Department of Neurology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Department of Cardiovascular surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
BMC Med Inform Decis Mak. 2024 Nov 19;24(1):349. doi: 10.1186/s12911-024-02762-2.
This study aimed to identify the risk factors of acute ischemic stroke (AIS) occurring during hospitalization in patients following off-pump coronary artery bypass grafting (OPCABG) and utilize Bayesian network (BN) methods to establish predictive models for this disease.
Data were collected from the electronic health records of adult patients who underwent OPCABG at Beijing Anzhen Hospital from January 2018 to December 2022. Patients were allocated to the training and test sets in an 8:2 ratio according to the principle of randomness. Subsequently, a BN model was established using the training dataset and validated against the testing dataset. The BN model was developed using a tabu search algorithm. Finally, receiver operating characteristic (ROC) and calibration curves were plotted to assess the extent of disparity in predictive performance between the BN and logistic models.
A total of 10,184 patients (mean (SD) age, 62.45 (8.7) years; 2524 (24.7%) females) were enrolled, including 151 (1.5%) with AIS and 10,033 (98.5%) without AIS. Female sex, history of ischemic stroke, severe carotid artery stenosis, high glycated albumin (GA) levels, high D-dimer levels, high erythrocyte distribution width (RDW), and high blood urea nitrogen (BUN) levels were strongly associated with AIS. Type 2 diabetes mellitus (T2DM) was indirectly linked to AIS through GA and BUN. The BN models exhibited superior performance to logistic regression in both the training and testing sets, achieving accuracies of 72.64% and 71.48%, area under the curve (AUC) of 0.899 (95% confidence interval (CI), 0.876-0.921) and 0.852 (95% CI, 0.769-0.935), sensitivities of 91.87% and 89.29%, and specificities of 72.35% and 71.24% (using the optimal cut-off), respectively.
Female gender, IS history, carotid stenosis (> 70%), RDW-CV, GA, D-dimer, BUN, and T2DM are potential predictors of IS in our Chinese cohort. The BN model demonstrated greater efficiency than the logistic regression model. Hence, employing BN models could be conducive to the early diagnosis and prevention of AIS after OPCABG.
本研究旨在确定非体外循环冠状动脉旁路移植术（OPCABG）后住院期间发生急性缺血性脑卒中（AIS）的风险因素，并利用贝叶斯网络（BN）方法建立该疾病的预测模型。
本研究从 2018 年 1 月至 2022 年 12 月在北京安贞医院接受 OPCABG 的成年患者的电子健康记录中收集数据。患者按照随机性原则分为训练集和测试集，比例为 8:2。随后，使用训练数据集建立 BN 模型，并在测试数据集上进行验证。BN 模型采用禁忌搜索算法开发。最后，绘制受试者工作特征（ROC）和校准曲线，以评估 BN 模型和逻辑模型在预测性能上的差异程度。
共纳入 10184 例患者（平均年龄 62.45（8.7）岁；2524 例[24.7%]为女性），其中 151 例（1.5%）发生 AIS，10033 例（98.5%）未发生 AIS。女性、缺血性脑卒中史、严重颈动脉狭窄、糖化白蛋白（GA）水平高、D-二聚体水平高、红细胞分布宽度（RDW）高和血尿素氮（BUN）水平高与 AIS 强烈相关。2 型糖尿病（T2DM）通过 GA 和 BUN 间接与 AIS 相关。BN 模型在训练集和测试集上的表现均优于逻辑回归，在训练集和测试集上的准确率分别为 72.64%和 71.48%，曲线下面积（AUC）分别为 0.899（95%置信区间[CI]，0.876-0.921）和 0.852（95%CI，0.769-0.935），敏感度分别为 91.87%和 89.29%，特异度分别为 72.35%和 71.24%（使用最佳截断值）。
本研究表明，在我国人群中，女性、IS 病史、颈动脉狭窄（＞70%）、RDW-CV、GA、D-二聚体、BUN 和 T2DM 是 OPCABG 后发生 IS 的潜在预测因素。BN 模型的效率优于逻辑回归模型。因此，采用 BN 模型有助于 AIS 的早期诊断和预防。
