Host-Response Subphenotypes Offer Prognostic Enrichment in Patients With or at Risk for Acute Respiratory Distress Syndrome

Plasma 1,3-β-d-glucan levels predict adverse clinical outcomes in critical illness

BACKGROUNDThe fungal cell wall constituent 1,3-β-d-glucan (BDG) is a pathogen-associated molecular pattern that can stimulate innate immunity. We hypothesized that BDG from colonizing fungi in critically ill patients may translocate into the systemic circulation and be associated with host inflammation and outcomes.METHODSWe enrolled 453 mechanically ventilated patients with acute respiratory failure (ARF) without invasive fungal infection and measured BDG, innate immunity, and epithelial permeability biomarkers in serially collected plasma samples.RESULTSCompared with healthy controls, patients with ARF had significantly higher BDG levels (median [IQR], 26 pg/mL [15-49 pg/mL], P < 0.001), whereas patients with ARF with high BDG levels (≥40 pg/mL, 31%) had higher odds for assignment to the prognostically adverse hyperinflammatory subphenotype (OR [CI], 2.88 [1.83-4.54], P < 0.001). Baseline BDG levels were predictive of fewer ventilator-free days and worse 30-day survival (adjusted P < 0.05). Integrative analyses of fungal colonization and epithelial barrier disruption suggested that BDG may translocate from either the lung or gut compartment. We validated the associations between plasma BDG and host inflammatory responses in 97 hospitalized patients with COVID-19.CONCLUSIONBDG measurements offered prognostic information in critically ill patients without fungal infections. Further research in the mechanisms of translocation and innate immunity recognition and stimulation may offer new therapeutic opportunities in critical illness.FUNDINGUniversity of Pittsburgh Clinical and Translational Science Institute, COVID-19 Pilot Award and NIH grants (K23 HL139987, U01 HL098962, P01 HL114453, R01 HL097376, K24 HL123342, U01 HL137159, R01 LM012087, K08HK144820, F32 HL142172, K23 GM122069).

Bedside clinical data subphenotypes of critically ill COVID-19 patients: a cohort study

Objective

To identify more severe COVID-19 presentations.

Methods

Consecutive intensive care unit-admitted patients were subjected to a stepwise clustering method.

Results

Data from 147 patients who were on average 56 ± 16 years old with a Simplified Acute Physiological Score 3 of 72 ± 18, of which 103 (70%) needed mechanical ventilation and 46 (31%) died in the intensive care unit, were analyzed. From the clustering algorithm, two well-defined groups were found based on maximal heart rate [Cluster A: 104 (95%CI 99 - 109) beats per minute versus Cluster B: 159 (95%CI 155 - 163) beats per minute], maximal respiratory rate [Cluster A: 33 (95%CI 31 - 35) breaths per minute versus Cluster B: 50 (95%CI 47 - 53) breaths per minute], and maximal body temperature [Cluster A: 37.4 (95%CI 37.1 - 37.7)°C versus Cluster B: 39.3 (95%CI 39.1 - 39.5)°C] during the intensive care unit stay, as well as the oxygen partial pressure in the blood over the oxygen inspiratory fraction at intensive care unit admission [Cluster A: 116 (95%CI 99 - 133) mmHg versus Cluster B: 78 (95%CI 63 - 93) mmHg]. Subphenotypes were distinct in inflammation profiles, organ dysfunction, organ support, intensive care unit length of stay, and intensive care unit mortality (with a ratio of 4.2 between the groups).

Conclusion

Our findings, based on common clinical data, revealed two distinct subphenotypes with different disease courses. These results could help health professionals allocate resources and select patients for testing novel therapies.

COVID-19 versus Non-COVID-19 Acute Respiratory Distress Syndrome: Comparison of Demographics, Physiologic Parameters, Inflammatory Biomarkers, and Clinical Outcomes

Rationale: There is an urgent need for improved understanding of the mechanisms and clinical characteristics of acute respiratory distress syndrome (ARDS) due to coronavirus disease (COVID-19).Objectives: To compare key demographic and physiologic parameters, biomarkers, and clinical outcomes of COVID-19 ARDS and ARDS secondary to direct lung injury from other etiologies of pneumonia.Methods: We enrolled 27 patients with COVID-19 ARDS in a prospective, observational cohort study and compared them with a historical, pre-COVID-19 cohort of patients with viral ARDS (n = 14), bacterial ARDS (n = 21), and ARDS due to culture-negative pneumonia (n = 30). We recorded clinical demographics; measured respiratory mechanical parameters; collected serial peripheral blood specimens for measurement of plasma interleukin (IL)-6, IL-8, and IL-10; and followed patients prospectively for patient-centered outcomes. We conducted between-group comparisons with nonparametric tests and analyzed time-to-event outcomes with Kaplan-Meier and Cox proportional hazards models.Results: Patients with COVID-19 ARDS had higher body mass index and were more likely to be Black, or residents of skilled nursing facilities, compared with those with non-COVID-19 ARDS (P < 0.05). Patients with COVID-19 had lower delivered minute ventilation compared with bacterial and culture-negative ARDS (post hoc P < 0.01) but not compared with viral ARDS. We found no differences in static compliance, hypoxemic indices, or carbon dioxide clearance between groups. Patients with COVID-19 had lower IL-6 levels compared with bacterial and culture-negative ARDS at early time points after intubation but no differences in IL-6 levels compared with viral ARDS. Patients with COVID-19 had longer duration of mechanical ventilation but similar 60-day mortality in both unadjusted and adjusted analyses.Conclusions: COVID-19 ARDS bears several similarities to viral ARDS but demonstrates lower minute ventilation and lower systemic levels of IL-6 compared with bacterial and culture-negative ARDS. COVID-19 ARDS was associated with longer dependence on mechanical ventilation compared with non-COVID-19 ARDS. Such detectable differences of COVID-19 do not merit deviation from evidence-based management of ARDS but suggest priorities for clinical research to better characterize and treat this new clinical entity.

Circulating microbial cell-free DNA is associated with inflammatory host-responses in severe pneumonia

Host inflammatory responses predict worse outcome in severe pneumonia, yet little is known about what drives dysregulated inflammation. We performed metagenomic sequencing of microbial cell-free DNA (mcfDNA) in 83 mechanically ventilated patients (26 culture-positive, 41 culture-negative pneumonia, 16 uninfected controls). Culture-positive patients had higher levels of mcfDNA than those with culture-negative pneumonia and uninfected controls (p<0.005). Plasma levels of inflammatory biomarkers (fractalkine, procalcitonin, pentraxin-3 and suppression of tumorigenicity-2) were independently associated with mcfDNA levels (adjusted p<0.05) among all patients with pneumonia. Such host-microbe interactions in the systemic circulation of patients with severe pneumonia warrant further large-scale clinical and mechanistic investigations.

Latent class analysis to predict intensive care outcomes in Acute Respiratory Distress Syndrome: a proposal of two pulmonary phenotypes

Background

Acute respiratory distress syndrome remains a heterogeneous syndrome for clinicians and researchers difficulting successful tailoring of interventions and trials. To this moment, phenotyping of this syndrome has been approached by means of inflammatory laboratory panels. Nevertheless, the systemic and inflammatory expression of acute respiratory distress syndrome might not reflect its respiratory mechanics and gas exchange.

Methods

Retrospective analysis of a prospective cohort of two hundred thirty-eight patients consecutively admitted patients under mechanical ventilation presenting with acute respiratory distress syndrome. All patients received standardized monitoring of clinical variables, respiratory mechanics and computed tomography scans at predefined PEEP levels. Employing latent class analysis, an unsupervised structural equation modelling method, on respiratory mechanics, gas-exchange and computed tomography-derived gas- and tissue-volumes at a PEEP level of 5cmH₂O, distinct pulmonary phenotypes of acute respiratory distress syndrome were identified.

Results

Latent class analysis was applied to 54 respiratory mechanics, gas-exchange and CT-derived gas- and tissue-volume variables, and a two-class model identified as best fitting. Phenotype 1 (non-recruitable) presented lower respiratory system elastance, alveolar dead space and amount of potentially recruitable lung volume than phenotype 2 (recruitable). Phenotype 2 (recruitable) responded with an increase in ventilated lung tissue, compliance and PaO₂/FiO₂ ratio (p < 0.001), in addition to a decrease in alveolar dead space (p < 0.001), to a standardized recruitment manoeuvre. Patients belonging to phenotype 2 (recruitable) presented a higher intensive care mortality (hazard ratio 2.9, 95% confidence interval 1.7–2.7, p = 0.001).

Conclusions

The present study identifies two ARDS phenotypes based on respiratory mechanics, gas-exchange and computed tomography-derived gas- and tissue-volumes. These phenotypes are characterized by distinctly diverse responses to a standardized recruitment manoeuvre and by a diverging mortality. Given multicentre validation, the simple and rapid identification of these pulmonary phenotypes could facilitate enrichment of future prospective clinical trials addressing mechanical ventilation strategies in ARDS.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03578-6.

Association of the systemic host immune response with acute hyperglycemia in mechanically ventilated septic patients

Hyperglycemia during sepsis is associated with increased organ dysfunction and higher mortality. The role of the host immune response in development of hyperglycemia during sepsis remains unclear. We performed a retrospective analysis of critically ill adult septic patients requiring mechanical ventilation (n = 153) to study the relationship between hyperglycemia and ten markers of the host injury and immune response measured on the first day of ICU admission (baseline). We determined associations between each biomarker and: (1) glucose, insulin, and c-peptide levels at the time of biomarker collection by Pearson correlation; (2) average glucose and glycemic variability in the first two days of ICU admission by linear regression; and (3) occurrence of hyperglycemia (blood glucose>180mg/dL) by logistic regression. Results were adjusted for age, pre-existing diabetes mellitus, severity of illness, and total insulin and glucocorticoid dose. Baseline plasma levels of ST2 and procalcitonin were positively correlated with average blood glucose and glycemic variability in the first two days of ICU admission in unadjusted and adjusted analyses. Additionally, higher baseline ST2, IL-1ra, procalcitonin, and pentraxin-3 levels were associated with increased risk of hyperglycemia. Our results suggest associations between the host immune response and hyperglycemia in critically ill septic patients particularly implicating the interleukin-1 axis (IL-1ra), the interleukin-33 axis (ST2), and the host response to bacterial infections (procalcitonin, pentraxin-3).

Precision medicine in acute respiratory distress syndrome: workshop report and recommendations for future research

Acute respiratory distress syndrome (ARDS) is a devastating critical illness that can be triggered by a wide range of insults and remains associated with a high mortality of around 40%. The search for targeted treatment for ARDS has been disappointing, possibly due to the enormous heterogeneity within the syndrome. In this perspective from the European Respiratory Society research seminar on "Precision medicine in ARDS", we will summarise the current evidence for heterogeneity, explore the evidence in favour of precision medicine and provide a roadmap for further research in ARDS. There is evident variation in the presentation of ARDS on three distinct levels: 1) aetiological; 2) physiological and 3) biological, which leads us to the conclusion that there is no typical ARDS. The lack of a common presentation implies that intervention studies in patients with ARDS need to be phenotype aware and apply a precision medicine approach in order to avoid the lack of success in therapeutic trials that we faced in recent decades. Deeper phenotyping and integrative analysis of the sources of variation might result in identification of additional treatable traits that represent specific pathobiological mechanisms, or so-called endotypes.

Biomarkers in acute respiratory distress syndrome

PURPOSE OF REVIEW

This article provides an overview of protein biomarkers for acute respiratory distress syndrome (ARDS) and their potential use in future clinical trials.

RECENT FINDINGS

The protein biomarkers studied as indices of biological processes involved in the pathogenesis of ARDS may have diagnostic and/or prognostic value. Recently, they also proved useful for identifying ARDS phenotypes and assessing heterogeneity of treatment effect in retrospective analyses of completed clinical trials.

SUMMARY

This article summarizes the current research on ARDS biomarkers and provides insights into how they should be integrated as prognostic and predictive enrichment tools in future clinical trials.

Exploring the metabolic phenotypes associated with different host inflammation of acute respiratory distress syndrome (ARDS) from lung metabolomics in mice

RATIONALE

The aim of this study was to analyze the metabolomics of lung with different host inflammation of acute respiratory distress syndrome (ARDS) for the identification of biomarkers for predicting severity under different inflammatory conditions.

METHODS

Cecal ligation and puncture (CLP) and lipopolysaccharide (LPS)-intratracheal injection induced acute lung injury (ALI) were used. A mouse model was used to explore lung metabolomic biomarkers in ALI/ARDS. The splenectomy model was used as an auxiliary method to distinguish between hyper- and hypo-inflammatory subtypes. Plasma, lung tissue and bronchoalveolar lavage fluid (BALF) samples were collected from mice after CLP/LPS. The severity of lung injury was evaluated. Expression of tumor necrosis factor-α (TNF-α) in mice serum and lung was tested by enzyme-linked immunosorbent assay (ELISA) and polymer chain reaction (PCR). Polymorphonuclear cells in BALF were counted. The lung metabolites were detected by gas chromatography/mass spectrometry (GC/MS), and the metabolic pathways predicted using the KEGG database.

RESULTS

The LPS/CLP-Splen group had more severe lung injury than the corresponding ALI group; that in the CLP-Splen group was more serious than in the LPS-Splen group. TNF-α expression was significantly elevated in the serum and lung tissue after LPS or CLP, and higher in the LPS/CLP-Splen group than in the corresponding ALI group. The level of TNF-α in the CLP-Splen group was elevated significantly over that in the LPS-Splen group. Both these groups also showed significant neutrophil exudation within the lungs. During differential inflammation, more differential metabolites were detected in the lungs of the CLP group ALI mice than in the LPS group. A total of 41 compounds were detected in the lungs of the CLP and CLP-Splen groups. Contrastingly, eight compounds were detected in the lungs of the LPS and LPS-Splen groups. The LPS-Splen and CLP-Splen groups had significant neutrophil exudation in the lung. Random forest analysis of lung-targeted metabolomics data indicated 4-hydroxyphenylacetic acid, 1-aminocyclopentanecarboxylic acid (ACPC), cis-aconitic acid, and hydroxybenzoic acid as strong predictors of the hyper-inflammatory subgroup in the CLP group. Furthermore, with splenectomy, 13 differential metabolic pathways between the CLP and LPS groups were revealed.

CONCLUSIONS

Hyper-inflammatory subgroups of ARDS have a greater inflammatory response and a more active lung metabolism. Combined with the host inflammation background, biomarkers from metabolomics could help evaluate the response severity of ARDS.

Respiratory Tract Dysbiosis Is Associated with Worse Outcomes in Mechanically Ventilated Patients

Rationale: Host inflammatory responses have been strongly associated with adverse outcomes in critically ill patients, but the biologic underpinnings of such heterogeneous responses have not been defined.Objectives: We examined whether respiratory tract microbiome profiles are associated with host inflammation and clinical outcomes of acute respiratory failure.Methods: We collected oral swabs, endotracheal aspirates (ETAs), and plasma samples from mechanically ventilated patients. We performed 16S ribosomal RNA gene sequencing to characterize upper and lower respiratory tract microbiota and classified patients into host-response subphenotypes on the basis of clinical variables and plasma biomarkers of innate immunity and inflammation. We derived diversity metrics and composition clusters with Dirichlet multinomial models and examined our data for associations with subphenotypes and clinical outcomes.Measurements and Main Results: Oral and ETA microbial communities from 301 mechanically ventilated subjects had substantial heterogeneity in α and β diversity. Dirichlet multinomial models revealed a cluster with low α diversity and enrichment for pathogens (e.g., high Staphylococcus or Pseudomonadaceae relative abundance) in 35% of ETA samples, associated with a hyperinflammatory subphenotype, worse 30-day survival, and longer time to liberation from mechanical ventilation (adjusted P < 0.05), compared with patients with higher α diversity and relative abundance of typical oral microbiota. Patients with evidence of dysbiosis (low α diversity and low relative abundance of "protective" oral-origin commensal bacteria) in both oral and ETA samples (17%, combined dysbiosis) had significantly worse 30-day survival and longer time to liberation from mechanical ventilation than patients without dysbiosis (55%; adjusted P < 0.05).Conclusions: Respiratory tract dysbiosis may represent an important, modifiable contributor to patient-level heterogeneity in systemic inflammatory responses and clinical outcomes.

The discovery of biological subphenotypes in ARDS: a novel approach to targeted medicine?

The acute respiratory distress syndrome (ARDS) is a severe lung disorder with a high morbidity and mortality which affects all age groups. Despite active research with intense, ongoing attempts in developing pharmacological agents to treat ARDS, its mortality rate remains unaltered high and treatment is still only supportive. Over the years, there have been many attempts to identify meaningful subgroups likely to react differently to treatment among the heterogenous ARDS population, most of them unsuccessful. Only recently, analysis of large ARDS cohorts from randomized controlled trials have identified the presence of distinct biological subphenotypes among ARDS patients: a hypoinflammatory (or uninflamed; named P1) and a hyperinflammatory (or reactive; named P2) subphenotype have been proposed and corroborated with existing retrospective data. The hyperinflammatory subphenotyope was clearly associated with shock state, metabolic acidosis, and worse clinical outcomes. Core features of the respective subphenotypes were identified consistently in all assessed cohorts, independently of the studied population, the geographical location, the study design, or the analysis method. Additionally and clinically even more relevant treatment efficacies, as assessed retrospectively, appeared to be highly dependent on the respective subphenotype. This discovery launches a promising new approach to targeted medicine in ARDS. Even though it is now widely accepted that each ARDS subphenotype has distinct functional, biological, and mechanistic differences, there are crucial gaps in our knowledge, hindering the translation to bedside application. First of all, the underlying driving biological factors are still largely unknown, and secondly, there is currently no option for fast and easy identification of ARDS subphenotypes. This narrative review aims to summarize the evidence in biological subphenotyping in ARDS and tries to point out the current issues that will need addressing before translation of biological subohenotypes into clinical practice will be possible.

Coronavirus disease 2019 (COVID-19): cytokine storms, hyper-inflammatory phenotypes, and acute respiratory distress syndrome

Coronavirus Disease 2019 (COVID-19) was first identified in China at the end of 2019. Acute respiratory distress syndrome (ARDS) represents the most common and serious complication of COVID-19. Cytokine storms are a pathophysiological feature of COVID-19 and play an important role in distinguishing hyper-inflammatory subphenotypes of ARDS. Accordingly, in this review, we focus on hyper-inflammatory host responses in ARDS that play a critical role in the differentiated development of COVID-19. Furthermore, we discuss inflammation-related indicators that have the potential to identify hyper-inflammatory subphenotypes of COVID-19, especially for those with a high risk of ARDS. Finally, we explore the possibility of improving the quality of monitoring and treatment of COVID-19 patients and in reducing the incidence of critical illness and mortality via better distinguishing hyper- and hypo-inflammatory subphenotypes of COVID-19.

Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome

Artificial intelligence (AI) has found its way into clinical studies in the era of big data. Acute respiratory distress syndrome (ARDS) or acute lung injury (ALI) is a clinical syndrome that encompasses a heterogeneous population. Management of such heterogeneous patient population is a big challenge for clinicians. With accumulating ALI datasets being publicly available, more knowledge could be discovered with sophisticated analytics. We reviewed literatures with big data analytics to understand the role of AI for improving the caring of patients with ALI/ARDS. Many studies have utilized the electronic medical records (EMR) data for the identification and prognostication of ARDS patients. As increasing number of ARDS clinical trials data is open to public, secondary analysis on these combined datasets provide a powerful way of finding solution to clinical questions with a new perspective. AI techniques such as Classification and Regression Tree (CART) and artificial neural networks (ANN) have also been successfully used in the investigation of ARDS problems. Individualized treatment of ARDS could be implemented with a support from AI as we are now able to classify ARDS into many subphenotypes by unsupervised machine learning algorithms. Interestingly, these subphenotypes show different responses to a certain intervention. However, current analytics involving ARDS have not fully incorporated information from omics such as transcriptome, proteomics, daily activities and environmental conditions. AI technology is assisting us to interpret complex data of ARDS patients and enable us to further improve the management of ARDS patients in future with individual treatment plans.

Subphenotypes in critical care: translation into clinical practice

Despite progress in the supportive care available for critically ill patients, few advances have been made in the search for effective disease-modifying therapeutic options. The fact that many trials in critical care medicine have not identified a treatment benefit is probably due, in part, to the underlying heterogeneity of critical care syndromes. Numerous approaches have been proposed to divide populations of critically ill patients into more meaningful subgroups (subphenotypes), some of which might be more useful than others. Subclassification systems driven by clinical features and biomarkers have been proposed for acute respiratory distress syndrome, sepsis, acute kidney injury, and pancreatitis. Identifying the systems that are most useful and biologically meaningful could lead to a better understanding of the pathophysiology of critical care syndromes and the discovery of new treatment targets, and allow recruitment in future therapeutic trials to focus on predicted responders. This Review discusses proposed subphenotypes of critical illness syndromes and highlights the issues that will need to be addressed to translate subphenotypes into clinical practice.

Lower respiratory tract myeloid cells harbor SARS-CoV-2 and display an inflammatory phenotype

SARS-CoV-2 pneumonia may induce an aberrant immune response with brisk recruitment of myeloid cells into the lower respiratory tract, which may contribute to morbidity and mortality. We describe endotracheal aspirate samples from seven patients with SARS-CoV-2 pneumonia requiring mechanical ventilation. We note SARS-CoV-2 virions within lower respiratory tract myeloid cells shown by electron tomography, immunofluorescence confocal imaging, and immuno-electron microscopy. Endotracheal aspirates are primarily composed of mononuclear and polymorphonuclear leukocytes. These myeloid cells that harbor virus are frequently positive for CD14 and/or CD16 and most display an inflammatory phenotype marked by expression of IL-6 and tissue factor mRNA transcript and protein expression.

Increased Alternative Complement Pathway Function and Improved Survival during Critical Illness

Rationale: Complement is crucial for host defense but may also drive dysregulated inflammation. There is limited understanding of alternative complement function, which can amplify all complement activity, during critical illness.Objectives: We examined the function and key components of the alternative complement pathway in a series of critically ill patients and in a mouse pneumonia model.Methods: Total classical (CH50) and alternative complement (AH50) function were quantified in serum from 321 prospectively enrolled critically ill patients and compared with clinical outcomes. Alternative pathway (AP) regulatory factors were quantified by ELISA (n = 181) and examined via transcriptomics data from external cohorts. Wild-type, Cfb-/-, and C3-/- mice were infected intratracheally with Klebsiella pneumoniae (KP) and assessed for extrapulmonary dissemination.Measurements and Main Results: AH50 greater than or equal to median, but not CH50 greater than or equal to median, was associated with decreased 30-day mortality (adjusted odds ratio [OR], 0.53 [95% confidence interval (CI), 0.31-0.91]), independent of chronic liver disease. One-year survival was improved in patients with AH50 greater than or equal to median (adjusted hazard ratio = 0.59 [95% CI, 0.41-0.87]). Patients with elevated AH50 had increased levels of AP factors B, H, and properdin, and fewer showed a "hyperinflammatory" subphenotype (OR, 0.30 [95% CI, 0.18-0.49]). Increased expression of proximal AP genes was associated with improved survival in two external cohorts. AH50 greater than or equal to median was associated with fewer bloodstream infections (OR, 0.67 [95% CI, 0.45-0.98). Conversely, depletion of AP factors, or AH50 less than median, impaired in vitro serum control of KP that was restored by adding healthy serum. Cfb-/- mice demonstrated increased extrapulmonary dissemination and serum inflammatory markers after intratracheal KP infection compared with wild type.Conclusions: Elevated AP function is associated with improved survival during critical illness, possibly because of enhanced immune capacity.

Single cell RNA sequencing identifies an early monocyte gene signature in acute respiratory distress syndrome

Acute respiratory distress syndrome (ARDS) results from overwhelming pulmonary inflammation. Prior bulk RNA sequencing provided limited insights into ARDS pathogenesis. We used single cell RNA sequencing to probe ARDS at a higher resolution. PBMCs of patients with pneumonia and sepsis with early ARDS were compared with those of sepsis patients who did not develop ARDS. Monocyte clusters from ARDS patients revealed multiple distinguishing characteristics in comparison with monocytes from patients without ARDS, including downregulation of SOCS3 expression, accompanied by a proinflammatory signature with upregulation of multiple type I IFN-induced genes, especially in CD16+ cells. To generate an ARDS risk score, we identified upregulation of 29 genes in the monocytes of these patients, and 17 showed a similar profile in cells of patients in independent cohorts. Monocytes had increased expression of RAB11A, known to inhibit neutrophil efferocytosis; ATP2B1, a calcium pump that exports Ca2+ implicated in endothelial barrier disruption; and SPARC, associated with processing of procollagen to collagen. These data show that monocytes of ARDS patients upregulate expression of genes not just restricted to those associated with inflammation. Together, our findings identify molecules that are likely involved in ARDS pathogenesis that may inform biomarker and therapeutic development.

Acute respiratory distress syndrome subphenotypes and therapy responsive traits among preclinical models: protocol for a systematic review and meta-analysis

BACKGROUND

Subphenotypes were recently reported within clinical acute respiratory distress syndrome (ARDS), with distinct outcomes and therapeutic responses. Experimental models have long been used to mimic features of ARDS pathophysiology, but the presence of distinct subphenotypes among preclinical ARDS remains unknown. This review will investigate whether: 1) subphenotypes can be identified among preclinical ARDS models; 2) such subphenotypes can identify some responsive traits.

METHODS

We will include comparative preclinical (in vivo and ex vivo) ARDS studies published between 2009 and 2019 in which pre-specified therapies were assessed (interleukin (IL)-10, IL-2, stem cells, beta-agonists, corticosteroids, fibroblast growth factors, modulators of the receptor for advanced glycation end-products pathway, anticoagulants, and halogenated agents) and outcomes compared to a control condition. The primary outcome will be a composite of the four key features of preclinical ARDS as per the American Thoracic Society consensus conference (histologic evidence of lung injury, altered alveolar-capillary barrier, lung inflammatory response, and physiological dysfunction). Secondary outcomes will include the single components of the primary composite outcome, net alveolar fluid clearance, and death. MEDLINE, Embase, and Cochrane databases will be searched electronically and data from eligible studies will be extracted, pooled, and analyzed using random-effects models. Individual study reporting will be assessed according to the Animal Research: Reporting of In Vivo Experiments guidelines. Meta-regressions will be performed to identify subphenotypes prior to comparing outcomes across subphenotypes and treatment effects.

DISCUSSION

This study will inform on the presence and underlying pathophysiological features of subphenotypes among preclinical models of ARDS and should help to determine whether sufficient evidence exists to perform preclinical trials of subphenotype-targeted therapies, prior to potential clinical translation.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO (ID: CRD42019157236).

The evolution of radiographic edema in ARDS and its association with clinical outcomes: A prospective cohort study in adult patients

PURPOSE

To assess the longitudinal evolution of radiographic edema using chest X-rays (CXR) in patients with Acute Respiratory Distress Syndrome (ARDS) and to examine its association with prognostic biomarkers, ARDS subphenotypes and outcomes.

MATERIALS AND METHODS

We quantified radiographic edema on CXRs from patients with ARDS or cardiogenic pulmonary edema (controls) using the Radiographic Assessment of Lung Edema (RALE) score on day of intubation and up to 10 days after. We measured baseline plasma biomarkers and recorded clinical variables.

RESULTS

The RALE score had good inter-rater agreement (r = 0.83, p < 0.0001) applied on 488 CXRs from 129 patients, with higher RALE scores in patients with ARDS (n = 108) compared to controls (n = 21, p = 0.01). Baseline RALE scores were positively correlated with levels of the receptor for end-glycation end products (RAGE) in ARDS patients (p < 0.05). Baseline RALE scores were not predictive of 30- or 90-day survival. Persistently elevated RALE scores were associated with prolonged need for mechanical ventilation (p = 0.002).

CONCLUSIONS

The RALE score is easily implementable with high inter-rater reliability. Longitudinal RALE scoring appears to be a reproducible approach to track the evolution of radiographic edema in patients with ARDS and can potentially predict prolonged need for mechanical ventilation.

Metagenomic identification of severe pneumonia pathogens in mechanically-ventilated patients: a feasibility and clinical validity study

BACKGROUND

Metagenomic sequencing of respiratory microbial communities for pathogen identification in pneumonia may help overcome the limitations of culture-based methods. We examined the feasibility and clinical validity of rapid-turnaround metagenomics with Nanopore™ sequencing of clinical respiratory specimens.

METHODS

We conducted a case-control study of mechanically-ventilated patients with pneumonia (nine culture-positive and five culture-negative) and without pneumonia (eight controls). We collected endotracheal aspirates and applied a microbial DNA enrichment method prior to metagenomic sequencing with the Oxford Nanopore MinION device. For reference, we compared Nanopore results against clinical microbiologic cultures and bacterial 16S rRNA gene sequencing.

RESULTS

Human DNA depletion enabled in depth sequencing of microbial communities. In culture-positive cases, Nanopore revealed communities with high abundance of the bacterial or fungal species isolated by cultures. In four cases with resistant clinical isolates, Nanopore detected antibiotic resistance genes corresponding to the phenotypic resistance in antibiograms. In culture-negative pneumonia, Nanopore revealed probable bacterial pathogens in 1/5 cases and Candida colonization in 3/5 cases. In controls, Nanopore showed high abundance of oral bacteria in 5/8 subjects, and identified colonizing respiratory pathogens in other subjects. Nanopore and 16S sequencing showed excellent concordance for the most abundant bacterial taxa.

CONCLUSIONS

We demonstrated technical feasibility and proof-of-concept clinical validity of Nanopore metagenomics for severe pneumonia diagnosis, with striking concordance with positive microbiologic cultures, and clinically actionable information obtained from sequencing in culture-negative samples. Prospective studies with real-time metagenomics are warranted to examine the impact on antimicrobial decision-making and clinical outcomes.