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Already at hospital admission, clinicians require simple tools to identify hospitalized COVID-19 patients at high risk of mortality. Such tools can significantly improve resource allocation and patient management within hospitals. From the statistical point of view, extended time-to-event models are required to account for competing risks (discharge from hospital) and censoring so that active cases can also contribute to the analysis. METHODS We used the hospital-based open Khorshid COVID Cohort (KCC) study with 630 COVID-19 patients from Isfahan, Iran. Competing risk methods are used to develop a death risk chart based on the following variables, which can simply be measured at hospital admission: sex, age, hypertension, oxygen saturation, and Charlson Comorbidity Index. The area under the receiver operator curve was used to assess accuracy concerning discrimination between patients discharged alive and dead. RESULTS Cause-specific hazard regression models show that these baseline variables are associated with both death, and discharge hazards. The risk chart reflects the combined results of the two cause-specific hazard regression models. The proposed risk assessment method had a very good accuracy (AUC = 0.872 [CI 95%: 0.835-0.910]). CONCLUSIONS This study aims to improve and validate a personalized mortality risk calculator based on hospitalized COVID-19 patients. The risk assessment of patient mortality provides physicians with additional guidance for making tough decisions.
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Affiliation(s)
- Hamid Reza Marateb
- Biomedical Engineering Department, Engineering Faculty, University of Isfahan, Isfahan, Iran
- Biomedical Engineering Research Centre (CREB), Automatic Control Department (ESAII), Universitat Politècnica de Catalunya-Barcelona Tech (UPC)Building H, Floor 4, Av. Diagonal 647, 08028 Barcelona, Spain
| | - Maja von Cube
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Ramin Sami
- Department of Internal Medicine, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Shaghayegh Haghjooy Javanmard
- Applied Physiology Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Marjan Mansourian
- Biomedical Engineering Research Centre (CREB), Automatic Control Department (ESAII), Universitat Politècnica de Catalunya-Barcelona Tech (UPC)Building H, Floor 4, Av. Diagonal 647, 08028 Barcelona, Spain
- Department of Epidemiology and Biostatistics, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Babak Amra
- Bamdad Respiratory Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Forogh Soltaninejad
- The Respiratory Research Center, Pulmonary Division, Department of Internal Medicine, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mojgan Mortazavi
- Isfahan Kidney Diseases Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Peyman Adibi
- Isfahan Gastroenterology and Hepatology Research Center (lGHRC), Isfahan University of Medical Sciences, Isfahan, Iran
| | - Nilufar Khademi
- School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | | | - Arash Toghyani
- School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Razieh Hassannejad
- Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Miquel Angel Mañanas
- Biomedical Engineering Research Centre (CREB), Automatic Control Department (ESAII), Universitat Politècnica de Catalunya-Barcelona Tech (UPC)Building H, Floor 4, Av. Diagonal 647, 08028 Barcelona, Spain
- Biomedical Research Networking Center in Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Harald Binder
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Martin Wolkewitz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
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Survival Analysis in Colon Cancer Patients. JOURNAL OF CONTEMPORARY MEDICINE 2021. [DOI: 10.16899/jcm.902588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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Kim SW, Schumacher M, Augustin NH. A shared frailty model for multivariate longitudinal data on adverse event of radiation therapy. Biom J 2021; 63:1493-1506. [PMID: 33949712 DOI: 10.1002/bimj.202000237] [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: 07/29/2020] [Revised: 02/01/2021] [Accepted: 04/06/2021] [Indexed: 11/07/2022]
Abstract
Oral mucositis is an inflammatory adverse event when treating head and neck cancer patients with radiation therapy (RT). The severity of its occurrence is believed to mainly depend on its site and the distribution of a cumulative radiation dose in the mouth area. The motivating study investigating differences in radiosensitivities (mucositis progression) at distinct sites where the severity of mucositis is assessed regularly at eight distinct sites on an ordinal scale results in multivariate longitudinal data and thus poses certain challenges. To deal with the multivariate longitudinal data in this particular setting, we take a time-to-event approach focusing on the first occurrence of severe mucositis at the distinct sites using the fact that the site-specific cumulative radiation dose thought to be the main driver of oral mucositis develops over time. Thereby, we may address multivariate longitudinal processes in a simpler and more compact fashion. In this article, to find out differences in mucositis progression at eight distinct sites we propose a shared frailty model for multivariate parallel processes within individuals. The shared frailty model directly incorporating 'process indicators' as covariates turns out to adequately explain the differences in the parallel processes (here, mucositis progressions at distinct sites) while taking individual effects into account. The parallel result with the one from the previous analysis based on the same data but conducted with an alternative statistical methodology shows adequacy of the proposed approach.
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Affiliation(s)
- Sung Won Kim
- Institute of Medical Biometry and Statistics, Medical Center-University of Freiburg, Freiburg, Germany
| | - Martin Schumacher
- Institute of Medical Biometry and Statistics, Medical Center-University of Freiburg, Freiburg, Germany
| | - Nicole H Augustin
- School of Mathematics, University of Edinburgh, James Clark Maxwell Building, The King's Buildings, Edinburgh, UK
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Samimi H, Mehta I, Docking TR, Zainulabadeen A, Karsan A, Zare H. DNA methylation analysis improves the prognostication of acute myeloid leukemia. EJHAEM 2021; 2:211-218. [PMID: 34308417 PMCID: PMC8294109 DOI: 10.1002/jha2.187] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 02/24/2021] [Accepted: 03/04/2021] [Indexed: 12/12/2022]
Abstract
Integration of orthogonal data could provide new opportunities to pinpoint the underlying molecular mechanisms of hematologic disorders. Using a novel gene network approach, we integrated DNA methylation data from The Cancer Genome Atlas (n = 194 cases) with the corresponding gene expression profile. Our integrated gene network analysis classified AML patients into low-, intermediate-, and high-risk groups. The identified high-risk group had significantly shorter overall survival compared to the low-risk group (p-value ≤10-11). Specifically, our approach identified a particular subgroup of nine high-risk AML cases that died within 2 years after diagnosis. These high-risk cases otherwise would be incorrectly classified as intermediate-risk solely based on cytogenetics, mutation profiles, and common molecular characteristics of AML. We confirmed the prognostic value of our integrative gene network approach using two independent datasets, as well as through comparison with European LeukemiaNet and LSC17 criteria. Our approach could be useful in the prognostication of a subset of borderline AML cases. These cases would not be classified into appropriate risk groups by other approaches that use gene expression, but not DNA methylation data. Our findings highlight the significance of epigenomic data, and they indicate integrating DNA methylation data with gene coexpression networks can have a synergistic effect.
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Affiliation(s)
- Hanie Samimi
- Department of Computer ScienceTexas State UniversitySan MarcosTexasUSA
| | - Isha Mehta
- Department of Cell Systems & AnatomyThe University of Texas Health Science CenterSan AntonioTexasUSA
| | - Thomas Roderick Docking
- Canada's Michael Smith Genome Sciences CentreBritish Columbia Cancer Research CentreVancouverBritish ColumbiaCanada
| | | | - Aly Karsan
- Canada's Michael Smith Genome Sciences CentreBritish Columbia Cancer Research CentreVancouverBritish ColumbiaCanada
| | - Habil Zare
- Department of Cell Systems & AnatomyThe University of Texas Health Science CenterSan AntonioTexasUSA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative DiseasesUniversity of Texas Health Sciences CenterSan AntonioTexasUSA
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Motion-Control Shoes Reduce the Risk of Pronation-Related Pathologies in Recreational Runners: A Secondary Analysis of a Randomized Controlled Trial. J Orthop Sports Phys Ther 2021; 51:135-143. [PMID: 33306927 DOI: 10.2519/jospt.2021.9710] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
OBJECTIVE To investigate whether motion-control shoes reduce the risk of pronation-related injuries in recreational runners. DESIGN Secondary analysis of a randomized controlled trial of the effect of shoes on running injuries. METHODS Three hundred seventy-two recreational runners were randomized to receive either standard neutral or motion-control shoes and were followed up for 6 months regarding running activity and injury. Running injuries that occurred during this period were registered and classified as pronation-related injuries (Achilles tendinopathy, plantar fasciopathy, exercise-related lower-leg pain, and anterior knee pain) or other running-related injuries. With the use of competing risk analysis, the relationship between pronation-related and other running-related injuries and shoe type was evaluated by estimating the cause-specific hazard, controlling for other possible confounders like age, sex, body mass index, previous injury, and sport participation pattern. RESULTS Twenty-five runners sustained pronation-related running injuries and 68 runners sustained other running-related injuries. Runners wearing the motion-control shoes had a lower risk of pronation-related running injuries compared with runners who wore standard neutral shoes (hazard ratio = 0.41; 95% confidence interval: 0.17, 0.98). There was no effect of shoe type (hazard ratio = 0.68; 95% confidence interval: 0.41, 1.10) on the risk of other running-related injuries. CONCLUSION Motion-control shoes may reduce the risk of pronation-related running injuries, but did not influence the risk of other running-related injuries. J Orthop Sports Phys Ther 2021;51(3):135-143. Epub 11 Dec 2020. doi:10.2519/jospt.2021.9710.
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Nguyen T, Zhang T, Fox G, Zeng S, Cao N, Pan C, Chen JY. Linking clinotypes to phenotypes and genotypes from laboratory test results in comprehensive physical exams. BMC Med Inform Decis Mak 2021; 21:51. [PMID: 33627109 PMCID: PMC7903607 DOI: 10.1186/s12911-021-01387-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 01/06/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In this work, we aimed to demonstrate how to utilize the lab test results and other clinical information to support precision medicine research and clinical decisions on complex diseases, with the support of electronic medical record facilities. We defined "clinotypes" as clinical information that could be observed and measured objectively using biomedical instruments. From well-known 'omic' problem definitions, we defined problems using clinotype information, including stratifying patients-identifying interested sub cohorts for future studies, mining significant associations between clinotypes and specific phenotypes-diseases, and discovering potential linkages between clinotype and genomic information. We solved these problems by integrating public omic databases and applying advanced machine learning and visual analytic techniques on two-year health exam records from a large population of healthy southern Chinese individuals (size n = 91,354). When developing the solution, we carefully addressed the missing information, imbalance and non-uniformed data annotation issues. RESULTS We organized the techniques and solutions to address the problems and issues above into CPA framework (Clinotype Prediction and Association-finding). At the data preprocessing step, we handled the missing value issue with predicted accuracy of 0.760. We curated 12,635 clinotype-gene associations. We found 147 Associations between 147 chronic diseases-phenotype and clinotypes, which improved the disease predictive performance to AUC (average) of 0.967. We mined 182 significant clinotype-clinotype associations among 69 clinotypes. CONCLUSIONS Our results showed strong potential connectivity between the omics information and the clinical lab test information. The results further emphasized the needs to utilize and integrate the clinical information, especially the lab test results, in future PheWas and omic studies. Furthermore, it showed that the clinotype information could initiate an alternative research direction and serve as an independent field of data to support the well-known 'phenome' and 'genome' researches.
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Affiliation(s)
- Thanh Nguyen
- Informatics Institute, School of Medicine, The University of Alabama at Birmingham, AL, Birmingham, USA
| | - Tongbin Zhang
- School of First Clinical Medical Sciences - School of Information and Engineering, Wenzhou Medical University, Zhejiang, China
- Department of Computer Technology and Information Management, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
| | - Geoffrey Fox
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | - Sisi Zeng
- School of First Clinical Medical Sciences - School of Information and Engineering, Wenzhou Medical University, Zhejiang, China
| | - Ni Cao
- School of First Clinical Medical Sciences - School of Information and Engineering, Wenzhou Medical University, Zhejiang, China
| | - Chuandi Pan
- School of First Clinical Medical Sciences - School of Information and Engineering, Wenzhou Medical University, Zhejiang, China
- Department of Computer Technology and Information Management, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
| | - Jake Y Chen
- Informatics Institute, School of Medicine, The University of Alabama at Birmingham, AL, Birmingham, USA.
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Tomotani BM, Gienapp P, de la Hera I, Terpstra M, Pulido F, Visser ME. Integrating Causal and Evolutionary Analysis of Life-History Evolution: Arrival Date in a Long-Distant Migrant. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.630823] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
In migratory species, the timing of arrival at the breeding grounds is a life-history trait with major fitness consequences. The optimal arrival date varies from year-to-year, and animals use cues to adjust their arrival dates to match this annual variation. However, which cues they use to time their arrival and whether these cues actually predict the annual optimal arrival date is largely unknown. Here, we integrate causal and evolutionary analysis by identifying the environmental variables used by a migratory songbird to time its arrival dates and testing whether these environmental variables also predicted the optimal time to arrive. We used 11 years of male arrival data of a pied flycatcher population. Specifically, we tested whether temperature and normalized difference vegetation index (NDVI) values from their breeding grounds in the Netherlands and from their wintering grounds in Ivory Coast explained the variation in arrival date, and whether these variables correlated with the position of the annual fitness peak at the breeding grounds. We found that temperature and NDVI, both from the wintering and the breeding grounds, explained the annual variation in arrival date, but did not correlate with the optimal arrival date. We explore three alternative explanations for this lack of correlation. Firstly, the date of the fitness peak may have been incorrectly estimated because a potentially important component of fitness (i.e., migration date dependent mortality en route or directly upon arrival) could not be measured. Secondly, we focused on male timing but the fitness landscape is also likely to be shaped by female timing. Finally, the correlation has recently disappeared because climate change disrupted the predictive value of the cues that the birds use to time their migration. In the latter case, birds may adapt by altering their sensitivity to temperature and NDVI.
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Wang Y, Yu Z. A kernel regression model for panel count data with nonparametric covariate functions. Biometrics 2021; 78:586-597. [PMID: 33559887 DOI: 10.1111/biom.13440] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Revised: 01/17/2021] [Accepted: 01/22/2021] [Indexed: 11/27/2022]
Abstract
The local kernel pseudo-partial likelihood is employed for estimation in a panel count model with nonparametric covariate functions. An estimator of the derivative of the nonparametric covariate function is derived first, and the nonparametric function estimator is then obtained by integrating the derivative estimator. Uniform consistency rates and pointwise asymptotic normality are obtained for the local derivative estimator under some regularity conditions. Moreover, the baseline function estimator is shown to be uniformly consistent. Demonstration of the asymptotic results strongly relies on the modern empirical theory, which generally does not require the Poisson assumption. Simulation studies also illustrate that the local derivative estimator performs well in a finite-sample regardless of whether the Poisson assumption holds. We also implement the proposed methodology to analyze a clinical study on childhood wheezing.
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Affiliation(s)
- Yang Wang
- Department of Statistics, SJTU-Yale Joint Centre for Biostatistics, Shanghai Jiao Tong University, Shanghai, China
| | - Zhangsheng Yu
- Department of Bioinformatics and Biostatistics, Department of Statistics, SJTU-Yale Joint Centre for Biostatistics, Shanghai Jiao Tong University, Shanghai, China
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Emmert-Streib F, Yli-Harja O, Dehmer M. Artificial Intelligence: A Clarification of Misconceptions, Myths and Desired Status. Front Artif Intell 2020; 3:524339. [PMID: 33733197 PMCID: PMC7944138 DOI: 10.3389/frai.2020.524339] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Accepted: 10/12/2020] [Indexed: 11/30/2022] Open
Abstract
The field artificial intelligence (AI) was founded over 65 years ago. Starting with great hopes and ambitious goals the field progressed through various stages of popularity and has recently undergone a revival through the introduction of deep neural networks. Some problems of AI are that, so far, neither the "intelligence" nor the goals of AI are formally defined causing confusion when comparing AI to other fields. In this paper, we present a perspective on the desired and current status of AI in relation to machine learning and statistics and clarify common misconceptions and myths. Our discussion is intended to lift the veil of vagueness surrounding AI to reveal its true countenance.
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Affiliation(s)
- Frank Emmert-Streib
- Predictive Society and Data Analytics Lab, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland
| | - Olli Yli-Harja
- Institute of Biosciences and Medical Technology, Tampere, Finland
- Computational System Biology, Faculty of Medicine and Health Technology, Tampere University, Finland
- Institute for Systems Biology, Seattle, WA, United States
| | - Matthias Dehmer
- Department of Mechatronics and Biomedical Computer Science, UMIT, Hall in Tyrol, IL, Austria
- Department of Computer Science, Swiss Distance University of Applied Sciences, Brig, Switzerland
- College of Artificial Intelligence, Nankai University, Tianjin, China
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Baek B, Lee H. Prediction of survival and recurrence in patients with pancreatic cancer by integrating multi-omics data. Sci Rep 2020; 10:18951. [PMID: 33144687 PMCID: PMC7609582 DOI: 10.1038/s41598-020-76025-1] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 10/20/2020] [Indexed: 01/08/2023] Open
Abstract
Predicting the prognosis of pancreatic cancer is important because of the very low survival rates of patients with this particular cancer. Although several studies have used microRNA and gene expression profiles and clinical data, as well as images of tissues and cells, to predict cancer survival and recurrence, the accuracies of these approaches in the prediction of high-risk pancreatic adenocarcinoma (PAAD) still need to be improved. Accordingly, in this study, we proposed two biological features based on multi-omics datasets to predict survival and recurrence among patients with PAAD. First, the clonal expansion of cancer cells with somatic mutations was used to predict prognosis. Using whole-exome sequencing data from 134 patients with PAAD from The Cancer Genome Atlas (TCGA), we found five candidate genes that were mutated in the early stages of tumorigenesis with high cellular prevalence (CP). CDKN2A, TP53, TTN, KCNJ18, and KRAS had the highest CP values among the patients with PAAD, and survival and recurrence rates were significantly different between the patients harboring mutations in these candidate genes and those harboring mutations in other genes (p = 2.39E-03, p = 8.47E-04, respectively). Second, we generated an autoencoder to integrate the RNA sequencing, microRNA sequencing, and DNA methylation data from 134 patients with PAAD from TCGA. The autoencoder robustly reduced the dimensions of these multi-omics data, and the K-means clustering method was then used to cluster the patients into two subgroups. The subgroups of patients had significant differences in survival and recurrence (p = 1.41E-03, p = 4.43E-04, respectively). Finally, we developed a prediction model for prognosis using these two biological features and clinical data. When support vector machines, random forest, logistic regression, and L2 regularized logistic regression were used as prediction models, logistic regression analysis generally revealed the best performance for both disease-free survival (DFS) and overall survival (OS) (accuracy [ACC] = 0.762 and area under the curve [AUC] = 0.795 for DFS; ACC = 0.776 and AUC = 0.769 for OS). Thus, we could classify patients with a high probability of recurrence and at a high risk of poor outcomes. Our study provides insights into new personalized therapies on the basis of mutation status and multi-omics data.
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Affiliation(s)
- Bin Baek
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, Korea
| | - Hyunju Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, Korea.
- Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju, 61005, Korea.
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Abstract
There has been increasing interest in modelling survival data using deep learning methods in medical research. Current approaches have focused on designing special cost functions to handle censored survival data. We propose a very different method with two simple steps. In the first step, we transform each subject's survival time into a series of jackknife pseudo conditional survival probabilities and then use these pseudo probabilities as a quantitative response variable in the deep neural network model. By using the pseudo values, we reduce a complex survival analysis to a standard regression problem, which greatly simplifies the neural network construction. Our two-step approach is simple, yet very flexible in making risk predictions for survival data, which is very appealing from the practice point of view. The source code is freely available at http://github.com/lilizhaoUM/DNNSurv.
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Kheiry F, Kargarian-Marvasti S, Afrashteh S, Mohammadbeigi A, Daneshi N, Naderi S, Saadat SH. Evaluation of goodness of fit of semiparametric and parametric models in analysis of factors associated with length of stay in neonatal intensive care unit. Clin Exp Pediatr 2020; 63:361-367. [PMID: 32517423 PMCID: PMC7462822 DOI: 10.3345/cep.2019.00437] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Accepted: 01/31/2020] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Length of stay is a significant indicator of care effectiveness and hospital performance. Owing to the limited number of healthcare centers and facilities, it is important to optimize length of stay and associated factors. PURPOSE The present study aimed to investigate factors associated with neonatal length of stay in the neonatal intensive care unit (NICU) using parametric and semiparametric models and compare model fitness according to Akaike information criterion (AIC) between 2016 and 2018. METHODS This retrospective cohort study reviewed 600 medical records of infants admitted to the NICU of Bandar Abbas Hospital. Samples were identified using census sampling. Factors associated with NICU length of stay were investigated based on semiparametric Cox model and 4 parametric models including Weibull, exponential, log-logistic, and log-normal to determine the best fitted model. The data analysis was conducted using R software. The significance level was set at 0.05. RESULTS The study findings suggest that breastfeeding, phototherapy, acute renal failure, presence of mechanical ventilation, and availability of central venous catheter were commonly identified as factors associated with NICU length of stay in all 5 models (P<0.05). Parametric models showed better fitness than the Cox model in this study. CONCLUSION Breastfeeding and availability of central venous catheter had protective effects against length of stay, whereas phototherapy, acute renal failure, and mechanical ventilation increased length of stay in NICU. Therefore, the identification of factors associated with NICU length of stay can help establish effective interventions aimed at decreasing the length of stay among infants.
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Affiliation(s)
- Fatemeh Kheiry
- Student Research Committee, School of Nursing and Midwifery, Shiraz University of Medical Sciences, Shiraz, Iran
| | | | - Sima Afrashteh
- Department of Public Health, Faculty of Health, Bushehr University of Medical Sciences, Bushehr, Iran
| | - Abolfazl Mohammadbeigi
- Department of Epidemiology and Biostatistics, Faculty of Health, Qom University of Medical Sciences, Qom, Iran
| | - Nima Daneshi
- Behbahan Faculty of Medical Sciences, Behbahan, Iran
| | - Salma Naderi
- Department of Pediatrics, Faculty of Medicine, Clinical Research Development Centre of Children Hospital, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
| | - Seyed Hossein Saadat
- Faculty of Medicine, Clinical Research Development Center of Children's Hospital, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
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46
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Qiu Y, Lyu J, Dunlap M, Harvey SE, Cheng C. A combinatorially regulated RNA splicing signature predicts breast cancer EMT states and patient survival. RNA (NEW YORK, N.Y.) 2020; 26:1257-1267. [PMID: 32467311 PMCID: PMC7430667 DOI: 10.1261/rna.074187.119] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 05/19/2020] [Indexed: 05/08/2023]
Abstract
During breast cancer metastasis, the developmental process epithelial-mesenchymal transition (EMT) is abnormally activated. Transcriptional regulatory networks controlling EMT are well-studied; however, alternative RNA splicing also plays a critical regulatory role during this process. A comprehensive understanding of alternative splicing (AS) and the RNA binding proteins (RBPs) that regulate it during EMT and their impact on breast cancer remains largely unknown. In this study, we annotated AS in the breast cancer TCGA data set and identified an AS signature that is capable of distinguishing epithelial and mesenchymal states of the tumors. This AS signature contains 25 AS events, among which nine showed increased exon inclusion and 16 showed exon skipping during EMT. This AS signature accurately assigns the EMT status of cells in the CCLE data set and robustly predicts patient survival. We further developed an effective computational method using bipartite networks to identify RBP-AS networks during EMT. This network analysis revealed the complexity of RBP regulation and nominated previously unknown RBPs that regulate EMT-associated AS events. This study highlights the importance of global AS regulation during EMT in cancer progression and paves the way for further investigation into RNA regulation in EMT and metastasis.
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Affiliation(s)
- Yushan Qiu
- Lester and Sue Smith Breast Center, Department of Molecular and Human Genetics, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
- Division of Hematology/Oncology, Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611, USA
- College of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, P.R. China
| | - Jingyi Lyu
- Lester and Sue Smith Breast Center, Department of Molecular and Human Genetics, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
- Integrative Molecular and Biomedical Sciences Graduate Program, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Mikayla Dunlap
- Lester and Sue Smith Breast Center, Department of Molecular and Human Genetics, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Samuel E Harvey
- Lester and Sue Smith Breast Center, Department of Molecular and Human Genetics, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
- Division of Hematology/Oncology, Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611, USA
| | - Chonghui Cheng
- Lester and Sue Smith Breast Center, Department of Molecular and Human Genetics, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
- Division of Hematology/Oncology, Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611, USA
- Integrative Molecular and Biomedical Sciences Graduate Program, Baylor College of Medicine, Houston, Texas 77030, USA
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Peng Y, Keenan TD, Chen Q, Agrón E, Allot A, Wong WT, Chew EY, Lu Z. Predicting risk of late age-related macular degeneration using deep learning. NPJ Digit Med 2020; 3:111. [PMID: 32904246 PMCID: PMC7453007 DOI: 10.1038/s41746-020-00317-z] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Accepted: 08/04/2020] [Indexed: 12/30/2022] Open
Abstract
By 2040, age-related macular degeneration (AMD) will affect ~288 million people worldwide. Identifying individuals at high risk of progression to late AMD, the sight-threatening stage, is critical for clinical actions, including medical interventions and timely monitoring. Although deep learning has shown promise in diagnosing/screening AMD using color fundus photographs, it remains difficult to predict individuals' risks of late AMD accurately. For both tasks, these initial deep learning attempts have remained largely unvalidated in independent cohorts. Here, we demonstrate how deep learning and survival analysis can predict the probability of progression to late AMD using 3298 participants (over 80,000 images) from the Age-Related Eye Disease Studies AREDS and AREDS2, the largest longitudinal clinical trials in AMD. When validated against an independent test data set of 601 participants, our model achieved high prognostic accuracy (5-year C-statistic 86.4 (95% confidence interval 86.2-86.6)) that substantially exceeded that of retinal specialists using two existing clinical standards (81.3 (81.1-81.5) and 82.0 (81.8-82.3), respectively). Interestingly, our approach offers additional strengths over the existing clinical standards in AMD prognosis (e.g., risk ascertainment above 50%) and is likely to be highly generalizable, given the breadth of training data from 82 US retinal specialty clinics. Indeed, during external validation through training on AREDS and testing on AREDS2 as an independent cohort, our model retained substantially higher prognostic accuracy than existing clinical standards. These results highlight the potential of deep learning systems to enhance clinical decision-making in AMD patients.
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Affiliation(s)
- Yifan Peng
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD USA
| | - Tiarnan D. Keenan
- National Eye Institute (NEI), National Institutes of Health (NIH), Bethesda, MD USA
| | - Qingyu Chen
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD USA
| | - Elvira Agrón
- National Eye Institute (NEI), National Institutes of Health (NIH), Bethesda, MD USA
| | - Alexis Allot
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD USA
| | - Wai T. Wong
- National Eye Institute (NEI), National Institutes of Health (NIH), Bethesda, MD USA
| | - Emily Y. Chew
- National Eye Institute (NEI), National Institutes of Health (NIH), Bethesda, MD USA
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD USA
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48
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Abstract
The gut microbiome plays a critical role in the health of many animals. Honeybees are no exception, as they host a core microbiome that affects their nutrition and immune function. However, the relationship between the honeybee immune system and its gut symbionts is poorly understood. Here, we explore how the beneficial symbiont Snodgrassella alvi affects honeybee immune gene expression. We show that both live and heat-killed S. alvi protect honeybees from the opportunistic pathogen Serratia marcescens and lead to the expression of host antimicrobial peptides. Honeybee immune genes respond differently to live S. alvi compared to heat-killed S. alvi, the latter causing a more extensive immune expression response. We show a preference for Toll pathway upregulation over the Imd pathway in the presence of both live and heat-killed S. alvi. Finally, we find that live S. alvi aids in clearance of S. marcescens from the honeybee gut, supporting a potential role for the symbiont in colonization resistance. Our results show that colonization by the beneficial symbiont S. alvi triggers a replicable honeybee immune response. These responses may benefit the host and the symbiont, by helping to regulate gut microbial members and preventing overgrowth or invasion by opportunists.
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Affiliation(s)
- Richard D Horak
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Sean P Leonard
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Nancy A Moran
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78712, USA
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49
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Li X, Liu L, Goodall GJ, Schreiber A, Xu T, Li J, Le TD. A novel single-cell based method for breast cancer prognosis. PLoS Comput Biol 2020; 16:e1008133. [PMID: 32833968 PMCID: PMC7470419 DOI: 10.1371/journal.pcbi.1008133] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 09/03/2020] [Accepted: 07/09/2020] [Indexed: 12/12/2022] Open
Abstract
Breast cancer prognosis is challenging due to the heterogeneity of the disease. Various computational methods using bulk RNA-seq data have been proposed for breast cancer prognosis. However, these methods suffer from limited performances or ambiguous biological relevance, as a result of the neglect of intra-tumor heterogeneity. Recently, single cell RNA-sequencing (scRNA-seq) has emerged for studying tumor heterogeneity at cellular levels. In this paper, we propose a novel method, scPrognosis, to improve breast cancer prognosis with scRNA-seq data. scPrognosis uses the scRNA-seq data of the biological process Epithelial-to-Mesenchymal Transition (EMT). It firstly infers the EMT pseudotime and a dynamic gene co-expression network, then uses an integrative model to select genes important in EMT based on their expression variation and differentiation in different stages of EMT, and their roles in the dynamic gene co-expression network. To validate and apply the selected signatures to breast cancer prognosis, we use them as the features to build a prediction model with bulk RNA-seq data. The experimental results show that scPrognosis outperforms other benchmark breast cancer prognosis methods that use bulk RNA-seq data. Moreover, the dynamic changes in the expression of the selected signature genes in EMT may provide clues to the link between EMT and clinical outcomes of breast cancer. scPrognosis will also be useful when applied to scRNA-seq datasets of different biological processes other than EMT.
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Affiliation(s)
- Xiaomei Li
- UniSA STEM, University of South Australia, Mawson Lakes, SA, Australia
| | - Lin Liu
- UniSA STEM, University of South Australia, Mawson Lakes, SA, Australia
| | - Gregory J. Goodall
- Centre for Cancer Biology, an alliance of SA Pathology and University of South Australia, Adelaide, SA, Australia
- School of Medicine, Discipline of Medicine, University of Adelaide, SA, Australia
| | - Andreas Schreiber
- Centre for Cancer Biology, an alliance of SA Pathology and University of South Australia, Adelaide, SA, Australia
| | - Taosheng Xu
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China
| | - Jiuyong Li
- UniSA STEM, University of South Australia, Mawson Lakes, SA, Australia
| | - Thuc D. Le
- UniSA STEM, University of South Australia, Mawson Lakes, SA, Australia
- * E-mail:
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50
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Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. ACTA ACUST UNITED AC 2020; 1:800-810. [DOI: 10.1038/s43018-020-0085-8] [Citation(s) in RCA: 326] [Impact Index Per Article: 65.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 05/26/2020] [Indexed: 02/07/2023]
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