Artificial Intelligence-Electrocardiography to Predict Incident Atrial Fibrillation and Clinical Outcomes in Kidney Transplant Recipients.
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.
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.
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.
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.
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.
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.
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.
Authors
Scalia Scalia, Farina Farina, Abdelnabi Abdelnabi, Pietri Pietri, Pathangey Pathangey, Ibrahim Ibrahim, Awad Awad, Abbas Abbas, Ali Ali, Mahmoud Mahmoud, Alsidawi Alsidawi, Steidley Steidley, El Masry El Masry, Sorajja Sorajja, Scott Scott, Lee Lee, Cho Cho, Johnson Johnson, Riad Riad, Wadei Wadei, Lester Lester, Chao Chao, Oh Oh, Ayoub Ayoub, Mour Mour, Arsanjani Arsanjani
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