Robust airway microbiome signatures in acute respiratory failure and hospital-acquired pneumonia

Transitions in lung microbiota landscape associate with distinct patterns of pneumonia progression

The precise microbial determinants driving clinical outcomes in severe pneumonia are unknown. Competing ecological forces produce dynamic microbiota states in health; infection and treatment effects on microbiota state must be defined to improve pneumonia therapy. Here, we leverage our unique clinical setting, which includes systematic and serial bronchoscopic sampling in patients with suspected pneumonia, to determine lung microbial ecosystem dynamics throughout pneumonia therapy. We combine 16S rRNA gene amplicon, metagenomic, and transcriptomic sequencing with bacterial load quantification to reveal clinically-relevant pneumonia progression drivers. Microbiota states are predictive of pneumonia category and exhibit differential stability and pneumonia therapy response. Disruptive forces, like aspiration, associate with cohesive changes in gene expression and microbial community structure. In summary, we show that host and microbiota landscapes change in unison with clinical phenotypes and that microbiota state dynamics reflect pneumonia progression. We suggest that distinct pathways of lung microbial community succession mediate pneumonia progression.

Aerosolized Antibiotic Therapy in Mechanically Ventilated Patients

Delivering antibiotics directly to the respiratory tract through inhalation to address lung infections has garnered clinical and scientific interest for decades, given the potential favorable pharmacokinetic profile of this administration route. Among critically ill patients, the burden of healthcare-associated pulmonary infections particularly drove continued interest in delivering inhaled antibiotics to intubated patients. We present a concise overview of the existing rationale and evidence and provide guidance for implementing inhaled antibiotics among ventilated critically ill patients, emphasizing insights from recent literature.

Rapid Molecular Diagnostics of Pneumonia Caused by Gram-Negative Bacteria: A Clinician's Review

With approximately half a billion events per year, lower respiratory tract infections (LRTIs) represent a major challenge for the global public health. Among LRTI cases, those caused by Gram-negative bacteria (GNB) are associated with a poorer prognostic. Standard-of-care etiologic diagnostics is lengthy and difficult to establish, with more than half of cases remaining microbiologically undocumented. Recently, syndromic molecular diagnostic panels became available, enabling simultaneous detection of tens of pathogen-related and antimicrobial-resistance genetic markers within a few hours. In this narrative review, we summarize the available data on the performance of molecular diagnostics in GNB pneumonia, highlighting the main strengths and limitations of these assays, as well as the main factors influencing their clinical utility. We searched MEDLINE and Web of Science databases for relevant English-language articles. Molecular assays have higher analytical sensitivity than cultural methods, and show good agreement with standard-of-care diagnostics regarding detection of respiratory pathogens, including GNB, and identification of frequent patterns of resistance to antibiotics. Clinical trials reported encouraging results on the usefulness of molecular assays in antibiotic stewardship. By providing early information on the presence of pathogens and their probable resistance phenotypes, these assays assist in the choice of targeted therapy, in shortening the time from sample collection to appropriate antimicrobial treatment, and in reducing unnecessary antibiotic use.

Microbial signatures predictive of short-term prognosis in severe pneumonia

Objective

This retrospective cohort study aimed to investigate the composition and diversity of lung microbiota in patients with severe pneumonia and explore its association with short-term prognosis.

Methods

A total of 301 patients diagnosed with severe pneumonia underwent bronchoalveolar lavage fluid metagenomic next-generation sequencing (mNGS) testing from February 2022 to January 2024. After applying exclusion criteria, 236 patients were included in the study. Baseline demographic and clinical characteristics were compared between survival and non-survival groups. Microbial composition and diversity were analyzed using alpha and beta diversity metrics. Additionally, LEfSe analysis and machine learning methods were employed to identify key pathogenic microorganism associated with short-term mortality. Microbial interaction modes were assessed through network co-occurrence analysis.

Results

The overall 28-day mortality rate was 37.7% in severe pneumonia. Non-survival patients had a higher prevalence of hypertension and exhibited higher APACHE II and SOFA scores, higher procalcitonin (PCT), and shorter hospitalization duration. Microbial α and β diversity analysis showed no significant differences between the two groups. However, distinct species diversity patterns were observed, with the non-survival group showing a higher abundance of Acinetobacter baumannii, Klebsiella pneumoniae, and Enterococcus faecium, while the survival group had a higher prevalence of Corynebacterium striatum and Enterobacter. LEfSe analysis identified 29 distinct terms, with 10 potential markers in the non-survival group, including Pseudomonas sp. and Enterococcus durans. Machine learning models selected 16 key pathogenic bacteria, such as Klebsiella pneumoniae, significantly contributing to predicting short-term mortality. Network co-occurrence analysis revealed greater complexity in the non-survival group compared to the survival group, with differences in central genera.

Conclusion

Our study highlights the potential significance of lung microbiota composition in predicting short-term prognosis in severe pneumonia patients. Differences in microbial diversity and composition, along with distinct microbial interaction modes, may contribute to variations in short-term outcomes. Further research is warranted to elucidate the clinical implications and underlying mechanisms of these findings.

Compartmentalization of the inflammatory response during bacterial sepsis and severe COVID-19

Acute infections cause local and systemic disorders which can lead in the most severe forms to multi-organ failure and eventually to death. The host response to infection encompasses a large spectrum of reactions with a concomitant activation of the so-called inflammatory response aimed at fighting the infectious agent and removing damaged tissues or cells, and the anti-inflammatory response aimed at controlling inflammation and initiating the healing process. Fine-tuning at the local and systemic levels is key to preventing local and remote injury due to immune system activation. Thus, during bacterial sepsis and Coronavirus disease 2019 (COVID-19), concomitant systemic and compartmentalized pro-inflammatory and compensatory anti-inflammatory responses are occurring. Immune cells (e.g., macrophages, neutrophils, natural killer cells, and T-lymphocytes), as well as endothelial cells, differ from one compartment to another and contribute to specific organ responses to sterile and microbial insult. Furthermore, tissue-specific microbiota influences the local and systemic response. A better understanding of the tissue-specific immune status, the organ immunity crosstalk, and the role of specific mediators during sepsis and COVID-19 can foster the development of more accurate biomarkers for better diagnosis and prognosis and help to define appropriate host-targeted treatments and vaccines in the context of precision medicine.

A multimodal integration pipeline for accurate diagnosis, pathogen identification, and prognosis prediction of pulmonary infections

Pulmonary infections pose formidable challenges in clinical settings with high mortality rates across all age groups worldwide. Accurate diagnosis and early intervention are crucial to improve patient outcomes. Artificial intelligence (AI) has the capability to mine imaging features specific to different pathogens and fuse multimodal features to reach a synergistic diagnosis, enabling more precise investigation and individualized clinical management. In this study, we successfully developed a multimodal integration (MMI) pipeline to differentiate among bacterial, fungal, and viral pneumonia and pulmonary tuberculosis based on a real-world dataset of 24,107 patients. The area under the curve (AUC) of the MMI system comprising clinical text and computed tomography (CT) image scans yielded 0.910 (95% confidence interval [CI]: 0.904-0.916) and 0.887 (95% CI: 0.867-0.909) in the internal and external testing datasets respectively, which were comparable to those of experienced physicians. Furthermore, the MMI system was utilized to rapidly differentiate between viral subtypes with a mean AUC of 0.822 (95% CI: 0.805-0.837) and bacterial subtypes with a mean AUC of 0.803 (95% CI: 0.775-0.830). Here, the MMI system harbors the potential to guide tailored medication recommendations, thus mitigating the risk of antibiotic misuse. Additionally, the integration of multimodal factors in the AI-driven system also provided an evident advantage in predicting risks of developing critical illness, contributing to more informed clinical decision-making. To revolutionize medical care, embracing multimodal AI tools in pulmonary infections will pave the way to further facilitate early intervention and precise management in the foreseeable future.

Longitudinal multicompartment characterization of host-microbiota interactions in patients with acute respiratory failure

Critical illness can significantly alter the composition and function of the human microbiome, but few studies have examined these changes over time. Here, we conduct a comprehensive analysis of the oral, lung, and gut microbiota in 479 mechanically ventilated patients (223 females, 256 males) with acute respiratory failure. We use advanced DNA sequencing technologies, including Illumina amplicon sequencing (utilizing 16S and ITS rRNA genes for bacteria and fungi, respectively, in all sample types) and Nanopore metagenomics for lung microbiota. Our results reveal a progressive dysbiosis in all three body compartments, characterized by a reduction in microbial diversity, a decrease in beneficial anaerobes, and an increase in pathogens. We find that clinical factors, such as chronic obstructive pulmonary disease, immunosuppression, and antibiotic exposure, are associated with specific patterns of dysbiosis. Interestingly, unsupervised clustering of lung microbiota diversity and composition by 16S independently predicted survival and performed better than traditional clinical and host-response predictors. These observations are validated in two separate cohorts of COVID-19 patients, highlighting the potential of lung microbiota as valuable prognostic biomarkers in critical care. Understanding these microbiome changes during critical illness points to new opportunities for microbiota-targeted precision medicine interventions.

Clinical evaluation of the BioFire Respiratory Pathogen Panel for the guidance of empirical antimicrobial therapy in critically ill patients with hospital-acquired pneumonia: A multicenter, quality improvement project

BACKGROUND

We aimed to determine whether implementing antimicrobial stewardship based on multiplex bacterial PCR examination of respiratory fluid can enhance outcomes of critically ill patients with hospital-acquired pneumonia (HAP).

METHODS

We conducted a quality improvement study in two hospitals in France. Adult patients requiring invasive mechanical ventilation with a diagnosis of HAP were included. In the pre-intervention period (August 2019 to April 2020), antimicrobial therapy followed European guidelines. In the «intervention» phase (June 2020 to October 2021), treatment followed a multiplex PCR-guided protocol. The primary endpoint was a composite endpoint made of mortality on day 28, clinical cure between days 7 and 10, and duration of invasive mechanical ventilation on day 28. The primary outcome was analyzed with a DOOR strategy.

RESULTS

A total of 443 patients were included in 3 ICUs from 2 hospitals (220 pre-intervention; 223 intervention). No difference in the ranking of the primary composite outcome was found (DOOR: 50.3%; 95%CI, 49.9%-50.8%). The number of invasive mechanical ventilation-free days at day 28 was 10.0 [0.0; 19.0] in the baseline period and 9.0 [0.0; 20.0] days during the intervention period (p = 0.95). The time-to-efficient antimicrobial treatment was 0.43 ± 1.29 days before versus 0.55 ± 1.13 days after the intervention (p = 0.56).

CONCLUSION

Implementation of Rapid Multiplex PCR to guide empirical antimicrobial therapy for critically ill patients with HAP was not associated with better outcomes. However, adherence to stewardship was low, and the study may have had limited power to detect a clinically important difference.

The epithelium takes the stage in asthma and inflammatory bowel diseases

The epithelium is a dynamic barrier and the damage to this epithelial layer governs a variety of complex mechanisms involving not only epithelial cells but all resident tissue constituents, including immune and stroma cells. Traditionally, diseases characterized by a damaged epithelium have been considered "immunological diseases," and research efforts aimed at preventing and treating these diseases have primarily focused on immuno-centric therapeutic strategies, that often fail to halt or reverse the natural progression of the disease. In this review, we intend to focus on specific mechanisms driven by the epithelium that ensure barrier function. We will bring asthma and Inflammatory Bowel Diseases into the spotlight, as we believe that these two diseases serve as pertinent examples of epithelium derived pathologies. Finally, we will argue how targeting the epithelium is emerging as a novel therapeutic strategy that holds promise for addressing these chronic diseases.

Pathogenesis and therapeutic opportunities of gut microbiome dysbiosis in critical illness

For many years, it has been hypothesized that pathological changes to the gut microbiome in critical illness is a driver of infections, organ dysfunction, and other adverse outcomes in the intensive care unit (ICU). The advent of contemporary microbiome methodologies and multi-omics tools have allowed researchers to test this hypothesis by dissecting host-microbe interactions in the gut to better define its contribution to critical illness pathogenesis. Observational studies of patients in ICUs have revealed that gut microbial communities are profoundly altered in critical illness, characterized by markedly reduced alpha diversity, loss of commensal taxa, and expansion of potential pathogens. These key features of ICU gut dysbiosis have been associated with adverse outcomes including life-threatening hospital-acquired (nosocomial) infections. Current research strives to define cellular and molecular mechanisms connecting gut dysbiosis with infections and other outcomes, and to identify opportunities for therapeutic modulation of host-microbe interactions. This review synthesizes evidence from studies of critically ill patients that have informed our understanding of intestinal dysbiosis in the ICU, mechanisms linking dysbiosis to infections and other adverse outcomes, as well as clinical trials of microbiota-modifying therapies. Additionally, we discuss novel avenues for precision microbial therapeutics to combat nosocomial infections and other life-threatening complications of critical illness.