Artificial Intelligence–Electrocardiography to Predict Incident Atrial Fibrillation and Clinical Outcomes in Kidney Transplant Recipients

Background: Incident atrial fibrillation (AF) is common following kidney transplantation (KTx) and is associated with worse clinical outcomes. Artificial intelligence electrocardiography (AI-ECG) algorithms have demonstrated efficacy in predicting risk of new-onset AF in the general population, however their prognostic value in KTx recipients is relatively unknown. Methods: Retrospective analysis was conducted on KTx recipients without AF, with at least one pre-transplant ECG between 2011 and 2021 across three tertiary centers in the United States (Mayo Clinic sites in Minnesota, Arizona, and Florida). A previously validated AI-ECG algorithm estimated the probability of incident AF for each patient. Based on AI-ECG probabilities, patients were categorized into high and low risk groups, with the optimal AI-ECG score cut-off determined. The incidence of new-onset AF, allograft failure, and mortality were compared between groups. Results: Overall, 6246 patients (age 53.5 ± 13.8 years; 58.9% male) were included. Pre-transplant AI-ECG probability of AF ≥5% was the optimal cutoff for high risk of incident AF (sensitivity 72%, specificity 62%). High risk scores were associated with true new-onset AF at 30 days (aHR 2.89, 95%CI 2.05-4.09, p<0.001), three years (aHR 2.54, 95%CI 1.99-3.26, p<0.001), and five years post-transplant (aHR 2.48, 95%CI 1.99-3.09, p<0.001). High risk AI-ECG scores were also associated with increased mortality (aHR 1.56, 95%CI 1.30-1.88, p<0.001) and overall allograft failure (aHR 1.50, 95%CI 1.30-1.75, p<0.001) through five year follow-up. Conclusions: This pre-transplant AI-ECG parameter identified patients at increased risk of new-onset AF post-KTx and provided prognostic utility. Overall, this easy to obtain tool allows for risk stratification of patients who may benefit from closer monitoring, targeted risk factor modification, and early intervention.