AUTHOR=Fang Caoyang , Li Jun , Wang Wei , Wang Yuqi , Chen Zhenfei , Zhang Jing TITLE=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 JOURNAL=Frontiers in Medicine VOLUME=10 YEAR=2023 URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2023.1158005 DOI=10.3389/fmed.2023.1158005 ISSN=2296-858X ABSTRACT=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 values of the nomogram; the DCA results showed that the graph had a high clinical application value.

Conclusion

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.