Predicting High-Risk CKD in Japanese People: Model Development and Validation.

Authors

Takaaki Kosugi, Masahiro Eriguchi, Hisako Yoshida, Hikari Tasaki, Masaru Matsui, Kunitoshi Iseki, Shouichi Fujimoto, Tsuneo Konta, Toshiki Moriyama, Kunihiro Yamagata, Ichiei Narita, Masato Kasahara, Yugo Shibagaki, Masahide Kondo, Koichi Asahi, Tsuyoshi Watanabe, Kazuhiko Tsuruya

Published in

Kidney international reports. Volume 11. Issue 10. Pages 107015. Epub Aug 12, 2026.

Abstract

Validated risk equations are recommended to identify patients at high risk of chronic kidney disease (CKD) progression; however, few models are applicable for early identification in primary care. This study developed and validated a predictive model to estimate the risk of progression to high-risk CKD according to the Kidney Disease: Improving Global Outcomes (KDIGO) risk classification.
This study used a nationwide Japanese health checkup cohort (2008-2014). The primary outcome was meeting the KDIGO high-risk CKD criteria at 5 years. The variables were selected using the least absolute shrinkage and selection operator method. The model was developed using Cox regression analysis in the Western Japan cohort and externally validated in the Eastern Japan cohort. A nomogram was constructed on the basis of this model to facilitate its clinical application. Net benefit was assessed using decision curve analysis.
The development and validation cohorts included 295,083 (13,453 events) and 119,225 (4593 events) participants, respectively. Age, sex, body mass index (BMI), systolic blood pressure (SBP), hemoglobin A1c (HbA1c), triglycerides (TG), uric acid (UA), estimated glomerular filtration rate, proteinuria, use of medication for hypertension and diabetes, smoking, and a history of stroke were selected as predictors. Discrimination and calibration were good in both the development cohort (C-index, 0.816; calibration slope, 0.998) and the validation cohort (C-index = 0.818; calibration slope = 1.042). Decision curve analysis confirmed the net benefit of the model.
A prediction model was developed and validated to facilitate the identification of individuals at high risk of CKD progression in primary care.

PMID:
42733929
Bibliographic data and abstract were imported from PubMed on 14 Sep 2026.

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