Intraoperative hypothermia is a prevalent complication that may significant clinical and economic burdens. Previous risk assessment models have demonstrated limitations in accurately predicting intraoperative hypothermia, particularly in diverse surgical populations. This study aims to develop and validate a model in adult surgical patients to improve outcomes.

This retrospective cohort study utilized data extracted from electronic medical records and anaesthesia information management systems between June 2022 and August 2023. The analysis included information of 3,405 adult surgical patients from three independent centres in China who underwent elective surgical procedures with body temperature monitoring. Intraoperative hypothermia was defined as a core temperature below 36 °C during surgery. The Least Absolute Shrinkage and Selection Operator (LASSO) regression employed to select optimal features and multivariate logistic regression was used to identify independent predictors of intraoperative hypothermia and then built the risk assessment model. We further evaluated the discriminative ability, calibration curves, and clinical utility of the predictive model.

The total incidences of intraoperative hypothermia in adult surgical patients were 42.5%. The predictors in the intraoperative hypothermia model included: age, BMI, baseline HR, baseline temperature, minimally invasive surgery, smoking, previous surgery and serum creatine level. In the training cohort, the model demonstrated strong discriminatory ability, with C-index values of 0.721 (95% CI 0.697-0.744). Internal and external validation further confirmed the model's robustness and generalizability.

These findings suggest that our model may help us more accurately identify patients at risk of intraoperative hypothermia.

China Clinical Trial Registration Center (ChiCTR2300071859), Date registered May/26/2023.

To develop a machine learning-based model to predict the relapse risk of Primary Autoimmune Haemolytic Anaemia (AIHA) after the last remission.

A retrospective study was conducted on primary AIHA cases who visited the Affiliated Hospital of Southwest Medical University and Xuyong County People's Hospital from May 2017 to May 2022. Cases were categorized as relapsed or non-relapsed based on the 1-year outcomes. Twenty-two features were analyzed to identify relapse risk factors. The least absolute shrinkage and selection operator (LASSO) regression model and multivariate logistic regression analysis were used to establish a predictive model. The C-index, Calibration curves, ROC, and Decision curve analysis (DCA) were used to evaluate the discriminatory, corrective, accurate, and clinical effectiveness of the predictive model.

A total of 232 cases of primary AIHA were included, and five potential variables including 'DAT results', 'Hb', 'Multiline therapy', 'Complicating ITP', and 'Complicating infection', have been screened for constructing a 1-year relapse risk prediction nomogram for primary AIHA. The nomogram has a C-index of 0.852 (95% CI: 0.797-0.907), confirmed by bootstrapping validation as 0.829. The area under the ROC was 0.846. The DCA shows that when the threshold probability is in the range of 1 ∼ 91%.

By following the current diagnostic and treatment criteria for AIHA in China, we retrospectively collect a multitude of medical records and analyze several relevant variables of AIHA, construct a predictive model by machine learning. Using this 1-year relapse risk nomogram can effectively predict the risk of relapse within 1 year after remission of primary AIHA.

Financial capability, encompassing both financial competence and financial performance, is essential for independent living. However, individuals with neurological and psychiatric disorders often demonstrate difficulties with financial capability. This study examined the influence of common neurological and psychiatric conditions, i.e., Alzheimer's disease (AD), Parkinson's disease (PD), stroke, and affective disturbances, on financial performance.

Prospective data from wave 8 (n= 53,695) and wave 9 (n= 69,477) of the Survey of Health, Retirement and Ageing in Europe were used, which included individuals aged 50+; part of Wave 8 and all of Wave 9 data were collected during the COVID-19 pandemic. Logistic regressions and group comparisons were conducted to analyze the influence of self-reported disease diagnosis on three aspects of (future) financial performance: difficulties in managing money, difficulties in making ends meet, and debt situation.

Compared to controls, participants with one of the four conditions reported significantly more often having difficulties managing money. Within the AD group, over half of the participants reported these difficulties. The different diagnoses also predicted both current and future difficulties in managing money and making ends meet. However, only affective disorders were associated with and predicted a higher likelihood of having household debts.

Compared to controls, individuals with PD, AD, stroke, or affective disorders are more prone to experiencing impairments with both current and future financial performance, potentially facing financial difficulties. These results emphasize the importance of recognizing financial difficulties in such individuals and offering financial assistance when needed.

Coarctation of the aorta is one of the most common congenital heart malformations, accounting for approximately 7% of all live births with congenital heart disease. It is crucial to make a definitive prenatal diagnosis as it can inform clinical treatment decisions.

The diagnostic criteria for coarctation of the aorta are still controversial, and there is currently no risk nomogram available to assess the probability of coarctation of the aorta using routine ultrasound parameters. We explored the prenatal diagnostic efficacy of ultrasound parameters and established a nomogram for coarctation of the aorta.

A total of 101 fetuses with suspected coarctation of the aorta diagnosed by prenatal ultrasound from July 2015 to June 2023 were collected retrospectively. The patients were divided into two groups according to the diagnostic results: a normal group (n=42; gestational weeks, 28.5±6.0) and a coarctation of the aorta group (n=59; gestational weeks, 26.7±5.1). Univariate and multivariate logistic regression analyses were used to identify echocardiographic predictors of coarctation of the aorta. Moreover, the patients were divided into a training set and a validation set in a ratio of 8:2, and a nomogram for the prenatal diagnosis of coarctation of the aorta was established using R.

(1) Aortic isthmus, aortic isthmus z-score, ascending aorta, ascending aorta z-score, pulmonary artery, pulmonary artery z-score, pulmonary artery/ascending aorta ratio, persistent left superior vena cava, and aortic arch dysplasia were the predictive markers of coarctation of the aorta in the univariate logistic regression analysis (P<0.05). (2) Aortic isthmus z-score, ascending aorta z-score, pulmonary artery/ascending aorta ratio, persistent left superior vena cava, and aortic arch dysplasia were identified as the final predictors after multivariate logistic regression analysis (P<0.05). (3) The combined model, which included aortic isthmus z-score, ascending aorta z-score, pulmonary artery/ascending aorta ratio, persistent left superior vena cava, and aortic arch dysplasia, demonstrated a larger area under the receiver operating characteristic curve (AUC) (AUC=0.96, sensitivity=93.22%, specificity=88.10%) than aortic isthmus z-score alone (AUC=0.77, sensitivity=77.97%, specificity=71.43%), ascending aorta z-score alone (AUC=0.78, sensitivity=54.24%, specificity=90.48%), pulmonary artery/ascending aorta ratio alone (AUC=0.68, sensitivity=72.88%, specificity=54.76%), aortic arch dysplasia alone (AUC=0.70, sensitivity=66.10%, specificity=73.81%), and persistent left superior vena cava alone (AUC=0.72, sensitivity=79.66%, specificity=64.29%). The nomogram, which was constructed with these parameters, also exhibited excellent calibration curves and a good decision curve analysis curve.

The nomogram established by aortic isthmus z-score, ascending aorta z-score, pulmonary artery/ascending aorta ratio, persistent left superior vena cava, and aortic arch dysplasia demonstrated excellent efficacy in the prenatal diagnosis of coarctation of the aorta.

In order to investigate the value of radiomic features derived from enhanced computed tomography (CT) for assessment of therapeutic efficacy in patients with Esophageal squamous cell carcinoma (ESCC) underwent neoadjuvant immunochemotherapy (NICT).

The primary cohort of this study included 120 ESCC patients who received NICT from April 2020 to August 2023, comprising 52 patients with good responders (GR) and 68 patients with non-good responders (non-GR) after NICT, the external validation cohort included 30 patients from another hospital, comprising 14 patients with GR and 16 patients with non-GR after NICT. Features were derived from both the intra-tumoral and peri-tumoral regions of the tumor in the enhanced CT image, and the least absolute shrinkage and selection operator (LASSO) regression was used to establish predictive radiomic models (Rad-Scores) and T-stage model for predicting therapeutic response to NICT.

The Rad-Score for predicting response to NICT generated the area under the curve (AUC) values of 0.838, 0.831, and 0.769 in the training, internal validation, and external validation cohorts, respectively. For T-stage, corresponding AUC values were 0.809, 0.800, and 0.716 in the same cohorts. Additionally, the nomogram model produced AUC values of 0.867, 0.871, and 0.818 in the training, internal validation, and external validation cohorts, respectively.

The established models demonstrate promising predictive potential for assessing the efficacy of NICT in ESCC patients, which may assist clinicians in formulating appropriate treatment strategies.

To construct a machine learning (ML) model to predict the progression of chronic atrophic gastritis (CAG) to gastric cancer (GC), given its precancerous significance.

Using medical records from the Affiliated Hospital of Qingdao University, common laboratory indicators were extracted. LASSO regression identified 10 core risk factors, which were further analyzed using binary logistic regression to develop a nomogram model in R. The model's performance was evaluated using receiver operating characteristic (ROC) curves, the concordance index (C-index), calibration curves, and decision curve analysis (DCA).

The model showed excellent performance, with a C-index of 0.887. The key factors included sex, coagulation, blood cell indexes, and blood lipid levels. The ROC areas were 0.892 (quantitative) and 0.853 (qualitative), confirming model reliability.

A new nomogram model for assessing GC risk in CAG patients was successfully developed. However, due to data collection and time limitations, future studies should expand the sample size, perfect the validation process, and optimize the model to achieve more accurate risk prediction.

Validation studies of prognostic models used in critical care have yet to be conducted in Palestine. The intense conflict in the Gaza Strip presents an opportunity to evaluate the performance of local ICUs and validate the performance of the MPM and SAPS models within a resource-limited and highly stressed healthcare system.

A prospective study conducted from October to December 2024 included all patients admitted to ICUs in three of the four critical care units operating in the Gaza Strip. Sociodemographic, clinical, physiological, and laboratory parameters were collected, along with information regarding the clinical course and ICU outcomes. The MPM-III and SAPS-III scores were calculated, and their discrimination and calibration were assessed using AUROC and the Hosmer-Lemeshow test, respectively. Furthermore, the difference between the predicted and actual mortality rates was visualized, and standardized mortality rates (SMR) were calculated. Except for the Hosmer-Lemeshow test, a p-value of less than 0.05 was deemed statistically significant. All statistical analyses were conducted using R Studio.

The cohort included 101 patients, of whom 72.27% were surgical cases and 58.41% were admitted from the ER. The ICU mortality rate was 30.69%. The median duration of ICU admission was four days [IQR 2-9] and was significantly longer for surgical cases than for medical cases. Physiological and laboratory parameters, along with interventions associated with higher mortality, included a lower GCS, burns, elevated leukocyte and platelet counts, lower PPO2, dysrhythmias, intracranial mass effect, and the need for mechanical ventilation or central venous catheterization. The predicted mortality rates were 16.63% for MPM0-III and 16.82% for SAPS-III. SMRs indicated that both models underestimated ICU mortality (SMR, MPM0-III 1.85; SAPS-III 1.83), with the discrepancy more likely to occur in high-risk patients. ROC curves demonstrated acceptable to good discriminatory power for both models (AUROC, MPM0-III 0.79 (95% CI 0.7-0.88); SAPS-III 0.87 (95% CI 0.80-0.94)). The Hosmer-Lemeshow test yielded statistically insignificant results for both models, indicating good calibration.

The outcomes of critical care units in the Gaza Strip during the studied period of the war were comparable to those of other hospitals in the West Bank and other LMICs without active conflicts. The MPM-III and SAPS-III demonstrated good discrimination and calibration, making them valid tools for enhancing ICU performance and improving resource utilization in the Gaza Strip.

Predictive models for intensive care unit (ICU) patients mainly focus on mortality, but short-term disease severity is more relevant for day-to-day decision-making.

The aim of this study was to develop and validate a daily prediction model for next-day disease severity in ICU patients.

Data from the Simple Intensive Care Studies-II prospective cohort study of acutely admitted critically ill adults, including data collected during the first 7 days of admission such as Sequential Organ Failure Assessment (SOFA) score-related measurements, were used to fit a mixed-effects logistic regression model for next-day deterioration. Deterioration was defined as a decline in the total (≥2) and organ-specific (≥1) SOFA scores. Performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis.

A total of 1009 patients were included. The final predictive model for overall next-day deterioration included six predictors (the total SOFA score on the current day, the minimum value of arterial pH, Glasgow Coma Scale score, mean arterial blood pressure, mechanical ventilation, and its effect differing between the first and subsequent ICU days). The model achieved an AUC of 0.74 (95% confidence interval: 0.70-0.78). In the decision curve analysis, within probability thresholds of 0.2-0.5, the model showed a higher net benefit than did strategies of treating all patients or treating no patients. Organ-specific prediction models for next-day deterioration in respiration, cardiovascular, and renal function showed slightly better performance than the overall model (AUCs: 0.79, 0.79, and 0.81, respectively).

Daily prediction models can predict next-day disease severity in overall, respiration, cardiovascular, and renal function amongst ICU patients. They offer clinical benefits within a range of probability thresholds and could support decision-making for ICU physicians.

Objective: Metabolic syndrome (MS) is the most important risk factor for Type 2 diabetes mellitus (T2DM) and cardiovascular disease. This study used a retrospective dataset from the First Affiliated Hospital of Shenzhen University and aimed to develop and validate a novel model nomogram based on clinical parameters to predict MS in patients with T2DM. Methods: A total of 2854 patients with T2DM between January 2014 and May 2022 were selected and divided into a training dataset (n = 2114) and a validation dataset (n = 740). This study used multivariate logistic regression analysis to develop a nomogram for predicting MS in patients with T2DM that included candidates selected in the LASSO regression model. The data were set standardized before LASSO regression. The area under the receiver operating characteristic curve (AUC-ROC) was used to assess discrimination in the prediction model. The calibration curve is used to evaluate the calibration of the calibration nomogram, and the clinical decision curve is used to determine the clinical utility of the calibration diagram. The validation dataset is used to evaluate the performance of predictive models. Results: A total of 2854 patients were eligible for this study. There were 1941 (68.01%) patients with MS. The training dataset included 20 potential risk factors of the patient's demographic, clinical, and laboratory indexes in the LASSO regression analysis. Gender, hypertension, BMI, WC, HbA1c, TG, LDL, and HDL were multivariate models. We obtained a model for estimating MS in patients with T2DM. The AUC-ROC of the training dataset in our model is 0.886, and the 95% CI is 0.871-0.901. Similar to the results obtained from the training dataset, the AUC-ROC of the validation dataset in our model is 0.859, and the 95% CI is 0.831-0.887, thus proving the robustness of the model. The prediction model is as follows: logit (MS) = -9.18209 + 0.14406 ∗ BMI (kg/m2) + 0.09218 ∗ WC (cm) + 1.05761 ∗ TG (mmol/L)-3.30013 ∗ HDL (mmol/L). The calibration plots of the predicted probabilities show excellent agreement with the observed MS rates. Decision curve analysis demonstrated that the new nomogram provided significant net benefits in clinical applications. Conclusion: The prediction model of this study covers four clinically easily obtained parameters: BMI, WC, TG, and HDL, and shows a high accuracy rate in the validation dataset. Our predictive model may provide an effective method for large-scale epidemiological studies of T2DM patients with MS and offer a practical tool for the early detection of MS in clinical work.

This study aimed to develop and validate radiomics-based nomograms for the identification of EGFR mutations in non-small cell lung cancer (NSCLC).

A retrospective analysis was performed on 313 NSCLC patients, who were randomly divided into training (n = 250) and validation (n = 63) groups. Radiomic features were extracted from 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) and thin-section computed tomography (CT) scans. After selecting optimal radiomic features, four machine learning algorithms, including logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost), were used to develop and validate radiomics models. A combined model, incorporating the Rad score from the best performing radiomics model with clinical and radiological features, was then formulated. Finally, the integrated nomogram was generated. Its predictive performance and clinical utility were evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis.

Among the radiomics models, the RF model showed the best performance with AUCs of 0.785 (95% CI, 0.726-0.844) and 0.776 (95% CI, 0.662-0.889) in the training and validation groups, respectively. The AUCs of the clinical and radiological models in both groups were 0.711 (95% CI, 0.645-0.776) and 0.758 (95% CI, 0.627-0.890), and 0.632 (95% CI, 0.564-0.699) and 0.677 (95% CI, 0.531-0.822), respectively. The combined model achieved the highest AUCs of 0.872 (95% CI, 0.829-0.915) and 0.831 (95% CI, 0.723-0.940) in the training and validation groups, respectively. The DeLong test confirmed the superiority of the combined model over the other three models. Both the calibration curve and the DCA indicated that the radiomics nomogram was consistent and clinically useful.

Radiomics combined with machine learning and based on 18F-FDG PET/CT images can effectively determine EGFR mutation status in NSCLC patients. Radiomics-based nomograms provide a non-invasive and visually intuitive prediction tool for screening NSCLC patients with EGFR mutations in a clinical setting.

To develop and validate a nomogram based on systemic inflammation response index (SIRI) and clinical risk factors to predict short-term prognosis in very elderly patients with hypertensive intracerebral hemorrhage (HICH).

A total of 324 very elderly HICH patients from January 2017 to June 2024 were retrospectively enrolled and randomly divided into two cohorts for training (n = 227) and validation (n = 97) according to the ratio of 7:3. Independent predictors of poor prognosis were analyzed using univariate and multivariate logistic regression analyses. Furthermore, a nomogram prediction model was built. The area under the receiver operating characteristic curves (AUC), calibration plots and decision curve analysis (DCA) were used to evaluate the performance of the nomogram in predicting the prognosis of very elderly HICH.

By univariate and stepwise multivariate logistic regression analyses, GCS score (p < 0.001), hematoma expansion (p = 0.049), chronic obstructive pulmonary disease (p = 0.010), and SIRI (p = 0.005) were independent predictors for the prognosis in very elderly patients with HICH. The nomogram showed the highest predictive efficiency in the training cohort (AUC = 0.940, 95% CI: 0.909 to 0.971) and the validation cohort (AUC = 0.884, 95% CI: 0.813 to 0.954). The calibration curve indicated that the nomogram had good calibration. DCA showed that the nomogram had high applicability in clinical practice.

The nomogram incorporated with the SIRI and clinical risk factors has good potential in predicting the short-term prognosis of very elderly HICH.

Von Willebrand factor (vWF) plays a key role in hemostasis and is reported to be related to the outcome of advanced chronic liver disease. The present study aimed to investigate the relationship between vWF and other potential variables and portal vein thrombosis (PVT) in patients with decompensated cirrhosis.

Consecutive cirrhotic patients with gastroesophageal varices were admitted to our hospital between January 2020 and September 2022. Patients were prospectively recruited and divided into PVT and non-PVT groups. We collected clinical tests, biochemical tests, coagulation tests, and hemostatic protein profile data to explore the associated factors of PVT.

A total of 128 patients were enrolled including 60 patients with PVT and 68 patients without PVT. Plasma levels of vWF [odds ratio (OR) = 1.015, 95% confidence interval (CI): 1.005-1.025, P = 0.005], D-dimer (OR = 1.967, 95% CI: 1.141-3.389, P = 0.015), and decreased portal vein velocity (PVV) (OR = 0.852, 95% CI: 0.769-0.944, P = 0.002) were the variables independently associated with the existence of PVT. Area under the curve (AUC) analyses for vWF, D-dimer, and PVV were 0.779, 0.848, and 0.832, respectively. A nomogram model was established involving the three parameters, and the AUC was 0.919 (95% CI: 0.869-0.969). In the internal validation using bootstrap, the AUC was 0.919 (95% CI: 0.868-0.970).

Higher vWF levels were related to PVT in patients with decompensated cirrhosis, indicating that vWF might serve as a relevant factor for PVT, and a nomogram containing vWF, D-dimer, and PVV could be an important tool for PVT identification in cirrhotic patients.

Pregnancy loss significantly affects physical and mental health. A nomogram for predicting spontaneous abortion risk was developed to improve pregnancy outcomes.

A total of 1346 pregnant women were enrolled from The Third Affiliated Hospital of Wenzhou Medical University (May 2020 - May 2022). The training set included 941 participants, and the validation set had 405. Feature selection was optimized using a random forest model, and a predictive model was constructed via multivariable logistic regression. The nomogram's performance was assessed with receiver operator characteristic (ROC), Hosmer-Lemeshow test, calibration curve, and clinical impact curve (CIC). Discrimination and clinical utility were compared between the nomogram and its individual variables.

Antithrombin III (AT-III), homocysteine (Hcy), complement component 3 (C3), protein C (PC), and anti-β2 glycoprotein I antibody (anti-β2GP1) were identified as risk factors. The nomogram demonstrated satisfactory discrimination (Training AUC: 0.813, 95% CI: 0.790-0.842; Validation AUC: 0.792, 95% CI: 0.741-0.838). The Hosmer-Lemeshow test (P = 0.331) indicated a good fit, and the CIC showed clinical net benefit. The nomogram outperformed individual variables in discrimination (AUC: 0.804, 95% CI: 0.779-0.829).

The developed nomogram, incorporating AT-III, Hcy, C3, PC, and anti-β2GP1, aids clinicians in identifying pregnant women at high risk for spontaneous abortion.

Patients with gastrointestinal surgery have a higher incidence of infection-related complications than the rest of those who undergo clean cut surgery. It can lead to a worse prognosis for patients. This study aimed to assess the association between glycemic variability (GV) and postoperative infection-related complications of gastrointestinal cancer patients. A total of 438 patients were included in this study. Using univariate and multivariate regression analyses, the risk factors for postoperative complications were determined. And nomogram prediction models were constructed through machine learning. The performance of the nomogram was assessed with respect to the calibration curves. Univariate and multivariate regression analysis showed that high GV on post operation day (POD)1 (P < .001), high leukocytes on POD4 (P = .003 < .01) and alcohol consumption (P = .005 < .01) were independent risk factors for postoperative infection-related complications in patients with gastrointestinal cancers. The area under the curve (AUC) showed that these 3 prediction models established through logistic regression (AUC = 0.81), XGBoost (AUC = 0.82) and random forest (AUC = 0.78) all performed well. Our study confirmed that higher GV on POD1 were independent risk factors for postoperative infection-related complications within 30 days of surgery in patients with gastrointestinal cancers. And the nomogram prediction model confirmed its capable for predicting infection-related complications.

Total hip arthroplasty (THA) is associated with considerable blood loss during the perioperative period, which commonly requires a blood transfusion, especially in patients who continue aspirin treatment preoperatively. Blood transfusion can significantly increase both the length of hospital stay and total treatment costs and is potentially associated with adverse reactions. However, a visual predictive model for assessing the risk of blood transfusion in THA patients is lacking. The aim of this study was to develop and validate a nomogram to predict the risk of blood transfusion during THA in patients who continue aspirin treatment preoperatively.

From June 2016 to December 2022, 228 consecutive patients who continued preoperative aspirin treatment and underwent primary unilateral THA were enrolled in this retrospective study. Potential risk factors were screened using least absolute shrinkage and selection operator (LASSO) regression, and univariate and multifactorial logistic regressions were performed on the factors screened using LASSO regression to further control for confounding effects. Finally, a nomogram was constructed on the basis of the variables identified through multiple regression analysis. Internal validation was carried out using the Bootstrap method to assess the performance of the model using the C-index, area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA).

Among the 228 patients, 43 (18.9%) received a blood transfusion. Patients who received a blood transfusion had a longer hospital stay (p = 0.01). The independent risk factors for blood transfusion included the concomitant use of clopidogrel (OR = 4.415), preoperative hemoglobin level (OR = 0.062), total estimated blood loss volume (OR = 3.411), American Society of Anesthesiologists (ASA) class (OR = 1.274), and the use of tranexamic acid (OR = 0.348). The prediction model had a C-index of 0.862, an internally validated C-index of 0.833, and an AUC of 0.833, indicating excellent discriminatory power. The calibration curve showed a good calibration effect, and DCA indicated that the nomogram has strong clinical applicability.

Based on these five independent risk factors, our nomogram can accurately predict the risk of blood transfusion in THA patients who continue aspirin treatment preoperatively, thereby assisting surgeons in clinical decision-making.

The performance of injury severity scores (ISSs), used widely to quantify injury severity and predict outcomes, has not been investigated in German pediatric cases. This study aims to identify the most feasible and accurate injury score predictor of mortality in German children with trauma using International Classification of Diseases 10 (ICD-10).

Between 2014 and 2020, a retrospective observational cohort study of hospital admissions cases aged <18 years with injury-related ICD-10 codes, using the German hospital database (GHD), was conducted. The maximum abbreviated injury scale and ISS were calculated using the International Classification of Diseases-Abbreviated Injury Scale (ICD-AIS) map provided by the Association for the Advancement of Automotive Medicine, adjusted to the German modification of the ICD-10 classification. The survival risk ratio was used to calculate the single-worst ICD-derived injury (single International Classification of Disease Injury Severity Score [ICISS]) and a multiplicative ICISS. Logistic regressions were conducted for each of the four above-mentioned scores (predictors) to predict in-hospital mortality (outcome) in the selected trauma population and within four clinically relevant subgroups using discrimination and calibration.

1,720,802 were trauma patients, and ICD-AIS mapping was possible in 1,328,377 cases. Cases with mapping failure (n = 392,425; 22.8%) were younger and had a higher mortality rate were excluded from the performance analysis. ICISS-derived scores had a better discrimination and calibration than ICD-AIS based scores in the overall cohort and all four subgroups (area under the curve [AUC] ranges between 0.985 and 0.998 vs 0.886 and- 0.972, respectively).

Empirically derived measures of injury severity were superior to ICD-AIS mapped scores in the GHD to predict mortality in pediatric trauma patients. Given the high percentage of mapping failure and high mortality among cases with single-coded injury, the single ICISS may be the most suitable measure of injury severity in this group of patients.

The first-line treatment for prolactinoma is drug therapy with dopamine agonists (DAs). However, some patients with resistance to DA treatment should prioritize surgical treatment. Therefore, it is crucial to accurately identify the drug treatment response of prolactinoma before treatment. The present study was performed to determine the DA treatment response of prolactinoma using a clinical radiomic model that incorporated radiomic and clinical features before treatment.

In total, 255 patients diagnosed with prolactinoma were retrospectively divided to training and validation sets. An elastic net algorithm was used to screen the radiomic features, and a fusion radiomic model was established. A clinical radiomic model was then constructed to integrate the fusion radiomic model and the most important clinical features through multivariate logistic regression analysis for individual prediction. The calibration, discrimination, and clinical applicability of the established models were evaluated. 60 patients with prolactinoma from other centers were used to validate the performance of the constructed model.

The fusion radiomic model was constructed from three significant radiomic features, and the area under the curve in the training set and validation set was 0.930 and 0.910, respectively. The clinical radiomic model was constructed using the radiomic model and three clinical features. The model exhibited good recognition and calibration abilities as evidenced by its area under the curve of 0.96, 0.92, and 0.92 in the training, validation, and external multicenter validation set, respectively. Analysis of the decision curve showed that the fusion radiomic model and clinical radiomic model had good clinical application value for DA treatment response prediction in patients with prolactinoma.

Our clinical radiomic model demonstrated high sensitivity and excellent performance in predicting DA treatment response in prolactinoma. This model holds promise for the noninvasive development of individualized diagnosis and treatment strategies for patients with prolactinoma.

Deep vein thrombosis (DVT) in patients undergoing endoscopic endonasal surgery remains underexplored, despite its potential impact on postoperative recovery. This study aimed to develop and validate a predictive nomogram for assessing the risk of lower-limb DVT in such patients without chemoprophylaxis. A retrospective analysis was conducted on 935 patients with postoperative lower-limb vein ultrasonography. Clinical data, including potential risk factors, were used to construct a predictive model via multivariate logistic regression analysis. The resulting nomogram was validated using an independent cohort and evaluated through concordance index (C-index), calibration plots, and decision curve analysis. The incidence of postoperative DVT was 28.9%, with most cases being distal (27.2%). Significant predictors included older age, intraoperative bleeding, female gender, prolonged surgery duration, elevated postoperative APTT and D-dimer levels, and disturbance of consciousness. The nomogram demonstrated good predictive performance, with C-index values of 0.81 in the training cohort and 0.75 in the validation cohort. Calibration and decision curve analyses confirmed the model's clinical applicability. This nomogram offers a practical tool for individualized DVT risk assessment in patients undergoing endoscopic endonasal surgery, facilitating more targeted prophylactic measures.

Early prediction of acute kidney injury (AKI) may provide a crucial opportunity for AKI prevention. To date, no prediction model targeting AKI among general hospitalized patients in developing countries has been published. Here we show a simple, real-time, interpretable AKI prediction model for general hospitalized patients developed from a large tertiary hospital in China, which has been validated across five independent, geographically distinct, different tiered hospitals. The model containing 20 readily available variables demonstrates consistent, high levels of predictive discrimination in validation cohort, with AUCs for serum creatinine-based AKI and severe AKI within 48 h ranging from 0.74-0.85 and 0.83-0.90 for transported models and from 0.81-0.90 and 0.88-0.95 for refitted models, respectively. With optimal probability cutoffs, the refitted model could predict AKI at a median of 72 (24-198) hours in advance in internal validation, and 54-90 h in advance in external validation. Broad application of the model in the future may provide an effective, convenient and cost-effective approach for AKI prevention.

Pathological complete response (pCR) is an essential criterion for adjusting follow-up treatment plans for patients with breast cancer (BC). The value of the visual geometry group and long short-term memory (VGG-LSTM) network using time-series dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for pCR identification in BC is unclear.

To identify pCR to neoadjuvant chemotherapy (NAC) using deep learning (DL) models based on the VGG-LSTM network.

Retrospective.

Center A: 235 patients (47.7 ± 10.0 years) were divided 7:3 into training (n = 164) and validation set (n = 71). Center B: 150 patients (48.5 ± 10.4 years) were used as test set.

3-T, T2-weighted spin-echo sequence imaging, and gradient echo DCE sequence imaging.

Patients underwent MRI examinations at three sequential time points: pretreatment, after three cycles of treatment, and prior to surgery, with tumor regions of interest manually delineated. Histopathology was the gold standard. We used VGG-LSTM network to establish seven DL models using time-series DCE-MR images: pre-NAC images (t0 model), early NAC images (t1 model), post-NAC images (t2 model), pre-NAC and early NAC images (t0 + t1 model), pre-NAC and post-NAC images (t0 + t2 model), pre-NAC, early NAC and post-NAC images (t0 + t1 + t2 model), and the optimal model combined with the clinical features and imaging features (combined model). The models were trained and optimized on the training and validation set, and tested on the test set.

The DeLong, Student's t-test, Mann-Whitney U, Chi-squared, Fisher's exact, Hosmer-Lemeshow tests, decision curve analysis, and receiver operating characteristics analysis were performed. P < 0.05 was considered significant.

Compared with the other six models, the combined model achieved the best performance in the test set yielding an AUC of 0.927.

The combined model that used time-series DCE-MR images, clinical features and imaging features shows promise for identifying pCR in BC.

Stage 4.

Microvascular invasion (MVI) is a significant indicator of the aggressive behavior of hepatocellular carcinoma (HCC). Expanding the surgical resection margin and performing anatomical liver resection may improve outcomes in patients with MVI. However, no reliable preoperative method currently exists to predict MVI status or to identify patients at high-risk group (M2).

To develop and validate models based on contrast-enhanced computed tomography (CECT) radiomics and clinicoradiological factors to predict MVI and identify M2 among patients with hepatitis B virus-related HCC (HBV-HCC). The ultimate goal of the study was to guide surgical decision-making.

A total of 270 patients who underwent surgical resection were retrospectively analyzed. The cohort was divided into a training dataset (189 patients) and a validation dataset (81) with a 7:3 ratio. Radiomics features were selected using intra-class correlation coefficient analysis, Pearson or Spearman's correlation analysis, and the least absolute shrinkage and selection operator algorithm, leading to the construction of radscores from CECT images. Univariate and multivariate analyses identified significant clinicoradiological factors and radscores associated with MVI and M2, which were subsequently incorporated into predictive models. The models' performance was evaluated using calibration, discrimination, and clinical utility analysis.

Independent risk factors for MVI included non-smooth tumor margins, absence of a peritumoral hypointensity ring, and a high radscore based on delayed-phase CECT images. The MVI prediction model incorporating these factors achieved an area under the curve (AUC) of 0.841 in the training dataset and 0.768 in the validation dataset. The M2 prediction model, which integrated the radscore from the 5 mm peritumoral area in the CECT arterial phase, α-fetoprotein level, enhancing capsule, and aspartate aminotransferase level achieved an AUC of 0.865 in the training dataset and 0.798 in the validation dataset. Calibration and decision curve analyses confirmed the models' good fit and clinical utility.

Multivariable models were constructed by combining clinicoradiological risk factors and radscores to preoperatively predict MVI and identify M2 among patients with HBV-HCC. Further studies are needed to evaluate the practical application of these models in clinical settings.

In recent years, the systemic immune-inflammatory index (SII) and prognostic nutritional index (PNI) have been considered potential predictors of survival outcomes in various solid tumors, including gastric cancer. However, there is a notable lack of research focusing on their prognostic implications specifically in the early stage of gastric cancer. This study aims to investigate the prognostic indicators of early gastric cancer (EGC), including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), SII, PNI, and lymph node metastasis (LNM).

In this retrospective analysis, we examined 490 patients diagnosed with EGC (pT1Nx). The peripheral blood indices of interest were SII, PNI, PLR, and NLR. The receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were used to determine optimal cutoff values and prognostic efficacy for each parameter. Additionally, Kaplan-Meier survival curves and multivariate Cox regression models were utilized to delineate independent prognostic factors.

The optimal cutoff values for SII and PNI were determined as 613.05 and 42.21, respectively. Patients in the low SII (SII-L) group demonstrated significantly higher 5-year Disease-Free Survival (DFS) and Overall Survival (OS) rates of 94.7% and 96.2%, compared to the high SII (SII-H) group (DFS: 78.7%; OS: 81.9%), with both differences proving statistically significant (P < 0.001, P < 0.001). Similarly, patients in the high PNI (PNI-H) group showed superior 5-year DFS (93.3%) and OS rates (95.1%) versus the low PNI (PNI-L) group (DFS: 71.4%; OS: 74.3%), also demonstrating statistical significance (P < 0.001, P < 0.001). Multivariate analysis identified SII, PNI, and LNM as independent prognostic factors for EGC. A combined analysis of SII, PNI, and LNM yielded a C-index of 0.723 (P = 0.008).

SII, PNI, and LNM are effective markers for predicting the survival outcomes of patients undergoing radical gastrectomy for EGC.

Tumor-infiltrating lymphocytes-ultrasonography (TILs-US) score is used to predict lymphocyte-predominant breast cancer (LPBC) in surgical specimens. We aimed to compare diagnostic performance of TILs-US score for predicting pathological complete response (pCR) with that of LPBC in biopsy specimens.

TILs ≥ 50% in biopsy specimens was defined as biopsy-LPBC, and TILs-US score ≥ 4 was categorized as TILs-US score-high. Basic nomogram for pCR was developed using stepwise logistic regression based on the smallest Akaike Information Criterion, and biopsy-LPBC and TILs-US score nomograms were developed by integrating biopsy-LPBC or TILs-US scores into a basic nomogram. The diagnostic performance of the nomograms for pCR was compared using area under the curve (AUC), categorical net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

This retrospective study evaluated 118 patients with breast cancer, including 33 (28.0%) with biopsy-LPBC, 52 (44.1%) with TILs-US score-high, with 34 (28.8%) achieving pCR. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and AUC for predicting pCR were 0.53, 0.82, 2.96, 0.57, and 0.68, respectively, for biopsy-LPBC, and 0.76, 0.69, 2.47, 0.34, and 0.73, respectively, for TILs-US score. The biopsy-LPBC nomogram showed significant improvements in categorical NRI (p = 0.023) and IDI (p = 0.007) but not in AUC (p = 0.25), compared with the basic nomogram. The TILs-US nomogram exhibited significant improvements in AUC (p = 0.039), categorical NRI (p = 0.010), and IDI (p < 0.001).

The TILs-US score may serve as a novel marker for prediction of pCR in patients with breast cancer. An external validation study is warranted to confirm our findings.

To investigate the feasibility and effectiveness of a deep learning (DL) super-resolution (SR) ultrasound image reconstruction model for predicting cervical lymph node status in patients with papillary thyroid carcinoma(PTC).

In this retrospective study, researchers recruited 544 patients with PTC and randomly assigned them to training and test sets. SR ultrasound images were acquired using SR technology to improve image resolution, and artificial features and DL features were extracted from the original (OR) and SR images, respectively, to construct a ML, DL model. The best model was selected and aggregated with clinical parameters to construct the nomogram. The performance of the model is evaluated by ROC curves, calibration curves and decision curves.

In distinguishing the presence or absence of metastatic lymph nodes, the predictive performance of the SR_ResNet 101 and SR_SVM models based on SR outperformed those based on OR. In the test set, SR_SVM AUC was 0.878 (95% CI 0.8203-0.9358), accuracy 0.854, while OR_SVM AUC was 0.822 (95% CI 0.7500-0.8937), accuracy 0.665. SR_ResNet 101 AUC was 0.799 (95% CI 0.7175-0.8806), accuracy 0.793, and OR_ResNet101 AUC was 0.751 (95% CI 0.6620-0.8401), accuracy 0.713. Subsequently, Nomogram_A and Nomogram_B were constructed by integrating the SR_SVM model and SR_ResNet 101 model, respectively, with clinical parameters, while Nomogram_C was constructed solely based on clinical indicators. In the test set, Nomogram_A demonstrated the best performance with an AUC of 0.930 (95% CI 0.8913-0.9682) and accuracy was 0.829. Nomogram_B AUC 0.868 (95% CI 0.8102-0.9261) and accuracy was 0.829, while Nomogram_C AUC 0.880 (95% CI 0.8257-0.9349) and accuracy was 0.787. The DeLong test revealed that the diagnostic performance of Nomogram_A based on SR_SVM was significantly higher than that of Nomogram_B, Nomogram_C, and the level of Radiologist (P < 0.05). The calibration curves and Hosmer-Lemeshow tests confirmed a high degree of fit, and the decision curve analysis demonstrated clinical value and potential patient benefit.

The predictive model constructed using SR reconstructed ultrasound images demonstrated superior performance in predicting preoperative cervical lymph node metastasis in PTC compared to OR images. The nomogram prediction model based on SR images has the potential to enhance the accuracy of predictive models and aid in clinical decision-making.

Local progression of primary skull base chordoma (PSBC) is a sign of treatment failure. Predicting the postoperative progression of PSBC can aid in the development of individualized treatment plans to improve patients' progression-free survival (PFS) after surgery. This study aimed to develop a multiparametric MRI-based fusion radiomic model (FRM) and clinicoradiomic model (CRM) via radiomic and clinical analysis and to explore their validity in predicting postoperative progression in PSBC patients before surgery. In this retrospective study, a total of 129 patients with PSBC from our institution, including 57 patients with progression, were enrolled and randomized to the training set (TS) or the validation set (VS) at a 2:1 ratio. Radiomic features were extracted and dimensionally reduced from 3.0 T/axial T2-weighted imaging (T2WI), T1-weighted imaging (T1WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) sequences for each patient, and the features were used for radiomic analysis. Univariate and multivariate Cox regression analyses were used to screen for key clinical factors. We constructed models on the basis of multivariate logistic regression analysis. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses were performed to evaluate the performance of the clinical model (CM), FRM and CRM. Through analysis, we found that blood supply was the only significantly different clinical factor in the CM. For the FRM, the area under the receiver operating characteristic curve (AUC) of the TS was 0.925, and the calibration curves were consistent across the TS. In the CRM, the AUC of the TS was 0.929, the calibration curve analysis was consistent for both the TS and the VS, and the DCA showed that the net benefit was greater at a threshold probability of > 0% for both the TS and the VS. Our proposed FRM can help clinicians better predict PSBC progression preoperatively, and the use of the CRM can lead to the development of more appropriate protocols to improve patients' PFS after surgery.

Accurate staging of liver fibrosis (LF) is essential for clinical management in chronic liver disease. While non-contrast MRI (NC-MRI) yields valuable information for liver assessment, its effectiveness in predicting LF remains underexplored. This study aimed to develop and validate artificial intelligence (AI)-powered models utilizing NC-MRI for staging LF.

A total of 1726 patients from Shengjing Hospital of China Medical University, registered between October 2003 and October 2022, were retrospectively collected, and divided into development (n = 1208) and internal test (n = 518) cohorts. An external test cohort consisting of 337 individuals from six centers, registered between June 2015 and November 2022, were also included. All participants underwent NC-MRI (T1-weighted imaging, T1WI; and T2-fat-suppressed imaging, T2FS) and liver biopsies. Two classification models (CMs), named T1 and T2FS, were trained on respective image types using 3D contextual transformer networks and evaluated on both test cohorts. Additionally, three CMs-Clinic, Image, and Fusion-were developed using clinical features, T1 and T2FS scores, and their integration via logistic regression. Classification effectiveness of CMs was assessed using the area under the receiver operating characteristic curve (AUC). A comparison was conducted between the optimal models (OMs) with highest AUC and other methods (transient elastography, five serum biomarkers, and six radiologists).

Fusion models (i.e., OM) yielded the highest AUC among the CMs, achieving AUCs of 0.810 for significant fibrosis, 0.881 for advanced fibrosis, and 0.918 for cirrhosis in the internal test cohort, and 0.808, 0.868, and 0.925, respectively, in the external test cohort. The OMs demonstrated superior performance in AUC, significantly surpassing transient elastography (only for staging ≥ F2 and ≥ F3 grades), serum biomarkers, and three junior radiologists for staging LF. Radiologists, with the aid of the OMs, can achieve a higher AUC in LF assessment.

AI-powered models utilizing NC-MRI, including T1WI and T2FS, accurately stage LF.

National Natural Science Foundation of China (No. 82071885); General Program of the Liaoning Provincial Department of Education (LJKMZ20221160); Liaoning Province Science and Technology Joint Plan (2023JH2/101700127); the Leading Young Talent Program of Xingliao Yingcai in Liaoning Province (XLYC2203037).

The survival rate of patients with distant metastasis (DM) of papillary thyroid carcinoma (PTC) is significantly reduced. It is of great significance to find an effective method for early prediction of the risk of DM for formulating individualized diagnosis and treatment plans and improving prognosis. Previous studies have significant limitations, and it is still necessary to develop new models for predicting the risk of DM of PTC. We aimed to develop and validate interpretable machine learning (ML) models for early prediction of DM in patients with PTC using a multicenter cohort.

We collected data on patients with PTC who were admitted between June 2013 and May 2023. Data from 1430 patients at Yunnan Cancer Hospital (YCH) served as the training and internal validation set, while data from 434 patients at the First Affiliated Hospital of Kunming Medical University (KMU 1st AH) was used as the external test set. Nine ML methods such as random forest (RF) were used to construct the model. Model prediction performance was compared using evaluation indicators such as the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanation (SHAP) method was used to rank the feature importance and explain the final model.

Among the nine ML models, the RF model performed the best. The RF model accurately predicted the risk of DM in patients with PTC in both the internal validation of the training set [AUC: 0.913, 95% confidence interval (CI) (0.9075-0.9185)] and the external test set [AUC: 0.8996, 95% CI (0.8483-0.9509)]. The calibration curve showed high agreement between the predicted and observed risks. In the sensitivity analysis focusing on DM sites of PTC, the RF model exhibited outstanding performance in predicting "lung-only metastasis" showing high AUC, specificity, sensitivity, F1 score, and a low Brier score. SHAP analysis identified variables that contributed to the model predictions. An online calculator based on the RF model was developed and made available for clinicians at https://predictingdistantmetastasis.shinyapps.io/shiny1/. 11 variables were included in the final RF model: age of the patient with PTC, whether the tumor size is > 2 cm, whether the tumor size is ≤ 1 cm, lymphocyte (LYM) count, monocyte (MONO) count, monocyte/lymphocyte ratio (MLR), thyroglobulin (TG) level, thyroid peroxidase antibody (TPOAb) level, whether the T stage is T1/2, whether the T stage is T3/4, and whether the N stage is N0.

On the basis of large-sample and multicenter data, we developed and validated an explainable ML model for predicting the risk of DM in patients with PTC. The model helps clinicians to identify high-risk patients early and provides a basis for individualized patient treatment plans.

This work was supported by the National Natural Science Foundation of China (No. 81960426, 82360345 and 82001986), the Outstanding Youth Science Foundation of Yunnan Basic Research Project (No. 202401AY070001-316), Yunnan Province Applied and Basic Research Foundation (No. 202401AT070008), and Ten Thousand Talent Plans for Young Top-notch Talents of Yunnan Province.

To predict high-risk prostate cancer (PCa) by combining the habitat features of biparametric magnetic resonance imaging (bp-MRI) with the omics features of contrast-enhanced ultrasound (CEUS).

This study retrospectively collected patients with PCa confirmed by histopathology from January 2020 to June 2023. All patients underwent bp-MRI and CEUS of the prostate, followed by a targeted and transrectal systematic prostate biopsy. The cases were divided into the intermediate-low-risk group (Gleason score ≤7, n = 59) and high-risk group (Gleason score ≥8, n = 33). Radiomics prediction models, namely, MRI_habitat, CEUS_intra, and MRI-CEUS models, were developed based on the habitat features of bp-MRI, the omics features of CEUS, and a merge of features of the two, respectively. Predicted probabilities, called radscores, were then obtained. Clinical-radiological indicators were screened to construct clinic models, which generated clinic scores. The omics-clinic model was constructed by combining the radscore of MRI-CEUS and the clinic score. The predictive performance of all the models was evaluated using the receiver operating characteristic curve.

The area under the curve (AUC) values of the MRI-CEUS model were 0.875 and 0.842 in the training set and test set, respectively, which were higher than those of the MR_habitat (training set: 0.846, test set: 0.813), CEUS_intra (training set: 0.801, test set: 0.743), and clinic models (training set: 0.722, test set: 0.611). The omics-clinic model achieved a higher AUC (train set: 0.986, test set: 0.898).

The combination of the habitat features of bp-MRI and the omics features of CEUS can help predict high-risk PCa.

Autologous arteriovenous fistula (AVF) is preferred in hemodialysis patients. Maintaining its patency is a critical problem. This study aimed to create a nomogram model for predicting 1-year primary patency of AVF. Consequently, a total of 414 patients were retrospectively enrolled and randomly allocated to training and validation cohorts. Risk factors were identified by multivariable logistic regression and used to create a nomogram model. Performance of the model was evaluated by receiver operating characteristic (ROC) curve, Hosmer-Lemeshow test, and calibration curve. The results suggested that diameter of cephalic vein, low-density lipoprotein, glycosylated hemoglobin (%), and C-reactive protein were risk factors which could predict the patency of AVF. Area under ROC curves for training and validation cohorts were 0.771 and 0.794, respectively. Calibration ability was satisfactory in both cohorts. Therefore, present nomogram model could predict the 1-year primary patency of AVF.

Depressive symptoms (DS) have become a global public health problem. However, a risk prediction model for DS in the elderly population has not been established. The purpose of this study was to develop and validate a predictive nomogram to screen for DS in the elderly population.

A cross-sectional data of 3396 participants aged 60 and over were obtained from the China Health and Retirement Longitudinal Study 2018 (CHARLS). Participants were divided into the development and validation set. Predictive factors were selected through a single-factor analysis, and then a predictive model nomogram was established. The discrimination, calibration, and clinical validity were evaluated using the receiver operating characteristic (ROC) curves, Hosmer-Lemeshow tests, and decision curve analyses (DCA).

A total of 2379 and 1017 participants were included in the development and validation set, respectively. The analysis found that gender, residence, dyslipidemia, self-rated health, and ADL disability were risk factors for DS in older adults, and were included in the final model. This nomogram showed an acceptable predictive performance as evaluated by the area under the ROC curve with values of 0.684 (95 % confidence interval (CI): 0.663-0.706) and 0.687 (95 % CI: 0.655-0.719) in the development and validation set, respectively. The calibration curve indicated that the model was accurate, and DCA demonstrated a good clinical application value.

Five factors were selected to establish a nomogram for predicting DS in older adults. The nomogram has a good evaluation performance and can be used as a reliable tool to predict DS among older adults.

Patients with neurogenic rosacea (NR) frequently demonstrate pronounced neurological manifestations, often unresponsive to conventional therapeutic approaches. A molecular-level understanding and diagnosis of this patient cohort could significantly guide clinical interventions. In this study, we amalgamated our sequencing data (n = 46) with a publicly accessible database (n = 38) to perform an unsupervised cluster analysis of the integrated dataset. The eighty-four rosacea patients were partitioned into two distinct clusters. Neurovascular biomarkers were found to be elevated in cluster 1 compared to cluster 2. Pathways in cluster 1 were predominantly involved in neurotransmitter synthesis, transmission, and functionality, whereas cluster 2 pathways were centered on inflammation-related processes. Differential gene expression analysis and WGCNA were employed to delineate the characteristic gene sets of the two clusters. Subsequently, a diagnostic model was constructed from the identified gene sets using linear regression methodologies. The model's C index, comprising genes PNPLA3, CUX2, PLIN2, and HMGCR, achieved a remarkable value of 0.9683, with an area under the curve (AUC) for the training cohort's nomogram of 0.9376. Clinical characteristics from our dataset (n = 46) were assessed by three seasoned dermatologists, forming the NR validation cohort (NR, n = 18; non-neurogenic rosacea, n = 28). Upon application of our model to NR diagnosis, the model's AUC value reached 0.9023. Finally, potential therapeutic candidates for both patient groups were predicted via the Connectivity Map. In summation, this study unveiled two clusters with unique molecular phenotypes within rosacea, leading to the development of a precise diagnostic model instrumental in NR diagnosis.

Radiomics is an emerging field that utilizes quantitative features extracted from medical images to predict clinically meaningful outcomes. Validating findings is crucial to assess radiomics applicability. We aimed to validate previously published magnetic resonance imaging (MRI) radiomics models to predict oncological outcomes in oral tongue squamous cell carcinoma (OTSCC).

Retrospective multicentric study on OTSCC surgically treated from 2010 to 2019. All patients performed preoperative MRI, including contrast-enhanced T1-weighted (CE-T1), diffusion-weighted sequences and apparent diffusion coefficient map. We evaluated overall survival (OS), locoregional recurrence-free survival (LRRFS), cause-specific mortality (CSM). We elaborated different models based on clinical and radiomic data. C-indexes assessed the prediction accuracy of the models.

We collected 112 consecutive independent patients from three Italian Institutions to validate the previously published MRI radiomic models based on 79 different patients. The C-indexes for the hybrid clinical-radiomic models in the validation cohort were lower than those in the training cohort but remained > 0.5 in most cases. CE-T1 sequence provided the best fit to the models: the C-indexes obtained were 0.61, 0.59, 0.64 (pretreatment model) and 0.65, 0.69, 0.70 (posttreatment model) for OS, LRRFS and CSM, respectively.

Our clinical-radiomic models retain a potential to predict OS, LRRFS and CSM in heterogeneous cohorts across different centers. These findings encourage further research, aimed at overcoming current limitations, due to the variability of imaging acquisition, processing and tumor volume delineation.

Vancomycin-induced acute kidney injury (VI-AKI) is one of its serious adverse reactions. The purpose of this study is to discuss the risk factors for VI-AKI in overweight patients and construct a clinical prediction model based on the results of the analysis.

Multivariable logistic regression analysis was used to identify risk factors for VI-AKI and constructed nomogram models. The performance of the nomogram was evaluated based on the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA).

Cancer (OR 4.186, 95% CI 1.473-11.896), vancomycin trough concentration >20.0 μg/mL (OR 6.251, 95% CI 2.275-17.180), concomitant furosemide (OR 2.722, 95% CI 1.071-6.919) and vasoactive agent (OR 2.824, 95% CI 1.086-7.340) were independent risk factors for VI-AKI. The AUC of the nomogram validation cohorts were 0.807 (95% CI 0.785-0.846). The calibration curve revealed that the predicted outcome was in agreement with the actual observations. Finally, the DCA curves showed that the nomogram had a good clinical applicability value.

There are four independent risk factors for the occurrence of VI-AKI in overweight patients, and the nomogram prediction model has good predictive ability, which can provide reference for clinical decision-making.

Prognostic models play a crucial role in providing personalised risk assessment, guiding treatment decisions, and facilitating the counselling of patients with cancer. However, previous imaging-based artificial intelligence models of epithelial ovarian cancer lacked interpretability. In this study, we aimed to develop an interpretable machine-learning model to predict progression-free survival in patients with epithelial ovarian cancer using clinical variables and radiomics features. A total of 102 patients with epithelial ovarian cancer who underwent contrast-enhanced computed tomography scans were enrolled in this retrospective study. Pre-surgery clinical data, including age, performance status, body mass index, tumour stage, venous blood cancer antigen-125 (CA125) level, white blood cell count, neutrophil count, red blood cell count, haemoglobin level, and platelet count, were obtained from medical records. The volume of interest for each tumour was manually delineated slice-by-slice along the boundary. A total of 2074 radiomic features were extracted from the pre- and post-contrast computed tomography images. Optimal radiomic features were selected using the Least Absolute Shrinkage and Selection Operator logistic regression. Multivariate Cox analysis was performed to identify independent predictors of three-year progression-free survival. The random forest algorithm developed radiomic and combined models using four-fold cross-validation. Finally, the Shapley additive explanation algorithm was applied to interpret the predictions of the combined model. Multivariate Cox analysis identified CA-125 levels (P = 0.015), tumour stage (P = 0.019), and Radscore (P < 0.001) as independent predictors of progression-free survival. The combined model based on these factors achieved an area under the curve of 0.812 (95 % confidence interval: 0.802-0.822) in the training cohort and 0.772 (95 % confidence interval: 0.727-0.817) in the validation cohort. The most impactful features on the model output were Radscore, followed by tumour stage and CA-125. In conclusion, the Shapley additive explanation-based interpretation of the prognostic model enables clinicians to understand the reasoning behind predictions better.

Patients with the target gene mutation frequently derive significant clinical benefits from target therapy. However, differences in the abundance level of mutations among patients resulted in varying survival benefits, even among patients with the same target gene mutations. Currently, there is a lack of rational and interpretable models to assess the risk of treatment failure. In this study, we investigated the underlying coupled factors contributing to variations in medication sensitivity and established a statistically interpretable framework, named SAFE-MIL, for risk estimation. We first constructed an effectiveness label for each patient from the perspective of exploring the optimal grouping of patients' positive judgment values and sampled patients into 600 and 1,000 groups, respectively, based on multi-instance learning (MIL). A novel and interpretable loss function was further designed based on the Hosmer-Lemeshow test for this framework. By integrating multi-instance learning with the Hosmer-Lemeshow test, SAFE-MIL is capable of accurately estimating the risk of drug treatment failure across diverse patient cohorts and providing the optimal threshold for assessing the risk stratification simultaneously. We conducted a comprehensive case study involving 457 non-small cell lung cancer patients with EGFR mutations treated with EGFR tyrosine kinase inhibitors. Results demonstrate that SAFE-MIL outperforms traditional regression methods with higher accuracy and can accurately assess patients' risk stratification. This underscores its ability to accurately capture inter-patient variability in risk while providing statistical interpretability. SAFE-MIL is able to effectively guide clinical decision-making regarding the use of drugs in targeted therapy and provides an interpretable computational framework for other patient stratification problems. The SAFE-MIL framework has proven its effectiveness in capturing inter-patient variability in risk and providing statistical interpretability. It outperforms traditional regression methods and can effectively guide clinical decision-making in the use of drugs for targeted therapy. SAFE-MIL offers a valuable interpretable computational framework that can be applied to other patient stratification problems, enhancing the precision of risk assessment in personalized medicine. The source code for SAFE-MIL is available for further exploration and application at https://github.com/Nevermore233/SAFE-MIL.