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中华重症医学电子杂志 ›› 2024, Vol. 10 ›› Issue (01) : 25 -30. doi: 10.3877/cma.j.issn.2096-1537.2024.01.004

临床研究

脓毒症合并低心功能指数患者PiCCO参数的聚类分析
胡杰1, 蔡国龙2,()   
  1. 1. 313012 杭州,浙江大学医学院;313000 浙江湖州,湖州市中心医院重症医学科
    2. 313012 杭州,浙江大学医学院;313012 杭州,浙江医院重症医学科
  • 收稿日期:2023-07-31 出版日期:2024-02-28
  • 通信作者: 蔡国龙
  • 基金资助:
    科技厅“翎雁”研发攻关计划项目(2022C03171)

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

Jie Hu1, Guolong Cai2,()   

  1. 1. Zhejiang University School of Medicine, Hangzhou 313012, China;Department of Critical Care Medicine, Huzhou Central Hospital, Huzhou 313000, China
    2. Zhejiang University School of Medicine, Hangzhou 313012, China;Department of Critical Care Medicine, Zhejiang Hospital, Hangzhou 313012, China
  • Received:2023-07-31 Published:2024-02-28
  • Corresponding author: Guolong Cai
引用本文:

胡杰, 蔡国龙. 脓毒症合并低心功能指数患者PiCCO参数的聚类分析[J/OL]. 中华重症医学电子杂志, 2024, 10(01): 25-30.

Jie Hu, Guolong Cai. Cluster analysis of PiCCO parameters in patients with sepsis combined with low cardiac function index[J/OL]. Chinese Journal of Critical Care & Intensive Care Medicine(Electronic Edition), 2024, 10(01): 25-30.

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-Ⅳ 2.0)(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(χ2=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 (MIMIC Ⅳ 2.0) (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 (χ2=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 parameters confirmed different phenotypes of haemodynamic status in patients with sepsis combined with low cardiac function index and identified critically ill patients based on the phenotypes, thus predicting the prognosis of patients.

图1 脓毒症合并低CI患者筛选流程 注:MIMIC-Ⅳ为美国监护室医学信息数据集;CI为心功能指数
表1 死亡组及生存组患者相关指标比较
表2 聚类分析下不同表型患者相关指标比较
相关指标 表型1(15例) 表型2(36例) 表型3(20例) 表型4(7例) 统计值 P
基线资料
年龄(岁, 76±14 59±17 67±16 65±8 F=4.1 0.090
男性[例(%)] 6(40.0) 15(41.7) 11(55.0) 1(14.3) χ2=3.9 0.295
BMI(kg/m2 28±5 30±10 29±5 28±8 F=0.2 0.879
SOFA评分[分,MQ25Q75)] 4(2,6) 4(2,7) 5(3,7) 3(2,8) Z=1.2 0.763
HR(次/min, 94±21 97±22 98±16 102±19 F=0.2 0.879
MAP(mmHg, 68±15 79±15a 85±20a 70±11 F=3.7 0.014
既往病史[例(%)]
心肌梗死 2(13.3) 4(11.1) 2(10.0) 1(14.3) χ2=0.2 >0.999
慢性肾功能不全 1(6.7) 7(19.4) 2(10.0) 2(28.6) χ2=2.7 0.394
慢性阻塞性肺病 6(40.0) 8(22.2) 7(35.0) 4(57.1) χ2=4.1 0.236
PiCCO参数(
CI[L/(min·m2)] 2.2±0.4 2.9±0.3a 2.0±0.4b 3.1±0.2ac F=32.0 <0.01
ELWI(ml/kg) 24.7±6.4 7.0±2.9a 11.9±4.3a 20.8±6.3bc F=40.9 <0.01
GEDI(ml/m2 904±135 697±121a 698±200a 1207±210b F=26.0 <0.01
SVRI(dyn·s·m2/cm5 2055±504 1728±43 3169±587ab 1369±352abc F=44.7 <0.01
临床结局
住院死亡[例(%)] 10(66.7) 10(27.8)a 5(25.0)a 2(28.6)a χ2=7.8 0.045
AKI(3级)[例(%)] 9(60.0) 21(58.3) 12(60.0) 5(71.4) χ2=4.5 0.932
住院时长[d,MQ25Q75)] 15.5(1.2,22.0) 21.6(13.7,31.4) 20.4(12.2,35.0) 19.2(12.2,31.7) Z=5.7 0.129
ICU住院时长[d,MQ25Q75)] 3.1(1.3,14.2) 13.7(7.3,20.2)a 12.5(7.4,24.4)a 15.8(10.7,20.8)a Z=10.7 0.013
表3 患者主要临床结局的logistic回归模型
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