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

Efficacy of early pulmonary rehabilitation in severe and critically ill COVID-19 patients: a retrospective cohort study

BACKGROUND

Respiratory sequelae, induced by lung injury, reduced muscle strength, and nutritional disturbance, are common in hospitalized patients with coronavirus disease 2019 (COVID-19). Therefore, optimal treatment is essential for reducing the mortality in severe forms of the disease and critically ill patients. Pulmonary rehabilitation (PR) has been used in many chronic respiratory diseases, but the role of early PR in severe and critically ill COVID-19 patients remains to be fully understood.

METHODS

Hospitalized severe to critically ill COVID-19 patients were recruited from Beijing Chaoyang Hospital between December 1, 2022, and June 30, 2023. In all, we recruited 272 patients, with 39 in the PR group and 233 in the control group. The PR intervention consisted of the prone position, airway clearance therapy (ACT), and resistance respiratory training (RRT). The primary outcome was the composite disease progression outcome rate, defined as death or intensive care unit (ICU) admission. Adverse events (AEs) and serious adverse events (SAEs) were recorded in the PR group. Inverse probability of treatment weighting (IPTW) and propensity score matching (PSM) was used to balance confounding bias, generating weighting cohort and matched cohort.

RESULTS

The rate of the primary outcome was lower in the PR group (28.2% [11/39] in the PR group vs. 48.9% [114/233] in the control group). Significant differences were observed in both the original and weighting cohorts. Subgroup analyses showed that receiving ≥ 2 types of PR, receiving RRT, length from admission to intervention ≤ 4 days, and baseline P/F ≤ 150 mmHg were associated with lower rates of progression. Total rates of 2.6% (1/39) for AEs and 10.26% (4/39) for SAEs were reported.

CONCLUSIONS

Early pulmonary rehabilitation may prevent disease progression and reduce mortality in patients with severe COVID-19. These findings may be helpful for formulating an optimal rehabilitation strategy.

Identification and validation of robust hospital-acquired pneumonia subphenotypes associated with all-cause mortality: a multi-cohort derivation and validation

PURPOSE

Despite optimal antimicrobial therapy, the treatment failure rate of hospital-acquired pneumonia (HAP) routinely reaches 40% in critically ill patients. Subphenotypes have been identified within sepsis and acute respiratory distress syndrome with important predictive and possibly therapeutic implications. We derived prognosis subphenotypes for HAP and explored whether they were associated with biological markers and response to treatment.

METHODS

We separately analysed data from four cohorts of critically ill patients in France (PNEUMOCARE, n = 511, ATLANREA, n = 401), Netherlands (MARS, n = 1351) and Europe-South America (ENIRRI, n = 900) to investigate HAP heterogeneity using unsupervised clustering based on clinical and routine biological variables available at HAP diagnosis. Then, we developed a machine learning-based workflow to create a simplified classification model using discovery data sets. This model was validated by applying it to an independent replication data set from an international randomized clinical trial comparing linezolid and tedizolid for the treatment of HAP (VITAL, n = 726 patients). The primary outcome was the association of subphenotypes with 28-day all-cause mortality. Secondary analyses included subphenotype associations with treatment failure at test-of-cure, respiratory microbiome and cytokine profiles in the ATLANREA subgroup, and treatment response in the VITAL trial.

RESULTS

We tested twelve metrics and determined that a two-cluster model best fits all cohorts. HAP subphenotype 2 had greater disease severity, lower body temperature, and worse PaO2/FiO2 ratio than subphenotype 1 patients. Although the prevalence of subphenotype 2 ranged from 26.9 to 66.9% across the four derivation cohorts, the rates of 28-day mortality and treatment failure at test-of-cure were consistently higher to subphenotype 1 (p < 0.01 for all comparisons). Subphenotype 2 was associated with greater respiratory microbiome dysbiosis and higher levels of proinflammatory cytokines in the ATLANREA cohort, as well as with statistically significant tedizolid effect modification in the VITAL trial (Relative Risk of treatment failure with tedizolid = 1.52; 95% CI 1.12-2.06 in subphenotype 1 vs. = 0.98; 95% CI 0.7-1.38 in subphenotype 2).

CONCLUSIONS

We identified two robust clinical subphenotypes by extensively analyzing HAP data sets. Their associations with respiratory microbiome composition, systemic inflammation, and treatment efficacy in independent data sets highlight their potential for prognostic value and predictive enrichment in future clinical trials aimed at personalized therapies.

Latent class analysis-derived classification improves the cancer-specific death stratification of lymphomas: A large retrospective cohort study

Lymphomas have diverse etiologies, treatment approaches, and prognoses. Accurate survival estimation is challenging for lymphoma patients due to their heightened susceptibility to non-lymphoma-related mortality. To overcome this challenge, we propose a novel lymphoma classification system that utilizes latent class analysis (LCA) and incorporates demographic and clinicopathological factors as indicators. We conducted LCA using data from 221,812 primary lymphoma patients in the Surveillance, Epidemiology, and End Results (SEER) database and identified four distinct LCA-derived classes. The LCA-derived classification efficiently stratified patients, thereby adjusting the bias induced by competing risk events such as non-lymphoma-related death. This remains effective even in cases of limited availability of cause-of-death information, leading to an enhancement in the accuracy of lymphoma prognosis assessment. Additionally, we validated the LCA-derived classification model in an external cohort and observed its improved prognostic stratification of molecular subtypes. We further explored the molecular characteristics of the LCA subgroups and identified potential driver genes specific to each subgroup. In conclusion, our study introduces a novel LCA-based lymphoma classification system that provides improved prognostic prediction by accounting for competing risk events. The proposed classification system enhances the clinical relevance of molecular subtypes and offers insights into potential therapeutic targets.

Subphenotyping prone position responders with machine learning

BACKGROUND

Acute respiratory distress syndrome (ARDS) is a heterogeneous condition with varying response to prone positioning. We aimed to identify subphenotypes of ARDS patients undergoing prone positioning using machine learning and assess their association with mortality and response to prone positioning.

METHODS

In this retrospective observational study, we enrolled 353 mechanically ventilated ARDS patients who underwent at least one prone positioning cycle. Unsupervised machine learning was used to identify subphenotypes based on respiratory mechanics, oxygenation parameters, and demographic variables collected in supine position. The primary outcome was 28-day mortality. Secondary outcomes included response to prone positioning in terms of respiratory system compliance, driving pressure, PaO2/FiO2 ratio, ventilatory ratio, and mechanical power.

RESULTS

Three distinct subphenotypes were identified. Cluster 1 (22.9% of whole cohort) had a higher PaO2/FiO2 ratio and lower Positive End-Expiratory Pressure (PEEP). Cluster 2 (51.3%) had a higher proportion of COVID-19 patients, lower driving pressure, higher PEEP, and higher respiratory system compliance. Cluster 3 (25.8%) had a lower pH, higher PaCO2, and higher ventilatory ratio. Mortality differed significantly across clusters (p = 0.03), with Cluster 3 having the highest mortality (56%). There were no significant differences in the proportions of responders to prone positioning for any of the studied parameters. Transpulmonary pressure measurements in a subcohort did not improve subphenotype characterization.

CONCLUSIONS

Distinct ARDS subphenotypes with varying mortality were identified in patients undergoing prone positioning; however, predicting which patients benefited from this intervention based on available data was not possible. These findings underscore the need for continued efforts in phenotyping ARDS through multimodal data to better understand the heterogeneity of this population.

Application of Macrophage Subtype Analysis in Acute Lung Injury/Acute Respiratory Distress Syndrome

Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) is a common critical illness. Supportive therapy is still the main strategy for ALI/ARDS. Macrophages are the predominant immune cells in the lungs and play a pivotal role in maintaining homeostasis, regulating metabolism, and facilitating tissue repair. During ALI/ARDS, these versatile cells undergo polarization into distinct subtypes with significant variations in transcriptional profiles, developmental trajectory, phenotype, and functionality. This review discusses developments in the analysis of alveolar macrophage subtypes in the study of ALI/ARDS, and the potential value of targeting new macrophage subtypes in the diagnosis, prognostic evaluation, and treatment of ALI/ARDS.

Future Directions in Therapies for Acute Respiratory Distress Syndrome

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

Steroids and Immunomodulatory Therapies for Acute Respiratory Distress Syndrome

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

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

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

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

Background

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

Methods

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

Results

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

Conclusion

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

Ventilator Weaning in Prolonged Mechanical Ventilation-A Narrative Review

Patients requiring mechanical ventilation (MV) beyond 21 days, usually referred to as prolonged MV, represent a unique group with significant medical needs and a generally poor prognosis. Research suggests that approximately 10% of all MV patients will need prolonged ventilatory care, and that number will continue to rise. Although we have extensive knowledge of MV in the acute care setting, less is known about care in the post-ICU setting. More than 50% of patients who were deemed unweanable in the ICU will be liberated from MV in the post-acute setting. Prolonged MV also presents a challenge in care for medically complex, elderly, socioeconomically disadvantaged and marginalized individuals, usually at the end of their life. Patients and their families often rely on ventilator weaning facilities and skilled nursing homes for the continuation of care, but home ventilation is becoming more common. The focus of this review is to discuss recent advances in the weaning strategies in prolonged MV, present their outcomes and provide insight into the complexity of care.