Predicting post-operative atrial fibrillation after cardiac surgery using an artificial intelligence-enabled electrocardiogram algorithm

Abstract

Abstract Introduction Postoperative atrial fibrillation (POAF) is common after cardiac surgery and is associated with adverse outcomes. Systematic monitoring of POAF is cumbersome, specifically beyond discharge. Therefore, risk stratification may aid to identify patients at high risk of POAF and guide monitoring strategies alongside preventive measures. However, the performance of bedside risk stratification models reliant on clinical risk factors remained underwhelming, necessitating the exploration of more sophisticated models that maintain clinical applicability. Hence, artificial intelligence algorithms (AI) have been suggested to reinforce or replace clinical risk scores. As such, a deep neural network (DNN) algorithm was developed to identify patients with AF based on a 12-lead electrocardiogram (ECG) in sinus rhythm. Whether this algorithm can identify patients at high risk of POAF remains unknown. Purpose To evaluate the usability of an AI-enabled ECG algorithm, that was trained to predict AF in non-surgical conditions, for the prediction of POAF. Methods This study retrospectively analyzed data from the SURGICAL-AF trial that monitored patients after cardiac surgery. The inclusion criteria for this subanalysis comprised: (1) patients without a history of AF prior to cardiac surgery; (2) availability of the raw data of a pre-operative 12-lead ECG in sinus rhythm; and (3) patients with POAF (during hospitalization or up to 91 days after discharge) or patients having completed PPG-based rhythm monitoring per protocol. The AF-risk score was calculated by the DNN described elsewere.1 Results In total, 127 patients (mean [SD] age, 63.4 [8.4] years; 30 women [23.6%];, median [interquartile range] CHA2DS2-VASc score, 2 [1-3]) complied with the inclusion criteria, out of the 450 patients randomized in the SURGICAL-AF trial. Testing the DNN on the last ECG before cardiac surgery resulted in an area under the receiver operating curve (AUC) of 0.66 (95% CI, 0.56 - 0.77) and an area under the precision-recall curve of 0.57 (95% CI, 0.42 -0.72). The optimal cut of score resulted in a sensitivity of 64.3% (95% CI, 48.0%-78.4%), specificity of 64.7% (95% CI, 53.6%-74.8%), accuracy of 0.65 (95% CI, 0.56 - 0.73), F1-score of 54.5% (95% CI, 42.9% - 66.8%), positive predictive value of 47.4% (95% CI, 34.0%-61.0%), and negative predictive value of 78.6% (95% CI, 67.1%-87.5%). POAF occurred within three months after cardiac surgery in 23 patients out of 57 patients classified in the high-risk group (40.4%) versus 15 patients (21.4%) out of 70 patients classified in the low-risk group (hazard ratio, 2.2; 95% CI, 1.2 – 4.3; P-value = 0.020). Conclusions The AI-enabled ECG algorithm, trained to predict AF on a pre-operative sinus rhythm ECG, was able to identify POAF with an accuracy comparable to existing clinical risk scores. Further studies are needed to determine whether the DNN score can be used as an independent predictor and improve existing risk scores.