Xu Lei, Zhang Yang, Zhang Jin, Xiao Wenyan, Liu Yu, Li Qi, Yang Min
The Second Department of Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Key Laboratory of Intelligent Computing and Signal Processing, Anhui University, Ministry of Education, Hefei, Anhui, China.
BMC Psychiatry. 2025 Aug 26;25(1):822. doi: 10.1186/s12888-025-07299-w.
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).
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.
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.
This study successfully constructed a BN predictive model for postoperative delirium following CABG, demonstrating robust predictive performance and high interpretability.
谵妄是冠状动脉旁路移植术（CABG）后常见的并发症。本研究旨在使用贝叶斯网络（BN）开发并验证CABG患者术后谵妄的预测模型。
分析了MIMIC-IV和eICU-CRD数据库中的数据，MIMIC-IV数据集用于模型训练和内部验证，eICU-CRD数据集用于外部验证。基于最大-最小爬山算法使用BN构建有向无环图，随后进行模型推理。使用受试者操作特征曲线下面积（AUROC）评估模型性能，并与逻辑回归、LightGBM以及基于爬山算法的BN模型进行比较。
共纳入来自MIMIC-IV数据库的3708例CABG患者和来自eICU-CRD数据库的630例患者，术后谵妄发生率分别为17%和14.9%。所开发的BN预测模型包含14个节点和22条有向边，里士满躁动镇静量表和序贯器官衰竭评估评分作为谵妄的父节点，表明网络内存在概率依赖性。该模型在内部验证队列中的AUROC为0.79，在外部验证队列中的AUROC为0.72。此外，还开发了基于BN模型的Shiny平台应用程序。
本研究成功构建了CABG术后谵妄的BN预测模型，具有强大的预测性能和高可解释性。
