Performance, Recalibration, and Implementation of In-Hospital Mortality Prediction Models for Myocardial Infarction

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.