平均动脉压变异度与重症患者短期预后的关系:基于大型临床数据库MIMIC-Ⅲ的回顾性分析

Mean arterial pressure variability and the prognosis in intensive care unit patients:a retrospective analysis of the MIMIC-Ⅲdatabase

目的探讨平均动脉压(mean arterial pressure,MAP)变异度与重症患者的重症医学科(intensive care unit,ICU)病死率之间的关系。方法回顾性分析重症监护医学信息数据库(Medical Information Mart for Intensive Care,MIMIC)-Ⅲ中38852例入ICU的重症患者的临床资料,计算入ICU后24 h内记录的MAP的变异系数作为MAP变异度,采用一般线性回归观察入ICU 24 h内MAP变异度与重症患者ICU病死率之间的相关性,并采用受试者工作特征(receiver operating characteristic,ROC)曲线下面积(area under curve,AUC)评估MAP变异度预测重症患者ICU病死率的能力。结果入ICU 24 h的MAP变异度与ICU病死率之间有很好的相关性(R2=0.860,P<0.001),MAP变异度越大,ICU病死率越高。24h的MAP变异程度预测ICU病死率的AUC为0.61。结论重症患者入ICU 24 h内的MAP变异度与ICU病死率有很好的相关性,MAP变异度越大,ICU病死率越高;MAP变异度能够为简单快速预测危重患者的ICU病死率提供一定的信息。

Objective To explore the relationship between mean arterial pressure(MAP)variability and the mortality in intensive care unit(ICU)at the critically ill patients.Methods This is a retrospective analysis with 38852 clinical data of critically ill patients in the Medical Information Mart for Intensive Care(MIMIC)-Ⅲdatabase.MAP variability was calculated by the coefficient of variation(CV)of MAP within 24 hours after admitted to ICU.According to the MAP variability within 24 hours,the patients were divided into 10 groups with equal frequency.Then,the general linear regression was used to assess the correlation between the MAP variability and the mortality in ICU.Area under the curve(AUC)of receiver operating characteristic(ROC)was applied to determine whether MAP variability can be a prognostic predictor on the in-ICU mortality in critically ill patients.Results There is a significant correlation between the 24-hour MAP variability and the mortality in ICU(R2=0.860,P<0.001).The larger the MAP variability,the higher the mortality in ICU.The AUC of 24-hour MAP variability prediction ICU mortality was 0.610.Conclusion The 24-hour MAP variability has a good correlation with the mortality in ICU at the critically ill patients.The greater the MAP variability,the higher the mortality in ICU.The MAP variability may provide some information for easily and quickly predicted the mortality in ICU of critically ill patients.