Construction of a predictive model for new-onset atrial fibrillation after acute myocardial infarction based on P-wave amplitude in lead V1

Background: In this study, we aimed to identify the risk factors for new -onset atrial fibrillation (NOAF) after postcoronary intervention in patients with acute myocardial infarction (AMI) and to establish a nomogram prediction model. Methods: The clinical data of 506 patients hospitalized for AMI from March 2020 to February 2023 were retrospectively collected, and the patients were randomized into a training cohort (70%; n = 354) and a validation cohort (30%; n = 152). Independent risk factors were determined using least absolute shrinkage and selection operator and multivariate logistic regression. Predictive nomogram modeling was performed using R software. Nomograms were evaluated based on discrimination, correction, and clinical efficacy using the Cstatistic, calibration plot, and decision curve analysis, respectively. Results: The multivariate logistic regression analysis showed that P -wave amplitude in lead V1, age, and infarct type were independent risk factors for NOAF, and the area under the receiver operating characteristic curve of the training and validation sets was 0.760 (95% confidence interval [CI] 0.674-0.846) and 0.732 (95% CI 0.580-0.883), respectively. The calibration curves showed good agreement between the predicted and observed values in both the training and validation sets, supporting that the actual predictive power was close to the ideal predictive power. Conclusions: P -wave amplitude in lead V1, age, and infarct type were independent risk factors for NOAF in patients with AMI after intervention. The nomogram model constructed in this study can be used to assess the risk of NOAF development and has some clinical application value.