Publication Date
6-1-2023
Journal
Transplantation
DOI
10.1097/TP.0000000000004504
PMID
36780487
PMCID
PMC10205652
PubMedCentral® Posted Date
6-1-2024
PubMedCentral® Full Text Version
Author MSS
Published Open-Access
yes
Keywords
Humans, Male, Female, Kidney Transplantation, Artificial Intelligence, Risk Factors, Risk Assessment, Electrocardiography
Abstract
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.
Included in
Diseases Commons, Infectious Disease Commons, Medical Sciences Commons, Nephrology Commons
Comments
Associated Data