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

Glucagon-Like Peptide-1 Is Prognostic of Mortality in Acute Respiratory Failure

OBJECTIVES

The incretin hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP) have therapeutic effects in diabetes mellitus. Prior clinical studies suggest incretins are prognostic of adverse outcomes in critical illness. We investigated whether incretin levels indicate disease severity and clinical outcomes in patients with acute respiratory failure, a common cause of critical illness.

DESIGN

Retrospective cohort study.

SETTING

ICUs in UPMC Health Systems hospitals within Western Pennsylvania.

PATIENTS

Two hundred ninety-seven critically ill adults with acute respiratory failure.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We measured GLP-1 and GIP levels in baseline samples collected at the time of study enrollment. We compared incretin levels across subgroups differing by severity of illness and investigated associations between incretins and markers of systemic host responses and intestinal permeability. In our primary analysis, we tested the association of each incretin level with 90-day mortality by logistic regression in unadjusted analyses and in analyses adjusted for age, Sequential Organ Failure Assessment score, and circulating interleukin-6 levels. GLP-1 levels were higher in nonsurvivors and patients with or at-risk for acute respiratory distress syndrome compared to those intubated for airway protection. GLP-1 levels also positively correlated with systemic immune response biomarkers but not with markers of intestinal permeability. GLP-1 levels positively correlated with mortality in unadjusted (odds ratio, 1.99 [1.55-2.56]; p < 0.01) and adjusted (2.02 [1.23-3.31]; p < 0.01) analyses. GIP levels were not associated with mortality or with host response biomarkers.

CONCLUSIONS

GLP-1 but not GIP levels were positively associated with systemic inflammation and mortality in critically ill patients with acute respiratory failure. Increased circulating GLP-1 levels may serve as prognostic biomarkers to identify patients who are likely to have worse outcomes.

Future Directions in Therapies for Acute Respiratory Distress Syndrome

Acute respiratory distress syndrome (ARDS) is caused by a complex interplay among hyperinflammation, endothelial dysfunction, and alveolar epithelial injury. Targeted treatments toward the underlying pathways have been unsuccessful in unselected patient populations. The first reliable biological subphenotypes reflective of these biological disease states have been identified in the past decade. Subphenotype targeted intervention studies are needed to advance the pharmacologic treatment of ARDS.

Precision Medicine in Acute Respiratory Distress Syndrome: Progress, Challenges, and the Road ahead

Several novel high-dimensional biologic measurements are increasingly being applied to biomedical sciences. Acute respiratory distress syndrome (ARDS) is a theoretically fertile ground for such approaches. Not only are these biologic and analytic tools available to better understand ARDS but also arguably, simpler approaches such as respiratory physiology has been vastly underutilized as a means of delivering precision-based care in the field. Here we review the progress made in ARDS toward discovering biologically homogeneous phenotypes, treatment responsive subgroups, the challenges to implement these discoveries at the bedside, and the road ahead that will enable precision medicine in ARDS.

Clinical and biologic profiles of patients with acute respiratory distress syndrome by prevalence of chronic obstructive pulmonary disease or emphysema; a cohort study

INTRODUCTION

Acute respiratory distress syndrome (ARDS) is characterized by diffuse lung injury. The impact of pre-existing chronic obstructive pulmonary disease (COPD) or emphysema on ARDS pathogenesis is not well characterized.

METHODS

Secondary analysis of ARDS patients enrolled in the Acute Lung Injury Registry and Biospecimen Repository at the University of Pittsburgh between June 2012 and September 2021. Patients were categorized into two mutually exclusive groups by the prevalence of COPD or emphysema at the time of ARDS diagnosis. The COPD/emphysema group comprised ARDS patients with radiological evidence of emphysema, chart diagnosis of COPD, or both. Demographics, lung mechanics, and clinical outcomes were obtained from the electronic medical record. Host-response biomarkers known to have validated associations with ARDS were previously measured in plasma and lower respiratory tract samples using a customized Luminex assay. Continuous and categorical variables were compared between groups with and without COPD/emphysema.

RESULTS

217 patients with ARDS were included in the study, 57 (27%) had COPD/emphysema. Patients with COPD/emphysema were older (median 62 [interquartile range 55-69] versus 53 [41-64] years, p < 0.01), more likely to be male (62% vs. 44%, p = 0.02) and had a higher prevalence of congestive heart failure (25% vs. 4%, p < 0.01) compared to patients without COPD/emphysema. Baseline demographics, laboratory parameters, and mechanical ventilatory characteristics were otherwise similar between the two groups. No difference in 90-day mortality was observed between groups; however, patients with COPD/emphysema had shorter duration of intensive care unit (ICU) stay (median 10 [7-18] versus 16 [9-28] days, p = 0.04) and shorter duration of mechanical ventilation (median 7 [4-16] vs. 12 [6-20] days, p = 0.01). Host response biomarkers in serum and lower respiratory tract samples did not significantly differ between groups.

CONCLUSION

ARDS patients with COPD or emphysema had similar respiratory mechanics, host response biomarker profiles, and mortality compared to those without COPD or emphysema but with a shorter median duration of mechanical ventilation and ICU length of stay. Future studies should address differences in clinical and biological responses by disease severity, and should investigate the impact of severity of COPD and emphysema on mechanical ventilation and targeted therapeutic strategies in ARDS.

CLINICAL TRIAL NUMBER

Not applicable.

Identification of novel sub-phenotypes of severe ARDS requiring ECMO using latent class analysis

BACKGROUND

Sub-phenotyping of acute respiratory distress syndrome (ARDS) could be useful for evaluating the severity of ARDS or predicting its responsiveness to given therapeutic strategies, but no studies have yet investigated the heterogeneity of patients with severe ARDS requiring veno-venous extracorporeal membrane oxygenation (V-V ECMO).

METHODS

We conducted this retrospective multicenter observational study in adult patients with severe ARDS treated by V-V ECMO. We performed latent class analysis (LCA) for identifying sub-phenotypes of severe ARDS based on the radiological and clinical findings at the start of ECMO support. Multivariate Cox regression analysis was conducted to investigate the differences in mortality and association between the PEEP setting of ≥ 10 cmH2O and mortality by the sub-phenotypes.

RESULTS

We identified three sub-phenotypes from analysis of the data of a total of 544 patients with severe ARDS treated by V-V ECMO, as follows: Dry type (n = 185; 34%); Wet type (n = 169; 31%); and Fibrotic type (n = 190; 35%). The 90-days in-hospital mortality risk was higher in the patients with the Fibrotic type than in those with the Dry type (adjusted hazard ratio [95% confidence interval] 1.75 [1.10-2.79], p = 0.019) or the Wet type (1.50 [1.02-2.23], p = 0.042). The PEEP setting of ≥ 10 cmH2O during the first 3 days of ECMO decreased the 90-days in-hospital mortality risk only in patients with the Wet type, and not in those with the Dry or Fibrotic type. A significant interaction effect was observed between the Wet type and the PEEP setting of ≥ 10 cmH2O in relation to the 90-day in-hospital mortality (pinteraction = 0.036).

CONCLUSIONS

The three sub-phenotypes showed different mortality rates and different relationships between higher PEEP settings in the early phase of V-V ECMO and patient outcomes. Our data suggest that we may need to change our management approach to patients with severe ARDS during V-V ECMO according to their clinical sub-phenotype.

The ARDS microenvironment enhances MSC-induced repair via VEGF in experimental acute lung inflammation

Clinical trials investigating the potential of mesenchymal stromal cells (MSCs) for the treatment of inflammatory diseases, such as acute respiratory distress syndrome (ARDS), have been disappointing, with less than 50% of patients responding to treatment. Licensed MSCs show enhanced therapeutic efficacy in response to cytokine-mediated activation signals. There are two distinct sub-phenotypes of ARDS: hypo- and hyper-inflammatory. We hypothesized that pre-licensing MSCs in a hyper-inflammatory ARDS environment would enhance their therapeutic efficacy in acute lung inflammation (ALI). Serum samples from patients with ARDS were segregated into hypo- and hyper-inflammatory categories based on interleukin (IL)-6 levels. MSCs were licensed with pooled serum from patients with hypo- or hyper-inflammatory ARDS or healthy serum controls. Our findings show that hyper-inflammatory ARDS pre-licensed MSC conditioned medium (MSC-CMHyper) led to a significant enrichment in tight junction expression and enhanced barrier integrity in lung epithelial cells in vitro and in vivo in a vascular endothelial growth factor (VEGF)-dependent manner. Importantly, while both MSC-CMHypo and MSC-CMHyper significantly reduced IL-6 and tumor necrosis factor alpha (TNF-α) levels in the bronchoalveolar lavage fluid (BALF) of lipopolysaccharide (LPS)-induced ALI mice, only MSC-CMHyper significantly reduced lung permeability and overall clinical outcomes including weight loss and clinical score. Thus, the hypo- and hyper-inflammatory ARDS environments may differentially influence MSC cytoprotective and immunomodulatory functions.

From ICU Syndromes to ICU Subphenotypes: Consensus Report and Recommendations for Developing Precision Medicine in the ICU

Critical care uses syndromic definitions to describe patient groups for clinical practice and research. There is growing recognition that a "precision medicine" approach is required and that integrated biologic and physiologic data identify reproducible subpopulations that may respond differently to treatment. This article reviews the current state of the field and considers how to successfully transition to a precision medicine approach. To impact clinical care, identification of subpopulations must do more than differentiate prognosis. It must differentiate response to treatment, ideally by defining subgroups with distinct functional or pathobiological mechanisms (endotypes). There are now multiple examples of reproducible subpopulations of sepsis, acute respiratory distress syndrome, and acute kidney or brain injury described using clinical, physiological, and/or biological data. Many of these subpopulations have demonstrated the potential to define differential treatment response, largely in retrospective studies, and that the same treatment-responsive subpopulations may cross multiple clinical syndromes (treatable traits). To bring about a change in clinical practice, a precision medicine approach must be evaluated in prospective clinical studies requiring novel adaptive trial designs. Several such studies are underway, but there are multiple challenges to be tackled. Such subpopulations must be readily identifiable and be applicable to all critically ill populations around the world. Subdividing clinical syndromes into subpopulations will require large patient numbers. Global collaboration of investigators, clinicians, industry, and patients over many years will therefore be required to transition to a precision medicine approach and ultimately realize treatment advances seen in other medical fields.

Longitudinal multicompartment characterization of host-microbiota interactions in patients with acute respiratory failure

Critical illness can significantly alter the composition and function of the human microbiome, but few studies have examined these changes over time. Here, we conduct a comprehensive analysis of the oral, lung, and gut microbiota in 479 mechanically ventilated patients (223 females, 256 males) with acute respiratory failure. We use advanced DNA sequencing technologies, including Illumina amplicon sequencing (utilizing 16S and ITS rRNA genes for bacteria and fungi, respectively, in all sample types) and Nanopore metagenomics for lung microbiota. Our results reveal a progressive dysbiosis in all three body compartments, characterized by a reduction in microbial diversity, a decrease in beneficial anaerobes, and an increase in pathogens. We find that clinical factors, such as chronic obstructive pulmonary disease, immunosuppression, and antibiotic exposure, are associated with specific patterns of dysbiosis. Interestingly, unsupervised clustering of lung microbiota diversity and composition by 16S independently predicted survival and performed better than traditional clinical and host-response predictors. These observations are validated in two separate cohorts of COVID-19 patients, highlighting the potential of lung microbiota as valuable prognostic biomarkers in critical care. Understanding these microbiome changes during critical illness points to new opportunities for microbiota-targeted precision medicine interventions.

Neutrophils and galectin-3 defend mice from lethal bacterial infection and humans from acute respiratory failure

Respiratory infection by Pseudomonas aeruginosa, common in hospitalized immunocompromised and immunocompetent ventilated patients, can be life-threatening because of antibiotic resistance. This raises the question of whether the host's immune system can be educated to combat this bacterium. Here we show that prior exposure to a single low dose of lipopolysaccharide (LPS) protects mice from a lethal infection by P. aeruginosa. LPS exposure trained the innate immune system by promoting expansion of neutrophil and interstitial macrophage populations distinguishable from other immune cells with enrichment of gene sets for phagocytosis- and cell-killing-associated genes. The cell-killing gene set in the neutrophil population uniquely expressed Lgals3, which encodes the multifunctional antibacterial protein, galectin-3. Intravital imaging for bacterial phagocytosis, assessment of bacterial killing and neutrophil-associated galectin-3 protein levels together with use of galectin-3-deficient mice collectively highlight neutrophils and galectin-3 as central players in LPS-mediated protection. Patients with acute respiratory failure revealed significantly higher galectin-3 levels in endotracheal aspirates (ETAs) of survivors compared to non-survivors, galectin-3 levels strongly correlating with a neutrophil signature in the ETAs and a prognostically favorable hypoinflammatory plasma biomarker subphenotype. Taken together, our study provides impetus for harnessing the potential of galectin-3-expressing neutrophils to protect from lethal infections and respiratory failure.

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.

Lung Epithelium Releases Growth Differentiation Factor 15 in Response to Pathogen-mediated Injury

GDF15 (growth differentiation factor 15) is a stress cytokine with several proposed roles, including support of stress erythropoiesis. Higher circulating GDF15 levels are prognostic of mortality during acute respiratory distress syndrome, but the cellular sources and downstream effects of GDF15 during pathogen-mediated lung injury are unclear. We quantified GDF15 in lower respiratory tract biospecimens and plasma from patients with acute respiratory failure. Publicly available data from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection were reanalyzed. We used mouse models of hemorrhagic acute lung injury mediated by Pseudomonas aeruginosa exoproducts in wild-type mice and mice genetically deficient for Gdf15 or its putative receptor, Gfral. In critically ill humans, plasma levels of GDF15 correlated with lower respiratory tract levels and were higher in nonsurvivors. SARS-CoV-2 infection induced GDF15 expression in human lung epithelium, and lower respiratory tract GDF15 levels were higher in coronavirus disease (COVID-19) nonsurvivors. In mice, intratracheal P. aeruginosa type II secretion system exoproducts were sufficient to induce airspace and plasma release of GDF15, which was attenuated with epithelial-specific deletion of Gdf15. Mice with global Gdf15 deficiency had decreased airspace hemorrhage, an attenuated cytokine profile, and an altered lung transcriptional profile during injury induced by P. aeruginosa type II secretion system exoproducts, which was not recapitulated in mice deficient for Gfral. Airspace GDF15 reconstitution did not significantly modulate key lung cytokine levels but increased circulating erythrocyte counts. Lung epithelium releases GDF15 during pathogen injury, which is associated with plasma levels in humans and mice and can increase erythrocyte counts in mice, suggesting a novel lung-blood communication pathway.

Respiratory drive heterogeneity associated with systemic inflammation and vascular permeability in acute respiratory distress syndrome

BACKGROUND

In acute respiratory distress syndrome (ARDS), respiratory drive often differs among patients with similar clinical characteristics. Readily observable factors like acid-base state, oxygenation, mechanics, and sedation depth do not fully explain drive heterogeneity. This study evaluated the relationship of systemic inflammation and vascular permeability markers with respiratory drive and clinical outcomes in ARDS.

METHODS

ARDS patients enrolled in the multicenter EPVent-2 trial with requisite data and plasma biomarkers were included. Neuromuscular blockade recipients were excluded. Respiratory drive was measured as PES0.1, the change in esophageal pressure during the first 0.1 s of inspiratory effort. Plasma angiopoietin-2, interleukin-6, and interleukin-8 were measured concomitantly, and 60-day clinical outcomes evaluated.

RESULTS

54.8% of 124 included patients had detectable respiratory drive (PES0.1 range of 0-5.1 cm H2O). Angiopoietin-2 and interleukin-8, but not interleukin-6, were associated with respiratory drive independently of acid-base, oxygenation, respiratory mechanics, and sedation depth. Sedation depth was not significantly associated with PES0.1 in an unadjusted model, or after adjusting for mechanics and chemoreceptor input. However, upon adding angiopoietin-2, interleukin-6, or interleukin-8 to models, lighter sedation was significantly associated with higher PES0.1. Risk of death was less with moderate drive (PES0.1 of 0.5-2.9 cm H2O) compared to either lower drive (hazard ratio 1.58, 95% CI 0.82-3.05) or higher drive (2.63, 95% CI 1.21-5.70) (p = 0.049).

CONCLUSIONS

Among patients with ARDS, systemic inflammatory and vascular permeability markers were independently associated with higher respiratory drive. The heterogeneous response of respiratory drive to varying sedation depth may be explained in part by differences in inflammation and vascular permeability.

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.

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.

Glucocorticoid use in acute respiratory failure from pulmonary causes and association with early changes in the systemic host immune response

BACKGROUND

Glucocorticoids are commonly used in patients with or at-risk for acute respiratory distress syndrome (ARDS), but optimal use remains unclear despite well-conducted clinical trials. We performed a secondary analysis in patients previously enrolled in the Acute Lung Injury and Biospecimen Repository at the University of Pittsburgh. The primary aim of our study was to investigate early changes in host response biomarkers in response to real-world use of glucocorticoids in patients with acute respiratory failure due to ARDS or at-risk due to a pulmonary insult. Participants had baseline plasma samples obtained on study enrollment and on follow-up 3 to 5 days later to measure markers of innate immunity (IL-6, IL-8, IL-10, TNFr1, ST2, fractalkine), epithelial injury (sRAGE), endothelial injury (angiopoietin-2), and host response to bacterial infections (procalcitonin, pentraxin-3). In our primary analyses, we investigated the effect of receiving glucocorticoids between baseline and follow-up samples on host response biomarkers measured at follow-up by doubly robust inverse probability weighting analysis. In exploratory analyses, we examined associations between glucocorticoid use and previously characterized host response subphenotypes (hyperinflammatory and hypoinflammatory).

RESULTS

67 of 148 participants (45%) received glucocorticoids between baseline and follow-up samples. Dose and type of glucocorticoids varied. Regimens that used hydrocortisone alone were most common (37%), and median daily dose was equivalent to 40 mg methylprednisolone (interquartile range: 21, 67). Participants who received glucocorticoids were more likely to be female, to be on immunosuppressive therapy at baseline, and to have higher baseline levels of ST-2, fractalkine, IL-10, pentraxin-3, sRAGE, and TNFr1. Glucocorticoid use was associated with decreases in IL-6 and increases in fractalkine. In exploratory analyses, glucocorticoid use was more frequent in participants in the hyperinflammatory subphenotype (58% vs 40%, p = 0.05), and was not associated with subphenotype classification at the follow-up time point (p = 0.16).

CONCLUSIONS

Glucocorticoid use varied in a cohort of patients with or at-risk for ARDS and was associated with early changes in the systemic host immune response.

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.

Advancing specificity in delirium: The delirium subtyping initiative

BACKGROUND

Delirium, a common syndrome with heterogeneous etiologies and clinical presentations, is associated with poor long-term outcomes. Recording and analyzing all delirium equally could be hindering the field's understanding of pathophysiology and identification of targeted treatments. Current delirium subtyping methods reflect clinically evident features but likely do not account for underlying biology.

METHODS

The Delirium Subtyping Initiative (DSI) held three sessions with an international panel of 25 experts.

RESULTS

Meeting participants suggest further characterization of delirium features to complement the existing Diagnostic and Statistical Manual of Mental Disorders Fifth Edition Text Revision diagnostic criteria. These should span the range of delirium-spectrum syndromes and be measured consistently across studies. Clinical features should be recorded in conjunction with biospecimen collection, where feasible, in a standardized way, to determine temporal associations of biology coincident with clinical fluctuations.

DISCUSSION

The DSI made recommendations spanning the breadth of delirium research including clinical features, study planning, data collection, and data analysis for characterization of candidate delirium subtypes.

HIGHLIGHTS

Delirium features must be clearly defined, standardized, and operationalized. Large datasets incorporating both clinical and biomarker variables should be analyzed together. Delirium screening should incorporate communication and reasoning.

A common single nucleotide polymorphism is associated with inflammation and critical illness outcomes

Acute inflammation is heterogeneous in critical illness and predictive of outcome. We hypothesized that genetic variability in novel, yet common, gene variants contributes to this heterogeneity and could stratify patient outcomes. We searched algorithmically for significant differences in systemic inflammatory mediators associated with any of 551,839 SNPs in one derivation (n = 380 patients with blunt trauma) and two validation (n = 75 trauma and n = 537 non-trauma patients) cohorts. This analysis identified rs10404939 in the LYPD4 gene. Trauma patients homozygous for the A allele (rs10404939AA; 27%) had different trajectories of systemic inflammation along with persistently elevated multiple organ dysfunction (MOD) indices vs. patients homozygous for the G allele (rs10404939GG; 26%). rs10404939AA homozygotes in the trauma validation cohort had elevated MOD indices, and non-trauma patients displayed more complex inflammatory networks and worse 90-day survival compared to rs10404939GG homozygotes. Thus, rs10404939 emerged as a common, broadly prognostic SNP in critical illness.

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.

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.

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.

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.

Prognostic Insights from Longitudinal Multicompartment Study of Host-Microbiota Interactions in Critically Ill Patients

Critical illness can disrupt the composition and function of the microbiome, yet comprehensive longitudinal studies are lacking. We conducted a longitudinal analysis of oral, lung, and gut microbiota in a large cohort of 479 mechanically ventilated patients with acute respiratory failure. Progressive dysbiosis emerged in all three body compartments, characterized by reduced alpha diversity, depletion of obligate anaerobe bacteria, and pathogen enrichment. Clinical variables, including chronic obstructive pulmonary disease, immunosuppression, and antibiotic exposure, shaped dysbiosis. Notably, of the three body compartments, unsupervised clusters of lung microbiota diversity and composition independently predicted survival, transcending clinical predictors, organ dysfunction severity, and host-response sub-phenotypes. These independent associations of lung microbiota may serve as valuable biomarkers for prognostication and treatment decisions in critically ill patients. Insights into the dynamics of the microbiome during critical illness highlight the potential for microbiota-targeted interventions in precision medicine.

Prognostic Insights from Longitudinal Multicompartment Study of Host-Microbiota Interactions in Critically Ill Patients

Critical illness can disrupt the composition and function of the microbiome, yet comprehensive longitudinal studies are lacking. We conducted a longitudinal analysis of oral, lung, and gut microbiota in a large cohort of 479 mechanically ventilated patients with acute respiratory failure. Progressive dysbiosis emerged in all three body compartments, characterized by reduced alpha diversity, depletion of obligate anaerobe bacteria, and pathogen enrichment. Clinical variables, including chronic obstructive pulmonary disease, immunosuppression, and antibiotic exposure, shaped dysbiosis. Notably, of the three body compartments, unsupervised clusters of lung microbiota diversity and composition independently predicted survival, transcending clinical predictors, organ dysfunction severity, and host-response sub-phenotypes. These independent associations of lung microbiota may serve as valuable biomarkers for prognostication and treatment decisions in critically ill patients. Insights into the dynamics of the microbiome during critical illness highlight the potential for microbiota-targeted interventions in precision medicine.

The upper and lower respiratory tract microbiome in severe aspiration pneumonia

Uncertainty persists whether anaerobic bacteria represent important pathogens in aspiration pneumonia. In a nested case-control study of mechanically ventilated patients classified as macro-aspiration pneumonia (MAsP, n = 56), non-macro-aspiration pneumonia (NonMAsP, n = 91), and uninfected controls (n = 11), we profiled upper (URT) and lower respiratory tract (LRT) microbiota with bacterial 16S rRNA gene sequencing, measured plasma host-response biomarkers, analyzed bacterial communities by diversity and oxygen requirements, and performed unsupervised clustering with Dirichlet Multinomial Models (DMM). MAsP and NonMAsP patients had indistinguishable microbiota profiles by alpha diversity and oxygen requirements with similar host-response profiles and 60-day survival. Unsupervised DMM clusters revealed distinct bacterial clusters in the URT and LRT, with low-diversity clusters enriched for facultative anaerobes and typical pathogens, associated with higher plasma levels of SPD and sCD14 and worse 60-day survival. The predictive inter-patient variability in these bacterial profiles highlights the importance of microbiome study in patient sub-phenotyping and precision medicine approaches for severe pneumonia.

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.

Distinct profiles of host responses between plasma and lower respiratory tract during acute respiratory failure

Current plasma-based subphenotyping approaches in acute respiratory failure represent host responses at a systemic level but do not capture important differences in lower respiratory tract biology https://bit.ly/40kTdDG.

Factor H preserves alternative complement function during ARDS, linked to improved survival

Background

Effective regulation of complement activation may be crucial to preserving complement function during acute respiratory distress syndrome (ARDS). Factor H is the primary negative regulator of the alternative pathway of complement. We hypothesised that preserved factor H levels are associated with decreased complement activation and reduced mortality during ARDS.

Methods

Total alternative pathway function was measured by serum haemolytic assay (AH50) using available samples from the ARDSnet Lisofylline and Respiratory Management of Acute Lung Injury (LARMA) trial (n=218). Factor B and factor H levels were quantified using ELISA using samples from the ARDSnet LARMA and Statins for Acutely Injured Lungs from Sepsis (SAILS) (n=224) trials. Meta-analyses included previously quantified AH50, factor B and factor H values from an observational registry (Acute Lung Injury Registry and Biospecimen Repository (ALIR)). Complement C3, and complement activation products C3a and Ba plasma levels were measured in SAILS.

Results

AH50 greater than the median was associated with reduced mortality in meta-analysis of LARMA and ALIR (hazard ratio (HR) 0.66, 95% CI 0.45-0.96). In contrast, patients in the lowest AH50 quartile demonstrated relative deficiency of both factor B and factor H. Relative deficiency of factor B (HR 1.99, 95% CI 1.44-2.75) or factor H (HR 1.52, 95% CI 1.09-2.11) was associated with increased mortality in meta-analysis of LARMA, SAILS and ALIR. Relative factor H deficiency was associated with increased factor consumption, as evidenced by lower factor B and C3 levels and Ba:B and C3a:C3 ratios. Higher factor H levels associated with lower inflammatory markers.

Conclusions

Relative factor H deficiency, higher Ba:B and C3a:C3 ratios and lower factor B and C3 levels suggest a subset of ARDS with complement factor exhaustion, impaired alternative pathway function, and increased mortality, that may be amenable to therapeutic targeting.

Trajectories of host-response biomarkers and inflammatory subphenotypes in COVID-19 patients across the spectrum of respiratory support

Purpose

Enhanced understanding of the dynamic changes in the dysregulated inflammatory response in COVID-19 may help improve patient selection and timing for immunomodulatory therapies.

Methods

We enrolled 323 COVID-19 inpatients on different levels of baseline respiratory support: i) Low Flow Oxygen (37%), ii) Non-Invasive Ventilation or High Flow Oxygen (NIV_HFO, 29%), iii) Invasive Mechanical Ventilation (IMV, 27%), and iv) Extracorporeal Membrane Oxygenation (ECMO, 7%). We collected plasma samples upon enrollment and days 5 and 10 to measure host-response biomarkers. We classified subjects into 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 were persistently elevated in patients at higher levels of respiratory support, whereas sRAGE displayed the inverse pattern. Patients on NIV_HFO at baseline had the most dynamic clinical trajectory, with 26% eventually requiring intubation and exhibiting worse 60-day mortality than IMV patients at baseline (67% vs. 35%, p<0.0001). sRAGE levels predicted NIV failure and worse 60-day mortality for NIV_HFO patients, whereas IL-6 levels were predictive in IMV or ECMO patients. Hyper-inflammatory subjects at baseline (<10% by both models) had worse 60-day survival (p<0.0001) and 50% of them remained classified as hyper-inflammatory on follow-up sampling at 5 days post-enrollment. Receipt of combined immunomodulatory therapies (steroids and anti-IL6 agents) was associated with markedly increased IL-6 and lower Angiopoietin-2 levels (p<0.05).

Conclusions

Longitudinal study of systemic host responses in COVID-19 revealed substantial and predictive inter-individual variability, influenced by baseline levels of respiratory support and concurrent immunomodulatory therapies.

Suppressed renoprotective purines in COVID-19 patients with acute kidney injury

Acute kidney injury (AKI) is common in patients hospitalized for COVID-19, complicating their clinical course and contributing to worse outcomes. Animal studies show that adenosine, inosine and guanosine protect the kidney against some types of AKI. However, until now there was no evidence in patients supporting the possibility that abnormally low kidney levels of adenosine, inosine and guanosine contribute to AKI. Here, we addressed the question as to whether these renoprotective purines are altered in the urine of COVID-19 patients with AKI. Purines were measured by employing ultra-high-performance liquid chromatography-tandem mass spectrometry with stable-isotope-labeled internal standards for each purine of interest. Compared with COVID-19 patients without AKI (n = 23), COVID-19 patients with AKI (n = 20) had significantly lower urine levels of adenosine (P < 0.0001), inosine (P = 0.0008), and guanosine (P = 0.0008) (medians reduced by 85%, 48% and 61%, respectively) and lower levels (P = 0.0003; median reduced by 67%) of the 2nd messenger for A2A and A2B adenosine receptors, i.e., 3',5'-cAMP. Moreover, in COVID-19 patients with AKI, urine levels of 8-aminoguanine (endogenous inhibitor of inosine and guanosine metabolism) were nearly abolished (P < 0.0001). In contrast, the "upstream" precursors of renoprotective purines, namely 5'-AMP and 5'-GMP, were not significantly altered in COVID-19 patients with AKI, suggesting defective conversion of these precursors by CD73 (converts 5'-AMP to adenosine and 5'-GMP to guanosine). These findings imply that an imbalance in renoprotective purines may contribute to AKI in COVID-19 patients and that pharmacotherapy targeted to restore levels of renoprotective purines may attenuate the risk of AKI in susceptible patients with COVID-19.

Stability of SARS-CoV-2-Encoded Proteins and Their Antibody Levels Correlate with Interleukin 6 in COVID-19 Patients

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), has become a severe global public health crisis. Therefore, understanding the molecular details of SARS-CoV-2 will be critical for fighting the virus's spread and preventing future pandemics. In this study, we globally profiled the stability of SARS-CoV-2-encoded proteins, studied their degradation pathways, and determined their correlation with the antibody responses in patient plasma. We identified 18 proteins with unstable half-lives and 6 relatively stable proteins with longer half-lives. The labile SARS-CoV-2 proteins were degraded mainly by the ubiquitin-proteasome pathway. We also observed a significant correlation between antibody levels and protein half-lives, which indicated that a stable antigen of SARS-CoV-2 could be more effective for eliciting antibody responses. In addition, levels of antiviral antibodies targeting NSP10 were found to be negatively correlated with systemic levels of interleukin 6 (IL-6) in patients. These findings may facilitate the development of novel therapeutic or diagnostic approaches. IMPORTANCE SARS-CoV-2, the etiological cause of COVID-19, carries 29 genes in its genome. However, our knowledge of the viral proteins in biological and biochemical aspects is limited. In this study, we globally profiled the stability of the viral proteins in living lung epithelial cells. Importantly, the labile SARS-CoV-2-encoded proteins were mainly degraded through the ubiquitin-proteasome pathway. Stable proteins, including spike and nucleocapsid, of SARS-CoV-2 were more effective in eliciting antibody production. The levels of antiviral antibodies targeting NSP10 were negatively correlated with systemic levels of IL-6 in COVID-19 patients.

Redefining critical illness

Research and practice in critical care medicine have long been defined by syndromes, which, despite being clinically recognizable entities, are, in fact, loose amalgams of heterogeneous states that may respond differently to therapy. Mounting translational evidence-supported by research on respiratory failure due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-suggests that the current syndrome-based framework of critical illness should be reconsidered. Here we discuss recent findings from basic science and clinical research in critical care and explore how these might inform a new conceptual model of critical illness. De-emphasizing syndromes, we focus on the underlying biological changes that underpin critical illness states and that may be amenable to treatment. We hypothesize that such an approach will accelerate critical care research, leading to a richer understanding of the pathobiology of critical illness and of the key determinants of patient outcomes. This, in turn, will support the design of more effective clinical trials and inform a more precise and more effective practice at the bedside.

Tertiary peritonitis: considerations for complex team-based care

Peritonitis, as a major consequence of hollow visceral perforation, anastomotic disruption, ischemic necrosis, or other injuries of the gastrointestinal tract, often drives acute care in the emergency department, operating room, and the ICU. Chronic critical illness (CCI) represents a devastating challenge in modern surgical critical care where successful interventions have fostered a growing cohort of patients with prolonged dependence on mechanical ventilation and other organ supportive therapies who would previously have succumbed much earlier in the acute phase of critical illness. An important subset of CCI patients are those who have survived an emergency abdominal operation, but who subsequently require prolonged open abdomen management complicated by persistent peritoneal space infection or colonization, fistula formation, and gastrointestinal (GI) tract dysfunction; these patients are described as having tertiary peritonitis (TP).The organ dysfunction cascade in TP terminates in death in between 30 and 64% of patients. This narrative review describes key-but not all-elements in a framework for the coordinate multiprofessional team-based management of a patient with tertiary peritonitis to mitigate this risk of death and promote recovery. Given the prolonged critical illness course of this unique patient population, early and recurrent Palliative Care Medicine consultation helps establish goals of care, support adjustment to changes in life circumstance, and enable patient and family centered care.

Towards a biological definition of ARDS: are treatable traits the solution?

The pathophysiology of acute respiratory distress syndrome (ARDS) includes the accumulation of protein-rich pulmonary edema in the air spaces and interstitial areas of the lung, variable degrees of epithelial injury, variable degrees of endothelial barrier disruption, transmigration of leukocytes, alongside impaired fluid and ion clearance. These pathophysiological features are different between patients contributing to substantial biological heterogeneity. In this context, it is perhaps unsurprising that a wide range of pharmacological interventions targeting these pathophysiological processes have failed to improve patient outcomes. In this manuscript, our goal is to provide a narrative summary of the potential methods to capture the underlying biological heterogeneity of ARDS and discuss how this information could inform future ARDS redefinitions. We discuss what biological tests are available to identify patients with any of the following predominant biological patterns: (1) epithelial and/or endothelial injury, (2) protein rich pulmonary edema and (3) systemic or within lung inflammatory responses.

Alveolar, Endothelial, and Organ Injury Marker Dynamics in Severe COVID-19

Rationale: Alveolar and endothelial injury may be differentially associated with coronavirus disease (COVID-19) severity over time. Objectives: To describe alveolar and endothelial injury dynamics and associations with COVID-19 severity, cardiorenovascular injury, and outcomes. Methods: This single-center observational study enrolled patients with COVID-19 requiring respiratory support at emergency department presentation. More than 40 markers of alveolar (including receptor for advanced glycation endproducts [RAGE]), endothelial (including angiopoietin-2), and cardiorenovascular injury (including renin, kidney injury molecule-1, and troponin-I) were serially compared between invasively and spontaneously ventilated patients using mixed-effects repeated-measures models. Ventilatory ratios were calculated for intubated patients. Associations of biomarkers with modified World Health Organization scale at Day 28 were determined with multivariable proportional-odds regression. Measurements and Main Results: Of 225 patients, 74 (33%) received invasive ventilation at Day 0. RAGE was 1.80-fold higher in invasive ventilation patients at Day 0 (95% confidence interval [CI], 1.50-2.17) versus spontaneous ventilation, but decreased over time in all patients. Changes in alveolar markers did not correlate with changes in endothelial, cardiac, or renal injury markers. In contrast, endothelial markers were similar to lower at Day 0 for invasive ventilation versus spontaneous ventilation, but then increased over time only among intubated patients. In intubated patients, angiopoietin-2 was similar (fold difference, 1.02; 95% CI, 0.89-1.17) to nonintubated patients at Day 0 but 1.80-fold higher (95% CI, 1.56-2.06) at Day 3; cardiorenovascular injury markers showed similar patterns. Endothelial markers were not consistently associated with ventilatory ratios. Endothelial markers were more often significantly associated with 28-day outcomes than alveolar markers. Conclusions: Alveolar injury markers increase early. Endothelial injury markers increase later and are associated with cardiorenovascular injury and 28-day outcome. Alveolar and endothelial injury likely contribute at different times to disease progression in severe COVID-19.

Phenotyping in acute respiratory distress syndrome: state of the art and clinical implications

PURPOSE OF REVIEW

Decades of research in acute respiratory distress syndrome (ARDS) have led to few interventions that impact clinical outcomes. The pandemic of patients with ARDS due to the novel SARS-CoV-2 infection has stressed the need for more effective therapies in ARDS. Phenotyping may enable successful trials and precision therapeutics in this patient population.

RECENT FINDINGS

Clinical phenotypes that group patients by shared cause, time-course or radiographic presentation are of prognostic value, but their use is limited by misclassification. Physiological phenotypes, including the P/F ratio, ventilatory ratio and dead space fraction, predict poor outcomes but can rapidly change, making them unstable over time. Biologic phenotypes have prognostic value with composite clinical and biomarker sub-phenotypes additionally impacting treatment response but are yet to be prospectively validated.

SUMMARY

Although much progress has been made in ARDS phenotyping, implementation of precision medicine practices will depend on conducting phenotype-aware trials using rapid point of care assays or machine learning algorithms. Omics studies will enhance our understanding of biologic determinants of clinical outcomes in ARDS sub-phenotypes. Whether biologic ARDS sub-phenotypes are specific to this syndrome or rather more broadly identify endotypes of critical illness remains to be determined.

Identification of acute respiratory distress syndrome subphenotypes de novo using routine clinical data: a retrospective analysis of ARDS clinical trials

OBJECTIVES

The acute respiratory distress syndrome (ARDS) is a heterogeneous condition, and identification of subphenotypes may help in better risk stratification. Our study objective is to identify ARDS subphenotypes using new simpler methodology and readily available clinical variables.

SETTING

This is a retrospective Cohort Study of ARDS trials. Data from the US ARDSNet trials and from the international ART trial.

PARTICIPANTS

3763 patients from ARDSNet data sets and 1010 patients from the ART data set.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome was 60-day or 28-day mortality, depending on what was reported in the original trial. K-means cluster analysis was performed to identify subgroups. Sets of candidate variables were tested to assess their ability to produce different probabilities for mortality in each cluster. Clusters were compared with biomarker data, allowing identification of subphenotypes.

RESULTS

Data from 4773 patients were analysed. Two subphenotypes (A and B) resulted in optimal separation in the final model, which included nine routinely collected clinical variables, namely heart rate, mean arterial pressure, respiratory rate, bilirubin, bicarbonate, creatinine, PaO₂, arterial pH and FiO₂. Participants in subphenotype B showed increased levels of proinflammatory markers, had consistently higher mortality, lower number of ventilator-free days at day 28 and longer duration of ventilation compared with patients in the subphenotype A.

CONCLUSIONS

Routinely available clinical data can successfully identify two distinct subphenotypes in adult ARDS patients. This work may facilitate implementation of precision therapy in ARDS clinical trials.

A Pilot Double-Blind Placebo-Controlled Randomized Clinical Trial to Investigate the Effects of Early Enteral Nutrients in Sepsis

Supplemental Digital Content is available in the text.

Preclinical studies from our laboratory demonstrated therapeutic effects of enteral dextrose administration in the acute phase of sepsis, mediated by the intestine-derived incretin hormone glucose-dependent insulinotropic peptide. The current study investigated the effects of an early enteral dextrose infusion on systemic inflammation and glucose metabolism in critically ill septic patients.

A Research Agenda for Precision Medicine in Sepsis and Acute Respiratory Distress Syndrome: An Official American Thoracic Society Research Statement

Background: Precision medicine focuses on the identification of therapeutic strategies that are effective for a group of patients based on similar unifying characteristics. The recent success of precision medicine in non-critical care settings has resulted from the confluence of large clinical and biospecimen repositories, innovative bioinformatics, and novel trial designs. Similar advances for precision medicine in sepsis and in the acute respiratory distress syndrome (ARDS) are possible but will require further investigation and significant investment in infrastructure. Methods: This project was funded by the American Thoracic Society Board of Directors. A multidisciplinary and diverse working group reviewed the available literature, established a conceptual framework, and iteratively developed recommendations for the Precision Medicine Research Agenda for Sepsis and ARDS. Results: The following six priority recommendations were developed by the working group: 1) the creation of large richly phenotyped and harmonized knowledge networks of clinical, imaging, and multianalyte molecular data for sepsis and ARDS; 2) the implementation of novel trial designs, including adaptive designs, and embedding trial procedures in the electronic health record; 3) continued innovation in the data science and engineering methods required to identify heterogeneity of treatment effect; 4) further development of the tools necessary for the real-time application of precision medicine approaches; 5) work to ensure that precision medicine strategies are applicable and available to a broad range of patients varying across differing racial, ethnic, socioeconomic, and demographic groups; and 6) the securement and maintenance of adequate and sustainable funding for precision medicine efforts. Conclusions: Precision medicine approaches that incorporate variability in genomic, biologic, and environmental factors may provide a path forward for better individualizing the delivery of therapies and improving care for patients with sepsis and ARDS.

Stratification for Identification of Prognostic Categories In the Acute RESpiratory Distress Syndrome (SPIRES) Score

OBJECTIVES

To develop a scoring model for stratifying patients with acute respiratory distress syndrome into risk categories (Stratification for identification of Prognostic categories In the acute RESpiratory distress syndrome score) for early prediction of death in the ICU, independent of the underlying disease and cause of death.

DESIGN

A development and validation study using clinical data from four prospective, multicenter, observational cohorts.

SETTING

A network of multidisciplinary ICUs.

PATIENTS

One-thousand three-hundred one patients with moderate-to-severe acute respiratory distress syndrome managed with lung-protective ventilation.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The study followed Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines for prediction models. We performed logistic regression analysis, bootstrapping, and internal-external validation of prediction models with variables collected within 24 hours of acute respiratory distress syndrome diagnosis in 1,000 patients for model development. Primary outcome was ICU death. The Stratification for identification of Prognostic categories In the acute RESpiratory distress syndrome score was based on patient's age, number of extrapulmonary organ failures, values of end-inspiratory plateau pressure, and ratio of Pao2 to Fio2 assessed at 24 hours of acute respiratory distress syndrome diagnosis. The pooled area under the receiver operating characteristic curve across internal-external validations was 0.860 (95% CI, 0.831-0.890). External validation in a new cohort of 301 acute respiratory distress syndrome patients confirmed the accuracy and robustness of the scoring model (area under the receiver operating characteristic curve = 0.870; 95% CI, 0.829-0.911). The Stratification for identification of Prognostic categories In the acute RESpiratory distress syndrome score stratified patients in three distinct prognostic classes and achieved better prediction of ICU death than ratio of Pao2 to Fio2 at acute respiratory distress syndrome onset or at 24 hours, Acute Physiology and Chronic Health Evaluation II score, or Sequential Organ Failure Assessment scale.

CONCLUSIONS

The Stratification for identification of Prognostic categories In the acute RESpiratory distress syndrome score represents a novel strategy for early stratification of acute respiratory distress syndrome patients into prognostic categories and for selecting patients for therapeutic trials.

Neurological Complications Acquired During Pediatric Critical Illness: Exploratory "Mixed Graphical Modeling" Analysis Using Serum Biomarker Levels

OBJECTIVES

Neurologic complications, consisting of the acute development of a neurologic disorder, that is, not present at admission but develops during the course of illness, can be difficult to detect in the PICU due to sedation, neuromuscular blockade, and young age. We evaluated the direct relationships of serum biomarkers and clinical variables to the development of neurologic complications. Analysis was performed using mixed graphical models, a machine learning approach that allows inference of cause-effect associations from continuous and discrete data.

DESIGN

Secondary analysis of a previous prospective observational study.

SETTING

PICU, single quaternary-care center.

PATIENTS

Individuals admitted to the PICU, younger than18 years old, with intravascular access via an indwelling catheter.

INTERVENTIONS

None.

MEASUREMENTS

About 101 patients were included in this analysis. Serum (days 1-7) was analyzed for glial fibrillary acidic protein, ubiquitin C-terminal hydrolase-L1, and alpha-II spectrin breakdown product 150 utilizing enzyme-linked immunosorbent assays. Serum levels of neuron-specific enolase, myelin basic protein, and S100 calcium binding protein B used in these models were reported previously. Demographic data, use of selected clinical therapies, lengths of stay, and ancillary neurologic testing (head CT, brain MRI, and electroencephalogram) results were recorded. The Mixed Graphical Model-Fast-Causal Inference-Maximum algorithm was applied to the dataset.

MAIN RESULTS

About 13 of 101 patients developed a neurologic complication during their critical illness. The mixed graphical model identified peak levels of the neuronal biomarker neuron-specific enolase and ubiquitin C-terminal hydrolase-L1, and the astrocyte biomarker glial fibrillary acidic protein to be the direct causal determinants for the development of a neurologic complication; in contrast, clinical variables including age, sex, length of stay, and primary neurologic diagnosis were not direct causal determinants.

CONCLUSIONS

Graphical models that include biomarkers in addition to clinical data are promising methods to evaluate direct relationships in the development of neurologic complications in critically ill children. Future work is required to validate and refine these models further, to determine if they can be used to predict which patients are at risk for/or with early neurologic complications.

Identification of persistent and resolving subphenotypes of acute hypoxemic respiratory failure in two independent cohorts

BACKGROUND

Acute hypoxemic respiratory failure (HRF) is associated with high morbidity and mortality, but its heterogeneity challenges the identification of effective therapies. Defining subphenotypes with distinct prognoses or biologic features can improve therapeutic trials, but prior work has focused on ARDS, which excludes many acute HRF patients. We aimed to characterize persistent and resolving subphenotypes in the broader HRF population.

METHODS

In this secondary analysis of 2 independent prospective ICU cohorts, we included adults with acute HRF, defined by invasive mechanical ventilation and PaO2-to-FIO2 ratio ≤ 300 on cohort enrollment (n = 768 in the discovery cohort and n = 1715 in the validation cohort). We classified patients as persistent HRF if still requiring mechanical ventilation with PaO2-to-FIO2 ratio ≤ 300 on day 3 following ICU admission, or resolving HRF if otherwise. We estimated relative risk of 28-day hospital mortality associated with persistent HRF, compared to resolving HRF, using generalized linear models. We also estimated fold difference in circulating biomarkers of inflammation and endothelial activation on cohort enrollment among persistent HRF compared to resolving HRF. Finally, we stratified our analyses by ARDS to understand whether this was driving differences between persistent and resolving HRF.

RESULTS

Over 50% developed persistent HRF in both the discovery (n = 386) and validation (n = 1032) cohorts. Persistent HRF was associated with higher risk of death relative to resolving HRF in both the discovery (1.68-fold, 95% CI 1.11, 2.54) and validation cohorts (1.93-fold, 95% CI 1.50, 2.47), after adjustment for age, sex, chronic respiratory illness, and acute illness severity on enrollment (APACHE-III in discovery, APACHE-II in validation). Patients with persistent HRF displayed higher biomarkers of inflammation (interleukin-6, interleukin-8) and endothelial dysfunction (angiopoietin-2) than resolving HRF after adjustment. Only half of persistent HRF patients had ARDS, yet exhibited higher mortality and biomarkers than resolving HRF regardless of whether they qualified for ARDS.

CONCLUSION

Patients with persistent HRF are common and have higher mortality and elevated circulating markers of lung injury compared to resolving HRF, and yet only a subset are captured by ARDS definitions. Persistent HRF may represent a clinically important, inclusive target for future therapeutic trials in HRF.

Understanding the Host in the Management of Pneumonia. An Official American Thoracic Society Workshop Report

Pneumonia causes a significant burden of disease worldwide. Although all populations are at risk of pneumonia, those at extremes of age and those with immunosuppressive disorders, underlying respiratory disease, and critical illness are particularly vulnerable. Although clinical practice guidelines addressing the management and treatment of pneumonia exist, few of the supporting studies focus on the crucial contributions of the host in pneumonia pathogenesis and recovery. Such essential considerations include the host risk factors that lead to susceptibility to lung infections; biomarkers reflecting the host response and the means to pursue host-directed pneumonia therapy; systemic effects of pneumonia on the host; and long-term health outcomes after pneumonia. To address these gaps, the Pneumonia Working Group of the Assembly on Pulmonary Infection and Tuberculosis led a workshop held at the American Thoracic Society meeting in May 2018 with overarching objectives to foster attention, stimulate research, and promote funding for short-term and long-term investigations into the host contributions to pneumonia. The workshop involved participants from various disciplines with expertise in lung infection, pneumonia, sepsis, immunocompromised patients, translational biology, data science, genomics, systems biology, and clinical trials. This workshop report summarizes the presentations and discussions and important recommendations for future clinical pneumonia studies. These recommendations include establishing consensus disease and outcome definitions, improved phenotyping, development of clinical study networks, standardized data and biospecimen collection and protocols, and development of innovative trial designs.

Biomarker-Based Classification of Patients With Acute Respiratory Failure Into Inflammatory Subphenotypes: A Single-Center Exploratory Study

OBJECTIVES

Hyper- and hypoinflammatory subphenotypes discovered in patients with acute respiratory distress syndrome predict clinical outcomes and therapeutic responses. These subphenotypes may be important in broader critically ill patient populations with acute respiratory failure regardless of clinical diagnosis. We investigated subphenotyping with latent class analysis in an inclusive population of acute respiratory failure, derived a parsimonious model for subphenotypic predictions based on a small set of variables, and examined associations with clinical outcomes.

DESIGN

Prospective, observational cohort study.

SETTING

Single-center, academic medical ICU.

PATIENTS

Mechanically ventilated patients with acute respiratory failure.

MEASUREMENTS AND MAIN RESULTS

We included 498 patients with acute respiratory failure (acute respiratory distress syndrome: 143, at-risk for acute respiratory distress syndrome: 198, congestive heart failure: 37, acute on chronic respiratory failure: 23, airway protection: 61, and multifactorial: 35) in our derivation cohort and measured 10 baseline plasma biomarkers. Latent class analysis considering clinical variables and biomarkers determined that a two-class model offered optimal fit (23% hyperinflammatory subphenotype). Distribution of hyperinflammatory subphenotype varied among acute respiratory failure etiologies (acute respiratory distress syndrome: 31%, at-risk for acute respiratory distress syndrome: 27%, congestive heart failure: 22%, acute on chronic respiratory failure 0%, airway protection: 5%, and multifactorial: 14%). Hyperinflammatory patients had higher Sequential Organ Failure Assessment scores, fewer ventilator-free days, and higher 30- and 90-day mortality (all p < 0.001). We derived a parsimonious model consisting of angiopoietin-2, soluble tumor necrosis factor receptor-1, procalcitonin, and bicarbonate and classified subphenotypes in a validation cohort (n = 139). Hyperinflammatory patients (19%) demonstrated higher levels of inflammatory biomarkers not included in the model (p < 0.01) and worse outcomes.

CONCLUSIONS

Host-response subphenotypes are observable in a heterogeneous population with acute respiratory failure and predict clinical outcomes. Simple, biomarker-based models can offer prognostic enrichment in patients with acute respiratory failure. The differential distribution of subphenotypes by specific etiologies of acute respiratory failure indicates that subphenotyping may be more relevant in patients with hypoxemic causes of acute respiratory failure and not in patients intubated for airway protection or acute on chronic decompensation.