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

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Maximização do teor de carbono fixo em biocarvão aplicado ao sequestro de carbono

RESUMOA emissão de dióxido de carbono é, atualmente, a principal causa do aquecimento global, devido ao efeito estufa. Ao redor do planeta pesquisadores têm-se esforçado na busca de soluções viáveis para o problema; com isto, o biocarvão está entre as mais recentes propostas de mitigação das mudanças climáticas em razão da sua simplicidade e eficiência. Desta forma, este trabalho teve, por objetivo, maximizar o teor de carbono fixo na produção de biocarvão. Para isso, analisaram-se sete tratamentos, temperaturas de pirólise: 200, 250, 300, 350, 400, 450 e 500 °C. A partir do rendimento gravimétrico de produção e do teor de carbono fixo calcularam-se os respectivos fatores de rendimento gravimétrico para cada tratamento, os quais indicam em qual temperatura se tem a maior quantidade de carbono fixo em função do biocarvão produzido. A pirólise com temperatura máxima de 300 °C indicou o maior fator de rendimento gravimétrico, atingindo 0,22672 C/biocarvão.</p

Quantitative Assessment of Acute Regurgitation Following TAVR: A Multicenter Pooled Analysis of 2,258 Valves

Objectives: The aim of this study was to assess acute regurgitation following transcatheter aortic valve replacement, comparing different implanted transcatheter heart valves. Background: Regurgitation following transcatheter aortic valve replacement influences all-cause mortality. Thus far, no quantitative comparison of regurgitation among multiple commercially available transcatheter heart valves has been performed. Methods: Aortograms from a multicenter cohort of consecutive 3,976 transcatheter aortic valve replacements were evaluated in this pooled analysis. A total of 2,258 (58.3%) were considered analyzable by an independent academic core laboratory using video densitometry. Results of quantitative regurgitation are shown as percentages. The valves evaluated were the ACURATE (n = 115), Centera (n = 11), CoreValve (n = 532), Direct Flow Medical (n = 21), Evolut PRO (n = 95), Evolut R (n = 295), Inovare (n = 4), Lotus (n = 546), Lotus Edge (n = 3), SAPIEN XT (n = 239), and SAPIEN 3 (n = 397). For the main analysis, only valves with more than 50 procedures (7 types) were used. Results: The Lotus valve had the lowest mean regurgitation (3.5 ± 4.4%), followed by Evolut PRO (7.4 ± 6.5%), SAPIEN 3 (7.6 ± 7.1%), Evolut R (7.9 ± 7.4%), SAPIEN XT (8.8 ± 7.5%), ACURATE (9.6 ± 9.2%) and CoreValve (13.7 ± 10.7%) (analysis of variance p < 0.001). The only valves that statistically differed from all their counterparts were Lotus (as the lowest regurgitation) and CoreValve (the highest). The proportion of patients presenting with moderate or severe regurgitation followed the same ranking order: Lotus (2.2%), Evolut PRO (5.3%), SAPIEN 3 (8.3%), Evolut R (8.8%), SAPIEN XT (10.9%), ACURATE (11.3%), and CoreValve (30.1%) (chi-square p < 0.001). Conclusions: In this pooled analysis stemming from daily clinical practice, the Lotus valve was shown to have the best immediate sealing. This analysis reflects the objective evaluation of regurgitation by an academic core laboratory (nonsponsored) in a real-world cohort of patients using a quantitative technique

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

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 < 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 < 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

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