A Bayesian network-based predictive model for postoperative delirium following coronary artery bypass grafting.

Background: Delirium is a common complication following coronary artery bypass grafting (CABG). This study aims to develop and validate a predictive model for postoperative delirium in CABG patients using a Bayesian Network (BN).

Methods: Data from the MIMIC-IV and eICU-CRD databases were analyzed, with the MIMIC-IV dataset used for model training and internal validation, and the eICU-CRD dataset for external validation. A directed acyclic graph was constructed using BN based on the Max-Min Hill-Climbing algorithm, followed by model inference. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC) and compared with logistic regression, LightGBM, and a BN model based on the Hill-Climbing algorithm.

Results: A total of 3,708 CABG patients from the MIMIC-IV database and 630 from the eICU-CRD database were included, with postoperative delirium incidence rates of 17% and 14.9%, respectively. The developed BN predictive model comprises 14 nodes and 22 directed edges, with Richmond Agitation-Sedation Scale and Sequential Organ Failure Assessment score appearing as parent nodes of delirium, indicating a probabilistic dependency within the network. The model achieved an AUROC of 0.79 in the internal validation cohort and 0.72 in the external validation cohort. Additionally, a Shiny platform application based on the BN model was developed.

Conclusions: This study successfully constructed a BN predictive model for postoperative delirium following CABG, demonstrating robust predictive performance and high interpretability.