Aug 2026· Vascular Health and Risk Management· Vol 22, pp. 1-18· 0 citations· 32 references
Medicine
TL;DR
Pro data from multiple follow-ups, combined with a model constructed using GRU, provides promising tool for predicting mortality risk in patients with chronic heart failure, and the self-developed web-based decision support system allows users to calculate risk scores simply by entering patient information.
Abstract
Background Prediction models for mortality risk in patients with chronic heart failure (CHF) have traditionally relied on static admission data, which restricts capturing disease dynamics. Longitudinal follow-up data were used to develop a dynamic model to improve accuracy and provide evidence for tailored interventions. Methods We enrolled 1,333 CHF patients from 3 Shanxi centres. Data included CHF patient-reported outcome (PRO) measures (CHF-PROM), lifestyle, medications, and prognosis. Endpoint: all-cause mortality. Using sequential data, we developed Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), Multi-layer Perceptron (MLP), and Logistic Regression (LR) for 3-year risk. Performance assessed by area under the receiver operating characteristic curve (AUC), accuracy, true negative rate (TNR), true positive rate (TPR), Brier score, and F1-score. Temporal Shapley Additive exPlanations (TimeSHAP) provided interpretability, and a web tool built. Results Among models tested, the GRU model demonstrated strongest predictive accuracy, with performance steadily increasing as follow-up progressed. By 24 months, the GRU-based model attained its peak predictive performance, yielding an AUC of 0.765 (95% confidence interval [CI]: 0.761–0.768), an F1-score of 0.537 (95% CI: 0.531–0.542), and a Brier score of 0.208 (95% CI: 0.199–0.216). TimeSHAP indicated that physical condition, appetite, sleep, physical independence, and anxiety within the CHF-PROM, together with age and New York Heart Association Functional Classification functional class, were key predictors of 3-year all-cause mortality in patients with CHF. Conclusion PRO data from multiple follow-ups, combined with a model constructed using GRU, provides promising tool for predicting mortality risk in patients with chronic heart failure (CHF). The self-developed web-based decision support system allows users to calculate risk scores simply by entering patient information. Trial Registration Study registered with the China Clinical Trial Registry [identifier: ChiCTR2100043337]. Experimental registration date is February 11, 2021.
It is demonstrated that machine learning models can effectively identify high-risk patients and that minority data resampling significantly improves mortality classification reliability, and the approach offers potential value for clinical decision support systems and prioritised care pathways.
Abiodun Ojo· Journal International Review...· 0 citations
The developed deep neural network model enables early identification of high-risk CHD patients with hypertension through interpretable, routinely available clinical variables through interpretable, routinely available clinical variables.
Li Wang, J. Song, Yingzhu Xie et al.· Frontiers in Medicine· 0 citations
BACKGROUND
Coronary artery disease (CAD) is the leading cause of death globally and a major contributor to hospital readmission. This study aimed to predict 30-day mortality in patients hospitalized with acute and chronic CAD using a structured machine learning approach with data from multiple centers.
METHODS
We conducted a retrospective cohort study using patient data from the Taipei Medical University Clinical Research Database (TMUCRD). Multiple machine learning algorithms were employed to develop predictive models for 30-day mortality. Model performance was evaluated using a stratified fivefold cross-validation approach. Key performance metrics included the area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and F1 score.
RESULTS
A total of 23,267 patients (mean age 64.9 years) were included, with 1215 deaths overall (5.2%): 570 (3.7%) in the internal cohort (n = 15,510) and 645 (8.3%) in the external validation cohort (n = 7757, Shuang Ho Hospital). XGBoost achieved the best performance for the overall and acute CAD cohorts (AUROC 0.845 and 0.820, respectively), while logistic regression performed best for chronic CAD (AUROC 0.766). Key predictive features included the Charlson Comorbidity Index, hemoglobin level, emergency room admission status, age, and creatinine level.
CONCLUSION
The use of a structured machine learning approach to predict 30-day mortality in patients with acute and chronic CAD demonstrated promising discriminative performance, providing valuable insights that could enhance personalized care and inform clinical decisions.
Septi Melisa, P. Phan, Sheng-Hsuan Chien et al.· International Journal of Car...· 0 citations
Chronic diseases remain a major cause of mortality and long-term disability, and proactive identification of high-risk individuals is difficult because early clinical changes are often subtle, incomplete, and distributed across heterogeneous hospital records. This study proposes a machine-learning-based predictive analytics framework for early detection of chronic disease risk using electronic health records, laboratory profiles, demographic factors, medication history, and derived clinical indicators. The study used 48,320 adult patient records collected from four tertiary hospitals between 2014 and 2023, with leakage-controlled preprocessing, multistage missing-data handling, correlation and SHAP-assisted feature selection, and stratified model development. Logistic Regression, Random Forest, XGBoost, Multilayer Perceptron, and TabNet were evaluated against clinical risk-score baselines using AUROC, PR-AUC, F1-score, recall, calibration, Brier score, and stability under missingness and imbalance. XGBoost achieved the strongest internal performance with AUROC of 0.942, PR-AUC of 0.901, F1-score of 0.901, and Brier score of 0.108. External validation on 12,950 patients from an unseen hospital produced AUROC of 0.931, confirming limited performance degradation and improved generalizability. SHAP analysis identified creatinine, HbA1c, age, systolic blood pressure, and triglycerides as dominant contributors, supporting clinically interpretable early-risk alerts for preventive care.
P. A. Prakash, Mamtha C, Manishathri R et al.· 2026 7th International Confe...· 0 citations
Objective Patients with hypertension and chronic heart failure (CHF) face high risks of poor prognostic outcomes. Effective prevention and management strategies remain a priority. However, the predictive value of platelet-to-high-density lipoprotein cholesterol ratio (PHR) for poor clinical outcomes is unclear. This study aimed to develop and internally validate a predictive nomogram incorporating PHR for assessing the risk of composite endpoint of all-cause death and rehospitalization due to HF in hypertensive CHF patients. Methods One hundred eighty-three hypertensive CHF patients admitted to Fuwai Central China Cardiovascular Hospital in 2021 were classified into Event-group (n = 83) and non-Event group (n = 100), with a 12-month follow-up for poor clinical outcomes (all-cause death and heart failure rehospitalization). The composite endpoint (all-cause death or HF rehospitalization) occurred in 83 patients, yielding an overall cumulative incidence of 45.36%. Independent predictors were identified by multivariate logistic regression, and a nomogram was developed and validated by bootstrap resampling. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration charts, and decision curve analysis. Results History of atrial fibrillation (AF), increased left atrial diameter (LAD), higher neutrophil-to-lymphocyte ratio (NLR), and elevated PHR are independent risk factors for poor clinical outcomes (all P < 0.05). The nomogram incorporating these factors showed strong discrimination with an AUC of 0.814 (95% CI: 0.750–0.878; Hosmer–Lemeshow χ2 = 4.0329, P = 0.2579) in the modeling group and 0.809 (95% CI: 0.746–0.872; Hosmer–Lemeshow χ2 = 2.5868, P = 0.4598) in the validation group. Decision curve analysis confirmed clinical utility across broad threshold probability ranges (modeling group: 12%–82%, validation group: 10%–84%). Conclusions The nomogram incorporating PHR effectively predicts all-cause death and heart failure rehospitalization risk in hypertensive CHF patients, showing robust clinical applicability.
Suqin Wang, Hongzhi Liu· Frontiers in Cardiovascular...· 0 citations
Abstract Background Despite advances in understanding and treating non–ST-elevation acute coronary syndrome (NSTE-ACS), patients continue to experience high rates of adverse outcomes, particularly those with non–ST-segment elevation myocardial infarction, which remains a leading cause of cardiovascular mortality. Existing risk models may not fully reflect contemporary patient populations due to substantial changes in clinical profiles. Developing new machine learning (ML)–based risk calculators may improve the prediction of in-hospital mortality (IHM) at different stages of the diagnostic process, and ultimately improve patient outcomes. Objective This study aimed to develop predictive models for IHM in patients with NSTE-ACS using ML methods and predictor sets obtained during the diagnostic process. Methods This retrospective observational study included 1144 patients with NSTE-ACS admitted between 2019 and 2021. IHM occurred in 94 (8.1%) of 1144 patients. Predictive models were developed using multivariable logistic regression, Random Forest, XGBoost (Extreme Gradient Boosting), and CatBoost algorithms. Model performance was evaluated using the area under the receiver operating characteristic curve (ROC-AUC), area under the precision-recall curve, calibration metrics, and decision curve analysis. Results A key feature of the developed models was their applicability at different stages of the diagnostic process, using predictors available at each specific stage. At admission, ML models achieved ROC-AUC values up to 0.93. After incorporating laboratory and echocardiographic data, predictive performance increased to ROC-AUC values of 0.94 to 0.95. The best-performing model demonstrated an ROC-AUC of 0.951 (95% CI 0.946‐0.956), a sensitivity of 0.902 (95% CI 0.889‐0.916), and a specificity of 0.891 (95% CI 0.886‐0.896). The area under the precision-recall curve reached 0.685, and the Brier score was 0.0331, indicating good calibration. Random Forest models demonstrated greater clinical utility than the Global Registry of Acute Coronary Events score (P<.001). Shapley Additive Explanations analysis identified the most significant predictors of mortality risk, including Killip class of acute heart failure, age, Charlson comorbidity index, creatinine level, and hematocrit level. Conclusions ML-based models enabled accurate prediction of IHM in patients with NSTE-ACS at different stages of the diagnostic process and may improve risk stratification and clinical decision-making in real-world practice.
V. V. Ryabov, A. Nesova, A. Vishnevskiy et al.· JMIR Medical Informatics· 0 citations