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Identification and validation of distinct biological phenotypes in patients with acute respiratory distress syndrome by cluster analysis.

ABSTRACT: RATIONALE:We hypothesised that patients with acute respiratory distress syndrome (ARDS) can be clustered based on concentrations of plasma biomarkers and that the thereby identified biological phenotypes are associated with mortality. METHODS:Consecutive patients with ARDS were included in this prospective observational cohort study. Cluster analysis of 20 biomarkers of inflammation, coagulation and endothelial activation provided the phenotypes in a training cohort, not taking any outcome data into account. Logistic regression with backward selection was used to select the most predictive biomarkers, and these predicted phenotypes were validated in a separate cohort. Multivariable logistic regression was used to quantify the independent association with mortality. RESULTS:Two phenotypes were identified in 454 patients, which we named 'uninflamed' (N=218) and 'reactive' (N=236). A selection of four biomarkers (interleukin-6, interferon gamma, angiopoietin 1/2 and plasminogen activator inhibitor-1) could be used to accurately predict the phenotype in the training cohort (area under the receiver operating characteristics curve: 0.98, 95% CI 0.97 to 0.99). Mortality rates were 15.6% and 36.4% (p<0.001) in the training cohort and 13.6% and 37.5% (p<0.001) in the validation cohort (N=207). The 'reactive phenotype' was independent from confounders associated with intensive care unit mortality (training cohort: OR 1.13, 95% CI 1.04 to 1.23; validation cohort: OR 1.18, 95% CI 1.06 to 1.31). CONCLUSIONS:Patients with ARDS can be clustered into two biological phenotypes, with different mortality rates. Four biomarkers can be used to predict the phenotype with high accuracy. The phenotypes were very similar to those found in cohorts derived from randomised controlled trials, and these results may improve patient selection for future clinical trials targeting host response in patients with ARDS.

SUBMITTER: Bos LD

PROVIDER: S-EPMC5964254 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Identification and validation of distinct biological phenotypes in patients with acute respiratory distress syndrome by cluster analysis.

Bos L D LD Schouten L R LR van Vught L A LA Wiewel M A MA Ong D S Y DSY Cremer O O Artigas A A Martin-Loeches I I Hoogendijk A J AJ van der Poll T T Horn J J Juffermans N N Calfee C S CS Schultz M J MJ

Thorax 20170427 10

<h4>Rationale</h4>We hypothesised that patients with acute respiratory distress syndrome (ARDS) can be clustered based on concentrations of plasma biomarkers and that the thereby identified biological phenotypes are associated with mortality.<h4>Methods</h4>Consecutive patients with ARDS were included in this prospective observational cohort study. Cluster analysis of 20 biomarkers of inflammation, coagulation and endothelial activation provided the phenotypes in a training cohort, not taking an ...[more]

PMID: 28450529

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Project description:BackgroundAcute respiratory distress syndrome (ARDS) is a heterogeneous syndrome, and the identification of homogeneous subgroups and phenotypes is the first step toward precision critical care. We aimed to explore whether ARDS phenotypes can be identified using clinical data, are reproducible and are associated with clinical outcomes and treatment response.MethodsThis study is based on a retrospective analysis of data from the telehealth intensive care unit (eICU) collaborative research database and three ARDS randomized controlled trials (RCTs) (ALVEOLI, FACTT and SAILS trials). We derived phenotypes in the eICU by cluster analysis based on clinical data and compared the clinical characteristics and outcomes of each phenotype. The reproducibility of the derived phenotypes was tested using the data from three RCTs, and treatment effects were evaluated.ResultsThree clinical phenotypes were identified in the training cohort of 3875 ARDS patients. Of the three phenotypes identified, phenotype I (n = 1565; 40%) was associated with fewer laboratory abnormalities, less organ dysfunction and the lowest in-hospital mortality rate (8%). Phenotype II (n = 1232; 32%) was correlated with more inflammation and shock and had a higher mortality rate (18%). Phenotype III (n = 1078; 28%) was strongly correlated with renal dysfunction and acidosis and had the highest mortality rate (22%). These results were validated using the data from the validation cohort (n = 3670) and three RCTs (n = 2289) and had reproducibility. Patients with these ARDS phenotypes had different treatment responses to randomized interventions. Specifically, in the ALVEOLI cohort, the effects of ventilation strategy (high PEEP vs low PEEP) on ventilator-free days differed by phenotype (p = 0.001); in the FACTT cohort, there was a significant interaction between phenotype and fluid-management strategy for 60-day mortality (p = 0.01). The fluid-conservative strategy was associated with improved mortality in phenotype II but had the opposite effect in phenotype III.ConclusionThree clinical phenotypes of ARDS were identified and had different clinical characteristics and outcomes. The analysis shows evidence of a phenotype-specific treatment benefit in the ALVEOLI and FACTT trials. These findings may improve the identification of distinct subsets of ARDS patients for exploration in future RCTs.

Project description:Rationale: Reverse triggering is an underexplored form of dyssynchrony with important clinical implications in patients with acute respiratory distress syndrome.Objectives: This retrospective study identified reverse trigger phenotypes and characterized their impacts on Vt and transpulmonary pressure.Methods: Fifty-five patients with acute respiratory distress syndrome on pressure-regulated ventilator modes were included. Four phenotypes of reverse triggering with and without breath stacking and their impact on lung inflation and deflation were investigated.Measurements and Main Results: Inflation volumes, respiratory muscle pressure generation, and transpulmonary pressures were determined and phenotypes differentiated using Campbell diagrams of respiratory activity. Reverse triggering was detected in 25 patients, 15 with associated breath stacking, and 13 with stable reverse triggering consistent with respiratory entrainment. Phenotypes were associated with variable levels of inspiratory effort (mean 4-10 cm H2O per phenotype). Early reverse triggering with early expiratory relaxation increased Vts (88 [64-113] ml) and inspiratory transpulmonary pressures (3 [2-3] cm H2O) compared with passive breaths. Early reverse triggering with delayed expiratory relaxation increased Vts (128 [86-170] ml) and increased inspiratory and mean-expiratory transpulmonary pressure (7 [5-9] cm H2O and 5 [4-6] cm H2O). Mid-cycle reverse triggering (initiation during inflation and maximal effort during deflation) increased Vt (51 [38-64] ml), increased inspiratory and mean-expiratory transpulmonary pressure (3 [2-4] cm H2O and 3 [2-3] cm H2O), and caused incomplete exhalation. Late reverse triggering (occurring exclusively during exhalation) increased mean expiratory transpulmonary pressure (2 [1-2] cm H2O) and caused incomplete exhalation. Breath stacking resulted in large delivered volumes (176 [155-197] ml).Conclusions: Reverse triggering causes variable physiological effects, depending on the phenotype. Differentiation of phenotype effects may be important to understand the clinical impacts of these events.

Project description:Background Acute respiratory distress syndrome (ARDS) is a common cause of death in ICU patients and its underlying mechanism remains unclear, which leads to its high mortality rate. This study aimed to identify candidate genes potentially implicating in the pathogenesis of ARDS and provide novel therapeutic targets. Methods Using bioinformatics tools, we searched for differentially expressed genes (DEGs) in an ARDS microarray dataset downloaded from the Gene Expression Omnibus (GEO) database. Afterwards, functional enrichment analysis of GO, KEGG, GSEA and WGCNA were carried out to investigate the potential involvement of these DEGs. Moreover, the Protein–protein interaction (PPI) network was constructed and molecular complexes and hub genes were identified, followed by prognosis analysis of the hub genes. Further, we performed qRT-PCR, Western Blot and flow cytometry analysis to detect candidate genes of CCR2 and FPR3 in macrophage model of LPS-induced ARDS and primary alveolar macrophages(AMs). Macrophage chemotaxis was evaluated using Transwell assay. Results DEGs mainly involved in myeloid leukocyte activation, cell chemotaxis, adenylate cyclase-modulating G protein-coupled receptor signaling pathway and cytokine-cytokine receptor interaction. Basing on the constructed PPI network, we identified five molecular complexes and 10 hub genes potentially participating in the pathogenesis of ARDS. It was observed that candidate genes of CCR2 and FPR3 were significantly over-expressed in primary alveolar macrophages from ARDS patients and macrophgae model of LPS-induced ARDS. Moreover, in vitro transwell assay demonstrated that CCR2 and FPR3 down-regulation, respectively, inhibited LPS-triggered macrophage chemotaxis toward CCL2. Finally, a positive correlation between FPR3 and CCR2 expression was confirmed using pearson correlation analysis and Western Blot assay. Conclusions Our study identified CCR2 and FPR3 as the candidate genes which can promote macrophage chemotaxis through a possible interaction between FPR3 and CCL2/CCR2 axis and provided novel insights into ARDS pathogenesis.

Project description:ObjectiveTo analyze the differential expression of autophagy-related genes of sepsis-induced acute respiratory distress syndrome (ARDS) as potential markers for early diagnosis.MethodsMale Sprague-Dawley rats (aged 8 weeks) were selected and randomly divided into sepsis-induced ARDS group (n = 6) and a normal control group (n = 6). Lung tissue samples were collected for high-throughput sequencing using Illumina HiSeq sequencing platform in the paired-end sequencing mode. Differentially expressed genes (DEGs) were screened by DESeq. 2 software [|log2FC | ≥1 and p < .05] and autophagy-related genes were identified using Mouse Genome Informatics. Co-expressed autophagy-related DEGs from these two datasets were filtered by construction of a Venn diagram. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on these autophagy-related DEGs and a protein interaction network was constructed using STRING and Cytoscape software to identify hub genes, which were verified by real-time quantitative polymerase chain reaction (qRT-PCR).ResultsA total of 42 autophagy-related DEGs (26 upregulated genes and 16 downregulated genes) were identified. The GO and KEGG pathway analyses showed enrichment in 969 biological processes (BPs), three cellular components (CCs), eight molecular functions (MFs) and 27 signaling pathways. The protein interaction (PPI) network revealed 42 node proteins and 75 interacting edges, with an average node degree of 3.52, and an average local clustering coefficient of 0.509. Among the top 10 hub genes with the RNA-Seq, six hub genes (Stat3, Il10, Ifng, Hmox1, Hif1a, and Nod2) were validated by qRT-PCR (all p < .05).Conclusion42 potential autophagy-related genes associated with sepsis-induced ARDS lung injury were identified and six hub genes (Stat3, Il10, Ifng, Hmox1, Hif1a, and Nod2) may affect the development of ARDS by regulating autophagy. These results expanded our understanding of ARDS and might be useful in treatment of exogenous sepsis-induced ARDS.

Dataset Information

Identification and validation of distinct biological phenotypes in patients with acute respiratory distress syndrome by cluster analysis.

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Identification and validation of distinct biological phenotypes in patients with acute respiratory distress syndrome by cluster analysis.

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