ArticleJournal of medical Internet research2024
A Machine Learning Model for Predicting In-Hospital Mortality in Chinese Patients With ST-Segment Elevation Myocardial Infarction: Findings From the China Myocardial Infarction Registry.
Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01874691 (China Acute Myocardial Infarction Registry), which is not on this map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
China Acute Myocardial Infarction Registry
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline 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
- Article
- CAMI-DM: Development and validation of a multi-algorithm model for in-hospital mortality risk prediction in diabetic patients with acute myocardial infarction - The China acute myocardial infarction registry.PLOS digital health · 2026Article
- Predicting mortality risk in hospitalized ACS patients with hypertensive comorbidity: an interpretable machine learning approach.BMC medical informatics and decision making · 2026Article
- Impact of MetS on Long-Term Prognosis Among STEMI Patients Treated with pPCI-Ten-Year Follow-Up Study.Medical sciences (Basel, Switzerland) · 2026Article
- Development and validation of a prediction model for respiratory complications and in-hospital mortality in trauma patients.Scientific reports · 2026Article
- Real-Time Integration of an AI-Based ECG Interpretation System in the Emergency Department: A Pragmatic Alternating-Day Study of Diagnostic Performance and Clinical Process Metrics.Healthcare (Basel, Switzerland) · 2026Article
- An interpretable machine learning model for predicting prognosis of medulloblastoma integrating genetic and clinical features.Communications medicine · 2026Article
- Machine learning-based prediction of mortality risk from air pollution-induced acute coronary syndrome in the Western Pacific region.Scientific reports · 2026Article
- Development and validation of an interpretable machine learning model for predicting medium-to-giant coronary aneurysms in Kawasaki disease.Frontiers in immunology · 2026Article
- Comparing Machine Learning Models for Predicting Mortality after Myocardial Infarction: A Systematic Review and Meta-analysis.Archives of academic emergency medicine · 2026Review
- On-scene machine learning prediction model for massive transfusion in trauma and its association with in-hospital mortality.BJS open · 2025Article
- Machine Learning Approach on Predictive Model Establishment for In-Hospital Mortality in Acute Myocardial Infarction Patients Post-Percutaneous Coronary Intervention: Solutions for Databases With Dimensionality Reduction and Class Imbalance.Reviews in cardiovascular medicine · 2025Article
- A systematic comparison of short-term and long-term mortality prediction in acute myocardial infarction using machine learning models.BMC medical informatics and decision making · 2025Observational
- Prediction of in-hospital mortality risk for patients with acute ST-elevation myocardial infarction after primary PCI based on predictors selected by GRACE score and two feature selection methods.Frontiers in cardiovascular medicine · 2024Article
- Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine learning (ML) risk prediction models, although much more accurate than traditional statistical methods, are inconvenient to use in clinical practice due to their nontransparency and requirement of a large number of input variables.
objectiveWe aimed to develop a precise, explainable, and flexible ML model to predict the risk of in-hospital mortality in patients with ST-segment elevation myocardial infarction (STEMI).
methodsThis study recruited 18,744 patients enrolled in the 2013 China Acute Myocardial Infarction (CAMI) registry and 12,018 patients from the China Patient-Centered Evaluative Assessment of Cardiac Events (PEACE)-Retrospective Acute Myocardial Infarction Study. The Extreme Gradient Boosting (XGBoost) model was derived from 9616 patients in the CAMI registry (2014, 89 variables) with 5-fold cross-validation and validated on both the 9125 patients in the CAMI registry (89 variables) and the independent China PEACE cohort (10 variables). The Shapley Additive Explanations (SHAP) approach was employed to interpret the complex relationships embedded in the proposed model.
resultsIn the XGBoost model for predicting all-cause in-hospital mortality, the variables with the top 8 most important scores were age, left ventricular ejection fraction, Killip class, heart rate, creatinine, blood glucose, white blood cell count, and use of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs). The area under the curve (AUC) on the CAMI validation set was 0.896 (95% CI 0.884-0.909), significantly higher than the previous models. The AUC for the Global Registry of Acute Coronary Events (GRACE) model was 0.809 (95% CI 0.790-0.828), and for the TIMI model, it was 0.782 (95% CI 0.763-0.800). Despite the China PEACE validation set only having 10 available variables, the AUC reached 0.840 (0.829-0.852), showing a substantial improvement to the GRACE (0.762, 95% CI 0.748-0.776) and TIMI (0.789, 95% CI 0.776-0.803) scores. Several novel and nonlinear relationships were discovered between patients' characteristics and in-hospital mortality, including a U-shape pattern of high-density lipoprotein cholesterol (HDL-C).
conclusionsThe proposed ML risk prediction model was highly accurate in predicting in-hospital mortality. Its flexible and explainable characteristics make the model convenient to use in clinical practice and could help guide patient management.
trial registrationClinicalTrials.gov NCT01874691; https://clinicaltrials.gov/study/NCT01874691.
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