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Using latent class analysis (LCA) of clinical and protein biomarkers, researchers have identified two phenotypes of the acute respiratory distress syndrome (ARDS) with divergent clinical trajectories and treatment responses. We investigated whether plasma metabolites differed among patients with LCA-derived hyperinflammatory and hypoinflammatory ARDS, and we tested the prognostic utility of adding metabolic clusters to LCA phenotypes. We analyzed data from 93 patients with ARDS and sepsis enrolled in a multicenter prospective cohort of critically ill patients, comparing 970 metabolites between the two LCA-derived phenotypes. In all, 188 metabolites were differentially abundant between the two LCA-derived phenotypes. After adjusting for age, sex, confounding medications, and comorbid liver and kidney disease, 82 metabolites remained significantly different. Patients with hyperinflammatory ARDS had reduced circulating lipids but high levels of pyruvate, lactate, and malate. Metabolic cluster and LCA-derived phenotypes were each significantly and independently associated with survival. Patients with hyperinflammatory ARDS may be experiencing a glycolytic shift leading to dysregulated lipid metabolism. Metabolic profiling offers prognostic information beyond what is captured by LCA phenotypes alone. Deeper biological profiling may identify key differences in pathogenesis among patients with ARDS and may lead to novel targeted therapies.

Heterogeneity in sepsis and acute respiratory distress syndrome (ARDS) is increasingly being recognized as one of the principal barriers to finding efficacious targeted therapies. The advent of multiple high-throughput biological data ("omics"), coupled with the widespread access to increased computational power, has led to the emergence of phenotyping in critical care. Phenotyping aims to use a multitude of data to identify homogenous subgroups within an otherwise heterogenous population. Increasingly, phenotyping schema are being applied to sepsis and ARDS to increase understanding of these clinical conditions and identify potential therapies. Here we present a selective review of the biological phenotyping schema applied to sepsis and ARDS. Further, we outline some of the challenges involved in translating these conceptual findings to bedside clinical decision-making tools. Expected final online publication date for the

Previous latent class analysis of adults with acute respiratory distress syndrome (ARDS) identified two phenotypes, distinguished by the degree of inflammation. We aimed to identify phenotypes in children with ARDS in whom developmental differences might be important, using a latent class analysis approach similar to that used in adults.This study was a secondary analysis of data aggregated from the Randomized Evaluation of Sedation Titration for Respiratory Failure (RESTORE) clinical trial and the Genetic Variation and Biomarkers in Children with Acute Lung Injury (BALI) ancillary study. We used latent class analysis, which included demographic, clinical, and plasma biomarker variables, to identify paediatric ARDS (PARDS) phenotypes within a cohort of children included in the RESTORE and BALI studies. The association of phenotypes with clinically relevant outcomes and the performance of paediatric data in adult ARDS classification algorithms were also assessed.304 children with PARDS were included in this secondary analysis. Using latent class analysis, a two-class model was a better fit for the cohort than a one-class model (p0·001). Latent class analysis identified two classes: class 1 (181 [60%] of 304 patients with PARDS) and class 2 (123 [40%] of 304 patients with PARDS), referred to as phenotype 1 and 2 hereafter. Phenotype 2 was characterised by higher concentrations of inflammatory biomarkers, a higher incidence of vasopressor use, and more frequent diagnosis of sepsis, consistent with the adult hyperinflammatory phenotype. All levels of severity of PARDS were observed across both phenotypes. Children with the hyperinflammatory phenotype (phenotype 2) had worse clinical outcomes than those with the hypoinflammatory phenotype (phenotype 1), with a longer duration of mechanical ventilation (median 10·0 days [IQR 6·3-21·0] for phenotype 2 vs 6·6 days [4·1-10·8] for phenotype 1, p0·0001), and higher incidence of mortality (17 [13·8%] of 123 patients vs four [2·2%] of 181 patients, p=0·0001). When using adult phenotype classification algorithms in children, the soluble tumour necrosis factor receptor-1 (sTNFr1), vasopressor use, and interleukin (IL)-6 variables gave an area under the curve (AUC) of 0·956, and the sTNFr1, vasopressor use, and IL-8 variables gave an AUC of 0·954, compared with the gold standard of latent class analysis.Latent class analysis identified two phenotypes in children with ARDS with characteristics similar to those in adults, including worse outcomes among patients with the hyperinflammatory phenotype. PARDS phenotypes should be considered in design and analysis of future clinical trials in children.US National Institutes of Health.

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.

Background: Both latent class analysis (LCA) assignment based upon a panel of plasma biomarkers and interleukin-18 (IL-18) plasma level have been shown to predict prognosis and treatment response in Acute Respiratory Distress Syndrome (ARDS). Interleukin-18 is a measure of inflammasome activation and plays a distinct role in inflammation that is not captured by the biomarkers used in LCA assignments. We hypothesized that elevated IL-18 would provide additive prognostic and therapeutic information to previously published LCA assignments in ARDS, identifying additional “high-risk” patients not captured by LCA who could be eligible for inclusion in future precision medicine-focused trials. Methods: IL-18 and a panel of protein markers used for LCA had been previously measured in plasma from 683/745 patients in the Statins for Acutely Injured Lungs from Sepsis (SAILS) and 511/540 patients in the Hydroxymethylglutaryl-CoA reductase inhibition with simvastatin in Acute lung injury to Reduce Pulmonary dysfunction (HARP-2) trials. We tested the association between high IL-18 (>800 pg/mL) and LCA class assignment using McNemar’s test and evaluated the association of each subgrouping as well as treatment with 60-day mortality using Fisher’s exact test. We assessed 60-day mortality in each combination (high/low IL-18, hypo-/hyper-inflammatory LCA class, and treatment/placebo) using Kaplan-Meier survival analysis. We evaluated the correlation between the log2 transformed IL-18 level and LCA biomarkers using Pearson’s correlation coefficient. Results: 33% of patients in SAILS and HARP-2 were discordant by IL-18 level and LCA class. Elevated IL-18 identified a high-risk group of individuals previously classified as hypo-inflammatory by LCA in both SAILS (OR 3.3, 95% CI 1.8-6.1, p of 0.17-0.47. Conclusions: High Plasma IL-18 level provides additional prognostic information to LCA sub-phenotypes in two large ARDS cohorts.

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.

Sepsis is a leading cause of death, and improved approaches for disease diagnosis and detection of etiologic pathogens are urgently needed. Here, we carried out integrated host and pathogen metagenomic next generation sequencing (mNGS) of whole blood (n=221) and plasma RNA and DNA (n=138) from critically ill patients following hospital admission. We assigned patients into sepsis groups based on clinical and microbiological criteria: 1) sepsis with bloodstream infection (SepsisBSI), 2) sepsis with peripheral site infection but not bloodstream infection (Sepsisnon-BSI), 3) suspected sepsis with negative clinical microbiological testing; 4) no evidence of infection (No-Sepsis), and 5) indeterminant sepsis status. From whole blood gene expression data, we first trained a bagged support vector machine (bSVM) classifier to distinguish SepsisBSI and Sepsisnon-BSI patients from No-Sepsis patients, using 75% of the cohort. This classifier performed with an area under the receiver operating characteristic curve (AUC) of 0.81 in the training set (75% of cohort) and an AUC of 0.82 in a held-out validation set (25% of cohort). Surprisingly, we found that plasma RNA also yielded a biologically relevant transcriptional signature of sepsis which included several genes previously reported as sepsis biomarkers (e.g., HLA-DRA, CD-177). A bSVM classifier for sepsis diagnosis trained on RNA gene expression data performed with an AUC of 0.97 in the training set and an AUC of 0.77 in a held-out validation set. We subsequently assessed the pathogen-detection performance of DNA and RNA mNGS by comparing against a practical reference standard of clinical bacterial culture and respiratory viral PCR. We found that sensitivity varied based on site of infection and pathogen, with an overall sensitivity of 83%, and a per-pathogen sensitivity of 100% for several key sepsis pathogens including S. aureus, E. coli, K. pneumoniae and P. aeruginosa. Pathogenic bacteria were also identified in 10/37 (27%) of patients in the No-Sepsis group. To improve detection of sepsis due to viral infections, we developed a secondary RNA host transcriptomic classifier which performed with an AUC of 0.94 in the training set and an AUC of 0.96 in the validation set. Finally, we combined host and microbial features to develop a proof-of-concept integrated sepsis diagnostic model that identified 72/73 (99%) of microbiologically confirmed sepsis cases, and predicted sepsis in 14/19 (74%) of suspected, and 8/9 (89%) of indeterminate sepsis cases. In summary, our findings suggest that integrating host transcriptional profiling and broad-range metagenomic pathogen detection from nucleic acid may hold promise as a tool for sepsis diagnosis.

It is believe that compulsion of attendance in colleges has direct impact on overall performance of students. A cross sectional study of the perception of students towards the mandatory attendance criteria for undergraduate MBBS students. Material & methodology: present study was conducted among the 247 students from second, third minor and major years from MGM Medical College and Hospital, Aurangabad were selected using the universal sampling protocol. Students from first year, and participants who did not consent to sharing their data were excluded. The data was collected in the period of 2 months. Results: in the present study we found that around 65% of students believe that compulsion of attendance do not have major impact on their overall performance. 52.2% students stated that the attendance criteria for the clinical postings should be mandatory. On the other hand 68% students stated attendance criteria should not be there for theory lectures. There are some reason for absenteeism in the class like for the preparation for the exam, for the attending coaching for the entrance tests etc. Most of the students believe that the compulsion of attendance hamper their exam performance, they should get less time for daily activities etc. Conclusion: From the present study we come to know about some conclusion like it should not be mandatory for attending classes and clinics before prelims/ final exams. Students should have some liberty to decide their curricular activities and arrange lectures according to their need. Institutions should arrange more guest lectures to provide students with some external exposure. College timings should be reduced to prevent overexertion of students. There should be some extracurricular activities should be taken for the refreshment of the students

How indices specific to respiratory compromise contribute to prognostication in patients with ARDS is not well characterized in general clinical populations. The primary objective of this study was to identify variables specific to respiratory failure that might add prognostic value to indicators of systemic illness severity in an observational cohort of subjects with ARDS.Fifty subjects with ARDS were enrolled in a single-center, prospective, observational cohort. We tested the contribution of respiratory variables (oxygenation index, ventilatory ratio [VR], and the radiographic assessment of lung edema score) to logistic regression models of 28-d mortality adjusted for indicators of systemic illness severity (the Acute Physiology and Chronic Health Evaluation [APACHE] III score or severity of shock as measured by the number of vasopressors required at baseline) using likelihood ratio testing. We also compared a model utilizing APACHE III with one including baseline number of vasopressors by comparing the area under the receiver operating curve (AUROC).VR significantly improved model performance by likelihood ratio testing when added to APACHE III (In this observational cohort of subjects with ARDS, the VR significantly improved discrimination for mortality when combined with indicators of severe systemic illness. The number of vasopressors required at baseline and APACHE III had similar discrimination for mortality when combined with VR. VR is easily obtained at the bedside and offers promise for clinical prognostication.

We carried out integrated host and pathogen metagenomic RNA and DNA next generation sequencing (mNGS) of whole blood (n = 221) and plasma (n = 138) from critically ill patients following hospital admission. We assigned patients into sepsis groups on the basis of clinical and microbiological criteria. From whole-blood gene expression data, we distinguished patients with sepsis from patients with non-infectious systemic inflammatory conditions using a trained bagged support vector machine (bSVM) classifier (area under the receiver operating characteristic curve (AUC) = 0.81 in the training set; AUC = 0.82 in a held-out validation set). Plasma RNA also yielded a transcriptional signature of sepsis with several genes previously reported as sepsis biomarkers, and a bSVM sepsis diagnostic classifier (AUC = 0.97 training set; AUC = 0.77 validation set). Pathogen detection performance of plasma mNGS varied on the basis of pathogen and site of infection. To improve detection of virus, we developed a secondary transcriptomic classifier (AUC = 0.94 training set; AUC = 0.96 validation set). We combined host and microbial features to develop an integrated sepsis diagnostic model that identified 99% of microbiologically confirmed sepsis cases, and predicted sepsis in 74% of suspected and 89% of indeterminate sepsis cases. In summary, we suggest that integrating host transcriptional profiling and broad-range metagenomic pathogen detection from nucleic acid is a promising tool for sepsis diagnosis.

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...

International audience; The acute respiratory distress syndrome (ARDS) is a common critical illness syndrome with high morbidity and mortality. There are no proven pharmacological therapies for ARDS. The current definition of ARDS is based on shared clinical characteristics but does not capture the heterogeneity in clinical risk factors, imaging characteristics, physiology, timing of onset and trajectory, and biology of the syndrome. There is increasing interest within the ARDS clinical trialist community to design clinical trials that reduce heterogeneity in the trial population. This effort must be balanced with ongoing work to craft an inclusive, global definition of ARDS, with important implications for trial design. Ultimately, the two aims—to design trials that are applicable to the diverse global ARDS population while also advancing opportunities to identify targetable traits—should coexist. In this Personal View, we recommend two primary strategies to improve future ARDS trials: the development of new methods to target treatable traits in clinical trial populations, and improvements in the representativeness of ARDS trials, with the inclusion of global populations. We emphasise that these two strategies are complementary. We also discuss how a proposed expansion of the definition of ARDS could affect the future of clinical trials.

Rationale: Cigarette smoke exposure is associated with an increased risk of developing acute respiratory distress syndrome (ARDS) in trauma, transfusion, and nonpulmonary sepsis. It is unknown whether this relationship exists in the general sepsis population. Furthermore, it is unknown if patients with ARDS have differences in underlying biology based on smoking status. Objectives: To assess the relationship between cigarette smoke exposure and ARDS in sepsis and identify tobacco-related biomarkers of lung injury. Methods: We studied a prospective cohort of 592 patients with sepsis from 2009 to 2017. Plasma cotinine and urine NNAL [urine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol] were measured to categorize smoking status. Plasma biomarkers of inflammation and lung injury were measured, including in a smaller cohort of trauma patients with ARDS to increase generalizability. Measurements and Main Results: Passive and active smoking were associated with increased odds of developing ARDS in patients with sepsis. Among patients with sepsis and ARDS, active cigarette smokers were younger and had lower severity of illness than nonsmokers. Patients with ARDS with cigarette smoke exposure had lower plasma levels of IL-8 (P = 0.01) and sTNFR-1 (soluble tumor necrosis factor 1; P = 0.01) compared with those without exposure. Similar biomarker patterns were observed in blunt trauma patients with ARDS. Conclusions: Passive and active smoking are associated with an increased risk of developing ARDS in patients with pulmonary and nonpulmonary sepsis. Among patients with ARDS, those with cigarette smoke exposure have less systemic inflammation, while active smokers also have lower severity of illness compared with nonsmokers, suggesting that smoking contributes to biological heterogeneity in ARDS.

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.