New combined risk score to predict atrial fibrillation after cardiac surgery: COM-AF

Background and Aims: Atrial fibrillation frequently occurs in the postoperative period of cardiac surgery, associated with an increase in morbidity and mortality. The scores POAF, CHA2DS2-VASc and HATCH demonstrated a validated ability to predict atrial fibrillation after cardiac surgery (AFCS). The objective is to develop and validate a risk score to predict AFCS from the combination of the variables with highest predictive value of POAF, CHA2DS2-VASc and HATCH models. Methods: We conducted a single-center cohort study, performing a retrospective analysis of prospectively collected data. The study included consecutive patients undergoing cardiac surgery in 2010-2016. The primary outcome was the development of new-onset AFCS. The variables of the POAF, CHA2DS2-VASc and HATCH scores were evaluated in a multivariate regression model to determine the predictive impact. Those variables that were independently associated with AFCS were included in the final model. Results: A total of 3113 patients underwent cardiac surgery, of which 21% presented AFCS. The variables included in the new score COM-AF were: age (≥75: 2 points, 65-74: 1 point), heart failure (2 points), female sex (1 point), hypertension (1 point), diabetes (1 point), previous stroke (2 points). For the prediction of AFCS, COM-AF presented an AUC of 0.78 (95% CI 0.76-0.80), the rest of the scores presented lower discrimination ability (P < 0.001): CHA2DS2-VASc AUC 0.76 (95% CI 0.74-0.78), POAF 0.71 (95% CI 0.69-0.73) and HATCH 0.70 (95% CI: 0, 67-0.72). Multivariable analysis demonstrated that COM-AF score was an independent predictor of AFCS: OR 1,91 (IC 95% 1,63-2,23). Conclusion: From the combination of variables with higher predictive value included in the POAF, CHA2DS2-VASc, and HATCH scores, a new risk model system called COM-AF was created to predict AFCS, presenting a greater predictive ability than the original ones. Being necessary future prospective validations.

Abstract licence: CC BY-NC-SA

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.