A Machine Learning Model for the Prediction of No-Reflow Phenomenon in Acute Myocardial Infarction Using the CALLY Index.

BACKGROUND

Acute myocardial infarction (AMI) constitutes a major health problem with high mortality rates worldwide. In patients with ST-segment elevation myocardial infarction (STEMI), no-reflow phenomenon is a condition that adversely affects response to therapy. Previous studies have demonstrated that the CALLY index, calculated using C-reactive protein (CRP), albumin, and lymphocytes, is a reliable indicator of mortality in patients with non-cardiac diseases. The objective of this study is to investigate the potential utility of the CALLY index in detecting no-reflow patients and to determine the predictability of this phenomenon using machine learning (ML) methods.

METHODS

This study included 1785 STEMI patients admitted to the clinic between January 2020 and June 2024 who underwent percutaneous coronary intervention (PCI). Patients were in no-reflow status, and other clinical data were analyzed. The CALLY index was calculated using data on patients' inflammatory status. The Extreme Gradient Boosting (XGBoost) ML algorithm was used for no-reflow prediction.

RESULTS

No-reflow was detected in a proportion of patients participating in this study. The model obtained with the XGBoost algorithm showed high accuracy rates in predicting no-reflow status. The role of the CALLY index in predicting no-reflow status was clearly demonstrated.

CONCLUSIONS

The CALLY index has emerged as a valuable tool for predicting no-reflow status in STEMI patients. This study demonstrates how machine learning methods can be effective in clinical applications and paves the way for innovative approaches for the management of no-reflow phenomenon. Future research needs to confirm and extend these findings with larger sample sizes.