Artificial intelligence assessment for early detection and prediction of renal impairment using electrocardiography.

ABSTRACT:

Purpose

Although renal failure is a major healthcare burden globally and the cornerstone for preventing its irreversible progression is an early diagnosis, an adequate and noninvasive tool to screen renal impairment (RI) reliably and economically does not exist. We developed an interpretable deep learning model (DLM) using electrocardiography (ECG) and validated its performance.

Methods

This retrospective cohort study included two hospitals. We included 115,361 patients who had at least one ECG taken with an estimated glomerular filtration rate measurement within 30 min of the index ECG. A DLM was developed using 96,549 ECGs of 55,222 patients. The internal validation included 22,949 ECGs of 22,949 patients. Furthermore, we conducted an external validation with 37,190 ECGs o

SUBMITTER: Kwon JM 

PROVIDER: S-EPMC9463260 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature