Citations 1
Artificial Intelligence–Electrocardiography to Predict Incident Atrial Fibrillation and Clinical Outcomes in Kidney Transplant Recipients
Scalia, Isabel G., Farina, Juan M., Abdelnabi, Mahmoud H., Pietri, Milagros Pereyra, Ibrahim, Ramzi, Awad, Kamal, Abbas, Mohammed Tiseer , Ali, Nima Baba , Mahmoud, Ahmed K., Steidley, D. Eric, Masry, Hicham El , Sorajja, Dan , Scott, Luis R., Lee, Kwan, Johnson, D. W., Riad, Samy M. , Lester, Steven J., Jk, Oh, Ayoub, Chadi, Arsanjani, Reza, Farina, Juan M. , Ibrahim, Ramzi , Scalia, Isabel G. , Scalia, Isabel G , Farina, Juan M , Pereyra Pietri, Milagros, Pathangey, Girish , Ibrahim, Ramzi , Tiseer Abbas, Mohammed, Baba Ali, Nima , Alsidawi, Said , El Masry, Hicham , Sorajja, Dan , Scott, Luis R , Lee, Kwan S , Cho, Yeoungjee , Johnson, David W , Wadei, Hani M , Chao, Chieh-Ju , Mour, Girish K , Arsanjani, Reza , Abbas, Mohammed Tiseer , Pereyra, Milagros , Pathangey, Girish , Awad, Kamal , Mahmoud, Ahmed , Alsidawi, Said , Steidley, D. Eric , Scott, Luis R. , Cho, Yeoungjee , Wadei, Hani M. , Chao, Chieh‐Ju , Mour, Girish , Arsanjani, Reza
Kidney360 | February 4, 2026 | Volume: 7 | Issue: 5 | Pages: 1128-1140
Key Points Atrial fibrillation after kidney transplantation is common and is associated with poor prognosis. Artificial intelligence applied to 12-lead electrocardiograms pretransplant accurately predicts the risk of new-onset atrial fibrillation in our cohort. High-risk artificial intelligence electrocardiography score was independently associated with increased risk of mortality and allograft failure. Background Incident atrial fibrillation (AF) is common after 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 pretransplant ECG between 2011 and 2021 across three tertiary centers in the United States (Mayo Clinic sites in MN, AZ, and FL). 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-risk and low-risk groups, with the optimal AI-ECG score cutoff determined. The incidence of new-onset AF, allograft failure, and mortality was compared between groups. Results Overall, 6246 patients (age 53.5±13.8 years; 58.9% male) were included. Pretransplant 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 (adjusted hazard ratio [aHR], 2.89; 95% confidence intervals [CI], 2.05 to 4.09; P < 0.001), 3 years (aHR, 2.54; 95% CI, 1.99 to 3.26, P < 0.001), and 5 years post-transplant (aHR, 2.48; 95% CI, 1.99 to 3.09, P < 0.001). High-risk AI-ECG scores were also associated with increased mortality (aHR, 1.56; 95% CI, 1.30 to 1.88, P < 0.001) and overall allograft failure (aHR, 1.50; 95% CI, 1.30 to 1.75, P < 0.001) through the 5-year follow-up. Conclusions This pretransplant 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.