| Title: | Non-invasive chronic kidney disease risk stratification tool derived from retina-based deep learning and clinical factors |
| Journal: | npj Digital Medicine |
| Published: | 17 Jun 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37330576/ |
| DOI: | https://doi.org/10.1038/s41746-023-00860-5 |
| URL: | https://www.nature.com/articles/s41746-023-00860-5.pdf |
| Citations: | 8 (8 in last 2 years) as of 8 Aug 2024 |
Publication 10496
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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.</p>
10 Authors
- 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
1 Application
| Application ID | Title |
|---|---|
| 68428 | Analyses of systemic ageing biomarkers related to retina via deep learning |
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