Latent class analysis of ARDS subphenotypes: a secondary analysis of the statins for acutely injured lungs from sepsis (SAILS) study

Patient stratification using plasma cytokines and their regulators in sepsis: relationship to outcomes, treatment effect and leucocyte transcriptomic subphenotypes

RATIONALE

Heterogeneity of the host response within sepsis, acute respiratory distress syndrome (ARDS) and more widely critical illness, limits discovery and targeting of immunomodulatory therapies. Clustering approaches using clinical and circulating biomarkers have defined hyper-inflammatory and hypo-inflammatory subphenotypes in ARDS associated with differential treatment response. It is unknown if similar subphenotypes exist in sepsis populations where leucocyte transcriptomic-defined subphenotypes have been reported.

OBJECTIVES

We investigated whether inflammatory clusters based on cytokine protein abundance were seen in sepsis, and the relationships with previously described transcriptomic subphenotypes.

METHODS

Hierarchical cluster and latent class analysis were applied to an observational study (UK Genomic Advances in Sepsis (GAinS)) (n=124 patients) and two clinical trial datasets (VANISH, n=155 and LeoPARDS, n=484) in which the plasma protein abundance of 65, 21, 11 circulating cytokines, cytokine receptors and regulators were quantified. Clinical features, outcomes, response to trial treatments and assignment to transcriptomic subphenotypes were compared between inflammatory clusters.

MEASUREMENTS AND MAIN RESULTS

We identified two (UK GAinS, VANISH) or three (LeoPARDS) inflammatory clusters. A group with high levels of pro-inflammatory and anti-inflammatory cytokines was seen that was associated with worse organ dysfunction and survival. No interaction between inflammatory clusters and trial treatment response was found. We found variable overlap of inflammatory clusters and leucocyte transcriptomic subphenotypes.

CONCLUSIONS

These findings demonstrate that differences in response at the level of cytokine biology show clustering related to severity, but not treatment response, and may provide complementary information to transcriptomic sepsis subphenotypes.

TRIAL REGISTRATION NUMBER

ISRCTN20769191, ISRCTN12776039.

Endotyping in ARDS: one step forward in precision medicine

BACKGROUND

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.

METHODS

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.

RESULTS

The LCM suggested three subgroups (n1 = 64, n2 = 86, and n3 = 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 PaO2/FiO2 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).

PERSPECTIVE

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.

Latent class analysis of chest CT abnormalities to define subphenotypes in patients with MPO-ANCA-positive microscopic polyangiitis

BACKGROUND

Patients with microscopic polyangiitis (MPA) and positive myeloperoxidase antineutrophil cytoplasmic antibody (MPO-ANCA) may present with various abnormalities in chest computed tomography (CT). This study aimed to identify subphenotypes using latent class analysis (LCA) and to explore the relationship between the subphenotypes and clinical patterns, as well as compare the clinical characteristics of these subphenotypes in patients with MPO-ANCA-positive MPA (MPO-MPA).

METHODS

The study identified subphenotypes using LCA based on chest CT findings in 178 patients with MPO-MPA and pulmonary involvement from June 2014 to August 2022.

RESULTS

LCA identified 27 participants (15.2%) in class 1, 43 (24.1%) in class 2, 35 (19.7%) in class 3, and 73 (41.0%) in class 4. Class 1 was characterized by prominent inflammatory exudation, class 2 by fibrosis and architectural distortion, class 3 by predominantly bronchiectasis, and class 4 by lesions mixed with inflammation and fibrosis. Class 1 had the highest level of extrapulmonary disease activity, with 77.8% of patients experiencing diffuse alveolar hemorrhage. Class 2 had the lowest level of extrapulmonary disease activity, with 41.9% of patients showing usual interstitial pneumonia. Class 3 patients were more likely to have complications involving the ear, nose, and throat, as well as pulmonary infections before treatment, and they exhibited the best outcomes. The characteristics and outcomes of class 4 were intermediate among the four classes.

CONCLUSIONS

These findings suggest that bronchiectasis may represent a unique pattern of pulmonary involvement in MPO-MPA, highlighting the importance of screening for bronchiectasis in MPO-MPA and identifying optimal management strategies.

Heterogeneity of immune cells and their communications unveiled by transcriptome profiling in acute inflammatory lung injury

Background

Acute Respiratory Distress Syndrome (ARDS) or its earlier stage Acute lung injury (ALI), is a worldwide health concern that jeopardizes human well-being. Currently, the treatment strategies to mitigate the incidence and mortality of ARDS are severely restricted. This limitation can be attributed, at least in part, to the substantial variations in immunity observed in individuals with this syndrome.

Methods

Bulk and single cell RNA sequencing from ALI mice and single cell RNA sequencing from ARDS patients were analyzed. We utilized the Seurat program package in R and cellmarker 2.0 to cluster and annotate the data. The differential, enrichment, protein interaction, and cell-cell communication analysis were conducted.

Results

The mice with ALI caused by pulmonary and extrapulmonary factors demonstrated differential expression including Clec4e, Retnlg, S100a9, Coro1a, and Lars2. We have determined that inflammatory factors have a greater significance in extrapulmonary ALI, while multiple pathways collaborate in the development of pulmonary ALI. Clustering analysis revealed significant heterogeneity in the relative abundance of immune cells in different ALI models. The autocrine action of neutrophils plays a crucial role in pulmonary ALI. Additionally, there was a significant increase in signaling intensity between B cells and M1 macrophages, NKT cells and M1 macrophages in extrapulmonary ALI. The CXCL, CSF3 and MIF, TGFβ signaling pathways play a vital role in pulmonary and extrapulmonary ALI, respectively. Moreover, the analysis of human single-cell revealed DCs signaling to monocytes and neutrophils in COVID-19-associated ARDS is stronger compared to sepsis-related ARDS. In sepsis-related ARDS, CD8+ T and Th cells exhibit more prominent signaling to B-cell nucleated DCs. Meanwhile, both MIF and CXCL signaling pathways are specific to sepsis-related ARDS.

Conclusion

This study has identified specific gene signatures and signaling pathways in animal models and human samples that facilitate the interaction between immune cells, which could be targeted therapeutically in ARDS patients of various etiologies.

Inflammatory and tissue injury marker dynamics in pediatric acute respiratory distress syndrome

BACKGROUNDThe molecular signature of pediatric acute respiratory distress syndrome (ARDS) is poorly described, and the degree to which hyperinflammation or specific tissue injury contributes to outcomes is unknown. Therefore, we profiled inflammation and tissue injury dynamics over the first 7 days of ARDS, and associated specific biomarkers with mortality, persistent ARDS, and persistent multiple organ dysfunction syndrome (MODS).METHODSIn a single-center prospective cohort of intubated pediatric patients with ARDS, we collected plasma on days 0, 3, and 7. Nineteen biomarkers reflecting inflammation, tissue injury, and damage-associated molecular patterns (DAMPs) were measured. We assessed the relationship between biomarkers and trajectories with mortality, persistent ARDS, or persistent MODS using multivariable mixed effect models.RESULTSIn 279 patients (64 [23%] nonsurvivors), hyperinflammatory cytokines, tissue injury markers, and DAMPs were higher in nonsurvivors. Survivors and nonsurvivors showed different biomarker trajectories. IL-1α, soluble tumor necrosis factor receptor 1, angiopoietin 2 (ANG2), and surfactant protein D increased in nonsurvivors, while DAMPs remained persistently elevated. ANG2 and procollagen type III N-terminal peptide were associated with persistent ARDS, whereas multiple cytokines, tissue injury markers, and DAMPs were associated with persistent MODS. Corticosteroid use did not impact the association of biomarker levels or trajectory with mortality.CONCLUSIONSPediatric ARDS survivors and nonsurvivors had distinct biomarker trajectories, with cytokines, endothelial and alveolar epithelial injury, and DAMPs elevated in nonsurvivors. Mortality markers overlapped with markers associated with persistent MODS, rather than persistent ARDS.FUNDINGNIH (K23HL-136688, R01-HL148054).

Association between Age and Mortality in Pediatric and Adult Acute Respiratory Distress Syndrome

Rationale: The epidemiology, management, and outcomes of acute respiratory distress syndrome (ARDS) differ between children and adults, with lower mortality rates in children despite comparable severity of hypoxemia. However, the relationship between age and mortality is unclear.Objective: We aimed to define the association between age and mortality in ARDS, hypothesizing that it would be nonlinear.Methods: We performed a retrospective cohort study using data from two pediatric ARDS observational cohorts (n = 1,236), multiple adult ARDS trials (n = 5,547), and an adult observational ARDS cohort (n = 1,079). We aligned all datasets to meet Berlin criteria. We performed unadjusted and adjusted logistic regression using fractional polynomials to assess the potentially nonlinear relationship between age and 90-day mortality, adjusting for sex, PaO2/FiO2, immunosuppressed status, year of study, and observational versus randomized controlled trial, treating each individual study as a fixed effect.Measurements and Main Results: There were 7,862 subjects with median ages of 4 years in the pediatric cohorts, 52 years in the adult trials, and 61 years in the adult observational cohort. Most subjects (43%) had moderate ARDS by Berlin criteria. Ninety-day mortality was 19% in the pediatric cohorts, 33% in the adult trials, and 67% in the adult observational cohort. We found a nonlinear relationship between age and mortality, with mortality risk increasing at an accelerating rate between 11 and 65 years of age, after which mortality risk increased more slowly.Conclusions: There was a nonlinear relationship between age and mortality in pediatric and adult ARDS.

Pharmacologic Treatments in Acute Respiratory Failure

Acute respiratory failure relies on supportive care using non-invasive and invasive oxygen and ventilatory support. Pharmacologic therapies for the most severe form of respiratory failure, acute respiratory distress syndrome (ARDS), are limited. This review focuses on the most promising therapies for ARDS, targeting different mechanisms that contribute to dysregulated inflammation and resultant hypoxemia. Significant heterogeneity exists within the ARDS population. Treatment requires prompt recognition of ARDS and an understanding of which patients may benefit most from specific pharmacologic interventions. The key to finding effective pharmacotherapies for ARDS may rely on deeper understanding of pathophysiology and bedside identification of ARDS subphenotypes.

Molecular Phenotypes of Acute Respiratory Distress Syndrome in the ROSE Trial Have Differential Outcomes and Gene Expression Patterns That Differ at Baseline and Longitudinally over Time

Rationale: Two molecular phenotypes have been identified in acute respiratory distress syndrome (ARDS). In the ROSE (Reevaluation of Systemic Early Neuromuscular Blockade) trial of cisatracurium in moderate to severe ARDS, we addressed three unanswered questions: 1) Do the same phenotypes emerge in a more severe ARDS cohort with earlier recruitment; 2) Do phenotypes respond differently to neuromuscular blockade? and 3) What biological pathways most differentiate inflammatory phenotypes?Methods: We performed latent class analysis in ROSE using preenrollment clinical and protein biomarkers. In a subset of patients (n = 134), we sequenced whole-blood RNA using enrollment and Day 2 samples and performed differential gene expression and pathway analyses. Informed by the differential gene expression analysis, we measured additional plasma proteins and evaluated their abundance relative to gene expression amounts.Measurements and Main Results: In ROSE, we identified the hypoinflammatory (60.4%) and hyperinflammatory (39.6%) phenotypes with similar biological and clinical characteristics as prior studies, including higher mortality at Day 90 for the hyperinflammatory phenotype (30.3% vs. 61.6%; P < 0.0001). We observed no treatment interaction between the phenotypes and randomized groups for mortality. The hyperinflammatory phenotype was enriched for genes associated with innate immune response, tissue remodeling, and zinc metabolism at Day 0 and collagen synthesis and neutrophil degranulation at Day 2. Longitudinal changes in gene expression patterns differed dependent on survivorship. For most highly expressed genes, we observed correlations with their corresponding plasma proteins' abundance. However, for the class-defining plasma proteins in the latent class analysis, no correlation was observed with their corresponding genes' expression.Conclusions: The hyperinflammatory and hypoinflammatory phenotypes have different clinical, protein, and dynamic transcriptional characteristics. These findings support the clinical and biological potential of molecular phenotypes to advance precision care in ARDS.

Host and Microbe Blood Metagenomics Reveals Key Pathways Characterizing Critical Illness Phenotypes

Rationale: Two molecular phenotypes of sepsis and acute respiratory distress syndrome, termed hyperinflammatory and hypoinflammatory, have been consistently identified by latent class analysis in numerous cohorts, with widely divergent clinical outcomes and differential responses to some treatments; however, the key biological differences between these phenotypes remain poorly understood.Objectives: We used host and microbe metagenomic sequencing data from blood to deepen our understanding of biological differences between latent class analysis-derived phenotypes and to assess concordance between the latent class analysis-derived phenotypes and phenotypes reported by other investigative groups (e.g., Sepsis Response Signature [SRS1-2], molecular diagnosis and risk stratification of sepsis [MARS1-4], reactive and uninflamed).Methods: We analyzed data from 113 patients with hypoinflammatory sepsis and 76 patients with hyperinflammatory sepsis enrolled in a two-hospital prospective cohort study. Molecular phenotypes had been previously assigned using latent class analysis.Measurements and Main Results: The hyperinflammatory and hypoinflammatory phenotypes of sepsis had distinct gene expression signatures, with 5,755 genes (31%) differentially expressed. The hyperinflammatory phenotype was associated with elevated expression of innate immune response genes, whereas the hypoinflammatory phenotype was associated with elevated expression of adaptive immune response genes and, notably, T cell response genes. Plasma metagenomic analysis identified differences in prevalence of bacteremia, bacterial DNA abundance, and composition between the phenotypes, with an increased presence and abundance of Enterobacteriaceae in the hyperinflammatory phenotype. Significant overlap was observed between these phenotypes and previously identified transcriptional subtypes of acute respiratory distress syndrome (reactive and uninflamed) and sepsis (SRS1-2). Analysis of data from the VANISH trial indicated that corticosteroids might have a detrimental effect in patients with the hypoinflammatory phenotype.Conclusions: The hyperinflammatory and hypoinflammatory phenotypes have distinct transcriptional and metagenomic features that could be leveraged for precision treatment strategies.

Acute Respiratory Distress Syndrome: Definition, Diagnosis, and Routine Management

Acute respiratory distress syndrome (ARDS) is an acute inflammatory lung injury characterized by severe hypoxemic respiratory failure, bilateral opacities on chest imaging, and low lung compliance. ARDS is a heterogeneous syndrome that is the common end point of a wide variety of predisposing conditions, with complex pathophysiology and underlying mechanisms. Routine management of ARDS is centered on lung-protective ventilation strategies such as low tidal volume ventilation and targeting low airway pressures to avoid exacerbation of lung injury, as well as a conservative fluid management strategy.

Investigational pharmacological agents for the treatment of ARDS

INTRODUCTION

Acute Respiratory Distress Syndrome (ARDS) is a heterogeneous form of lung injury with severe hypoxemia and bilateral infiltrates after an inciting event that results in diffuse lung inflammation with a high mortality rate. While research in COVID-related ARDS has resulted in several pharmacotherapeutic agents that have undergone successful investigation, non-COVID ARDS studies have not resulted in many widely accepted pharmacotherapeutic agents despite exhaustive research.

AREAS COVERED

The aim of this review is to discuss adjuvant pharmacotherapies targeting non-COVID Acute Lung Injury (ALI)/ARDS and novel therapeutics in COVID associated ALI/ARDS. In ARDS, variable data may support selective use of neuromuscular blocking agents, corticosteroids and neutrophil elastase inhibitors, but are not yet universally used. COVID-ALI/ARDS has data supporting the use of IL-6 monoclonal antibodies, corticosteroids, and JAK inhibitor therapy.

EXPERT OPINION

Although ALI/ARDS modifying pharmacological agents have been identified in COVID-related disease, the data in non-COVID ALI/ARDS has been less compelling. The increased use of more specific molecular phenotyping based on physiologic parameters and biomarkers, will ensure equipoise between groups, and will likely allow more precision in confirming pharmacological agent efficacy in future studies.

Novel subtypes of severe COVID-19 respiratory failure based on biological heterogeneity: a secondary analysis of a randomized controlled trial

BACKGROUND

Despite evidence associating inflammatory biomarkers with worse outcomes in hospitalized adults with COVID-19, trials of immunomodulatory therapies have met with mixed results, likely due in part to biological heterogeneity of participants. Latent class analysis (LCA) of clinical and protein biomarker data has identified two subtypes of non-COVID acute respiratory distress syndrome (ARDS) with different clinical outcomes and treatment responses. We studied biological heterogeneity and clinical outcomes in a multi-institutional platform randomized controlled trial of adults with severe COVID-19 hypoxemic respiratory failure (I-SPY COVID).

METHODS

Clinical and plasma protein biomarker data were analyzed from 400 trial participants enrolled from September 2020 until October 2021 with severe COVID-19 requiring ≥ 6 L/min supplemental oxygen. Seventeen hypothesis-directed protein biomarkers were measured at enrollment using multiplex Luminex panels or single analyte enzyme linked immunoassay methods (ELISA). Biomarkers and clinical variables were used to test for latent subtypes and longitudinal biomarker changes by subtype were explored. A validated parsimonious model using interleukin-8, bicarbonate, and protein C was used for comparison with non-COVID hyper- and hypo-inflammatory ARDS subtypes.

RESULTS

Average participant age was 60 ± 14 years; 67% were male, and 28-day mortality was 25%. At trial enrollment, 85% of participants required high flow oxygen or non-invasive ventilation, and 97% were receiving dexamethasone. Several biomarkers of inflammation (IL-6, IL-8, IL-10, sTNFR-1, TREM-1), epithelial injury (sRAGE), and endothelial injury (Ang-1, thrombomodulin) were associated with 28- and 60-day mortality. Two latent subtypes were identified. Subtype 2 (27% of participants) was characterized by persistent derangements in biomarkers of inflammation, endothelial and epithelial injury, and disordered coagulation and had twice the mortality rate compared with Subtype 1. Only one person was classified as hyper-inflammatory using the previously validated non-COVID ARDS model.

CONCLUSIONS

We discovered evidence of two novel biological subtypes of severe COVID-19 with significantly different clinical outcomes. These subtypes differed from previously established hyper- and hypo-inflammatory non-COVID subtypes of ARDS. Biological heterogeneity may explain inconsistent findings from trials of hospitalized patients with COVID-19 and guide treatment approaches.

An appraisal of lung computer tomography in very early anti-inflammatory treatment of two different ovine ARDS phenotypes

Mortality and morbidity of Acute Respiratory Distress Syndrome (ARDS) are largely unaltered. A possible new approach to treatment of ARDS is offered by the discovery of inflammatory subphenotypes. In an ovine model of ARDS phenotypes, matching key features of the human subphenotypes, we provide an imaging characterization using computer tomography (CT). Nine animals were randomized into (a) OA (oleic acid, hypoinflammatory; n = 5) and (b) OA-LPS (oleic acid and lipopolysaccharides, hyperinflammatory; n = 4). 48 h after ARDS induction and anti-inflammatory treatment, CT scans were performed at high (H) and then low (L) airway pressure. After CT, the animals were euthanized and lung tissue was collected. OA-LPS showed a higher air fraction and OA a higher tissue fraction, resulting in more normally aerated lungs in OA-LPS in contrast to more non-aerated lung in OA. The change in lung and air volume between H and L was more accentuated in OA-LPS, indicating a higher recruitment potential. Strain was higher in OA, indicating a higher level of lung damage, while the amount of lung edema and histological lung injury were largely comparable. Anti-inflammatory treatment might be beneficial in terms of overall ventilated lung portion and recruitment potential, especially in the OA-LPS group.

Identification of four latent classes of acute respiratory distress syndrome using PaO2/FIO2 ratio: an observational cohort study

Biological phenotypes in patients with the acute respiratory distress syndrome (ARDS) have previously been described. We hypothesized that the trajectory of PaO2/FIO2 ratio could be used to identify phenotypes of ARDS. We used a retrospective cohort analysis of an ARDS database to identify latent classes in the trajectory of PaO2/FIO2 ratio over time. We included all adult patients admitted to an intensive care unit who met the Berlin criteria for ARDS over a 4-year period in tertiary adult intensive care units in Manitoba, Canada. Baseline demographics were collected along with the daily PaO2/FIO2 ratio collected on admission and on days 1-7, 14 and 28. We used joint growth mixture modeling to test whether ARDS patients exhibit distinct phenotypes with respect to both longitudinal PaO2/FIO2 ratio and survival. The resulting latent classes were compared on several demographic variables. In our study group of 209 patients, we found that four latent trajectory classes of PaO2/FIO2 ratio was optimal. These four classes differed in their baseline PaO2/FIO2 ratio and their trajectory of improvement during the 28 days of the study. Despite similar baseline characteristics the hazard for death for the classes differed over time. This difference was largely driven by withdrawal of life sustaining therapy in one of the classes. Latent classes were identified in the trajectory of the PaO2/FIO2 ratio over time, suggesting the presence of different ARDS phenotypes. Future studies should confirm the existence of this finding and determine the cause of mortality differences between classes.

Identification of molecular subphenotypes in two cohorts of paediatric ARDS

BACKGROUND

Two subphenotypes of acute respiratory distress syndrome (ARDS), hypoinflammatory and hyperinflammatory, have been reported in adults and in a single paediatric cohort. The relevance of these subphenotypes in paediatrics requires further investigation. We aimed to identify subphenotypes in two large observational cohorts of paediatric ARDS and assess their congruence with prior descriptions.

METHODS

We performed latent class analysis (LCA) separately on two cohorts using biomarkers as inputs. Subphenotypes were compared on clinical characteristics and outcomes. Finally, we assessed overlap with adult cohorts using parsimonious classifiers.

FINDINGS

In two cohorts from the Children's Hospital of Philadelphia (n=333) and from a multicentre study based at the University of California San Francisco (n=293), LCA identified two subphenotypes defined by differential elevation of biomarkers reflecting inflammation and endotheliopathy. In both cohorts, hyperinflammatory subjects had greater illness severity, more sepsis and higher mortality (41% and 28% in hyperinflammatory vs 11% and 7% in hypoinflammatory). Both cohorts demonstrated overlap with adult subphenotypes when assessed using parsimonious classifiers.

INTERPRETATION

We identified hypoinflammatory and hyperinflammatory subphenotypes of paediatric ARDS from two separate cohorts with utility for prognostic and potentially predictive, enrichment. Future paediatric ARDS trials should identify and leverage biomarker-defined subphenotypes in their analysis.

COAGULOPATHY PARAMETERS PREDICTIVE OF OUTCOMES IN SEPSIS-INDUCED ACUTE RESPIRATORY DISTRESS SYNDROME: A SUBANALYSIS OF THE TWO PROSPECTIVE MULTICENTER COHORT STUDIES

ABSTRACT

Background: Although coagulopathy is often observed in acute respiratory distress syndrome (ARDS), its clinical impact remains poorly understood. Objectives: This study aimed to clarify the coagulopathy parameters that are clinically applicable for prognostication and to determine anticoagulant indications in sepsis-induced ARDS. Method: This study enrolled patients with sepsis-derived ARDS from two nationwide multicenter, prospective observational studies. We explored coagulopathy parameters that could predict outcomes in the Focused Outcome Research on Emergency Care for Acute Respiratory Distress Syndrome, Sepsis, and Trauma (FORECAST) cohort, and the defined coagulopathy criteria were validated in the Sepsis Prognostication in Intensive Care Unit and Emergency Room-Intensive Care Unit (SPICE-ICU) cohort. The correlation between anticoagulant use and outcomes was also evaluated. Results: A total of 181 patients with sepsis-derived ARDS in the FORECAST study and 61 patients in the SPICE-ICU study were included. In a preliminary study, we found the set of prothrombin time-international normalized ratio ≥1.4 and platelet count ≤12 × 10 4 /μL, and thrombocytopenia and elongated prothrombin time (TEP) coagulopathy as the best coagulopathy parameters and used it for further analysis; the odds ratio (OR) of TEP coagulopathy for in-hospital mortality adjusted for confounding was 3.84 (95% confidence interval [CI], 1.66-8.87; P = 0.005). In the validation cohort, the adjusted OR for in-hospital mortality was 32.99 (95% CI, 2.60-418.72; P = 0.002). Although patients without TEP coagulopathy showed significant improvements in oxygenation over the first 4 days, patients with TEP coagulopathy showed no significant improvement (ΔPaO 2 /FiO 2 ratio, 24 ± 20 vs. 90 ± 9; P = 0.026). Furthermore, anticoagulant use was significantly correlated with mortality and oxygenation recovery in patients with TEP coagulopathy but not in patients without TEP coagulopathy. Conclusion: Thrombocytopenia and elongated prothrombin time coagulopathy is closely associated with better outcomes and responses to anticoagulant therapy in sepsis-induced ARDS, and our coagulopathy criteria may be clinically useful.

Subphenotypes of Acute Respiratory Distress Syndrome: Advancing Towards Precision Medicine

Acute respiratory distress syndrome (ARDS) is a common cause of severe hypoxemia defined by the acute onset of bilateral non-cardiogenic pulmonary edema. The diagnosis is made by defined consensus criteria. Supportive care, including prevention of further injury to the lungs, is the only treatment that conclusively improves outcomes. The inability to find more advanced therapies is due, in part, to the highly sensitive but relatively non-specific current syndromic consensus criteria, combining a heterogenous population of patients under the umbrella of ARDS. With few effective therapies, the morality rate remains 30% to 40%. Many subphenotypes of ARDS have been proposed to cluster patients with shared combinations of observable or measurable traits. Subphenotyping patients is a strategy to overcome heterogeneity to advance clinical research and eventually identify treatable traits. Subphenotypes of ARDS have been proposed based on radiographic patterns, protein biomarkers, transcriptomics, and/or machine-based clustering of clinical and biological variables. Some of these strategies have been reproducible across patient cohorts, but at present all have practical limitations to their implementation. Furthermore, there is no agreement on which strategy is the most appropriate. This review will discuss the current strategies for subphenotyping patients with ARDS, including the strengths and limitations, and the future directions of ARDS subphenotyping.

A New Global Definition of Acute Respiratory Distress Syndrome

Background: Since publication of the 2012 Berlin definition of acute respiratory distress syndrome (ARDS), several developments have supported the need for an expansion of the definition, including the use of high-flow nasal oxygen, the expansion of the use of pulse oximetry in place of arterial blood gases, the use of ultrasound for chest imaging, and the need for applicability in resource-limited settings. Methods: A consensus conference of 32 critical care ARDS experts was convened, had six virtual meetings (June 2021 to March 2022), and subsequently obtained input from members of several critical care societies. The goal was to develop a definition that would 1) identify patients with the currently accepted conceptual framework for ARDS, 2) facilitate rapid ARDS diagnosis for clinical care and research, 3) be applicable in resource-limited settings, 4) be useful for testing specific therapies, and 5) be practical for communication to patients and caregivers. Results: The committee made four main recommendations: 1) include high-flow nasal oxygen with a minimum flow rate of ⩾30 L/min; 2) use PaO2:FiO2 ⩽ 300 mm Hg or oxygen saturation as measured by pulse oximetry SpO2:FiO2 ⩽ 315 (if oxygen saturation as measured by pulse oximetry is ⩽97%) to identify hypoxemia; 3) retain bilateral opacities for imaging criteria but add ultrasound as an imaging modality, especially in resource-limited areas; and 4) in resource-limited settings, do not require positive end-expiratory pressure, oxygen flow rate, or specific respiratory support devices. Conclusions: We propose a new global definition of ARDS that builds on the Berlin definition. The recommendations also identify areas for future research, including the need for prospective assessments of the feasibility, reliability, and prognostic validity of the proposed global definition.

Simvastatin in Critically Ill Patients with Covid-19

BACKGROUND

The efficacy of simvastatin in critically ill patients with coronavirus disease 2019 (Covid-19) is unclear.

METHODS

In an ongoing international, multifactorial, adaptive platform, randomized, controlled trial, we evaluated simvastatin (80 mg daily) as compared with no statin (control) in critically ill patients with Covid-19 who were not receiving statins at baseline. The primary outcome was respiratory and cardiovascular organ support-free days, assessed on an ordinal scale combining in-hospital death (assigned a value of -1) and days free of organ support through day 21 in survivors; the analyis used a Bayesian hierarchical ordinal model. The adaptive design included prespecified statistical stopping criteria for superiority (>99% posterior probability that the odds ratio was >1) and futility (>95% posterior probability that the odds ratio was <1.2).

RESULTS

Enrollment began on October 28, 2020. On January 8, 2023, enrollment was closed on the basis of a low anticipated likelihood that prespecified stopping criteria would be met as Covid-19 cases decreased. The final analysis included 2684 critically ill patients. The median number of organ support-free days was 11 (interquartile range, -1 to 17) in the simvastatin group and 7 (interquartile range, -1 to 16) in the control group; the posterior median adjusted odds ratio was 1.15 (95% credible interval, 0.98 to 1.34) for simvastatin as compared with control, yielding a 95.9% posterior probability of superiority. At 90 days, the hazard ratio for survival was 1.12 (95% credible interval, 0.95 to 1.32), yielding a 91.9% posterior probability of superiority of simvastatin. The results of secondary analyses were consistent with those of the primary analysis. Serious adverse events, such as elevated levels of liver enzymes and creatine kinase, were reported more frequently with simvastatin than with control.

CONCLUSIONS

Although recruitment was stopped because cases had decreased, among critically ill patients with Covid-19, simvastatin did not meet the prespecified criteria for superiority to control. (REMAP-CAP ClinicalTrials.gov number, NCT02735707.).

Angiotensin II treatment is associated with improved oxygenation in ARDS patients with refractory vasodilatory shock

BACKGROUND

The physiological effects of renin-angiotensin system modulation in acute respiratory distress syndrome (ARDS) remain controversial and have not been investigated in randomized trials. We sought to determine whether angiotensin-II treatment is associated with improved oxygenation in shock-associated ARDS.

METHODS

Post-hoc subgroup analysis of the Angiotensin Therapy for High Output Shock (ATHOS-3) trial. We studied patients who met modified Berlin ARDS criteria at enrollment. The primary outcome was PaO2/FiO2-ratio (P:F) at 48-h adjusted for baseline P:F. Secondary outcomes included oxygenation index, ventilatory ratio, PEEP, minute-ventilation, hemodynamic measures, patients alive and ventilator-free by day-7, and mortality.

RESULTS

Of 81 ARDS patients, 34 (42%) and 47 (58%) were randomized to angiotensin-II or placebo, respectively. In angiotensin-II patients, mean P:F increased from 155 mmHg (SD: 69) at baseline to 265 mmHg (SD: 160) at hour-48 compared with no change with placebo (148 mmHg (SD: 63) at baseline versus 164 mmHg (SD: 74) at hour-48)(baseline-adjusted difference: + 98.4 mmHg [95%CI 35.2-161.5], p = 0.0028). Similarly, oxygenation index decreased by - 6.0 cmH2O/mmHg at hour-48 with angiotensin-II versus - 0.4 cmH2O/mmHg with placebo (baseline-adjusted difference: -4.8 cmH2O/mmHg, [95%CI - 8.6 to - 1.1], p = 0.0273). There was no difference in PEEP, minute ventilation, or ventilatory ratio. Twenty-two (64.7%) angiotensin-II patients had sustained hemodynamic response to treatment at hour-3 versus 17 (36.2%) placebo patients (absolute risk-difference: 28.5% [95%CI 6.5-47.0%], p = 0.0120). At day-7, 7/34 (20.6%) angiotensin-II patients were alive and ventilator-free versus 5/47(10.6%) placebo patients. Day-28 mortality was 55.9% in the angiotensin-II group versus 68.1% in the placebo group.

CONCLUSIONS

In post-hoc analysis of the ATHOS-3 trial, angiotensin-II was associated with improved oxygenation versus placebo among patients with ARDS and catecholamine-refractory vasodilatory shock. These findings provide a physiologic rationale for trials of angiotensin-II as treatment for ARDS with vasodilatory shock.

TRIAL REGISTRATION

ClinicalTrials.Gov Identifier: NCT02338843 (Registered January 14th 2015).

Current Practice Review in the Management of Acute Respiratory Distress Syndrome

Acute respiratory distress syndrome (ARDS) presents as an acute inflammatory lung injury characterized by refractory hypoxemia and non-cardiac pulmonary edema. An estimated 10% of patients in the intensive care unit and 25% of those who are mechanically ventilated are diagnosed with ARDS. Increased awareness is warranted as mortality rates remain high and delays in diagnosing ARDS are common. The COVID-19 pandemic highlights the importance of understanding ARDS management. Treatment of ARDS can be challenging due to the complexity of the disease state and conflicting existing evidence. Therefore, it is imperative that pharmacists understand both pharmacologic and non-pharmacologic treatment strategies to optimize patient care. This narrative review provides a critical evaluation of current literature describing management practices for ARDS. A review of treatment modalities and supportive care strategies will be presented.

'Immune-inflammatory markers & clinical characteristics for outcomes in hospitalized SARS-CoV-2 infected patients of Pakistan: a retrospective analysis'

OBJECTIVE

Accumulating evidence suggests the role of immune-inflammatory markers in early risk stratification and prognostication of COVID-19 patients. We aimed to evaluate their association with severity and the development of diagnostic scores with optimal thresholds in critical patients.

SETTING AND PARTICIPANTS

This retrospective case study includes hospitalized COVID-19 patients from March 2019 to March, 2022, in the developing area teaching hospital in Pakistan. Polymerase chain reaction (PCR) positive patients, n = 467 were investigated for clinical outcomes, comorbidities and disease prognosis. The plasma levels of Interleukin-6 (IL-6), Lactate dehydrogenase (LDH), C-reactive protein (CRP), Procalcitonin (PCT), ferritin and Complete blood count markers were measured.

RESULTS

Majority were males (58.8%) and patients with comorbidities had more severe disease. Hypertension and diabetes mellitus were the commonest comorbidities. Shortness of breath, myalgia and cough were the main symptoms. The hematological markers NLR, as well as the plasma levels of immune-inflammatory variables, IL-6, LDH, Procalcitonin, Erythrocyte sedimentation rate, Ferritin were markedly raised in severe and critical patients (p < 0.0001 for these markers). ROC analysis supports IL-6 as the most accurate marker with high prognostic relevance with proposed cut-off threshold (43 pg/ml), determining >90% of patients in terms of COVID-19 severity (AUC = 0.93, 91.7%, se; 90.3%sp). Furthermore, positive correlation with all other markers including NLR with cut-off = 2.99 (AUC = 0.87, se = 89.8%, sp = 88.4%), CRP with cut-offs at 42.9 mg/l, (AUC = 0.883, se = 89.3% and sp = 78.6%), LDH cut-off at 267μg/L, evidenced in >80% patients (AUC = 0.834 se = 84% and sp = 80%). Additionally, ESR and ferritin have the corresponding AUC 0.81 and 0.813 with cut-off at 55 mm/hr and 370, respectively.

CONCLUSION

Investigating the immune-inflammatory markers can assist physicians in providing prompt treatment and ICU admission in terms of COVID-19 severity. As a result, which may reduce the overall mortality of COVID-19 patients.

Inflammatory subphenotypes in patients at risk of ARDS: evidence from the LIPS-A trial

PURPOSE

Latent class analysis (LCA) has identified hyper- and non-hyper-inflammatory subphenotypes in patients with acute respiratory distress syndrome (ARDS). It is unknown how early inflammatory subphenotypes can be identified in patients at risk of ARDS. We aimed to test for inflammatory subphenotypes upon presentation to the emergency department.

METHODS

LIPS-A was a trial of aspirin to prevent ARDS in at-risk patients presenting to the emergency department. In this secondary analysis, we performed LCA using clinical, blood test, and biomarker variables.

RESULTS

Among 376 (96.4%) patients from the LIPS-A trial, two classes were identified upon presentation to the emergency department (day 0): 72 (19.1%) patients demonstrated characteristics of a hyper-inflammatory and 304 (80.9%) of a non-hyper-inflammatory subphenotype. 15.3% of patients in the hyper- and 8.2% in the non-hyper-inflammatory class developed ARDS (p = 0.07). Patients in the hyper-inflammatory class had fewer ventilator-free days (median [interquartile range, IQR] 28[23-28] versus 28[27-28]; p = 0.010), longer intensive care unit (3[2-6] versus 0[0-3] days; p < 0.001) and hospital (9[6-18] versus 5[3-9] days; p < 0.001) length of stay, and higher 1-year mortality (34.7% versus 20%; p = 0.008). Subphenotypes were identified on day 1 and 4 in a subgroup with available data (n = 244). 77.9% of patients remained in their baseline class throughout day 4. Patients with a hyper-inflammatory subphenotype throughout the study period (n = 22) were at higher risk of ARDS (36.4% versus 10.4%; p = 0.003).

CONCLUSION

Hyper- and non-hyper-inflammatory subphenotypes may precede ARDS development, remain identifiable over time, and can be identified upon presentation to the emergency department. A hyper-inflammatory subphenotype predicts worse outcomes.

COVID-19 subphenotypes at hospital admission are associated with mortality: a cross-sectional study

BACKGROUND

We have an incomplete understanding of COVID-19 characteristics at hospital presentation and whether underlying subphenotypes are associated with clinical outcomes and therapeutic responses.

METHODS

For this cross-sectional study, we extracted electronic health data from adults hospitalized between 1 March and 30 August 2020 with a PCR-confirmed diagnosis of COVID-19 at five New York City Hospitals. We obtained clinical and laboratory data from the first 24 h of the patient's hospitalization. Treatment with tocilizumab and convalescent plasma was assessed over hospitalization. The primary outcome was mortality; secondary outcomes included intubation, intensive care unit (ICU) admission and length of stay (LOS). First, we employed latent class analysis (LCA) to identify COVID-19 subphenotypes on admission without consideration of outcomes and assigned each patient to a subphenotype. We then performed robust Poisson regression to examine associations between COVID-19 subphenotype assignment and outcome. We explored whether the COVID-19 subphenotypes had a differential response to tocilizumab and convalescent plasma therapies.

RESULTS

A total of 4620 patients were included. LCA identified six subphenotypes, which were distinct by level of inflammation, clinical and laboratory derangements and ranged from a hypoinflammatory subphenotype with the fewest derangements to a hyperinflammatory with multiorgan dysfunction subphenotypes. Multivariable regression analyses found differences in risk for mortality, intubation, ICU admission and LOS, as compared to the hypoinflammatory subphenotype. For example, in multivariable analyses the moderate inflammation with fever subphenotype had 3.29 times the risk of mortality (95% CI 2.05, 5.28), while the hyperinflammatory with multiorgan failure subphenotype had 17.87 times the risk of mortality (95% CI 11.56, 27.63), as compared to the hypoinflammatory subphenotype. Exploratory analyses suggested that subphenotypes may differential respond to convalescent plasma or tocilizumab therapy.

CONCLUSION

COVID-19 subphenotype at hospital admission may predict risk for mortality, ICU admission and intubation and differential response to treatment.KEY MESSAGEThis cross-sectional study of COVID patients admitted to the Mount Sinai Health System, identified six distinct COVID subphenotypes on admission. Subphenotypes correlated with ICU admission, intubation, mortality and differential response to treatment.

Trajectories of Host-Response Subphenotypes in Patients With COVID-19 Across the Spectrum of Respiratory Support

BACKGROUND

Hospitalized patients with severe COVID-19 follow heterogeneous clinical trajectories, requiring different levels of respiratory support and experiencing diverse clinical outcomes. Differences in host immune responses to SARS-CoV-2 infection may account for the heterogeneous clinical course, but we have limited data on the dynamic evolution of systemic biomarkers and related subphenotypes. Improved understanding of the dynamic transitions of host subphenotypes in COVID-19 may allow for improved patient selection for targeted therapies.

RESEARCH QUESTION

We examined the trajectories of host-response profiles in severe COVID-19 and evaluated their prognostic impact on clinical outcomes.

STUDY DESIGN AND METHODS

In this prospective observational study, we enrolled 323 inpatients with COVID-19 receiving different levels of baseline respiratory support: (1) low-flow oxygen (37%), (2) noninvasive ventilation (NIV) or high-flow oxygen (HFO; 29%), (3) invasive mechanical ventilation (27%), and (4) extracorporeal membrane oxygenation (7%). We collected plasma samples on enrollment and at days 5 and 10 to measure host-response biomarkers. We classified patients by inflammatory subphenotypes using two validated predictive models. We examined clinical, biomarker, and subphenotype trajectories and outcomes during hospitalization.

RESULTS

IL-6, procalcitonin, and angiopoietin 2 persistently were elevated in patients receiving higher levels of respiratory support, whereas soluble receptor of advanced glycation end products (sRAGE) levels displayed the inverse pattern. Patients receiving NIV or HFO at baseline showed the most dynamic clinical trajectory, with 24% eventually requiring intubation and exhibiting worse 60-day mortality than patients receiving invasive mechanical ventilation at baseline (67% vs 35%; P < .0001). sRAGE levels predicted NIV failure and worse 60-day mortality for patients receiving NIV or HFO, whereas IL-6 levels were predictive in all patients regardless of level of support (P < .01). Patients classified to a hyperinflammatory subphenotype at baseline (< 10%) showed worse 60-day survival (P < .0001) and 50% of them remained classified as hyperinflammatory at 5 days after enrollment.

INTERPRETATION

Longitudinal study of the systemic host response in COVID-19 revealed substantial and predictive interindividual variability influenced by baseline levels of respiratory support.

Elevated Plasma Interleukin-18 Identifies High-Risk Acute Respiratory Distress Syndrome Patients not Distinguished by Prior Latent Class Analyses Using Traditional Inflammatory Cytokines: A Retrospective Analysis of Two Randomized Clinical Trials

OBJECTIVES

Interleukin-18 (IL-18) plasma level and latent class analysis (LCA) have separately been shown to predict prognosis and treatment response in acute respiratory distress syndrome (ARDS). IL-18 is a measure of inflammasome activation, a pathway potentially distinct from inflammation captured by biomarkers defining previously published LCA classes. We hypothesized that elevated IL-18 would identify distinct "high-risk" patients not captured by prior LCA classifications.

DESIGN

Statins for acutely injured lungs from sepsis (SAILS) and hydroxymethylglutaryl-CoA reductase inhibition with simvastatin in acute lung injury to reduce pulmonary dysfunction trial (HARP-2) are two large randomized, controlled trials in ARDS in which both LCA assignments and IL-18 levels were shown to predict mortality. We first evaluated the overlap between high IL-18 levels (≥ 800 pg/mL) with prior LCA class assignments using McNemar's test and then tested the correlation between IL-18 and LCA biomarkers using Pearson's exact test on log-2 transformed values. Our primary analysis was the association of IL-18 level with 60-day mortality in the hypoinflammatory LCA class, which was assessed using the Fisher exact test and Cox proportional hazards modeling adjusting for age, Acute Physiology and Chronic Health Evaluation score, and gender. Secondary analyses included the association of IL-18 and LCA with mortality within each IL-18/LCA subgroup.

SETTING

Secondary analysis of two multicenter, randomized controlled clinical trials of ARDS patients.

SUBJECTS

Six hundred eighty-three patients in SAILS and 511 patients in HARP-2.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We found that 33% of patients in SAILS and HARP-2 were discordant by IL-18 level and LCA class. We further found that IL-18 level was only modestly correlated (0.17-0.47) with cytokines used in the LCA assignment. A substantial subset of individuals classified as hypoinflammatory by LCA (14% of SAILS and 43% of HARP-2) were classified as high risk by elevated IL-18. These individuals were at high risk for mortality in both SAILS (42% 60-d mortality, odds ratio [OR] 3.3; 95% CI, 1.8-6.1; p < 0.001) and HARP-2 (27% 60-d mortality, OR 2.1; 95% CI, 1.2-3.8; p = 0.009).

CONCLUSIONS

Plasma IL-18 level provides important additional prognostic information to LCA subphenotypes defined largely by traditional inflammatory biomarkers in two large ARDS cohorts.

Acute respiratory distress syndrome heterogeneity and the septic ARDS subgroup

Acute respiratory distress syndrome (ARDS) is an acute diffuse inflammatory lung injury characterized by the damage of alveolar epithelial cells and pulmonary capillary endothelial cells. It is mainly manifested by non-cardiogenic pulmonary edema, resulting from intrapulmonary and extrapulmonary risk factors. ARDS is often accompanied by immune system disturbance, both locally in the lungs and systemically. As a common heterogeneous disease in critical care medicine, researchers are often faced with the failure of clinical trials. Latent class analysis had been used to compensate for poor outcomes and found that targeted treatment after subgrouping contribute to ARDS therapy. The subphenotype of ARDS caused by sepsis has garnered attention due to its refractory nature and detrimental consequences. Sepsis stands as the most predominant extrapulmonary cause of ARDS, accounting for approximately 32% of ARDS cases. Studies indicate that sepsis-induced ARDS tends to be more severe than ARDS caused by other factors, leading to poorer prognosis and higher mortality rate. This comprehensive review delves into the immunological mechanisms of sepsis-ARDS, the heterogeneity of ARDS and existing research on targeted treatments, aiming to providing mechanism understanding and exploring ideas for accurate treatment of ARDS or sepsis-ARDS.

Subphenotypes in patients with acute respiratory distress syndrome treated with high-flow oxygen

BACKGROUND

Acute respiratory distress syndrome (ARDS) subphenotypes differ in outcomes and treatment responses. Subphenotypes in high-flow nasal oxygen (HFNO)-treated ARDS patients have not been investigated.

OBJECTIVES

To identify biological subphenotypes in HFNO-treated ARDS patients.

METHODS

Secondary analysis of a prospective multicenter observational study including ARDS patients supported with HFNO. Plasma inflammation markers (interleukin [IL]-6, IL-8, and IL-33 and soluble suppression of tumorigenicity-2 [sST2]) and lung epithelial (receptor for advanced glycation end products [RAGE] and surfactant protein D [SP-D]) and endothelial (angiopoietin-2 [Ang-2]) injury were measured. These biomarkers and bicarbonate were used in K-means cluster analysis to identify subphenotypes. Logistic regression was performed on biomarker combinations to predict clustering. We chose the model with the best AUROC and the lowest number of variables. This model was used to describe the HAIS (High-flow ARDS Inflammatory Subphenotype) score.

RESULTS

Among 41 HFNO patients, two subphenotypes were identified. Hyperinflammatory subphenotype (n = 17) showed higher biomarker levels than hypoinflammatory (n = 24). Despite similar baseline characteristics, the hyperinflammatory subphenotype had higher 60-day mortality (47 vs 8.3% p = 0.014) and longer ICU length of stay (22.0 days [18.0-30.0] vs 39.5 [25.5-60.0], p = 0.034). The HAIS score, based on IL-8 and sST2, accurately distinguished subphenotypes (AUROC 0.96 [95%CI: 0.90-1.00]). A HAIS score ≥ 7.45 was predictor of hyperinflammatory subphenotype.

CONCLUSION

ARDS patients treated with HFNO exhibit two biological subphenotypes that have similar clinical characteristics, but hyperinflammatory patients have worse outcomes. The HAIS score may identify patients with hyperinflammatory subphenotype and might be used for enrichment strategies in future clinical trials.

Uncovering heterogeneity in sepsis: a comparative analysis of subphenotypes

PURPOSE

The heterogeneity in sepsis is held responsible, in part, for the lack of precision treatment. Many attempts to identify subtypes of sepsis patients identify those with shared underlying biology or outcomes. To date, though, there has been limited effort to determine overlap across these previously identified subtypes. We aimed to determine the concordance of critically ill patients with sepsis classified by four previously described subtype strategies.

METHODS

This secondary analysis of a multicenter prospective observational study included 522 critically ill patients with sepsis assigned to four previously established subtype strategies, primarily based on: (i) clinical data in the electronic health record (α, β, γ, and δ), (ii) biomarker data (hyper- and hypoinflammatory), and (iii-iv) transcriptomic data (Mars1-Mars4 and SRS1-SRS2). Concordance was studied between different subtype labels, clinical characteristics, biological host response aberrations, as well as combinations of subtypes by sepsis ensembles.

RESULTS

All four subtype labels could be adjudicated in this cohort, with the distribution of the clinical subtype varying most from the original cohort. The most common subtypes in each of the four strategies were γ (61%), which is higher compared to the original classification, hypoinflammatory (60%), Mars2 (35%), and SRS2 (54%). There was no clear relationship between any of the subtyping approaches (Cramer's V = 0.086-0.456). Mars2 and SRS1 were most alike in terms of host response biomarkers (p = 0.079-0.424), while other subtype strategies showed no clear relationship. Patients enriched for multiple subtypes revealed that characteristics and outcomes differ dependent on the combination of subtypes made.

CONCLUSION

Among critically ill patients with sepsis, subtype strategies using clinical, biomarker, and transcriptomic data do not identify comparable patient populations and are likely to reflect disparate clinical characteristics and underlying biology.

Identifying molecular phenotypes in sepsis: an analysis of two prospective observational cohorts and secondary analysis of two randomised controlled trials

BACKGROUND

In sepsis and acute respiratory distress syndrome (ARDS), heterogeneity has contributed to difficulty identifying effective pharmacotherapies. In ARDS, two molecular phenotypes (hypoinflammatory and hyperinflammatory) have consistently been identified, with divergent outcomes and treatment responses. In this study, we sought to derive molecular phenotypes in critically ill adults with sepsis, determine their overlap with previous ARDS phenotypes, and evaluate whether they respond differently to treatment in completed sepsis trials.

METHODS

We used clinical data and plasma biomarkers from two prospective sepsis cohorts, the Validating Acute Lung Injury biomarkers for Diagnosis (VALID) study (N=1140) and the Early Assessment of Renal and Lung Injury (EARLI) study (N=818), in latent class analysis (LCA) to identify the optimal number of classes in each cohort independently. We used validated models trained to classify ARDS phenotypes to evaluate concordance of sepsis and ARDS phenotypes. We applied these models retrospectively to the previously published Prospective Recombinant Human Activated Protein C Worldwide Evaluation in Severe Sepsis and Septic Shock (PROWESS-SHOCK) trial and Vasopressin and Septic Shock Trial (VASST) to assign phenotypes and evaluate heterogeneity of treatment effect.

FINDINGS

A two-class model best fit both VALID and EARLI (p<0·0001). In VALID, 804 (70·5%) of the 1140 patients were classified as hypoinflammatory and 336 (29·5%) as hyperinflammatory; in EARLI, 530 (64·8%) of 818 were hypoinflammatory and 288 (35·2%) hyperinflammatory. We observed higher plasma pro-inflammatory cytokines, more vasopressor use, more bacteraemia, lower protein C, and higher mortality in the hyperinflammatory than in the hypoinflammatory phenotype (p<0·0001 for all). Classifier models indicated strong concordance between sepsis phenotypes and previously identified ARDS phenotypes (area under the curve 0·87-0·96, depending on the model). Findings were similar excluding participants with both sepsis and ARDS. In PROWESS-SHOCK, 1142 (68·0%) of 1680 patients had the hypoinflammatory phenotype and 538 (32·0%) had the hyperinflammatory phenotype, and response to activated protein C differed by phenotype (p=0·0043). In VASST, phenotype proportions were similar to other cohorts; however, no treatment interaction with the type of vasopressor was observed (p=0·72).

INTERPRETATION

Molecular phenotypes previously identified in ARDS are also identifiable in multiple sepsis cohorts and respond differently to activated protein C. Molecular phenotypes could represent a treatable trait in critical illness beyond the patient's syndromic diagnosis.

FUNDING

US National Institutes of Health.

Development of genomic phenotype and immunophenotype of acute respiratory distress syndrome using autophagy and metabolism-related genes

Background

Distinguishing ARDS phenotypes is of great importance for its precise treatment. In the study, we attempted to ascertain its phenotypes based on metabolic and autophagy-related genes and infiltrated immune cells.

Methods

Transcription datasets of ARDS patients were obtained from Gene expression omnibus (GEO), autophagy and metabolic-related genes were from the Human Autophagy Database and the GeneCards Database, respectively. Autophagy and metabolism-related differentially expressed genes (AMRDEGs) were further identified by machine learning and processed for constructing the nomogram and the risk prediction model. Functional enrichment analyses of differentially expressed genes were performed between high- and low-risk groups. According to the protein-protein interaction network, these hub genes closely linked to increased risk of ARDS were identified with CytoHubba. ssGSEA and CIBERSORT was applied to analyze the infiltration pattern of immune cells in ARDS. Afterwards, immunologically characterized and molecular phenotypes were constructed according to infiltrated immune cells and hub genes.

Results

A total of 26 AMRDEGs were obtained, and CTSB and EEF2 were identified as crucial AMRDEGs. The predictive capability of the risk score, calculated based on the expression levels of CTSB and EEF2, was robust for ARDS in both the discovery cohort (AUC = 1) and the validation cohort (AUC = 0.826). The mean risk score was determined to be 2.231332, and based on this score, patients were classified into high-risk and low-risk groups. 371 differential genes in high- and low-risk groups were analyzed. ITGAM, TYROBP, ITGB2, SPI1, PLEK, FGR, MPO, S100A12, HCK, and MYC were identified as hub genes. A total of 12 infiltrated immune cells were differentially expressed and have correlations with hub genes. According to hub genes and implanted immune cells, ARDS patients were divided into two different molecular phenotypes (Group 1: n = 38; Group 2: n = 19) and two immune phenotypes (Cluster1: n = 22; Cluster2: n = 35), respectively.

Conclusion

This study picked up hub genes of ARDS related to autophagy and metabolism and clustered ARDS patients into different molecular phenotypes and immunophenotypes, providing insights into the precision medicine of treating patients with ARDS.

Application of anti-inflammatory treatment in two different ovine Acute Respiratory Distress Syndrome injury models: a preclinical randomized intervention study

Whilst the presence of 2 subphenotypes among the heterogenous Acute Respiratory Distress Syndrome (ARDS) population is becoming clinically accepted, subphenotype-specific treatment efficacy has yet to be prospectively tested. We investigated anti-inflammatory treatment in different ARDS models in sheep, previously shown similarities to human ARDS subphenotypes, in a preclinical, randomized, blinded study. Thirty anesthetized sheep were studied up to 48 h and randomized into: (a) OA: oleic acid (n = 15) and (b) OA-LPS: oleic acid and subsequent lipopolysaccharide (n = 15) to achieve a PaO2/FiO2 ratio of < 150 mmHg. Then, animals were randomly allocated to receive treatment with methylprednisolone or erythromycin or none. Assessed outcomes were oxygenation, pulmonary mechanics, hemodynamics and survival. All animals reached ARDS. Treatment with methylprednisolone, but not erythromycin, provided the highest therapeutic benefit in Ph2 animals, leading to a significant increase in PaO2/FiO2 ratio by reducing pulmonary edema, dead space ventilation and shunt fraction. Animals treated with methylprednisolone displayed a higher survival up to 48 h than all others. In animals treated with erythromycin, there was no treatment benefit regarding assessed physiological parameters and survival in both phenotypes. Treatment with methylprednisolone improves oxygenation and survival, more so in ovine phenotype 2 which resembles the human hyperinflammatory subphenotype.

Imaging Reactive Oxygen Radicals in Excised Mouse Lung Trapped by Reaction with Hydroxylamine Probes Using 1 GHz Rapid Scan Electron Paramagnetic Resonance

PURPOSE

Oxidative stress is proposed to be critical in acute lung disease, but methods to monitor radicals in lungs are lacking. Our goal is to develop low-frequency electron paramagnetic resonance (EPR) methods to monitor radicals that contribute to the disease.

PROCEDURES

Free radicals generated in a lipopolysaccharide-induced mouse model of acute respiratory distress syndrome reacted with cyclic hydroxylamines CPH (1-hydroxy-3-carboxy-2,2,5,5-tetramethylpyrrolidine hydrochloride) and DCP-AM-H (4-acetoxymethoxycarbonyl-1-hydroxy-2,2,5,5-tetramethylpyrrolidine-3-carboxylic acid), which were converted into the corresponding nitroxide radicals, CP• and DCP•. The EPR signals of the nitroxide radicals in excised lungs were imaged with a 1 GHz EPR spectrometer/imager that employs rapid scan technology.

RESULTS

The small numbers of nitroxides formed by reaction of the hydroxylamine with superoxide result in low signal-to-noise in the spectra and images. However, since the spectral properties of the nitroxides are known, we can use prior knowledge of the line shape and hyperfine splitting to fit the noisy data, yielding well-defined spectra and images. Two-dimensional spectral-spatial images are shown for lung samples containing (4.5 ± 0.5) ×1014 CP• and (9.9 ± 1.0) ×1014 DCP• nitroxide spins. These results suggest that a probe that accumulates in cells gives a stronger nitroxide signal than a probe that is more easily washed out of cells.

CONCLUSION

The nitroxide radicals in excised mouse lungs formed by reaction with hydroxylamine probes CPH and DCP-AM-H can be imaged at 1 GHz.

Identification and Prediction of Clinical Phenotypes in Hospitalized Patients With COVID-19: Machine Learning From Medical Records

BACKGROUND

There is significant heterogeneity in disease progression among hospitalized patients with COVID-19. The pathogenesis of SARS-CoV-2 infection is attributed to a complex interplay between virus and host immune response that in some patients unpredictably and rapidly leads to "hyperinflammation" associated with increased risk of mortality. The early identification of patients at risk of progression to hyperinflammation may help inform timely therapeutic decisions and lead to improved outcomes.

OBJECTIVE

The primary objective of this study was to use machine learning to reproducibly identify specific risk-stratifying clinical phenotypes across hospitalized patients with COVID-19 and compare treatment response characteristics and outcomes. A secondary objective was to derive a predictive phenotype classification model using routinely available early encounter data that may be useful in informing optimal COVID-19 bedside clinical management.

METHODS

This was a retrospective analysis of electronic health record data of adult patients (N=4379) who were admitted to a Johns Hopkins Health System hospital for COVID-19 treatment from 2020 to 2021. Phenotypes were identified by clustering 38 routine clinical observations recorded during inpatient care. To examine the reproducibility and validity of the derived phenotypes, patient data were randomly divided into 2 cohorts, and clustering analysis was performed independently for each cohort. A predictive phenotype classifier using the gradient-boosting machine method was derived using routine clinical observations recorded during the first 6 hours following admission.

RESULTS

A total of 2 phenotypes (designated as phenotype 1 and phenotype 2) were identified in patients admitted for COVID-19 in both the training and validation cohorts with similar distributions of features, correlations with biomarkers, treatments, comorbidities, and outcomes. In both the training and validation cohorts, phenotype-2 patients were older; had elevated markers of inflammation; and were at an increased risk of requiring intensive care unit-level care, developing sepsis, and mortality compared with phenotype-1 patients. The gradient-boosting machine phenotype prediction model yielded an area under the curve of 0.89 and a positive predictive value of 0.83.

CONCLUSIONS

Using machine learning clustering, we identified and internally validated 2 clinical COVID-19 phenotypes with distinct treatment or response characteristics consistent with similar 2-phenotype models derived from other hospitalized populations with COVID-19, supporting the reliability and generalizability of these findings. COVID-19 phenotypes can be accurately identified using machine learning models based on readily available early encounter clinical data. A phenotype prediction model based on early encounter data may be clinically useful for timely bedside risk stratification and treatment personalization.

Leveraging Data Science and Novel Technologies to Develop and Implement Precision Medicine Strategies in Critical Care

Precision medicine aims to identify treatments that are most likely to result in favorable outcomes for subgroups of patients with similar clinical and biological characteristics. The gaps for the development and implementation of precision medicine strategies in the critical care setting are many, but the advent of data science and multi-omics approaches, combined with the rich data ecosystem in the intensive care unit, offer unprecedented opportunities to realize the promise of precision critical care. In this article, the authors review the data-driven and technology-based approaches being leveraged to discover and implement precision medicine strategies in the critical care setting.

Peripheral Blood Omics and Other Multiplex-based Systems in Pulmonary and Critical Care Medicine

Over the last years, the use of peripheral blood-derived big datasets in combination with machine learning technology has accelerated the understanding, prediction, and management of pulmonary and critical care conditions. The goal of this article is to provide readers with an introduction to the methods and applications of blood omics and other multiplex-based technologies in the pulmonary and critical care medicine setting to better appreciate the current literature in the field. To accomplish that, we provide essential concepts needed to rationalize this approach and introduce readers to the types of molecules that can be obtained from the circulating blood to generate big datasets; elaborate on the differences between bulk, sorted, and single-cell approaches; and the basic analytical pipelines required for clinical interpretation. Examples of peripheral blood-derived big datasets used in recent literature are presented, and limitations of that technology are highlighted to qualify both the current and future value of these methodologies.

Estimating the attributable fraction of mortality from acute respiratory distress syndrome to inform enrichment in future randomised clinical trials

BACKGROUND

Efficiency of randomised clinical trials of acute respiratory distress syndrome (ARDS) depends on the fraction of deaths attributable to ARDS (AFARDS) to which interventions are targeted. Estimates of AFARDS in subpopulations of ARDS could improve design of ARDS trials.

METHODS

We performed a matched case-control study using the Large observational study to UNderstand the Global impact of Severe Acute respiratory FailurE cohort. Primary outcome was intensive care unit mortality. We used nearest neighbour propensity score matching without replacement to match ARDS to non-ARDS populations. We derived two separate AFARDS estimates by matching patients with ARDS to patients with non-acute hypoxaemic respiratory failure (non-AHRF) and to patients with AHRF with unilateral infiltrates only (AHRF-UL). We also estimated AFARDS in subgroups based on severity of hypoxaemia, number of lung quadrants involved and hyperinflammatory versus hypoinflammatory phenotypes. Additionally, we derived AFAHRF estimates by matching patients with AHRF to non-AHRF controls, and AFAHRF-UL estimates by matching patients with AHRF-UL to non-AHRF controls.

RESULTS

Estimated AFARDS was 20.9% (95% CI 10.5% to 31.4%) when compared with AHRF-UL controls and 38.0% (95% CI 34.4% to 41.6%) compared with non-AHRF controls. Within subgroups, estimates for AFARDS compared with AHRF-UL controls were highest in patients with severe hypoxaemia (41.1% (95% CI 25.2% to 57.1%)), in those with four quadrant involvement on chest radiography (28.9% (95% CI 13.4% to 44.3%)) and in the hyperinflammatory subphenotype (26.8% (95% CI 6.9% to 46.7%)). Estimated AFAHRF was 33.8% (95% CI 30.5% to 37.1%) compared with non-AHRF controls. Estimated AFAHRF-UL was 21.3% (95% CI 312.8% to 29.7%) compared with non-AHRF controls.

CONCLUSIONS

Overall AFARDS mean values were between 20.9% and 38.0%, with higher AFARDS seen with severe hypoxaemia, four quadrant involvement on chest radiography and hyperinflammatory ARDS.

Clinical Sepsis Phenotypes in Critically Ill Patients

Sepsis, defined as the life-threatening dysregulated host response to an infection leading to organ dysfunction, is considered as one of the leading causes of mortality worldwide, especially in intensive care units (ICU). Moreover, sepsis remains an enigmatic clinical syndrome, with complex pathophysiology incompletely understood and a great heterogeneity both in terms of clinical expression, patient response to currently available therapeutic interventions and outcomes. This heterogeneity proves to be a major obstacle in our quest to deliver improved treatment in septic critical care patients; thus, identification of clinical phenotypes is absolutely necessary. Although this might be seen as an extremely difficult task, nowadays, artificial intelligence and machine learning techniques can be recruited to quantify similarities between individuals within sepsis population and differentiate them into distinct phenotypes regarding not only temperature, hemodynamics or type of organ dysfunction, but also fluid status/responsiveness, trajectories in ICU and outcome. Hopefully, we will eventually manage to determine both the subgroup of septic patients that will benefit from a therapeutic intervention and the correct timing of applying the intervention during the disease process.

Biomarker-Based Risk Stratification in Pediatric Sepsis From a Low-Middle Income Country

OBJECTIVES

Most biomarker studies of sepsis originate from high-income countries, whereas mortality risk is higher in low- and middle-income countries. The second version of the Pediatric Sepsis Biomarker Risk Model (PERSEVERE-II) has been validated in multiple North American PICUs for prognosis. Given differences in epidemiology, we assessed the performance of PERSEVERE-II in septic children from Pakistan, a low-middle income country. Due to uncertainty regarding how well PERSEVERE-II would perform, we also assessed the utility of other select biomarkers reflecting endotheliopathy, coagulopathy, and lung injury.

DESIGN

Prospective cohort study.

SETTING

PICU in Aga Khan University Hospital in Karachi, Pakistan.

PATIENTS

Children (< 18 yr old) meeting pediatric modifications of adult Sepsis-3 criteria between November 2020 and February 2022 were eligible.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Plasma was collected within 24 hours of admission and biomarkers quantified. The area under the receiver operating characteristic curve for PERSEVERE-II to discriminate 28-day mortality was determined. Additional biomarkers were compared between survivors and nonsurvivors and between subjects with and without acute respiratory distress syndrome. In 86 subjects (20 nonsurvivors, 23%), PERSEVERE-II discriminated mortality (area under the receiver operating characteristic curve, 0.83; 95% CI, 0.72-0.94) and stratified the cohort into low-, medium-, and high-risk of mortality. Biomarkers reflecting endotheliopathy (angiopoietin 2, intracellular adhesion molecule 1) increased across worsening risk strata. Angiopoietin 2, soluble thrombomodulin, and plasminogen activator inhibitor 1 were higher in nonsurvivors, and soluble receptor for advanced glycation end-products and surfactant protein D were higher in children meeting acute respiratory distress syndrome criteria.

CONCLUSIONS

PERSEVERE-II performs well in septic children from Aga Khan University Hospital, representing the first validation of PERSEVERE-II in a low-middle income country. Patients possessed a biomarker profile comparable to that of sepsis from high-income countries, suggesting that biomarker-based enrichment strategies may be effective in this setting.

ESICM guidelines on acute respiratory distress syndrome: definition, phenotyping and respiratory support strategies

The aim of these guidelines is to update the 2017 clinical practice guideline (CPG) of the European Society of Intensive Care Medicine (ESICM). The scope of this CPG is limited to adult patients and to non-pharmacological respiratory support strategies across different aspects of acute respiratory distress syndrome (ARDS), including ARDS due to coronavirus disease 2019 (COVID-19). These guidelines were formulated by an international panel of clinical experts, one methodologist and patients' representatives on behalf of the ESICM. The review was conducted in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement recommendations. We followed the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to assess the certainty of evidence and grade recommendations and the quality of reporting of each study based on the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) network guidelines. The CPG addressed 21 questions and formulates 21 recommendations on the following domains: (1) definition; (2) phenotyping, and respiratory support strategies including (3) high-flow nasal cannula oxygen (HFNO); (4) non-invasive ventilation (NIV); (5) tidal volume setting; (6) positive end-expiratory pressure (PEEP) and recruitment maneuvers (RM); (7) prone positioning; (8) neuromuscular blockade, and (9) extracorporeal life support (ECLS). In addition, the CPG includes expert opinion on clinical practice and identifies the areas of future research.

Biomarkers to guide immunomodulatory treatment: where do we stand?

INTRODUCTION

This review summarizes current progress in the development of biomarkers to guide immunotherapy in oncology, rheumatology, and critical illness.

AREAS COVERED

An extensive literature search was performed about biomarkers classifying patients' immune responses to guide immunotherapy in oncology, rheumatology, and critical illness. Surface markers, such as programmed death-ligand 1 (PD-L1), genetic biomarkers, such as tumor mutation load, and circulating tumor DNA are biomarkers associated with the effectiveness of immunotherapy in oncology. Genomics, metabolomics, and proteomics play a crucial role in selecting the most suitable therapeutic options for rheumatologic patients. Phenotypes and endotypes are a promising approach to detect critically ill patients with hyper- or hypo-inflammation. Sepsis trials using biomarkers such as ferritin, lymphopenia, HLA-DR expression on monocytes and PD-L1 to guide immunotherapy have been already conducted or are currently ongoing. Immunotherapy in COVID-19 pneumonia, guided by C-reactive protein and soluble urokinase plasminogen activator receptor (suPAR) has improved patient outcomes globally. More research is needed into immunotherapy in other critical conditions.

EXPERT OPINION

Targeted immunotherapy has improved outcomes in oncology and rheumatology, paving the way for precision medicine in the critically ill. Transcriptomics will play a crucial role in detecting the most suitable candidates for immunomodulation.

Endothelial dysfunction triggers acute respiratory distress syndrome in patients with sepsis: a narrative review

Acute respiratory distress syndrome (ARDS) is a severe organ failure occurring mainly in critically ill patients as a result of different types of insults such as sepsis, trauma or aspiration. Sepsis is the main cause of ARDS, and it contributes to a high mortality and resources consumption both in hospital setting and in the community. ARDS develops mainly an acute respiratory failure with severe and often refractory hypoxemia. ARDS also has long term implications and sequelae. Endothelial damage plays an important role in the pathogenesis of ARDS. Understanding the mechanisms of ARDS presents opportunities for novel diagnostic and therapeutic targets. Biochemical signals can be used in concert to identify and classify patients into ARDS phenotypes allowing earlier effective treatment with personalised therapies. This is a narrative review where we aimed to flesh out the pathogenetic mechanisms and heterogeneity of ARDS. We examine the links between endothelium damage and its contribution to organ failure. We have also investigated future strategies for treatment with a special emphasis in endothelial damage.

Machine learning-based COVID-19 acute respiratory distress syndrome phenotyping and clinical outcomes: A systematic review

COVID-19-related acute respiratory distress syndrome (CARDS) has been suggested to differ from the typical ARDS. While distinct phenotypes of ARDS have been identified through latent class analysis (LCA), it is unclear whether such phenotypes exist for CARDS and how they affect clinical outcomes. To address this question, we conducted a systematic review of the current evidence.We searched several, including PubMed, EBSCO Host, and Web of Science, from inception to July 1, 2022. Our exposure and outcome of interest were different CARDS phenotypes identified and their associated outcomes, such as 28-day, 90-day, 180-day mortality, ventilator-free days, and other relevant outcomes.We identified four studies comprising a total of 1776 CARDS patients.Of the four studies, three used LCA to identify subphenotypes (SPs) of CARDS. One study based on longitudinal data identified two SPs, with SP2 associated with worse ventilation and mechanical parameters than SP1. The other two studies based on baseline data also identified two SPs, with SP2 and SP1 were associated with hyperinflammatory and hypoinflammatory CARDS, respectively. The fourth study identified three SPs primarily stratified by comorbidities using multifactorial analysis.All studies identified a subphenotype associated with poorer outcomes, including mortality, ventilator-free days, multiple-organ injury, and pulmonary embolism. Two studies reported differential responses to corticosteroids among the SPs, with improved mortality in the hyperinflammatory and worse in the hypoinflammatory SPs.Overall, our review highlights the importance of phenotyping in understanding CARDS and its impact on disease management and prognostication. However, a consensus approach to phenotyping is necessary to ensure consistency and comparability across studies. We recommend that randomized clinical trials stratified by phenotype should only be initiated after such consensus is reached.

Short title

COVID-19 ARDS subphenotypes and outcomes.

Cardiovascular Subphenotypes in ARDS: Diagnostic and Therapeutic Implications and Overlap with Other ARDS Subphenotypes

Acute respiratory distress syndrome (ARDS) is a highly heterogeneous clinical condition. Shock is a poor prognostic sign in ARDS, and heterogeneity in its pathophysiology may be a barrier to its effective treatment. Although right ventricular dysfunction is commonly implicated, there is no consensus definition for its diagnosis, and left ventricular function is neglected. There is a need to identify the homogenous subgroups within ARDS, that have a similar pathobiology, which can then be treated with targeted therapies. Haemodynamic clustering analyses in patients with ARDS have identified two subphenotypes of increasingly severe right ventricular injury, and a further subphenotype of hyperdynamic left ventricular function. In this review, we discuss how phenotyping the cardiovascular system in ARDS may align with haemodynamic pathophysiology, can aid in optimally defining right ventricular dysfunction and can identify tailored therapeutic targets for shock in ARDS. Additionally, clustering analyses of inflammatory, clinical and radiographic data describe other subphenotypes in ARDS. We detail the potential overlap between these and the cardiovascular phenotypes.

Serum metabolomic signatures of fatty acid oxidation defects differentiate host-response subphenotypes of acute respiratory distress syndrome

BACKGROUND

Fatty acid oxidation (FAO) defects have been implicated in experimental models of acute lung injury and associated with poor outcomes in critical illness. In this study, we examined acylcarnitine profiles and 3-methylhistidine as markers of FAO defects and skeletal muscle catabolism, respectively, in patients with acute respiratory failure. We determined whether these metabolites were associated with host-response ARDS subphenotypes, inflammatory biomarkers, and clinical outcomes in acute respiratory failure.

METHODS

In a nested case-control cohort study, we performed targeted analysis of serum metabolites of patients intubated for airway protection (airway controls), Class 1 (hypoinflammatory), and Class 2 (hyperinflammatory) ARDS patients (N = 50 per group) during early initiation of mechanical ventilation. Relative amounts were quantified by liquid chromatography high resolution mass spectrometry using isotope-labeled standards and analyzed with plasma biomarkers and clinical data.

RESULTS

Of the acylcarnitines analyzed, octanoylcarnitine levels were twofold increased in Class 2 ARDS relative to Class 1 ARDS or airway controls (P = 0.0004 and < 0.0001, respectively) and was positively associated with Class 2 by quantile g-computation analysis (P = 0.004). In addition, acetylcarnitine and 3-methylhistidine were increased in Class 2 relative to Class 1 and positively correlated with inflammatory biomarkers. In all patients within the study with acute respiratory failure, increased 3-methylhistidine was observed in non-survivors at 30 days (P = 0.0018), while octanoylcarnitine was increased in patients requiring vasopressor support but not in non-survivors (P = 0.0001 and P = 0.28, respectively).

CONCLUSIONS

This study demonstrates that increased levels of acetylcarnitine, octanoylcarnitine, and 3-methylhistidine distinguish Class 2 from Class 1 ARDS patients and airway controls. Octanoylcarnitine and 3-methylhistidine were associated with poor outcomes in patients with acute respiratory failure across the cohort independent of etiology or host-response subphenotype. These findings suggest a role for serum metabolites as biomarkers in ARDS and poor outcomes in critically ill patients early in the clinical course.

Use of Electron Paramagnetic Resonance (EPR) to Evaluate Redox Status in a Preclinical Model of Acute Lung Injury

PURPOSE

Patients with hyper- vs. hypo-inflammatory subphenotypes of acute respiratory distress syndrome (ARDS) exhibit different clinical outcomes. Inflammation increases the production of reactive oxygen species (ROS) and increased ROS contributes to the severity of illness. Our long-term goal is to develop electron paramagnetic resonance (EPR) imaging of lungs in vivo to precisely measure superoxide production in ARDS in real time. As a first step, this requires the development of in vivo EPR methods for quantifying superoxide generation in the lung during injury, and testing if such superoxide measurements can differentiate between susceptible and protected mouse strains.

PROCEDURES

In WT mice, mice lacking total body extracellular superoxide dismutase (EC-SOD) (KO), or mice overexpressing lung EC-SOD (Tg), lung injury was induced with intraperitoneal (IP) lipopolysaccharide (LPS) (10 mg/kg). At 24 h after LPS treatment, mice were injected with the cyclic hydroxylamines 1-hydroxy-3-carboxy-2,2,5,5-tetramethylpyrrolidine hydrochloride (CPH) or 4-acetoxymethoxycarbonyl-1-hydroxy-2,2,5,5-tetramethylpyrrolidine-3-carboxylic acid (DCP-AM-H) probes to detect, respectively, cellular and mitochondrial ROS - specifically superoxide. Several probe delivery strategies were tested. Lung tissue was collected up to one hour after probe administration and assayed by EPR.

RESULTS

As measured by X-band EPR, cellular and mitochondrial superoxide increased in the lungs of LPS-treated mice compared to control. Lung cellular superoxide was increased in EC-SOD KO mice and decreased in EC-SOD Tg mice compared to WT. We also validated an intratracheal (IT) delivery method, which enhanced the lung signal for both spin probes compared to IP administration.

CONCLUSIONS

We have developed protocols for delivering EPR spin probes in vivo, allowing detection of cellular and mitochondrial superoxide in lung injury by EPR. Superoxide measurements by EPR could differentiate mice with and without lung injury, as well as mouse strains with different disease susceptibilities. We expect these protocols to capture real-time superoxide production and enable evaluation of lung EPR imaging as a potential clinical tool for subphenotyping ARDS patients based on redox status.

Sustained Perturbation of Metabolism and Metabolic Subphenotypes Are Associated With Mortality and Protein Markers of the Host Response

Perturbed host metabolism is increasingly recognized as a pillar of sepsis pathogenesis, yet the dynamic alterations in metabolism and its relationship to other components of the host response remain incompletely understood. We sought to identify the early host-metabolic response in patients with septic shock and to explore biophysiological phenotyping and differences in clinical outcomes among metabolic subgroups.

DESIGN

We measured serum metabolites and proteins reflective of the host-immune and endothelial response in patients with septic shock.

SETTING

We considered patients from the placebo arm of a completed phase II, randomized controlled trial conducted at 16 U.S. medical centers. Serum was collected at baseline (within 24 hr of the identification of septic shock), 24-hour, and 48-hour postenrollment. Linear mixed models were built to assess the early trajectory of protein analytes and metabolites stratified by 28-day mortality status. Unsupervised clustering of baseline metabolomics data was conducted to identify subgroups of patients.

PATIENTS

Patients with vasopressor-dependent septic shock and moderate organ dysfunction that were enrolled in the placebo arm of a clinical trial.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Fifty-one metabolites and 10 protein analytes were measured longitudinally in 72 patients with septic shock. In the 30 patients (41.7%) who died prior to 28 days, systemic concentrations of acylcarnitines and interleukin (IL)-8 were elevated at baseline and persisted at T24 and T48 throughout early resuscitation. Concentrations of pyruvate, IL-6, tumor necrosis factor-α, and angiopoietin-2 decreased at a slower rate in patients who died. Two groups emerged from clustering of baseline metabolites. Group 1 was characterized by higher levels of acylcarnitines, greater organ dysfunction at baseline and postresuscitation (p < 0.05), and greater mortality over 1 year (p < 0.001).

CONCLUSIONS

Among patients with septic shock, nonsurvivors exhibited a more profound and persistent dysregulation in protein analytes attributable to neutrophil activation and disruption of mitochondrial-related metabolism than survivors.

Are circulating endothelial cells the next target for transcriptome-level pathway analysis in ARDS?

Acute respiratory distress syndrome (ARDS) has had no mortality-improving pharmacological intervention despite 50 years of high-caliber research due to its heterogeneity (Huppert LA, Matthay MA, Ware LB. Semin Respir Crit Care Med 40: 31-39, 2019). For the field to advance, better definitions for ARDS subgroups that more uniformly respond to therapies are needed (Bos LDJ, Scicluna BP, Ong DSY, Cremer O, van der Poll T, Schultz MJ. Am J Respir Crit Care Med 200: 42-50, 2019; Dickson RP, Schultz MJ, T van der P, Schouten LR, Falkowski NR, Luth JE, Sjoding MW, Brown CA, Chanderraj R, Huffnagle GB, Bos LDJ, Biomarker Analysis in Septic ICU Patients (BASIC) Consortium. Am J Respir Crit Care Med 201: 555-563, 2020; Sinha P, Calfee CS. Am J Respir Crit Care Med 200: 4-6, 2019; Calfee CS, Delucchi K, Parsons PE, Thompson BT, Ware LB, Matthay MA, NHLBI ARDS Network. Lancet Respir Med 2: 611-620, 2014; Hendrickson CM, Matthay MA. Pulm Circ 8: 1-12, 2018). A plethora of high-quality clinical research has uncovered the next generation of soluble biomarkers that provide the predictive enrichment necessary for trial recruitment; however, plasma-soluble markers do not specify the damaged organ of origin nor do they provide insight into disease mechanisms. In this perspective, we make the case for querying the transcriptome of circulating endothelial cells (CECs), which when shed from vessels after inflammatory insult, become heralds of site-specific inflammatory damage. We review the application of CEC quantification to multiple disease phenotypes (including myocardial infarction, vasculitides, cancer, and ARDS), in each case supporting the association of CEC number with disease severity. We also argue for the utility of single-cell RNA transcriptomics to the understanding of cell-specific contributions to disease pathophysiology and its potential to uncover novel insight on signals contributing to CEC shedding in ARDS.

Pulmonary drug delivery for acute respiratory distress syndrome

The acute respiratory distress syndrome (ARDS) is a life-threatening condition that causes respiratory failure. Despite numerous clinical trials, there are no molecularly targeted pharmacologic therapies to prevent or treat ARDS. Drug delivery during ARDS is challenging due to the heterogenous nature of lung injury and occlusion of lung units by edema fluid and inflammation. Pulmonary drug delivery during ARDS offers several potential advantages including limiting the off-target and off-organ effects and directly targeting the damaged and inflamed lung regions. In this review we summarize recent ARDS clinical trials using both systemic and pulmonary drug delivery. We then discuss the advantages of pulmonary drug delivery and potential challenges to its implementation. Finally, we discuss the use of nanoparticle drug delivery and surfactant-based drug carriers as potential strategies for delivering therapeutics to the injured lung in ARDS.