Latent Class Analysis Reveals COVID-19-related Acute Respiratory Distress Syndrome Subgroups with Differential Responses to Corticosteroids

Shadows and lights in sepsis immunotherapy

INTRODUCTION

Sepsis remains a major global public health challenge. The host's response in sepsis involves both an exaggerated inflammatory reaction and immunosuppressive mechanisms. A better understanding of this response has shed light on the failure of anti-inflammatory therapies administered under the 'one size fits all' approach during last decades.

AREAS COVERED

To date, patients' management has moved toward a comprehensive precision medicine approach that aims to personalize immunotherapy, whether anti-inflammatory or immunostimulatory. Large Prospective interventional randomized controlled trials validating this approach are about to start. A crucial prerequisite for these studies is to stratify patients based on biomarkers that will help defining the patients' immuno-inflammatory trajectory.

EXPERT OPINION

Some biomarkers are already available in routine clinical care, while improvements are anticipated through the standardized use of transcriptomics and other multi-omics technologies in this field. With these precautions in mind, it is reasonable to anticipate improvement in outcomes in sepsis.

Invasive Fungal Infections in Immunocompromised Conditions: Emphasis on COVID-19

The COVID-19 pandemic caused death of 6 million lives globally, primarily from respiratory failure, but also a significant number from invasive fungal co-infections in these patients, owing to the immune dysfunction in hospitalized patients. Such complications occurred more often in critically ill, hospitalized patients particularly those admitted in intensive care units and were reported as the major reason associated with a high mortality rate worldwide. Fungal pathogens most commonly associated with COVID-19 patients comprise members of the Mucorales (such as Rhizopus, Mucor, and Lichtheimia), as well as genera Aspergillus and Candida. In India, the prevalence rate of mucormycosis is relatively high than aspergillosis and candidiasis, and the predisposing risk factors associated with such infections included uncontrolled diabetes, underlying lung disease, leukopenia, neutropenia, malignancies and prolonged steroid therapy. However, co-infection with other fungi, including Alternaria and Scedosporium was also sporadically reported. These devastating invasive fungal infections are associated with differential mortality (high-low) and morbidity rates even after active management. The diagnosis of such infections is often challenging due to lack of sensitivity in contemporary diagnostic methods and poses an enormous challenge to healthcare experts. Thus, the role of early and accurate diagnosis, and management of such fungal infections, is vital in preventing life-threatening situations. Hence, this review focusses primarily on the epidemiology, predisposing risk factors, host environment, diagnosis and treatment of the most common medically important invasive fungal infections in immunocompromised conditions associated with COVID-19.

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.

Specific and Non-specific Aspects and Future Challenges of ICU Care Among COVID-19 Patients with Obesity: A Narrative Review

PURPOSE OF REVIEW

Since the end of 2019, the coronavirus disease 2019 (COVID-19) pandemic has infected nearly 800 million people and caused almost seven million deaths. Obesity was quickly identified as a risk factor for severe COVID-19, ICU admission, acute respiratory distress syndrome, organ support including mechanical ventilation and prolonged length of stay. The relationship among obesity; COVID-19; and respiratory, thrombotic, and renal complications upon admission to the ICU is unclear.

RECENT FINDINGS

The predominant effect of a hyperinflammatory status or a cytokine storm has been suggested in patients with obesity, but more recent studies have challenged this hypothesis. Numerous studies have also shown increased mortality among critically ill patients with obesity and COVID-19, casting doubt on the obesity paradox, with survival advantages with overweight and mild obesity being reported in other ICU syndromes. Finally, it is now clear that the increase in the global prevalence of overweight and obesity is a major public health issue that must be accompanied by a transformation of our ICUs, both in terms of equipment and human resources. Research must also focus more on these patients to improve their care. In this review, we focused on the central role of obesity in critically ill patients during this pandemic, highlighting its specificities during their stay in the ICU, identifying the lessons we have learned, and identifying areas for future research as well as the future challenges for ICU activity.

Clinical phenotyping uncovers heterogeneous associations between corticosteroid treatment and survival in critically ill COVID-19 patients

PURPOSE

Disease heterogeneity in coronavirus disease 2019 (COVID-19) may render the current one-size-fits-all treatment approach suboptimal. We aimed to identify and immunologically characterize clinical phenotypes among critically ill COVID-19 patients, and to assess heterogeneity of corticosteroid treatment effect.

METHODS

We applied consensus k-means clustering on 21 clinical parameters obtained within 24 h after admission to the intensive care unit (ICU) from 13,279 COVID-19 patients admitted to 82 Dutch ICUs from February 2020 to February 2022. Derived phenotypes were reproduced in 6225 COVID-19 ICU patients from Spain (February 2020 to December 2021). Longitudinal immunological characterization was performed in three COVID-19 ICU cohorts from the Netherlands and Germany, and associations between corticosteroid treatment and survival were assessed across phenotypes.

RESULTS

We derived three phenotypes: COVIDICU1 (43% of patients) consisted of younger patients with the lowest Acute Physiology And Chronic Health Evaluation (APACHE) scores, highest body mass index (BMI), lowest PaO2/FiO2 ratio, and a 90-day in-hospital mortality rate of 18%. COVIDICU2 patients (37%) had the lowest BMI, were older and had higher APACHE scores and mortality rate (24%) than COVIDICU1. Patients with COVIDICU3 (20%) were the eldest with the most comorbidities, the highest APACHE scores, acute kidney injury and metabolic dysregulations, and the highest mortality rate (47%). These patients also displayed the most pronounced inflammatory response. Corticosteroid therapy started at day 5 [2-9] after ICU admission and administered for 5 [3-7] days was associated with an increased risk for 90-day mortality in patients with the COVIDICU1 and COVIDICU2 phenotypes (hazard ratio [HR] 1.59 [1.09-2.31], p = 0.015 and HR 1.79 [1.42-2.26], p < 0.001, respectively), but not in patients with the COVIDICU3 phenotype (HR 1.08 [0.76-1.54], p = 0.654).

CONCLUSION

Our multinational study identified three distinct clinical COVID-19 phenotypes, each exhibiting marked differences in demographic, clinical, and immunological features, and in the response to late and short-term corticosteroid treatment.

Platelet Metabolites as Candidate Biomarkers in Sepsis Diagnosis and Management Using the Proposed Explainable Artificial Intelligence Approach

Background: Sepsis is characterized by an atypical immune response to infection and is a dangerous health problem leading to significant mortality. Current diagnostic methods exhibit insufficient sensitivity and specificity and require the discovery of precise biomarkers for the early diagnosis and treatment of sepsis. Platelets, known for their hemostatic abilities, also play an important role in immunological responses. This study aims to develop a model integrating machine learning and explainable artificial intelligence (XAI) to identify novel platelet metabolomics markers of sepsis. Methods: A total of 39 participants, 25 diagnosed with sepsis and 14 control subjects, were included in the study. The profiles of platelet metabolites were analyzed using quantitative 1H-nuclear magnetic resonance (NMR) technology. Data were processed using the synthetic minority oversampling method (SMOTE)-Tomek to address the issue of class imbalance. In addition, missing data were filled using a technique based on random forests. Three machine learning models, namely extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and kernel tree boosting (KTBoost), were used for sepsis prediction. The models were validated using cross-validation. Clinical annotations of the optimal sepsis prediction model were analyzed using SHapley Additive exPlanations (SHAP), an XAI technique. Results: The results showed that the KTBoost model (0.900 accuracy and 0.943 AUC) achieved better performance than the other models in sepsis diagnosis. SHAP results revealed that metabolites such as carnitine, glutamate, and myo-inositol are important biomarkers in sepsis prediction and intuitively explained the prediction decisions of the model. Conclusion: Platelet metabolites identified by the KTBoost model and XAI have significant potential for the early diagnosis and monitoring of sepsis and improving patient outcomes.

Immunomodulators in patients receiving extracorporeal membrane oxygenation for COVID-19: a propensity-score adjusted analysis of the ELSO registry

BACKGROUND

Mortality for patients receiving extracorporeal membrane oxygenation (ECMO) for COVID-19 increased over the course of the pandemic. We investigated the association between immunomodulators and mortality for patients receiving ECMO for COVID-19.

METHODS

We retrospectively analysed the Extracorporeal Life Support Organisation registry from 1 January, 2020, through 31 December, 2021, to compare the outcomes of patients who received no immunomodulators, only corticosteroids, only other immunomodulators (selective interleukin blockers, janus-kinase inhibitors, convalescent plasma, and intravenous immunoglobulin), and a combination of corticosteroids and other immunomodulators administered either before or during ECMO. We used Cox regression models to estimate survival time until 90 days. We estimated the propensity score of receiving different immunomodulators using multinomial regression, and incorporated these scores into the regression models.

RESULTS

We included 7181 patients in the final analysis; 6169 patients received immunomodulators either before or during ECMO. The 90-day survival was 58.1% (95%-CI 55.1-61.2%) for patients receiving no immunomodulators, 50.7% (95%-CI 49.0-52.5%) for those receiving only corticosteroids, 62.2% (95%-CI 57.4-67.0%) for those receiving other immunomodulators, and 48.5% (95%-CI 46.7-50.4%) for those receiving corticosteroids and other immunomodulators. Compared to patients without immunomodulators, patients receiving either corticosteroids alone (HR: 1.13, 95%-CI 1.01-1.28) or with other immunomodulators (HR: 1.21, 95%-CI: 1.07-1.54) had significantly shorter survival time, while patients receiving only other immunomodulators had significantly longer survival time (HR: 0.79, 95%-CI: 0.66-0.96). The receipt of immunomodulators (across all three groups) was associated with an increase in secondary infections.

CONCLUSIONS

In this cohort study, we found that immunomodulators, in particular corticosteroids, were associated with significantly higher mortality amongst patients receiving ECMO for COVID-19, after adjusting for potential confounding variables and propensity score. In addition, patients receiving corticosteroids with or without other immunomodulators had longer ECMO runs, which has potential implications for resource allocation. While residual confounding likely remains, further studies are required to evaluate the timing of immunomodulators and better understand the possible mechanisms behind this association, including secondary infections.

Early inflammatory profiles predict maximal disease severity in COVID-19: An unsupervised cluster analysis

Background

The inflammatory changes that underlie the heterogeneous presentations of COVID-19 remain incompletely understood. In this study we aimed to identify inflammatory profiles that precede the development of severe COVID-19, that could serve as targets for optimised delivery of immunomodulatory therapies and provide insights for the development of new therapies.

Methods

We included individuals sampled <10 days from COVID-19 symptom onset, recruited from both inpatient and outpatient settings. We measured 61 biomarkers in plasma, including markers of innate immune and T cell activation, coagulation, tissue repair and lung injury. We used principal component analysis and hierarchical clustering to derive biomarker clusters, and ordinal logistic regression to explore associations between cluster membership and maximal disease severity, adjusting for known risk factors for severe COVID-19.

Results

In 312 individuals, median (IQR) 7 (4-9) days from symptom onset, we found four clusters. Cluster 1 was characterised by low overall inflammation, cluster 2 was characterised by higher levels of growth factors and markers of endothelial activation (EGF, VEGF, PDGF, TGFα, PAI-1 and p-selectin). Cluster 3 and 4 both had higher overall inflammation. Cluster 4 had the highest levels of most markers including markers of innate immune activation (IL6, procalcitonin, CRP, TNFα), and coagulation (D-dimer, TPO), in contrast cluster 3 had the highest levels of alveolar epithelial injury markers (RAGE, ST2), but relative downregulation of growth factors and endothelial activation markers, suggesting a dysfunctional inflammatory pattern. In unadjusted and adjusted analysis, compared to cluster 1, cluster 3 had the highest odds of progressing to more severe disease (unadjusted OR (95%CI) 9.02 (4.53-17.96), adjusted OR (95%CI) 6.02 (2.70-13.39)).

Conclusion

Early inflammatory profiles predicted subsequent maximal disease severity independent of risk factors for severe COVID-19. A cluster with downregulation of growth factors and endothelial activation markers, and early evidence of alveolar epithelial injury, had the highest risk of severe COVID-19.

The life-saving benefit of dexamethasone in severe COVID-19 is linked to a reversal of monocyte dysregulation

Dexamethasone is a life-saving treatment for severe COVID-19, yet its mechanism of action is unknown, and many patients deteriorate or die despite timely treatment initiation. Here, we identify dexamethasone treatment-induced cellular and molecular changes associated with improved survival in COVID-19 patients. We observed a reversal of transcriptional hallmark signatures in monocytes associated with severe COVID-19 and the induction of a monocyte substate characterized by the expression of glucocorticoid-response genes. These molecular responses to dexamethasone were detected in circulating and pulmonary monocytes, and they were directly linked to survival. Monocyte single-cell RNA sequencing (scRNA-seq)-derived signatures were enriched in whole blood transcriptomes of patients with fatal outcome in two independent cohorts, highlighting the potential for identifying non-responders refractory to dexamethasone. Our findings link the effects of dexamethasone to specific immunomodulation and reversal of monocyte dysregulation, and they highlight the potential of single-cell omics for monitoring in vivo target engagement of immunomodulatory drugs and for patient stratification for precision medicine approaches.

Phenotyping Refractory Cardiogenic Shock Patients Receiving Venous-Arterial Extracorporeal Membrane Oxygenation Using Machine Learning Algorithms

Background

This study used machine learning to categorize cardiogenic shock (CS) patients treated with venous-arterial extracorporeal membrane oxygenation (VA-ECMO) into distinct phenotypes. Subsequently, it aimed to clarify the wide mortality variance observed in refractory CS, attributing it to the condition's inherent heterogeneity.

Methods

This study enrolled a cohort of CS patients who received VA-ECMO support. By employing rigorous machine learning (ML) techniques, we generated and validated clusters based on determinants identified through algorithmic analysis. These clusters, characterized by distinct clinical outcomes, facilitated the examination of clinical and laboratory profiles to enhance the understanding of patient responses to VA-ECMO treatment.

Results

In a study of 210 CS patients undergoing VA-ECMO treatment, 70.5% were male with a median age of 62, ranging from 53 to 67 years. Survival rates were 67.6% during VA-ECMO and 49.5% post-discharge. Patients were classified into three phenotypes based on the clinical and laboratory findings: "platelet preserved (I)", those with stable platelet counts, "hyperinflammatory (II)", those indicating significant inflammation, and "hepatic-renal (III)", those showing compromised liver and kidney functions. Mortality rates (25.0%, 52.8%, and 55.9% for phenotypes I, Ⅱ, and Ⅲ, respectively (p = 0.005)) varied significantly among these groups, highlighting the importance of phenotype identification in patient management.

Conclusions

This study identified three distinct phenotypes among refractory CS patients treated using VA-ECMO, each with unique clinical characteristics and mortality risks. Thus, highlighting the importance of early detection and targeted intervention, these findings suggest that proactive management could improve outcomes for those showing critical signs.

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.

ECMO is associated with decreased hospital mortality in COVID-19 ARDS

This study determined whether compared to conventional mechanical ventilation (MV), extracorporeal membrane oxygenation (ECMO) is associated with decreased hospital mortality or fibrotic changes in patients with COVID-19 acute respiratory distress syndrome. A cohort of 72 patients treated with ECMO and 390 with conventional MV were analyzed (February 2020-December 2021). A target trial was emulated comparing the treatment strategies of initiating ECMO vs no ECMO within 7 days of MV in patients with a PaO2/FiO2 < 80 or a PaCO2 ≥ 60 mmHg. A total of 222 patients met the eligibility criteria for the emulated trial, among whom 42 initiated ECMO. ECMO was associated with a lower risk of hospital mortality (hazard ratio [HR], 0.56; 95% confidence interval [CI] 0.36-0.96). The risk was lower in patients who were younger (age < 70 years), had less comorbidities (Charlson comorbidity index < 2), underwent prone positioning before ECMO, and had driving pressures ≥ 15 cmH2O at inclusion. Furthermore, ECMO was associated with a lower risk of fibrotic changes (HR, 0.30; 95% CI 0.11-0.70). However, the finding was limited due to relatively small number of patients and differences in observability between the ECMO and conventional MV groups.

Association between sub-phenotypes identified using latent class analysis and neurological outcomes in patients with out-of-hospital cardiac arrest in Japan

BACKGROUND

In patients who experience out-of-hospital cardiac arrest (OHCA), it is important to assess the association of sub-phenotypes identified by latent class analysis (LCA) using pre-hospital prognostic factors and factors measurable immediately after hospital arrival with neurological outcomes at 30 days, which would aid in making treatment decisions.

METHODS

This study retrospectively analyzed data obtained from the Japanese OHCA registry between June 2014 and December 2019. The registry included a complete set of data on adult patients with OHCA, which was used in the LCA. The association between the sub-phenotypes and 30-day survival with favorable neurological outcomes was investigated. Furthermore, adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were estimated by multivariate logistic regression analysis using in-hospital data as covariates.

RESULTS

A total of, 22,261 adult patients who experienced OHCA were classified into three sub-phenotypes. The factor with the highest discriminative power upon patient's arrival was Glasgow Coma Scale followed by partial pressure of oxygen. Thirty-day survival with favorable neurological outcome as the primary outcome was evident in 66.0% participants in Group 1, 5.2% in Group 2, and 0.5% in Group 3. The 30-day survival rates were 80.6%, 11.8%, and 1.3% in groups 1, 2, and 3, respectively. Logistic regression analysis revealed that the ORs (95% CI) for 30-day survival with favorable neurological outcomes were 137.1 (99.4-192.2) for Group 1 and 4.59 (3.46-6.23) for Group 2 in comparison to Group 3. For 30-day survival, the ORs (95%CI) were 161.7 (124.2-212.1) for Group 1 and 5.78 (4.78-7.04) for Group 2, compared to Group 3.

CONCLUSIONS

This study identified three sub-phenotypes based on the prognostic factors available immediately after hospital arrival that could predict neurological outcomes and be useful in determining the treatment strategy of patients experiencing OHCA upon their arrival at the hospital.

Lung ultrasound is associated with distinct clinical phenotypes in COVID-19 ARDS: A retrospective observational study

BACKGROUND

ARDS is a heterogeneous syndrome with distinct clinical phenotypes. Here we investigate whether the presence or absence of large pulmonary ultrasonographic consolidations can categorize COVID-19 ARDS patients requiring mechanical ventilation into distinct clinical phenotypes.

METHODS

This is a retrospective study performed in a tertiary-level intensive care unit in Israel between April and September 2020. Data collected included lung ultrasound (LUS) findings, respiratory parameters, and treatment interventions. The primary outcome was a composite of three ARDS interventions: prone positioning, high PEEP, or a high dose of inhaled nitric oxide.

RESULTS

A total of 128 LUS scans were conducted among 23 patients. The mean age was 65 and about two-thirds were males. 81 scans identified large consolidation and were classified as "C-type", and 47 scans showed multiple B-lines with no or small consolidation and were classified as "B-type". The presence of a "C-type" study had 2.5 times increased chance of receiving the composite primary outcome of advanced ARDS interventions despite similar SOFA scores, Pao2/FiO2 ratio, and markers of disease severity (OR = 2.49, %95CI 1.40-4.44).

CONCLUSION

The presence of a "C-type" profile with LUS consolidation potentially represents a distinct COVID-19 ARDS subphenotype that is more likely to require aggressive ARDS interventions. Further studies are required to validate this phenotype in a larger cohort and determine causality, diagnostic, and treatment responses.

Quantitative SARS-CoV-2 RT-PCR and Bronchoalveolar Cytokine Concentrations Redefine the COVID-19 Phenotypes in Critically Ill Patients

RATIONALE

Recent studies suggest that both hypo- and hyperinflammatory acute respiratory distress syndrome (ARDS) phenotypes characterize severe COVID-19-related pneumonia. The role of lung Severe Acute Respiratory Syndrome - Coronavirus 2 (SARS-CoV-2) viral load in contributing to these phenotypes remains unknown.

OBJECTIVES

To redefine COVID-19 ARDS phenotypes when considering quantitative SARS-CoV-2 RT-PCR in the bronchoalveolar lavage of intubated patients. To compare the relevance of deep respiratory samples versus plasma in linking the immune response and the quantitative viral loads.

METHODS

Eligible subjects were adults diagnosed with COVID-19 ARDS who required mechanical ventilation and underwent bronchoscopy. We recorded the immune response in the bronchoalveolar lavage and plasma and the quantitative SARS-CoV-2 RT-PCR in the bronchoalveolar lavage. Hierarchical clustering on principal components was applied separately on the 2 compartments' datasets. Baseline characteristics were compared between clusters.

MEASUREMENTS AND RESULTS

Twenty subjects were enrolled between August 2020 and March 2021. Subjects underwent bronchoscopy on average 3.6 days after intubation. All subjects were treated with dexamethasone prior to bronchoscopy, 11 of 20 (55.6%) received remdesivir and 1 of 20 (5%) received tocilizumab. Adding viral load information to the classic 2-cluster model of ARDS revealed a new cluster characterized by hypoinflammatory responses and high viral load in 23.1% of the cohort. Hyperinflammatory ARDS was noted in 15.4% of subjects. Bronchoalveolar lavage clusters were more stable compared to plasma.

CONCLUSIONS

We identified a unique group of critically ill subjects with COVID-19 ARDS who exhibit hypoinflammatory responses but high viral loads in the lower airways. These clusters may warrant different treatment approaches to improve clinical outcomes.

Association between emergency department disposition and mortality in patients with COVID-19 acute respiratory distress syndrome

Objectives

Patients hospitalized for COVID-19 frequently develop hypoxemia and acute respiratory distress syndrome (ARDS) after admission. In non-COVID-19 ARDS studies, admission to hospital wards with subsequent transfer to intensive care unit (ICU) is associated with worse outcomes. We hypothesized that initial admission to the ward may affect outcomes in patient with COVID-19 ARDS.

Methods

This was a retrospective study of consecutive adults admitted for COVID-19 ARDS between March 2020 and March 2021 at Stanford Health Care. Mortality scores at hospital admission (Coronavirus Clinical Characterization Consortium Mortality Score [4C score]) and ICU admission (Simplified Acute Physiology Score III [SAPS-III]) were calculated, as well as ROX index for patients on high flow nasal oxygen. Patients were classified by emergency department (ED) disposition (ward-first vs. ICU-direct), and 28- and 60-day mortality and highest level of respiratory support within 1 day of ICU admission were compared. A second cohort (April 2021‒July 2022, n = 129) was phenotyped to validate mortality outcome.

Results

A total of 157 patients were included, 48% of whom were first admitted to the ward (n = 75). Ward-first patients had more comorbidities, including lung disease. Ward-first patients had lower 4C and similar SAPS-III score, yet increased mortality at 28 days (32% vs. 17%, hazard ratio [HR] 2.0, 95% confidence interval [95% CI] 1.0‒3.7, p = 0.039) and 60 days (39% vs. 23%, HR 1.83, 95% CI 1.04‒3.22, p = 0.037) compared to ICU-direct patients. More ward-first patients escalated to mechanical ventilation on day 1 of ICU admission (36% vs. 14%, p = 0.002) despite similar ROX index. Ward-first patients who upgraded to ICU within 48 h of ED presentation had the highest mortality. Mortality findings were replicated in a sensitivity analysis.

Conclusion

Despite similar baseline risk scores, ward-first patients with COVID-19 ARDS had increased mortality and escalation to mechanical ventilation compared to ICU-direct patients. Ward-first patients requiring ICU upgrade within 48 h were at highest risk, highlighting a need for improved identification of this group at ED admission.

Dynamic heterogeneity in COVID-19: Insights from a mathematical model

Critical illness, such as severe COVID-19, is heterogenous in presentation and treatment response. However, it remains possible that clinical course may be influenced by dynamic and/or random events such that similar patients subject to similar injuries may yet follow different trajectories. We deployed a mechanistic mathematical model of COVID-19 to determine the range of possible clinical courses after SARS-CoV-2 infection, which may follow from specific changes in viral properties, immune properties, treatment modality and random external factors such as initial viral load. We find that treatment efficacy and baseline patient or viral features are not the sole determinant of outcome. We found patients with enhanced innate or adaptive immune responses can experience poor viral control, resolution of infection or non-infectious inflammatory injury depending on treatment efficacy and initial viral load. Hypoxemia may result from poor viral control or ongoing inflammation despite effective viral control. Adaptive immune responses may be inhibited by very early effective therapy, resulting in viral load rebound after cessation of therapy. Our model suggests individual disease course may be influenced by the interaction between external and patient-intrinsic factors. These data have implications for the reproducibility of clinical trial cohorts and timing of optimal treatment.

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.

Racial and ethnic disparities post-hospitalization for COVID-19: barriers to access to care for survivors of COVID-19 acute respiratory distress syndrome

Racial and ethnic health disparities in the incidence and severity of Coronavirus Disease 2019 (COVID-19) have been observed globally and in the United States. Research has focused on transmission, hospitalization, and mortality among racial and ethnic minorities, but Long COVID-19 health disparities research is limited. This study retrospectively evaluated 195 adults who survived COVID-19 associated acute respiratory distress syndrome (C-ARDS) in New York City from March-April 2020. Among survivors, 54% met the criteria for Long COVID syndrome. Hispanic/Latinx patients, were more likely to be uninsured (p = 0.027) and were less frequently discharged to rehabilitation facilities (p < 0.001). A cross-sectional telephone survey and interview were conducted with a subset of survivors (n = 69). Among these, 11% reported a lack of follow-up primary care post-discharge and 38% had subsequent emergency room visits. Notably, 38% reported poor treatment within the health care system, with 67% attributing this to racial or ethnic bias. Thematic analysis of interviews identified four perceived challenges: decline in functional status, discrimination during hospitalization, healthcare system inequities, and non-healthcare-related structural barriers. Sources of resilience included survivorship, faith, and family support. This study highlights structural and healthcare-related barriers rooted in perceived racism and poverty as factors impacting post-COVID-19 care.

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).

A Bayesian finite mixture model approach to evaluate dichotomization method for correlated ELISA tests

In diagnostic accuracy studies, a commonly employed approach involves dichotomizing continuous data and subsequently analyzing them using a Bayesian latent class model (BLCM), often relying on binomial or multinomial distributions, rather than preserving their continuous nature. However, this procedure can inadvertently lead to less reliable outcomes due to the inherent loss of information when converting the original continuous measurements into binary values. Through comprehensive simulations, we demonstrated the limitations and disadvantages of dichotomizing continuous biomarkers from two correlated tests. Our findings highlighted notable disparities between the true values and the model estimates as a result of dichotomization. We discovered the crucial significance of selecting a reference test with high diagnostic accuracy in test evaluation in order to obtain reliable estimates of test accuracy and prevalences. Our study served as a call to action for veterinary researchers to exercise caution when utilizing dichotomization.

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.

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.

A Nomogram for Predicting Mortality in Patients with Pneumonia-Associated Acute Respiratory Distress Syndrome (ARDS)

Background

There is no predictive tool developed for pneumonia-associated acute respiratory distress syndrome (ARDS) specifically so far, and the clinical risk classification of these patients is not well defined. Our study aims to construct an early prediction model for hospital mortality in patients with pneumonia-associated ARDS.

Methods

In this single-center retrospective study, consecutive patients with pneumonia-associated ARDS admitted into intensive care units (ICUs) in West China Hospital of Sichuan University in China between January 2012 and December 2018 were enrolled. The least absolute shrinkage and selection operator (LASSO) regression and then multivariate logistic regression analysis were used to identify independent predictors which were used to develop a nomogram. We evaluated the performance of differentiation, calibration, and clinical utility of the nomogram.

Results

The included patients were divided into the training cohort (442 patients) and the testing cohort (190 patients) with comparable baseline characteristics. The independent predictors for hospital mortality included age (OR: 1.04; 95% CI: 1.02, 1.05), chronic cardiovascular diseases (OR: 2.62; 95% CI: 1.54, 4.45), chronic respiratory diseases (OR: 1.87; 95% CI: 1.02, 3.43), lymphocytes (OR: 0.56; 95% CI: 0.39, 0.81), albumin (OR: 0.94; 95% CI: 0.90, 1.00), creatinine (OR: 1.00; 95% CI: 1.00, 1.01), D-dimer (OR: 1.06; 95% CI: 1.03, 1.09) and procalcitonin (OR: 1.14; 95% CI: 1.07, 1.22). A web-based dynamic nomogram (https://h1234.shinyapps.io/dynnomapp/) was constructed based on these factors. The concordance index (C index) of the nomogram was 0.798 (95% CI: 0.756, 0.840) in the training cohort and 0.808 (95% CI: 0.747, 0.870) in testing cohort. The precision-recall (PR) curves, calibration curves, decision curve analyses (DCA) and clinical impact curves showed that the nomogram has good predictive value and clinical utility.

Conclusion

We developed and evaluated a convenient nomogram consisting of 8 clinical characteristics for predicting mortality in patients with pneumonia-associated ARDS.

Glucocorticoid use in acute respiratory failure from pulmonary causes and association with early changes in the systemic host immune response

BACKGROUND

Glucocorticoids are commonly used in patients with or at-risk for acute respiratory distress syndrome (ARDS), but optimal use remains unclear despite well-conducted clinical trials. We performed a secondary analysis in patients previously enrolled in the Acute Lung Injury and Biospecimen Repository at the University of Pittsburgh. The primary aim of our study was to investigate early changes in host response biomarkers in response to real-world use of glucocorticoids in patients with acute respiratory failure due to ARDS or at-risk due to a pulmonary insult. Participants had baseline plasma samples obtained on study enrollment and on follow-up 3 to 5 days later to measure markers of innate immunity (IL-6, IL-8, IL-10, TNFr1, ST2, fractalkine), epithelial injury (sRAGE), endothelial injury (angiopoietin-2), and host response to bacterial infections (procalcitonin, pentraxin-3). In our primary analyses, we investigated the effect of receiving glucocorticoids between baseline and follow-up samples on host response biomarkers measured at follow-up by doubly robust inverse probability weighting analysis. In exploratory analyses, we examined associations between glucocorticoid use and previously characterized host response subphenotypes (hyperinflammatory and hypoinflammatory).

RESULTS

67 of 148 participants (45%) received glucocorticoids between baseline and follow-up samples. Dose and type of glucocorticoids varied. Regimens that used hydrocortisone alone were most common (37%), and median daily dose was equivalent to 40 mg methylprednisolone (interquartile range: 21, 67). Participants who received glucocorticoids were more likely to be female, to be on immunosuppressive therapy at baseline, and to have higher baseline levels of ST-2, fractalkine, IL-10, pentraxin-3, sRAGE, and TNFr1. Glucocorticoid use was associated with decreases in IL-6 and increases in fractalkine. In exploratory analyses, glucocorticoid use was more frequent in participants in the hyperinflammatory subphenotype (58% vs 40%, p = 0.05), and was not associated with subphenotype classification at the follow-up time point (p = 0.16).

CONCLUSIONS

Glucocorticoid use varied in a cohort of patients with or at-risk for ARDS and was associated with early changes in the systemic host immune response.

Phenotypes and Lung Microbiota Signatures of Immunocompromised Patients with Pneumonia-Related Acute Respiratory Distress Syndrome

Objective

We aim to identify the clinical phenotypes of immunocompromised patients with pneumonia-related ARDS, to investigate the lung microbiota signatures and the outcomes of different phenotypes, and finally, to develop a machine learning classifier for a specified phenotype.

Methods

This prospective study included immunocompromised patients with pneumonia-related ARDS. We identified phenotypes using hierarchical clustering to analyze clinical variables and serum cytokine levels. We then compared outcomes and lung microbiota signatures between phenotypes. Based on lung microbiota markers, we developed a random forest classifier for a specified phenotype with worse outcomes.

Results

This study included 92 patients, who were divided into three phenotypes, namely "type α" (N = 33), "type β" (N = 12), and "type γ" (N = 47). Compared to type α or type β, patients with type γ had no obvious inflammatory presentation and had significantly lower IL-6 levels and more severe oxygenation failure. Type γ was also related to higher 30-day mortality and lower ventilator free days. The microbiota signatures of type γ were characterized by lower alpha diversity and distinct compositions than those of other patients. We developed a lung microbiota-derived random forest model to differentiate patients with type γ from other phenotypes.

Conclusion

Immunocompromised patients with pneumonia-related ARDS can be clustered into three clinical phenotypes, namely type α, type β, and type γ. Phenotypes were distinguished from each other with different outcomes and lung microbiota signatures. Type γ, which was characterized by insufficient inflammation response and worse outcomes, can be detected with a random forest model based on lung microbiota markers.

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.

Bacterial Community- and Hospital-Acquired Pneumonia in Patients with Critical COVID-19-A Prospective Monocentric Cohort Study

The impact of bacterial pneumonia on patients with COVID-19 infection remains unclear. This prospective observational monocentric cohort study aims to determine the incidence of bacterial community- and hospital-acquired pneumonia (CAP and HAP) and its effect on mortality in critically ill COVID-19 patients admitted to the intensive care unit (ICU) at University Hospital Olomouc between 1 November 2020 and 31 December 2022. The secondary objectives of this study include identifying the bacterial etiology of CAP and HAP and exploring the capabilities of diagnostic tools, with a focus on inflammatory biomarkers. Data were collected from the electronic information hospital system, encompassing biomarkers, microbiological findings, and daily visit records, and subsequently evaluated by ICU physicians and clinical microbiologists. Out of 171 patients suffering from critical COVID-19, 46 (27%) had CAP, while 78 (46%) developed HAP. Critically ill COVID-19 patients who experienced bacterial CAP and HAP exhibited higher mortality compared to COVID-19 patients without any bacterial infection, with rates of 38% and 56% versus 11%, respectively. In CAP, the most frequent causative agents were chlamydophila and mycoplasma; Enterobacterales, which were multidrug-resistant in 71% of cases; Gram-negative non-fermenting rods; and Staphylococcus aureus. Notably, no strains of Streptococcus pneumoniae were detected, and only a single strain each of Haemophilus influenzae and Moraxella catarrhalis was isolated. The most frequent etiologic agents causing HAP were Enterobacterales and Gram-negative non-fermenting rods. Based on the presented results, commonly used biochemical markers demonstrated poor predictive and diagnostic accuracy. To confirm the diagnosis of bacterial CAP in our patient cohort, it was necessary to assess the initial values of inflammatory markers (particularly procalcitonin), consider clinical signs indicative of bacterial infection, and/or rely on positive microbiological findings. For HAP diagnostics, it was appropriate to conduct regular detailed clinical examinations (with a focus on evaluating respiratory functions) and closely monitor the dynamics of inflammatory markers (preferably Interleukin-6).

Host Response Changes and Their Association with Mortality in COVID-19 Patients with Lymphopenia

Rationale: Lymphopenia in coronavirus disease (COVID-19) is associated with increased mortality. Objectives: To explore the association between lymphopenia, host response aberrations, and mortality in patients with lymphopenic COVID-19. Methods: We determined 43 plasma biomarkers reflective of four pathophysiological domains: endothelial cell and coagulation activation, inflammation and organ damage, cytokine release, and chemokine release. We explored if decreased concentrations of lymphocyte-derived proteins in patients with lymphopenia were associated with an increase in mortality. We sought to identify host response phenotypes in patients with lymphopenia by cluster analysis of plasma biomarkers. Measurements and Main Results: A total of 439 general ward patients with COVID-19 were stratified by baseline lymphocyte counts: normal (>1.0 × 109/L; n = 167), mild lymphopenia (>0.5 to ⩽1.0 × 109/L; n = 194), and severe lymphopenia (⩽0.5 × 109/L; n = 78). Lymphopenia was associated with alterations in each host response domain. Lymphopenia was associated with increased mortality. Moreover, in patients with lymphopenia (n = 272), decreased concentrations of several lymphocyte-derived proteins (e.g., CCL5, IL-4, IL-13, IL-17A) were associated with an increase in mortality (at P < 0.01 or stronger significance levels). A cluster analysis revealed three host response phenotypes in patients with lymphopenia: "hyporesponsive" (23.2%), "hypercytokinemic" (36.4%), and "inflammatory-injurious" (40.4%), with substantially differing mortality rates of 9.5%, 5.1%, and 26.4%, respectively. A 10-biomarker model accurately predicted these host response phenotypes in an external cohort with similar mortality distribution. The inflammatory-injurious phenotype showed a remarkable combination of relatively high inflammation and organ damage markers with high antiinflammatory cytokine levels yet low proinflammatory cytokine levels. Conclusions: Lymphopenia in COVID-19 signifies a heterogenous group of patients with distinct host response features. Specific host responses contribute to lymphopenia-associated mortality in COVID-19, including reduced CCL5 levels.

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.

Longitudinal plasma proteomics reveals biomarkers of alveolar-capillary barrier disruption in critically ill COVID-19 patients

The pathobiology of respiratory failure in COVID-19 consists of a complex interplay between viral cytopathic effects and a dysregulated host immune response. In critically ill patients, imatinib treatment demonstrated potential for reducing invasive ventilation duration and mortality. Here, we perform longitudinal profiling of 6385 plasma proteins in 318 hospitalised patients to investigate the biological processes involved in critical COVID-19, and assess the effects of imatinib treatment. Nine proteins measured at hospital admission accurately predict critical illness development. Next to dysregulation of inflammation, critical illness is characterised by pathways involving cellular adhesion, extracellular matrix turnover and tissue remodelling. Imatinib treatment attenuates protein perturbations associated with inflammation and extracellular matrix turnover. These proteomic alterations are contextualised using external pulmonary RNA-sequencing data of deceased COVID-19 patients and imatinib-treated Syrian hamsters. Together, we show that alveolar capillary barrier disruption in critical COVID-19 is reflected in the plasma proteome, and is attenuated with imatinib treatment. This study comprises a secondary analysis of both clinical data and plasma samples derived from a clinical trial that was registered with the EU Clinical Trials Register (EudraCT 2020-001236-10, https://www.clinicaltrialsregister.eu/ctr-search/trial/2020-001236-10/NL ) and Netherlands Trial Register (NL8491, https://www.trialregister.nl/trial/8491 ).

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.

Prone Positioning and Molecular Biomarkers in COVID and Non-COVID ARDS: A Narrative Review

Prone positioning (PP) represents a therapeutic intervention with the proven capacity of ameliorating gas exchanges and ventilatory mechanics indicated in acute respiratory distress syndrome (ARDS). When PP is selectively applied to moderate-severe cases of ARDS, it sensitively affects clinical outcomes, including mortality. After the COVID-19 outbreak, clinical application of PP peaked worldwide and was applied in 60% of treated cases, according to large reports. Research on this topic has revealed many physiological underpinnings of PP, focusing on regional ventilation redistribution and the reduction of parenchymal stress and strain. However, there is a lack of evidence on biomarkers behavior in different phases and phenotypes of ARDS. Patients response to PP are, to date, decided on PaO2/FiO2 ratio improvement, whereas scarce data exist on biomarker tracking during PP. The purpose of this review is to explore current evidence on the clinical relevance of biomarkers in the setting of moderate-severe ARDS of different etiologies (i.e., COVID and non-COVID-related ARDS). Moreover, this review focuses on how PP may modulate biomarkers and which biomarkers may have a role in outcome prediction in ARDS patients.

The influence of propolis plus Hyoscyamus niger L. against COVID-19: A phase II, multicenter, placebo-controlled, randomized trial

The incubation period of COVID-19 symptoms, along with the proliferation and high transmission rate of the SARS-CoV-2 virus, is the cause of an uncontrolled epidemic worldwide. Vaccination is the front line of prevention, and antiinflammatory and antiviral drugs are the treatment of this disease. In addition, some herbal therapy approaches can be a good way to deal with this disease. The aim of this study was to evaluate the effect of propolis syrup with Hyoscyamus niger L. extract in hospitalized patients with COVID-19 with acute disease conditions in a double-blinded approach. The study was performed on 140 patients with COVID-19 in a double-blind, randomized, and multicentral approach. The main inclusion criterion was the presence of a severe type of COVID-19 disease. The duration of treatment with syrup was 6 days and 30 CC per day in the form of three meals. On Days 0, 2, 4, and 6, arterial blood oxygen levels, C-reactive protein (CRP), erythrocyte sedimentation rate, and white blood cell, as well as the patient's clinical symptoms such as fever and chills, cough and shortness of breath, chest pain, and other symptoms, were recorded and analyzed. Propolis syrup with H. niger L. significantly reduces cough from the second day, relieving shortness of breath on the fourth day, and significantly reduces CRP, weakness, and lethargy, as well as significantly increased arterial blood oxygen pressure on the sixth day compared to the placebo group (p < 0.05). The results in patients are such that in the most severe conditions of the disease 80% < SpO2 (oxygen saturation), the healing process of the syrup on reducing CRP and increasing arterial blood oxygen pressure from the fourth day is significantly different compared with the placebo group (p < 0.05). The use of syrup is associated with a reduction of 3.6 days in the hospitalization period compared with the placebo group. Propolis syrup with H. niger L. has effectiveness in the viral and inflammatory phases on clinical symptoms and blood parameters and arterial blood oxygen levels of patients with COVID-19. Also, it reduces referrals to the intensive care unit and mortality in hospitalized patients with COVID-19. So, this syrup promises to be an effective treatment in the great challenge of COVID-19.

[ARDS Diagnostics and Treatment after the Coronavirus Pandemic - Everything as it was?]

ARDS is a syndrome that can develop as a result of various underlying diseases. For a long time, the prevailing belief was that the course of the disease was comparable regardless of the underlying disease. However, even before the COVID-19 pandemic, it was suspected that there were different manifestations that could be treated more individually and thus reduce the high mortality rate of ARDS, which has remained unchanged for years. The various findings on the heterogeneity of the course of the disease in COVID-related ARDS appear to confirm these assumptions. It is therefore to be expected that the diagnosis and treatment of non-COVID-related ARDS will also have to be individualised according to such phenotypes in the future. However, as long as the effectiveness of such strategies has not been proven in clinical trials, the current recommendations for ARDS therapy will remain valid for the time being. However, the adjustments already formulated in this context to individual pathophysiological conditions with regard to respiratory mechanics, ventilation-perfusion distribution and possible cardiac dysfunction should be made more meticulously than has usually been the case to date.

[Pathophysiology of Acute Respiratory Distress Syndrome]

Acute respiratory distress syndrome (ARDS) is a common condition in intensive care medicine. Various intra- and extrapulmonal causes may trigger an epithelial and endothelial permeability increase, which leads to impaired gas exchange due to fluid overload of the alveoli and transmigration of leukocytes. This results in hypoxemia and hypercapnia, as well as deleterious consequences for the macro- and microcirculation with the risk of multi-organ failure and high mortality. This review summarizes ARDS pathophysiology and clinical consequences.

Simvastatin in Critically Ill Patients with Covid-19

BACKGROUND

The efficacy of simvastatin in critically ill patients with coronavirus disease 2019 (Covid-19) is unclear.

METHODS

In an ongoing international, multifactorial, adaptive platform, randomized, controlled trial, we evaluated simvastatin (80 mg daily) as compared with no statin (control) in critically ill patients with Covid-19 who were not receiving statins at baseline. The primary outcome was respiratory and cardiovascular organ support-free days, assessed on an ordinal scale combining in-hospital death (assigned a value of -1) and days free of organ support through day 21 in survivors; the analyis used a Bayesian hierarchical ordinal model. The adaptive design included prespecified statistical stopping criteria for superiority (>99% posterior probability that the odds ratio was >1) and futility (>95% posterior probability that the odds ratio was <1.2).

RESULTS

Enrollment began on October 28, 2020. On January 8, 2023, enrollment was closed on the basis of a low anticipated likelihood that prespecified stopping criteria would be met as Covid-19 cases decreased. The final analysis included 2684 critically ill patients. The median number of organ support-free days was 11 (interquartile range, -1 to 17) in the simvastatin group and 7 (interquartile range, -1 to 16) in the control group; the posterior median adjusted odds ratio was 1.15 (95% credible interval, 0.98 to 1.34) for simvastatin as compared with control, yielding a 95.9% posterior probability of superiority. At 90 days, the hazard ratio for survival was 1.12 (95% credible interval, 0.95 to 1.32), yielding a 91.9% posterior probability of superiority of simvastatin. The results of secondary analyses were consistent with those of the primary analysis. Serious adverse events, such as elevated levels of liver enzymes and creatine kinase, were reported more frequently with simvastatin than with control.

CONCLUSIONS

Although recruitment was stopped because cases had decreased, among critically ill patients with Covid-19, simvastatin did not meet the prespecified criteria for superiority to control. (REMAP-CAP ClinicalTrials.gov number, NCT02735707.).

Derivation, validation, and transcriptomic assessment of pediatric septic shock phenotypes identified through latent profile analyses: Results from a prospective multi-center observational cohort

Background

Sepsis poses a grave threat, especially among children, but treatments are limited due to clinical and biological heterogeneity among patients. Thus, there is an urgent need for precise subclassification of patients to guide therapeutic interventions.

Methods

We used clinical, laboratory, and biomarker data from a prospective multi-center pediatric septic shock cohort to derive phenotypes using latent profile analyses. Thereafter, we trained a support vector machine model to assign phenotypes in a hold-out validation set. We tested interactions between phenotypes and common sepsis therapies on clinical outcomes and conducted transcriptomic analyses to better understand the phenotype-specific biology. Finally, we compared whether newly identified phenotypes overlapped with established gene-expression endotypes and tested the utility of an integrated subclassification scheme.

Findings

Among 1,071 patients included, we identified two phenotypes which we named 'inflamed' (19.5%) and an 'uninflamed' phenotype (80.5%). The 'inflamed' phenotype had an over 4-fold risk of 28-day mortality relative to those 'uninflamed'. Transcriptomic analysis revealed overexpression of genes implicated in the innate immune response and suggested an overabundance of developing neutrophils, pro-T/NK cells, and NK cells among those 'inflamed'. There was no significant overlap between endotypes and phenotypes. However, an integrated subclassification scheme demonstrated varying survival probabilities when comparing endophenotypes.

Interpretation

Our research underscores the reproducibility of latent profile analyses to identify clinical and biologically informative 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.

Subphenotypes of SARS-CoV-2-Associated ARDS Overlap Each Other: A Retrospective Analysis

Background  Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus-associated pneumonia and acute respiratory distress syndrome (ARDS) were often associated with hyperinflammation and elevation of several serum inflammatory markers but usually less than what is observed in non-coronavirus disease (COVID) ARDS. Elevated inflammatory markers such as C-reactive protein, interleukin (IL)-6, etc., are associated with severe infection. This study identified subphenotypes of COVID-19 ARDS patients by latent profile analysis in a cohort of Indian patients. Methods  Data of n  = 233 adult Indian patients with laboratory-confirmed SARS-CoV-2 infection admitted to a tertiary care teaching hospital were analyzed in this retrospective study. Only patients with acute respiratory failure (defined by partial pressure of oxygen/fraction of inspired oxygen ratio < 200 mm Hg) and chest X-ray showing bilateral infiltrates were included. Results  The patients' mean (standard deviation) age was 53.3 (14.9) years, and 62% were male. A two subphenotypic model was formulated based on the lowest Bayesian information criterion. Neutrophil-to-lymphocyte ratio and serum IL-6 were latent variables in that model (entropy 0.91). The second phenotype (hyperinflammatory) had lower platelet count ( p  = 0.02), higher serum creatinine ( p  = 0.004), higher C-reactive protein ( p  = 0.001), higher ferritin ( p  < 0.001), and serum lactate dehydrogenase ( p  = 0.009). Age-adjusted hospital mortality ( p  = 0.007), duration of hospital stay ( p  < 0.001), and duration of intensive care unit stay ( p  < 0.001) were significantly higher in the second subphenotype. Conclusion  Two distinct but overlapping subphenotypes were identified in SARS-CoV-2-associated respiratory failure. Hyperinflammatory subphenotype was associated with significantly poor short-term outcomes.

Inflammatory subphenotypes in patients at risk of ARDS: evidence from the LIPS-A trial

PURPOSE

Latent class analysis (LCA) has identified hyper- and non-hyper-inflammatory subphenotypes in patients with acute respiratory distress syndrome (ARDS). It is unknown how early inflammatory subphenotypes can be identified in patients at risk of ARDS. We aimed to test for inflammatory subphenotypes upon presentation to the emergency department.

METHODS

LIPS-A was a trial of aspirin to prevent ARDS in at-risk patients presenting to the emergency department. In this secondary analysis, we performed LCA using clinical, blood test, and biomarker variables.

RESULTS

Among 376 (96.4%) patients from the LIPS-A trial, two classes were identified upon presentation to the emergency department (day 0): 72 (19.1%) patients demonstrated characteristics of a hyper-inflammatory and 304 (80.9%) of a non-hyper-inflammatory subphenotype. 15.3% of patients in the hyper- and 8.2% in the non-hyper-inflammatory class developed ARDS (p = 0.07). Patients in the hyper-inflammatory class had fewer ventilator-free days (median [interquartile range, IQR] 28[23-28] versus 28[27-28]; p = 0.010), longer intensive care unit (3[2-6] versus 0[0-3] days; p < 0.001) and hospital (9[6-18] versus 5[3-9] days; p < 0.001) length of stay, and higher 1-year mortality (34.7% versus 20%; p = 0.008). Subphenotypes were identified on day 1 and 4 in a subgroup with available data (n = 244). 77.9% of patients remained in their baseline class throughout day 4. Patients with a hyper-inflammatory subphenotype throughout the study period (n = 22) were at higher risk of ARDS (36.4% versus 10.4%; p = 0.003).

CONCLUSION

Hyper- and non-hyper-inflammatory subphenotypes may precede ARDS development, remain identifiable over time, and can be identified upon presentation to the emergency department. A hyper-inflammatory subphenotype predicts worse outcomes.

COVID-19 subphenotypes at hospital admission are associated with mortality: a cross-sectional study

BACKGROUND

We have an incomplete understanding of COVID-19 characteristics at hospital presentation and whether underlying subphenotypes are associated with clinical outcomes and therapeutic responses.

METHODS

For this cross-sectional study, we extracted electronic health data from adults hospitalized between 1 March and 30 August 2020 with a PCR-confirmed diagnosis of COVID-19 at five New York City Hospitals. We obtained clinical and laboratory data from the first 24 h of the patient's hospitalization. Treatment with tocilizumab and convalescent plasma was assessed over hospitalization. The primary outcome was mortality; secondary outcomes included intubation, intensive care unit (ICU) admission and length of stay (LOS). First, we employed latent class analysis (LCA) to identify COVID-19 subphenotypes on admission without consideration of outcomes and assigned each patient to a subphenotype. We then performed robust Poisson regression to examine associations between COVID-19 subphenotype assignment and outcome. We explored whether the COVID-19 subphenotypes had a differential response to tocilizumab and convalescent plasma therapies.

RESULTS

A total of 4620 patients were included. LCA identified six subphenotypes, which were distinct by level of inflammation, clinical and laboratory derangements and ranged from a hypoinflammatory subphenotype with the fewest derangements to a hyperinflammatory with multiorgan dysfunction subphenotypes. Multivariable regression analyses found differences in risk for mortality, intubation, ICU admission and LOS, as compared to the hypoinflammatory subphenotype. For example, in multivariable analyses the moderate inflammation with fever subphenotype had 3.29 times the risk of mortality (95% CI 2.05, 5.28), while the hyperinflammatory with multiorgan failure subphenotype had 17.87 times the risk of mortality (95% CI 11.56, 27.63), as compared to the hypoinflammatory subphenotype. Exploratory analyses suggested that subphenotypes may differential respond to convalescent plasma or tocilizumab therapy.

CONCLUSION

COVID-19 subphenotype at hospital admission may predict risk for mortality, ICU admission and intubation and differential response to treatment.KEY MESSAGEThis cross-sectional study of COVID patients admitted to the Mount Sinai Health System, identified six distinct COVID subphenotypes on admission. Subphenotypes correlated with ICU admission, intubation, mortality and differential response to treatment.

Analysis of Protein Biomarkers From Hospitalized COVID-19 Patients Reveals Severity-Specific Signatures and Two Distinct Latent Profiles With Differential Responses to Corticosteroids

OBJECTIVES

To identify and validate novel COVID-19 subphenotypes with potential heterogenous treatment effects (HTEs) using electronic health record (EHR) data and 33 unique biomarkers.

DESIGN

Retrospective cohort study of adults presenting for acute care, with analysis of biomarkers from residual blood collected during routine clinical care. Latent profile analysis (LPA) of biomarker and EHR data identified subphenotypes of COVID-19 inpatients, which were validated using a separate cohort of patients. HTE for glucocorticoid use among subphenotypes was evaluated using both an adjusted logistic regression model and propensity matching analysis for in-hospital mortality.

SETTING

Emergency departments from four medical centers.

PATIENTS

Patients diagnosed with COVID-19 based on International Classification of Diseases , 10th Revision codes and laboratory test results.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Biomarker levels generally paralleled illness severity, with higher levels among more severely ill patients. LPA of 522 COVID-19 inpatients from three sites identified two profiles: profile 1 ( n = 332), with higher levels of albumin and bicarbonate, and profile 2 ( n = 190), with higher inflammatory markers. Profile 2 patients had higher median length of stay (7.4 vs 4.1 d; p < 0.001) and in-hospital mortality compared with profile 1 patients (25.8% vs 4.8%; p < 0.001). These were validated in a separate, single-site cohort ( n = 192), which demonstrated similar outcome differences. HTE was observed ( p = 0.03), with glucocorticoid treatment associated with increased mortality for profile 1 patients (odds ratio = 4.54).

CONCLUSIONS

In this multicenter study combining EHR data with research biomarker analysis of patients with COVID-19, we identified novel profiles with divergent clinical outcomes and differential treatment responses.

Efficacy and Safety of Glucocorticoid in the Treatment of Acute Respiratory Distress Syndrome caused by Covid-19: A Systematic Review and Meta-Analysis

BACKGROUND

Glucocorticoids are often used to treat acute respiratory distress syndrome (ARDS) and novel coronavirus disease 2019 (COVID-19). However, the efficacy and safety of glucocorticoids in the treatment of ARDS caused by COVID-19 are still controversial; therefore, we conducted this meta-analysis of the literature on this topic.

METHODS

Four databases (PubMed, EMBASE, Cochrane Library, and Web of Science) were searched from the establishment of the databases to August 16, 2023. Randomized controlled trials (RCTs) and cohort studies that compared glucocorticoid versus standard treatment for ARDS caused by COVID-19 were included. The Newcastle-Ottawa Scale (NOS) checklist and the Cochrane Handbook for Systematic Reviews of Interventions were used to evaluate the risk of bias. Review Manager 5.4 software and STATA 17.0 were used for meta-analy-sis, and the relative risk (RR), mean difference, and 95% confidence intervals (CIs) were then determined. Results: A total of 17 studies involving 8592 patients were evaluated, including 14 retrospective studies and 3 RCTs. Sixteen studies reported data on all-cause mortality. The results of the meta-analysis showed that glucocorticoids did not reduce all-cause (RR, 0.96; 95% CI 0.82-1.13, P = .62) or 28-day (RR, 1.01; 95% CI 0.78-1.32, P = .93) mortality. Subgroup analysis showed that only methylprednisolone reduced all-cause mortality. No matter whether glucocorticoid use was early or delayed, high-dose or low-dose, long-term or short-term, no regimen reduced all-cause mortality. Furthermore, there were no significant differences in length of intensive care unit (ICU) stay, length of hospital stay, hyperglycemia, and ventilator-associated pneumonia (VAP); how-ever, glucocorticoids increased the number of ventilator-free days.

CONCLUSIONS

Although methylprednisolone may reduce all-cause mortality from ARDS caused by COVID-19, this effect was not found with other types of glucocorticoids. At the same time, glucocorticoid use was associ-ated with more ventilator-free days, without increasing the incidence of hyperglycemic events or VAP. Con-sidering that almost all of the included studies were retrospective cohort studies, more RCTs are needed to confirm these findings.

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.