Development and evaluation of a machine learning model for post-surgical acute kidney injury in active infective endocarditis.

ABSTRACT:

Introduction

Acute kidney injury (AKI) is notably prevalent after cardiac surgery for patients with active infective endocarditis. This study aims to create a machine learning model to predict AKI in this high-risk group, improving upon existing models by focusing specifically on endocarditis-related surgeries.

Methods

We analyzed medical records from 527 patients who underwent cardiac surgery for active infective endocarditis from January 2012 to December 2023. Feature selection was performed using LASSO regression. These features informed the development of machine learning models, including logistic regression, linear and radial basis function support vector machines, XGBoost, decision trees, and random forests. The optimal model was selected based on ROC curve AUC. Model

SUBMITTER: Liu X 

PROVIDER: S-EPMC11659755 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature