Development and Validation of a Machine Learning-Based Prognostic Model for IgA Nephropathy with Chronic Kidney Disease Stage 3 or 4.

ABSTRACT:

Introduction

Immunoglobulin A nephropathy (IgAN) patients with lower estimated glomerular filtration rate (eGFR) and higher proteinuria are at a higher risk for end-stage kidney disease (ESKD) and their prognosis is still unclear. We aim to develop and validate prognostic models in IgAN patients with chronic kidney disease (CKD) stage 3 or 4 and proteinuria ≥1.0 g/d.

Methods

Patients who came from Xijing Hospital, spanning December 2008 to January 2020 were divided into training and test cohorts randomly, with a ratio of 7:3, achieving ESKD and death as study endpoints. Created prediction models for IgAN patients based on 66 clinical and pathological characteristics using the random survival forests (RSF), survival support vector machine (SSVM), eXtreme Gradient Boosting (XG

SUBMITTER: Yu Z 

PROVIDER: S-EPMC11631042 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature