대한신장학회

Skip Navigation: Skip to contents

KOR
ENG
전체메뉴보기

간행물 검색

현재 페이지 경로

HOME
간행물
간행물 검색

논문분류	춘계학술대회 초록집
제목	Machine Learning Survival Analysis for Predicting Kidney Disease Progression in Patients With Acute Kidney Injury Undergoing Continuous Kidney Replacement Therapy: an Analysis of the LINKA Database
저자	Donghwan Yun
출판정보	2025; 2025(1):
키워드	machine learning, survival analysis, continuous kidney replacement therapy, end-stage kidney disease, risk prediction
초록	Progression to end-stage kidney disease (ESKD) from acute kidney injury (AKI) presents challenges due to high comorbidity risks and poor outcomes. This study aimed to develop and validate machine learning survival models for predicting ESKD in patients receiving continuous kidney replacement therapy (CKRT) for AKI. A total of 1,444 AKI patients who survived beyond one week after CKRT were included. Data from six hospitals were used to develop the model, while data from two hospitals formed the validation cohort. A comprehensive set of 122 clinical and laboratory variables was utilized to construct prediction models, including CoxBoost, Elastic-Net Cox, Random Survival Forest, and Cox proportional hazards models. Model performance was assessed using the concordance index (C-index). The CoxBoost model demonstrated superior performance, achieving a C-index of 0.881 (95% confidence interval, 0.756–0.865) in internal validation and 0.742 (0.700–0.788) in external validation. This model reduced the variable set to 23 key parameters, with 24-hour urine output on day 7 of CKRT and pre-existing chronic kidney disease identified as the most critical predictors. A simplified scoring system derived from six binarized variables effectively stratified patients into low-, intermediate-, and high-risk groups for ESKD progression. This machine learning survival approach highlights the importance of specific clinical variables in risk assessment and may aid in personalized interventions and resource allocation to improve outcomes for AKI patients receiving CKRT.
원문(PDF)	PDF 원문보기

목록

위로가기