Development of postoperative delirium prediction models in patients undergoing cardiovascular surgery using machine learning algorithms.

ABSTRACT: Associations between delirium and postoperative adverse events in cardiovascular surgery have been reported and the preoperative identification of high-risk patients of delirium is needed to implement focused interventions. We aimed to develop and validate machine learning models to predict post-cardiovascular surgery delirium. Patients aged ≥ 40 years who underwent cardiovascular surgery at a single hospital were prospectively enrolled. Preoperative and intraoperative factors were assessed. Each patient was evaluated for postoperative delirium 7 days after surgery. We developed machine learning models using the Bernoulli naive Bayes, Support vector machine, Random forest, Extra-trees, and XGBoost algorithms. Stratified fivefold cross-validation was performed for each developed model. Of t

SUBMITTER: Nagata C 

PROVIDER: S-EPMC10689441 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature