Machine Learning to Develop and Internally Validate a Predictive Model for Post-operative Delirium in a Prospective, Observational Clinical Cohort Study of Older Surgical Patients.

ABSTRACT:

Background

Our objective was to assess the performance of machine learning methods to predict post-operative delirium using a prospective clinical cohort.

Methods

We analyzed data from an observational cohort study of 560 older adults (≥ 70 years) without dementia undergoing major elective non-cardiac surgery. Post-operative delirium was determined by the Confusion Assessment Method supplemented by a medical chart review (N = 134, 24%). Five machine learning algorithms and a standard stepwise logistic regression model were developed in a training sample (80% of participants) and evaluated in the remaining hold-out testing sample. We evaluated three overlapping feature sets, restricted to variables that are readily available or minimally burdensome to collect in clinical sett

SUBMITTER: Racine AM 

PROVIDER: S-EPMC7878663 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature