カタオカ　ヒロシ   KATAOKA Hiroshi   片岡　浩史    所属   医学部 医学科（東京女子医科大学病院）    職種   講師
言語種別	英語
発表タイトル	Prognostic Prediction Model for CKD Stages by Machine Learning in Patients with ADPKD: A Nationwide Cohort Study in Japan
会議名	Kidney Week 2025
主催者	American Society of Nephrology
学会区分	国際学会及び海外の学会
発表形式	ポスター掲示
講演区分	一般
発表者・共同発表者	◎Shimada Yosuke, Kataoka Hiroshi, Nishio Saori, Hoshino Junichi, Hiromura Keiju, Isaka Yoshitaka, Muto Satoru
発表年月日	2025/11/07
国名	アメリカ合衆国
開催地 （都市, 国名）	Houston, USA
開催期間	2025/11/05～2025/11/09
概要	Background Autosomal dominant polycystic kidney disease (ADPKD) is a common cause of chronic kidney disease (CKD), and frequently progresses to end-stage renal disease. Accurately predicting CKD progression in ADPKD patients is essential for personalized treatment strategies. Several prognostic prediction models have been proposed, however, the utilization of machine learning (ML) in this context has not been sufficiently explored. Therefore, this study aims to develop a ML model to predict the progression of CKD stage in ADPKD patients. Methods This study analyzed data from 2,737 ADPKD patients enrolled in the Japanese Nationwide Cohort. Using this dataset, we developed three ML models to predict CKD stages; random forest, support vector machine, and naïve Bayes. These models were evaluated for their predictive accuracy. Feature importance analysis was performed to identify key predictive variables. Results Random forest exhibited the highest predictive accuracy among the three models. Feature importance analysis identified the estimated glomerular filtration rate (eGFR), serum creatinine, the CKD heat map, urinary protein, and total kidney volume as the most significant predictors of CKD stage. As a nonlinear model, random forest effectively captured the complex interactions between the variables, outperforming the linear support vector machine. Conclusion These findings suggest that considering complex interactions among explanatory variables may be important for predicting accurate CKD stage, as demonstrated by the superior performance of the nonlinear random forest model. These findings emphasize ML’s potential in personalized CKD management and highlight the need for individualized treatment approaches.