Abstract
BACKGROUND: Atrial fibrillation (AF) is a frequent complication in patients with acute myocardial infarction (AMI) undergoing percutaneous coronary intervention (PCI), contributing to adverse outcomes. Early identification of individuals at high risk for post-PCI AF is crucial for tailored monitoring and intervention strategies. This study aimed to develop and validate a predictive nomogram for new-onset AF in AMI patients treated with PCI. METHODS: A retrospective observational cohort of 268 AMI patients undergoing PCI between January 2023 and December 2024 was analyzed. Patients were divided into AF (n = 56) and non-AF (n = 212) groups based on documented AF within 6 months post-PCI. Baseline clinical, echocardiographic, laboratory, and procedural variables were collected. Multivariate logistic regression was used to identify independent predictors of post-PCI AF. A nomogram was constructed based on the final model. Discriminatory performance was assessed using the area under the receiver operating characteristic curve (AUC), and calibration was evaluated using the Hosmer-Lemeshow test and internal bootstrap validation. RESULTS: Six independent predictors were identified: Gensini score, left atrial diameter, serum creatinine (SCr), prognostic nutritional index (PNI), symptom onset-to-PCI time, and post-PCI thrombolysis in myocardial infarction (TIMI) flow grade. The resulting nomogram demonstrated excellent discrimination (AUC = 0.916) and strong calibration (Hosmer-Lemeshow χ(2) = 0.967, p = 0.551; optimism-corrected C-index = 0.909). The model showed good internal validity and potential clinical utility for early risk stratification. CONCLUSION: This study identified Gensini score, left atrial diameter, SCr, PNI, onset-to-PCI time, and final TIMI flow grade as independent predictors of post-PCI AF, and developed a nomogram with excellent predictive accuracy requiring validation in larger multicenter cohorts.