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

Frontiers in Medicine

Medicine

Jun Li¹  Zhenfei Chen²  Jing Zhang²  Wei Wang²  Yuqi Wang³  Caoyang Fang³ 

[1] Department of Cardiology, The Lu’an Hospital Affiliated to Anhui Medical University, Lu’an, Anhui, China;Department of Cardiology, The Lu’an People's Hospital, Lu’an, Anhui, China;Department of Cardiology, The Second People's Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University, Hefei, Anhui, China;Department of Cardiology, The Second People's Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University, Hefei, Anhui, China;Graduate School, Bengbu Medical College, Bengbu, Anhui, China;

关键词: serum YKL-40;    acute ST-elevation myocardial infarction;    STEMI;    adverse cardiovascular events;    MACE;    nomogram;   

DOI  :  10.3389/fmed.2023.1158005

 received in 2023-02-03, accepted in 2023-05-02,  发布年份 2023

来源: Frontiers

【 摘 要 】

ObjectiveThis 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).MethodsIn 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.ResultsAccording 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 values of the nomogram; the DCA results showed that the graph had a high clinical application value.ConclusionIn conclusion, we constructed and validated a nomogram based on serum YKL-40 to predict the risk of in-hospital MACE in STEMI patients. This model can provide a scientific reference for predicting the occurrence of in-hospital MACE and improving the prognosis of STEMI patients.

【 授权许可】

Unknown   
Copyright © 2023 Fang, Li, Wang, Wang, Chen and Zhang.