Development of a machine learning-based prediction model for extremely rapid decline in estimated glomerular filtration rate in patients with chronic kidney disease: a retrospective cohort study using a large data set from a hospital in Japan.

ABSTRACT:

Objectives

Trajectories of estimated glomerular filtration rate (eGFR) decline vary highly among patients with chronic kidney disease (CKD). It is clinically important to identify patients who have high risk for eGFR decline. We aimed to identify clusters of patients with extremely rapid eGFR decline and develop a prediction model using a machine learning approach.

Design

Retrospective single-centre cohort study.

Settings

Tertiary referral university hospital in Toyoake city, Japan.

Participants

A total of 5657 patients with CKD with baseline eGFR of 30 mL/min/1.73 m² and eGFR decline of ≥30% within 2 years.

Primary outcome

Our main outcome was extremely rapid eGFR decline. To study-complicated eGFR behaviours, we first applied a variation of

SUBMITTER: Inaguma D 

PROVIDER: S-EPMC9185577 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature