Anesthetic Management Recommendations Using a Machine Learning Algorithm to Reduce the Risk of Acute Kidney Injury After Cardiac Surgeries.

ABSTRACT:

Background

Open heart surgeries are a common surgical approach among patients with heart disease. Acute kidney injury (AKI) is one of the most common postoperative complications following cardiac surgeries, with an average incidence of 6 - 10%. Additionally, AKI has a mortality rate of 5 - 10%. One of the challenges of cardiac surgeries is selecting the appropriate anesthetic approaches to reduce the risk of AKI.

Objectives

This study presents a machine learning-based method that consists of two regression models. These models can inform the anesthesiologist about the risk of AKI resulting from the improper selection of anesthetic parameters.

Methods

In this cohort study, the medical records of 998 patients who underwent cardiac surgery were collected. The proposed me

SUBMITTER: Abin AA 

PROVIDER: S-EPMC11474233 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature