• Na Wu, Junzheng Li, Xiang Xu, Zhiquan Yuan, Lili Yang, Yanxiu Chen, Tingting Xia, Qin Hu, Zheng Chen, Chengying Li, Ying Xiang, Zhihui Zhang, Li Zhong, and Yafei Li.
• Department of Epidemiology, College of Preventive Medicine, Army Medical University, Chongqing 400038, China.
• Int. J. Clin. Pract. 2023 Jan 1; 2023: 34736033473603.

ObjectiveAtrial fibrillation (AF) is one of the most common complications of acute coronary syndrome (ACS) patients. Possible risk factors related to new-onset AF (NOAF) in ACS patients have been reported in some studies, and several prediction models have been established. However, the predictive power of these models was modest and lacked independent validation. The aim of this study is to define risk factors of NOAF in patients with ACS during hospitalization and to develop a prediction model and nomogram for individual risk prediction.MethodsRetrospective cohort studies were conducted. A total of 1535 eligible ACS patients from one hospital were recruited for model development. External validation was performed using an external cohort of 1635 ACS patients from another hospital. The prediction model was created using multivariable logistic regression and validated in an external cohort. The discrimination, calibration, and clinical utility of the model were evaluated, and a nomogram was constructed. A subgroup analysis was performed for unstable angina (UA) patients.ResultsDuring hospitalization, the incidence of NOAF was 8.21% and 6.12% in the training and validation cohorts, respectively. Age, admission heart rate, left atrial diameter, right atrial diameter, heart failure, brain natriuretic peptide (BNP) level, less statin use, and no percutaneous coronary intervention (PCI) were independent predictors of NOAF. The AUC was 0.891 (95% CI: 0.863-0.920) and 0.839 (95% CI: 0.796-0.883) for the training and validation cohort, respectively, and the model passed the calibration test (P > 0.05). The clinical utility evaluation shows that the model has a clinical net benefit within a certain range of the threshold probability.ConclusionA model with strong predictive power was constructed for predicting the risk of NOAF in patients with ACS during hospitalization. It might help with the identification of ACS patients at risk and early intervention of NOAF during hospitalization.Copyright © 2023 Na Wu et al.

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