ArticleCardiovascular diabetology2024
Machine learning for predicting in-hospital mortality in elderly patients with heart failure combined with hypertension: a multicenter retrospective study.
Article in Cardiovascular diabetology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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34 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Development and Validation of an XGBoost-SHAP Model for Predicting Adverse Outcomes in Elderly Cardiovascular Patients With Polypharmacy: A Retrospective Cohort Study.Pharmacology research & perspectives · 2026Article
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- Association of triglyceride-cholesterol-body weight index with in-hospital mortality in critically ill patients with heart failure.Scientific reports · 2026Article
- Malnutrition Severity Drives Mortality in Geriatric Heart Failure: A Multicenter Extreme Gradient Boosting Analysis.Reviews in cardiovascular medicine · 2026Article
- Identification and Validation of an Explainable Predictive Model For Heart Failure in Patients With Hypertension.Reviews in cardiovascular medicine · 2026Article
- Real-time monitoring of in-hospital mortality risk in intensive care units heart failure patients using an extreme gradient boosting model.Journal of geriatric cardiology : JGC · 2026Article
- Impact of in-hospital ACEI use on long-term prognosis in discharged type 2 myocardial infarction patients: a retrospective propensity score-matched cohort study.BMC cardiovascular disorders · 2026Observational
- An explainable machine learning model predicts 30-day readmission after vertebral augmentation.iScience · 2026Article
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- Identification of cardiovascular disease in patients with kidney stone disease using explainable machine learning.Frontiers in cardiovascular medicine · 2026Article
- Nonlinear association of HbA1c/HDL-C ratio with heart failure in adults: a large cross-sectional study.Frontiers in cardiovascular medicine · 2026Article
- Machine learning to identify heart failure with preserved ejection fraction in type 2 diabetes mellitus patients.Frontiers in cardiovascular medicine · 2026Article
- Development and External Validation of Machine Learning Model to Predict Live Birth Following Assisted Reproductive Technology in Women with Ovarian Endometriomas: A Decision-Support Tool.Clinical epidemiology · 2026Article
- Development and external validation of an explainable machine learning model for in-hospital mortality risk stratification in intensive care unit patients with heart failure.Frontiers in cardiovascular medicine · 2026Article
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12 authors.
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Abstract
backgroundHeart failure combined with hypertension is a major contributor for elderly patients (≥ 65 years) to in-hospital mortality. However, there are very few models to predict in-hospital mortality in such elderly patients. We aimed to develop and test an individualized machine learning model to assess risk factors and predict in-hospital mortality in in these patients.
methodsFrom January 2012 to December 2021, this study collected data on elderly patients with heart failure and hypertension from the Chongqing Medical University Medical Data Platform. Least absolute shrinkage and the selection operator was used for recognizing key clinical variables. The optimal predictive model was chosen among eight machine learning algorithms on the basis of area under curve. SHapley Additive exPlanations and Local Interpretable Model-agnostic Explanations was employed to interpret the outcome of the predictive model.
resultsThis study ultimately comprised 4647 elderly individuals with hypertension and heart failure. The Random Forest model was chosen with the highest area under curve for 0.850 (95% CI 0.789-0.897), high accuracy for 0.738, recall 0.837, specificity 0.734 and brier score 0.178. According to SHapley Additive exPlanations results, the most related factors for in-hospital mortality in elderly patients with heart failure and hypertension were urea, length of stay, neutrophils, albumin and high-density lipoprotein cholesterol.
conclusionsThis study developed eight machine learning models to predict in-hospital mortality in elderly patients with hypertension as well as heart failure. Compared to other algorithms, the Random Forest model performed significantly better. Our study successfully predicted in-hospital mortality and identified the factors most associated with in-hospital mortality.
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