The alveolar fibroproliferative response in moderate to severe COVID-19-related acute respiratory distress syndrome and 1-yr follow-up

COVID-19-related acute respiratory distress syndrome (ARDS) can lead to long-term pulmonary fibrotic lesions. Alveolar fibroproliferative response (FPR) is a key factor in the development of pulmonary fibrosis. N-terminal peptide of procollagen III (NT-PCP-III) is a validated biomarker for activated FPR in ARDS. This study aimed to assess the association between dynamic changes in alveolar FPR and long-term outcomes, as well as mortality in COVID-19 ARDS patients. We conducted a prospective cohort study of 154 COVID-19 ARDS patients. We collected bronchoalveolar lavage (BAL) and blood samples for measurement of 17 pulmonary fibrosis biomarkers, including NT-PCP-III. We assessed pulmonary function and chest computed tomography (CT) at 3 and 12 mo after hospital discharge. We performed joint modeling to assess the association between longitudinal changes in biomarker levels and mortality at day 90 after starting mechanical ventilation. 154 patients with 284 BAL samples were analyzed. Of all patients, 40% survived to day 90, of whom 54 completed the follow-up procedure. A longitudinal increase in NT-PCP-III was associated with increased mortality (HR 2.89, 95% CI: 2.55-3.28; P < 0.001). Forced vital capacity and diffusion for carbon monoxide were impaired at 3 mo but improved significantly at one year after hospital discharge (P = 0.03 and P = 0.004, respectively). There was no strong evidence linking alveolar FPR during hospitalization and signs of pulmonary fibrosis in pulmonary function or chest CT images during 1-yr follow-up. In COVID-19 ARDS patients, alveolar FPR during hospitalization was associated with higher mortality but not with the presence of long-term fibrotic lung sequelae within survivors.NEW & NOTEWORTHY This is the first prospective study on the longitudinal alveolar fibroproliferative response in COVID-19 ARDS and its relationship with mortality and long-term follow-up. We used the largest cohort of COVID-19 ARDS patients who had consecutive bronchoalveolar lavages and measured 17 pulmonary fibroproliferative biomarkers. We found that a higher fibroproliferative response during admission was associated with increased mortality, but not correlated with long-term fibrotic lung sequelae in survivors.

Augmented intelligence facilitates concept mapping across different electronic health records

INTRODUCTION

With the advent of artificial intelligence, the secondary use of routinely collected medical data from electronic healthcare records (EHR) has become increasingly popular. However, different EHR systems typically use different names for the same medical concepts. This obviously hampers scalable model development and subsequent clinical implementation for decision support. Therefore, converting original parameter names to a so-called ontology, a standardized set of predefined concepts, is necessary but time-consuming and labor-intensive. We therefore propose an augmented intelligence approach to facilitate ontology alignment by predicting correct concepts based on parameter names from raw electronic health record data exports.

METHODS

We used the manually mapped parameter names from the multicenter "Dutch ICU data warehouse against COVID-19" sourced from three types of EHR systems to train machine learning models for concept mapping. Data from 29 intensive care units on 38,824 parameters mapped to 1,679 relevant and unique concepts and 38,069 parameters labeled as irrelevant were used for model development and validation. We used the Natural Language Toolkit (NLTK) to preprocess the parameter names based on WordNet cognitive synonyms transformed by term-frequency inverse document frequency (TF-IDF), yielding numeric features. We then trained linear classifiers using stochastic gradient descent for multi-class prediction. Finally, we fine-tuned these predictions using information on distributions of the data associated with each parameter name through similarity score and skewness comparisons.

RESULTS

The initial model, trained using data from one hospital organization for each of three EHR systems, scored an overall top 1 precision of 0.744, recall of 0.771, and F1-score of 0.737 on a total of 58,804 parameters. Leave-one-hospital-out analysis returned an average top 1 recall of 0.680 for relevant parameters, which increased to 0.905 for the top 5 predictions. When reducing the training dataset to only include relevant parameters, top 1 recall was 0.811 and top 5 recall was 0.914 for relevant parameters. Performance improvement based on similarity score or skewness comparisons affected at most 5.23% of numeric parameters.

CONCLUSION

Augmented intelligence is a promising method to improve concept mapping of parameter names from raw electronic health record data exports. We propose a robust method for mapping data across various domains, facilitating the integration of diverse data sources. However, recall is not perfect, and therefore manual validation of mapping remains essential.

Persistent alveolar inflammatory response in critically ill patients with COVID-19 is associated with mortality

INTRODUCTION

Patients with COVID-19-related acute respiratory distress syndrome (ARDS) show limited systemic hyperinflammation, but immunomodulatory treatments are effective. Little is known about the inflammatory response in the lungs and if this could be targeted using high-dose steroids (HDS). We aimed to characterise the alveolar immune response in patients with COVID-19-related ARDS, to determine its association with mortality, and to explore the association between HDS treatment and the alveolar immune response.

METHODS

In this observational cohort study, a comprehensive panel of 63 biomarkers was measured in repeated bronchoalveolar lavage (BAL) fluid and plasma samples of patients with COVID-19 ARDS. Differences in alveolar-plasma concentrations were determined to characterise the alveolar inflammatory response. Joint modelling was performed to assess the longitudinal changes in alveolar biomarker concentrations, and the association between changes in alveolar biomarker concentrations and mortality. Changes in alveolar biomarker concentrations were compared between HDS-treated and matched untreated patients.

RESULTS

284 BAL fluid and paired plasma samples of 154 patients with COVID-19 were analysed. 13 biomarkers indicative of innate immune activation showed alveolar rather than systemic inflammation. A longitudinal increase in the alveolar concentration of several innate immune markers, including CC motif ligand (CCL)20 and CXC motif ligand (CXCL)1, was associated with increased mortality. Treatment with HDS was associated with a subsequent decrease in alveolar CCL20 and CXCL1 levels.

CONCLUSIONS

Patients with COVID-19-related ARDS showed an alveolar inflammatory state related to the innate host response, which was associated with a higher mortality. HDS treatment was associated with decreasing alveolar concentrations of CCL20 and CXCL1.

Lung Microbiota of Critically Ill Patients with COVID-19 Are Associated with Nonresolving Acute Respiratory Distress Syndrome

Rationale: Bacterial lung microbiota are correlated with lung inflammation and acute respiratory distress syndrome (ARDS) and altered in severe coronavirus disease (COVID-19). However, the association between lung microbiota (including fungi) and resolution of ARDS in COVID-19 remains unclear. We hypothesized that increased lung bacterial and fungal burdens are related to nonresolving ARDS and mortality in COVID-19. Objectives: To determine the relation between lung microbiota and clinical outcomes of COVID-19-related ARDS. Methods: This observational cohort study enrolled mechanically ventilated patients with COVID-19. All patients had ARDS and underwent bronchoscopy with BAL. Lung microbiota were profiled using 16S rRNA gene sequencing and quantitative PCR targeting the 16S and 18S rRNA genes. Key features of lung microbiota (bacterial and fungal burden, α-diversity, and community composition) served as predictors. Our primary outcome was successful extubation adjudicated 60 days after intubation, analyzed using a competing risk regression model with mortality as competing risk. Measurements and Main Results: BAL samples of 114 unique patients with COVID-19 were analyzed. Patients with increased lung bacterial and fungal burden were less likely to be extubated (subdistribution hazard ratio, 0.64 [95% confidence interval, 0.42-0.97]; P = 0.034 and 0.59 [95% confidence interval, 0.42-0.83]; P = 0.0027 per log₁₀ increase in bacterial and fungal burden, respectively) and had higher mortality (bacterial burden, P = 0.012; fungal burden, P = 0.0498). Lung microbiota composition was associated with successful extubation (P = 0.0045). Proinflammatory cytokines (e.g., tumor necrosis factor-α) were associated with the microbial burdens. Conclusions: Bacterial and fungal lung microbiota are related to nonresolving ARDS in COVID-19 and represent an important contributor to heterogeneity in COVID-19-related ARDS.

Probiotics in the Intensive Care Unit

The understanding of the gut microbiome in health and disease has shown tremendous progress in the last decade. Shaped and balanced throughout life, the gut microbiome is intricately related to the local and systemic immune system and a multitude of mechanisms through which the gut microbiome contributes to the host’s defense against pathogens have been revealed. Similarly, a plethora of negative consequences, such as superinfections and an increased rate of hospital re-admissions, have been identified when the gut microbiome is disturbed by disease or by the iatrogenic effects of antibiotic treatment and other interventions. In this review, we describe the role that probiotics may play in the intensive care unit (ICU). We discuss what is known about the gut microbiome of the critically ill, and the concept of probiotic intervention to positively modulate the gut microbiome. We summarize the evidence derived from randomized clinical trials in this context, with a focus on the prevention of ventilator-associated pneumonia. Finally, we consider what lessons we can learn in terms of the current challenges, efficacy and safety of probiotics in the ICU and what we may expect from the future. Throughout the review, we highlight studies that have provided conceptual advances to the field or have revealed a specific mechanism; this narrative review is not intended as a comprehensive summary of the literature.

Gut microbiota of adults with asthma is broadly similar to non-asthmatics in a large population with varied ethnic origins

Bacterial gut communities might predispose children to develop asthma. Yet, little is known about the role of these micro-organisms in adult asthmatics. We aimed to profile the relationship between fecal microbiota and asthma in a large-scale, ethnically diverse, observational cohort of adults. Fecal microbiota composition of 1632 adults (172 asthmatics and 1460 non-asthmatics) was analyzed using 16S ribosomal RNA gene sequencing. Using extremely randomized trees machine learning models, we assessed the discriminatory ability of gut bacterial features to identify asthmatics from non-asthmatics. Asthma contributed 0.019% to interindividual dissimilarities in intestinal microbiota composition, which was not significant (P = .97). Asthmatics could not be distinguished from non-asthmatics based on individual microbiota composition by an extremely randomized trees classifier model (area under the receiver operating characteristic curve = 0.54). In conclusion, there were no prominent differences in fecal microbiota composition in adult asthmatics when compared to non-asthmatics in an urban, large-sized and ethnically diverse cohort.

Gut microbiota and sepsis: from pathogenesis to novel treatments

PURPOSE OF REVIEW

This review summarizes recent progress in our understanding of the role of the gut microbiota in sepsis pathogenesis and outlines the potential role of microbiota-targeted therapies.

RECENT FINDINGS

The composition of the gut microbiome is profoundly distorted during sepsis, with a loss of commensal bacteria and an overgrowth of potential pathogenic micro-organisms. These alterations also extend to nonbacterial intestinal inhabitants. Disruptions of these intestinal communities are associated with both an increased susceptibility to develop sepsis, as well as a higher risk of adverse outcomes. Preclinical studies have characterized the effects of several microbiota-derived metabolites (such as D-lactate, butyrate, and deoxycholic acid) on enhancing the host immune response during critical illness. Microbiota-targeted therapies (e.g. probiotics or fecal microbiota transplantation) might be of benefit, but can also be associated with increased risks of bloodstream infections.

SUMMARY

Emerging evidence display an important role of gut micro-organisms (including bacteria, fungi, eukaryotic viruses, and bacteriophages) and their derived metabolites in both the susceptibility to, as well as outcomes of sepsis. Despite recent progress in the mechanistic understanding of microbiota-mediated protection, clinical breakthroughs in the development of microbiota-based prognostic tools or therapies are thus far lacking in the field of sepsis.

Bacterial and viral respiratory tract microbiota and host characteristics in adults with lower respiratory tract infections: a case-control study

Background

Viruses and bacteria from the nasopharynx are capable of causing community-acquired pneumonia (CAP), which can be difficult to diagnose. We aimed to investigate whether shifts in the composition of these nasopharyngeal microbial communities can be used as diagnostic biomarkers for CAP in adults.

Methods

We collected nasopharyngeal swabs from adult CAP patients and controls without infection in a prospective multicenter case-control study design. We generated bacterial and viral profiles using 16S ribosomal RNA gene sequencing and multiplex polymerase chain reaction (PCR), respectively. Bacterial, viral, and clinical data were subsequently used as inputs for extremely randomized trees classification models aiming to distinguish subjects with CAP from healthy controls.

Results

We enrolled 117 cases and 48 control subjects. Cases displayed significant beta diversity differences in nasopharyngeal microbiota (P = .016, R² = .01) compared to healthy controls. Our extremely randomized trees classification models accurately discriminated CAP caused by bacteria (area under the curve [AUC] .83), viruses (AUC .95) or mixed origin (AUC .81) from healthy control subjects. We validated this approach using a dataset of nasopharyngeal samples from 140 influenza patients and 38 controls, which yielded highly accurate (AUC .93) separation between cases and controls.

Conclusions

Relative proportions of different bacteria and viruses in the nasopharynx can be leveraged to diagnose CAP and identify etiologic agent(s) in adult patients. Such data can inform the development of a microbiota-based diagnostic panel used to identify CAP patients and causative agents from nasopharyngeal samples, potentially improving diagnostic specificity, efficiency, and antimicrobial stewardship practices.

Sepsis: deriving biological meaning and clinical applications from high-dimensional data

The pathophysiology of sepsis is multi-facetted and highly complex. As sepsis is a leading cause of global mortality that still lacks targeted therapies, increased understanding of its pathogenesis is vital for improving clinical care and outcomes. An increasing number of investigations seeks to unravel the complexity of sepsis through high-dimensional data analysis, enabled by advances in -omics technologies. Here, we summarize progress in the following major -omics fields: genomics, epigenomics, transcriptomics, proteomics, lipidomics, and microbiomics. We describe what these fields can teach us about sepsis, and highlight current trends and future challenges. Finally, we focus on multi-omics integration, and discuss the challenges in deriving biological meaning and clinical applications from these types of data.