脓毒症合并低心功能指数患者PiCCO参数的聚类分析  

Cluster analysis of PiCCO parameters in patients with sepsis combined with low cardiac function index

作　　者：胡杰 蔡国龙 Hu Jie;Cai Guolong(Zhejiang University School of Medicine,Hangzhou 313012,China;Department of Critical Care Medicine,Huzhou Central Hospital,Huzhou 313000,China;Department of Critical Care Medicine,Zhejiang Hospital,Hangzhou 313012,China)

机构地区：[1]浙江大学医学院,杭州313012 [2]湖州市中心医院重症医学科,浙江湖州313000 [3]浙江医院重症医学科,杭州313012

出　　处：《中华重症医学电子杂志》2024年第1期25-30,共6页Chinese Journal Of Critical Care & Intensive Care Medicine(Electronic Edition)

基　　金：科技厅“翎雁”研发攻关计划项目(2022C03171)

摘　　要：目的通过对脓毒症合并低心功能指数(CI)患者脉搏指示连续心输出量(PiCCO)参数聚类分析,确认不同表型,筛选出预后最差表型,从而识别危重型患者。方法在美国监护室医学信息数据集(MIMIC-Ⅳ2.0)(2008年至2019年)中筛选脓毒症合并低CI且有PiCCO记录的成人患者78例,根据PiCCO参数[CI、全心舒张末期容积指数(GEDI)、全身血管阻力指数(SVRI)、血管外肺水指数(ELWI)]进行K-mean聚类成不同表型,比较各表型间年龄、性别、体重指数(BMI)、序贯器官衰竭评分(SOFA)、既往疾病史;CI、GEDI、SVRI、ELWI、心率(HR)、平均动脉压(MAP)、主要临床结局(住院病死率)、次要临床结局[急性肾损伤(AKI)3级发生率、住院时长、ICU住院时长]的差异,并建立单因素及多因素logistic回归模型。结果共确认4种不同表型,表型1:高血容量,高血管阻力,极高血管外肺水;表型2:正常血容量,正常血管阻力,正常血管外肺水;表型3:正常血容量,高血管阻力,高血管外肺水;表型4:高血容量,正常血管阻力,极高血管外肺水。通过分析发现,表型1的预后最差,住院病死率最高(66.7%),表型1、表型2、表型3、表型4间住院病死率比较,差异有统计学意义(χ^(2)=7.8,P=0.045)。多因素logistic回归分析显示,与表型1相比,表型2、表型3、表型4的OR值及95%CI分别为0.095(0.017~0.540)、0.087(0.013~0.580)及0.067(0.006~0.719),差异亦有统计学意义(P<0.05)。结论基于PiCCO参数聚类分析能确认脓毒症合并低CI患者的血流动力学状态不同表型,并能根据表型识别危重型患者,预测预后。Objective To identify different phenotypes and screen the prognostic phenotypes by cluster analysis of pulse-indicated continuous cardiac output monitoring technique(PiCCO)parameters in septic patients combined with low cardiac function.Methods Seventy-eight septic patients with low cardiac function index and PiCCO recordings were screened in the US Intensive Care Database(MIMICⅣ2.0)(2008-2019).Based on the PiCCO parameters Cardiac Function Index CI,Whole Heart End-Diastolic Volume Index GEDI,Systemic Vascular Resistance Index SVRI,Extravascular Lung Water Index(ELWI)K-mean clustering characterized patients into different phenotypes.The inter-phenotypic parameters were compared in different phenotypes,such as CI,GEDI,SVRI,ELWI,heart rate(HR),mean arterial pressure(MAP),age,gender,body mass index(BMI),sequential organ failure score(SOFA),history of previous illness,in-hospital mortality for the primary clinical outcome,and incidence of acute kidney injury(AKI grade 3)for the secondary clinical outcome,difference in the length of hospital and ICU stay.Univariate and multivariate logistic regression models were established.Results Four different phenotypes were identified in this study.Phenotype 1:hypervolemic,high vascular resistance,very high extravascular lung water.Phenotype 2:normal blood volume,normal vascular resistance,normal extravascular lung water.Phenotype 3:normal blood volume,high vascular resistance,high extravascular lung water.Phenotype 4:high blood volume,normal vascular resistance,very high extravascular lung water.Phenotype 1 had the worst prognosis and the highest in-hospital mortality rate(66.7%).The difference of in-hospital mortality among the four phenotypes was statistically significant different(χ^(2)=7.8,P=0.045).Multifactorial logistic regression showed,compared to phenotype 1,phenotype 2,phenotype 3,and phenotype 4 had an OR and 95%CI of 0.095(0.017-0.540),0.087(0.013-0.580)and 0.067(0.006-0.719),with significant differences(P<0.05).Conclusion Cluster analysis based on PiCCO paramete

关 键 词：脓毒症 低心功能指数 脉搏指示连续心输出量 聚类分析 表型 住院病死率 

分 类 号：R459.7[医药卫生—急诊医学]