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논문분류	춘계학술대회 초록집
제목	Impact of retinal photography-based deep learning system on risk stratification for chronic kidney disease progression
저자	Young Su Joo
출판정보	2024; 2024(1):
키워드
초록	Objectives: We had previously developed a deep-learning-based risk evaluation system from retinal photographs, Reti-CKD, for stratifying chronic kidney development risk in kidney function preserved people. This study aims to evaluate whether Reti-CKD can improve risk assessment of kidney disease progression in diabetic patients with prevalent CKD. Methods: Total of 5348 diabetic patients from two tertiary hospitals in Korea were evaluated. Patients with estimated glomerular filtration rate (eGFR) <90 ml/min/1.73m2 or albuminuria were included. Those with missing data for retinal photograph, serum creatinine, or albuminuria were excluded. Patients were categorized into low-risk, moderate-risk, and high-risk groups according to the KDIGO criteria for prognosis of CKD. The KDIGO groups were additionally dichotomized based on Reti-CKD score (Reti-CKD <20 and ≥20). CKD progression was compared between the categories using Cox regression models. Primary outcome was CKD progression, defined as incremental progression to a higher NKF-KDOQI CKD stage. Results: The mean age of the patients was 62.4 ± 11.4 years and 60.6% were male. Mean eGFR was 86.6 ± 15.3 mL/min per 1.73 m2 and albuminuria was present in 46.9%. During a median follow-up of 5.0 (interquartile range, 2.5-7.8) years, primary outcome developed in 1379 (25.8%) patients. The primary outcome incidence rate gradually increased with higher KDIGO and Reti-CKD combined risk categories. The risk for CKD progression progressively increased in KDIG moderate-risk and high-risk groups compared to low-risk. When Reti-CKD was incorporated to the KDIGO category, significant stratification of CKD progression risk was noted in the KDIGO low-risk and moderate-risk groups (Table 1 and figure 1). Additionally, the combination of KDIGO and Reti-CKD classification showed better discrimination power compared to the KDIGO only classification (delta c-statistics, 0.03; 95% CI 0.02 to 0.040). Conclusions: Retinal photography-based deep learning system (Reti-CKD) further stratifies the risk of CKD progression and improves predictability in diabetic patients with reduced renal function.
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