Prediction of acute kidney injury risk after cardiac surgery: using a hybrid machine learning algorithm.

ABSTRACT:

Background

Acute kidney injury (AKI) is a serious complication after cardiac surgery. We derived and internally validated a Machine Learning preoperative model to predict cardiac surgery-associated AKI of any severity and compared its performance with parametric statistical models.

Methods

We conducted a retrospective study of adult patients who underwent major cardiac surgery requiring cardiopulmonary bypass between November 1st, 2009 and March 31st, 2015. AKI was defined according to the KDIGO criteria as stage 1 or greater, within 7 days of surgery. We randomly split the cohort into derivation and validation datasets. We developed three AKI risk models: (1) a hybrid machine learning (ML) algorithm, using Random Forests for variable selection, followed by high performance

SUBMITTER: Petrosyan Y 

PROVIDER: S-EPMC9118758 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature