Development of a Nomogram to Classify In-Hospital Atrial Fibrillation Among Patients Hospitalized With Acute Myocardial Infarction: A Retrospective Case–Control Study

Background: Previous studies on acute myocardial infarction (AMI) complicated by atrial fibrillation (AF) have mainly focused on anatomy or underlying disease state, and its prognostic predictors have not been fully explored. Therefore, there is a need for an effective prognosis model for patients with AMI-AF. Methods: We retrospectively selected 126 patients with acute myocardial infarction complicated with AF hospitalized in Beijing Anzhen Hospital from January 2020 to December 2024 as the case group, and 1719 patients without AF as the control group. The clinical characteristics and laboratory test results of the two groups were compared to determine independent risk factors for AF in patients with acute myocardial infarction. The predictive performance of the model was evaluated by plotting Receiver Operating Characteristic (ROC) for each independent predictor. For the combined model, we used R software to build pattern plots, calibration plots, and Decision Curve Analysis (DCA) based on a multivariate logistic regression model. Results: Multivariate Logistic regression analysis showed that older age (Odds Ratio (OR) = 1.067, 95% CI: 1.044–1.092), longer hospitalization days (OR = 1.039, 95% CI: 1.013–1.066). The AUCs for age, hospitalization days, history of coronary heart disease, heart rate, International Normalized Ratio (INR), Hemoglobin, and mean platelet volume were 0.721, 0.663, 0.577, 0.614, 0.688, 0.438, and 0.607. The AUC of nomogram model for predicting AF in AMI patients was 0.833 (95% CI: 0.796–0.870, p < 0.001), the sensitivity was 0.817, and the specificity was 0.726. The nomogram model indicated a clinical net benefit when the predicted risk threshold exceeded 0.06. Conclusions: Multivariable prediction model has good prediction effect. The variables in this nomogram model are easily obtained in clinical practice and can provide reference for individualized prediction of AF in AMI patients.