The development and use of hierarchical composite endpoints (HCEs) in pneumonia clinical trials are recommended by federal regulatory agencies, expert professional societies and pharmaceutical stakeholders alike. However, the selection and hierarchy of nonfatal outcomes in HCEs often reflect investigator opinion rather than the bedside priorities of frontline clinicians. To date, no study has systematically evaluated how clinicians prioritize nonfatal outcomes in clinical decision-making for patients with pneumonia.Table 1.Patient Outcome Features and LevelsTable 2.Demographic Characteristics of Survey RespondentsIndividual question response sizes vary per question given respondent attribution. Not all responses shown. We conducted an international web-based survey using choice-based conjoint analysis. Respondents were presented with and asked to choose a more desirable overall outcome between two hypothetical patient profiles from a ventilator-associated pneumonia (VAP) trial, each comprised of a bundle of nonfatal outcomes of varying severity (Table 1). Feature order and level selection were randomized per respondent. Bayesian hierarchical modeling was used to calculate normalized part-worth utilities and determine the relative feature importance of each nonfatal outcome. The survey targeted practicing clinicians who care for patients with VAP and was deployed via professional society listservs.Figure 1.Bar Plot of Relative Feature Importance of Nonfatal OutcomesFeature importance values reflect the measure of influence of each nonfatal outcome on the desirability of the overall patient outcome and were were derived by dividing the range of part-worth utilities across a given feature's levels by the sum of all attribute part-worth utility score ranges. 95% confidence intervals derived via bootstrapping and display in error bars.Figure 2.Lollipop Plot of Part-Worth Utility Scores of Nonfatal Outcomes.Relative utility values (normalized part-worth utility scores) represent the weighted value respondents assigned to each nonfatal outcome level. A higher relative utility value implies a greater preference for a given feature level and a greater weight the presence of said feature level has on respondent choice, whereas a lower/negative relative utility value implies a lower preference for a given feature level and a lower weight the presence of said feature level has on respondent choice. The survey had 506 respondents and a 93% completion rate; demographics are shown in Table 2. Clinicians prioritized antibiotic-related adverse events (median feature importance 26.4% [95% CI 25.7-26.8%]), duration of invasive mechanical ventilation (24.9% [95% CI 24.4-25.9%]), and 30-day discharge disposition (24.2% [95% CI 23.6-25.1%]), over time to symptoms resolution (13.8% [95% CI 13.4-14.2%]), and pneumonia recurrence (9.2% [95% CI 8.6-9.6%]) (Figure 1). Part-worth utility scores for individual feature levels are shown in Figure 2. In this international conjoint analysis survey, clinicians prioritized outcomes often excluded from current pneumonia HCEs, such as discharge disposition and antibiotic-related adverse events. In contrast, they placed less value on pneumonia recurrence and time to clinical cure, despite their frequent use in existing pneumonia regulatory/comparative effectiveness trials. These findings suggest that pneumonia trial HCEs may be misaligned with the priorities of frontline clinicians. Krishna Rao, MD, MS, Merck & Co, Inc.: Grant/Research Support|Rebiotix Inc.: Advisor/Consultant|Seres Therapeutics: Advisor/Consultant|SUmmit Therapeutics Inc: Advisor/Consultant Keith S. Kaye, MD, MPH, AbbVie: Advisor/Consultant|GSK: Advisor/Consultant|Merck: Advisor/Consultant|Shionogi: Advisor/Consultant Owen Albin, MD, Biomerieux: Advisor/Consultant|Biomerieux: Grant/Research Support|Charles River Laboratories: Advisor/Consultant

Introduction Current guideline-recommended antibiotic treatment durations for ventilator-associated pneumonia (VAP) are largely standardised, with limited consideration of individual patient characteristics, pathogens or clinical context. This one-size-fits-all approach risks both overtreatment—promoting antimicrobial resistance and adverse drug events—as well as undertreatment, increasing the likelihood of pneumonia recurrence and sepsis-related complications. There is a critical need for VAP-specific biomarkers to enable individualised treatment strategies. The Ventilator-associated pneumonia Biomarker Evaluation (VIBE) study aims to identify a dynamic alveolar biomarker signature associated with treatment response, with the goal of informing personalised antibiotic duration in future clinical trials.Methods and analysis VIBE is a prospective, observational, case-cohort study of 125 adult patients with VAP in Michigan Medicine University Hospital intensive care units. Study subjects will undergo non-bronchoscopic bronchoalveolar lavage on the day of VAP diagnosis (Day 1) and then on Days 3 and 5. Alveolar biomarkers (quantitative respiratory culture bioburden, alveolar neutrophil percentage and pathogen genomic load assessed via BioFire FilmArray polymerase chain reaction) will be assessed. An expert panel of intensivists, blinded to biomarker data, will adjudicate each patient’s Day 10 outcome as VAP clinical cure (control) or treatment failure (case). Absolute biomarker levels and mean-fold changes in biomarker levels will be compared between groups. Data will be used to derive a composite temporal alveolar biomarker signature predictive of VAP treatment failure.Ethics and dissemination Ethical approval was obtained from the University of Michigan Institutional Review Board (IRB #HUM00251780). Informed consent will be obtained from all study participants or their legally authorised representatives. Findings will be disseminated through peer-reviewed publications, conferences and feedback into clinical guidelines committees.

While incorporation of microbiome techniques into the study of respiratory microbiology has proven revolutionary in the past decade, the respiratory tract presents unique challenges with regard to sample acquisition. Sampling for respiratory microbiome studies is constrained both by the relative inaccessibility of the lower airways and alveoli and by the low microbial biomass of lower respiratory tract specimens. Contamination of microbial DNA can confound every step of the sampling and sequencing process, and special consideration must be paid to the unique anatomy and microbiology of the respiratory tract. This chapter reviews the common techniques used for sampling respiratory microbiota in human and animal studies, and provides guidance regarding how best to handle the methodological challenges of sampling and sequencing contamination.

PurposeChronic lung allograft dysfunction (CLAD) is the leading long-term contributor to mortality after lung transplant. The lung microbiome predicts subsequent development of CLAD, but whether lung bacteria differ between CLAD phenotypes is unknown.MethodsUsing a biorepository of acellular bronchoalveolar lavage (BAL) fluid, we identified specimens which were collected within 90 days of CLAD onset. CLAD phenotype was assigned based on the presence of absence of obstruction, restriction, and computed tomography (CT) scan opacities, in accordance with ISHLT guidelines. Bacterial DNA burden was measured with BioRad QX200 Droplet Digital PCR and 16S rRNA gene sequencing was performed using the Illumina MiSeq platform. Bacterial burden was compared using Wilcoxon rank-sum. Community composition was compared using PERMANOVA and mvabund (a model-based approach to analysis of multivariable abundance data).ResultsEighty (80) patients had a BAL specimen from near CLAD onset available for analysis. Of these, 41 (51%) had BOS, 13 (16%) had RAS, 6 (8%) had Mixed CLAD, and 20 (25%) had an undefined/unclassifiable phenotype. There were no differences in lung bacterial burden (p=0.56, Panel A) or overall community composition between CLAD phenotypes (p=0.53, Panel C). Shannon diversity index did differ between CLAD phenotypes, with lower average diversity observed in the patients with an unclassifiable CLAD phenotype (1.0 ± 0.4) vs. BOS (2.2 ± 0.8), RAS (2.1 ± 0.8), Mixed (2.1 ± 0.7), or undefined CLAD (2.1 ± 0.4, overall p=0.02, Panel B). This difference persisted even after accounting for clinical evidence of infection (BAL neutrophilia and bacterial culture results did not differ between groups).ConclusionLung microbiome characteristics are largely similar across CLAD phenotypes, although lower within-specimen diversity is seen in patients with unclassified CLAD. Chronic lung allograft dysfunction (CLAD) is the leading long-term contributor to mortality after lung transplant. The lung microbiome predicts subsequent development of CLAD, but whether lung bacteria differ between CLAD phenotypes is unknown. Using a biorepository of acellular bronchoalveolar lavage (BAL) fluid, we identified specimens which were collected within 90 days of CLAD onset. CLAD phenotype was assigned based on the presence of absence of obstruction, restriction, and computed tomography (CT) scan opacities, in accordance with ISHLT guidelines. Bacterial DNA burden was measured with BioRad QX200 Droplet Digital PCR and 16S rRNA gene sequencing was performed using the Illumina MiSeq platform. Bacterial burden was compared using Wilcoxon rank-sum. Community composition was compared using PERMANOVA and mvabund (a model-based approach to analysis of multivariable abundance data). Eighty (80) patients had a BAL specimen from near CLAD onset available for analysis. Of these, 41 (51%) had BOS, 13 (16%) had RAS, 6 (8%) had Mixed CLAD, and 20 (25%) had an undefined/unclassifiable phenotype. There were no differences in lung bacterial burden (p=0.56, Panel A) or overall community composition between CLAD phenotypes (p=0.53, Panel C). Shannon diversity index did differ between CLAD phenotypes, with lower average diversity observed in the patients with an unclassifiable CLAD phenotype (1.0 ± 0.4) vs. BOS (2.2 ± 0.8), RAS (2.1 ± 0.8), Mixed (2.1 ± 0.7), or undefined CLAD (2.1 ± 0.4, overall p=0.02, Panel B). This difference persisted even after accounting for clinical evidence of infection (BAL neutrophilia and bacterial culture results did not differ between groups). Lung microbiome characteristics are largely similar across CLAD phenotypes, although lower within-specimen diversity is seen in patients with unclassified CLAD.

In ecology, population density is a key feature of community analysis. Yet in studies of the gut microbiome, bacterial density is rarely reported. Studies of hospitalized patients commonly use rectal swabs for microbiome analysis, yet variation in their bacterial density—and the clinical and methodologic significance of this variation—remains undetermined. We used an ultra-sensitive quantification approach—droplet digital PCR (ddPCR)—to quantify bacterial density in rectal swabs from 118 hospitalized patients. We compared bacterial density with bacterial community composition (via 16S rRNA amplicon sequencing) and clinical data to determine if variation in bacterial density has methodological, clinical, and prognostic significance. Bacterial density in rectal swab specimens was highly variable, spanning five orders of magnitude (1.2 × 104–3.2 × 109 16S rRNA gene copies/sample). Low bacterial density was strongly correlated with the detection of sequencing contamination (Spearman ρ = − 0.95, p < 10−16). Low-density rectal swab communities were dominated by peri-rectal skin bacteria and sequencing contaminants (p < 0.01), suggesting that some variation in bacterial density is explained by sampling variation. Yet bacterial density was also associated with important clinical exposures, conditions, and outcomes. Bacterial density was lower among patients who had received piperacillin-tazobactam (p = 0.017) and increased among patients with multiple medical comorbidities (Charlson score, p = 0.0040) and advanced age (p = 0.043). Bacterial density at the time of hospital admission was independently associated with subsequent extraintestinal infection (p = 0.0028), even when controlled for severity of illness and comorbidities. The bacterial density of rectal swabs is highly variable, and this variability is of methodological, clinical, and prognostic significance. Microbiome studies using rectal swabs are vulnerable to sequencing contamination and should include appropriate negative sequencing controls. Among hospitalized patients, gut bacterial density is associated with clinical exposures (antibiotics, comorbidities) and independently predicts infection risk. Bacterial density is an important and under-studied feature of gut microbiome community analysis.