Acute respiratory distress syndrome (ARDS) is an acute respiratory illness characterised by bilateral chest radiographical opacities with severe hypoxaemia due to non-cardiogenic pulmonary oedema. ...The COVID-19 pandemic has caused an increase in ARDS and highlighted challenges associated with this syndrome, including its unacceptably high mortality and the lack of effective pharmacotherapy. In this Seminar, we summarise current knowledge regarding ARDS epidemiology and risk factors, differential diagnosis, and evidence-based clinical management of both mechanical ventilation and supportive care, and discuss areas of controversy and ongoing research. Although the Seminar focuses on ARDS due to any cause, we also consider commonalities and distinctions of COVID-19-associated ARDS compared with ARDS from other causes.
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.
This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2020. Other selected articles can be found online at ...https://www.biomedcentral.com/collections/annualupdate2020. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901.
Two distinct phenotypes of acute respiratory distress syndrome (ARDS) with differential clinical outcomes and responses to randomly assigned treatment have consistently been identified in randomized ...controlled trial cohorts using latent class analysis. Plasma biomarkers, key components in phenotype identification, currently lack point-of-care assays and represent a barrier to the clinical implementation of phenotypes.
The objective of this study was to develop models to classify ARDS phenotypes using readily available clinical data only.
Three randomized controlled trial cohorts served as the training data set (ARMA High vs. Low Vt, ALVEOLI Assessment of Low Vt and Elevated End-Expiratory Pressure to Obviate Lung Injury, and FACTT Fluids and Catheter Treatment Trial;
= 2,022), and a fourth served as the validation data set (SAILS Statins for Acutely Injured Lungs from Sepsis;
= 745). A gradient-boosted machine algorithm was used to develop classifier models using 24 variables (demographics, vital signs, laboratory, and respiratory variables) at enrollment. In two secondary analyses, the ALVEOLI and FACTT cohorts each, individually, served as the validation data set, and the remaining combined cohorts formed the training data set for each analysis. Model performance was evaluated against the latent class analysis-derived phenotype.
For the primary analysis, the model accurately classified the phenotypes in the validation cohort (area under the receiver operating characteristic curve AUC, 0.95; 95% confidence interval CI, 0.94-0.96). Using a probability cutoff of 0.5 to assign class, inflammatory biomarkers (IL-6, IL-8, and sTNFR-1;
< 0.0001) and 90-day mortality (38% vs. 24%;
= 0.0002) were significantly higher in the hyperinflammatory phenotype as classified by the model. Model accuracy was similar when ALVEOLI (AUC, 0.94; 95% CI, 0.92-0.96) and FACTT (AUC, 0.94; 95% CI, 0.92-0.95) were used as the validation cohorts. Significant treatment interactions were observed with the clinical classifier model-assigned phenotypes in both ALVEOLI (
= 0.0113) and FACTT (
= 0.0072) cohorts.
ARDS phenotypes can be accurately identified using machine learning models based on readily available clinical data and may enable rapid phenotype identification at the bedside.
To provide an overview of the current research in identifying homogeneous subgroups and phenotypes in ARDS.
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.
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.
The COVID-19 pandemic has seen a surge of patients with acute respiratory distress syndrome (ARDS) in intensive care units across the globe. As experience of managing patients with ...COVID-19-associated ARDS has grown, so too have efforts to classify patients according to respiratory system mechanics, with a view to optimising ventilatory management. Personalised lung-protective mechanical ventilation reduces mortality and has become the mainstay of treatment in ARDS. In this Viewpoint, we address ventilatory strategies in the context of recent discussions on phenotypic heterogeneity in patients with COVID-19-associated ARDS. Although early reports suggested that COVID-19-associated ARDS has distinctive features that set it apart from historical ARDS, emerging evidence indicates that the respiratory system mechanics of patients with ARDS, with or without COVID-19, are broadly similar. In the absence of evidence to support a shift away from the current paradigm of ventilatory management, we strongly recommend adherence to evidence-based management, informed by bedside physiology, as resources permit.
BACKGROUND ARDS is a heterogeneous syndrome that encompasses lung injury from both direct and indirect sources. Direct ARDS (pneumonia, aspiration) has been hypothesized to cause more severe lung ...epithelial injury than indirect ARDS (eg, nonpulmonary sepsis); however, this hypothesis has not been well studied in humans. METHODS We measured plasma biomarkers of lung epithelial and endothelial injury and inflammation in a single-center study of 100 patients with ARDS and severe sepsis and in a secondary analysis of 853 patients with ARDS drawn from a multicenter randomized controlled trial. Biomarker levels in patients with direct vs indirect ARDS were compared in both cohorts. RESULTS In both studies, patients with direct ARDS had significantly higher levels of a biomarker of lung epithelial injury (surfactant protein D) and significantly lower levels of a biomarker of endothelial injury (angiopoietin-2) than those with indirect ARDS. These associations were robust to adjustment for severity of illness and ARDS severity. In the multicenter study, patients with direct ARDS also had lower levels of von Willebrand factor antigen and IL-6 and IL-8, markers of endothelial injury and inflammation, respectively. The prognostic value of the biomarkers was similar in direct and indirect ARDS. CONCLUSIONS Direct lung injury in humans is characterized by a molecular phenotype consistent with more severe lung epithelial injury and less severe endothelial injury. The opposite pattern was identified in indirect lung injury. Clinical trials of novel therapies targeted specifically at the lung epithelium or endothelium may benefit from preferentially enrolling patients with direct and indirect ARDS, respectively.
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.
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
(mm Hg)/(predicted body weight × 100 × 37.5).
To determine the relation of ventilatory ratio with Vd/Vt in ARDS.
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.
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
/Fi
, 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.
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.
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