Machine learning-based prediction model for postoperative delirium in non-cardiac surgery.

ABSTRACT:

Background

Postoperative delirium is a common complication that is distressing. This study aimed to demonstrate a prediction model for delirium.

Methods

Among 203,374undergoing non-cardiac surgery between January 2011 and June 2019 at Samsung Medical Center, 2,865 (1.4%) were diagnosed with postoperative delirium. After comparing performances of machine learning algorithms, we chose variables for a prediction model based on an extreme gradient boosting algorithm. Using the top five variables, we generated a prediction model for delirium and conducted an external validation. The Kaplan-Meier and Cox survival analyses were used to analyse the difference of delirium occurrence in patients classified as a prediction model.

Results

The top five variables selected for the p

SUBMITTER: Lee DY 

PROVIDER: S-EPMC10161528 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature