A dynamic nomogram for predicting in-hospital major adverse cardiovascular and cerebrovascular events in patients with both coronary artery disease and atrial fibrillation: a multicenter retrospective study.

Abstract

Patients with both coronary artery disease (CAD) and atrial fibrillation (AF) are at a high risk of major adverse cardiovascular and cerebrovascular events (MACCE) during hospitalization. Accurate prediction of MACCE can help identify high-risk patients and guide treatment decisions. This study was to elaborate and validate a dynamic nomogram for predicting the occurrence of MACCE during hospitalization in Patients with CAD combined with AF. A total of 3550 patients with AF and CAD were collected. They were randomly assigned to a training group and a validation group in a ratio of 7 : 3. Univariate and multivariate analyses were utilized to identify risk factors (P < 0.05). To avoid multicollinearity and overfit of the model, the least absolute shrinkage and selection operator was conducted to further screen the risk factors. Calibration curves, receiver operating characteristic curves, and decision curve analyses are employed to assess the nomogram. For external validation, a cohort consisting of 249 patients was utilized from the Medical Information Mart for Intensive Care IV Clinical Database, version 2.2. Eight indicators with statistical differences were screened by univariate analysis, multivariate analysis, and the least absolute shrinkage and selection operator method (P < 0.05). The prediction model based on eight risk factors demonstrated good prediction performance in the training group, with an area under the curve (AUC) of 0.838. This performance was also maintained in the internal validation group (AUC = 0.835) and the external validation group (AUC = 0.806). Meanwhile, the calibration curve indicates that the nomogram was well-calibrated, and decision curve analysis revealed that the nomogram exhibited good clinical utility. The nomogram we constructed may aid in stratifying the risk and predicting the prognosis for patients with CAD and AF.