Performance, Recalibration, and Implementation of In-Hospital Mortality Prediction Models for Myocardial Infarction
- 1The First School of Clinical Medicine, Lanzhou University, 730000 Lanzhou, Gansu, China
- 2Heart Center, The First Hospital of Lanzhou University, 730000 Lanzhou, Gansu, China
- 3Gansu Province Clinical Research Center for Cardiovascular Diseases, 730000 Lanzhou, Gansu, China
Abstract
Acute myocardial infarction remains a leading cause of cardiovascular mortality worldwide. Although reperfusion therapy has improved short-term outcomes, in-hospital mortality remains high, particularly among patients with complex conditions. Therefore, accurate mortality prediction models are essential for optimizing clinical decision-making and individualized management. This review summarizes the evolution of these models over the past two decades, from traditional statistical methods to machine learning. Indeed, machine-learning models may achieve slightly higher discrimination than traditional statistical models in selected datasets, particularly when high-dimensional and nonlinear clinical information is available. However, the magnitude of improvement is often incremental rather than transformative, and higher area under the curve (AUC) values do not necessarily indicate better calibration, interpretability, transportability, or clinical utility. Multicenter external validation studies indicate that performance is influenced by population heterogeneity, evolving treatment strategies, and data quality, with notable variation across subgroups, including Asian patients, women, and patients with diabetes. Recalibration is key to improving transportability and can be achieved through intercept adjustment, slope calibration, or model reconstruction; dynamic updating may help maintain long-term performance. Inadequate data standardization, concerns regarding black-box models, workflow misalignment, and limited policy support can all constrain clinical implementation. Therefore, this study proposes a technology–clinical–management pathway: promoting standardized data and federated learning, building lightweight interpretable embedded decision support, establishing model registration/certification and reimbursement linkages, and strengthening multidisciplinary training. Future work should prioritize multimodal data fusion, real-time prediction, improved interpretability, multicenter prospective validation, and cost-effectiveness assessment to support safe implementation across diverse healthcare systems. Notably, China represents an important regional context for evaluating these strategies.
Related
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- Development and Validation of a Prediction Model for Acute Coronary Occlusion in Patients With Non-ST-Segment Elevation Myocardial Infarction.
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- Mortality prediction in patients with acute myocardial infarction and prior ischemic stroke: a prospective cohort study with explainable machine learning and SHAP analysis
BibTeX
@article{xing2026performance,
title = {{Performance, Recalibration, and Implementation of In-Hospital Mortality Prediction Models for Myocardial Infarction}},
author = {Erkun Xing and Yurun Su and Zekun Lang and Yongling Wa and Wei Hu and Xiaojuan Wang and Youqi Zhu and Jing Zhao and Ming Bai},
journal = {Reviews in Cardiovascular Medicine},
year = {2026},
month = oct,
doi = {10.31083/rcm51293},
url = {https://doi.org/10.31083/rcm51293},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Performance, Recalibration, and Implementation of In-Hospital Mortality Prediction Models for Myocardial Infarction
AU - Erkun Xing
AU - Yurun Su
AU - Zekun Lang
AU - Yongling Wa
AU - Wei Hu
AU - Xiaojuan Wang
AU - Youqi Zhu
AU - Jing Zhao
AU - Ming Bai
PY - 2026
DA - 2026/10/21
JO - Reviews in Cardiovascular Medicine
DO - 10.31083/rcm51293
UR - https://doi.org/10.31083/rcm51293
AB - Acute myocardial infarction remains a leading cause of cardiovascular mortality worldwide. Although reperfusion therapy has improved short-term outcomes, in-hospital mortality remains high, particularly among patients with complex conditions. Therefore, accurate mortality prediction models are essential for optimizing clinical decision-making and individualized management. This review summarizes the evolution of these models over the past two decades, from traditional statistical methods to machine learning. Indeed, machine-learning models may achieve slightly higher discrimination than traditional statistical models in selected datasets, particularly when high-dimensional and nonlinear clinical information is available. However, the magnitude of improvement is often incremental rather than transformative, and higher area under the curve (AUC) values do not necessarily indicate better calibration, interpretability, transportability, or clinical utility. Multicenter external validation studies indicate that performance is influenced by population heterogeneity, evolving treatment strategies, and data quality, with notable variation across subgroups, including Asian patients, women, and patients with diabetes. Recalibration is key to improving transportability and can be achieved through intercept adjustment, slope calibration, or model reconstruction; dynamic updating may help maintain long-term performance. Inadequate data standardization, concerns regarding black-box models, workflow misalignment, and limited policy support can all constrain clinical implementation. Therefore, this study proposes a technology–clinical–management pathway: promoting standardized data and federated learning, building lightweight interpretable embedded decision support, establishing model registration/certification and reimbursement linkages, and strengthening multidisciplinary training. Future work should prioritize multimodal data fusion, real-time prediction, improved interpretability, multicenter prospective validation, and cost-effectiveness assessment to support safe implementation across diverse healthcare systems. Notably, China represents an important regional context for evaluating these strategies.
SN - 1530-6550
SN - 2153-8174
ER -