Department of Medicine, Institut Universitaire de Cardiologie et de Pneumologie de Quebec-Université Laval, Quebec, Canada.
Department of Critical Care Medicine, Medicine and Biochemistry and Molecular Biology, Health Research Innovation Center (HRIC), University of Calgary, Room 4C64, 3280 Hospital Drive N.W., Calgary, AB, T2N 4Z6, Canada.
Eur J Med Res. 2024 May 14;29(1):284. doi: 10.1186/s40001-024-01876-7.
The Berlin definition of acute respiratory distress syndrome (ARDS) includes only clinical characteristics. Understanding unique patient pathobiology may allow personalized treatment. We aimed to define and describe ARDS phenotypes/endotypes combining clinical and pathophysiologic parameters from a Canadian ARDS cohort.
A cohort of adult ARDS patients from multiple sites in Calgary, Canada, had plasma cytokine levels and clinical parameters measured in the first 24 h of ICU admission. We used a latent class model (LCM) to group the patients into several ARDS subgroups and identified the features differentiating those subgroups. We then discuss the subgroup effect on 30 day mortality.
The LCM suggested three subgroups (n = 64, n = 86, and n = 30), and 23 out of 69 features made these subgroups distinct. The top five discriminating features were IL-8, IL-6, IL-10, TNF-a, and serum lactate. Mortality distinctively varied between subgroups. Individual clinical characteristics within the subgroup associated with mortality included mean PaO/FiO ratio, pneumonia, platelet count, and bicarbonate negatively associated with mortality, while lactate, creatinine, shock, chronic kidney disease, vasopressor/ionotropic use, low GCS at admission, and sepsis were positively associated. IL-8 and Apache II were individual markers strongly associated with mortality (Area Under the Curve = 0.84).
ARDS subgrouping using biomarkers and clinical characteristics is useful for categorizing a heterogeneous condition into several homogenous patient groups. This study found three ARDS subgroups using LCM; each subgroup has a different level of mortality. This model may also apply to developing further trial design, prognostication, and treatment selection.
柏林急性呼吸窘迫综合征（ARDS）定义仅包括临床特征。了解独特的患者病理生理学可能允许进行个体化治疗。我们旨在通过结合加拿大 ARDS 队列的临床和病理生理参数，定义和描述 ARDS 表型/亚型。
来自加拿大卡尔加里多个地点的成年 ARDS 患者队列，在 ICU 入院的前 24 小时内测量了血浆细胞因子水平和临床参数。我们使用潜在类别模型（LCM）将患者分为几个 ARDS 亚组，并确定了区分这些亚组的特征。然后，我们讨论了亚组对 30 天死亡率的影响。
LCM 提示存在三个亚组（n=64、n=86 和 n=30），23 个特征中的 69 个特征使这些亚组具有区别。五个最具区分性的特征是 IL-8、IL-6、IL-10、TNF-a 和血清乳酸。死亡率在亚组之间明显不同。亚组内的个体临床特征与死亡率相关，包括平均 PaO/FiO 比值、肺炎、血小板计数和碳酸氢盐与死亡率呈负相关，而乳酸、肌酐、休克、慢性肾脏病、血管加压素/离子otropic 药物的使用、入院时低 GCS 和败血症与死亡率呈正相关。IL-8 和 Apache II 是与死亡率密切相关的个体标志物（曲线下面积=0.84）。
使用生物标志物和临床特征对 ARDS 进行亚组分类有助于将异质疾病分为几个同质患者群体。本研究使用 LCM 发现了三个 ARDS 亚组；每个亚组的死亡率不同。该模型也可能适用于进一步的试验设计、预后和治疗选择。
