Table 1_Development and validation of a nomogram for predicting atrial fibrillation after percutaneous coronary intervention in patients with acute myocardial infarction.docx

BackgroundAtrial 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.
MethodsA 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.
ResultsSix 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 χ2 = 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.
ConclusionThis 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.