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

 Young Su Joo  ;  Tyler Hyungtaek Rim  ;  Hee Byung Koh  ;  Joseph Yi  ;  Hyeonmin Kim  ;  Geunyoung Lee  ;  Young Ah Kim  ;  Shin-Wook Kang  ;  Sung Soo Kim  ;  Jung Tak Park 
 NPJ DIGITAL MEDICINE(Nature partner journals digital medicine Digital medicine), Vol.6(1) : 114, 2023-06 
Journal Title
NPJ DIGITAL MEDICINE(Nature partner journals digital medicine Digital medicine)
Issue Date
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.

© 2023. The Author(s).
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1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Ophthalmology (안과학교실) > 1. Journal Papers
Yonsei Authors
Kang, Shin Wook(강신욱) ORCID logo https://orcid.org/0000-0002-5677-4756
Koh, Hee Byung(고희병)
Kim, Sung Soo(김성수) ORCID logo https://orcid.org/0000-0002-0574-7993
Park, Jung Tak(박정탁) ORCID logo https://orcid.org/0000-0002-2325-8982
Joo, Young Su(주영수) ORCID logo https://orcid.org/0000-0002-7890-0928
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