ArticleBMC medical informatics and decision making2025
ECG-based deep learning for chronic kidney disease detection and cardiovascular risk prediction.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Bridging the gap from clinical to home ECG: quantifying and overcoming accuracy loss in AI-enabled single-lead ECG models.NPJ digital medicine · 2026Article
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5 authors.
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Abstract
backgroundChronic kidney disease (CKD) is a global health burden with low awareness among both patients and healthcare providers. Deep learning models (DLMs) have shown promise in interpreting electrocardiograms (ECGs) for various disease and may offer new opportunities for early CKD detection.
methodsWe enrolled 66,587 outpatients with estimated glomerular filtration rate (eGFR) data from January 2010 to October 2020. A total of 72,618 ECGs from 49,632 patients were used to develop DLMs. Internal validation was performed on 16,955 nonoverlapping patients, and external validation involved 10,476 patients from a community hospital. The primary outcome was the detection of CKD, defined as eGFR < 60 mL/min/1.73 m². Secondary outcomes included all-cause mortality and major cardiovascular events.
resultsThe DLM achieved an AUC of 0.885 and 0.861 in the internal and external validation sets, respectively. Patients flagged by the DLM as having CKD showed more clinical risk factors for CKD progression and cardiovascular disease. Among patients without baseline CKD, those with a positive DLM screen had a significantly higher risk of incident CKD (hazard ratios 2.14 and 1.38; 95% CIs: 1.76-2.60 and 1.09-1.74). DLM stratification also predicted adverse outcomes such as stroke, heart failure, and atrial fibrillation more effectively than eGFR classification alone.
conclusionAn ECG-based deep learning model can help identify individuals at risk for CKD and its complications, even before laboratory abnormalities emerge. This approach may support early detection and risk stratification in clinical practice. CLINICAL TRIAL NUMBER: Not applicable.
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