Key result
Higher polygenic risk score linked to ~83% higher odds of postoperative AF per SD.
Why the study?
Postoperative AF is the most common complication following cardiac surgery and increases stroke and mortality risk, motivating development of a prediction model integrating clinical variables and polygenic risk and comparison against existing AF prediction models.
Does a multimodal prediction model integrating clinical variables and polygenic risk improve the prediction of postoperative atrial fibrillation within 30 days compared to existing AF prediction models in patients undergoing cardiac surgery without prior AF?
Cohort (n=2,340)
No
Does a multimodal prediction model integrating clinical variables and polygenic risk improve the prediction of postoperative atrial fibrillation within 30 days compared to existing AF prediction models in patients undergoing cardiac surgery without prior AF?
Integrating polygenic risk scores with clinical and surgical variables in the LASSO and simplified PAC-AF models improves the prediction of postoperative atrial fibrillation after cardiac surgery compared to existing clinical, genetic, or ECG-AI models alone.
Answers are grounded in this paper and cite their sources.
May enhance POAF prediction after cardiac surgery; extends prior models but remains hypothesis-generating pending validation.
Background/Introduction Postoperative atrial fibrillation (POAF) is the most common complication following cardiac surgery, prolongs hospital stay and increases risk of stroke and death. Purpose To develop a prediction model for POAF integrating clinical variables and polygenic risk, and to compare its performance with existing atrial fibrillation (AF) prediction models, including CHARGE-AF, polygenic risk scores (PGS-AF), and an ECG-based artificial intelligence (ECG-AI) model. Methods This was a prospective hospital biobank-based cohort study with retrospective adjudication of POAF. We included 2,340 participants in a Biobank at our institution who underwent cardiac surgery between 2005 and 2020 and did not have AF prior to surgery. POAF was defined as new-onset AF occurring within 30 days after surgery. PGS-AF was calculated using the Multi-t-EUR model, a European ancestry-specific tuned score integrating individual polygenic scores for AF and related traits (1). A least absolute shrinkage and selection operator (LASSO) logistic regression model and a simplified point-based score (PAC-AF), incorporating PGS-AF percentiles, age ≥65 years, and surgical characteristics, were derived using clinical variables and PGS-AF (Figure, panel B). Model tuning was conducted using the training set (70%). Performance assessment is reported on the test set (30%). The ECG-AI model used for comparison was previously developed to predict long-term incident AF rather than POAF (2). Performance was assessed using odds ratios (OR) between risk categories, calibration plots and the area under the receiver operating characteristic curve (AUC). Results Among 2,340 patients, 871 (37.2%) developed POAF (Table). In the test cohort, the LASSO model demonstrated the highest discrimination (AUC 0.72, 95% CI 0.68–0.75), with PGS-AF emerging as the most powerful independent predictor (OR 1.83, 95% CI 1.64–2.08 per 1-SD increase). The simplified point-based PAC-AF score yielded an AUC of 0.68 (95% CI 0.64–0.72), with no statistically significant difference compared to LASSO model (DeLong p=0.19). POAF prevalence increased stepwise across PAC-AF score categories (Figure), from 21% in patients with ≤2 points to 83% in those with ≥6 points, corresponding to an odds ratio of 18.5 (95% CI 5.2–66.2). CHARGE-AF (AUC 0.64, 95% CI 0.61–0.66), PGS-AF (AUC 0.64, 95% CI 0.62–0.67), and ECG-AI (AUC 0.65, 95% CI 0.62–0.67) showed lower discrimination. The LASSO and PAC-AF models showed good calibration (slopes 1.14 and 1.08, respectively). Conclusion A multimodal model integrating clinical variables and polygenic risk demonstrated good performance in predicting POAF, with PGS-AF emerging as the most powerful independent predictor. The PAC-AF score could be used to identify a high-risk population that may be targeted for aggressive POAF preventive and screening measures, or to include in clinical trials of AF or stroke-preventive strategies following cardiac surgery.
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Jabbour et al. (2026) conducted a cohort in Postoperative atrial fibrillation (POAF) following cardiac surgery (n=2,340). Multimodal LASSO and PAC-AF prediction models (integrating clinical variables and polygenic risk score PGS-AF) vs. CHARGE-AF, PGS-AF alone, and ECG-AI models (or lowest PAC-AF risk category ≤2 points) was evaluated on New-onset postoperative atrial fibrillation (POAF) occurring within 30 days after cardiac surgery (OR 1.83, 95% CI 1.64-2.08). A multimodal LASSO model integrating clinical and polygenic scores predicted postoperative AF with an AUC of 0.72 (95% CI 0.68-0.75), with PGS-AF as the strongest predictor (OR 1.83 per SD).
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