Acute Respiratory Distress Syndrome Phenotypes and Identifying Treatable Traits. The Dawn of Personalized Medicine for ARDS

Identification of four latent classes of acute respiratory distress syndrome using PaO2/FIO2 ratio: an observational cohort study

Biological phenotypes in patients with the acute respiratory distress syndrome (ARDS) have previously been described. We hypothesized that the trajectory of PaO2/FIO2 ratio could be used to identify phenotypes of ARDS. We used a retrospective cohort analysis of an ARDS database to identify latent classes in the trajectory of PaO2/FIO2 ratio over time. We included all adult patients admitted to an intensive care unit who met the Berlin criteria for ARDS over a 4-year period in tertiary adult intensive care units in Manitoba, Canada. Baseline demographics were collected along with the daily PaO2/FIO2 ratio collected on admission and on days 1-7, 14 and 28. We used joint growth mixture modeling to test whether ARDS patients exhibit distinct phenotypes with respect to both longitudinal PaO2/FIO2 ratio and survival. The resulting latent classes were compared on several demographic variables. In our study group of 209 patients, we found that four latent trajectory classes of PaO2/FIO2 ratio was optimal. These four classes differed in their baseline PaO2/FIO2 ratio and their trajectory of improvement during the 28 days of the study. Despite similar baseline characteristics the hazard for death for the classes differed over time. This difference was largely driven by withdrawal of life sustaining therapy in one of the classes. Latent classes were identified in the trajectory of the PaO2/FIO2 ratio over time, suggesting the presence of different ARDS phenotypes. Future studies should confirm the existence of this finding and determine the cause of mortality differences between classes.

Identification and Prediction of Clinical Phenotypes in Hospitalized Patients With COVID-19: Machine Learning From Medical Records

BACKGROUND

There is significant heterogeneity in disease progression among hospitalized patients with COVID-19. The pathogenesis of SARS-CoV-2 infection is attributed to a complex interplay between virus and host immune response that in some patients unpredictably and rapidly leads to "hyperinflammation" associated with increased risk of mortality. The early identification of patients at risk of progression to hyperinflammation may help inform timely therapeutic decisions and lead to improved outcomes.

OBJECTIVE

The primary objective of this study was to use machine learning to reproducibly identify specific risk-stratifying clinical phenotypes across hospitalized patients with COVID-19 and compare treatment response characteristics and outcomes. A secondary objective was to derive a predictive phenotype classification model using routinely available early encounter data that may be useful in informing optimal COVID-19 bedside clinical management.

METHODS

This was a retrospective analysis of electronic health record data of adult patients (N=4379) who were admitted to a Johns Hopkins Health System hospital for COVID-19 treatment from 2020 to 2021. Phenotypes were identified by clustering 38 routine clinical observations recorded during inpatient care. To examine the reproducibility and validity of the derived phenotypes, patient data were randomly divided into 2 cohorts, and clustering analysis was performed independently for each cohort. A predictive phenotype classifier using the gradient-boosting machine method was derived using routine clinical observations recorded during the first 6 hours following admission.

RESULTS

A total of 2 phenotypes (designated as phenotype 1 and phenotype 2) were identified in patients admitted for COVID-19 in both the training and validation cohorts with similar distributions of features, correlations with biomarkers, treatments, comorbidities, and outcomes. In both the training and validation cohorts, phenotype-2 patients were older; had elevated markers of inflammation; and were at an increased risk of requiring intensive care unit-level care, developing sepsis, and mortality compared with phenotype-1 patients. The gradient-boosting machine phenotype prediction model yielded an area under the curve of 0.89 and a positive predictive value of 0.83.

CONCLUSIONS

Using machine learning clustering, we identified and internally validated 2 clinical COVID-19 phenotypes with distinct treatment or response characteristics consistent with similar 2-phenotype models derived from other hospitalized populations with COVID-19, supporting the reliability and generalizability of these findings. COVID-19 phenotypes can be accurately identified using machine learning models based on readily available early encounter clinical data. A phenotype prediction model based on early encounter data may be clinically useful for timely bedside risk stratification and treatment personalization.

Cardiovascular Subphenotypes in Acute Respiratory Distress Syndrome

OBJECTIVES

To use clustering methods on transthoracic echocardiography (TTE) findings and hemodynamic parameters to characterize circulatory failure subphenotypes and potentially elucidate underlying mechanisms in patients with acute respiratory distress syndrome (ARDS) and to describe their association with mortality compared with current definitions of right ventricular dysfunction (RVD).

DESIGN

Retrospective, single-center cohort study.

SETTING

University Hospital ICU, Birmingham, United Kingdom.

PATIENTS

ICU patients that received TTE within 7 days of ARDS onset between April 2016 and December 2021.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Latent class analysis (LCA) of TTE/hemodynamic parameters was performed in 801 patients, 62 years old (interquartile range, 50-72 yr old), 63% male, and 40% 90-day mortality rate. Four cardiovascular subphenotypes were identified: class 1 (43%; mostly normal left and right ventricular [LV/RV] function), class 2 (24%; mostly dilated RV with preserved systolic function), class 3 (13%, mostly dilated RV with impaired systolic function), and class 4 (21%; mostly high cardiac output, with hyperdynamic LV function). The four subphenotypes differed in their characteristics and outcomes, with 90-day mortality rates of 19%, 40%, 78%, and 59% in classes 1-4, respectively ( p < 0.0001). Following multivariable logistic regression analysis, class 3 had the highest odds ratio (OR) for mortality (OR, 6.9; 95% CI, 4.0-11.8) compared with other RVD definitions. Different three-variable models had high diagnostic accuracy in identifying each of these latent subphenotypes.

CONCLUSIONS

LCA of TTE parameters identified four cardiovascular subphenotypes in ARDS that more closely aligned with circulatory failure mechanisms and mortality than current RVD definitions.

Coronavirus disease 2019 (COVID-19): cytokine storms, hyper-inflammatory phenotypes, and acute respiratory distress syndrome

Coronavirus Disease 2019 (COVID-19) was first identified in China at the end of 2019. Acute respiratory distress syndrome (ARDS) represents the most common and serious complication of COVID-19. Cytokine storms are a pathophysiological feature of COVID-19 and play an important role in distinguishing hyper-inflammatory subphenotypes of ARDS. Accordingly, in this review, we focus on hyper-inflammatory host responses in ARDS that play a critical role in the differentiated development of COVID-19. Furthermore, we discuss inflammation-related indicators that have the potential to identify hyper-inflammatory subphenotypes of COVID-19, especially for those with a high risk of ARDS. Finally, we explore the possibility of improving the quality of monitoring and treatment of COVID-19 patients and in reducing the incidence of critical illness and mortality via better distinguishing hyper- and hypo-inflammatory subphenotypes of COVID-19.

Compliance Phenotypes in Early Acute Respiratory Distress Syndrome before the COVID-19 Pandemic

Rationale: A novel model of phenotypes based on set thresholds of respiratory system compliance (Crs) was recently postulated in context of coronavirus disease (COVID-19) acute respiratory distress syndrome (ARDS). In particular, the dissociation between the degree of hypoxemia and Crs was characterized as a distinct ARDS phenotype.

Objectives: To determine whether such Crs-based phenotypes existed among patients with ARDS before the COVID-19 pandemic and to closely examine the Crs–mortality relationship.

Methods: We undertook a secondary analysis of patients with ARDS, who were invasively ventilated on controlled modes and enrolled in a large, multinational, epidemiological study. We assessed Crs, degree of hypoxemia, and associated Crs-based phenotypic patterns with their characteristics and outcomes.

Measurements and Main Results: Among 1,117 patients with ARDS who met inclusion criteria, the median Crs was 30 (interquartile range, 23–40) ml/cm H₂O. One hundred thirty-six (12%) patients had preserved Crs (≥50 ml/cm H₂O; phenotype with low elastance [“phenotype L”]), and 827 (74%) patients had poor Crs (<40 ml/cm H₂O; phenotype with high elastance [“phenotype H”]). Compared with those with phenotype L, patients with phenotype H were sicker and had more comorbidities and higher hospital mortality (32% vs. 45%; P < 0.05). A near complete dissociation between Pa_O2/Fi_O2 and Crs was observed. Of 136 patients with phenotype L, 58 (43%) had a Pa_O2/Fi_O2 < 150. In a multivariable-adjusted analysis, the Crs was independently associated with hospital mortality (adjusted odds ratio per ml/cm H₂O increase, 0.988; 95% confidence interval, 0.979–0.996; P = 0.005).

Conclusions: A wide range of Crs was observed in non–COVID-19 ARDS. Approximately one in eight patients had preserved Crs. Pa_O2/Fi_O2 and Crs were dissociated. Lower Crs was independently associated with higher mortality. The Crs–mortality relationship lacked a clear transition threshold.

Metabolic Imaging and Biological Assessment: Platforms to Evaluate Acute Lung Injury and Inflammation

Pulmonary inflammation is a hallmark of several pulmonary disorders including acute lung injury and acute respiratory distress syndrome. Moreover, it has been shown that patients with hyperinflammatory phenotype have a significantly higher mortality rate. Despite this, current therapeutic approaches focus on managing the injury rather than subsiding the inflammatory burden of the lung. This is because of the lack of appropriate non-invasive biomarkers that can be used clinically to assess pulmonary inflammation. In this review, we discuss two metabolic imaging tools that can be used to non-invasively assess lung inflammation. The first method, Positron Emission Tomography (PET), is widely used in clinical oncology and quantifies flux in metabolic pathways by measuring uptake of a radiolabeled molecule into the cells. The second method, hyperpolarized ¹³C MRI, is an emerging tool that interrogates the branching points of the metabolic pathways to quantify the fate of metabolites. We discuss the differences and similarities between these techniques and discuss their clinical applications.

Understanding Heterogeneity in Biologic Phenotypes of Acute Respiratory Distress Syndrome by Leukocyte Expression Profiles

Rationale: Two biologic phenotypes of acute respiratory distress syndrome (ARDS) have been identified based on plasma protein markers in four previous studies. Objectives: To determine if blood leukocyte gene expression is different between the "reactive" and "uninflamed" phenotype. Methods: This is a new study adding blood leukocyte transcriptomics and bioinformatics analysis to an existing patient cohort of ARDS in patients with sepsis admitted to two ICUs during a 1.5-year period. Canonical pathway analysis was performed. Measurements and Main Results: A total of 210 patients with sepsis and ARDS were included, of whom 128 had a reactive and 82 an uninflamed phenotype. A total of 3,332/11,443 (29%) transcripts were significantly different between the phenotypes. Canonical pathway analysis showed upregulation of oxidative phosphorylation genes indicative of mitochondrial dysfunction (52% of genes in pathway). The uninflamed phenotype was characterized by upregulation of mitogen-activated protein kinase pathways. Conclusions: A third of genes are differentially expressed between biologic phenotypes of ARDS supporting the observation that the subgroups of ARDS are incomparable in terms of pathophysiology. These data provide additional support for biologic heterogeneity in patients with ARDS and suggests that a personalized approach to intervention focusing on oxidative phosphorylation is pivotal in this condition.

Distinct Metabolic Endotype Mirroring Acute Respiratory Distress Syndrome (ARDS) Subphenotype and its Heterogeneous Biology

Predisposing aetiologies in Acute Respiratory Distress Syndrome (ARDS), perpetuates to heterogeneous clinical course hampering therapeutic response. Therefore, physiological variables need to be identified by stratifying ARDS subphenotypes and endotype, to target ARDS heterogeneity. The present study is stimulated by the fact that the ARDS heterogeneity arises from diverse pathophysiological changes leading to distinct ARDS endotypes characterized by perturbed biological mechanism which can be exploited in terms of metabolic profile by metabolomics. Biological endotypes using (n = 464 patients and controls), mBALF and serum samples were identified by high - resolution NMR spectroscopy from two clinically diagnosed ARDS subtypes grouped under mild, moderate and severe ARDS as subphenotype1and pulmonary and extra - pulmonary ARDS as subphenotype2. The identified mBALF endotypes (isoleucine, leucine, valine, lysine/arginine, tyrosine, threonine) and serum endotypes (proline, glutamate, phenylalanine, valine) in both subphenotypes by statistical analysis were tested for their reproducibility and robustness. By combining metabolic endotypes with clinical based mortality score (APACHE and SOFA) added to their predictive performance as ARDS mortality predictors. Thus, a comprehensive set of mBALF endotypes representing compartmentalized lung milieu and serological endotypes representing systemic markers of ARDS subtypes were validated. The interlinked biological pathway of these disease specific endotype further elucidated their role as candidate biomarker in governing ARDS heterogeneous biology.

Stability of ARDS subphenotypes over time in two randomised controlled trials

RATIONALE

Two distinct acute respiratory distress syndrome (ARDS) subphenotypes have been identified using data obtained at time of enrolment in clinical trials; it remains unknown if these subphenotypes are durable over time.

OBJECTIVE

To determine the stability of ARDS subphenotypes over time.

METHODS

Secondary analysis of data from two randomised controlled trials in ARDS, the ARMA trial of lung protective ventilation (n=473; patients randomised to low tidal volumes only) and the ALVEOLI trial of low versus high positive end-expiratory pressure (n=549). Latent class analysis (LCA) and latent transition analysis (LTA) were applied to data from day 0 and day 3, independent of clinical outcomes.

MEASUREMENTS AND MAIN RESULTS

In ALVEOLI, LCA indicated strong evidence of two ARDS latent classes at days 0 and 3; in ARMA, evidence of two classes was stronger at day 0 than at day 3. The clinical and biological features of these two classes were similar to those in our prior work and were largely stable over time, though class 2 demonstrated evidence of progressive organ failures by day 3, compared with class 1. In both LCA and LTA models, the majority of patients (>94%) stayed in the same class from day 0 to day 3. Clinical outcomes were statistically significantly worse in class 2 than class 1 and were more strongly associated with day 3 class assignment.

CONCLUSIONS

ARDS subphenotypes are largely stable over the first 3 days of enrolment in two ARDS Network trials, suggesting that subphenotype identification may be feasible in the context of clinical trials.

Lung Metabolism and Inflammation during Mechanical Ventilation; An Imaging Approach

Acute respiratory distress syndrome (ARDS) is a major cause of mortality in critically ill patients. Patients are currently managed by protective ventilation and alveolar recruitment using positive-end expiratory pressure (PEEP). However, the PEEP's effect on both pulmonary metabolism and regional inflammation is poorly understood. Here, we demonstrate the effect of PEEP on pulmonary anaerobic metabolism in mechanically ventilated injured rats, using hyperpolarized carbon-13 imaging. Pulmonary lactate-to-pyruvate ratio was measured in 21 rats; 14 rats received intratracheal instillation of hydrochloric-acid, while 7 rats received sham saline. 1 hour after acid/saline instillation, PEEP was lowered to 0 cmH2O in 7 injured rats (ZEEP group) and in all sham rats; PEEP was continued in the remaining 7 injured rats (PEEP group). Pulmonary compliance, oxygen saturation, histological injury scores, ICAM-1 expression and myeloperoxidase expression were measured. Lactate-to-pyruvate ratio progressively increased in the dependent lung during mechanical ventilation at ZEEP (p < 0.001), but remained unchanged in PEEP and sham rats. Lactate-to-pyruvate ratio was correlated with hyaline membrane deposition (r = 0.612), edema severity (r = 0.663), ICAM-1 (r = 0.782) and myeloperoxidase expressions (r = 0.817). Anaerobic pulmonary metabolism increases during lung injury progression and is contained by PEEP. Pulmonary lactate-to-pyruvate ratio may indicate in-vivo neutrophil activity due to atelectasis.

Effects of cigarette smoke on pulmonary endothelial cells

Cigarette smoking is the leading cause of preventable disease and death in the United States. Cardiovascular comorbidities associated with both active and secondhand cigarette smoking indicate the vascular toxicity of smoke exposure. Growing evidence supports the injurious effect of cigarette smoke on pulmonary endothelial cells and the roles of endothelial cell injury in development of acute respiratory distress syndrome (ARDS), emphysema, and pulmonary hypertension. This review summarizes results from studies of humans, preclinical animal models, and cultured endothelial cells that document toxicities of cigarette smoke exposure on pulmonary endothelial cell functions, including barrier dysfunction, endothelial activation and inflammation, apoptosis, and vasoactive mediator production. The discussion is focused on effects of cigarette smoke-induced endothelial injury in the development of ARDS, emphysema, and vascular remodeling in chronic obstructive pulmonary disease.

Estimating the impact of tuberculosis anatomical classification on treatment outcomes: A patient and surveillance perspective analysis

Introduction

Tuberculosis anatomical classification is inconsistent in the literature, which limits current tuberculosis knowledge and control. We aimed to evaluate whether tuberculosis classification impacts on treatment outcomes at patient and aggregate level.

Methods

We analyzed adults from São Paulo State, Brazil with newly diagnosed tuberculosis from 2010–2013. We used an extended clinical classification of tuberculosis, categorizing cases as pulmonary, pulmonary and extrapulmonary, extrapulmonary and miliary/disseminated. Our primary outcome was unsuccessful outcome of treatment. To investigate the reported treatment outcome at the aggregate level, we sampled 500 different “countries” from the dataset and compared the impact of pulmonary and extrapulmonary classifications on the reported treatment success.

Results

Of 62,178 patients, 49,999 (80.4%) were pulmonary, 9,026 (14.5%) extrapulmonary, 1,651 (2.7%) pulmonary-extrapulmonary and 1,502 (2.4%) miliary/disseminated. Pulmonary-extrapulmonary cases had similar unsuccessful outcome of treatment compared with pulmonary (adjusted-OR 1.00, 95%CI, 0.88–1.13, p = 0.941), while extrapulmonary were associated with better (adjusted-OR 0.65, 95%CI, 0.60–0.71, p<0.001) and miliary/disseminated with worse outcomes (adjusted-OR 1.51, 95%CI, 1.33–1.71, p<0.001). We found that 60 (12%) countries would report a difference ≥10% in treatment success depending on whether they reported all clinical forms together (current WHO recommendation) or pulmonary forms alone, overestimating the treatment success of pulmonary forms.

Conclusions

The expanded anatomical classification of tuberculosis was strongly associated with treatment outcomes at the patient level. Remarkably, pulmonary with concomitant extrapulmonary forms had similar treatment outcomes compared with pulmonary forms after adjustment for potential confounders. At the aggregate level, reporting treatment success for all clinical forms together might hide differences in progress between pulmonary and extrapulmonary tuberculosis control.