Prospective External Validation of Three Preoperative Risk Scores for Prediction of New Onset Atrial Fibrillation After Cardiac Surgery

BACKGROUND: Postoperative atrial fibrillation (POAF) is associated with early and late morbidity and mortality of cardiac surgical patients. Prophylactic treatment of atrial fibrillation (AF) has been recommended to improve outcome in cardiac surgical patients at high risk of developing POAF. Reliable models for prediction of POAF are needed to achieve that goal. This study attempted to externally validate 3 risk models proposed for preoperative prediction of POAF in cardiac surgical patients: the POAF score, the CHA(2)DS(2)-VASc score, and the Atrial Fibrillation Risk Index. METHODS: This was a prospective cohort study of 1416 adult patients who underwent nonemergent coronary artery bypass graft and/or valve surgery in a single cardiac surgical center between February 2014 and September 2015. A risk score for each of the 3 prediction models was calculated in each patient. All patients were followed for up to 2 weeks, or until hospital discharge, to observe the primary outcome of new onset AF requiring treatment. Discrimination was assessed using receiver operating characteristic curves. Calibration was assessed using the Pearson (2) goodness-of-fit test and calibration plots. Utility of the score to implement AF prophylaxis based on the risk of POAF, in comparison to strategies of treating all patients, or not treating any patients, was assessed via a net benefit analysis. RESULTS: Of the 1416 patients included in this study, 478 had the primary outcome (33.8%). The areas under the receiver operating characteristic curve for prediction of POAF in the population subsets for which the scores were validated were as follows: 0.651 (95% confidence interval [CI], 0.621-0.681) for the POAF score, 0.593 (95% CI, 0.557-0.629) for the CHA(2)DS(2)-VASc score (P < .001 versus POAF score, P < .222 versus Atrial Fibrillation Risk Index), and 0.563 (95% CI, 0.522-0.604) for the Atrial Fibrillation Risk Index (P < .001 versus POAF score). The calibration analysis showed that the predictive models had a poor fit between the observed and expected rates of POAF. Net benefit analysis showed that AF preventive strategies based on these scores, and targeting patients with moderate or high risk of POAF, improve decision-making in comparison to preventive strategies of treating all patients. CONCLUSIONS: The 3 prediction scores evaluated in this study have limited ability to predict POAF in cardiac surgical patients. Despite this, they may be useful in preventive strategies targeting patients with moderate or high risk of PAOF in comparison with preventive strategies applied to all patients.