Frontiers in cardiovascular medicine
Phenotyping of AMI patients using machine learning: integration of clinical, metabolic, and behavioral features to identify high-risk "frailty-inflammation" phenotype
By Zhao Y, Ding W, Li H and 2 others.
Abstract
Background
Prognosis among patients with acute myocardial infarction (AMI) shows substantial heterogeneity, making precise identification of high-risk phenotypes essential for improving long-term outcomes. Machine learning techniques were used to identify such phenotypes by integrating multidimensional data and to examine their association with major adverse cardiovascular events (MACE).
Methods
A total of 1,139 patients with AMI were enrolled from the First Affiliated Hospital of Harbin Medical University between March 2023 and June 2024. Multidimensional data, including clinical features, metabolic characteristics, and behavioral patterns, were collected. Phenotyping was performed using a hierarchical clustering algorithm. Associations between derived phenotypes and 1-year MACE were evaluated using Kaplan-Meier survival analysis and multivariable Cox regression models. Shapley Additive Explanations (SHAP) values derived from an XGBoost model were used to quantify the contributions of individual variables to cluster assignment.