TY - JOUR
T1 - Physiological Age by Artificial Intelligence-Enhanced Electrocardiograms as a Novel Risk Factor of Mortality in Kidney Transplant Candidates
AU - Lorenz, Elizabeth C.
AU - Zaniletti, Isabella
AU - Johnson, Bradley K.
AU - Petterson, Tanya M.
AU - Kremers, Walter K.
AU - Schinstock, Carrie A.
AU - Amer, Hatem
AU - Cheville, Andrea L.
AU - Lebrasseur, Nathan K.
AU - Winkelmayer, Wolfgang C.
AU - Navaneethan, Sankar D.
AU - Baez-Suarez, Abraham
AU - Attia, Zachi I.
AU - Lopez-Jimenez, Francisco
AU - Friedman, Paul A.
AU - Kennedy, Cassie C.
AU - Rule, Andrew D.
N1 - Publisher Copyright:
© 2023 Lippincott Williams and Wilkins. All rights reserved.
PY - 2023/6/1
Y1 - 2023/6/1
N2 - Background. Mortality risk assessment before kidney transplantation (KT) is imperfect. An emerging risk factor for death in nontransplant populations is physiological age as determined by the application of artificial intelligence to the electrocardiogram (ECG). The aim of this study was to examine the relationship between ECG age and KT waitlist mortality. Methods. We applied a previously developed convolutional neural network to the ECGs of KT candidates evaluated 2014 to 2019 to determine ECG age. We used a Cox proportional hazard model to examine whether ECG age was associated with waitlist mortality. Results. Of the 2183 patients evaluated, 59.1% were male, 81.4% were white, and 11.4% died during follow-up. Mean ECG age was 59.0 ± 12.0 y and mean chronological age at ECG was 53.3 ± 13.6 y. After adjusting for chronological age, comorbidities, and other characteristics associated with mortality, each increase in ECG age of >10 y than the average ECG age for patients of a similar chronological age was associated with an increase in mortality risk (hazard ratio 3.59 per 10-y increase; 95% confidence interval, 2.06-5.72; P < 0.0001). Conclusions. ECG age is a risk factor for KT waitlist mortality. Determining ECG age through artificial intelligence may help guide risk-benefit assessment when evaluating candidates for KT.
AB - Background. Mortality risk assessment before kidney transplantation (KT) is imperfect. An emerging risk factor for death in nontransplant populations is physiological age as determined by the application of artificial intelligence to the electrocardiogram (ECG). The aim of this study was to examine the relationship between ECG age and KT waitlist mortality. Methods. We applied a previously developed convolutional neural network to the ECGs of KT candidates evaluated 2014 to 2019 to determine ECG age. We used a Cox proportional hazard model to examine whether ECG age was associated with waitlist mortality. Results. Of the 2183 patients evaluated, 59.1% were male, 81.4% were white, and 11.4% died during follow-up. Mean ECG age was 59.0 ± 12.0 y and mean chronological age at ECG was 53.3 ± 13.6 y. After adjusting for chronological age, comorbidities, and other characteristics associated with mortality, each increase in ECG age of >10 y than the average ECG age for patients of a similar chronological age was associated with an increase in mortality risk (hazard ratio 3.59 per 10-y increase; 95% confidence interval, 2.06-5.72; P < 0.0001). Conclusions. ECG age is a risk factor for KT waitlist mortality. Determining ECG age through artificial intelligence may help guide risk-benefit assessment when evaluating candidates for KT.
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U2 - 10.1097/TP.0000000000004504
DO - 10.1097/TP.0000000000004504
M3 - Article
C2 - 36780487
AN - SCOPUS:85160008026
SN - 0041-1337
VL - 107
SP - 1365
EP - 1372
JO - Transplantation
JF - Transplantation
IS - 6
ER -