血流动力学不稳定的临床特征分析及预测模型的构建

Analysis of clinical features and construction of prediction models for hemodynamic instability

目的探讨不同的临床特征预测血流动力学不稳定的重要性,并构建不同应用场景最佳预测模型。方法回顾性选取重症医学数据库(MIMIC)-Ⅳ中符合纳入标准的重症监护病房(ICU)住院患者,根据临床干预分为血流动力学不稳定组和稳定组。提取患者一般人口学特征、临床资料及实验室检验等特征信息,进行组间比较。使用机器学习算法评估各特征重要性,构建成人血流动力学不稳定(AHI)模型、改良AHI模型、无创模型、有创模型、血压模型及休克指数模型。结果以AUC评估模型性能,预测能力最强的是AHI模型(AUC=0.862),其他依次是改良AHI模型(AUC=0.810)、有创模型(AUC=0.787)、无创模型(AUC=0.760)、血压模型(AUC=0.720)和休克指数模型(AUC=0.716)。F1分数结果显示,AHI模型是最佳模型,其次是改良AHI模型、有创模型和无创模型,血压模型和休克指数模型预测效果较差。结论在特征信息较全的情况下,AHI模型是最佳预测模型,其中无创血压是识别血流动力学不稳定性最有用的特征。在特征信息有限情况下,无创模型比单一的血压模型和休克指数模型具有优势。

Objective To investigate the importance of different clinical features in predicting hemodynamic instability,and to construct best prediction model for different application scenarios.Methods Patients admitted to the intensive care unit(ICU)who met the inclusion criteria in the Medical Information Mart for Intensive Care-Ⅳ(MIMIC-Ⅳ)database were retrospectively selected and divided into hemodynamic instable group and hemodynamic stable group according to their clinical interventions.General characteristics,clinical data,laboratory results of all the patients were collected and compared between groups.Machine learning algorithms were used to assess the importance of each feature and construct adult hemodynamic instability(AHI)model,modified AHI model,noninvasive model,invasive model,blood pressure model,and shock index model.Results Model performance was evaluated by using the area under the receiver operating characteristic(AUC)curve,and the AHI model demonstrated the best predictive performance(AUC=0.862).The performance of other models was as follows:the modified AHI model(AUC=0.810),the invasive model(AUC=0.787),the non-invasive model(AUC=0.760),the blood pressure model(AUC=0.720),and the shock index model(AUC=0.716).F1 score showed that the AHI model was best,followed by the modified AHI model,the invasive model and the non-invasive model.The blood pressure model and the shock index model had the worst predictive performance.Conclusions In the case of complete feature information,the AHI model is the best predictive model,and the non-invasive blood pressure is identified as the most useful feature for predicting hemodynamic instability.Under the conditions with limited feature information,the non-invasive model has the advantage over single blood pressure model and shock index model.