PDF(Pubmed)
Abstract：
UNASSIGNED: We aim to identify the clinical phenotypes of immunocompromised patients with pneumonia-related ARDS, to investigate the lung microbiota signatures and the outcomes of different phenotypes, and finally, to develop a machine learning classifier for a specified phenotype.
UNASSIGNED: This prospective study included immunocompromised patients with pneumonia-related ARDS. We identified phenotypes using hierarchical clustering to analyze clinical variables and serum cytokine levels. We then compared outcomes and lung microbiota signatures between phenotypes. Based on lung microbiota markers, we developed a random forest classifier for a specified phenotype with worse outcomes.
UNASSIGNED: This study included 92 patients, who were divided into three phenotypes, namely \"type α\" (N = 33), \"type β\" (N = 12), and \"type γ\" (N = 47). Compared to type α or type β, patients with type γ had no obvious inflammatory presentation and had significantly lower IL-6 levels and more severe oxygenation failure. Type γ was also related to higher 30-day mortality and lower ventilator free days. The microbiota signatures of type γ were characterized by lower alpha diversity and distinct compositions than those of other patients. We developed a lung microbiota-derived random forest model to differentiate patients with type γ from other phenotypes.
UNASSIGNED: Immunocompromised patients with pneumonia-related ARDS can be clustered into three clinical phenotypes, namely type α, type β, and type γ. Phenotypes were distinguished from each other with different outcomes and lung microbiota signatures. Type γ, which was characterized by insufficient inflammation response and worse outcomes, can be detected with a random forest model based on lung microbiota markers.
UNASSIGNED: This prospective study included immunocompromised patients with pneumonia-related ARDS. We identified phenotypes using hierarchical clustering to analyze clinical variables and serum cytokine levels. We then compared outcomes and lung microbiota signatures between phenotypes. Based on lung microbiota markers, we developed a random forest classifier for a specified phenotype with worse outcomes.
UNASSIGNED: This study included 92 patients, who were divided into three phenotypes, namely \"type α\" (N = 33), \"type β\" (N = 12), and \"type γ\" (N = 47). Compared to type α or type β, patients with type γ had no obvious inflammatory presentation and had significantly lower IL-6 levels and more severe oxygenation failure. Type γ was also related to higher 30-day mortality and lower ventilator free days. The microbiota signatures of type γ were characterized by lower alpha diversity and distinct compositions than those of other patients. We developed a lung microbiota-derived random forest model to differentiate patients with type γ from other phenotypes.
UNASSIGNED: Immunocompromised patients with pneumonia-related ARDS can be clustered into three clinical phenotypes, namely type α, type β, and type γ. Phenotypes were distinguished from each other with different outcomes and lung microbiota signatures. Type γ, which was characterized by insufficient inflammation response and worse outcomes, can be detected with a random forest model based on lung microbiota markers.
摘要：
■我们的目标是确定肺炎相关性ARDS的免疫功能低下患者的临床表型，为了研究肺部微生物群的特征和不同表型的结果，最后，为指定的表型开发机器学习分类器。
■这项前瞻性研究包括患有肺炎相关性ARDS的免疫功能低下患者。我们使用层次聚类分析临床变量和血清细胞因子水平来鉴定表型。然后，我们比较了表型之间的结果和肺部微生物群特征。基于肺部微生物群标记，我们为结果较差的指定表型开发了一个随机森林分类器.
■这项研究包括92名患者，他们被分为三种表型，即“类型α”(N=33)，“β型”(N=12)，和“γ型”(N=47)。与α型或β型相比，γ型患者无明显的炎症表现,IL-6水平明显降低,氧合衰竭更为严重.γ型也与较高的30天死亡率和较低的无呼吸机天数有关。与其他患者相比，γ型的微生物群特征具有较低的α多样性和不同的组成。我们开发了一种肺微生物群衍生的随机森林模型，以区分γ型患者与其他表型。
■肺炎相关性ARDS的免疫功能低下患者可以分为三种临床表型，即α型，β型,和γ型。表型彼此区分,具有不同的结果和肺微生物群特征。γ型,其特征是炎症反应不足和预后较差，可以用基于肺部微生物群标记的随机森林模型检测。
■这项前瞻性研究包括患有肺炎相关性ARDS的免疫功能低下患者。我们使用层次聚类分析临床变量和血清细胞因子水平来鉴定表型。然后，我们比较了表型之间的结果和肺部微生物群特征。基于肺部微生物群标记，我们为结果较差的指定表型开发了一个随机森林分类器.
■这项研究包括92名患者，他们被分为三种表型，即“类型α”(N=33)，“β型”(N=12)，和“γ型”(N=47)。与α型或β型相比，γ型患者无明显的炎症表现,IL-6水平明显降低,氧合衰竭更为严重.γ型也与较高的30天死亡率和较低的无呼吸机天数有关。与其他患者相比，γ型的微生物群特征具有较低的α多样性和不同的组成。我们开发了一种肺微生物群衍生的随机森林模型，以区分γ型患者与其他表型。
■肺炎相关性ARDS的免疫功能低下患者可以分为三种临床表型，即α型，β型,和γ型。表型彼此区分,具有不同的结果和肺微生物群特征。γ型,其特征是炎症反应不足和预后较差，可以用基于肺部微生物群标记的随机森林模型检测。
