Treffer: 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.
(© 2025. The Author(s).)

Declarations. Ethics approval and consent to participate: The study analyzed data obtained from the MIMIC-IV and eICU-CRD public databases. The data used in this study were de-identified and publicly available, ensuring that patient confidentiality and privacy were maintained. The Institutional Review Boards (IRB) of the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center provided ethical approval for the use of these databases in research. The IRBs of the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center granted a waiver of informed consent due to the retrospective nature of the study and the use of de-identified data. Details of the databases are available at the following links: MIMIC-IV: https://mimic.mit.edu, eICU-CRD: https://eicu-crd.mit.edu. Consent for publication: All the authors agree to the publication of this work. Competing interests: The authors declare no competing interests.