Machine learning-based 2-year risk prediction tool in immunoglobulin A nephropathy.

ABSTRACT:

Background

This study aimed to develop a machine learning-based 2-year risk prediction model for early identification of patients with rapid progressive immunoglobulin A nephropathy (IgAN). We also assessed the model's performance to predict the long-term kidney-related outcome of patients.

Methods

A retrospective cohort of 1,301 patients with biopsy-proven IgAN from two tertiary hospitals was used to derive and externally validate a random forest-based prediction model predicting primary outcome (30% decline in estimated glomerular filtration rate from baseline or end-stage kidney disease requiring renal replacement therapy) and secondary outcome (improvement of proteinuria) within 2 years after kidney biopsy.

Results

For the 2-year prediction of primary outcomes, pr

SUBMITTER: Kim Y 

PROVIDER: S-EPMC11615444 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature