2026-10-30

Background: Major adverse renal and cardiovascular events (MARCE) have been proposed as a comprehensive end point reflecting both cardiac and renal outcomes in patients with acute myocardial infarction (AMI) and chronic kidney disease (CKD). However, existing prediction tools lack accuracy and generalizability for this high-risk population. Objective: This study aimed to develop and externally validate explainable machine learning models and a nomogram for predicting in-hospital MARCE among patients with AMI and CKD. Methods: We enrolled patients with AMI and CKD who underwent emergency treatment at the First Affiliated Hospital of Wenzhou Medical University as the discovery cohort and those at Wenzhou TCM (traditional Chinese medicine) Hospital of Zhejiang Chinese Medical University as the external validation cohort. Clinical features, including demographic details, comorbidities, laboratory results, echocardiography measurements, procedural details, and medication parameters, were collected for all participants. Important variables were selected using least absolute shrinkage and selection operator regression in the training set. We developed extreme gradient boosting (XGBoost), random forest, decision tree, and logistic regression (LR) models and evaluated their discrimination, calibration, and classification metrics in the hold-out test set and external cohort. Results: In the training set, we identified 6 important variables (Killip class, lactate, estimated glomerular filtration rate, use of diuretics, use of beta-blockers, and use of renin-angiotensin system inhibitors). In the test set, model discrimination was comparable across XGBoost (area under the receiver operating characteristic curve [AUC] 0.882, 95% CI 0.816-0.947), LR (AUC 0.875, 95% CI 0.799-0.950), and random forest (AUC 0.870, 95% CI 0.800-0.940), while decision tree showed lower performance (AUC 0.814, 95% CI 0.720-0.886). Shapley additive explanations (SHAP) values illustrated both global and individual feature contributions in the XGBoost model. A nomogram based on LR achieved an AUC of 0.873 (95% CI 0.714-0.926) in external validation. Conclusions: Our study identified 6 important predictors and provided a series of prediction models to enhance risk stratification of MARCE for patients with AMI and CKD. These models might aid in stratifying risk and choosing appropriate therapeutic strategies in the management of patients with AMI and CKD.

Background: This study developed and validated an machine-learning-based radiomics model integrating tumor habitat radiomics, peritumoral radiomics, and clinical features to predict progression-free survival (PFS) following repeat transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC). Methods: This retrospective multicenter study included 270 patients with HCC. Patients from Center 1 were assigned to training (n = 94) and internal validation (n = 41) cohorts, while Centers 2 (n = 82) and 3 (n = 53) served as external validation cohorts. Tumor habitats and 5-mm peritumoral regions were generated from contrast-enhanced CT images. Radiomics features were selected using univariate Cox and LASSO-Cox regression. A fusion model and HPC risk score were constructed by integrating selected radiomics signatures and clinical variables. Performance was assessed using time-dependent ROC, C-index, calibration, decision curve analysis, and Kaplan-Meier analyses. Results: The optimal habitat cluster number was K = 2. Eleven habitat features, three peritumoral features, and alpha-fetoprotein were retained in the fusion model. The model achieved AUCs of 0.923, 0.921, and 0.897 for 3-, 6-, and 9-month progression-free survival in the training cohort, and 0.932, 0.863, and 0.916 in the internal validation cohort, respectively; all external AUCs exceeded 0.82. The fusion model showed higher C-index values than the clinical model across cohorts. High-risk patients had significantly shorter PFS than low-risk patients (all P < 0.001). Calibration and decision curve analyses demonstrated good agreement and clinical utility. Conclusion: The CT-based fusion model and HPC risk score effectively predicted progression risk in HCC.

Objectives: To develop and validate a deep learning (DL)-based CT model integrating pancreatic imaging and CT-derived body composition for severity prediction in acute pancreatitis (AP). Materials And Methods: In this retrospective dual-center study, patients with AP who underwent CT within 72 h of symptom onset were enrolled from January 2021 to September 2025 at Center 1 and January 2023 to September 2024 at Center 2. The primary dataset (n = 426) was randomly divided into a training set (n = 298) and an internal test set (n = 128), while 110 patients from Center 2 served as the external test set. A three-dimensional DL model was developed for pancreatic imaging, and two-dimensional DL models were constructed for L3-level visceral adipose tissue (VAT) and skeletal muscle (SM). Six predictive models were constructed: clinical, pancreas, VAT, SM, pancreas-clinical (PC), and combined. Performance was evaluated using receiver operating characteristic analysis, DeLong testing, calibration, decision curve analysis, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results: The combined model achieved area under the curve (AUCs) of 0.897 (95% CI: 0.862-0.932), 0.909 (95% CI: 0.861-0.958), and 0.910 (95% CI: 0.856-0.964) in the training, internal test, and external test sets, respectively. The combined model outperformed the PC model in the training set (p = 0.002) and external test set (p = 0.020). NRI and IDI analyses confirmed the incremental predictive value of body composition features. Conclusions: A CT-based DL model integrating pancreatic imaging, body composition, and clinical variables demonstrated good performance for AP severity prediction. Key Points: Question Early severity stratification in AP remains challenging because conventional clinical and imaging assessments may not fully capture pancreatic injury and systemic disease status. Findings A CT-based DL model integrating pancreatic imaging, body composition, and clinical variables demonstrated good performance for severity prediction in AP. Critical Relevance Statement This study evaluates the value of integrating pancreatic imaging and CT-derived body composition information and supports the use of routinely acquired CT data for early risk stratification, potentially facilitating more individualized monitoring and management of AP.

Background: Machine learning-based disease prediction models can assist with disease diagnosis and early detection. This study aimed to identify the risk factors for developmental dysplasia of the hip (DDH) and develop multiple machine learning-based models to support screening and risk stratification for DDH. Methods: A retrospective analysis of 12,105 infants screened for DDH between January 2020 and December 2022 at Shenzhen Children's Hospital was performed to collect information on physical examination and risk factors. After univariate and multivariate screening, age-stratified sampling was performed on 11,898 DDH-negative patients to match the age distribution of 207 DDH-positive patients, thereby reducing potential bias. These balanced cohorts were randomly split into training and test sets (at an 8:2 ratio) under both 1:1 and 1:2 positive-to-negative ratios. Predictive modeling was based on random forest, support vector machine, logistic regression, k-nearest neighbor, light gradient boosting machine, and extreme gradient boosting (XGBoost). Results: Twelve variables were included in the model. The XGBoost model trained on 1:1 data was chosen as the best model [area under the curve (AUC): 0.883, accuracy: 0.807, precision: 0.744, F1 score: 0.800, recall (sensitivity): 0.865, and specificity: 0.761]. External validation using data from Jinhua Municipal Central Hospital provided preliminary evidence of transferability [AUC: 0.909, accuracy: 0.838, precision: 0.829, F1 score: 0.840, recall (sensitivity): 0.850, and specificity: 0.825]. Skin pattern asymmetry, restriction of hip abduction, swaddling, breech presentation, and female sex were the top five most important predictive variables. Conclusions: The XGBoost model can be used to support early clinical screening and risk stratification for DDH, helping clinicians identify infants who should undergo hip ultrasonography.

Background: Early mortality prediction in critically ill patients with chronic obstructive pulmonary disease (COPD) coexisting pulmonary hypertension (PH) remains challenging, particularly because prediction models developed in intensive care unit (ICU) database may not perform consistently across other healthcare systems. Methods: This retrospective multicenter study employed two large-scale databases to establish and validate ML models for predicting early mortality in ICU patients with COPD and PH. The MIMIC-IV database served as the model-development cohort (2555 patients), with external validation conducted on eICU CRD (292 patients) database. Six ML algorithms were adopted: Random Forest (RF), Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Light Gradient Boosting Machine (LightGBM), Support Vector Machines (SVM), and Artificial Neural Network (ANN).The baseline variables were screened to identify potential predictors via least absolute shrinkage and selection operator (Lasso) regression. Model interpretability was evaluated via SHapley Additive exPlanations (SHAP) analysis. Results: The overall cohort consisted of 2,847 ICU patients with COPD and PH. Twelve consistently crucial clinical features were identified, including acute kidney injury, pneumonia, age, weight, heart rate, red blood cell distribution width (RDW), white blood cell (WBC), anion gap, chloride, creatinine, base excess, potential of hydrogen (pH). The Random Forest algorithm achieved the optimal performance, with a training Area Under Curve (AUC) of 0.938, internal validation AUC of 0.748, and eICU CRD external validation AUC of 0.800. SHAP analysis indicated that acute kidney injury was the most influential predictor, followed by base excess and RDW. Additionally, a web tool was developed in this study to facilitate the application of early death risk prediction in clinical practice. Conclusions: This study is the multi-database validation of ML models for early mortality prediction in ICU patients with COPD and PH. The Random Forest model integrated with SHAP interpretability offers a reliable tool for clinical decision-making support, with consistent validity verified across two databases, including our internal validation cohort. This work provides a decision support tool to assist in identifying high-risk individuals within this complex comorbid population, and establishes a foundation for future studies with a more dynamic and personalized prognostic approach.

Background: Liver transplantation remains limited by organ shortage, necessitating precise waitlist risk stratification. The Model for End-Stage Liver Disease (MELD) remains the cornerstone of liver allocation and effectively predicts near-term mortality when assessed dynamically during waitlist follow-up. However, MELD measured at initial transplant evaluation may not fully capture baseline multisystem vulnerability, particularly among candidates with lower MELD scores who may remain on the waitlist for prolonged periods. Methods: The BioWaitIndex (BWI) was developed using a random forest model in an NHANES development cohort comprising 13,830 participants. The trained model was first temporally validated in a separate, non-overlapping sample from subsequent NHANES cycles comprising 8,151 participants. It was then independently clinically validated in a separate, non-overlapping, IRB-approved cohort of 1,502 adult liver transplant candidates from Houston Methodist Hospital. In the clinical validation cohort, associations between BWI measured at initial transplant evaluation and subsequent waitlist outcomes were assessed using competing-risk regression. Results: In multivariable competing-risks analyses with BWI and MELD both measured at initial evaluation, each unit increase in BWI was associated with a greater likelihood of transplantation (sHR 1.02, 95% CI 1.01-1.03; p < 0.001) and a lower risk of waitlist death or clinical deterioration (sHR 0.97, 95% CI 0.97-0.98; p < 0.001). Adding BWI to MELD at evaluation improved net reclassification for waitlist mortality by 31.2% (p < 0.001) and for transplantation by 11.1% (p = 0.026), with consistent associations across sexes. Conclusion: BWI may complement dynamic MELD assessment by providing earlier information about baseline multisystem vulnerability and identifying candidates whose risk may not be fully reflected by their MELD score at evaluation.

Background: Invasive pulmonary aspergillosis (IPA) is increasingly recognized in non-neutropenic patients, but validated bedside risk stratification tools remain scarce. We developed a machine learning model to predict IPA and tested whether corticosteroid effect depends on bacterial co-pathogen. Methods: This multicenter retrospective cohort study enrolled 837 non-neutropenic adults hospitalized with pulmonary infections across three tertiary hospitals in China between December 2020 and December 2025. The discovery cohort was split into training and internal validation sets. Four classifiers were compared, with Random Forest selected for optimal performance. The final model was externally validated in an independent cohort. Feature importance was assessed using SHAP (SHapley Additive exPlanations) analysis. Corticosteroid-bacteria interactions were examined using subgroup analyses and propensity score matching, with the synergy index S calculated to quantify biological interaction. A simplified bedside score was derived from the top-ranking predictors. Results: The Random Forest model achieved external validation AUC of 0.865. Six predictors consistently dominated across all assessments: Pseudomonas aeruginosa isolation, hypoxemia (PaO2/FiO2 <200 mmHg), diabetes mellitus, organ failure (Sequential Organ Failure Assessment, SOFA >5), lymphopenia (lymphocyte count <0.8×109/L), and ICU (intensive care unit) admission. Corticosteroid exposure did not rank among the top six predictors. However, its association with IPA diverged sharply by bacterial context: OR below 1 with P. aeruginosa (OR 0.52, 95% CI 0.214-1.283) but OR above 1 with other Gram-negative bacilli (OR 2.17, 95% CI 0.722-6.535); P for interaction = 0.131; a directional pattern that persisted after propensity matching. The six-variable score derived from these predictors showed AUCs of 0.869 (training), 0.774 (internal validation), and 0.719 (external validation). Conclusions: IPA risk in non-neutropenic ICU patients is shaped more by bacterial co-pathogen context than by corticosteroid exposure alone. The divergent corticosteroid effects across bacterial species challenge the conventional assumption of a uniform steroid-associated risk. The externally validated six-variable score offers a practical, software-free tool to support pathogen-informed, individualized risk stratification and clinical decision-making in the ICU.

Background: Traumatic injury induces rapid systemic immune-inflammatory alterations, but whether routinely measurable admission biomarkers can provide an early biological estimate of anatomical injury burden remains uncertain. This study aimed to characterize trauma-associated immune-inflammatory profiles across trauma severity levels defined by injury severity score (ISS) and evaluate their combined value for early severity stratification. Methods: This two-center retrospective study included healthy participants as a physiological reference group, non-trauma acute-care patients as a clinically relevant comparator, and trauma patients classified as mild (ISS <16), moderate (ISS 16-25), or severe (ISS >25). Nineteen admission laboratory biomarkers and demographic variables were evaluated in a development cohort of 775 participants and an independent external validation cohort of 349 participants. Multinomial least absolute shrinkage and selection operator regression was used to identify severity-associated biomarkers. Ten classifiers were compared on the same predefined trauma-only development split, and the selected model was evaluated in an independent external trauma cohort. Results: A 10-feature panel was retained following feature selection and collectively defined an immune-inflammatory profile associated with increasing trauma severity. Increasing injury burden was associated with heterogeneous and partly nonlinear inflammatory and hematologic alterations, including changes in neutrophil-related indices, lymphocyte-related measures, and albumin. The multinomial logistic regression model achieved the best overall performance, with an accuracy of 0.941 and 0.973 in the internal holdout and external validation cohorts, respectively, with an external macro-AUC of 0.993. Model interpretation consistently identified neutrophil count and RDW-CV as major contributors to severity stratification. Conclusion: Admission-stage blood biomarkers capture reproducible immune-inflammatory alterations associated with increasing anatomical injury severity. Their combined analysis may provide a complementary approach for early biological assessment of injury burden and supports further investigation of clinically accessible biomarkers of trauma-associated immune-inflammatory dysregulation.

Aim: To develop and externally validate an interpretable machine learning model using routine laboratory data to distinguish current Stage III-IV from Stage I-II periodontitis among adults with established periodontitis. Materials And Methods: This two-centre retrospective study included 1321 patients for development and 584 for external validation at two hospitals of the Zhejiang University School of Medicine. The stacking model used age, sex, smoking status and 13 blood-derived predictors and was frozen before external validation. Performance was assessed by discrimination, calibration, classification measures, decision-curve analysis and comparison with simpler models. SHAP and restricted cubic spline analyses were exploratory. Results: The stacking model had an out-of-fold AUC of 0.827 (95% CI: 0.802-0.851) in development and an external AUC of 0.774 (95% CI: 0.730-0.818). External calibration was less favourable (intercept 0.785, slope 0.822). External AUC was 0.745 for age, sex and smoking status and 0.763 after adding the 13 blood-derived predictors. Conclusions: Among adults with established periodontitis and sufficiently complete predictor data, the model showed moderate external discrimination but less favourable calibration. Blood-derived predictors added modest discrimination beyond age, sex and smoking status. The model does not replace periodontal examination. Further multi-centre validation and calibration are needed before clinical use.