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 - Fang, Caoyang; Li, Jun; Wang, Wei; Wang, Yuqi; Chen, Zhenfei; Zhang, Jing (Frontiers in Medicine, 2023)
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, 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; nomogram model was constructed and the discrimination, calibration and clinical effectiveness of the model were verified. Results We identified serum YKL-40, hemoglobin, fasting glucose, LVEF, and uric acid as independent predictors of in-hospital MACE in STEMI patients based on random forest and multivariate analyses. A nomogram was established using the above parameters, with a model C-index of 0.865 (95% CI: 0.745–0.820) and an internally validated C-index of 0.852, which had good predictive power; the H-L goodness of fit test results showedχ²= 1.5067, P =0.4708, and the calibration curve showed good nomogram prediction values and observed values; the DCA results showed that this figure had high clinical application value; the nomogram model predicted that the AUC of in-hospital MACE after PCI in STEMI patients (0.865) was greater than the TIMI score (0.727), P < 0.05.Conclusions In 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.