Development and validation of a predictive model for new-onset atrial fibrillation in sepsis based on clinical risk factors - Li, Zhuanyun; Pang, Ming; Li, Yongkai; Yu, Yaling; Peng, Tianfeng; Hu, Zhenghao; Niu, Ruijie; Li, Jiming; Wang, Xiaorong (Frontiers in Cardiovascular Medicine, 2022)
Objective: New-onset atrial fibrillation (NOAF) is a common complication and one of the primary causes of increased mortality in critically ill adults. Since early assessment of the risk of developing NOAF is difficult, it is critical to establish predictive tools to identify the risk of NOAF. Method: This was a multicenter retrospective study. Data from 1568 patients with sepsis who were treated at the Union Hospital of Tongji Medical College, Huazhong University of Science and Technology (Wuhan, China) were used as the training cohort. For external validation of the model, we enrolled 924 patients with sepsis from The First Affiliated Hospital of Xinjiang Medical University (Urumqi, China) as the validation cohort. All patients with NOAF were confirmed by case data records or electrocardiogram reports. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve were used to assess the value of the predictive model in NOAF. Results: A total of 2492 patients with sepsis (1592 (63.88%) male; mean [SD] age, 59.47 [16.42] years) were enrolled in this study. Age, international normalized ratio, fibrinogen, C-reaction protein, sequential organ failure assessment score, congestive heart failure, and dopamine use were used as risk variables to develop the nomogram model. The AUCs of the nomogram model were 0.861 (95% CI, 0.830-0.892) and 0.845 (95% CI, 0.804-0.886) in the internal and external validation, respectively. The clinical prediction model showed excellent calibration and higher net clinical benefit. Conclusion: The nomogram model can be used as a reliable and simple predictive tool for the early identification of NOAF in patients with sepsis, which will provide practical information for individualized treatment decisions.