Prediction models for postoperative delirium in elderly patients with machine-learning algorithms and SHapley Additive exPlanations.

ABSTRACT: Postoperative delirium (POD) is a common and severe complication in elderly patients with hip fractures. Identifying high-risk patients with POD can help improve the outcome of patients with hip fractures. We conducted a retrospective study on elderly patients (≥65 years of age) who underwent orthopedic surgery with hip fracture between January 2014 and August 2019. Conventional logistic regression and five machine-learning algorithms were used to construct prediction models of POD. A nomogram for POD prediction was built with the logistic regression method. The area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, and precision were calculated to evaluate different models. Feature importance of individuals was interpreted using Shapley Additive Explanati

SUBMITTER: Song Y 

PROVIDER: S-EPMC10808214 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature