Latent class analysis of ARDS subphenotypes: a secondary analysis of the statins for acutely injured lungs from sepsis (SAILS) study

Ventilator-Induced Lung Injury: The Unseen Challenge in Acute Respiratory Distress Syndrome Management

Invasive mechanical ventilation is a cornerstone therapy for supporting patients with acute respiratory distress syndrome (ARDS) by relieving respiratory muscle strain and ensuring gas exchange. Despite its life-saving benefits, mechanical ventilation can induce ventilator-induced lung injury (VILI), a critical condition characterized by mechanisms such as barotrauma, volutrauma, atelectrauma, ergotrauma, and biotrauma. This review examines the pathophysiological mechanisms of VILI and their impact on lung function, particularly in patients with ARDS. It highlights the importance of lung-protective ventilation strategies, including low tidal volume and tailored positive end-expiratory pressure, which have been shown to improve outcomes in ARDS. The role of prone positioning in enhancing lung homogeneity and improving outcomes is also discussed. Furthermore, emerging concepts such as mechanical power and individual respiratory mechanics are explored as potential avenues for personalized ventilation strategies. Despite advancements, the optimal approach to mechanical ventilation remains a subject of ongoing research.

Functional immune profiling of hyper- and hypo-inflammatory subphenotypes of critical illness: a secondary analysis

Introduction

Recent studies of adult sepsis patients demonstrate the existence of two subphenotypes that differ in risk of mortality: a hyper-inflammatory subphenotype with a high risk of mortality, and a hypo-inflammatory or "not hyper-inflamed" subphenotype with a relatively lower risk of mortality. We recently investigated the association of organ dysfunction with ex vivo immune profiling in sixty (60) critically ill adult patients with sepsis. In this secondary analysis we measured cytokine biomarkers with an automated, microfluidic immunoassay device (Ella™) and sought to investigate the functional immune profiles of patients in the hyper/hypo-inflammatory subphenotype groups.

Methods

Subjects were consecutively identified adults, older than 18 years, and enrolled within 48 hours of sepsis onset. Whole blood cytokine analysis was performed in all patients. Additionally, ex vivo cytokine production was measured following 4h of stimulation. Cytokine concentrations were measured using the Ella™ automated immunoassay system.

Results

Subjects were divided into hypo-inflammatory (42 patients) and hyper-inflammatory (18 patients) subtypes using a previously validated parsimonious model based on concentrations of IL-6, TNFR1 and bicarbonate. The hyper- and hypo-inflammatory clusters demonstrated a near four-fold difference in 30-day mortality (44.4% vs 11.9%, p=0.0046). Following 4h of ex vivo stimulation with LPS, TNF production was lower in the hyper-inflammatory group as compared with the hypo-inflammatory group (p=0.0159). Ex vivo phorbol 12-myristate 13-acetate (PMA)-stimulated IFN-γ production (4h) by whole blood did not differ between groups.

Conclusions

These data further validate the use of IL-6, TNFR1 and bicarbonate to discern inflammatory sub-groups of patients with critical illness. They also confirm the observation that the presence of the hyper-inflammatory subphenotype is often accompanied by a compensatory anti-inflammatory response syndrome. Future investigations should focus on prospective validation of this panel for prognostic enrichment of clinical research studies.

Identifying progression subphenotypes of Alzheimer's disease from large-scale electronic health records with machine learning

OBJECTIVE

Identification of clinically meaningful subphenotypes of disease progression can enhance the understanding of disease heterogeneity and underlying pathophysiology. In this study, we propose a machine learning framework to identify subphenotypes of Alzheimer's disease progression based on longitudinal real-world patient records.

METHODS

The framework, dynaPhenoM, extracts coherent clinical topics across patient visits and employs a time-aware latent class analysis to characterize subphenotypes. We validated dynaPhenoM using three patient databases with a total of 3952 AD patients across the United States, demonstrating its effectiveness in revealing mild cognitive impairment (MCI) progression to AD.

RESULTS

Our study identified five subphenotypes associated with distinct organ systems for disease progression from MCI to AD, including common subtypes across cohorts-respiratory, musculoskeletal, cardiovascular, and endocrine/metabolic-as well as a cohort-specific digestive subtype.

CONCLUSION

Our study unravels the complexity and heterogeneity of the progression from MCI to AD. These findings highlight disease progression heterogeneity and can inform both diagnostic and therapeutic strategies, thereby advancing precision medicine for Alzheimer's disease.

Sepsis subphenotypes, theragnostics and personalized sepsis care

Heterogeneity between critically ill patients with sepsis is a major barrier to the discovery of effective therapies. The use of machine learning techniques, coupled with improved understanding of sepsis biology, has led to the identification of patient subphenotypes. This exciting development may help overcome the problem of patient heterogeneity and lead to the identification of patient subgroups with treatable traits. Re-analyses of completed clinical trials have demonstrated that patients with different subphenotypes may respond differently to treatments. This suggests that future clinical trials that take a precision medicine approach will have a higher likelihood of identifying effective therapeutics for patients based on their subphenotype. In this review, we describe the emerging subphenotypes identified in the critically ill and outline the promising immune modulation therapies which could have a beneficial treatment effect within some of these subphenotypes. Furthermore, we will also highlight how bringing subphenotype identification to the bedside could enable a new generation of precision-medicine clinical trials.

Subphenotypic classification of COVID-19 survivors and response to telerehabilitation: a latent class analysis

OBJECTIVE

Investigating the role of telerehabilitation in aiding recovery and societal reintegration for COVID-19 survivors, this study aims to identify distinct subphenotypes among survivors and assess their responsiveness to telerehabilitation.

DESIGN

A secondary analysis of a multicentre, parallel-group randomized controlled trial from April 2020 through to follow-up in 2021.

SUBJECTS/PATIENTS

The study included 377 COVID-19 survivors (47.1% male), with a mean age of 56.4 years.

METHODS

Data from the Telerehabilitation Programme for COVID-19 (TERECO) were analysed using Latent Class Analysis to identify subphenotypes based on baseline characteristics. Clinical outcomes were compared between subphenotypes and treatment groups.

RESULTS

Latent Class Analysis identified 2 phenotypes: Phenotype 1 (52.9%) characterized by impaired lung function and Phenotype 2 (47.1%) with better lung function. Among those receiving corticosteroids, only Phenotype 1 showed significant benefits from the TERECO intervention. Discrimination accuracy using forced expiratory volume in 1 s (FEV1) and peak expiratory flow was high (AUC = 0.936).

CONCLUSION

Two distinct phenotypes were identified in COVID-19 survivors, suggesting potential improvements in clinical trial design and personalized treatment strategies based on initial pulmonary function. This insight can guide more targeted rehabilitation approaches, enhancing recovery outcomes for specific survivor groups.

Advances in acute respiratory distress syndrome: focusing on heterogeneity, pathophysiology, and therapeutic strategies

In recent years, the incidence of acute respiratory distress syndrome (ARDS) has been gradually increasing. Despite advances in supportive care, ARDS remains a significant cause of morbidity and mortality in critically ill patients. ARDS is characterized by acute hypoxaemic respiratory failure with diffuse pulmonary inflammation and bilateral edema due to excessive alveolocapillary permeability in patients with non-cardiogenic pulmonary diseases. Over the past seven decades, our understanding of the pathology and clinical characteristics of ARDS has evolved significantly, yet it remains an area of active research and discovery. ARDS is highly heterogeneous, including diverse pathological causes, clinical presentations, and treatment responses, presenting a significant challenge for clinicians and researchers. In this review, we comprehensively discuss the latest advancements in ARDS research, focusing on its heterogeneity, pathophysiological mechanisms, and emerging therapeutic approaches, such as cellular therapy, immunotherapy, and targeted therapy. Moreover, we also examine the pathological characteristics of COVID-19-related ARDS and discuss the corresponding therapeutic approaches. In the face of challenges posed by ARDS heterogeneity, recent advancements offer hope for improved patient outcomes. Further research is essential to translate these findings into effective clinical interventions and personalized treatment approaches for ARDS, ultimately leading to better outcomes for patients suffering from ARDS.

Temporal Transitions of the Hyperinflammatory and Hypoinflammatory Phenotypes in Critical Illness

Rationale: Systemic molecular phenotypes of critical illness are prognostically informative, yet their temporal kinetics and implications of changing phenotypes remain incompletely understood. Objectives: To determine the temporal nature of the Hyperinflammatory and Hypoinflammatory phenotypes and assess the impact of transition between the phenotypes on mortality. Methods: We used data from one prospective observational cohort (MARS [Molecular Diagnosis and Risk Stratification of Sepsis]) and two randomized controlled trials in acute respiratory distress syndrome (ALVEOLI [Assessment of Low Tidal Volume and Elevated End-Expiratory Pressure to Obviate Lung Injury]) and sepsis (CLOVERS [Crystalloid Liberal or Vasopressors Early Resuscitation in Sepsis]). Critically ill patients with biomarkers available at multiple time points (Days 0-4) were included. We used a validated classifier model incorporating plasma IL-8, protein C, and serum bicarbonate to assign phenotypes on each day. We determined the association of longitudinal phenotype transition and 90-day all-cause mortality. Measurements and Main Results: Data from 2,407, 527, and 868 patients were included in MARS, ALVEOLI, and CLOVERS, respectively. In MARS, 36.0% were classified by the parsimonious model as Hyperinflammatory at Day 0, decreasing to 15.6% by Day 2 and 6.3% by Day 4. In ALVEOLI and CLOVERS, 26.4% and 24.8% of patients were Hyperinflammatory at Day 0, decreasing to 13.4% and 5.7% at Day 3, respectively. In all three cohorts, switching classification from Hyperinflammatory (Day 0) to Hypoinflammatory over time was associated with significantly improved mortality compared with persistently Hyperinflammatory patients. Mediation analysis indicated that only a minor proportion of this improvement could be attributed to ameliorating organ failure. Conclusions: The prevalence of the Hyperinflammatory phenotype, as assigned using a parsimonious biomarker classifier model, decreases over the first several days of critical illness, irrespective of acute respiratory distress syndrome diagnosis. The transition from Hyperinflammatory to Hypoinflammatory mediates a trajectory toward recovery beyond the resolution of organ failure.

Comparison of artificial intelligence and logistic regression models for mortality prediction in acute respiratory distress syndrome: a systematic review and meta-analysis

BACKGROUND

The application of artificial intelligence (AI) in predicting the mortality of acute respiratory distress syndrome (ARDS) has garnered significant attention. However, there is still a lack of evidence-based support for its specific diagnostic performance. Thus, this systematic review and meta-analysis was conducted to evaluate the effectiveness of AI algorithms in predicting ARDS mortality.

METHOD

We conducted a comprehensive electronic search across Web of Science, Embase, PubMed, Scopus, and EBSCO databases up to April 28, 2024. The QUADAS-2 tool was used to assess the risk of bias in the included articles. A bivariate mixed-effects model was applied for the meta-analysis. Sensitivity analysis, meta-regression analysis, and tests for heterogeneity were also performed.

RESULTS

Eight studies were included in the analysis. The sensitivity, specificity, and summarized receiver operating characteristic (SROC) of the AI-based model in the validation set were 0.89 (95% CI 0.79-0.95), 0.72 (95% CI 0.65-0.78), and 0.84 (95% CI 0.80-0.87), respectively. For the logistic regression (LR) model, the sensitivity, specificity, and SROC were 0.78 (95% CI 0.74-0.82), 0.68 (95% CI 0.60-0.76), and 0.81 (95% CI 0.77-0.84). The AI model demonstrated superior predictive accuracy compared to the LR model. Notably, the predictive model performed better in patients with moderate to severe ARDS (SAUC: 0.84 [95% CI 0.80-0.87] vs. 0.81 [95% CI 0.77-0.84]).

CONCLUSION

The AI algorithms showed superior performance in predicting the mortality of ARDS patients and demonstrated strong potential for clinical application. Additionally, we found that for ARDS, a highly heterogeneous condition, the accuracy of the model is influenced by the severity of the disease.

Sepsis subphenotypes: bridging the gaps in sepsis treatment strategies

Sepsis, a heterogeneous illness produced by a dysregulated host response to infection, remains a severe mortality risk. Recent discoveries in sepsis research have stressed phenotyping as a feasible strategy for tackling heterogeneity and enhancing therapy precision. Sepsis phenotyping has moved from traditional stratifications based on severity and prognosis to dynamic, phenotype-driven therapeutic options. This review covers recent progress in connecting sepsis subgroups to personalized treatments, with a focus on phenotype-based therapeutic predictions and decision-support systems. Despite ongoing challenges, such as standardizing phenotyping frameworks and incorporating findings into clinical practice, this topic has enormous promise. By investigating phenotypic variation in therapy responses, we hope to uncover new biomarkers and phenotype-driven therapeutic solutions, laying the groundwork for more effective therapies and, ultimately improving patient outcomes.

Clinical subtypes in critically ill patients with sepsis: validation and parsimonious classifier model development

BACKGROUND

The application of sepsis subtypes to enhance personalized medicine in critically ill patients is hindered by the lack of validation across diverse cohorts and the absence of a simple classification model. We aimed to validate the previously identified SENECA clinical sepsis subtypes in multiple large ICU cohorts, and to develop parsimonious classifier models for δ-type adjudication in clinical practice.

METHODS

Data from four cohorts between 2008 and 2023 were used to assign α, β, γ and δ-type in patients fulfilling the Sepsis-3 criteria using clinical variables: (I) The Molecular diAgnosis and Risk stratification of Sepsis (MARS, n = 2449), (II) a contemporary continuation of the MARS study (MARS2, n = 2445) (III) the Dutch National Intensive Care Evaluation registry (NICE, n = 28,621) and (IV) the Medical Information Mart for Intensive Care including (MIMIC-IV, n = 18,661). K-means clustering using clinical variables was conducted to assess the optimal number of classes and compared to the SENECA subtypes. Parsimonious models were built in the SENECA derivation cohort to predict subtype membership using logistic regression, and validated in MARS and MIMIC-IV.

RESULTS

Among 52.226 patients with sepsis, the subtype distribution in MARS, MARS2 and NICE was 2-6% for the α-type, 1-5% for the β-type, 49-65% for the γ-type and 26-48% for the δ-type compared to 33%, 27%, 27% and 13% in the original SENECA derivation cohort, whereas subtype distribution in MIMIC-IV was more similar at 25%, 24%, 27% and 25%, respectively. In-hospital mortality rates were significantly different between the four cohorts for α, γ and δ-type (p < 0.001). Method-based validation showed moderate overlap with the original subtypes in both MARS and MIMIC-IV. A parsimonious model for all four subtypes had moderate to low accuracy (accuracy 62.2%), while a parsimonious classifier model with 3 variables (aspartate aminotransferase, serum lactate, and bicarbonate) had excellent accuracy in predicting the δ-type patients from all other types in the derivation cohort and moderate accuracy in the validation cohorts (MARS: area under the receiver operator characteristic curve (AUC) 0.93, 95% CI [0.92-0.94], accuracy 85.5% [84.0-86.8%]; MIMIC-IV: AUC 0.86 [0.85-0.87], accuracy 82.9% [82.4-83.4%]).

CONCLUSIONS

The distribution and mortality rates of clinical sepsis subtypes varied between US and European cohorts. A three-variable model could accurately identify the δ-type sepsis patients.

Biological subphenotypes in patients hospitalized with suspected infection in Thailand: a secondary analysis of a prospective observational study

Background

Subphenotypes of infected patients have been reported in Europe and North America, but few studies have investigated populations in Southeast Asia. We sought to identify and differentiate subphenotypes of patients hospitalized with suspected infection in rural Thailand using biological markers implicated in the dysregulated host response.

Methods

In a cohort of prospectively enrolled patients hospitalized with suspected infection in northeastern Thailand, we measured 15 circulating biomarkers from a random selection of 585 subjects and developed latent profile models to identify subphenotypes. Patient characteristics were compared after subphenotype assignment, and a parsimonious model was developed to identify patient subphenotypes.

Findings

We identified and assigned 585 patients to three subphenotypes termed latent biological profile (LBP)-1 (52%), LBP-2 (39%) and LBP-3 (9%). Patients assigned to LBP-3 had a higher risk of 28-day mortality compared to those in LBP-1 and LBP-2 (adjusted relative risk 1.8, 95% confidence interval [CI] 1.1-2.9, P = 0.02). Patient clinical characteristics and biomarker concentrations also differed by subphenotype assignment. A parsimonious three-biomarker model identified subphenotypes in an internal validation cohort (LBP-1: area under the receiver operating curve [AUC] 0.96, 95% CI: 0.94-0.98; LBP-2: AUC 0.77, 95% CI 0.71-0.83; LBP-3: AUC 0.99, 95% CI 0.98-1.00).

Interpretation

We identified three biological subphenotypes of patients with suspected infection in rural Thailand, where the burden of infection is high but understudied. Patient subphenotype assignment was characterized by distinct clinical outcomes and biological profiles which could inform contextualized future study design.

Funding

The US National Institutes of Health, the Wellcome Trust, and the Firland Foundation.

Evolving concepts in intracranial pressure monitoring - from traditional monitoring to precision medicine

A wide range of acute brain injuries, including both traumatic and non-traumatic causes, can result in elevated intracranial pressure (ICP), which in turn can cause further secondary injury to the brain, initiating a vicious cascade of propagating injury. Elevated ICP is therefore a neurological injury that requires intensive monitoring and time-sensitive interventions. Patients at high risk for developing elevated ICP undergo placement of invasive ICP monitors including external ventricular drains, intraparenchymal ICP monitors, and lumbar drains. These monitors all generate an ICP waveform, but each has its own unique caveats in monitoring and accuracy. Current ICP monitoring and management clinical guidelines focus on the mean ICP derived from the ICP waveform, with standard thresholds of treating ICP greater than 20 ​mmHg or 22 ​mmHg applied broadly to a wide range of patients. However, this one-size fits all approach has been criticized and there is a need to develop personalized, evidence-based and possibly multi-factorial precision-medicine based approaches to the problem. This paper provides historical and physiological context to the problem of elevated ICP, provides an overview of the challenges of the current paradigm of ICP management strategies, and discusses advances in ICP waveform analysis, emerging non-invasive ICP monitoring techniques, and applications of machine learning to create predictive algorithms.

Complex Thrombo-Inflammatory Responses versus Outcomes of Non-COVID-19 Community-Acquired Pneumonia and COVID-19

INTRODUCTION

The thrombo-inflammatory response and outcomes of community-acquired pneumonia (CAP) due to various organisms (non-COVID-19 CAP) versus CAP due to a single virus, SARS-CoV-2 (i.e., COVID-19) may differ.

METHODS

Adults hospitalized with non-COVID-19 CAP (December 1, 2021-June 15, 2023) or COVID-19 (March 2, 2020-June 15, 2023) in Canada. We compared non-COVID-19 CAP and COVID-19 baseline, thrombo-inflammatory response, and mortality. We measured plasma cytokine and coagulation factor levels in a sample of patients, did hierarchical clustering, and compared cytokine and coagulation factor levels.

RESULTS

In 2,485 patients (non-COVID-19 CAP, n = 719; COVID-19 patients, n = 2,157), non-COVID-19 CAP patients had significantly lower 28-day mortality (CAP vs. COVID-19 waves 1 and 2; 10% vs. 18% and 16%, respectively), intensive care unit admission (CAP vs. all waves; 15% vs. 39%, 37%, 33%, and 24%, respectively), invasive ventilation (CAP vs. waves 1, 2, and 3 patients; 11% vs. 25%, 20%, and 16%), vasopressor use (CAP 12% vs. 23%, 21%, and 18%), and renal replacement therapy use (CAP 3% vs. Omicron 7%). Complexity of hierarchical clustering aligned directly with mortality: COVID-19 wave 1 and 2 patients had six clusters at admission and higher mortality than non-COVID-19 CAP and Omicron that had three clusters at admission. Pooling all COVID-19 waves increased complexity with seven clusters on admission.

CONCLUSION

Complex thrombo-inflammatory responses aligned with mortality of CAP. At a fundamental level, the human thrombo-inflammatory response to a brand new virus was "confused" whereas humans had eons of time to develop a more concise efficient thrombo-inflammatory host response to CAP.

Future Directions in Therapies for Acute Respiratory Distress Syndrome

Acute respiratory distress syndrome (ARDS) is caused by a complex interplay among hyperinflammation, endothelial dysfunction, and alveolar epithelial injury. Targeted treatments toward the underlying pathways have been unsuccessful in unselected patient populations. The first reliable biological subphenotypes reflective of these biological disease states have been identified in the past decade. Subphenotype targeted intervention studies are needed to advance the pharmacologic treatment of ARDS.

Biomarkers of Acute Respiratory Distress Syndrome: Current State and Future Prospects

Biomarkers are an important tool aiding researchers in the study of acute respiratory distress syndrome (ARDS). Mechanisms involving injury to the alveolar-capillary membrane, endothelium and epithelium resulting in lung inflammation and alterations in coagulation pathways have been validated in human trials and have been used to discover promising phenotypes that share similar characteristics and differential treatment responses. The emergence of powerful point-of-care technologies will enable the prospective study of biomarkers for future enrichment trials with the goal of transforming biomarkers into the clinical realm to inform delivery of personalized medicine at the bedside.

Steroids and Immunomodulatory Therapies for Acute Respiratory Distress Syndrome

Acute respiratory distress syndrome (ARDS) is characterized by a dysregulated immune response to infection or injury. This framework has driven long-standing interest in immunomodulatory therapies as treatments for ARDS. In this narrative review, we first define what constitutes a dysregulated immune response in ARDS. In this context, we describe the rationale and available evidence for immunomodulatory therapies studied in randomized controlled trials of ARDS patients to date. Finally, we address factors that have contributed to the failure to develop therapies in the past and highlight current and future developments designed to address them.

Precision Medicine in Acute Respiratory Distress Syndrome: Progress, Challenges, and the Road ahead

Several novel high-dimensional biologic measurements are increasingly being applied to biomedical sciences. Acute respiratory distress syndrome (ARDS) is a theoretically fertile ground for such approaches. Not only are these biologic and analytic tools available to better understand ARDS but also arguably, simpler approaches such as respiratory physiology has been vastly underutilized as a means of delivering precision-based care in the field. Here we review the progress made in ARDS toward discovering biologically homogeneous phenotypes, treatment responsive subgroups, the challenges to implement these discoveries at the bedside, and the road ahead that will enable precision medicine in ARDS.

Acute respiratory distress syndrome

The understanding of acute respiratory distress syndrome (ARDS) has evolved greatly since it was first described in a 1967 case series, with several subsequent updates to the definition of the syndrome. Basic science advances and clinical trials have provided insight into the mechanisms of lung injury in ARDS and led to reduced mortality through comprehensive critical care interventions. This review summarizes the current understanding of the epidemiology, pathophysiology, and management of ARDS. Key highlights include a recommended new global definition of ARDS and updated guidelines for managing ARDS on a backbone of established interventions such as low tidal volume ventilation, prone positioning, and a conservative fluid strategy. Future priorities for investigation of ARDS are also highlighted.

Identification of novel sub-phenotypes of severe ARDS requiring ECMO using latent class analysis

BACKGROUND

Sub-phenotyping of acute respiratory distress syndrome (ARDS) could be useful for evaluating the severity of ARDS or predicting its responsiveness to given therapeutic strategies, but no studies have yet investigated the heterogeneity of patients with severe ARDS requiring veno-venous extracorporeal membrane oxygenation (V-V ECMO).

METHODS

We conducted this retrospective multicenter observational study in adult patients with severe ARDS treated by V-V ECMO. We performed latent class analysis (LCA) for identifying sub-phenotypes of severe ARDS based on the radiological and clinical findings at the start of ECMO support. Multivariate Cox regression analysis was conducted to investigate the differences in mortality and association between the PEEP setting of ≥ 10 cmH2O and mortality by the sub-phenotypes.

RESULTS

We identified three sub-phenotypes from analysis of the data of a total of 544 patients with severe ARDS treated by V-V ECMO, as follows: Dry type (n = 185; 34%); Wet type (n = 169; 31%); and Fibrotic type (n = 190; 35%). The 90-days in-hospital mortality risk was higher in the patients with the Fibrotic type than in those with the Dry type (adjusted hazard ratio [95% confidence interval] 1.75 [1.10-2.79], p = 0.019) or the Wet type (1.50 [1.02-2.23], p = 0.042). The PEEP setting of ≥ 10 cmH2O during the first 3 days of ECMO decreased the 90-days in-hospital mortality risk only in patients with the Wet type, and not in those with the Dry or Fibrotic type. A significant interaction effect was observed between the Wet type and the PEEP setting of ≥ 10 cmH2O in relation to the 90-day in-hospital mortality (pinteraction = 0.036).

CONCLUSIONS

The three sub-phenotypes showed different mortality rates and different relationships between higher PEEP settings in the early phase of V-V ECMO and patient outcomes. Our data suggest that we may need to change our management approach to patients with severe ARDS during V-V ECMO according to their clinical sub-phenotype.

Heterogeneity, Bayesian thinking, and phenotyping in critical care: A primer

PURPOSE

To familiarize clinicians with the emerging concepts in critical care research of Bayesian thinking and personalized medicine through phenotyping and explain their clinical relevance by highlighting how they address the issues of frequent negative trials and heterogeneity of treatment effect.

SUMMARY

The past decades have seen many negative (effect-neutral) critical care trials of promising interventions, culminating in calls to improve the field's research through adopting Bayesian thinking and increasing personalization of critical care medicine through phenotyping. Bayesian analyses add interpretive power for clinicians as they summarize treatment effects based on probabilities of benefit or harm, contrasting with conventional frequentist statistics that either affirm or reject a null hypothesis. Critical care trials are beginning to include prospective Bayesian analyses, and many trials have undergone reanalysis with Bayesian methods. Phenotyping seeks to identify treatable traits to target interventions to patients expected to derive benefit. Phenotyping and subphenotyping have gained prominence in the most syndromic and heterogenous critical care disease states, acute respiratory distress syndrome and sepsis. Grouping of patients has been informative across a spectrum of clinically observable physiological parameters, biomarkers, and genomic data. Bayesian thinking and phenotyping are emerging as elements of adaptive clinical trials and predictive enrichment, paving the way for a new era of high-quality evidence. These concepts share a common goal, sifting through the noise of heterogeneity in critical care to increase the value of existing and future research.

CONCLUSION

The future of critical care medicine will inevitably involve modification of statistical methods through Bayesian analyses and targeted therapeutics via phenotyping. Clinicians must be familiar with these systems that support recommendations to improve decision-making in the gray areas of critical care practice.

Alveolar epithelial cells mitigate neutrophilic inflammation in lung injury through regulating mitochondrial fatty acid oxidation

Type 2 alveolar epithelial (AT2) cells of the lung are fundamental in regulating alveolar inflammation in response to injury. Impaired mitochondrial long-chain fatty acid β-oxidation (mtLCFAO) in AT2 cells is assumed to aggravate alveolar inflammation in acute lung injury (ALI), yet the importance of mtLCFAO to AT2 cell function needs to be defined. Here we show that expression of carnitine palmitoyltransferase 1a (CPT1a), a mtLCFAO rate limiting enzyme, in AT2 cells is significantly decreased in acute respiratory distress syndrome (ARDS). In mice, Cpt1a deletion in AT2 cells impairs mtLCFAO without reducing ATP production and alters surfactant phospholipid abundance in the alveoli. Impairing mtLCFAO in AT2 cells via deleting either Cpt1a or Acadl (acyl-CoA dehydrogenase long chain) restricts alveolar inflammation in ALI by hindering the production of the neutrophilic chemokine CXCL2 from AT2 cells. This study thus highlights mtLCFAO as immunometabolism to injury in AT2 cells and suggests impaired mtLCFAO in AT2 cells as an anti-inflammatory response in ARDS.

Social Determinants of Health Phenotypes and Cardiometabolic Condition Prevalence Among Patients in a Large Academic Health System: Latent Class Analysis

Background

Adverse social determinants of health (SDoH) have been associated with cardiometabolic disease; however, disparities in cardiometabolic outcomes are rarely the result of a single risk factor.

Objective

This study aimed to identify and characterize SDoH phenotypes based on patient-reported and neighborhood-level data from the institutional electronic medical record and evaluate the prevalence of diabetes, obesity, and other cardiometabolic diseases by phenotype status.

Methods

Patient-reported SDoH were collected (January to December 2020) and neighborhood-level social vulnerability, neighborhood socioeconomic status, and rurality were linked via census tract to geocoded patient addresses. Diabetes status was coded in the electronic medical record using International Classification of Diseases codes; obesity was defined using measured BMI ≥30 kg/m2. Latent class analysis was used to identify clusters of SDoH (eg, phenotypes); we then examined differences in the prevalence of cardiometabolic conditions based on phenotype status using prevalence ratios (PRs).

Results

Complete data were available for analysis for 2380 patients (mean age 53, SD 16 years; n=1405, 59% female; n=1198, 50% non-White). Roughly 8% (n=179) reported housing insecurity, 30% (n=710) reported resource needs (food, health care, or utilities), and 49% (n=1158) lived in a high-vulnerability census tract. We identified 3 patient SDoH phenotypes: (1) high social risk, defined largely by self-reported SDoH (n=217, 9%); (2) adverse neighborhood SDoH (n=1353, 56%), defined largely by adverse neighborhood-level measures; and (3) low social risk (n=810, 34%), defined as low individual- and neighborhood-level risks. Patients with an adverse neighborhood SDoH phenotype had higher prevalence of diagnosed type 2 diabetes (PR 1.19, 95% CI 1.06-1.33), hypertension (PR 1.14, 95% CI 1.02-1.27), peripheral vascular disease (PR 1.46, 95% CI 1.09-1.97), and heart failure (PR 1.46, 95% CI 1.20-1.79).

Conclusions

Patients with the adverse neighborhood SDoH phenotype had higher prevalence of poor cardiometabolic conditions compared to phenotypes determined by individual-level characteristics, suggesting that neighborhood environment plays a role, even if individual measures of socioeconomic status are not suboptimal.

Phenotyping COVID-19 respiratory failure in spontaneously breathing patients with AI on lung CT-scan

BACKGROUND

Automated analysis of lung computed tomography (CT) scans may help characterize subphenotypes of acute respiratory illness. We integrated lung CT features measured via deep learning with clinical and laboratory data in spontaneously breathing subjects to enhance the identification of COVID-19 subphenotypes.

METHODS

This is a multicenter observational cohort study in spontaneously breathing patients with COVID-19 respiratory failure exposed to early lung CT within 7 days of admission. We explored lung CT images using deep learning approaches to quantitative and qualitative analyses; latent class analysis (LCA) by using clinical, laboratory and lung CT variables; regional differences between subphenotypes following 3D spatial trajectories.

RESULTS

Complete datasets were available in 559 patients. LCA identified two subphenotypes (subphenotype 1 and 2). As compared with subphenotype 2 (n = 403), subphenotype 1 patients (n = 156) were older, had higher inflammatory biomarkers, and were more hypoxemic. Lungs in subphenotype 1 had a higher density gravitational gradient with a greater proportion of consolidated lungs as compared with subphenotype 2. In contrast, subphenotype 2 had a higher density submantellar-hilar gradient with a greater proportion of ground glass opacities as compared with subphenotype 1. Subphenotype 1 showed higher prevalence of comorbidities associated with endothelial dysfunction and higher 90-day mortality than subphenotype 2, even after adjustment for clinically meaningful variables.

CONCLUSIONS

Integrating lung-CT data in a LCA allowed us to identify two subphenotypes of COVID-19, with different clinical trajectories. These exploratory findings suggest a role of automated imaging characterization guided by machine learning in subphenotyping patients with respiratory failure.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT04395482. Registration date: 19/05/2020.

Potential Impact of Subphenotyping in Pharmacologic Management of Acute Respiratory Distress Syndrome

Background: Acute respiratory distress syndrome (ARDS) is an acute inflammatory process in the lungs associated with high morbidity and mortality. Previous research has studied both nonpharmacologic and pharmacologic interventions aimed at targeting this inflammatory process and improving ventilation. Hypothesis: To date, only nonpharmacologic interventions including lung protective ventilation, prone positioning, and high positive end-expiratory pressure ventilation strategies have resulted in significant improvements in patient outcomes. Given the high mortality associated with ARDS despite these advancements, interest in subphenotyping has grown, aiming to improve diagnosis and develop personalized treatment approaches. Data Collection: Previous trials evaluating pharmacologic therapies in heterogeneous populations have primarily demonstrated no positive effect, but hope to show benefit when targeting specific subphenotypes, thus increasing their efficacy, while simultaneously decreasing adverse effects. Results: Although most studies evaluating pharmacologic therapies for ARDS have not demonstrated a mortality benefit, there is limited data evaluating pharmacologic therapies in ARDS subphenotypes, which have found promising results. Neuromuscular blocking agents, corticosteroids, and simvastatin have resulted in a mortality benefit when used in patients with the hyper-inflammatory ARDS subphenotype. Therapeutic Opinion: The use of subphenotyping could revolutionize the way ARDS therapies are applied and therefore improve outcomes while also limiting the adverse effects associated with their ineffective use. Future studies should evaluate ARDS subphenotypes and their response to pharmacologic intervention to advance this area of precision medicine.

From ICU Syndromes to ICU Subphenotypes: Consensus Report and Recommendations for Developing Precision Medicine in the ICU

Critical care uses syndromic definitions to describe patient groups for clinical practice and research. There is growing recognition that a "precision medicine" approach is required and that integrated biologic and physiologic data identify reproducible subpopulations that may respond differently to treatment. This article reviews the current state of the field and considers how to successfully transition to a precision medicine approach. To impact clinical care, identification of subpopulations must do more than differentiate prognosis. It must differentiate response to treatment, ideally by defining subgroups with distinct functional or pathobiological mechanisms (endotypes). There are now multiple examples of reproducible subpopulations of sepsis, acute respiratory distress syndrome, and acute kidney or brain injury described using clinical, physiological, and/or biological data. Many of these subpopulations have demonstrated the potential to define differential treatment response, largely in retrospective studies, and that the same treatment-responsive subpopulations may cross multiple clinical syndromes (treatable traits). To bring about a change in clinical practice, a precision medicine approach must be evaluated in prospective clinical studies requiring novel adaptive trial designs. Several such studies are underway, but there are multiple challenges to be tackled. Such subpopulations must be readily identifiable and be applicable to all critically ill populations around the world. Subdividing clinical syndromes into subpopulations will require large patient numbers. Global collaboration of investigators, clinicians, industry, and patients over many years will therefore be required to transition to a precision medicine approach and ultimately realize treatment advances seen in other medical fields.

Association between inflammation and post-intensive care syndrome: a systematic review

Post-intensive care syndrome describes the physical, cognitive and emotional symptoms which persist following critical illness. At present there is limited understanding of the pathological mechanisms contributing to the development of post-intensive care syndrome. The aim of this systematic review was to synthesise current evidence exploring the association between inflammation and features of post-intensive care syndrome in survivors of critical illness. Relevant databases were systematically searched for studies of human participants exposed to critical illness. We sought studies that reported results for biomarkers with an identified role in the pathophysiology of inflammation obtained at any time-point in the patient journey and an outcome measure of any feature of post-intensive care syndrome at any point following hospital discharge. We included 32 studies, with 23 in the primary analysis and nine in a brain injury subgroup analysis. In the primary analysis, 47 different biomarkers were sampled and 44 different outcome measures were employed. Of the biomarkers which were sampled in five or more studies, interleukin-8, C-reactive protein and interleukin-10 most frequently showed associations with post-intensive care syndrome outcomes in 71%, 62% and 60% of studies, respectively. There was variability in terms of which biomarkers were sampled, time-points of sampling and outcome measures reported. Overall, there was mixed evidence of a potential association between an inflammatory process and long-term patient outcomes following critical illness. Further high-quality research is required to develop a longitudinal inflammatory profile of survivors of critical illness over the recovery period and evaluate the association with outcomes.

Early serum biomarkers to characterise different phenotypes of primary graft dysfunction after lung transplantation: a systematic scoping review

Background

Lung transplantation (LUTX) is often complicated by primary graft dysfunction (PGD). Plasma biomarkers hold potential for PGD phenotyping and targeted therapy. This scoping review aims to collect the available literature in search of serum biomarkers for PGD phenotyping.

Methods

Following JBI and PRISMA guidelines, we conducted a systematic review searching MEDLINE, Web of Science, EMBASE and The Cochrane Library for papers reporting the association between serum biomarkers measured within 72 h of reperfusion and PGD, following International Society for Heart and Lung Transplantation (ISHLT) guidelines. We extracted study details, patient demographics, PGD definition and timing, biomarker concentration, and their performance in identifying PGD cases.

Results

Among the 1050 papers screened, 25 prospective observational studies were included, with only nine conducted in the last decade. These papers included 1793 unique adult patients (1195 double LUTX, median study size 100 (IQR 44-119)). Most (n=21) compared PGD grade 3 to less severe PGD, but only four adhered to 2016 PGD definitions. Enzyme-linked immunosorbent assays and the multiplex bead array technique were utilised in 23 and two papers, respectively. In total, 26 candidate biomarkers were identified, comprising 13 inflammatory, three endothelial activation, three epithelial injury, three cellular damage and two coagulation dysregulation markers. Only five biomarkers (sRAGE, ICAM-1, PAI-1, SP-D, FSTL-1) underwent area under the receiver operating characteristic curve analysis, yielding a median value of 0.58 (0.51-0.78) in 406 patients (276 double LUTX).

Conclusions

Several biomarkers exhibit promise for future studies aimed at PGD phenotyping after LUTX. To uncover the significant existing knowledge gaps, further international prospective studies incorporating updated diagnostic criteria, modern platforms and advanced statistical approaches are essential.

A systematic review of machine learning models for management, prediction and classification of ARDS

AIM

Acute respiratory distress syndrome or ARDS is an acute, severe form of respiratory failure characterised by poor oxygenation and bilateral pulmonary infiltrates. Advancements in signal processing and machine learning have led to promising solutions for classification, event detection and predictive models in the management of ARDS.

METHOD

In this review, we provide systematic description of different studies in the application of Machine Learning (ML) and artificial intelligence for management, prediction, and classification of ARDS. We searched the following databases: Google Scholar, PubMed, and EBSCO from 2009 to 2023. A total of 243 studies was screened, in which, 52 studies were included for review and analysis. We integrated knowledge of previous work providing the state of art and overview of explainable decision models in machine learning and have identified areas for future research.

RESULTS

Gradient boosting is the most common and successful method utilised in 12 (23.1%) of the studies. Due to limitation of data size available, neural network and its variation is used by only 8 (15.4%) studies. Whilst all studies used cross validating technique or separated database for validation, only 1 study validated the model with clinician input. Explainability methods were presented in 15 (28.8%) of studies with the most common method is feature importance which used 14 times.

CONCLUSION

For databases of 5000 or fewer samples, extreme gradient boosting has the highest probability of success. A large, multi-region, multi centre database is required to reduce bias and take advantage of neural network method. A framework for validating with and explaining ML model to clinicians involved in the management of ARDS would be very helpful for development and deployment of the ML model.

Lung microbiome: new insights into bronchiectasis' outcome

The present treatments for bronchiectasis, which is defined by pathological dilatation of the airways, are confined to symptom relief and minimizing exacerbations. The condition is becoming more common worldwide. Since the disease's pathophysiology is not entirely well understood, developing novel treatments is critically important. The interplay of chronic infection, inflammation, and compromised mucociliary clearance, which results in structural alterations and the emergence of new infection, is most likely responsible for the progression of bronchiectasis. Other than treating bronchiectasis caused by cystic fibrosis, there are no approved treatments. Understanding the involvement of the microbiome in this disease is crucial, the microbiome is defined as the collective genetic material of all bacteria in an environment. In clinical practice, bacteria in the lungs have been studied using cultures; however, in recent years, researchers use next-generation sequencing methods, such as 16S rRNA sequencing. Although the microbiome in bronchiectasis has not been entirely investigated, what is known about it suggests that Haemophilus, Pseudomonas and Streptococcus dominate the lung bacterial ecosystems, they present significant intraindividual stability and interindividual heterogeneity. Pseudomonas and Haemophilus-dominated microbiomes have been linked to more severe diseases and frequent exacerbations, however additional research is required to fully comprehend the role of microbiome in the evolution of bronchiectasis. This review discusses recent findings on the lung microbiota and its association with bronchiectasis.

Association of Sociodemographic Factors With Overtriage, Undertriage, and Value of Care After Major Surgery

Objective

To determine whether certain patients are vulnerable to errant triage decisions immediately after major surgery and whether there are unique sociodemographic phenotypes within overtriaged and undertriaged cohorts.

Background

In a fair system, overtriage of low-acuity patients to intensive care units (ICUs) and undertriage of high-acuity patients to general wards would affect all sociodemographic subgroups equally.

Methods

This multicenter, longitudinal cohort study of hospital admissions immediately after major surgery compared hospital mortality and value of care (risk-adjusted mortality/total costs) across 4 cohorts: overtriage (N = 660), risk-matched overtriage controls admitted to general wards (N = 3077), undertriage (N = 2335), and risk-matched undertriage controls admitted to ICUs (N = 4774). K-means clustering identified sociodemographic phenotypes within overtriage and undertriage cohorts.

Results

Compared with controls, overtriaged admissions had a predominance of male patients (56.2% vs 43.1%, P < 0.001) and commercial insurance (6.4% vs 2.5%, P < 0.001); undertriaged admissions had a predominance of Black patients (28.4% vs 24.4%, P < 0.001) and greater socioeconomic deprivation. Overtriage was associated with increased total direct costs [$16.2K ($11.4K-$23.5K) vs $14.1K ($9.1K-$20.7K), P < 0.001] and low value of care; undertriage was associated with increased hospital mortality (1.5% vs 0.7%, P = 0.002) and hospice care (2.2% vs 0.6%, P < 0.001) and low value of care. Unique sociodemographic phenotypes within both overtriage and undertriage cohorts had similar outcomes and value of care, suggesting that triage decisions, rather than patient characteristics, drive outcomes and value of care.

Conclusions

Postoperative triage decisions should ensure equality across sociodemographic groups by anchoring triage decisions to objective patient acuity assessments, circumventing cognitive shortcuts and mitigating bias.

An update on the pharmacological management of acute respiratory distress syndrome

INTRODUCTION

Acute respiratory distress syndrome (ARDS) is characterized by acute inflammatory injury to the lungs, alterations in vascular permeability, loss of aerated tissue, bilateral infiltrates, and refractory hypoxemia. ARDS is considered a heterogeneous syndrome, which complicates the search for effective therapies. The goal of this review is to provide an update on the pharmacological management of ARDS.

AREAS COVERED

The difficulties in finding effective pharmacological therapies are mainly due to the challenges in designing clinical trials for this unique, varied population of critically ill patients. Recently, some trials have been retrospectively analyzed by dividing patients into hyper-inflammatory and hypo-inflammatory sub-phenotypes. This approach has led to significant outcome improvements with some pharmacological treatments that previously failed to demonstrate efficacy, which suggests that a more precise selection of ARDS patients for clinical trials could be the key to identifying effective pharmacotherapies. This review is provided after searching the main studies on this topics on the PubMed and clinicaltrials.gov databases.

EXPERT OPINION

The future of ARDS therapy lies in precision medicine, innovative approaches to drug delivery, immunomodulation, cell-based therapies, and robust clinical trial designs. These should lead to more effective and personalized treatments for patients with ARDS.

Imaging Reactive Oxygen Radicals in Excised Mouse Lung Trapped by Reaction with Hydroxylamine Probes Using 1 GHz Rapid Scan Electron Paramagnetic Resonance

PURPOSE

Oxidative stress is proposed to be critical in acute lung disease, but methods to monitor radicals in lungs are lacking. Our goal is to develop low-frequency electron paramagnetic resonance (EPR) methods to monitor radicals that contribute to the disease.

PROCEDURES

Free radicals generated in a lipopolysaccharide-induced mouse model of acute respiratory distress syndrome reacted with cyclic hydroxylamines CPH (1-hydroxy-3-carboxy-2,2,5,5-tetramethylpyrrolidine hydrochloride) and DCP-AM-H (4-acetoxymethoxycarbonyl-1-hydroxy-2,2,5,5-tetramethylpyrrolidine-3-carboxylic acid), which were converted into the corresponding nitroxide radicals, CP• and DCP•. The EPR signals of the nitroxide radicals in excised lungs were imaged with a 1 GHz EPR spectrometer/imager that employs rapid scan technology.

RESULTS

The small numbers of nitroxides formed by reaction of the hydroxylamine with superoxide result in low signal-to-noise in the spectra and images. However, since the spectral properties of the nitroxides are known, we can use prior knowledge of the line shape and hyperfine splitting to fit the noisy data, yielding well-defined spectra and images. Two-dimensional spectral-spatial images are shown for lung samples containing (4.5 ± 0.5) ×1014 CP• and (9.9 ± 1.0) ×1014 DCP• nitroxide spins. These results suggest that a probe that accumulates in cells gives a stronger nitroxide signal than a probe that is more easily washed out of cells.

CONCLUSION

The nitroxide radicals in excised mouse lungs formed by reaction with hydroxylamine probes CPH and DCP-AM-H can be imaged at 1 GHz.

Use of Electron Paramagnetic Resonance (EPR) to Evaluate Redox Status in a Preclinical Model of Acute Lung Injury

PURPOSE

Patients with hyper- vs. hypo-inflammatory subphenotypes of acute respiratory distress syndrome (ARDS) exhibit different clinical outcomes. Inflammation increases the production of reactive oxygen species (ROS) and increased ROS contributes to the severity of illness. Our long-term goal is to develop electron paramagnetic resonance (EPR) imaging of lungs in vivo to precisely measure superoxide production in ARDS in real time. As a first step, this requires the development of in vivo EPR methods for quantifying superoxide generation in the lung during injury, and testing if such superoxide measurements can differentiate between susceptible and protected mouse strains.

PROCEDURES

In WT mice, mice lacking total body extracellular superoxide dismutase (EC-SOD) (KO), or mice overexpressing lung EC-SOD (Tg), lung injury was induced with intraperitoneal (IP) lipopolysaccharide (LPS) (10 mg/kg). At 24 h after LPS treatment, mice were injected with the cyclic hydroxylamines 1-hydroxy-3-carboxy-2,2,5,5-tetramethylpyrrolidine hydrochloride (CPH) or 4-acetoxymethoxycarbonyl-1-hydroxy-2,2,5,5-tetramethylpyrrolidine-3-carboxylic acid (DCP-AM-H) probes to detect, respectively, cellular and mitochondrial ROS - specifically superoxide. Several probe delivery strategies were tested. Lung tissue was collected up to one hour after probe administration and assayed by EPR.

RESULTS

As measured by X-band EPR, cellular and mitochondrial superoxide increased in the lungs of LPS-treated mice compared to control. Lung cellular superoxide was increased in EC-SOD KO mice and decreased in EC-SOD Tg mice compared to WT. We also validated an intratracheal (IT) delivery method, which enhanced the lung signal for both spin probes compared to IP administration.

CONCLUSIONS

We have developed protocols for delivering EPR spin probes in vivo, allowing detection of cellular and mitochondrial superoxide in lung injury by EPR. Superoxide measurements by EPR could differentiate mice with and without lung injury, as well as mouse strains with different disease susceptibilities. We expect these protocols to capture real-time superoxide production and enable evaluation of lung EPR imaging as a potential clinical tool for subphenotyping ARDS patients based on redox status.

Patient stratification using plasma cytokines and their regulators in sepsis: relationship to outcomes, treatment effect and leucocyte transcriptomic subphenotypes

RATIONALE

Heterogeneity of the host response within sepsis, acute respiratory distress syndrome (ARDS) and more widely critical illness, limits discovery and targeting of immunomodulatory therapies. Clustering approaches using clinical and circulating biomarkers have defined hyper-inflammatory and hypo-inflammatory subphenotypes in ARDS associated with differential treatment response. It is unknown if similar subphenotypes exist in sepsis populations where leucocyte transcriptomic-defined subphenotypes have been reported.

OBJECTIVES

We investigated whether inflammatory clusters based on cytokine protein abundance were seen in sepsis, and the relationships with previously described transcriptomic subphenotypes.

METHODS

Hierarchical cluster and latent class analysis were applied to an observational study (UK Genomic Advances in Sepsis (GAinS)) (n=124 patients) and two clinical trial datasets (VANISH, n=155 and LeoPARDS, n=484) in which the plasma protein abundance of 65, 21, 11 circulating cytokines, cytokine receptors and regulators were quantified. Clinical features, outcomes, response to trial treatments and assignment to transcriptomic subphenotypes were compared between inflammatory clusters.

MEASUREMENTS AND MAIN RESULTS

We identified two (UK GAinS, VANISH) or three (LeoPARDS) inflammatory clusters. A group with high levels of pro-inflammatory and anti-inflammatory cytokines was seen that was associated with worse organ dysfunction and survival. No interaction between inflammatory clusters and trial treatment response was found. We found variable overlap of inflammatory clusters and leucocyte transcriptomic subphenotypes.

CONCLUSIONS

These findings demonstrate that differences in response at the level of cytokine biology show clustering related to severity, but not treatment response, and may provide complementary information to transcriptomic sepsis subphenotypes.

TRIAL REGISTRATION NUMBER

ISRCTN20769191, ISRCTN12776039.

Endotyping in ARDS: one step forward in precision medicine

BACKGROUND

The Berlin definition of acute respiratory distress syndrome (ARDS) includes only clinical characteristics. Understanding unique patient pathobiology may allow personalized treatment. We aimed to define and describe ARDS phenotypes/endotypes combining clinical and pathophysiologic parameters from a Canadian ARDS cohort.

METHODS

A cohort of adult ARDS patients from multiple sites in Calgary, Canada, had plasma cytokine levels and clinical parameters measured in the first 24 h of ICU admission. We used a latent class model (LCM) to group the patients into several ARDS subgroups and identified the features differentiating those subgroups. We then discuss the subgroup effect on 30 day mortality.

RESULTS

The LCM suggested three subgroups (n1 = 64, n2 = 86, and n3 = 30), and 23 out of 69 features made these subgroups distinct. The top five discriminating features were IL-8, IL-6, IL-10, TNF-a, and serum lactate. Mortality distinctively varied between subgroups. Individual clinical characteristics within the subgroup associated with mortality included mean PaO2/FiO2 ratio, pneumonia, platelet count, and bicarbonate negatively associated with mortality, while lactate, creatinine, shock, chronic kidney disease, vasopressor/ionotropic use, low GCS at admission, and sepsis were positively associated. IL-8 and Apache II were individual markers strongly associated with mortality (Area Under the Curve = 0.84).

PERSPECTIVE

ARDS subgrouping using biomarkers and clinical characteristics is useful for categorizing a heterogeneous condition into several homogenous patient groups. This study found three ARDS subgroups using LCM; each subgroup has a different level of mortality. This model may also apply to developing further trial design, prognostication, and treatment selection.

Latent class analysis of chest CT abnormalities to define subphenotypes in patients with MPO-ANCA-positive microscopic polyangiitis

BACKGROUND

Patients with microscopic polyangiitis (MPA) and positive myeloperoxidase antineutrophil cytoplasmic antibody (MPO-ANCA) may present with various abnormalities in chest computed tomography (CT). This study aimed to identify subphenotypes using latent class analysis (LCA) and to explore the relationship between the subphenotypes and clinical patterns, as well as compare the clinical characteristics of these subphenotypes in patients with MPO-ANCA-positive MPA (MPO-MPA).

METHODS

The study identified subphenotypes using LCA based on chest CT findings in 178 patients with MPO-MPA and pulmonary involvement from June 2014 to August 2022.

RESULTS

LCA identified 27 participants (15.2%) in class 1, 43 (24.1%) in class 2, 35 (19.7%) in class 3, and 73 (41.0%) in class 4. Class 1 was characterized by prominent inflammatory exudation, class 2 by fibrosis and architectural distortion, class 3 by predominantly bronchiectasis, and class 4 by lesions mixed with inflammation and fibrosis. Class 1 had the highest level of extrapulmonary disease activity, with 77.8% of patients experiencing diffuse alveolar hemorrhage. Class 2 had the lowest level of extrapulmonary disease activity, with 41.9% of patients showing usual interstitial pneumonia. Class 3 patients were more likely to have complications involving the ear, nose, and throat, as well as pulmonary infections before treatment, and they exhibited the best outcomes. The characteristics and outcomes of class 4 were intermediate among the four classes.

CONCLUSIONS

These findings suggest that bronchiectasis may represent a unique pattern of pulmonary involvement in MPO-MPA, highlighting the importance of screening for bronchiectasis in MPO-MPA and identifying optimal management strategies.

Heterogeneity of immune cells and their communications unveiled by transcriptome profiling in acute inflammatory lung injury

Background

Acute Respiratory Distress Syndrome (ARDS) or its earlier stage Acute lung injury (ALI), is a worldwide health concern that jeopardizes human well-being. Currently, the treatment strategies to mitigate the incidence and mortality of ARDS are severely restricted. This limitation can be attributed, at least in part, to the substantial variations in immunity observed in individuals with this syndrome.

Methods

Bulk and single cell RNA sequencing from ALI mice and single cell RNA sequencing from ARDS patients were analyzed. We utilized the Seurat program package in R and cellmarker 2.0 to cluster and annotate the data. The differential, enrichment, protein interaction, and cell-cell communication analysis were conducted.

Results

The mice with ALI caused by pulmonary and extrapulmonary factors demonstrated differential expression including Clec4e, Retnlg, S100a9, Coro1a, and Lars2. We have determined that inflammatory factors have a greater significance in extrapulmonary ALI, while multiple pathways collaborate in the development of pulmonary ALI. Clustering analysis revealed significant heterogeneity in the relative abundance of immune cells in different ALI models. The autocrine action of neutrophils plays a crucial role in pulmonary ALI. Additionally, there was a significant increase in signaling intensity between B cells and M1 macrophages, NKT cells and M1 macrophages in extrapulmonary ALI. The CXCL, CSF3 and MIF, TGFβ signaling pathways play a vital role in pulmonary and extrapulmonary ALI, respectively. Moreover, the analysis of human single-cell revealed DCs signaling to monocytes and neutrophils in COVID-19-associated ARDS is stronger compared to sepsis-related ARDS. In sepsis-related ARDS, CD8+ T and Th cells exhibit more prominent signaling to B-cell nucleated DCs. Meanwhile, both MIF and CXCL signaling pathways are specific to sepsis-related ARDS.

Conclusion

This study has identified specific gene signatures and signaling pathways in animal models and human samples that facilitate the interaction between immune cells, which could be targeted therapeutically in ARDS patients of various etiologies.

Inflammatory and tissue injury marker dynamics in pediatric acute respiratory distress syndrome

BACKGROUNDThe molecular signature of pediatric acute respiratory distress syndrome (ARDS) is poorly described, and the degree to which hyperinflammation or specific tissue injury contributes to outcomes is unknown. Therefore, we profiled inflammation and tissue injury dynamics over the first 7 days of ARDS, and associated specific biomarkers with mortality, persistent ARDS, and persistent multiple organ dysfunction syndrome (MODS).METHODSIn a single-center prospective cohort of intubated pediatric patients with ARDS, we collected plasma on days 0, 3, and 7. Nineteen biomarkers reflecting inflammation, tissue injury, and damage-associated molecular patterns (DAMPs) were measured. We assessed the relationship between biomarkers and trajectories with mortality, persistent ARDS, or persistent MODS using multivariable mixed effect models.RESULTSIn 279 patients (64 [23%] nonsurvivors), hyperinflammatory cytokines, tissue injury markers, and DAMPs were higher in nonsurvivors. Survivors and nonsurvivors showed different biomarker trajectories. IL-1α, soluble tumor necrosis factor receptor 1, angiopoietin 2 (ANG2), and surfactant protein D increased in nonsurvivors, while DAMPs remained persistently elevated. ANG2 and procollagen type III N-terminal peptide were associated with persistent ARDS, whereas multiple cytokines, tissue injury markers, and DAMPs were associated with persistent MODS. Corticosteroid use did not impact the association of biomarker levels or trajectory with mortality.CONCLUSIONSPediatric ARDS survivors and nonsurvivors had distinct biomarker trajectories, with cytokines, endothelial and alveolar epithelial injury, and DAMPs elevated in nonsurvivors. Mortality markers overlapped with markers associated with persistent MODS, rather than persistent ARDS.FUNDINGNIH (K23HL-136688, R01-HL148054).

Association between Age and Mortality in Pediatric and Adult Acute Respiratory Distress Syndrome

Rationale: The epidemiology, management, and outcomes of acute respiratory distress syndrome (ARDS) differ between children and adults, with lower mortality rates in children despite comparable severity of hypoxemia. However, the relationship between age and mortality is unclear.Objective: We aimed to define the association between age and mortality in ARDS, hypothesizing that it would be nonlinear.Methods: We performed a retrospective cohort study using data from two pediatric ARDS observational cohorts (n = 1,236), multiple adult ARDS trials (n = 5,547), and an adult observational ARDS cohort (n = 1,079). We aligned all datasets to meet Berlin criteria. We performed unadjusted and adjusted logistic regression using fractional polynomials to assess the potentially nonlinear relationship between age and 90-day mortality, adjusting for sex, PaO2/FiO2, immunosuppressed status, year of study, and observational versus randomized controlled trial, treating each individual study as a fixed effect.Measurements and Main Results: There were 7,862 subjects with median ages of 4 years in the pediatric cohorts, 52 years in the adult trials, and 61 years in the adult observational cohort. Most subjects (43%) had moderate ARDS by Berlin criteria. Ninety-day mortality was 19% in the pediatric cohorts, 33% in the adult trials, and 67% in the adult observational cohort. We found a nonlinear relationship between age and mortality, with mortality risk increasing at an accelerating rate between 11 and 65 years of age, after which mortality risk increased more slowly.Conclusions: There was a nonlinear relationship between age and mortality in pediatric and adult ARDS.

Molecular Phenotypes of Acute Respiratory Distress Syndrome in the ROSE Trial Have Differential Outcomes and Gene Expression Patterns That Differ at Baseline and Longitudinally over Time

Rationale: Two molecular phenotypes have been identified in acute respiratory distress syndrome (ARDS). In the ROSE (Reevaluation of Systemic Early Neuromuscular Blockade) trial of cisatracurium in moderate to severe ARDS, we addressed three unanswered questions: 1) Do the same phenotypes emerge in a more severe ARDS cohort with earlier recruitment; 2) Do phenotypes respond differently to neuromuscular blockade? and 3) What biological pathways most differentiate inflammatory phenotypes?Methods: We performed latent class analysis in ROSE using preenrollment clinical and protein biomarkers. In a subset of patients (n = 134), we sequenced whole-blood RNA using enrollment and Day 2 samples and performed differential gene expression and pathway analyses. Informed by the differential gene expression analysis, we measured additional plasma proteins and evaluated their abundance relative to gene expression amounts.Measurements and Main Results: In ROSE, we identified the hypoinflammatory (60.4%) and hyperinflammatory (39.6%) phenotypes with similar biological and clinical characteristics as prior studies, including higher mortality at Day 90 for the hyperinflammatory phenotype (30.3% vs. 61.6%; P < 0.0001). We observed no treatment interaction between the phenotypes and randomized groups for mortality. The hyperinflammatory phenotype was enriched for genes associated with innate immune response, tissue remodeling, and zinc metabolism at Day 0 and collagen synthesis and neutrophil degranulation at Day 2. Longitudinal changes in gene expression patterns differed dependent on survivorship. For most highly expressed genes, we observed correlations with their corresponding plasma proteins' abundance. However, for the class-defining plasma proteins in the latent class analysis, no correlation was observed with their corresponding genes' expression.Conclusions: The hyperinflammatory and hypoinflammatory phenotypes have different clinical, protein, and dynamic transcriptional characteristics. These findings support the clinical and biological potential of molecular phenotypes to advance precision care in ARDS.

Pharmacologic Treatments in Acute Respiratory Failure

Acute respiratory failure relies on supportive care using non-invasive and invasive oxygen and ventilatory support. Pharmacologic therapies for the most severe form of respiratory failure, acute respiratory distress syndrome (ARDS), are limited. This review focuses on the most promising therapies for ARDS, targeting different mechanisms that contribute to dysregulated inflammation and resultant hypoxemia. Significant heterogeneity exists within the ARDS population. Treatment requires prompt recognition of ARDS and an understanding of which patients may benefit most from specific pharmacologic interventions. The key to finding effective pharmacotherapies for ARDS may rely on deeper understanding of pathophysiology and bedside identification of ARDS subphenotypes.

Host and Microbe Blood Metagenomics Reveals Key Pathways Characterizing Critical Illness Phenotypes

Rationale: Two molecular phenotypes of sepsis and acute respiratory distress syndrome, termed hyperinflammatory and hypoinflammatory, have been consistently identified by latent class analysis in numerous cohorts, with widely divergent clinical outcomes and differential responses to some treatments; however, the key biological differences between these phenotypes remain poorly understood.Objectives: We used host and microbe metagenomic sequencing data from blood to deepen our understanding of biological differences between latent class analysis-derived phenotypes and to assess concordance between the latent class analysis-derived phenotypes and phenotypes reported by other investigative groups (e.g., Sepsis Response Signature [SRS1-2], molecular diagnosis and risk stratification of sepsis [MARS1-4], reactive and uninflamed).Methods: We analyzed data from 113 patients with hypoinflammatory sepsis and 76 patients with hyperinflammatory sepsis enrolled in a two-hospital prospective cohort study. Molecular phenotypes had been previously assigned using latent class analysis.Measurements and Main Results: The hyperinflammatory and hypoinflammatory phenotypes of sepsis had distinct gene expression signatures, with 5,755 genes (31%) differentially expressed. The hyperinflammatory phenotype was associated with elevated expression of innate immune response genes, whereas the hypoinflammatory phenotype was associated with elevated expression of adaptive immune response genes and, notably, T cell response genes. Plasma metagenomic analysis identified differences in prevalence of bacteremia, bacterial DNA abundance, and composition between the phenotypes, with an increased presence and abundance of Enterobacteriaceae in the hyperinflammatory phenotype. Significant overlap was observed between these phenotypes and previously identified transcriptional subtypes of acute respiratory distress syndrome (reactive and uninflamed) and sepsis (SRS1-2). Analysis of data from the VANISH trial indicated that corticosteroids might have a detrimental effect in patients with the hypoinflammatory phenotype.Conclusions: The hyperinflammatory and hypoinflammatory phenotypes have distinct transcriptional and metagenomic features that could be leveraged for precision treatment strategies.

Acute Respiratory Distress Syndrome: Definition, Diagnosis, and Routine Management

Acute respiratory distress syndrome (ARDS) is an acute inflammatory lung injury characterized by severe hypoxemic respiratory failure, bilateral opacities on chest imaging, and low lung compliance. ARDS is a heterogeneous syndrome that is the common end point of a wide variety of predisposing conditions, with complex pathophysiology and underlying mechanisms. Routine management of ARDS is centered on lung-protective ventilation strategies such as low tidal volume ventilation and targeting low airway pressures to avoid exacerbation of lung injury, as well as a conservative fluid management strategy.

Investigational pharmacological agents for the treatment of ARDS

INTRODUCTION

Acute Respiratory Distress Syndrome (ARDS) is a heterogeneous form of lung injury with severe hypoxemia and bilateral infiltrates after an inciting event that results in diffuse lung inflammation with a high mortality rate. While research in COVID-related ARDS has resulted in several pharmacotherapeutic agents that have undergone successful investigation, non-COVID ARDS studies have not resulted in many widely accepted pharmacotherapeutic agents despite exhaustive research.

AREAS COVERED

The aim of this review is to discuss adjuvant pharmacotherapies targeting non-COVID Acute Lung Injury (ALI)/ARDS and novel therapeutics in COVID associated ALI/ARDS. In ARDS, variable data may support selective use of neuromuscular blocking agents, corticosteroids and neutrophil elastase inhibitors, but are not yet universally used. COVID-ALI/ARDS has data supporting the use of IL-6 monoclonal antibodies, corticosteroids, and JAK inhibitor therapy.

EXPERT OPINION

Although ALI/ARDS modifying pharmacological agents have been identified in COVID-related disease, the data in non-COVID ALI/ARDS has been less compelling. The increased use of more specific molecular phenotyping based on physiologic parameters and biomarkers, will ensure equipoise between groups, and will likely allow more precision in confirming pharmacological agent efficacy in future studies.

Novel subtypes of severe COVID-19 respiratory failure based on biological heterogeneity: a secondary analysis of a randomized controlled trial

BACKGROUND

Despite evidence associating inflammatory biomarkers with worse outcomes in hospitalized adults with COVID-19, trials of immunomodulatory therapies have met with mixed results, likely due in part to biological heterogeneity of participants. Latent class analysis (LCA) of clinical and protein biomarker data has identified two subtypes of non-COVID acute respiratory distress syndrome (ARDS) with different clinical outcomes and treatment responses. We studied biological heterogeneity and clinical outcomes in a multi-institutional platform randomized controlled trial of adults with severe COVID-19 hypoxemic respiratory failure (I-SPY COVID).

METHODS

Clinical and plasma protein biomarker data were analyzed from 400 trial participants enrolled from September 2020 until October 2021 with severe COVID-19 requiring ≥ 6 L/min supplemental oxygen. Seventeen hypothesis-directed protein biomarkers were measured at enrollment using multiplex Luminex panels or single analyte enzyme linked immunoassay methods (ELISA). Biomarkers and clinical variables were used to test for latent subtypes and longitudinal biomarker changes by subtype were explored. A validated parsimonious model using interleukin-8, bicarbonate, and protein C was used for comparison with non-COVID hyper- and hypo-inflammatory ARDS subtypes.

RESULTS

Average participant age was 60 ± 14 years; 67% were male, and 28-day mortality was 25%. At trial enrollment, 85% of participants required high flow oxygen or non-invasive ventilation, and 97% were receiving dexamethasone. Several biomarkers of inflammation (IL-6, IL-8, IL-10, sTNFR-1, TREM-1), epithelial injury (sRAGE), and endothelial injury (Ang-1, thrombomodulin) were associated with 28- and 60-day mortality. Two latent subtypes were identified. Subtype 2 (27% of participants) was characterized by persistent derangements in biomarkers of inflammation, endothelial and epithelial injury, and disordered coagulation and had twice the mortality rate compared with Subtype 1. Only one person was classified as hyper-inflammatory using the previously validated non-COVID ARDS model.

CONCLUSIONS

We discovered evidence of two novel biological subtypes of severe COVID-19 with significantly different clinical outcomes. These subtypes differed from previously established hyper- and hypo-inflammatory non-COVID subtypes of ARDS. Biological heterogeneity may explain inconsistent findings from trials of hospitalized patients with COVID-19 and guide treatment approaches.

An appraisal of lung computer tomography in very early anti-inflammatory treatment of two different ovine ARDS phenotypes

Mortality and morbidity of Acute Respiratory Distress Syndrome (ARDS) are largely unaltered. A possible new approach to treatment of ARDS is offered by the discovery of inflammatory subphenotypes. In an ovine model of ARDS phenotypes, matching key features of the human subphenotypes, we provide an imaging characterization using computer tomography (CT). Nine animals were randomized into (a) OA (oleic acid, hypoinflammatory; n = 5) and (b) OA-LPS (oleic acid and lipopolysaccharides, hyperinflammatory; n = 4). 48 h after ARDS induction and anti-inflammatory treatment, CT scans were performed at high (H) and then low (L) airway pressure. After CT, the animals were euthanized and lung tissue was collected. OA-LPS showed a higher air fraction and OA a higher tissue fraction, resulting in more normally aerated lungs in OA-LPS in contrast to more non-aerated lung in OA. The change in lung and air volume between H and L was more accentuated in OA-LPS, indicating a higher recruitment potential. Strain was higher in OA, indicating a higher level of lung damage, while the amount of lung edema and histological lung injury were largely comparable. Anti-inflammatory treatment might be beneficial in terms of overall ventilated lung portion and recruitment potential, especially in the OA-LPS group.

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 of molecular subphenotypes in two cohorts of paediatric ARDS

BACKGROUND

Two subphenotypes of acute respiratory distress syndrome (ARDS), hypoinflammatory and hyperinflammatory, have been reported in adults and in a single paediatric cohort. The relevance of these subphenotypes in paediatrics requires further investigation. We aimed to identify subphenotypes in two large observational cohorts of paediatric ARDS and assess their congruence with prior descriptions.

METHODS

We performed latent class analysis (LCA) separately on two cohorts using biomarkers as inputs. Subphenotypes were compared on clinical characteristics and outcomes. Finally, we assessed overlap with adult cohorts using parsimonious classifiers.

FINDINGS

In two cohorts from the Children's Hospital of Philadelphia (n=333) and from a multicentre study based at the University of California San Francisco (n=293), LCA identified two subphenotypes defined by differential elevation of biomarkers reflecting inflammation and endotheliopathy. In both cohorts, hyperinflammatory subjects had greater illness severity, more sepsis and higher mortality (41% and 28% in hyperinflammatory vs 11% and 7% in hypoinflammatory). Both cohorts demonstrated overlap with adult subphenotypes when assessed using parsimonious classifiers.

INTERPRETATION

We identified hypoinflammatory and hyperinflammatory subphenotypes of paediatric ARDS from two separate cohorts with utility for prognostic and potentially predictive, enrichment. Future paediatric ARDS trials should identify and leverage biomarker-defined subphenotypes in their analysis.

COAGULOPATHY PARAMETERS PREDICTIVE OF OUTCOMES IN SEPSIS-INDUCED ACUTE RESPIRATORY DISTRESS SYNDROME: A SUBANALYSIS OF THE TWO PROSPECTIVE MULTICENTER COHORT STUDIES

ABSTRACT

Background: Although coagulopathy is often observed in acute respiratory distress syndrome (ARDS), its clinical impact remains poorly understood. Objectives: This study aimed to clarify the coagulopathy parameters that are clinically applicable for prognostication and to determine anticoagulant indications in sepsis-induced ARDS. Method: This study enrolled patients with sepsis-derived ARDS from two nationwide multicenter, prospective observational studies. We explored coagulopathy parameters that could predict outcomes in the Focused Outcome Research on Emergency Care for Acute Respiratory Distress Syndrome, Sepsis, and Trauma (FORECAST) cohort, and the defined coagulopathy criteria were validated in the Sepsis Prognostication in Intensive Care Unit and Emergency Room-Intensive Care Unit (SPICE-ICU) cohort. The correlation between anticoagulant use and outcomes was also evaluated. Results: A total of 181 patients with sepsis-derived ARDS in the FORECAST study and 61 patients in the SPICE-ICU study were included. In a preliminary study, we found the set of prothrombin time-international normalized ratio ≥1.4 and platelet count ≤12 × 10 4 /μL, and thrombocytopenia and elongated prothrombin time (TEP) coagulopathy as the best coagulopathy parameters and used it for further analysis; the odds ratio (OR) of TEP coagulopathy for in-hospital mortality adjusted for confounding was 3.84 (95% confidence interval [CI], 1.66-8.87; P = 0.005). In the validation cohort, the adjusted OR for in-hospital mortality was 32.99 (95% CI, 2.60-418.72; P = 0.002). Although patients without TEP coagulopathy showed significant improvements in oxygenation over the first 4 days, patients with TEP coagulopathy showed no significant improvement (ΔPaO 2 /FiO 2 ratio, 24 ± 20 vs. 90 ± 9; P = 0.026). Furthermore, anticoagulant use was significantly correlated with mortality and oxygenation recovery in patients with TEP coagulopathy but not in patients without TEP coagulopathy. Conclusion: Thrombocytopenia and elongated prothrombin time coagulopathy is closely associated with better outcomes and responses to anticoagulant therapy in sepsis-induced ARDS, and our coagulopathy criteria may be clinically useful.