Risk prediction for cardiovascular events and all-cause mortality in maintenance hemodialysis patients.

ABSTRACT:

Objective

This study is designed to develop predictive models for cardiovascular events (CVE) and all-cause mortality in maintenance hemodialysis (MHD) patients using machine learning (ML) algorithms. Furthermore, we aim to compare the performance of these ML-based models with that of traditional Cox regression models.

Methods

We conducted a retrospective study that included 275 patients who underwent MHD treatment from January 1, 2020, to January 1, 2022. We collected comprehensive data on their demographic characteristics, comorbidities, medication history, and baseline laboratory values, and followed up with them throughout the study period. To develop predictive models for CVE and all-cause mortality, we employed several ML algorithms, including Logistic Regression (LR),

SUBMITTER: Cao M 

PROVIDER: S-EPMC12597953 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature