基于机器学习构建住院患者深静脉血栓风险预测模型的系统评价

Machine learning-based a risk predictive model for deep vein thrombosis in hospitalized patients: a systematic review

目的 系统评价基于机器学习构建的住院患者深静脉血栓风险预测模型。方法 检索PubMed、Embase、CHINHAL、Cochrane Library、Web of Science、中国知网、万方数据库中关于机器学习对住院患者深静脉血栓预测模型构建相关研究,检索时限为建库至2023年3月。2名研究者独立完成文献筛选并提取资料,使用预测模型构建研究数据提取和质量评价清单对纳入文献进行质量评价。结果 最终纳入11篇研究,包括28个机器学习模型,ROC曲线下面积为0.710~0.976。年龄、血栓史、住院时间、用药史、D-二聚体等实验室指标是主要预测因子。结论 机器学习可更准确识别住院患者发生深静脉血栓的风险,预测性能优于传统风险预测模型。文献整体偏倚风险较低,预测模型适用性水平一般。

Objective To systematically evaluate the risk prediction model for deep vein thrombosis in hospitalized patients based on machine learning.Methods We conducted literature research in PubMed,Embase,CHINHAL,Cochrane Library,Web of Science,CNKI,and Wanfang databases for literature on risk prediction models for deep vein thrombosis in hospitalized patients constructed by machine learning.The search period spanned from the inception to March 2023.Two researchers completed literature screening and data extraction independently,and used predictive models to construct a research data extraction and quality evaluation checklist(CHARMS)to evaluate the quality of the included literature and screened high-quality literature for discussion.Results Totally 11 high-quality studies were collected,including 28 machine learning models,with an area under the ROC curve ranging from 0.710 to 0.976.Laboratory indicators such as age,VTE history,length of hospital stay,medication history,and D-dimer were are the main predictive factors.Conclusions Risk prediction models constructed using machine learning can accurately identify the risk of DVT events in hospitalized patients,and its predictive performance is superior to traditional risk prediction models.The available literature on the topic exhibits a low overall risk of bias,however,the applicability level of the prediction model is considered average.