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危险因素和预测模型,急性缺血性中风后,不抽动脉冠状动脉旁路移植基于贝叶斯网络的贝叶斯网络

Wenlong Zou¹, Haipeng Zhao², Ming Ren³

¹Department of Neurology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.

BMC medical informatics and decision making

|November 20, 2024

概括

贝叶斯网络模型可以预测急性缺血性中风 (AIS) 在非抽动冠状动脉旁路移植 (OPCABG) 后. 关键的危险因素包括女性性别,中风史,大脑动脉狭窄症以及高的GA,D-二聚体,RDW和BUN水平.

科学领域:

心血管外科心血管外科
神经学神经学
医疗信息学医疗信息学

背景情况:

急性缺血性中风 (AIS) 是一种潜在的并发症,它是在非抽动冠状动脉旁路移植 (OPCABG) 之后出现的.
识别风险因素和开发OPCABG后AIS的预测模型对于患者管理至关重要.

研究的目的:

在接受OPCABG的患者中识别AIS的风险因素.
使用贝叶斯网络 (BN) 方法开发和验证AIS的预测模型.

主要方法:

从10184名接受OPCABG的成年患者的电子健康记录的回顾性分析.
在80%的训练集上使用 tabu 搜索算法开发 BN 模型.
对20%的测试集进行BN模型的验证,将其性能与使用ROC和校准曲线的逻辑回归模型进行比较.

主要成果:

151名患者 (1.5%) 在OPCABG后发展出AIS.
对AIS的显著危险因素包括女性性别,有史以来的缺血性中风,严重的动脉狭窄,以及高的糖性白蛋白 (GA),D-二次体,红细胞分布宽度 (RDW) 和血液尿素 (BUN) 水平.
BN模型的性能优于逻辑回归模型,在训练和测试数据集中实现了更高的准确性和AUC.

结论:

女性性别,缺血性中风病史,心血管狭窄,RDW-CV,GA,D-二次数,BUN和2型糖尿病是中国人群中OPCABG后AIS的潜在预测因素.
与物流回归相比,BN模型显示出更高的预测效率.
在接受OPCABG的患者中,BN模型对早期诊断和预防AIS有希望.

关键词:

贝叶斯网络是一个贝叶斯网络.冠状动脉绕道移植冠状动脉绕道移植预测模型的预测模型.风险因素风险因素风险因素一次性中风,中风.2型糖尿病是什么? 2型糖尿病是什么?

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