Key result
Novel model predicts postoperative AF after lung resection with strong 0.89 AUC in external validation.
Why the study?
Postoperative atrial fibrillation is the most prevalent and potentially life-threatening arrhythmia after thoracic surgery, creating a need for a predictive risk model.
Can a novel prediction model accurately predict the risk of new-onset atrial fibrillation after lung resection?
Cohort (n=1,546)
Can a novel prediction model accurately predict the risk of new-onset atrial fibrillation after lung resection?
Effect estimate: AUC 0.89 (95% CI 0.81-0.83)
p-value: p=< 0.05
A novel prediction model based on easily accessible clinical variables accurately predicts the risk of postoperative atrial fibrillation after lung resection, potentially guiding early interventions.
May support risk stratification after lung resection; leaves open prospective validation before clinical use.
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: < 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.
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Chen et al. (2025) conducted a cohort in Postoperative atrial fibrillation (n=1,546). Prediction model for POAF was evaluated on Prediction of new-onset atrial fibrillation (AUC 0.89, 95% CI 0.81-0.83, p=< 0.05). A novel prediction model for postoperative atrial fibrillation after lung resection demonstrated strong discrimination in an external validation cohort (AUC 0.89; 95% CI 0.81-0.83).
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