Postoperative delirium prediction using machine learning models and preoperative electronic health record data.

ABSTRACT:

Background

Accurate, pragmatic risk stratification for postoperative delirium (POD) is necessary to target preventative resources toward high-risk patients. Machine learning (ML) offers a novel approach to leveraging electronic health record (EHR) data for POD prediction. We sought to develop and internally validate a ML-derived POD risk prediction model using preoperative risk features, and to compare its performance to models developed with traditional logistic regression.

Methods

This was a retrospective analysis of preoperative EHR data from 24,885 adults undergoing a procedure requiring anesthesia care, recovering in the main post-anesthesia care unit, and staying in the hospital at least overnight between December 2016 and December 2019 at either of two hospitals in a

SUBMITTER: Bishara A 

PROVIDER: S-EPMC8722098 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature