Predictive value of platelet-to-lymphocyte ratio for new-onset atrial fibrillation after percutaneous coronary intervention in patients with ST-segment elevation myocardial infarction

Inflammation represents a key pathophysiological link between acute myocardial infarction and atrial fibrillation. Despite this, the platelet-to-lymphocyte ratio (PLR), as a composite inflammatory biomarker, remains underexplored for predicting new-onset atrial fibrillation (NOAF) specifically in patients with ST-segment elevation myocardial infarction (STEMI). This study aimed to evaluate the predictive value of PLR for in-hospital NOAF following percutaneous coronary intervention (PCI) in this population. This was a single-center, retrospective observational study. A total of 335 patients diagnosed with STEMI at the Affiliated Hospital of Xuzhou Medical University from January 2023 to December 2023 were included. All patients underwent primary PCI within 12 h of symptom onset and received continuous electrocardiographic monitoring during hospitalization. Logistic regression analysis and receiver operating characteristic (ROC) curves were used to assess the relationship between PLR and the incidence of NOAF during hospitalization in STEMI patients. Among the cohort, 30 patients (9.0%) developed NOAF during their hospital stay. Multivariable logistic regression, adjusted for potential confounders, identified age, systolic blood pressure, heart rate, and the coronary angiography-derived index of microcirculatory resistance (caIMR) as independent factors associated with NOAF. The PLR demonstrated good predictive performance for NOAF development, corresponding to an area under the curve (AUC) of 0.749 (95% CI: 0.664–0.834, P < 0.001). Incorporating PLR into a baseline clinical model significantly improved risk prediction, with a NRI of 0.608 (P = 0.001) and an IDI of 0.071 (P = 0.004). This study demonstrates that a high PLR is independently associated with in-hospital NOAF following primary PCI for STEMI. Furthermore, PLR offers significant improvement to the predictive capability of clinical risk models for NOAF, suggesting its utility in enhancing patient risk assessment.