Hyperoxemia and excess oxygen use in early acute respiratory distress syndrome : Insights from the LUNG SAFE study

Publisher Copyright: © 2020 The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Background: Concerns exist regarding the prevalence and impact of unnecessary oxygen use in patients with acute respiratory distress syndrome (ARDS). We examined this issue in patients with ARDS enrolled in the Large observational study to UNderstand the Global impact of Severe Acute respiratory FailurE (LUNG SAFE) study. Methods: In this secondary analysis of the LUNG SAFE study, we wished to determine the prevalence and the outcomes associated with hyperoxemia on day 1, sustained hyperoxemia, and excessive oxygen use in patients with early ARDS. Patients who fulfilled criteria of ARDS on day 1 and day 2 of acute hypoxemic respiratory failure were categorized based on the presence of hyperoxemia (PaO2 > 100 mmHg) on day 1, sustained (i.e., present on day 1 and day 2) hyperoxemia, or excessive oxygen use (FIO2 ≥ 0.60 during hyperoxemia). Results: Of 2005 patients that met the inclusion criteria, 131 (6.5%) were hypoxemic (PaO2 < 55 mmHg), 607 (30%) had hyperoxemia on day 1, and 250 (12%) had sustained hyperoxemia. Excess FIO2 use occurred in 400 (66%) out of 607 patients with hyperoxemia. Excess FIO2 use decreased from day 1 to day 2 of ARDS, with most hyperoxemic patients on day 2 receiving relatively low FIO2. Multivariate analyses found no independent relationship between day 1 hyperoxemia, sustained hyperoxemia, or excess FIO2 use and adverse clinical outcomes. Mortality was 42% in patients with excess FIO2 use, compared to 39% in a propensity-matched sample of normoxemic (PaO2 55-100 mmHg) patients (P = 0.47). Conclusions: Hyperoxemia and excess oxygen use are both prevalent in early ARDS but are most often non-sustained. No relationship was found between hyperoxemia or excessive oxygen use and patient outcome in this cohort. Trial registration: LUNG-SAFE is registered with ClinicalTrials.gov, NCT02010073publishersversionPeer reviewe

Secretome analysis of Trypanosoma cruzi by proteomics studies

International audienceBackgroundChagas disease is a debilitating often fatal disease resulting from infection by the protozoan parasite Trypanosoma cruzi. Chagas disease is endemic in 21 countries of the Americas, and it is an emerging disease in other countries as a result of migration. Given the chronic nature of the infection where intracellular parasites persist for years, the diagnosis of T. cruzi by direct detection is difficult, whereas serologic tests though sensitive may yield false-positive results. The development of new rapid test based on the identification of soluble parasitic antigens in serum would be a real innovation in the diagnosis of Chagas disease.MethodsTo identify new soluble biomarkers that may improve diagnostic tests, we investigated the proteins secreted by T. cruzi using mass spectrometric analyses of conditioned culture media devoid of serum collected during the emergence of trypomastigotes from infected Vero cells. In addition, we compared the secretomes of two T. cruzi strains from DTU Tc VI (VD and CL Brener).ResultsAnalysis of the secretome collected during the emergence of trypomastigotes from Vero cells led to the identification of 591 T. cruzi proteins. Three hundred sixty three proteins are common to both strains and most belong to different multigenic super families (i.e. TcS, GP63, MASP, and DGF1). Ultimately we have established a list of 94 secreted proteins, common to both DTU Tc VI strains that do not belong to members of multigene families.ConclusionsThis study provides the first comparative analysis of the secretomes from two distinct T. cruzi strains of DTU TcVI. This led us to identify a subset of common secreted proteins that could potentially serve as serum markers for T. cruzi infection. Their potential could now be evaluated, with specific antibodies using sera collected from patients and residents from endemic regions

Secretome analysis of Trypanosoma cruzi by proteomics studies

Background: Chagas disease is a debilitating often fatal disease resulting from infection by the protozoan parasite Trypanosoma cruzi. Chagas disease is endemic in 21 countries of the Americas, and it is an emerging disease in other countries as a result of migration. Given the chronic nature of the infection where intracellular parasites persist for years, the diagnosis of T. cruzi by direct detection is difficult, whereas serologic tests though sensitive may yield false-positive results. The development of new rapid test based on the identification of soluble parasitic antigens in serum would be a real innovation in the diagnosis of Chagas disease. Methods: To identify new soluble biomarkers that may improve diagnostic tests, we investigated the proteins secreted by T. cruzi using mass spectrometric analyses of conditioned culture media devoid of serum collected during the emergence of trypomastigotes from infected Vero cells. In addition, we compared the secretomes of two T. cruzi strains from DTU Tc VI (VD and CL Brener). Results: Analysis of the secretome collected during the emergence of trypomastigotes from Vero cells led to the identification of 591 T. cruzi proteins. Three hundred sixty three proteins are common to both strains and most belong to different multigenic super families (i.e. TcS, GP63, MASP, and DGF1). Ultimately we have established a list of 94 secreted proteins, common to both DTU Tc VI strains that do not belong to members of multigene families. Conclusions: This study provides the first comparative analysis of the secretomes from two distinct T. cruzi strains of DTU TcVI. This led us to identify a subset of common secreted proteins that could potentially serve as serum markers for T. cruzi infection. Their potential could now be evaluated, with specific antibodies using sera collected from patients and residents from endemic regions.Fil: Brossas, Jean Yves. Inserm; Francia. Universite de Paris VI; Francia. Hôpital Pitié-Salpêtrière; FranciaFil: Gulin, Julián Ernesto Nicolás. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutiérrez"; Argentina. Gobierno de la Ciudad de Buenos Aires. Instituto Multidisciplinario de Investigaciones en Patologías Pediátricas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto Multidisciplinario de Investigaciones en Patologías Pediátricas.; ArgentinaFil: Bisio, Margarita María Catalina. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutiérrez"; Argentina. Gobierno de la Ciudad de Buenos Aires. Instituto Multidisciplinario de Investigaciones en Patologías Pediátricas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto Multidisciplinario de Investigaciones en Patologías Pediátricas.; ArgentinaFil: Chapelle, Manuel. Bruker Daltonics; FranciaFil: Marinach Patrice, Carine. Inserm; Francia. Universite de Paris VI; FranciaFil: Bordessoulles, Mallaury. Universite de Paris VI; Francia. Inserm; FranciaFil: Palazon Ruiz, George. Inserm; FranciaFil: Vion, Jeremy. Inserm; FranciaFil: Paris, Luc. Hôpital Pitié-Salpêtrière; Francia. Universite de Paris VI; FranciaFil: Altcheh, Jaime Marcelo. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutiérrez"; Argentina. Gobierno de la Ciudad de Buenos Aires. Instituto Multidisciplinario de Investigaciones en Patologías Pediátricas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto Multidisciplinario de Investigaciones en Patologías Pediátricas.; ArgentinaFil: Mazier, Dominique. Inserm; Francia. Universite de Paris VI; Francia. Hôpital Pitié-Salpêtrière; Franci

Analyse of trans-sialidase (TcS) proteins found in secretome of 2 strains.

<p>Classification of TcS proteins for each strain into 8 group previously described [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185504#pone.0185504.ref022" target="_blank">22</a>]. Groups IV, V and VI are less than I, II, III groups VII and VIII for two strains. On the x-axis, the number of group is indicated. The Y-axis shows the percentage of each TcS identified in our analyses.</p

List of secreted proteins that are common in both <i>T</i>.<i>cruzi</i> strains without of multigene proteins.

<p>List of secreted proteins that are common in both <i>T</i>.<i>cruzi</i> strains without of multigene proteins.</p

Functional categories classification of <i>T</i>. <i>cruzi</i> proteins from CL Brener and VD strains.

<p>Proteins were classified into 15 functional categories using literature and UniprotKB annotation. Y-axis, categories are indicated. X-axis shows the percentage of each category for each strain.</p

Overlap between secretomes of two different <i>T</i>. <i>cruzi</i> strains DTU Tc VI.

<p>A total of 591 proteins of <i>T</i>. <i>cruzi</i> were identified. We note that 78 proteins are specific to the CL Brener strain whereas 151 proteins are specific to VD strain. However, 363 proteins are common to both strains.</p

Immunocompromised patients with acute respiratory distress syndrome: Secondary analysis of the LUNG SAFE database

Background: The aim of this study was to describe data on epidemiology, ventilatory management, and outcome of acute respiratory distress syndrome (ARDS) in immunocompromised patients. Methods: We performed a post hoc analysis on the cohort of immunocompromised patients enrolled in the Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE) study. The LUNG SAFE study was an international, prospective study including hypoxemic patients in 459 ICUs from 50 countries across 5 continents. Results: Of 2813 patients with ARDS, 584 (20.8%) were immunocompromised, 38.9% of whom had an unspecified cause. Pneumonia, nonpulmonary sepsis, and noncardiogenic shock were their most common risk factors for ARDS. Hospital mortality was higher in immunocompromised than in immunocompetent patients (52.4% vs 36.2%; p &lt; 0.0001), despite similar severity of ARDS. Decisions regarding limiting life-sustaining measures were significantly more frequent in immunocompromised patients (27.1% vs 18.6%; p &lt; 0.0001). Use of noninvasive ventilation (NIV) as first-line treatment was higher in immunocompromised patients (20.9% vs 15.9%; p = 0.0048), and immunodeficiency remained independently associated with the use of NIV after adjustment for confounders. Forty-eight percent of the patients treated with NIV were intubated, and their mortality was not different from that of the patients invasively ventilated ab initio. Conclusions: Immunosuppression is frequent in patients with ARDS, and infections are the main risk factors for ARDS in these immunocompromised patients. Their management differs from that of immunocompetent patients, particularly the greater use of NIV as first-line ventilation strategy. Compared with immunocompetent subjects, they have higher mortality regardless of ARDS severity as well as a higher frequency of limitation of life-sustaining measures. Nonetheless, nearly half of these patients survive to hospital discharge. Trial registration: ClinicalTrials.gov, NCT02010073. Registered on 12 December 2013

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

International audienceTwo 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