Establishment and validation of a clinical nomogram model based on serum YKL-40 to predict major adverse cardiovascular events during hospitalization in patients with acute ST-segment elevation myocardial infarction

作者：Caoyang Fang, Jun Li, Wei Wang, Yuqi Wang, Zhenfei Chen, Jing Zhang · 发表于：Frontiers in Medicine · 年份：2023 · DOI：10.3389/fmed.2023.1158005 · 被引用次数：3 · 研究领域：Studies on Chitinases and Chitosanases、IL-33, ST2, and ILC Pathways、Cardiac Fibrosis and Remodeling

Objective This study aimed to investigate the predictive value of a clinical nomogram model based on serum YKL-40 for major adverse cardiovascular events (MACE) during hospitalization in patients with acute ST-segment elevation myocardial infarction (STEMI). Methods In this study, 295 STEMI patients from October 2020 to March 2023 in the Second People’s Hospital of Hefei were randomly divided into a training group ( n = 206) and a validation group ( n = 89). Machine learning random forest model was used to select important variables and multivariate logistic regression was included to analyze the influencing factors of in-hospital MACE in STEMI patients; a nomogram model was constructed and the discrimination, calibration, and clinical effectiveness of the model were verified. Results According to the results of random forest and multivariate analysis, we identified serum YKL-40, albumin, blood glucose, hemoglobin, LVEF, and uric acid as independent predictors of in-hospital MACE in STEMI patients. Using the above parameters to establish a nomogram, the model C-index was 0.843 (95% CI: 0.79–0.897) in the training group; the model C-index was 0.863 (95% CI: 0.789–0.936) in the validation group, with good predictive power; the AUC (0.843) in the training group was greater than the TIMI risk score (0.648), p < 0.05; and the AUC (0.863) in the validation group was greater than the TIMI risk score (0.795). The calibration curve showed good predictive values and observed val...