Machine learning to predict post-operative acute kidney injury stage 3 after heart transplantation.

ABSTRACT:

Background

Acute kidney injury (AKI) stage 3, one of the most severe complications in patients with heart transplantation (HT), is associated with substantial morbidity and mortality. We aimed to develop a machine learning (ML) model to predict post-transplant AKI stage 3 based on preoperative and perioperative features.

Methods

Data from 107 consecutive HT recipients in the provincial center between 2018 and 2020 were included for analysis. Logistic regression with L2 regularization was used for the ML model building. The predictive performance of the ML model was assessed using the area under the curve (AUC) in tenfold stratified cross-validation and was compared with that of the Cleveland-clinical model.

Results

Post-transplant AKI occurred in 76 (71.0%) patients i

SUBMITTER: Li T 

PROVIDER: S-EPMC9233761 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature