Plasma sRAGE is independently associated with increased mortality in ARDS: a meta-analysis of individual patient data

The soluble receptor for advanced glycation end-products (sRAGE) is a marker of lung epithelial injury and alveolar fluid clearance (AFC), with promising values for assessing prognosis and lung injury severity in acute respiratory distress syndrome (ARDS). Because AFC is impaired in most patients with ARDS and is associated with higher mortality, we hypothesized that baseline plasma sRAGE would predict mortality, independently of two key mediators of ventilator-induced lung injury. We conducted a meta-analysis of individual data from 746 patients enrolled in eight prospective randomized and observational studies in which plasma sRAGE was measured in ARDS articles published through March 2016. The primary outcome was 90-day mortality. Using multivariate and mediation analyses, we tested the association between baseline plasma sRAGE and mortality, independently of driving pressure and tidal volume. Higher baseline plasma sRAGE [odds ratio (OR) for each one-log increment, 1.18; 95% confidence interval (CI) 1.01-1.38; P = 0.04], driving pressure (OR for each one-point increment, 1.04; 95% CI 1.02-1.07; P = 0.002), and tidal volume (OR for each one-log increment, 1.98; 95% CI 1.07-3.64; P = 0.03) were independently associated with higher 90-day mortality in multivariate analysis. Baseline plasma sRAGE mediated a small fraction of the effect of higher Delta P on mortality but not that of higher V (T). Higher baseline plasma sRAGE was associated with higher 90-day mortality in patients with ARDS, independently of driving pressure and tidal volume, thus reinforcing the likely contribution of alveolar epithelial injury as an important prognostic factor in ARDS. Registration: PROSPERO (ID: CRD42018100241)

Increased risk of severe clinical course of COVID-19 in carriers of HLA-C*04:01

BACKGROUND: Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic, there has been increasing urgency to identify pathophysiological characteristics leading to severe clinical course in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Human leukocyte antigen alleles (HLA) have been suggested as potential genetic host factors that affect individual immune response to SARS-CoV-2. We sought to evaluate this hypothesis by conducting a multicenter study using HLA sequencing. METHODS: We analyzed the association between COVID-19 severity and HLAs in 435 individuals from Germany ((n) = 135), Spain ((n) = 133), Switzerland ((n) = 20) and the United States ((n) = 147), who had been enrolled from March 2020 to August 2020. This study included patients older than 18 years, diagnosed with COVID-19 and representing the full spectrum of the disease. Finally, we tested our results by meta-analysing data from prior genome-wide association studies (GWAS). FINDINGS: We describe a potential association of HLA-C*04:01 with severe clinical course of COVID-19. Carriers of HLA-C*04:01 had twice the risk of intubation when infected with SARS-CoV-2 (risk ratio 1.5 [95% CI 1.1-2.1], odds ratio 3.5 [95% CI 1.9-6.6], adjusted (p)-value = 0.0074). These findings are based on data from four countries and corroborated by independent results from GWAS. Our findings are biologically plausible, as HLA-C*04:01 has fewer predicted bindings sites for relevant SARS-CoV-2 peptides compared to other HLA alleles. INTERPRETATION: HLA-C*04:01 carrier state is associated with severe clinical course in SARS-CoV-2. Our findings suggest that HLA class I alleles have a relevant role in immune defense against SARS-CoV-2

Personalized medicine for ARDS : the 2035 research agenda

In the last 20 years, survival among patients with acute respiratory distress syndrome (ARDS) has increased substantially with advances in lung-protective ventilation and resuscitation. Building on this success, personalizing mechanical ventilation to patient-specific physiology for enhanced lung protection will be a top research priority for the years ahead. However, the ARDS research agenda must be broader in scope. Further understanding of the heterogeneous biology, from molecular to mechanical, underlying early ARDS pathogenesis is essential to inform therapeutic discovery and tailor treatment and prevention strategies to the individual patient. The ARDSne(x)t research agenda for the next 20 years calls for bringing personalized medicine to ARDS, asking simultaneously both whether a treatment affords clinically meaningful benefit and for whom. This expanded scope necessitates standard acquisition of highly granular biological, physiological, and clinical data across studies to identify biologically distinct subgroups that may respond differently to a given intervention. Clinical trials will need to consider enrichment strategies and incorporate long-term functional outcomes. Tremendous investment in research infrastructure and global collaboration will be vital to fulfilling this agenda

Acute respiratory distress syndrome phenotypes with distinct clinical outcomes in PHARLAP trial cohort

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.Shailesh Bihari, Andrew Bersten, Eldho Paul, Shay McGuinness, Dani Dixon, Pratik Sinha, Carolyn S Calfee, Alistair Nichol and Carol Hodgson, for the PHARLAP Study Investigator

Estimated dead space fraction and the ventilatory ratio are associated with mortality in early ARDS

Background: Indirect indices for measuring impaired ventilation, such as the estimated dead space fraction and the ventilatory ratio, have been shown to be independently associated with an increased risk of mortality. This study aimed to compare various methods for dead space estimation and the ventilatory ratio in patients with acute respiratory distress syndrome (ARDS) and to determine their independent values for predicting death at day 30. The present study is a post hoc analysis of a prospective observational cohort study of ICUs of two tertiary care hospitals in the Netherlands. Results: Individual patient data from 940 ARDS patients were analyzed. Estimated dead space fraction and the ventilatory ratio at days 1 and 2 were significantly higher among non-survivors (p < 0.01). Dead space fraction calculation using the estimate from physiological variables [V/V] and the ventilatory ratio at day 2 showed independent association with mortality at 30 days (odds ratio 1.28 [95% CI 1.02-1.61], p < 0.03 and 1.20 [95% CI, 1.01-1.40], p < 0.03, respectively); whereas, the Harris-Benedict [V/V] and Penn State [V/V] estimations were not associated with mortality. The predicted validity of the estimated dead space fraction and the ventilatory ratio improved the baseline model based on PEEP, PaO/FiO, driving pressure and compliance of the respiratory system at day 2 (AUROCC 0.72 vs. 0.69, p < 0.05). Conclusions: Estimated methods for dead space calculation and the ventilatory ratio during the early course of ARDS are associated with mortality at day 30 and add statistically significant but limited improvement in the predictive accuracy to indices of oxygenation and respiratory system mechanics at the second day of mechanical ventilation

Plasma sRAGE is independently associated with increased mortality in ARDS: a meta-analysis of individual patient data.

International audienceThe soluble receptor for advanced glycation end-products (sRAGE) is a marker of lung epithelial injury and alveolar fluid clearance (AFC), with promising values for assessing prognosis and lung injury severity in acute respiratory distress syndrome (ARDS). Because AFC is impaired in most patients with ARDS and is associated with higher mortality, we hypothesized that baseline plasma sRAGE would predict mortality, independently of two key mediators of ventilator-induced lung injury

Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis

BACKGROUND: Two acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS. METHODS: In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the dependent variable. FINDINGS: The primary clinical classifier model had an area under receiver operating characteristic curve (AUC) of 0·92 (95% CI 0·90-0·95) in EARLI and 0·88 (0·84-0·91) in VALID. Performance of the primary model was similar when using exclusively EHR-derived predictors compared with manually curated predictors (AUC=0·88 [95% CI 0·81-0·94] vs 0·92 [0·88-0·97]). In LUNG SAFE, 90-day mortality was higher in patients assigned the hyperinflammatory subphenotype than in those with the hypoinflammatory phenotype (414 [57%] of 725 vs 694 [33%] of 2088; p<0·0001). There was a significant treatment interaction with PEEP strategy and ARDS subphenotype (p=0·041), with lower 90-day mortality in the high PEEP group of patients with the hyperinflammatory subphenotype (hyperinflammatory subphenotype: 169 [54%] of 313 patients in the high PEEP group vs 127 [62%] of 205 patients in the low PEEP group; hypoinflammatory subphenotype: 231 [34%] of 675 patients in the high PEEP group vs 233 [32%] of 734 patients in the low PEEP group). INTERPRETATION: Classifier models using clinical variables alone can accurately assign ARDS subphenotypes in observational cohorts. Application of these models can provide valuable prognostic information and could inform management strategies for personalised treatment, including application of PEEP, once prospectively validated. FUNDING: US National Institutes of Health and European Society of Intensive Care Medicine