CHU Clermont-Ferrand, Service de Médecine intensive et réanimation, Clermont-Ferrand, France.
CHU Clermont-Ferrand, Service de Radiologie, Clermont-Ferrand, France.
Crit Care Med. 2024 Feb 1;52(2):e38-e46. doi: 10.1097/CCM.0000000000006105. Epub 2023 Oct 27.
Inconsistent results from COVID-19 studies raise the issue of patient heterogeneity.
The objective of this study was to identify homogeneous subgroups of patients (clusters) using baseline characteristics including inflammatory biomarkers and the extent of lung parenchymal lesions on CT, and to compare their outcomes.
Retrospective single-center study.
Medical ICU of the University Hospital of Clermont-Ferrand, France.
All consecutive adult patients aged greater than or equal to 18 years, admitted between March 20, 2020, and August 31, 2021, for COVID-19 pneumonia.
Characteristics at baseline, during ICU stay, and outcomes at day 60 were recorded. On the chest CT performed at admission the extent of lung parenchyma lesions was established by artificial intelligence software.
Clusters were determined by hierarchical clustering on principal components using principal component analysis of admission characteristics including plasma interleukin-6, human histocompatibility leukocyte antigen-DR expression rate on blood monocytes (HLA-DR) monocytic-expression rate (mHLA-DR), and the extent of lung parenchymal lesions. Factors associated with day 60 mortality were investigated by univariate survival analysis. Two hundred seventy patients were included. Four clusters were identified and three were fully described. Cluster 1 (obese patients, with moderate hypoxemia, moderate extent of lung parenchymal lesions, no inflammation, and no down-regulation of mHLA-DR) had a better prognosis at day 60 (hazard ratio [HR] = 0.27 [0.15-0.46], p < 0.01), whereas cluster 2 (older patients with comorbidities, moderate extent of lung parenchyma lesions but significant hypoxemia, inflammation, and down-regulation of mHLA-DR) and cluster 3 (patients with severe parenchymal disease, hypoxemia, inflammatory reaction, and down-regulation of mHLA-DR) had an increased risk of mortality (HR = 2.07 [1.37-3.13], p < 0.01 and HR = 1.52 [1-2.32], p = 0.05, respectively). In multivariate analysis, only clusters 1 and 2 were independently associated with day 60 death.
Three clusters with distinct characteristics and outcomes were identified. Such clusters could facilitate the identification of targeted populations for the next trials.
COVID-19 研究结果不一致,引发了患者异质性的问题。
本研究旨在使用包括炎症生物标志物和 CT 上肺实质病变程度在内的基线特征,识别同质的患者亚组(簇),并比较其结局。
回顾性单中心研究。
法国克莱蒙费朗大学医院的重症监护病房。
所有连续≥18 岁的成年患者,于 2020 年 3 月 20 日至 2021 年 8 月 31 日期间因 COVID-19 肺炎入院。
记录入院时、入住 ICU 期间和第 60 天的特征和结局。在入院时进行的胸部 CT 上,使用人工智能软件确定肺实质病变的范围。
使用基于主成分的层次聚类和主成分分析,根据入院时的特征(包括血浆白细胞介素 6、血液单核细胞上的人类组织相容性白细胞抗原-DR 表达率(HLA-DR)、单核细胞表达率(mHLA-DR)和肺实质病变程度)确定簇。使用单变量生存分析调查与第 60 天死亡率相关的因素。共纳入 270 例患者。确定了 4 个簇,其中 3 个簇得到了充分描述。簇 1(肥胖患者,中度低氧血症,中度肺实质病变程度,无炎症,mHLA-DR 无下调)在第 60 天的预后更好(风险比 [HR] = 0.27 [0.15-0.46],p < 0.01),而簇 2(合并症较多的老年患者,中度肺实质病变程度但严重低氧血症、炎症和 mHLA-DR 下调)和簇 3(严重实质疾病、低氧血症、炎症反应和 mHLA-DR 下调的患者)死亡风险增加(HR = 2.07 [1.37-3.13],p < 0.01 和 HR = 1.52 [1-2.32],p = 0.05)。多变量分析显示,只有簇 1 和 2 与第 60 天死亡独立相关。
确定了具有不同特征和结局的 3 个簇。这种聚类方法可以帮助确定下一次试验的目标人群。