Machine learning redevelopment of GRACE, ACEF, and TIMI scores for 6-month mortality

作者：Bing Han, Z W Zhu, Rui Guo, Jizhe Xu, Yixin Zhang, Zheng Zhang, Wenqiang Li · 发表于：Frontiers in Artificial Intelligence · 年份：2026 · DOI：10.3389/frai.2026.1838324 · 研究领域：Machine Learning in Healthcare、Artificial Intelligence in Healthcare、Acute Myocardial Infarction Research

Background: In recent years, advancements in our understanding of the pathophysiological mechanisms underlying coronary artery disease (CAD) have introduced new challenges regarding the clinical application of traditional risk scores. While studies suggest that machine learning (ML) algorithms surpass traditional statistical methods in risk prediction, their conclusions are often derived from heterogeneous datasets and varying model structures, which restrict their generalizability and persuasive power. Objective: This study aims to evaluate the clinical performance of the GRACE, ACEF, and TIMI risk scores, while also enhancing their predictive accuracy through redevelopment utilizing six ML algorithms. Methods: This retrospective study was undertaken at the First Hospital of Lanzhou University between January 1, 2019, and December 31, 2020. Six ML algorithms were employed to redevelop the original GRACE, TIMI, and ACEF risk scores. Model performance was evaluated using accuracy, sensitivity, precision, F1-score, the area under the receiver operating characteristic curve (AUROC), and the area under the precision-recall curve (AUPRC). Results: We retrospectively enrolled 1,682 patients diagnosed with acute myocardial infarction (AMI) who underwent emergency percutaneous coronary intervention (PCI). The derivation cohort from 2019 included 883 patients, among whom 48 (5.4%) experienced death during the follow-up period. The temporal validation cohort from 2020 consisted of 799 ...