Prediction of the development of acute kidney injury following cardiac surgery by machine learning.

ABSTRACT:

Background

Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication that results in increased morbidity and mortality after cardiac surgery. Most established prediction models are limited to the analysis of nonlinear relationships and fail to fully consider intraoperative variables, which represent the acute response to surgery. Therefore, this study utilized an artificial intelligence-based machine learning approach thorough perioperative data-driven learning to predict CSA-AKI.

Methods

A total of 671 patients undergoing cardiac surgery from August 2016 to August 2018 were enrolled. AKI following cardiac surgery was defined according to criteria from Kidney Disease: Improving Global Outcomes (KDIGO). The variables used for analysis included demograph

SUBMITTER: Tseng PY 

PROVIDER: S-EPMC7395374 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature