Identification and validation of distinct biological phenotypes in patients with acute respiratory distress syndrome by cluster analysis

Accuracy study of Angiotensin 1-7 composite index test to predict pulmonary fibrosis and guide treatment

BACKGROUND

Pulmonary fibrosis can develop after acute respiratory distress syndrome (ARDS). The hypothesis is we are able to measure phenotypes that lie at the origin of ARDS severity and fibrosis development. The aim is an accuracy study of prognostic circulating biomarkers.

METHODS

A longitudinal study followed COVID-related ARDS patients with medical imaging, pulmonary function tests and biomarker analysis, generating 444 laboratory data. Comparison to controls used non-parametrical statistics; p < 0·05 was considered significant. Cut-offs were obtained through receiver operating curve. Contingency tables revealed predictive values. Odds ratio was calculated through logistic regression.

RESULTS

Angiotensin 1-7 beneath 138 pg/mL defined Angiotensin imbalance phenotype. Hyper-inflammatory phenotype showed a composite index test above 34, based on high Angiotensin 1-7, C-Reactive Protein, Ferritin and Transforming Growth Factor-β. Analytical study showed conformity to predefined goals. Clinical performance gave a positive predictive value of 95 % (95 % confidence interval, 82 %-99 %), and a negative predictive value of 100 % (95 % confidence interval, 65 %-100 %). Those severe ARDS phenotypes represented 34 (Odds 95 % confidence interval, 3-355) times higher risk for pulmonary fibrosis development (p < 0·001).

CONCLUSIONS

Angiotensin 1-7 composite index is an early and objective predictor of ARDS evolving to pulmonary fibrosis. It may guide therapeutic decisions in targeted phenotypes.

Longitudinal analysis of ARDS variability and biomarker predictive power in burn patients

Acute Respiratory Distress Syndrome (ARDS) is a severe complication in burn patients, characterized by rapid lung inflammation and hypoxemia. Managing ARDS is challenging due to its high mortality rates and the complex interplay of various pathophysiological factors. This study aims to investigate the heterogeneity of ARDS in burn patients admitted to the Intensive Care Unit (ICU) and evaluate the predictive efficacy of biomarkers in comparison to the PaO2/FiO2 (PF) ratio. A retrospective cohort study was conducted at the Burn Intensive Care Unit of Hangang Sacred Heart Hospital in Seoul, Korea, from July 2010 to December 2022, involving 2,318 patients. Longitudinal k-means clustering was employed to identify patient subgroups based on PF ratio trajectories. Biomarkers, including albumin, white blood cell (WBC) count, and lactate, were assessed for their predictive accuracy using multivariable logistic regression, area under the curve (AUC) analysis, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Four distinct patient clusters were identified. Cluster A exhibited the highest mortality rate at 54.9%, while Cluster D showed the lowest at 7.9%. Albumin demonstrated significantly higher predictive accuracy in Cluster A (AUC: 0.905, NRI: 0.421) and Cluster C (AUC: 0.915, NRI: 0.845), outperforming the PF ratio in both groups. However, in Clusters B and D, biomarkers did not significantly improve upon the predictive power of the PF ratio. Across all clusters, the integration of biomarkers with the PF ratio led to modest improvements but did not consistently outperform the PF ratio as a standalone predictor. This study reveals substantial heterogeneity in ARDS progression among burn patients, with varying mortality rates and biomarker efficacy across clusters. While biomarkers such as albumin showed potential in specific subgroups, their overall contribution to predictive accuracy was limited. Further multicenter, prospective studies are required to validate these findings and develop more refined predictive models. Personalized treatment strategies, based on biomarker profiles and traditional clinical metrics, could enhance ARDS management in burn patients.

Personalized ventilation adjustment in ARDS: A systematic review and meta-analysis of image, driving pressure, transpulmonary pressure, and mechanical power

BACKGROUND

Acute Respiratory Distress Syndrome (ARDS) necessitates personalized treatment strategies due to its heterogeneity, aiming to mitigate Ventilator-Induced Lung Injury (VILI). Advanced monitoring techniques, including imaging, driving pressure, transpulmonary pressure, and mechanical power, present potential avenues for tailored interventions.

OBJECTIVE

To review some of the most important techniques for achieving greater personalization of mechanical ventilation in ARDS patients as evaluated in randomized clinical trials, by analyzing their effect on three clinically relevant aspects: mortality, ventilator-free days, and gas exchange.

METHODS

Following PRISMA guidelines, we conducted a systematic review and meta-analysis of Randomized Clinical Trials (RCTs) involving adult ARDS patients undergoing personalized ventilation adjustments. Outcomes were mortality (primary end-point), ventilator-free days, and oxygenation improvement.

RESULTS

Among 493 identified studies, 13 RCTs (n = 1255) met inclusion criteria. No personalized ventilation strategy demonstrated superior outcomes compared to traditional protocols. Meta-analysis revealed no significant reduction in mortality with image-guided (RR 0.88, 95 % CI 0.70-1.11), driving pressure-guided (RR 0.61, 95 % CI 0.29-1.30), or transpulmonary pressure-guided (RR 0.85, 95 % CI 0.58-1.24) strategies. Ventilator-free days and oxygenation outcomes showed no significant differences.

CONCLUSION

Our study does not support the superiority of personalized ventilation techniques over traditional protocols in ARDS patients. Further research is needed to standardize ventilation strategies and determine their impact on mechanical ventilation outcomes.

Glucocorticoid therapy for acute respiratory distress syndrome: Current concepts

Acute respiratory distress syndrome (ARDS), a fatal critical disease, is induced by various insults. ARDS represents a major global public health burden, and the management of ARDS continues to challenge healthcare systems globally, especially during the pandemic of the coronavirus disease 2019 (COVID-19). There remains no confirmed specific pharmacotherapy for ARDS, despite advances in understanding its pathophysiology. Debate continues about the potential role of glucocorticoids (GCs) as a promising ARDS clinical therapy. Questions regarding GC agent, dose, and duration in patients with ARDS need to be answered, because of substantial variations in GC administration regimens across studies. ARDS heterogeneity likely affects the therapeutic actions of exogenous GCs. This review includes progress in determining the GC mechanisms of action and clinical applications in ARDS, especially during the COVID-19 pandemic.

Developing a predictive nomogram for mortality in patients with extrapulmonary acute respiratory distress syndrome: the prognostic value of serum soluble thrombomodulin, lung ultrasound score, and lactate

Objective: This study aimed to elucidate the prognostic significance of serum soluble thrombomodulin (sTM), lung ultrasound score (LUS), and lactate levels in patients with extrapulmonary acute respiratory distress syndrome (ARDS), with the goal of refining mortality risk prediction in this cohort. Methods: In a prospective cohort of 95 patients with extrapulmonary ARDS admitted to the intensive care unit, we investigated the primary endpoint of 28-day mortality. Utilizing Lasso-Cox regression analysis, we identified independent prognostic factors for mortality. A predictive nomogram was developed incorporating these factors, and its performance was validated through several statistical measures, including the consistency index, calibration plot, internal validation curve, decision curve analysis, interventions avoided analysis, receiver operating characteristic curve analysis, and Kaplan-Meier survival analysis. We further conducted a subgroup analysis to examine the impact of prone positioning on patient outcomes. Results: The study identified baseline serum sTM, LUS, and lactate levels as independent predictors of 28-day mortality in extrapulmonary ARDS patients. The predictive nomogram demonstrated superior prognostic accuracy compared to the use of sTM, LUS, or lactate levels alone, and outperformed traditional prognostic tools such as the Acute Physiology and Chronic Health Evaluation II score and the partial pressure of arterial oxygen to fractional inspired oxygen ratio. The subgroup analysis did not show a significant impact of prone positioning on the predictive value of the identified biomarkers. Conclusion: Our study results support the development and validation of a novel prognostic nomogram that integrates key clinical biomarkers and ultrasound imaging scores to predict mortality in patients with extrapulmonary ARDS. While our research is preliminary, further studies and validation are required.

Prehospital Post-Resuscitation Vital Sign Phenotypes are Associated with Outcomes Following Out-of-Hospital Cardiac Arrest

OBJECTIVES

The use of machine learning to identify patient 'clusters' using post-return of spontaneous circulation (ROSC) vital signs may facilitate the identification of patient subgroups at high risk of rearrest and mortality. Our objective was to use k-means clustering to identify post-ROSC vital sign clusters and determine whether these clusters were associated with rearrest and mortality.

METHODS

The ESO Data Collaborative 2018-2022 datasets were used for this study. We included adult, non-traumatic OHCA patients with >2 post-ROSC vital sign sets. Patients were excluded if they had an EMS-witnessed OHCA or were encountered during an interfacility transfer. Unsupervised (k-means) clustering was performed using minimum, maximum, and delta (last minus first) systolic blood pressure (BP), heart rate, SpO2, shock index, and pulse pressure. The assessed outcomes were mortality and rearrest. To explore the association between rearrest, mortality, and cluster, multivariable logistic regression modeling was used.

RESULTS

Within our cohort of 12,320 patients, five clusters were identified. Patients in cluster 1 were hypertensive, patients in cluster 2 were normotensive, patients in cluster 3 were hypotensive and tachycardic (n = 2164; 17.6%), patients in cluster 4 were hypoxemic and exhibited increasing systolic BP, and patients in cluster 5 were severely hypoxemic and exhibited a declining systolic BP. The overall proportion of patients who experienced mortality stratified by cluster was 63.4% (c1), 68.1% (c2), 78.8% (c3), 84.8% (c4), and 86.6% (c5). In comparison to the cluster with the lowest mortality (c1), each other cluster was associated with greater odds of mortality and rearrest.

CONCLUSIONS

Unsupervised k-means clustering yielded 5 post-ROSC vital sign clusters that were associated with rearrest and mortality.

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.

Identification and transcriptomic assessment of latent profile pediatric septic shock phenotypes

BACKGROUND

Sepsis poses a grave threat, especially among children, but treatments are limited owing to heterogeneity among patients. We sought to test the clinical and biological relevance of pediatric septic shock subclasses identified using reproducible approaches.

METHODS

We performed latent profile analyses using clinical, laboratory, and biomarker data from a prospective multi-center pediatric septic shock observational cohort to derive phenotypes and trained a support vector machine model to assign phenotypes in an internal validation set. We established the clinical relevance of phenotypes and tested for their interaction with common sepsis treatments on patient outcomes. We conducted transcriptomic analyses to delineate phenotype-specific biology and inferred underlying cell subpopulations. Finally, we compared whether latent profile phenotypes overlapped with established gene-expression endotypes and compared survival among patients based on an integrated subclassification scheme.

RESULTS

Among 1071 pediatric septic shock patients requiring vasoactive support on day 1 included, we identified two phenotypes which we designated as Phenotype 1 (19.5%) and Phenotype 2 (80.5%). Membership in Phenotype 1 was associated with ~ fourfold adjusted odds of complicated course relative to Phenotype 2. Patients belonging to Phenotype 1 were characterized by relatively higher Angiopoietin-2/Tie-2 ratio, Angiopoietin-2, soluble thrombomodulin (sTM), interleukin 8 (IL-8), and intercellular adhesion molecule 1 (ICAM-1) and lower Tie-2 and Angiopoietin-1 concentrations compared to Phenotype 2. We did not identify significant interactions between phenotypes, common treatments, and clinical outcomes. Transcriptomic analysis revealed overexpression of genes implicated in the innate immune response and driven primarily by developing neutrophils among patients designated as Phenotype 1. There was no statistically significant overlap between established gene-expression endotypes, reflective of the host adaptive response, and the newly derived phenotypes, reflective of the host innate response including microvascular endothelial dysfunction. However, an integrated subclassification scheme demonstrated varying survival probabilities when comparing patient endophenotypes.

CONCLUSIONS

Our research underscores the reproducibility of latent profile analyses to identify pediatric septic shock phenotypes with high prognostic relevance. Pending validation, an integrated subclassification scheme, reflective of the different facets of the host response, holds promise to inform targeted intervention among those critically ill.

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.

Characterisation of phenotypic patterns in equine exercise-associated myopathies

BACKGROUND

Equine exercise-associated myopathies are prevalent, clinically heterogeneous, generally idiopathic disorders characterised by episodes of myofibre damage that occur in association with exercise. Episodes are intermittent and vary within and between affected horses and across breeds. The aetiopathogenesis is often unclear; there might be multiple causes. Poor phenotypic characterisation hinders genetic and other disease analyses.

OBJECTIVES

The aim of this study was to characterise phenotypic patterns across exercise-associated myopathies in horses.

STUDY DESIGN

Historical cross-sectional study, with subsequent masked case-control validation study.

METHODS

Historical clinical and histological features from muscle samples (n = 109) were used for k-means clustering and validated using principal components analysis and hierarchical clustering. For further validation, a blinded histological study (69 horses) was conducted comparing two phenotypic groups with selected controls and horses with histopathological features characterised by myofibrillar disruption.

RESULTS

We identified two distinct broad phenotypes: a non-classic exercise-associated myopathy syndrome (EAMS) subtype was associated with practitioner-described signs of apparent muscle pain (p < 0.001), reluctance to move (10.85, p = 0.001), abnormal gait (p < 0.001), ataxia (p = 0.001) and paresis (p = 0.001); while a non-specific classic RER subtype was not uniquely associated with any particular variables. No histological differences were identified between subtypes in the validation study, and no identifying histopathological features for other equine myopathies identified in either subtype.

MAIN LIMITATIONS

Lack of an independent validation population; small sample size of smaller identified subtypes; lack of positive control myofibrillar myopathy cases; case descriptions derived from multiple independent and unblinded practitioners.

CONCLUSIONS

This is the first study using computational clustering methods to identify phenotypic patterns in equine exercise-associated myopathies, and suggests that differences in patterns of presenting clinical signs support multiple disease subtypes, with EAMS a novel subtype not previously described. Routine muscle histopathology was not helpful in sub-categorising the phenotypes in our population.

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 narrative review of chemokine receptors CXCR1 and CXCR2 and their role in acute respiratory distress syndrome

Acute respiratory distress syndrome (ARDS) is a severe form of acute respiratory failure characterised by extensive inflammatory injury to the alveolocapillary barrier leading to alveolar oedema, impaired gas exchange and, ultimately, hypoxaemia necessitating the use of supplemental oxygen combined with some degree of positive airway pressure. Although much heterogeneity exists regarding the aetiology, localisation and endotypic characterisation of ARDS, what remains largely undisputed is the role of the innate immune system, and in particular of neutrophils, in precipitating and propagating lung injury. Activated neutrophils, recruited to the lung through chemokine gradients, promote injury by releasing oxidants, proteases and neutrophil extracellular traps, which ultimately cause platelet aggregation, microvascular thrombosis and cellular death. Among various neutrophilic chemoattractants, interleukin-8/C-X-C motif ligand 8 and related chemokines, collectively called ELR+ chemokines, acting on neutrophils through the G protein-coupled receptors CXCR1 and CXCR2, are pivotal in orchestrating the neutrophil activation status and chemotaxis in the inflamed lung. This allows efficient elimination of infectious agents while at the same time minimising collateral damage to host tissue. Therefore, understanding how CXCR1 and CXCR2 receptors are regulated is important if we hope to effectively target them for therapeutic use in ARDS. In the following narrative review, we provide an overview of the role of ELR+ chemokines in acute lung injury (ALI) and ARDS, we summarise the relevant regulatory pathways of their cognisant receptors CXCR1/2 and highlight current preclinical and clinical evidence on the therapeutic role of CXCR1 and CXCR2 inhibition in animal models of ALI, as well as in ARDS patients.

TM9SF1 offers utility as an efficient predictor of clinical severity and mortality among acute respiratory distress syndrome patients

Introduction

Acute respiratory distress syndrome (ARDS) is a major cause of death among critically ill patients in intensive care settings, underscoring the need to identify biomarkers capable of predicting ARDS patient clinical status and prognosis at an early time point. This study specifically sought to explore the utility and clinical relevance of TM9SF1 as a biomarker for the early prediction of disease severity and prognostic outcomes in patients with ARDS.

Methods

This study enrolled 123 patients with severe ARDS and 116 patients with non-severe ARDS for whom follow-up information was available. The mRNA levels of TM9SF1 and cytokines in peripheral blood mononuclear cells from these patients were evaluated by qPCR. The predictive performance of TM9SF1 and other clinical indicators was evaluated using received operating characteristic (ROC) curves. A predictive nomogram was developed based on TM9SF1 expression and evaluated for its ability in the early prediction of severe disease and mortality in patients with ARDS.

Results

TM9SF1 mRNA expression was found to be significantly increased in patients with severe ARDS relative to those with non-severe disease or healthy controls. ARDS severity increased in correspondence with the level of TM9SF1 expression (odds ratio [OR] = 2.43, 95% confidence interval [CI] = 2.15-3.72, P = 0.005), and high TM9SF1 levels were associated with a greater risk of mortality (hazard ratio [HR] = 2.27, 95% CI = 2.20-4.39, P = 0.001). ROC curves demonstrated that relative to other clinical indicators, TM9SF1 offered superior performance in the prediction of ARDS severity and mortality. A novel nomogram incorporating TM9SF1 expression together with age, D-dimer levels, and C-reactive protein (CRP) levels was developed and was used to predict ARDS severity (AUC = 0.887, 95% CI = 0.715-0.943). A separate model incorporating TM9SF1 expression, age, neutrophil-lymphocyte ratio (NLR), and D-dimer levels (C-index = 0.890, 95% CI = 0.627-0.957) was also developed for predicting mortality.

Conclusion

Increases in ARDS severity and patient mortality were observed with rising levels of TM9SF1 expression. TM9SF1 may thus offer utility as a novel biomarker for the early prediction of ARDS patient disease status and clinical outcomes.

Biological basis of critical illness subclasses: from the bedside to the bench and back again

Critical illness syndromes including sepsis, acute respiratory distress syndrome, and acute kidney injury (AKI) are associated with high in-hospital mortality and long-term adverse health outcomes among survivors. Despite advancements in care, clinical and biological heterogeneity among patients continues to hamper identification of efficacious therapies. Precision medicine offers hope by identifying patient subclasses based on clinical, laboratory, biomarker and 'omic' data and potentially facilitating better alignment of interventions. Within the previous two decades, numerous studies have made strides in identifying gene-expression based endotypes and clinico-biomarker based phenotypes among critically ill patients associated with differential outcomes and responses to treatment. In this state-of-the-art review, we summarize the biological similarities and differences across the various subclassification schemes among critically ill patients. In addition, we highlight current translational gaps, the need for advanced scientific tools, human-relevant disease models, to gain a comprehensive understanding of the molecular mechanisms underlying critical illness subclasses.

Causes and attributable fraction of death from ARDS in inflammatory phenotypes of sepsis

BACKGROUND

Hypoinflammatory and hyperinflammatory phenotypes have been identified in both Acute Respiratory Distress Syndrome (ARDS) and sepsis. Attributable mortality of ARDS in each phenotype of sepsis is yet to be determined. We aimed to estimate the population attributable fraction of death from ARDS (PAFARDS) in hypoinflammatory and hyperinflammatory sepsis, and to determine the primary cause of death within each phenotype.

METHODS

We studied 1737 patients with sepsis from two prospective cohorts. Patients were previously assigned to the hyperinflammatory or hypoinflammatory phenotype using latent class analysis. The PAFARDS in patients with sepsis was estimated separately in the hypo and hyperinflammatory phenotypes. Organ dysfunction, severe comorbidities, and withdrawal of life support were abstracted from the medical record in a subset of patients from the EARLI cohort who died (n = 130/179). Primary cause of death was defined as the organ system that most directly contributed to death or withdrawal of life support.

RESULTS

The PAFARDS was 19% (95%CI 10,28%) in hypoinflammatory sepsis and, 14% (95%CI 6,20%) in hyperinflammatory sepsis. Cause of death differed between the two phenotypes (p < 0.001). Respiratory failure was the most common cause of death in hypoinflammatory sepsis, whereas circulatory shock was the most common cause in hyperinflammatory sepsis. Death with severe underlying comorbidities was more frequent in hypoinflammatory sepsis (81% vs. 67%, p = 0.004).

CONCLUSIONS

The PAFARDS is modest in both phenotypes whereas primary cause of death among patients with sepsis differed substantially by phenotype. This study identifies challenges in powering future clinical trials to detect changes in mortality outcomes among patients with sepsis and ARDS.

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.

Inflammatory subphenotypes previously identified in ARDS are associated with mortality at intensive care unit discharge: a secondary analysis of a prospective observational study

BACKGROUND

Intensive care unit (ICU)-survivors have an increased risk of mortality after discharge compared to the general population. On ICU admission subphenotypes based on the plasma biomarker levels of interleukin-8, protein C and bicarbonate have been identified in patients admitted with acute respiratory distress syndrome (ARDS) that are prognostic of outcome and predictive of treatment response. We hypothesized that if these inflammatory subphenotypes previously identified among ARDS patients are assigned at ICU discharge in a more general critically ill population, they are associated with short- and long-term outcome.

METHODS

A secondary analysis of a prospective observational cohort study conducted in two Dutch ICUs between 2011 and 2014 was performed. All patients discharged alive from the ICU were at ICU discharge adjudicated to the previously identified inflammatory subphenotypes applying a validated parsimonious model using variables measured median 10.6 h [IQR, 8.0-31.4] prior to ICU discharge. Subphenotype distribution at ICU discharge, clinical characteristics and outcomes were analyzed. As a sensitivity analysis, a latent class analysis (LCA) was executed for subphenotype identification based on plasma protein biomarkers at ICU discharge reflective of coagulation activation, endothelial cell activation and inflammation. Concordance between the subphenotyping strategies was studied.

RESULTS

Of the 8332 patients included in the original cohort, 1483 ICU-survivors had plasma biomarkers available and could be assigned to the inflammatory subphenotypes. At ICU discharge 6% (n = 86) was assigned to the hyperinflammatory and 94% (n = 1397) to the hypoinflammatory subphenotype. Patients assigned to the hyperinflammatory subphenotype were discharged with signs of more severe organ dysfunction (SOFA scores 7 [IQR 5-9] vs. 4 [IQR 2-6], p < 0.001). Mortality was higher in patients assigned to the hyperinflammatory subphenotype (30-day mortality 21% vs. 11%, p = 0.005; one-year mortality 48% vs. 28%, p < 0.001). LCA deemed 2 subphenotypes most suitable. ICU-survivors from class 1 had significantly higher mortality compared to class 2. Patients belonging to the hyperinflammatory subphenotype were mainly in class 1.

CONCLUSIONS

Patients assigned to the hyperinflammatory subphenotype at ICU discharge showed significantly stronger anomalies in coagulation activation, endothelial cell activation and inflammation pathways implicated in the pathogenesis of critical disease and increased mortality until one-year follow up.

Application of laboratory models for sarcoidosis research

This manuscript will review the implications and applications of sarcoidosis models towards advancing our understanding of sarcoidosis disease mechanisms, identification of biomarkers, and preclinical testing of novel therapies. Emerging disease models and innovative research tools will also be considered.

Elevated ferritin, mediated by IL-18 is associated with systemic inflammation and mortality in acute respiratory distress syndrome (ARDS)

BACKGROUND

Inflammatory subphenotypes have been identified in acute respiratory distress syndrome (ARDS). Hyperferritinaemia in sepsis is associated with hyperinflammation, worse clinical outcomes, and may predict benefit with immunomodulation. Our aim was to determine if raised ferritin identified a subphenotype in patients with ARDS.

METHODS

Baseline plasma ferritin concentrations were measured in patients with ARDS from two randomised controlled trials of simvastatin (Hydroxymethylglutaryl-CoA Reductase Inhibition with Simvastatin in Acute Lung Injury to Reduce Pulmonary Dysfunction-2 (HARP-2); discovery cohort, UK) and neuromuscular blockade (ROSE; validation cohort, USA). Results were analysed using a logistic regression model with restricted cubic splines, to determine the ferritin threshold associated with 28-day mortality.

RESULTS

Ferritin was measured in 511 patients from HARP-2 (95% of patients enrolled) and 847 patients (84% of patients enrolled) from ROSE. Ferritin was consistently associated with 28-day mortality in both studies and following a meta-analysis, a log-fold increase in ferritin was associated with an OR 1.71 (95% CI 1.01 to 2.90) for 28-day mortality. Patients with ferritin >1380 ng/mL (HARP-2 28%, ROSE 24%) had a significantly higher 28-day mortality and fewer ventilator-free days in both studies. Mediation analysis, including confounders (acute physiology and chronic health evaluation-II score and ARDS aetiology) demonstrated a statistically significant contribution of interleukin (IL)-18 as an intermediate pathway between ferritin and mortality.

CONCLUSIONS

Ferritin is a clinically useful biomarker in ARDS and is associated with worse patient outcomes. These results provide support for prospective interventional trials of immunomodulatory agents targeting IL-18 in this hyperferritinaemic subgroup of patients with ARDS.

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.

Signaling pathways and potential therapeutic targets in acute respiratory distress syndrome (ARDS)

Acute respiratory distress syndrome (ARDS) is a common condition associated with critically ill patients, characterized by bilateral chest radiographical opacities with refractory hypoxemia due to noncardiogenic pulmonary edema. Despite significant advances, the mortality of ARDS remains unacceptably high, and there are still no effective targeted pharmacotherapeutic agents. With the outbreak of coronavirus disease 19 worldwide, the mortality of ARDS has increased correspondingly. Comprehending the pathophysiology and the underlying molecular mechanisms of ARDS may thus be essential to developing effective therapeutic strategies and reducing mortality. To facilitate further understanding of its pathogenesis and exploring novel therapeutics, this review provides comprehensive information of ARDS from pathophysiology to molecular mechanisms and presents targeted therapeutics. We first describe the pathogenesis and pathophysiology of ARDS that involve dysregulated inflammation, alveolar-capillary barrier dysfunction, impaired alveolar fluid clearance and oxidative stress. Next, we summarize the molecular mechanisms and signaling pathways related to the above four aspects of ARDS pathophysiology, along with the latest research progress. Finally, we discuss the emerging therapeutic strategies that show exciting promise in ARDS, including several pharmacologic therapies, microRNA-based therapies and mesenchymal stromal cell therapies, highlighting the pathophysiological basis and the influences on signal transduction pathways for their use.

Chinese experts' consensus on the application of intensive care big data

The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts' Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.

Subphenotypes of Acute Respiratory Distress Syndrome: Advancing Towards Precision Medicine

Acute respiratory distress syndrome (ARDS) is a common cause of severe hypoxemia defined by the acute onset of bilateral non-cardiogenic pulmonary edema. The diagnosis is made by defined consensus criteria. Supportive care, including prevention of further injury to the lungs, is the only treatment that conclusively improves outcomes. The inability to find more advanced therapies is due, in part, to the highly sensitive but relatively non-specific current syndromic consensus criteria, combining a heterogenous population of patients under the umbrella of ARDS. With few effective therapies, the morality rate remains 30% to 40%. Many subphenotypes of ARDS have been proposed to cluster patients with shared combinations of observable or measurable traits. Subphenotyping patients is a strategy to overcome heterogeneity to advance clinical research and eventually identify treatable traits. Subphenotypes of ARDS have been proposed based on radiographic patterns, protein biomarkers, transcriptomics, and/or machine-based clustering of clinical and biological variables. Some of these strategies have been reproducible across patient cohorts, but at present all have practical limitations to their implementation. Furthermore, there is no agreement on which strategy is the most appropriate. This review will discuss the current strategies for subphenotyping patients with ARDS, including the strengths and limitations, and the future directions of ARDS subphenotyping.

A New Global Definition of Acute Respiratory Distress Syndrome

Background: Since publication of the 2012 Berlin definition of acute respiratory distress syndrome (ARDS), several developments have supported the need for an expansion of the definition, including the use of high-flow nasal oxygen, the expansion of the use of pulse oximetry in place of arterial blood gases, the use of ultrasound for chest imaging, and the need for applicability in resource-limited settings. Methods: A consensus conference of 32 critical care ARDS experts was convened, had six virtual meetings (June 2021 to March 2022), and subsequently obtained input from members of several critical care societies. The goal was to develop a definition that would 1) identify patients with the currently accepted conceptual framework for ARDS, 2) facilitate rapid ARDS diagnosis for clinical care and research, 3) be applicable in resource-limited settings, 4) be useful for testing specific therapies, and 5) be practical for communication to patients and caregivers. Results: The committee made four main recommendations: 1) include high-flow nasal oxygen with a minimum flow rate of ⩾30 L/min; 2) use PaO2:FiO2 ⩽ 300 mm Hg or oxygen saturation as measured by pulse oximetry SpO2:FiO2 ⩽ 315 (if oxygen saturation as measured by pulse oximetry is ⩽97%) to identify hypoxemia; 3) retain bilateral opacities for imaging criteria but add ultrasound as an imaging modality, especially in resource-limited areas; and 4) in resource-limited settings, do not require positive end-expiratory pressure, oxygen flow rate, or specific respiratory support devices. Conclusions: We propose a new global definition of ARDS that builds on the Berlin definition. The recommendations also identify areas for future research, including the need for prospective assessments of the feasibility, reliability, and prognostic validity of the proposed global definition.

Subphenotypes in patients with acute respiratory distress syndrome treated with high-flow oxygen

BACKGROUND

Acute respiratory distress syndrome (ARDS) subphenotypes differ in outcomes and treatment responses. Subphenotypes in high-flow nasal oxygen (HFNO)-treated ARDS patients have not been investigated.

OBJECTIVES

To identify biological subphenotypes in HFNO-treated ARDS patients.

METHODS

Secondary analysis of a prospective multicenter observational study including ARDS patients supported with HFNO. Plasma inflammation markers (interleukin [IL]-6, IL-8, and IL-33 and soluble suppression of tumorigenicity-2 [sST2]) and lung epithelial (receptor for advanced glycation end products [RAGE] and surfactant protein D [SP-D]) and endothelial (angiopoietin-2 [Ang-2]) injury were measured. These biomarkers and bicarbonate were used in K-means cluster analysis to identify subphenotypes. Logistic regression was performed on biomarker combinations to predict clustering. We chose the model with the best AUROC and the lowest number of variables. This model was used to describe the HAIS (High-flow ARDS Inflammatory Subphenotype) score.

RESULTS

Among 41 HFNO patients, two subphenotypes were identified. Hyperinflammatory subphenotype (n = 17) showed higher biomarker levels than hypoinflammatory (n = 24). Despite similar baseline characteristics, the hyperinflammatory subphenotype had higher 60-day mortality (47 vs 8.3% p = 0.014) and longer ICU length of stay (22.0 days [18.0-30.0] vs 39.5 [25.5-60.0], p = 0.034). The HAIS score, based on IL-8 and sST2, accurately distinguished subphenotypes (AUROC 0.96 [95%CI: 0.90-1.00]). A HAIS score ≥ 7.45 was predictor of hyperinflammatory subphenotype.

CONCLUSION

ARDS patients treated with HFNO exhibit two biological subphenotypes that have similar clinical characteristics, but hyperinflammatory patients have worse outcomes. The HAIS score may identify patients with hyperinflammatory subphenotype and might be used for enrichment strategies in future clinical trials.

Identifying molecular phenotypes in sepsis: an analysis of two prospective observational cohorts and secondary analysis of two randomised controlled trials

BACKGROUND

In sepsis and acute respiratory distress syndrome (ARDS), heterogeneity has contributed to difficulty identifying effective pharmacotherapies. In ARDS, two molecular phenotypes (hypoinflammatory and hyperinflammatory) have consistently been identified, with divergent outcomes and treatment responses. In this study, we sought to derive molecular phenotypes in critically ill adults with sepsis, determine their overlap with previous ARDS phenotypes, and evaluate whether they respond differently to treatment in completed sepsis trials.

METHODS

We used clinical data and plasma biomarkers from two prospective sepsis cohorts, the Validating Acute Lung Injury biomarkers for Diagnosis (VALID) study (N=1140) and the Early Assessment of Renal and Lung Injury (EARLI) study (N=818), in latent class analysis (LCA) to identify the optimal number of classes in each cohort independently. We used validated models trained to classify ARDS phenotypes to evaluate concordance of sepsis and ARDS phenotypes. We applied these models retrospectively to the previously published Prospective Recombinant Human Activated Protein C Worldwide Evaluation in Severe Sepsis and Septic Shock (PROWESS-SHOCK) trial and Vasopressin and Septic Shock Trial (VASST) to assign phenotypes and evaluate heterogeneity of treatment effect.

FINDINGS

A two-class model best fit both VALID and EARLI (p<0·0001). In VALID, 804 (70·5%) of the 1140 patients were classified as hypoinflammatory and 336 (29·5%) as hyperinflammatory; in EARLI, 530 (64·8%) of 818 were hypoinflammatory and 288 (35·2%) hyperinflammatory. We observed higher plasma pro-inflammatory cytokines, more vasopressor use, more bacteraemia, lower protein C, and higher mortality in the hyperinflammatory than in the hypoinflammatory phenotype (p<0·0001 for all). Classifier models indicated strong concordance between sepsis phenotypes and previously identified ARDS phenotypes (area under the curve 0·87-0·96, depending on the model). Findings were similar excluding participants with both sepsis and ARDS. In PROWESS-SHOCK, 1142 (68·0%) of 1680 patients had the hypoinflammatory phenotype and 538 (32·0%) had the hyperinflammatory phenotype, and response to activated protein C differed by phenotype (p=0·0043). In VASST, phenotype proportions were similar to other cohorts; however, no treatment interaction with the type of vasopressor was observed (p=0·72).

INTERPRETATION

Molecular phenotypes previously identified in ARDS are also identifiable in multiple sepsis cohorts and respond differently to activated protein C. Molecular phenotypes could represent a treatable trait in critical illness beyond the patient's syndromic diagnosis.

FUNDING

US National Institutes of Health.

Distinct clinical phenotypes in paediatric cancer patients with sepsis are associated with different outcomes-an international multicentre retrospective study

Background

Identifying phenotypes in sepsis patients may enable precision medicine approaches. However, the generalisability of these phenotypes to specific patient populations is unclear. Given that paediatric cancer patients with sepsis have different host response and pathogen profiles and higher mortality rates when compared to non-cancer patients, we determined whether unique, reproducible, and clinically-relevant sepsis phenotypes exist in this specific patient population.

Methods

We studied patients with underlying malignancies admitted with sepsis to one of 25 paediatric intensive care units (PICUs) participating in two large, multi-centre, observational cohorts from the European SCOTER study (n = 383 patients; study period between January 1, 2018 and January 1, 2020) and the U.S. Novel Data-Driven Sepsis Phenotypes in Children study (n = 1898 patients; study period between January 1, 2012 and January 1, 2018). We independently used latent class analysis (LCA) in both cohorts to identify phenotypes using demographic, clinical, and laboratory data from the first 24 h of PICU admission. We then tested the association of the phenotypes with clinical outcomes in both cohorts.

Findings

LCA identified two distinct phenotypes that were comparable across both cohorts. Phenotype 1 was characterised by lower serum bicarbonate and albumin, markedly increased lactate and hepatic, renal, and coagulation abnormalities when compared to phenotype 2. Patients with phenotype 1 had a higher 90-day mortality (European cohort 29.2% versus 13.4%, U.S. cohort 27.3% versus 11.4%, p < 0.001) and received more vasopressor and renal replacement therapy than patients with phenotype 2. After adjusting for severity of organ dysfunction, haematological cancer, prior stem cell transplantation and age, phenotype 1 was associated with an adjusted OR of death at 90-day of 1.9 (1.04-3.34) in the European cohort and 1.6 (1.2-2.2) in the U.S. cohort.

Interpretation

We identified two clinically-relevant sepsis phenotypes in paediatric cancer patients that are reproducible across two international, multicentre cohorts with prognostic implications. These results may guide further research regarding therapeutic approaches for these specific phenotypes.

Funding

Part of this study is funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

Application of anti-inflammatory treatment in two different ovine Acute Respiratory Distress Syndrome injury models: a preclinical randomized intervention study

Whilst the presence of 2 subphenotypes among the heterogenous Acute Respiratory Distress Syndrome (ARDS) population is becoming clinically accepted, subphenotype-specific treatment efficacy has yet to be prospectively tested. We investigated anti-inflammatory treatment in different ARDS models in sheep, previously shown similarities to human ARDS subphenotypes, in a preclinical, randomized, blinded study. Thirty anesthetized sheep were studied up to 48 h and randomized into: (a) OA: oleic acid (n = 15) and (b) OA-LPS: oleic acid and subsequent lipopolysaccharide (n = 15) to achieve a PaO2/FiO2 ratio of < 150 mmHg. Then, animals were randomly allocated to receive treatment with methylprednisolone or erythromycin or none. Assessed outcomes were oxygenation, pulmonary mechanics, hemodynamics and survival. All animals reached ARDS. Treatment with methylprednisolone, but not erythromycin, provided the highest therapeutic benefit in Ph2 animals, leading to a significant increase in PaO2/FiO2 ratio by reducing pulmonary edema, dead space ventilation and shunt fraction. Animals treated with methylprednisolone displayed a higher survival up to 48 h than all others. In animals treated with erythromycin, there was no treatment benefit regarding assessed physiological parameters and survival in both phenotypes. Treatment with methylprednisolone improves oxygenation and survival, more so in ovine phenotype 2 which resembles the human hyperinflammatory subphenotype.

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.

Leveraging Data Science and Novel Technologies to Develop and Implement Precision Medicine Strategies in Critical Care

Precision medicine aims to identify treatments that are most likely to result in favorable outcomes for subgroups of patients with similar clinical and biological characteristics. The gaps for the development and implementation of precision medicine strategies in the critical care setting are many, but the advent of data science and multi-omics approaches, combined with the rich data ecosystem in the intensive care unit, offer unprecedented opportunities to realize the promise of precision critical care. In this article, the authors review the data-driven and technology-based approaches being leveraged to discover and implement precision medicine strategies in the critical care setting.

2-deoxy-2-[18F]fluoro-D-glucose Positron Emission Tomography to Monitor Lung Inflammation and Therapeutic Response to Dexamethasone in a Murine Model of Acute Lung Injury

PURPOSE

To image inflammation and monitor therapeutic response to anti-inflammatory intervention using 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography (PET) in a preclinical model of acute lung injury (ALI).

PROCEDURES

Mice were intratracheally administered lipopolysaccharide (LPS, 2.5 mg/kg) to induce ALI or phosphate-buffered saline as the vehicle control. A subset of mice in the ALI group received two intraperitoneal doses of dexamethasone 1 and 24 h after LPS. [18F]FDG PET/CT was performed 2 days after the induction of ALI. [18F]FDG uptake in the lungs was quantified by PET (%ID/mLmean and standardized uptake value (SUVmean)) and ex vivo γ-counting (%ID/g). The severity of lung inflammation was determined by quantifying the protein level of inflammatory cytokines/chemokines and the activity of neutrophil elastase and glycolytic enzymes. In separate groups of mice, flow cytometry was performed to estimate the contribution of individual immune cell types to the total pulmonary inflammatory cell burden under different treatment conditions.

RESULTS

Lung uptake of [18F]FDG was significantly increased during LPS-induced ALI, and a decreased [18F]FDG uptake was observed following dexamethasone treatment to an intermediate level between that of LPS-treated and control mice. Protein expression of inflammatory biomarkers and the activity of neutrophil elastase and glycolytic enzymes were increased in the lungs of LPS-treated mice versus those of control mice, and correlated with [18F]FDG uptake. Furthermore, dexamethasone-induced decreases in cytokine/chemokine protein levels and enzyme activities correlated with [18F]FDG uptake. Neutrophils were the most abundant cells in LPS-induced ALI, and the pattern of total cell burden during ALI with or without dexamethasone therapy mirrored that of [18F]FDG uptake.

CONCLUSIONS

[18F]FDG PET noninvasively detects lung inflammation in ALI and its response to anti-inflammatory therapy in a preclinical model. However, high [18F]FDG uptake by bone, brown fat, and myocardium remains a technical limitation for quantification of [18F]FDG in the lungs.

ESICM guidelines on acute respiratory distress syndrome: definition, phenotyping and respiratory support strategies

The aim of these guidelines is to update the 2017 clinical practice guideline (CPG) of the European Society of Intensive Care Medicine (ESICM). The scope of this CPG is limited to adult patients and to non-pharmacological respiratory support strategies across different aspects of acute respiratory distress syndrome (ARDS), including ARDS due to coronavirus disease 2019 (COVID-19). These guidelines were formulated by an international panel of clinical experts, one methodologist and patients' representatives on behalf of the ESICM. The review was conducted in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement recommendations. We followed the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to assess the certainty of evidence and grade recommendations and the quality of reporting of each study based on the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) network guidelines. The CPG addressed 21 questions and formulates 21 recommendations on the following domains: (1) definition; (2) phenotyping, and respiratory support strategies including (3) high-flow nasal cannula oxygen (HFNO); (4) non-invasive ventilation (NIV); (5) tidal volume setting; (6) positive end-expiratory pressure (PEEP) and recruitment maneuvers (RM); (7) prone positioning; (8) neuromuscular blockade, and (9) extracorporeal life support (ECLS). In addition, the CPG includes expert opinion on clinical practice and identifies the areas of future research.

Recent progress and prospects for anti-cytokine therapy in preclinical and clinical acute lung injury

Acute respiratory distress syndrome (ARDS) is a heterogeneous cause of respiratory failure that has a rapid onset, a high mortality rate, and for which there is no effective pharmacological treatment. Current evidence supports a critical role of excessive inflammation in ARDS, resulting in several cytokines, cytokine receptors, and proteins within their downstream signalling pathways being putative therapeutic targets. However, unsuccessful trials of anti-inflammatory drugs have thus far hindered progress in the field. In recent years, the prospects of precision medicine and therapeutic targeting of cytokines coevolving into effective treatments have gained notoriety. There is an optimistic and growing understanding of ARDS subphenotypes as well as advances in treatment strategies and clinical trial design. Furthermore, large trials of anti-cytokine drugs in patients with COVID-19 have provided an unprecedented amount of information that could pave the way for therapeutic breakthroughs. While current clinical and nonclinical ARDS research suggest relatively limited potential in monotherapy with anti-cytokine drugs, combination therapy has emerged as an appealing strategy and may provide new perspectives on finding safe and effective treatments. Accurate evaluation of these drugs, however, also relies on well-founded experimental research and the implementation of biomarker-guided stratification in future trials. In this review, we provide an overview of anti-cytokine therapy for acute lung injury and ARDS, highlighting the current preclinical and clinical evidence for targeting the main cytokines individually and the therapeutic prospects for combination therapy.

Genetic Determinants of the Acute Respiratory Distress Syndrome

Acute respiratory distress syndrome (ARDS) is a life-threatening lung condition that arises from multiple causes, including sepsis, pneumonia, trauma, and severe coronavirus disease 2019 (COVID-19). Given the heterogeneity of causes and the lack of specific therapeutic options, it is crucial to understand the genetic and molecular mechanisms that underlie this condition. The identification of genetic risks and pharmacogenetic loci, which are involved in determining drug responses, could help enhance early patient diagnosis, assist in risk stratification of patients, and reveal novel targets for pharmacological interventions, including possibilities for drug repositioning. Here, we highlight the basis and importance of the most common genetic approaches to understanding the pathogenesis of ARDS and its critical triggers. We summarize the findings of screening common genetic variation via genome-wide association studies and analyses based on other approaches, such as polygenic risk scores, multi-trait analyses, or Mendelian randomization studies. We also provide an overview of results from rare genetic variation studies using Next-Generation Sequencing techniques and their links with inborn errors of immunity. Lastly, we discuss the genetic overlap between severe COVID-19 and ARDS by other causes.

Physiological and clinical variables identify ARDS classes and therapeutic heterogeneity to glucocorticoids: a retrospective study

OBJECTIVE

We aimed to identify new classes in acute respiratory distress syndrome (ARDS) using physiological and clinical variables and to explore heterogeneity in the effects of glucocorticoid therapy between classes.

METHODS

Using the Medical Information Mart for Intensive Care-IV database, we identified patients with ARDS. Potential profile analysis was used to identify classes with physiological and clinical data as delineating variables. Baseline characteristics and clinical outcomes were compared between classes. The effect of glucocorticoid treatment was explored by stratifying by class and glucocorticoid treatment.

RESULTS

From 2008 to 2019, 1104 patients with ARDS were enrolled in the study. The 2-class potential analysis model had the best fit (P < 0.0001), with 78% of patients falling into class 1 and 22% into class 2. Additional classes did not improve the model fit. Patients in class 2 had higher anion gap, lactate, creatinine, and glucose levels and lower residual base, blood pressure, and bicarbonate compared with class 1. In-hospital mortality and 28-day mortality were significantly higher among patients in class 2 than those in class 1 (P < 0.001). Heterogeneity of glucocorticoid treatment was observed, stratified by class and treatment, with no significant effect in class 1 (P = 0.496), increased mortality in class 2 (P = 0.001), and a significant interaction (P = 0.0381). In class 2, 28-day survival was significantly lower with glucocorticoid treatment compared with no hormone treatment (P = 0.001).

CONCLUSION

We used clinical and physiological variables to identify two classes of non-COVID-19-associated ARDS with different baseline characteristics and clinical outcomes. The response to glucocorticoid therapy varied among different classes of patients.

Identification of a pediatric acute hypoxemic respiratory failure signature in peripheral blood leukocytes at 24 hours post-ICU admission with machine learning

Background

There is no generalizable transcriptomics signature of pediatric acute respiratory distress syndrome. Our goal was to identify a whole blood differential gene expression signature for pediatric acute hypoxemic respiratory failure (AHRF) using transcriptomic microarrays within twenty-four hours of diagnosis. We used publicly available human whole-blood gene expression arrays of a Berlin-defined pediatric acute respiratory distress syndrome (GSE147902) cohort and a sepsis-triggered AHRF (GSE66099) cohort within twenty-four hours of diagnosis and compared those children with a PaO2/FiO2 < 200 to those with a PaO2/FiO2 ≥ 200.

Results

We used stability selection, a bootstrapping method of 100 simulations using logistic regression as a classifier, to select differentially expressed genes associated with a PaO2/FiO2 < 200 vs. PaO2/FiO2 ≥ 200. The top-ranked genes that contributed to the AHRF signature were selected in each dataset. Genes common to both of the top 1,500 ranked gene lists were selected for pathway analysis. Pathway and network analysis was performed using the Pathway Network Analysis Visualizer (PANEV) and Reactome was used to perform an over-representation gene network analysis of the top-ranked genes common to both cohorts. Changes in metabolic pathways involved in energy balance, fundamental cellular processes such as protein translation, mitochondrial function, oxidative stress, immune signaling, and inflammation are differentially regulated early in pediatric ARDS and sepsis-induced AHRF compared to both healthy controls and to milder acute hypoxemia. Specifically, fundamental pathways related to the severity of hypoxemia emerged and included (1) ribosomal and eukaryotic initiation of factor 2 (eIF2) regulation of protein translation and (2) the nutrient, oxygen, and energy sensing pathway, mTOR, activated via PI3K/AKT signaling.

Conclusions

Cellular energetics and metabolic pathways are important mechanisms to consider to further our understanding of the heterogeneity and underlying pathobiology of moderate and severe pediatric acute respiratory distress syndrome. Our findings are hypothesis generating and support the study of metabolic pathways and cellular energetics to understand heterogeneity and underlying pathobiology of moderate and severe acute hypoxemic respiratory failure in children.

Functional enhancement strategies to potentiate the therapeutic properties of mesenchymal stromal cells for respiratory diseases

Respiratory diseases remain a major health concern worldwide because they subject patients to considerable financial and psychosocial burdens and result in a high rate of morbidity and mortality. Although significant progress has been made in understanding the underlying pathologic mechanisms of severe respiratory diseases, most therapies are supportive, aiming to mitigate symptoms and slow down their progressive course but cannot improve lung function or reverse tissue remodeling. Mesenchymal stromal cells (MSCs) are at the forefront of the regenerative medicine field due to their unique biomedical potential in promoting immunomodulation, anti-inflammatory, anti-apoptotic and antimicrobial activities, and tissue repair in various experimental models. However, despite several years of preclinical research on MSCs, therapeutic outcomes have fallen far short in early-stage clinical trials for respiratory diseases. This limited efficacy has been associated with several factors, such as reduced MSC homing, survival, and infusion in the late course of lung disease. Accordingly, genetic engineering and preconditioning methods have emerged as functional enhancement strategies to potentiate the therapeutic actions of MSCs and thus achieve better clinical outcomes. This narrative review describes various strategies that have been investigated in the experimental setting to functionally potentiate the therapeutic properties of MSCs for respiratory diseases. These include changes in culture conditions, exposure of MSCs to inflammatory environments, pharmacological agents or other substances, and genetic manipulation for enhanced and sustained expression of genes of interest. Future directions and challenges in efficiently translating MSC research into clinical practice are discussed.

Integrating biology into clinical trial design

PURPOSE OF REVIEW

Critical care medicine revolves around syndromes, such as acute respiratory distress syndrome (ARDS), sepsis and acute kidney injury. Few interventions have shown to be effective in large clinical trials, likely because of between-patient heterogeneity. Translational evidence suggests that more homogeneous biological subgroups can be identified and that differential treatment effects exist. Integrating biological considerations into clinical trial design is therefore an important frontier of critical care research.

RECENT FINDINGS

The pathophysiology of critical care syndromes involves a multiplicity of processes, which emphasizes the difficulty of integrating biology into clinical trial design. Biological assessment can be integrated into clinical trials using predictive enrichment at trial inclusion, time-dependent variation to better understand treatment effects and biological markers as surrogate outcomes.

SUMMARY

Integrating our knowledge on biological heterogeneity into clinical trial design, which has revolutionized other medical fields, could serve as a solution to implement personalized treatment in critical care syndromes. Changing the trial design by using predictive enrichment, incorporation of the evaluation of time-dependent changes and biological markers as surrogate outcomes may improve the likelihood of detecting a beneficial effect from targeted therapeutic interventions and the opportunity to test multiple lines of treatment per patient.

Biological Phenotyping in Sepsis and Acute Respiratory Distress Syndrome

Heterogeneity in sepsis and acute respiratory distress syndrome (ARDS) is increasingly being recognized as one of the principal barriers to finding efficacious targeted therapies. The advent of multiple high-throughput biological data ("omics"), coupled with the widespread access to increased computational power, has led to the emergence of phenotyping in critical care. Phenotyping aims to use a multitude of data to identify homogenous subgroups within an otherwise heterogenous population. Increasingly, phenotyping schemas are being applied to sepsis and ARDS to increase understanding of these clinical conditions and identify potential therapies. Here we present a selective review of the biological phenotyping schemas applied to sepsis and ARDS. Further, we outline some of the challenges involved in translating these conceptual findings to bedside clinical decision-making tools.

Identification of two specific transcriptomic clusters of COVID-19 acute respiratory distress syndrome patients with different immune profiles and different outcomes

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the respiratory illness COVID-19 (coronavirus disease 2019). The virus was first identified in December 2019 in Wuhan, China and has since then spread globally, resulting in the ongoing SARS-CoV-2 pandemic, causing more than 615 million confirmed cases of infection (https://covid19.who.int/). Although the largest proportion of SARS-CoV-2 infections in humans is characterised by a mild course of disease, about 5% to 20% of patients are hospitalised with COVID-19 due to a more severe course of disease, and require admission to the intensive care unit for diffuse lung infiltrates and severe hypoxaemia [1].

Transcriptomic clustering of patients with ARDS due to COVID-19 identified different immune profiles and outcomes. This demonstrates heterogeneity among COVID-19 ARDS patients and may help to establish personalised therapies.https://bit.ly/3h61sCj

Research Progress on Phenotypic Classification of Acute Respiratory Distress Syndrome: A Narrative Review

Acute respiratory distress syndrome (ARDS) is a clinical syndrome that is characterized by an acute onset and refractory hypoxemia. It remains an important contributor to high mortality in critically ill patients, and the majority of clinical randomized controlled trials on ARDS provide underwhelming findings, which is attributed in large part to its pathophysiological and clinical heterogeneity, among other aspects. It is now widely accepted that ARDS is highly heterogeneous, growing evidences support this. ARDS phenotypic and subphenotypic studies aim to further differentiate and identify ARDS heterogeneity in the hope that clinicians can benefit from it, then can diagnose ARDS faster and more accurately and provide targeted treatments. This review collates and evaluates the major phenotype-related research advances of recent years, with a specific focus on ARDS biomarkers and clinical factors.

Respiratory strategy at birth initiates distinct lung injury phenotypes in the preterm lamb lung

Background

A lack of clear trial evidence often hampers clinical decision-making during support of the preterm lung at birth. Protein biomarkers have been used to define acute lung injury phenotypes and improve patient selection for specific interventions in adult respiratory distress syndrome. The objective of the study was to use proteomics to provide a deeper biological understanding of acute lung injury phenotypes resulting from different aeration strategies at birth in the preterm lung.

Methods

Changes in protein abundance against an unventilated group (n = 7) were identified via mass spectrometry in a biobank of gravity dependent and non-dependent lung tissue from preterm lambs managed with either a Sustained Inflation (SI, n = 20), Dynamic PEEP (DynPEEP, n = 19) or static PEEP (StatPEEP, n = 11). Ventilation strategy-specific pathways and functions were identified (PANTHER and WebGestalt Tool) and phenotypes defined using integrated analysis of proteome, physiological and clinical datasets (MixOmics package).

Results

2372 proteins were identified. More altered proteins were identified in the non-dependent lung, and in SI group than StatPEEP and DynPEEP. Different inflammation, immune system, apoptosis and cytokine pathway enrichment were identified for each strategy and lung region. Specific integration maps of clinical and physiological outcomes to specific proteins could be generated for each strategy.

Conclusions

Proteomics mapped the molecular events initiating acute lung injury and identified detailed strategy-specific phenotypes. This study demonstrates the potential to characterise preterm lung injury by the direct aetiology and response to lung injury; the first step towards true precision medicine in neonatology.

Immunomodulation and endothelial barrier protection mediate the association between oral imatinib and mortality in hospitalised COVID-19 patients

BACKGROUND

Imatinib reduced 90-day mortality in hospitalised coronavirus disease 2019 (COVID-19) patients in a recent clinical trial, but the biological effects that cause improved clinical outcomes are unknown. We aimed to determine the biological changes elicited by imatinib in patients with COVID-19 and what baseline biological profile moderates the effect of imatinib.

METHODS

We undertook a secondary analysis of a randomised, double-blind, placebo-controlled trial of oral imatinib in hospitalised, hypoxaemic COVID-19 patients. Mediating effects of changes in plasma concentration of 25 plasma host response biomarkers on the association between randomisation group and 90-day mortality were studied by combining linear mixed effect modelling and joint modelling. Moderation of baseline biomarker concentrations was evaluated by Cox regression modelling. We identified subphenotypes using Ward's method clustering and evaluated moderation of these subphenotypes using the aforementioned method.

RESULTS

332 out of 385 participants had plasma samples available. Imatinib increased the concentration of surfactant protein D (SP-D), and decreased the concentration of interleukin-6, procalcitonin, angiopoietin (Ang)-2/Ang-1 ratio, E-selectin, tumour necrosis factor (TNF)-α, and TNF receptor I. The effect of imatinib on 90-day mortality was fully mediated by changes in these biomarkers. Cluster analysis revealed three host response subphenotypes. Mortality benefit of imatinib was only present in the subphenotype characterised by alveolar epithelial injury indicated by increased SP-D levels in the context of systemic inflammation and endothelial dysfunction (hazard ratio 0.30, 95% CI 0.10-0.92).

CONCLUSIONS

The effect of imatinib on mortality in hospitalised COVID-19 patients is mediated through modulation of innate immune responses and reversal of endothelial dysfunction, and possibly moderated by biological subphenotypes.

Subphenotypes of acute kidney injury in adults

PURPOSE OF REVIEW

Acute kidney injury is a heterogeneous syndrome and as such is associated with multiple predisposing conditions and causes all of which affect outcomes. Such heterogeneity may conceal the potential benefit of therapies when generally applied to patients with acute kidney injury (AKI). The discovery of pathophysiology-based subphenotypes could be of benefit in allocating current and future therapies to specific groups.

RECENT FINDINGS

Clinical subphenotypes group patients into categories according to predisposing factors, disease severity, and trajectory. These may be helpful in assessing patient outcomes. Analyses of existing databases have revealed biological subphenotypes that are characterized by levels of biomarkers indicative of hyperinflammation and endothelial injury. Patients with increased levels of these biomarkers display higher mortality rates compared with those with lower levels and there is potential that this group might respond differently to therapies. However, challenges remain in the validation, generalizability, and application of these subphenotypes.

SUMMARY

Subphenotyping may help reduce heterogeneity under the umbrella term of acute kidney injury. Despite challenges remain, the identification of AKI subphenotypes has opened the potential of AKI research focused on better targeted therapies.

Validating a Proteomic Signature of Severe COVID-19

COVID-19 is a heterogenous disease. Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19.

DESIGN

Prospective observational cohort study.

SETTING

Two hospitals in the United States.

PATIENTS

One hundred sixty-seven hospitalized adults with COVID-19.

INTERVENTION

None.

MEASUREMENTS AND MAIN RESULTS

We measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88-0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006).

CONCLUSIONS

Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia.

Personalized medicine using omics approaches in acute respiratory distress syndrome to identify biological phenotypes

In the last decade, research on acute respiratory distress syndrome (ARDS) has made considerable progress. However, ARDS remains a leading cause of mortality in the intensive care unit. ARDS presents distinct subphenotypes with different clinical and biological features. The pathophysiologic mechanisms of ARDS may contribute to the biological variability and partially explain why some pharmacologic therapies for ARDS have failed to improve patient outcomes. Therefore, identifying ARDS variability and heterogeneity might be a key strategy for finding effective treatments. Research involving studies on biomarkers and genomic, metabolomic, and proteomic technologies is increasing. These new approaches, which are dedicated to the identification and quantitative analysis of components from biological matrixes, may help differentiate between different types of damage and predict clinical outcome and risk. Omics technologies offer a new opportunity for the development of diagnostic tools and personalized therapy in ARDS. This narrative review assesses recent evidence regarding genomics, proteomics, and metabolomics in ARDS research.