Acute respiratory infections (ARIs) cause ≈5% of all deaths globally1 and are particularly problematic in children with a tracheostomy requiring home ventilation.2 Although this high-risk population group is increasing in number,3 there is no consensus about the best clinical practices for ARI treatment in this at-risk group, even within the 2016 American Thoracic Society guidelines for the care of children requiring chronic home ventilation.4 Given the life-threatening potential of ARIs in this population and the lack of clear guidance, many clinicians choose to treat ARIs in these patients with antibiotics based on conventional cultures or to help prevent clinical deterioration.5 Unfortunately, these current testing and treatment approaches5 not only do not distinguish between colonizing bacteria and true pathogens but also do not account for the variation in intrapatient dynamics of the tracheal microbial communities.6 More effective treatment of ARIs in this population, and possibly in healthy populations as well, will require a more nuanced understanding of ARIs that helps clinicians move beyond their current reductionist approach of classifying ARIs as viral, bacterial or secondary bacterial.7
One new conceptual model of pneumonia pathogenesis that addresses this need suggests that pneumonia is an emergent phenomenon caused by bacterial blooms arising from the colonizing microbiota.7 The objective of this study was to extend this complex adaptive system model7 to ARIs in patients with a tracheostomy by examining the dynamics of the tracheal microbiota before, during and after an ARI. We hypothesized that at the beginning of the ARI, there would be a “bloom” of at least 1 genus already present in the airway.
The present cohort was a convenience sample of children cared for by the Critical Care, Anesthesia, Perioperative Extension and Home Ventilation Program. Between November 2013 and May 2014, we stored 1 tracheal aspirate per week per patient. When patients developed symptoms of an ARI, we retrieved the sample from the week before infection (day 0 [D0]) and from the first day of symptoms (D1). Additionally, after ARI onset, we collected 1 sample every week for 4 weeks (W1–W4). An ARI was defined as any illness with increased mucus production that required increased oxygen delivery or higher ventilator settings over baseline. We ensured standardized sample collection by observing parents or visiting nurses collecting the first sample in person during a home or clinic visit. The aspirates were stored at 0°C within 15 minutes of collection. The study team retrieved the samples during home visits and subsequently stored them at −80°C. The Institutional Review Board approved this study. (Supplementary Digital Content 1, http://links.lww.com/INF/C997; for a description of the detailed methods.)
We used D1 samples and the Luminex xTAG Respiratory Viral Panel FAST v2 multiplex polymerase chain reaction (Toronto, ON, Canada) to test for 18 respiratory viruses.
We sequenced the V4 region (≈250 bp) of the 16S rRNA gene on the Illumina MiSeq sequencing platform. Raw sequence files were processed and clustered into operational taxonomic units and normalized as previously reported (see SDC, http://links.lww.com/INF/C997; for detailed methods).6
The estimation of α-diversity, β-diversity and dissimilarity between samples and a complete description of the analytic methods are described in the methods SDC. Briefly, linear mixed-effects models, as implemented in the lmer4 R package, were applied to both α-diversity indices and microbial genera abundances (ie, sum of operational taxonomic units sharing a taxonomic genus) while accounting for nonindependence of subjects and time. β-diversity Unifrac indices were compared using permutational multivariate analysis of variance (adonis). We performed multiple rounds of analysis that included time and the following covariables: meteorologic seasons, age, gender, feeding route (ie, oral, gastrostomy tube, oral + gastrostomy tube, gastro-jejunal tube), ventilator use (ie, none, when sleeping or continuous), oxygen requirement, tracheostomy change frequency (ie, more than once per month or not), prophylactic antibiotics, daily inhaled corticosteroids and antibiotics during ARI. Our preliminary analyses showed that only feeding route had a significant association with microbial diversity and taxon abundance. Hence, our final, most parsimonious linear mixed-effects and adonis models included 1 predictor (time) and 1 covariable (feeding route). Benjamini–Hochberg false discovery rate (FDR) multiple test correction was applied.
Twenty patients had an ARI during the study period. The median age was 12 years (interquartile range, 4–24 years), and 70% were male. Sixty percent had neuromuscular disorders, and the remainder had lung disease, other than cystic fibrosis. Fifteen (75%) received antibiotics for their ARI. Clinical characteristics for the study cohort are presented in Table, Supplemental Digital Content 2, http://links.lww.com/INF/C998. We collected a total of 92 tracheal samples during the study period. Twenty-eight (23%) tracheal samples were missing because of missed sample collections and 2 patients dying from their ARI during the study. From the 92 samples, we obtained a total of 1,485,077 sequences ranging from 1030 to 63,835 sequences per sample (mean=17,070; median=10,076) after quality control analyses and operational taxonomic unit filtering.
Of the 17 patients with sample available for virology testing on D1, 5 (29%) had enterovirus/rhinovirus, 3 (18%) had respiratory syncytial virus, 3 (18%) had coronavirus, 1 (6%) had human metapneumovirus, 1 (6%) had influenza A 5 five (29%) had no virus detected. One patient (6%) had viral coinfection.
Taxonomic Composition of the Tracheal Microbiota
The tracheal microbiota across all 20 patients was dominated by the 8 genera listed in Table 1. The most abundant genera in the tracheal microbiotas were Streptococcus (21%), Haemophilus (10%), Corynebacterium (9%), Neisseria (9%) and Moraxella (8%). The remaining genera each accounted for <3% of the total sequences.
Dynamics of the Tracheal Microbiota Around ARIs
α-diversity varied significantly [P (>F) < 0.05] over time in 3 of 4 indices (Table 1). Compared with the pre-ARI sample (D0), the mean α-diversity was significantly lower in samples from D1, W2 and W4 after the ARI with W2 to W4 having the lowest α-diversity.
β-diversity unweighted Unifrac distances (uUd) varied significantly (P = 0.045) from D0 to W4, but weighted Unifrac distances did not. uUd distances also varied significantly (P < 0.05) between D0–W1 samples and D0–W2 samples; all other pairs did not significantly differ.
Among the 8 most abundant bacterial genera, the relative mean proportions of Haemophilus and Moraxella fluctuated significantly (P < 0.05) over time (Table 1). Both genera showed significantly (P < 0.05) higher abundances (ie, Haemophilus increased 274% and Moraxella 64%) on D1 of ARI compared with the pre-ARI (D0) sample (Table 1).
Patients’ microbiota varied over the 1 month post-ARI as indicated by the principal coordinates analysis (PCoA) of uUd (Fig., Supplemental Digital Content 3, http://links.lww.com/INF/C999). Intrapatient microbiomes before ARI (D0) and on D1 were more similar to each other than samples from subsequent weeks. Indeed, after the onset of ARI most tracheal microbiomes appeared unique, as suggested by the lack of overlap (colored dots) in the longitudinal PCoA plots.
In this study, we investigated the composition and temporal dynamics of microbial communities inhabiting the trachea of children and young adults with a tracheostomy before, during and after ARI. The results demonstrate lower species richness and evenness during and after ARI as would be expected with a respiratory infection in addition to variation in microbial community composition (as suggested by uUd) over 1 month, but not significant changes in microbial structure (as suggested by weighted Unifrac distances). Moreover, the results confirm that similar to previously observed ecologic phenomena,8 2 previously present genera (ie, Haemophilus and Moraxella) bloom by day 1 of ARI despite the highly variable baseline microbiota of patients with a tracheostomy.7
The traditional reductionist approach of categorizing ARIs as either viral or bacterial may be too simplistic a clinical framework for ARIs in individuals with a tracheostomy, and possibly all people with ARIs.7 On D1 of ARI, the majority of the current prospective cohort had a virus detected and a “bloom” of already present genera (ie, Haemophilus and Moraxella). The tracheal finding of Haemophilus and Moraxella blooming are consistent with findings from previous studies utilizing nasopharyngeal samples to examine acute respiratory illness outcomes.9 And although viral–bacterial interactions during ARIs have been described,10 the present results extend previous research by suggesting that these ARIs were not infections due to acquisition of a new bacterial pathogen as Koch’s postulates suggest, but rather a bloom of colonizing genera in the context of a viral infection. Conceptualizing ARIs as “blooms” may be more complex to operationalize clinically than the current reductionist approach, but may eventually provide opportunities for novel, targeted treatment methods. Although beyond the scope of these data, ARIs may be best understood as an emergent phenomenon7 that (1) is driven by a complex interplay among the infecting virus, microbiome and host response9 and (2) results in a continuum of ARI severity anchored by pneumonia.
The next step is to better understand the pathobiology of ARI in this high-risk population with variable underlying microbiota to develop novel targets for ARI treatment and to provide guidance about when to use antimicrobials and which bacteria to treat. Until this time of improved ARI understanding and clinical guidance, many clinicians will continue to overuse and misuse antimicrobials for ARIs in children with a tracheostomy.
We thank the GWU Colonial One High-Performance Computing Cluster for computational time.
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