Non-invasive chronic kidney disease risk stratification tool derived from retina-based deep learning and clinical factors

Abstract

Despite the importance of preventing chronic kidney disease (CKD), predicting high-risk patients who require active intervention is challenging, especially in people with preserved kidney function. In this study, a predictive risk score for CKD (Reti-CKD score) was derived from a deep learning algorithm using retinal photographs. The performance of the Reti-CKD score was verified using two longitudinal cohorts of the UK Biobank and Korean Diabetic Cohort. Validation was done in people with preserved kidney function, excluding individuals with eGFR <90 mL/min/1.73 m² or proteinuria at baseline. In the UK Biobank, 720/30,477 (2.4%) participants had CKD events during the 10.8-year follow-up period. In the Korean Diabetic Cohort, 206/5014 (4.1%) had CKD events during the 6.1-year follow-up period. When the validation cohorts were divided into quartiles of Reti-CKD score, the hazard ratios for CKD development were 3.68 (95% Confidence Interval [CI], 2.88-4.41) in the UK Biobank and 9.36 (5.26-16.67) in the Korean Diabetic Cohort in the highest quartile compared to the lowest. The Reti-CKD score, compared to eGFR based methods, showed a superior concordance index for predicting CKD incidence, with a delta of 0.020 (95% CI, 0.011-0.029) in the UK Biobank and 0.024 (95% CI, 0.002-0.046) in the Korean Diabetic Cohort. In people with preserved kidney function, the Reti-CKD score effectively stratifies future CKD risk with greater performance than conventional eGFR-based methods.

Fig. 1. Augmented saliency map according to deep-learning-derived retina-CKD probability.

Augmented saliency maps according to quartiles of deep-learning-derived retina-CKD probability in the a UK Biobank and b Korean Diabetic Cohort are shown. Saliency is represented in color-scale (scaled between 0 and 255). Highlighted areas along the arcade vessels were more prominent in images with higher deep-learning-derived retina-CKD probability.

Fig. 2. Cumulative incidence of chronic kidney disease events according to Reti-CKD score quartiles in the UK Biobank and Korean Diabetic Cohort.

Cumulative chronic kidney disease (CKD) incidences are illustrated according to the Reti-CKD score quartile in the a UK Biobank and b Korean Diabetic Cohort. There was a clear association between CKD development and the Reti-CKD scores in both cohorts.

Fig. 3. Study flow chart for derivation and validation of Reti-CKD score.

In phase 1, health-screening center data was used for the development of deep-learning algorithm. In phase 2, data from longitudinal cohorts were utilized for derivation and validation of Reti-CKD score. Reti-CKD score was derived based on a Cox model using the UK Biobank cohort. The performance of Reti-CKD score was subsequently validated using the UK Biobank and Korean Diabetic Cohort.