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 of 37,190 patients from another hospital. The endpoint was to detect a moderate to severe RI (estimated glomerular filtration rate < 45 ml/min/1.73m²).

Results: The area under the receiver operating characteristic curve (AUC) of a DLM using a 12-lead ECG for detecting RI during the internal and external validation was 0.858 (95% confidence interval 0.851-0.866) and 0.906 (0.900-0.912), respectively. In the initial evaluation of 25,536 individuals without RI patients whose DLM was defined as having a higher risk had a significantly higher chance of developing RI than those in the low-risk group (17.2% vs. 2.4%, p < 0.001). The sensitivity map indicated that the DLM focused on the QRS complex and T-wave for detecting RI.

Conclusion: The DLM demonstrated high performance for RI detection and prediction using 12-, 6-, single-lead ECGs.

Keywords: Artificial intelligence; Deep learning; Electrocardiography; Renal insufficiency.

Fig. 1

Architecture of deep learning based model for detecting renal impairment. Legend: 1D denotes 1-dimension and Conv convolution neural network

Fig. 2

Performances of deep learning-based model for detecting renal impairment. Legend: †The alternative hypothesis for this p value was that there was a difference of AUC between the 12-lead ECG model and others. AUC denotes area under the receiver operating characteristic curve, ECG electrocardiography, EGFR estimated glomerular filtration rate, NPV negative predictive value, PPV positive predictive value, SEN sensitivity, and SPE specificity

Fig. 3

Sensitivity map of deep learning based model for detecting renal impairment

Fig. 4

Cumulative risk of patients with no initial renal impairment developing renal impairment