Deep learning-based electrocardiographic screening for chronic kidney disease.

ABSTRACT:

Background

Undiagnosed chronic kidney disease (CKD) is a common and usually asymptomatic disorder that causes a high burden of morbidity and early mortality worldwide. We developed a deep learning model for CKD screening from routinely acquired ECGs.

Methods

We collected data from a primary cohort with 111,370 patients which had 247,655 ECGs between 2005 and 2019. Using this data, we developed, trained, validated, and tested a deep learning model to predict whether an ECG was taken within one year of the patient receiving a CKD diagnosis. The model was additionally validated using an external cohort from another healthcare system which had 312,145 patients with 896,620 ECGs between 2005 and 2018.

Results

Using 12-lead ECG waveforms, our deep learning algorithm achieve

SUBMITTER: Holmstrom L 

PROVIDER: S-EPMC10220039 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature