Cited 3 times in
Non-invasive chronic kidney disease risk stratification tool derived from retina-based deep learning and clinical factors
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 강신욱 | - |
dc.contributor.author | 김성수 | - |
dc.contributor.author | 박정탁 | - |
dc.contributor.author | 주영수 | - |
dc.contributor.author | 고희병 | - |
dc.date.accessioned | 2023-07-12T03:05:29Z | - |
dc.date.available | 2023-07-12T03:05:29Z | - |
dc.date.issued | 2023-06 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/195494 | - |
dc.description.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. © 2023. The Author(s). | - |
dc.description.statementOfResponsibility | open | - |
dc.format | application/pdf | - |
dc.language | English | - |
dc.publisher | Nature Publishing Group | - |
dc.relation.isPartOf | NPJ DIGITAL MEDICINE(Nature partner journals digital medicine Digital medicine) | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Non-invasive chronic kidney disease risk stratification tool derived from retina-based deep learning and clinical factors | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Internal Medicine (내과학교실) | - |
dc.contributor.googleauthor | Young Su Joo | - |
dc.contributor.googleauthor | Tyler Hyungtaek Rim | - |
dc.contributor.googleauthor | Hee Byung Koh | - |
dc.contributor.googleauthor | Joseph Yi | - |
dc.contributor.googleauthor | Hyeonmin Kim | - |
dc.contributor.googleauthor | Geunyoung Lee | - |
dc.contributor.googleauthor | Young Ah Kim | - |
dc.contributor.googleauthor | Shin-Wook Kang | - |
dc.contributor.googleauthor | Sung Soo Kim | - |
dc.contributor.googleauthor | Jung Tak Park | - |
dc.identifier.doi | 10.1038/s41746-023-00860-5 | - |
dc.contributor.localId | A00053 | - |
dc.contributor.localId | A00571 | - |
dc.contributor.localId | A01654 | - |
dc.contributor.localId | A03956 | - |
dc.relation.journalcode | J03796 | - |
dc.identifier.eissn | 2398-6352 | - |
dc.identifier.pmid | 37330576 | - |
dc.contributor.alternativeName | Kang, Shin Wook | - |
dc.contributor.affiliatedAuthor | 강신욱 | - |
dc.contributor.affiliatedAuthor | 김성수 | - |
dc.contributor.affiliatedAuthor | 박정탁 | - |
dc.contributor.affiliatedAuthor | 주영수 | - |
dc.citation.volume | 6 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 114 | - |
dc.identifier.bibliographicCitation | NPJ DIGITAL MEDICINE(Nature partner journals digital medicine Digital medicine), Vol.6(1) : 114, 2023-06 | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.