Abstract
BACKGROUND: Postoperative atrial fibrillation (POAF) is the most prevalent and potentially life-threatening arrhythmia following thoracic surgery. This study aimed to construct and validate a predictive model for assessing POAF risk. METHODS: A meta-analysis was conducted to rank risk factors associated with POAF based on their respective risk ratios (RRs). Significant risk factors identified from the meta-analyses were incorporated into the model and assigned weights. External validation was performed using a retrospective cohort from China. Receiver operating characteristic (ROC) curves, calibration plots and decision curve analysis (DCA) were employed to assess the model's predictive performance, calibration and clinical utility. RESULTS: We screened 40 cohort studies involving 58,899 patients. We developed a risk model that incorporated age ≥ 70 years (RR 2.10, 95% CI 1.34-3.30; p < 0.05), male sex (RR 1.46, 95% CI 1.34-1.60; p < 0.05), COPD (RR 2.28, 95% CI 1.81-2.89; p < 0.05), CAD (RR 1.72, 95% CI 1.49-1.99; p < 0.05), heart failure (RR 1.62, 95% CI 1.12-2.35; p < 0.05), pneumonectomy (RR 2.32, 95% CI 2.01-2.67; p < 0.05) and lobectomy (RR 1.86, 95% CI 1.38-2.51; p < 0.05) and thoracotomy (RR 1.46, 95% CI 1.30-1.64; p < 0.05). Validation was performed in an external cohort of 1546 participants, demonstrating strong discrimination with an area under the receiver operating characteristic curve (95% CI) of 0.89 (95% CI 0.81-0.83). The calibration curve and DCA curve results demonstrated good concordance and applicability. CONCLUSIONS: This model, built with easily accessible clinical variables, could accurately predict the risk of POAF. This holds promise for improving clinical decision making and guiding early interventions.