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Rationale Two distinct subphenotypes have been identified in acute respiratory distress syndrome (ARDS), but the presence of subgroups in ARDS associated with COVID-19 is unknown. The objective of this study was to identify clinically relevant, novel subgroups in COVID-19-related ARDS, and compare them to previously described ARDS subphenotypes. Methods Eligible participants were adults with COVID-19 and ARDS at Columbia University Irving Medical Center. Latent class analysis (LCA) was used to identify subgroups with baseline clinical, respiratory, and laboratory data serving as partitioning variables. A previously-developed machine learning model was used to classify patients as the hypoinflammatory and hyperinflammatory subphenotypes. Baseline characteristics and clinical outcomes were compared between subgroups. Heterogeneity of treatment effect (HTE) for corticosteroid-use in subgroups was tested. Measurements and Main Results From 3/2-4/30/2020, 483 patients with COVID-19-related ARDS met study criteria. A two-class LCA model best fit the population (p=0.0075). Class 2 (23%) had higher pro-inflammatory markers, troponin, creatinine and lactate, lower bicarbonate and lower blood pressure than Class 1 (77%). 90-day mortality was higher in Class 2 versus Class 1 (75% vs 48%; p

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.

Acute respiratory distress syndrome (ARDS) is a heterogeneous clinical syndrome that manifests secondary to numerous etiologic insults, and consequently it is associated with a multitude of pathophysiological abnormalities. Despite more than 50 years of experimental studies, translation of these benchside discoveries into effective biological therapies has been elusive. In this review, some of the key advances made in our knowledge of the pathophysiology of ARDS, based on histopathology, imaging, protein, and transcriptomic biomarkers, are presented. Finally, the role of such human studies in understanding the pathophysiology of coronavirus disease 2019-related ARDS is reviewed.

RationaleUsing latent class analysis (LCA), two subphenotypes of acute respiratory distress syndrome (ARDS) have consistently been identified in five randomised controlled trials (RCTs), with distinct biological characteristics, divergent outcomes and differential treatment responses to randomised interventions. Their existence in unselected populations of ARDS remains unknown. We sought to identify subphenotypes in observational cohorts of ARDS using LCA.MethodsLCA was independently applied to patients with ARDS from two prospective observational cohorts of patients admitted to the intensive care unit, derived from the Validating Acute Lung Injury markers for Diagnosis (VALID) (n=624) and Early Assessment of Renal and Lung Injury (EARLI) (n=335) studies. Clinical and biological data were used as class-defining variables. To test for concordance with prior ARDS subphenotypes, the performance metrics of parsimonious classifier models (interleukin 8, bicarbonate, protein C and vasopressor-use), previously developed in RCTs, were evaluated in EARLI and VALID with LCA-derived subphenotypes as the gold-standard.ResultsA 2-class model best fit the population in VALID (p=0.0010) and in EARLI (pConclusionPreviously described subphenotypes are generalisable to unselected populations of non-trauma ARDS.

BackgroundPrior acute respiratory distress syndrome (ARDS) trials have identified hypoinflammatory and hyperinflammatory subphenotypes, with distinct differences in short-term outcomes. It is unknown if such differences extend beyond 90 days or are associated with physical, mental health or cognitive outcomes.Methods568 patients in the multicentre Statins for Acutely Injured Lungs from Sepsis trial of rosuvastatin versus placebo were included and assigned a subphenotype. Among 6-month and 12-month survivors (N=232 and 219, respectively, representing 243 unique survivors), subphenotype status was evaluated for association with a range of patient-reported outcomes (eg, mental health symptoms, quality of life). Patient subsets also were evaluated with performance-based tests of physical function (eg, 6 min walk test) and cognition.FindingsThe hyperinflammatory versus hypoinflammatory subphenotype had lower overall 12-month cumulative survival (58% vs 72%, pInterpretationComparing the hyperinflammatory versus hypoinflammatory ARDS subphenotype, there was no significant difference in survival beyond 90 days and no consistent findings of important differences in 6-month or 12-month physical, cognitive and mental health outcomes. These findings, when considered with prior results, suggest that inflammatory subphenotypes largely reflect the acute phase of illness and its short-term impact.

Background: The Permissive Hypercapnia, Alveolar Recruitment and Low Airway Pressure (PHARLAP) randomised controlled trial compared an open lung ventilation strategy with control ventilation, and found that open lung ventilation did not reduce the number of ventilator-free days (VFDs) or mortality in patients with moderate-to-severe acute respiratory distress syndrome (ARDS). Parsimonious models can identify distinct phenotypes of ARDS (hypo-inflammatory and hyperinflammatory) which are associated with different outcomes and treatment responses. Objective: To test the hypothesis that a parsimonious model would identify patients with distinctly different clinical outcomes in the PHARLAP study. Design, setting and participants: Blood and lung lavage samples were collected in a subset of PHARLAP patients who were recruited in Australian and New Zealand centres. A previously validated parsimonious model (interleukin-8, soluble tumour necrosis factor receptor-1 and bicarbonate) was used to classify patients with blood samples into hypo-inflammatory and hyperinflammatory groups. Generalised linear modelling was used to examine the interaction between inflammatory phenotype and treatment group (intervention or control). Main outcome measure: The primary outcome was number of VFDs at Day 28. Results: Data for the parsimonious model were available for 56 of 115 patients (49%). Within this subset, 38 patients (68%) and 18 patients (32%) were classified as having hypo-inflammatory and hyperinflammatory phenotypes, respectively. Patients with the hypo-inflammatory phenotype had more VFDs at Day 28 when compared with those with the hyperinflammatory phenotype (median [IQR], 19.5 [11–24] versus 8 [0–21]; P = 0.03). Patients with the hyperinflammatory phenotype had numerically fewer VFDs when managed with an open lung strategy than when managed with control “protective” ventilation (median [IQR], 0 [0–19] versus 16 [8–22]). Conclusion: In the PHARLAP trial, ARDS patients classified as having a hyperinflammatory phenotype, with a parsimonious three-variable model, had fewer VFDs at Day 28 compared with patients classified as having a hypo-inflammatory phenotype. Future clinical studies of ventilatory strategies should consider incorporating distinct ARDS phenotypes into their trial design.

Background The unprecedented increase in critically ill patients due to the COVID-19 pandemic mandated rapid training in critical care for redeployed staff to work safely in intensive care units (ICU). Methods The COVID-19 ICU Remote-Learning Course (CIRLC) is a remote delivery course developed in response to the pandemic. This was a one-day course focused on the fundamentals of Intensive Care. The course used blended learning with recorded lectures and interactive tutorials delivered by shielding and frontline ICU trained professionals. The course was developed within one week and piloted at three NHS Trusts. It was then made publicly available free of charge to redeployed healthcare professionals across the UK and Ireland. An iterative cycle of improvement was used to update the course content weekly. A course confidence questionnaire with quantitative and qualitative questions was used to evaluate effectiveness. Data is reported as n (%), means (SD) and thematic analysis was used for the open questions. Results 1,269 candidates from 171 organisations completed the course, with 99 volunteer trainers. 96% of respondents rated the course as very or extremely useful. 86% rated the online platform as excellent. Overall confidence improved from 2.7/5 to 3.9/5. Qualitative data showed that the course was pitched at the appropriate level, accessible and built clinicians confidence to work in intensive care. Conclusion This model of educational delivery with a rapid iteration cycle was a pragmatic, effective solution to knowledge-based training under social distancing measures. Whilst full course evaluation was not possible, we believe that this work demonstrates practical guidance on educational response in a pandemic as well as highlighting the altruistic nature of the critical care community.

Latent class analysis is a probabilistic modeling algorithm that allows clustering of data and statistical inference. There has been a recent upsurge in the application of latent class analysis in the fields of critical care, respiratory medicine, and beyond. In this review, we present a brief overview of the principles behind latent class analysis. Furthermore, in a stepwise manner, we outline the key processes necessary to perform latent class analysis including some of the challenges and pitfalls faced at each of these steps. The review provides a one-stop shop for investigators seeking to apply latent class analysis to their data.

Rationale: Two distinct phenotypes of acute respiratory distress syndrome (ARDS) with differential clinical outcomes and responses to randomly assigned treatment have consistently been identified i...

Background Patients with COVID-19-related acute respiratory distress syndrome (ARDS) have been postulated to present with distinct respiratory subphenotypes. However, most phenotyping schema have been limited by sample size, disregard for temporal dynamics, and insufficient validation. We aimed to identify respiratory subphenotypes of COVID-19-related ARDS using unbiased data-driven approaches.Methods PRoVENT-COVID was an investigator-initiated, national, multicentre, prospective, observational cohort study at 22 intensive care units (ICUs) in the Netherlands. Consecutive patients who had received invasive mechanical ventilation for COVID-19 (aged 18 years or older) served as the derivation cohort, and similar patients from two ICUs in the USA served as the replication cohorts. COVID-19 was confirmed by positive RT-PCR. We used latent class analysis to identify subphenotypes using clinically available respiratory data cross-sectionally at baseline, and longitudinally using 8-hourly data from the first 4 days of invasive ventilation. We used group-based trajectory modelling to evaluate trajectories of individual variables and to facilitate potential clinical translation. The PRoVENT-COVID study is registered with ClinicalTrials.gov, NCT04346342.Findings Between March 1, 2020, and May 15, 2020, 1007 patients were admitted to participating ICUs in the Netherlands, and included in the derivation cohort. Data for 288 patients were included in replication cohort 1 and 326 in replication cohort 2. Cross-sectional latent class analysis did not identify any underlying subphenotypes. Longitudinal latent dass analysis identified two distinct subphenotypes. Subphenotype 2 was characterised by higher mechanical power, minute ventilation, and ventilatory ratio over the first 4 days of invasive mechanical ventilation than subphenotype 1, but PaO2/FiO(2), pH, and compliance of the respiratory system did not differ between the two subphenotypes. 185 (28%) of 671 patients with subphenotype 1 and 109 (32%) of 336 patients with subphenotype 2 had died at day 28 (p=0.10). However, patients with subphenotype 2 had fewer ventilator-free days at day 28 (median 0, IQR 0-15 vs 5, 0-17; p=0.016) and more frequent venous thrombotic events (109[32%] of 336 patients vs 176 [26%] of 671 patients; p=0.048) compared with subphenotype 1. Group-based trajectory modelling revealed trajectories of ventilatory ratio and mechanical power with similar dynamics to those observed in latent class analysis-derived trajectory subphenotypes. The two trajectories were: a stable value for ventilatory ratio or mechanical power over the first 4 days of invasive mechanical ventilation (trajectory A) or an upward trajectory (trajectory B). However, upward trajectories were better independent prognosticators for 28-day mortality (OR 1.64, 95% CI 1.17-2.29 for ventilatory ratio; 1.82, 1.24-2.66 for mechanical power). The association between upward ventilatory ratio trajectories (trajectory B) and 28-day mortality was confirmed in the replication cohorts (OR 4.65, 95% CI 1. 87-11 6 for ventilatory ratio in replication cohort 1; 1-89, 1.05-3-37 for ventilatory ratio in replication cohort 2).Interpretation At baseline, COVID-19-related ARDS has no consistent respiratory subphenotype. Patients diverged from a fairly homogenous to a more heterogeneous population, with trajectories of ventilatory ratio and mechanical power being the most discriminatory. Modelling these parameters alone provided prognostic value for duration of mechanical ventilation and mortality. Copyright (C) 2021 Published by Elsevier Ltd. All rights reserved.

Rationale: The biological mechanisms of long-term cognitive impairment and disability after critical illness are unclear. Objectives: To test the hypothesis that markers of acute inflammation and coagulation are associated with subsequent long-term cognitive impairment and disability. Methods: We obtained plasma samples from adults with respiratory failure or shock on Study Days 1, 3, and 5 and measured concentrations of CRP (C-reactive protein), IFN-γ, IL-1β, IL-6, IL-8, IL-10, IL-12, MMP-9 (matrix metalloproteinase-9), TNF-α (tumor necrosis factor-α), soluble TNF receptor 1, and protein C. At 3 and 12 months after discharge, we assessed global cognition, executive function, and activities of daily living. We analyzed associations between markers and outcomes using multivariable regression, adjusting for age, sex, education, comorbidities, baseline cognition, doses of sedatives and opioids, stroke risk (in cognitive models), and baseline disability scores (in disability models). Measurements and Main Results: We included 548 participants who were a median (interquartile range) of 62 (53–72) years old, 88% of whom were mechanically ventilated, and who had an enrollment Sequential Organ Failure Assessment score of 9 (7–11). After adjusting for covariates, no markers were associated with long-term cognitive function. Two markers, CRP and MMP-9, were associated with greater disability in basic and instrumental activities of daily living at 3 and 12 months. No other markers were consistently associated with disability outcomes. Conclusions: Markers of systemic inflammation and coagulation measured early during critical illness are not associated with long-term cognitive outcomes and demonstrate inconsistent associations with disability outcomes. Future studies that pair longitudinal measurement of inflammation and related pathways throughout the course of critical illness and during recovery with long-term outcomes are needed.

Supplemental Digital Content is available in the text., OBJECTIVES: Unbiased global metabolomic profiling has not been used to identify distinct subclasses in patients with early sepsis and sepsis-associated acute respiratory distress syndrome. In this study, we examined whether the plasma metabolome reflects systemic illness in early sepsis and in acute respiratory distress syndrome. DESIGN: Plasma metabolites were measured in subjects with early sepsis. SETTING: Patients were admitted from the emergency department to the ICU in a plasma sample collected within 24 hours of ICU admission. Metabolic profiling of 970 metabolites was performed by Metabolon (Durham, NC). Hierarchical clustering and partial least squares discriminant clustering were used to identify distinct clusters among patients with early sepsis and sepsis-associated acute respiratory distress syndrome. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among critically ill patients with early sepsis (n = 197), three metabolically distinct subgroups were identified, with metabolic subtype driven by plasma lipids. Group 1, with 45 subjects (23% of cohort), had increased 60-day mortality (odds ratio, 2; 95% CI, 0.99–4.0; p = 0.04 for group 1 vs all others). This group also had higher rates of vasopressor-dependent shock, acute kidney injury, and met Berlin acute respiratory distress syndrome criteria more often (all p < 0.05). Conversely, metabolic group 3, with 76 subjects (39% of cohort), had the lowest risk of 60-day mortality (odds ratio, 0.44; 95% CI, 0.22–0.86; p = 0.01) and lower rates of organ dysfunction as reflected in a lower Simplified Acute Physiology Score II (p < 0.001). In contrast, global metabolomic profiling did not separate patient with early sepsis with moderate-to-severe acute respiratory distress syndrome (n = 78) from those with sepsis without acute respiratory distress syndrome (n = 75). CONCLUSIONS: Plasma metabolomic profiling in patients with early sepsis identified three metabolically distinct groups that were characterized by different plasma lipid profiles, distinct clinical phenotypes, and 60-day mortality. Plasma metabolites did not distinguish patients with early sepsis who developed acute respiratory distress syndrome from those who did not.

The immunological features that distinguish COVID-19-associated acute respiratory distress syndrome (ARDS) from other causes of ARDS are incompletely understood. Here, we report the results of comparative lower respiratory tract transcriptional profiling of tracheal aspirate from 52 critically ill patients with ARDS from COVID-19 or from other etiologies, as well as controls without ARDS. In contrast to a “cytokine storm,” we observe reduced proinflammatory gene expression in COVID-19 ARDS when compared to ARDS due to other causes. COVID-19 ARDS is characterized by a dysregulated host response with increased PTEN signaling and elevated expression of genes with non-canonical roles in inflammation and immunity. In silico analysis of gene expression identifies several candidate drugs that may modulate gene expression in COVID-19 ARDS, including dexamethasone and granulocyte colony stimulating factor. Compared to ARDS due to other types of viral pneumonia, COVID-19 is characterized by impaired interferon-stimulated gene (ISG) expression. The relationship between SARS-CoV-2 viral load and expression of ISGs is decoupled in patients with COVID-19 ARDS when compared to patients with mild COVID-19. In summary, assessment of host gene expression in the lower airways of patients reveals distinct immunological features of COVID-19 ARDS., Here, the authors perform transcriptional profiling on tracheal aspirates of adults requiring mechanical ventilation for SARS-CoV2-induced acute respiratory distress syndrome (ARDS) and identify a dysregulated host response predicted to predicted to be potentially modulated by dexamethasone.

Rationale: Pulmonary dead space fraction (Vd/Vt) is an independent predictor of mortality in acute respiratory distress syndrome (ARDS). Yet, it is seldom used in practice. The ventilatory ratio is a simple bedside index that can be calculated using routinely measured respiratory variables and is a measure of impaired ventilation. Ventilatory ratio is defined as [minute ventilation (ml/min) × Pa(CO(2)) (mm Hg)]/(predicted body weight × 100 × 37.5). Objectives: To determine the relation of ventilatory ratio with Vd/Vt in ARDS. Methods: First, in a single-center, prospective observational study of ARDS, we tested the association of Vd/Vt with ventilatory ratio. With in-hospital mortality as the primary outcome and ventilator-free days as the secondary outcome, we tested the role of ventilatory ratio as an outcome predictor. The findings from this study were further verified in secondary analyses of two NHLBI ARDS Network randomized controlled trials. Measurements and Main Results: Ventilatory ratio positively correlated with Vd/Vt. Ordinal groups of ventilatory ratio had significantly higher Vd/Vt. Ventilatory ratio was independently associated with increased risk of mortality after adjusting for Pa(O(2))/Fi(O(2)), and positive end-expiratory pressure (odds ratio, 1.51; P = 0.024) and after adjusting for Acute Physiologic Assessment and Chronic Health Evaluation II score (odds ratio, 1.59; P = 0.04). These findings were further replicated in secondary analyses of two separate NHLBI randomized controlled trials. Conclusions: Ventilatory ratio correlates well with Vd/Vt in ARDS, and higher values at baseline are associated with increased risk of adverse outcomes. These results are promising for the use of ventilatory ratio as a simple bedside index of impaired ventilation in ARDS.

There are limited data on longitudinal outcomes for coronavirus disease 2019 (COVID-19) hospitalizations that account for transitions between clinical states over time. Using electronic health record data from a hospital network in the St. Louis, Missouri, region, we performed multistate analyses to examine longitudinal transitions and outcomes among hospitalized adults with laboratory-confirmed COVID-19 with respect to 15 mutually exclusive clinical states. Between March 15 and July 25, 2020, a total of 1,577 patients in the network were hospitalized with COVID-19 (49.9% male; median age, 63 years (interquartile range, 50–75); 58.8% Black). Overall, 34.1% (95% confidence interval (CI): 26.4, 41.8) had an intensive care unit admission and 12.3% (95% CI: 8.5, 16.1) received invasive mechanical ventilation (IMV). The risk of decompensation peaked immediately after admission; discharges peaked around days 3–5, and deaths plateaued between days 7 and 16. At 28 days, 12.6% (95% CI: 9.6, 15.6) of patients had died (4.2% (95% CI: 3.2, 5.2) had received IMV) and 80.8% (95% CI: 75.4, 86.1) had been discharged. Among those receiving IMV, 35.1% (95% CI: 28.2, 42.0) remained intubated after 14 days; after 28 days, 37.6% (95% CI: 30.4, 44.7) had died and only 37.7% (95% CI: 30.6, 44.7) had been discharged. Multistate methods offer granular characterizations of the clinical course of COVID-19 and provide essential information for guiding both clinical decision-making and public health planning.

We performed comparative lower respiratory tract transcriptional profiling of 52 critically ill patients with ARDS from COVID-19 or other etiologies, or without ARDS. We found no evidence of cytokine storm but instead observed complex host response dysregulation driven by genes with non-canonical roles in inflammation and immunity that were predicted to be modulated by dexamethasone. Compared to other viral ARDS, COVID-19 was characterized by impaired interferon-stimulated gene expression.

The description of a so-called cytokine storm in patients with COVID-19 has prompted consideration of anti-cytokine therapies, particularly interleukin-6 antagonists. However, direct systematic comparisons of COVID-19 with other critical illnesses associated with elevated cytokine concentrations have not been reported. In this Rapid Review, we report the results of a systematic review and meta-analysis of COVID-19 studies published or posted as preprints between Nov 1, 2019, and April 14, 2020, in which interleukin-6 concentrations in patients with severe or critical disease were recorded. 25 COVID-19 studies (n=1245 patients) were ultimately included. Comparator groups included four trials each in sepsis (n=5320), cytokine release syndrome (n=72), and acute respiratory distress syndrome unrelated to COVID-19 (n=2767). In patients with severe or critical COVID-19, the pooled mean serum interleukin-6 concentration was 36·7 pg/mL (95% CI 21·6-62·3 pg/mL; I2=57·7%). Mean interleukin-6 concentrations were nearly 100 times higher in patients with cytokine release syndrome (3110·5 pg/mL, 632·3-15 302·9 pg/mL; p

BACKGROUND: In acute respiratory distress syndrome (ARDS) unrelated to COVID-19, two phenotypes, based on the severity of systemic inflammation (hyperinflammatory and hypoinflammatory), have been described. The hyperinflammatory phenotype is known to be associated with increased multiorgan failure and mortality. In this study, we aimed to identify these phenotypes in COVID-19-related ARDS.METHODS: In this prospective observational study done at two UK intensive care units, we recruited patients with ARDS due to COVID-19. Demographic, clinical, and laboratory data were collected at baseline. Plasma samples were analysed for interleukin-6 (IL-6) and soluble tumour necrosis factor receptor superfamily member 1A (TNFR1) using a novel point-of-care assay. A parsimonious regression classifier model was used to calculate the probability for the hyperinflammatory phenotype in COVID-19 using IL-6, soluble TNFR1, and bicarbonate levels. Data from this cohort was compared with patients with ARDS due to causes other than COVID-19 recruited to a previous UK multicentre, randomised controlled trial of simvastatin (HARP-2).FINDINGS: Between March 17 and April 25, 2020, 39 patients were recruited to the study. Median ratio of partial pressure of arterial oxygen to fractional concentration of oxygen in inspired air (PaO2/FiO2) was 18 kpa (IQR 15-21) and acute physiology and chronic health evaluation II score was 12 (10-16). 17 (44%) of 39 patients had died by day 28 of the study. Compared with survivors, patients who died were older and had lower PaO2/FiO2. The median probability for the hyperinflammatory phenotype was 0·03 (IQR 0·01-0·2). Depending on the probability cutoff used to assign class, the prevalence of the hyperinflammatory phenotype was between four (10%) and eight (21%) of 39, which is lower than the proportion of patients with the hyperinflammatory phenotype in HARP-2 (186 [35%] of 539). Using the Youden index cutoff (0·274) to classify phenotype, five (63%) of eight patients with the hyperinflammatory phenotype and 12 (39%) of 31 with the hypoinflammatory phenotype died. Compared with matched patients recruited to HARP-2, levels of IL-6 were similar in our cohort, whereas soluble TNFR1 was significantly lower in patients with COVID-19-associated ARDS.INTERPRETATION: In this exploratory analysis of 39 patients, ARDS due to COVID-19 was not associated with higher systemic inflammation and was associated with a lower prevalence of the hyperinflammatory phenotype than that observed in historical ARDS data. This finding suggests that the excess mortality observed in COVID-19-related ARDS is unlikely to be due to the upregulation of inflammatory pathways described by the parsimonious model.FUNDING: US National Institutes of Health, Innovate UK, and Randox.

Care for patients with acute respiratory distress syndrome (ARDS) has changed considerably over the 50 years since its original description. Indeed, standards of care continue to evolve as does how this clinical entity is defined and how patients are grouped and treated in clinical practice. In this narrative review we discuss current standards - treatments that have a solid evidence base and are well established as targets for usual care - and also evolving standards - treatments that have promise and may become widely adopted in the future. We focus on three broad domains of ventilatory management, ventilation adjuncts, and pharmacotherapy. Current standards for ventilatory management include limitation of tidal volume and airway pressure and standard approaches to setting PEEP, while evolving standards might focus on limitation of driving pressure or mechanical power, individual titration of PEEP, and monitoring efforts during spontaneous breathing. Current standards in ventilation adjuncts include prone positioning in moderate-severe ARDS and veno-venous extracorporeal life support after prone positioning in patients with severe hypoxemia or who are difficult to ventilate. Pharmacotherapy current standards include corticosteroids for patients with ARDS due to COVID-19 and employing a conservative fluid strategy for patients not in shock; evolving standards may include steroids for ARDS not related to COVID-19, or specific biological agents being tested in appropriate sub-phenotypes of ARDS. While much progress has been made, certainly significant work remains to be done and we look forward to these future developments.

Sepsis-associated acute kidney injury (AKI) is a complex clinical disorder associated with inflammation, endothelial dysfunction, and dysregulated coagulation. With standard regression methods, collinearity among biomarkers may lead to the exclusion of important biological pathways in a single final model. Best subset regression is an analytic technique that identifies statistically equivalent models, allowing for more robust evaluation of correlated variables. Our objective was to identify common clinical characteristics and biomarkers associated with sepsis-associated AKI. We enrolled 453 septic adults within 24 h of intensive care unit admission. Using best subset regression, we evaluated for associations using a range of models consisting of 1−38 predictors (composed of clinical risk factors and plasma and urine biomarkers) with AKI as the outcome [defined as a serum creatinine (SCr) increase of ≥0.3 mg/dL within 48 h or ≥1.5× baseline SCr within 7 days]. Two hundred ninety-seven patients had AKI. Five-variable models were found to be of optimal complexity, as the best subset of five- and six-variable models were statistically equivalent. Within the subset of five-variable models, 46 permutations of predictors were noted to be statistically equivalent. The most common predictors in this subset included diabetes, baseline SCr, angiopoetin-2, IL-8, soluble tumor necrosis factor receptor-1, and urine neutrophil gelatinase-associated lipocalin. The models had a c-statistic of ∼0.70 (95% confidence interval: 0.65–0.75). In conclusion, using best subset regression, we identified common clinical characteristics and biomarkers associated with sepsis-associated AKI. These variables may be especially relevant in the pathogenesis of sepsis-associated AKI.

Phenotyping of COVID-19-related ARDS should be done using careful, data-driven approaches https://bit.ly/3eX65Yu

BackgroundUsing latent class analysis (LCA) in five randomised controlled trial (RCT) cohorts, two distinct phenotypes of acute respiratory distress syndrome (ARDS) have been identified: hypoinflammatory and hyperinflammatory. The phenotypes are associated with differential outcomes and treatment response. The objective of this study was to develop parsimonious models for phenotype identification that could be accurate and feasible to use in the clinical setting.MethodsIn this retrospective study, three RCT cohorts from the National Lung, Heart, and Blood Institute ARDS Network (ARMA, ALVEOLI, and FACTT) were used as the derivation dataset (n=2022), from which the machine learning and logistic regression classifer models were derived, and a fourth (SAILS; n=715) from the same network was used as the validation test set. LCA-derived phenotypes in all of these cohorts served as the reference standard. Machine-learning algorithms (random forest, bootstrapped aggregating, and least absolute shrinkage and selection operator) were used to select a maximum of six important classifier variables, which were then used to develop nested logistic regression models. Only cases with complete biomarker data in the derivation dataset were used for variable selection. The best logistic regression models based on parsimony and predictive accuracy were then evaluated in the validation test set. Finally, the models' prognostic validity was tested in two external ARDS clinical trial datasets (START and HARP-2) by assessing mortality at days 28, 60, and 90 and ventilator-free days to day 28.FindingsThe six most important classifier variables were interleukin (IL)-8, IL-6, protein C, soluble tumour necrosis factor receptor 1, bicarbonate, and vasopressor use. From the nested models, three-variable (IL-8, bicarbonate, and protein C) and four-variable (3-variable plus vasopressor use) models were adjudicated to be the best performing. In the validation test set, both models showed good accuracy (AUC 0·94 [95% CI 0·92-0·95] for the three-variable model and 0·95 [95% CI 0·93-0·96] for the four-variable model) against LCA classifications. As with LCA-derived phenotypes, the hyperinflammatory phenotype as identified by the classifier model was associated with higher mortality at day 90 (87 [39%] of 223 patients vs 112 [23%] of 492 patients; p

The description of a so-called cytokine storm in patients with COVID-19 has prompted consideration of anti-cytokine therapies, particularly interleukin-6 antagonists. However, direct systematic comparisons of COVID-19 with other critical illnesses associated with elevated cytokine concentrations have not been reported. In this Rapid Review, we report the results of a systematic review and meta-analysis of COVID-19 studies published or posted as preprints between Nov 1, 2019, and April 14, 2020, in which interleukin-6 concentrations in patients with severe or critical disease were recorded. 25 COVID-19 studies (n=1245 patients) were ultimately included. Comparator groups included four trials each in sepsis (n=5320), cytokine release syndrome (n=72), and acute respiratory distress syndrome unrelated to COVID-19 (n=2767). In patients with severe or critical COVID-19, the pooled mean serum interleukin-6 concentration was 36·7 pg/mL (95% CI 21·6-62·3 pg/mL; I

A complex myriad of aetiologies and pathologies coalesce within the clinical syndrome of acute respiratory distress syndrome (ARDS) often obfuscating our understanding of the disorder. Amidst the complexity, acute lung inflammation stands out as the fundamental unifying pathophysiology underlying ARDS. A better understanding of the mechanisms driving inflammation in the lung and the ensuing response from the innate immune system, endothelial and epithelial barriers are key to developing disease-modifying drugs in ARDS. Targeted inhibition of proinflammatory molecules such as tumour necrosis factor (TNF)-α would seem a logical and fruitful line of investigation for disease-modifying intervention in ARDS. As suggested by its name, TNF-α was first described as a mediator in abrogating malignant proliferation of cancerous cells.1 Since then, TNF-α has also been identified as a key cytokine in modulating inflammation. TNF-α is mainly produced by macrophages and monocytes, but it is released by most cells, including endothelial cells, epithelial cells and most other leucocytes. A pleiotropic cytokine, TNF-α has a wide range of biological activities. TNF-α has been implicated as a chemoattractant for neutrophils and eosinophils, as a mediator of apoptosis and a major stimulant of other proinflammatory cytokine release.2 In ARDS, elevated levels of TNF-α are found in both plasma and bronchioalveolar lavage (BAL).3 4 In the lungs, TNF-α expression directly leads to increased endothelial permeability5 6 and downregulates epithelial sodium channel expression leading to impaired alveolar fluid clearance.7 Aside from ARDS, TNF-α is also a critical mediator in other inflammatory conditions such as rheumatoid arthritis, psoriasis and Crohn’s disease. Moreover, in these diseases, inhibitors of TNF-α are now in widespread therapeutic use, setting a precedent for their use in ARDS. As a ligand, TNF-α binds to two members of TNF-receptor superfamily: TNF-receptor 1 (TNFR1) and TNF-receptor 2 (TNFR2). TNFR1 is a transmembrane receptor with …

Despite progress in the supportive care available for critically ill patients, few advances have been made in the searchfor effective disease-modifying therapeutic options. The fact that many trials in critical care medicine have notidentified a treatment benefit is probably due, in part, to the underlying heterogeneity of critical care syndromes.Numerous approaches have been proposed to divide populations of critically ill patients into more meaningfulsubgroups (subphenotypes), some of which might be more useful than others. Subclassification systems driven byclinical features and biomarkers have been proposed for acute respiratory distress syndrome, sepsis, acute kidneyinjury, and pancreatitis. Identifying the systems that are most useful and biologically meaningful could lead to abetter understanding of the pathophysiology of critical care syndromes and the discovery of new treatment targets,and allow recruitment in future therapeutic trials to focus on predicted responders. This Review discusses proposedsubphenotypes of critical illness syndromes and highlights the issues that will need to be addressed to translatesubphenotypes into clinical practice.

BACKGROUND: Sepsis profoundly alters immune homeostasis. Cytokine release after whole blood lipopolysaccharide (LPS)-stimulation reflects cell function across multiple immune cell classes and represents the immune response to LPS. The main goal of this study was to evaluate the prognostic value of ex vivo stimulation of whole blood with LPS in sepsis. METHODS: Blood was drawn on day 1 and day 7 after admission, and stimulated ex vivo with LPS. Tumour necrosis factor (TNF)-α, interleukin (IL)-1β, IL-6 and IL-10 were measured with and without stimulation. Our primary outcome measure was the persistence of at least one organ dysfunction at day 7. Organ dysfunction was defined according to the SOFA components by a score ≥ 2. RESULTS: Forty-nine patients with sepsis from a 21-bed intensive care unit, and 23 healthy volunteers were enrolled. The blood of septic patients was less responsive to ex vivo stimulation with LPS than that of healthy controls at day 1 and 7, as demonstrated by lower TNF-α, IL-1β, IL-6 and IL-10 release. Persistent organ dysfunction was more frequent in patients with lower IL-10 release at day 1 but such an association was not found for pro-inflammatory cytokines. A persistent low IL-10 release at day 7 was also associated with persistent organ dysfunction. CONCLUSION: These data suggest that the capacity to produce IL-10 in response to whole blood ex vivo stimulation early in sepsis, as well as persistent low IL-10 response over time, may help in prognostication and patient stratification. These results will need to be confirmed in future studies.

Purpose of review To provide an overview of the current research in identifying homogeneous subgroups and phenotypes in ARDS. Recent findings In recent years, investigations have used either physiology, clinical data, biomarkers or a combination of these to stratify patients with ARDS into distinct subgroups with divergent clinical outcomes. In some studies, there has also been evidence of differential treatment response within subgroups. Physiologic approaches include stratification based on P/F ratio and ventilatory parameters; stratification based on P/F ratio is already being employed in clinical trials. Clinical approaches include stratification based on ARDS risk factor or direct vs. indirect ARDS. Combined clinical and biological data has been used to identify two phenotypes across five cohorts of ARDS, termed hyperinflammatory and hypoinflammatory. These phenotypes have widely divergent clinical outcomes and differential response to mechanical ventilation, fluid therapy, and simvastatin in secondary analysis of completed trials. Next steps in the field include prospective validation of inflammatory phenotypes and integration of high-dimensional 'omics' data into our understanding of ARDS heterogeneity. Summary Identification of distinct subgroups or phenotypes in ARDS may impact future conduct of clinical trials and can enhance our understanding of the disorder, with potential future clinical implications.

Mitochondrial transfer from donor cells to cells of injured tissues is a promising cell-based therapy for effectively bringing about recovery of tissue bioenergetics. Here, we review recent studies on intercellular mitochondrial transfer in organs and cells. We also review studies that shed light on our current understanding of the known mechanisms and conditions that lead to intercellular mitochondrial transfer.

In their letter, Drs. Cherian et al. take issue with our interpretation of the respiratory physiology of COVID-19, arguing that it is based merely on “small cohort studies,” instead arguing that “a high proportion of mechanically ventilated COVID-19 patients exhibit near-normal lung compliance” [1]. Yet the low respiratory compliance of COVID-19 patients has now been extensively demonstrated by studies totalling more than 800 COVID-19 patients [2–8], including a direct comparison with non-COVID ARDS patients that revealed no difference in respiratory compliance [8]., We argue that phenotyping of COVID-19 related ARDS should be done using careful, data-driven approaches.

Purpose: Using latent class analysis (LCA), we have consistently identified two distinct subphenotypes in four randomized controlled trial cohorts of ARDS. One subphenotype has hyper-inflammatory characteristics and is associated with worse clinical outcomes. Further, within three negative clinical trials, we observed differential treatment response by subphenotype to randomly assigned interventions. The main purpose of this study was to identify ARDS subphenotypes in a contemporary NHLBI Network trial of infection-associated ARDS (SAILS) using LCA and to test for differential treatment response to rosuvastatin therapy in the subphenotypes. Methods: LCA models were constructed using a combination of biomarker and clinical data at baseline in the SAILS study (n = 745). LCA modeling was then repeated using an expanded set of clinical class-defining variables. Subphenotypes were tested for differential treatment response to rosuvastatin. Results: The two-class LCA model best fit the population. Forty percent of the patients were classified as the “hyper-inflammatory” subphenotype. Including additional clinical variables in the LCA models did not identify new classes. Mortality at day 60 and day 90 was higher in the hyper-inflammatory subphenotype. No differences in outcome were observed between hyper-inflammatory patients randomized to rosuvastatin therapy versus placebo. Conclusions: LCA using a two-subphenotype model best described the SAILS population. The subphenotypes have features consistent with those previously reported in four other cohorts. Addition of new class-defining variables in the LCA model did not yield additional subphenotypes. No treatment effect was observed with rosuvastatin. These findings further validate the presence of two subphenotypes and demonstrate their utility for patient stratification in ARDS.

Background: Precision medicine approaches targeting patients based on disease subtype have transformed approaches to cancer, asthma, and other heterogeneous syndromes. Two distinct subphenotypes of ARDS have been identified in three US-based clinical trials and respond differently to positive end-expiratory pressure and fluid management. It remains unknown if these subphenotypes exist in different populations and respond differently to pharmacotherapies. Methods: We conducted a secondary analysis using data from 539 patients enrolled in a UK multicenter, placebo-controlled randomized trial of simvastatin for ARDS (HARP-2). Latent class analysis was applied to baseline data without consideration of outcomes to identify subphenotypes. Clinical outcomes were compared across subphenotypes and treatment groups. Findings: A two class (two-subphenotype) model was an improvement over a one class model (pInterpretation: Two subphenotypes of ARDS were identified in the HARP-2 cohort, withdistinct clinical and biological features and disparate clinical outcomes; the hyper-inflammatory subphenotype had improved survival with simvastatin compared to placebo. These findings support further pursuit of predictive enrichment strategies in critical care clinical trials.

ARDS has a high mortality in the acute setting, with long-term disability among disease survivors. In 1967, David Ashbaugh and colleagues first described the clinical features of ARDS, which were notably similar to the infantile respiratory distress syndrome. Half a century later, ARDS remains underrecognized and is associated with high mortality rates. Valuable insights from observational studies fail to demonstrate a mortality benefit in randomized controlled trials (RCTs). In the absence of a pharmacologic cure, supportive ventilator strategies limit rather than treat the ongoing lung injury. Interestingly, ARDS has higher mortality rates in observational studies compared to RCTs. Comparing mortality rates between ARDS studies and trials is problematic, partly due to varying time-points at which mortality is reported. Discerning the true mortality attributable to ARDS is also difficult. The diagnostic criteria for ARDS are mainly clinical and lack the objectivity of a laboratory test or biomarker. Nonetheless, these factors are common to both studies and trials, and fail to explain the higher mortality rate of ARDS observational studies. Disease heterogeneity and complex patient characteristics can also confound mortality estimation in ARDS. We therefore examined patient and trial factors that could influence mortality outcomes in ARDS observational studies and RCTs. Unlike RCTs, observational studies include ARDS subjects with severe comorbidities and those requesting limited care. Less stringent selection criteria could thereby contribute to high mortality rates in ARDS observational studies. In contrast, exclusion criteria in RCTs meticulously scrutinize patient characteristics, confining the type and number of eligible subjects. As a result, the task of identifying, consenting, and randomizing eligible patients within the enrollment window is challenging, further decreasing the number of subjects enrolled. Moreover, ARDS RCTs strictly adhere to lung-protective strategies, while ARDS observational studies continually demonstrate variable compliance. This review highlights the impact of patient- and trial-related factors on influencing mortality rates in ARDS observational studies and RCTs.

Owing to complexities of measuring dead space, ventilatory failure is difficult to quantify in critical care. A simple, novel index called ventilatory ratio (VR) can quantify ventilatory efficiency at the bedside. The study objectives were to evaluate physiological properties of VR and examine its clinical applicability in acute respiratory distress syndrome (ARDS) patients.A validated computational model of cardiopulmonary physiology [Nottingham Physiology Simulator (NPS)] was used to evaluate VR ex vivo in three virtual patients with varying degrees of gas exchange defects. Arterial P(CO₂) and mixed expired P(CO₂) were obtained from the simulator while either dead space or CO₂ production was altered in isolation. VR and deadspace fraction was calculated using these values. A retrospective analysis of a previously presented prospective ARDS database was then used to evaluate the clinical utility of VR. Basic characteristics of VR and its association with mortality were examined.The NPS showed that VR behaved in an intuitive manner as would be predicted by its physiological properties. When CO₂ production was constant, there was strong positive correlation between dead space and VR (modified Pearson's r 0.98, P0.01). The ARDS database had a mean VR of 1.47 (standard deviation 0.58). Non-survivors had a significantly higher VR compared with survivors [1.70 vs 1.34, mean difference 0.35, 95% confidence interval (CI) 0.16-0.56, P0.01]. VR was an independent predictor of mortality (odds ratio 3.05, CI 1.35-6.91, P0.01).VR is influenced by dead space and CO₂ production. In ARDS, high VR was associated with increased mortality.