The discovery of biological subphenotypes in ARDS: a novel approach to targeted medicine?

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.

Polymorphic markers of several immune regulatory genes modulate the susceptibility for eczema and related phenotypes in children

Background

Eczema is associated with multiple genes regulating epidermal barrier functions and immunological pathways. However, their epistatic interactions are not well studied. This cross-sectional study investigated the relationship between childhood eczema phenotypes and single-nucleotide polymorphisms (SNPs) of immune regulatory genes.

Methods

One thousand three hundred and twenty-nine Chinese eczematous children and 1,179 non-allergic controls were recruited. Nine SNPs of immune regulatory genes signal transducer and activator of transcription 3 (STAT3), interleukin-10 (IL10), transforming growth factor-beta 1 (TGFB1), and IL-6 receptor (IL6R) were genotyped by TaqMan genotyping assays. Logistic regression was used to analyze the associations between SNPs and eczema phenotypes. Generalized multifactor dimensionality reduction (GMDR) was used to examine epistatic interactions among these SNPs as well as those reported by our group [filaggrin (FLG) and 11q13] for eczema phenotypes.

Results

TGFB1_rs1800469 was found to be associated with eczema [odds ratio (OR), 0.82; 95% confidence interval (CI): 0.73-0.92; P=0.001], atopic eczema (OR, 0.83; 95% CI: 0.72-0.95; P=0.009) and allergic rhinitis (OR, 0.84; 95% CI: 0.74-0.95; P=0.005). We also found a trend between IL10_rs1800872 and increased total immunoglobulin E (IgE) levels (P=0.009). Epistatic interaction among IL10_rs3021094, TGFB1_rs1800469, IL6R_rs2228145, and STAT3_rs4796793 were found for total IgE [testing accuracy (TA), 0.551; cross-validation consistency (CVC), 10; P=0.014]. Mean log-transformed total IgE (logIgE) levels in high-risk cases, low-risk cases, high-risk controls, and low-risk controls were 2.75, 2.60, 1.90, and 1.81 respectively (P=0.019 for trend).

Conclusions

Functional TGFB1 polymorphism is associated with both eczema and allergic rhinitis, suggesting the role of TGF-β1 in allergy susceptibility. IL10 may be associated with increased total IgE levels. Interaction among immune regulatory genes modulates total IgE levels.

ARDS Mortality Prediction Model Using Evolving Clinical Data and Chest Radiograph Analysis

INTRODUCTION

Within primary ARDS, SARS-CoV-2-associated ARDS (C-ARDS) emerged in late 2019, reaching its peak during the subsequent two years. Recent efforts in ARDS research have concentrated on phenotyping this heterogeneous syndrome to enhance comprehension of its pathophysiology.

METHODS AND RESULTS

A retrospective study was conducted on C-ARDS patients from April 2020 to February 2021, encompassing 110 participants with a mean age of 63.2 ± 11.92 (26-83 years). Of these, 61.2% (68) were male, and 25% (17) experienced severe ARDS, resulting in a mortality rate of 47.3% (52). Ventilation settings, arterial blood gases, and chest X-ray (CXR) were evaluated on the first day of invasive mechanical ventilation and between days two and three. CXR images were scrutinized using a convolutional neural network (CNN). A binary logistic regression model for predicting C-ARDS mortality was developed based on the most influential variables: age, PaO2/FiO2 ratio (P/F) on days one and three, CNN-extracted CXR features, and age. Initial performance assessment on test data (23 patients out of the 110) revealed an area under the receiver operating characteristic (ROC) curve of 0.862 with a 95% confidence interval (0.654-0.969).

CONCLUSION

Integrating data available in all intensive care units enables the prediction of C-ARDS mortality by utilizing evolving P/F ratios and CXR. This approach can assist in tailoring treatment plans and initiating early discussions to escalate care and extracorporeal life support. Machine learning algorithms for imaging classification can uncover otherwise inaccessible patterns, potentially evolving into another form of ARDS phenotyping. The combined features of these algorithms and clinical variables demonstrate superior performance compared to either element alone.

AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges

Aim

Artificial intelligence (AI) and machine learning (ML) are important areas of computer science that have recently attracted attention for their application to medicine. However, as techniques continue to advance and become more complex, it is increasingly challenging for clinicians to stay abreast of the latest research. This overview aims to translate research concepts and potential concerns to healthcare professionals interested in applying AI and ML to resuscitation research but who are not experts in the field.

Main text

We present various research including prediction models using structured and unstructured data, exploring treatment heterogeneity, reinforcement learning, language processing, and large-scale language models. These studies potentially offer valuable insights for optimizing treatment strategies and clinical workflows. However, implementing AI and ML in clinical settings presents its own set of challenges. The availability of high-quality and reliable data is crucial for developing accurate ML models. A rigorous validation process and the integration of ML into clinical practice is essential for practical implementation. We furthermore highlight the potential risks associated with self-fulfilling prophecies and feedback loops, emphasizing the importance of transparency, interpretability, and trustworthiness in AI and ML models. These issues need to be addressed in order to establish reliable and trustworthy AI and ML models.

Conclusion

In this article, we overview concepts and examples of AI and ML research in the resuscitation field. Moving forward, appropriate understanding of ML and collaboration with relevant experts will be essential for researchers and clinicians to overcome the challenges and harness the full potential of AI and ML in resuscitation.

Anti-inflammatory therapies for acute respiratory distress syndrome

INTRODUCTION

Treatments for the acute respiratory distress syndrome (ARDS) are mainly supportive, and ventilatory management represents a key approach in these patients. Despite progress in pharmacotherapy, anti-inflammatory strategies for the treatment of ARDS have shown controversial results. Positive outcomes with pharmacologic and nonpharmacologic treatments have been found in two different biological subphenotypes of ARDS, suggesting that, with a personalized medicine approach, pharmacotherapy for ARDS can be effective.

AREAS COVERED

This article reviews the literature concerning anti-inflammatory therapies for ARDS, focusing on pharmacological and stem-cell therapies, including extracellular vesicles.

EXPERT OPINION

Despite advances, ARDS treatments remain primarily supportive. Ventilatory and fluid management are important strategies in these patients that have demonstrated significant impacts on outcome. Anti-inflammatory drugs have shown some benefits, primarily in preclinical research and in specific clinical scenarios, but no recommendations are available from guidelines to support their use in patients with ARDS, except in particular settings such as different subphenotypes, specific etiologies, or clinical trials. Personalized medicine seems promising insofar as it may identify specific subgroups of patients with ARDS who may benefit from anti-inflammatory treatment. However, additional efforts are needed to move subphenotype characterization from bench to bedside.

Genetic Determinants of the Acute Respiratory Distress Syndrome

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

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

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

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

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

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

Affective-cognitive-behavioral heterogeneity of Attention-Deficit/Hyperactivity Disorder (ADHD): Emotional dysregulation as a sentinel symptom differentiating "ADHD-simplex" and "ADHD-complex" syndromes?

BACKGROUND

Current DSM and ICD classifications of Attention-Deficit/Hyperactivity Disorder (ADHD) exclude emotional dysregulation (ED) in their diagnostic criteria, despite ED symptoms frequently co-occurring in ADHD and likely sharing common neurobiological substrates. In this study, we examined whether consideration of ED symptoms could delineate more informative "ADHD+ED" subphenotypes.

METHOD

4106 children with ADHD were recruited. ED and inattentive (IA) and hyperactive/impulsive (HI) symptoms were profiled using latent class analyses (LCA). The derived latent class (LC) subphenotypes were evaluated and validated in relation to comorbidity patterns, executive functions, and functional impairments.

RESULTS

Five LC subphenotypes with ED symptoms were identified: IA/HI + ED profile (LC1); HI + ED profile (LC2); IA + ED profile (LC3); IA/HI profile (LC4); and IA profile (LC5). Cross-validation of the LCA model using support vector machine analysis confirmed 83% accuracy. ED positive (ED+ve) subphenotypes were associated with higher rates of oppositional defiant disorder, mood disorders, anxiety disorders, as well as more severe autistic traits and sluggish cognitive tempo symptoms. Higher rates of ecological executive functioning impairments (BRIEF ratings) were found among ED+ve subphenotypes (though no differences were detected by laboratory-based measures). Functional impairments were also more severe among participants with ED+ve subphenotypes.

LIMITATIONS

The data for our LCA were cross-sectional and based primarily on parent ratings.

CONCLUSION

Our classification model has parcellated IA, HI, and ED symptoms into novel informative subphenotypes. These classifications provide preliminary evidence that ED symptoms could serve as sentinel features to identify a potential "ADHD-complex" syndrome, which demarcates a more pervasive condition of greater severity, complexity, and impairment.

Clinical Phenotyping of Out-of-Hospital Cardiac Arrest Patients With Shockable Rhythm - Machine Learning-Based Unsupervised Cluster Analysis

BACKGROUND

The hypothesis of this study is that latent class analysis could identify the subphenotypes of out-of-hospital cardiac arrest (OHCA) patients associated with the outcomes and allow us to explore heterogeneity in the effects of extracorporeal cardiopulmonary resuscitation (ECPR).Methods and Results:This study was a retrospective analysis of a multicenter prospective observational study (CRITICAL study) of OHCA patients. It included adult OHCA patients with initial shockable rhythm. Patients from 2012 to 2016 (development dataset) were included in the latent class analysis, and those from 2017 (validation dataset) were included for evaluation. The association between subphenotypes and outcomes was investigated. Further, the heterogeneity of the association between ECPR implementation and outcomes was explored. In the study results, a total of 920 patients were included for latent class analysis. Three subphenotypes (Groups 1, 2, and 3) were identified, mainly characterized by the distribution of partial pressure of O₂(PO₂), partial pressure of CO₂(PCO₂) value of blood gas assessment, cardiac rhythm on hospital arrival, and estimated glomerular filtration rate. The 30-day survival outcomes were varied across the groups: 15.7% in Group 1; 30.7% in Group 2; and 85.9% in Group 3. Further, the association between ECPR and 30-day survival outcomes by subphenotype groups in the development dataset was as varied. These results were validated using the validation dataset.

CONCLUSIONS

The latent class analysis identified 3 subphenotypes with different survival outcomes and potential heterogeneity in the effects of ECPR.

Influence of rosuvastatin treatment on cerebral inflammation and nitro-oxidative stress in experimental lung injury in pigs

BACKGROUND

Many patients with acute respiratory distress syndrome (ARDS) suffer from cognitive impairment after hospital discharge. Different mechanisms have been implicated as potential causes for this impairment, inter alia cerebral inflammation. A class of drugs with antioxidant and anti-inflammatory properties are β-HMG-CoA-reductase inhibitors ("statins"). We hypothesized that treatment with rosuvastatin attenuates cerebral cytokine mRNA expression and nitro-oxidative stress in an animal model of acute lung injury.

METHODS

After approval of the institutional and state animal care committee, we performed this prospective randomized controlled animal study in accordance with the international guidelines for the care and use of laboratory animals. Thirty-two healthy male pigs were randomized to one of four groups: lung injury by central venous injection of oleic acid (n = 8), statin treatment before and directly after lung injury (n = 8), statin treatment after lung injury (n = 8), or ventilation-only controls (n = 8). About 18 h after lung injury and standardized treatment, the animals were euthanised, and the brains and lungs were collected for further examinations. We determined histologic lung injury and cerebral and pulmonal cytokine and 3-nitrotyrosine production.

RESULTS

We found a significant increase in hippocampal IL-6 mRNA after lung injury (p < 0.05). Treatment with rosuvastatin before and after induction of lung injury led to a significant reduction of hippocampal IL-6 mRNA (p < 0.05). Cerebral 3-nitrotyrosine was significantly higher in lung-injured animals compared with all other groups (p < 0.05 vs. animals treated with rosuvastatin after lung injury induction; p < 0.001 vs. all other groups). 3-Nitrotyrosine was also increased in the lungs of the lung-injured pigs compared to all other groups (p < 0.05 each).

CONCLUSIONS

Our findings highlight cerebral cytokine production and nitro-oxidative stress within the first day after induction of lung injury. The treatment with rosuvastatin reduced IL-6 mRNA and 3-nitrotyrosine concentration in the brains of the animals. In earlier trials, statin treatment did not reduce mortality in ARDS patients but seemed to improve quality of life in ARDS survivors. Whether this is attributable to better cognitive function because of reduced nitro-oxidative stress and inflammation remains to be elucidated.

Correction to: The discovery of biological subphenotypes in ARDS: a novel approach to targeted medicine?

An amendment to this paper has been published and can be accessed via the original article.