ArticleClinical cardiology2023
Use of machine learning models to predict in-hospital mortality in patients with acute coronary syndrome.
Article in Clinical cardiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- In-hospital mortality risk prediction models for patients with acute coronary syndrome: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2025Pooled it
- Prediction of Hospital Mortality in Patients with ST Segment Elevation Myocardial Infarction: Evolution of Risk Measurement Techniques and Assessment of Their Effectiveness (Review).Sovremennye tekhnologii v meditsine · 2024Pooled it
- Predicting mortality risk in hospitalized ACS patients with hypertensive comorbidity: an interpretable machine learning approach.BMC medical informatics and decision making · 2026Article
- A Multicenter Prospective Study to Develop a Prediction Model for Postherpetic Neuralgia Using Clinical and Laboratory Indicators.Pain and therapy · 2026Article
- Machine learning approaches for risk prediction in aortic dissection: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2026Review
- Development and validation of the C-reactive protein-triglyceride-glucose index for predicting short- and long-term mortality in critically ill patients with coronary artery disease: a multicenter cohort study.Frontiers in cardiovascular medicine · 2026Article
- Developing an explainable machine learning model to predict false-negative citrin deficiency cases in newborn screening.Orphanet journal of rare diseases · 2025Article
- The efficacy of artificial intelligence in predicting mortality rate and cardiogenic shock in acute coronary syndrome patients.International journal of cardiology. Cardiovascular risk and prevention · 2025Review
- Predicting In-Hospital Mortality in Patients With Acute Myocardial Infarction: A Comparison of Machine Learning Approaches.Clinical cardiology · 2025Article
- Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.Advances in therapy · 2025Review
- The Efficacy of Artificial Intelligence in Predicting the Postoperative Mortality Rate in Patients with Congenital Heart Disease: A Systematic Review and Meta-Analysis.Research in heart yield and translational medicine · 2025Review
- Performance of the Models Predicting In-Hospital Mortality in Patients with ST-Segment Elevation Myocardial Infarction with Predictors in Categorical and Continuous Forms.Sovremennye tekhnologii v meditsine · 2024Article
- Artificial intelligence based prediction model of in-hospital mortality among females with acute coronary syndrome: for the Jerusalem Platelets Thrombosis and Intervention in Cardiology (JUPITER-12) Study Group.Frontiers in cardiovascular medicine · 2024Article
- Use of machine learning models to predict in-hospital mortality in patients with acute coronary syndrome.Clinical cardiology · 2023Article
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Authors and funding
9 authors.
Funding
Abstract
backgroundCardiovascular diseases are a significant health burden with the prevalence increasing worldwide. Thus, a highly accurate assessment and prediction of death risk are crucial to meet the clinical demand. This study sought to develop and validate a model to predict in-hospital mortality among patients with the acute coronary syndrome (ACS) using nonlinear algorithms.
methodsA total of 2414 ACS patients were enrolled in this study. All samples were divided into five groups for cross-validation. The logistic regression (LR) model and XGboost model were applied to predict in-hospital mortality. The results of two models were compared between the variable set by the global registry of acute coronary events (GRACE) score and the selected variable set.
resultsThe in-hospital mortality rate was 3.5% in the dataset. Model performance on the selected variable set was better than that on GRACE variables: a 3% increase in area under the receiver operating characteristic (ROC) curve (AUC) for LR and 1.3% for XGBoost. The AUC of XGBoost is 0.913 (95% confidence interval [CI]: 0.910-0.916), demonstrating a better discrimination ability than LR (AUC = 0.904, 95% CI: 0.902-0.905) on the selected variable set. Almost perfect calibration was found in XGBoost (slope of predicted to observed events, 1.08; intercept, -0.103; p < .001).
conclusionsXGboost modeling, an advanced machine learning algorithm, identifies new variables and provides high accuracy for the prediction of in-hospital mortality in ACS patients.
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