Lung Microbiota Predict Clinical Outcomes in Critically Ill Patients

Abstract

Rationale: Recent studies have revealed that, in critically ill patients, lung microbiota are altered and correlate with alveolar inflammation. The clinical significance of altered lung bacteria in critical illness is unknown.Objectives: To determine if clinical outcomes of critically ill patients are predicted by features of the lung microbiome at the time of admission.Methods: We performed a prospective, observational cohort study in an ICU at a university hospital. Lung microbiota were quantified and characterized using droplet digital PCR and bacterial 16S ribosomal RNA gene quantification and sequencing. Primary predictors were the bacterial burden, community diversity, and community composition of lung microbiota. The primary outcome was ventilator-free days, determined at 28 days after admission.Measurements and Main Results: Lungs of 91 critically ill patients were sampled using miniature BAL within 24 hours of ICU admission. Patients with increased lung bacterial burden had fewer ventilator-free days (hazard ratio, 0.43; 95% confidence interval, 0.21-0.88), which remained significant when the analysis was controlled for pneumonia and severity of illness. The community composition of lung bacteria predicted ventilator-free days (P = 0.003), driven by the presence of gut-associated bacteria (e.g., species of the Lachnospiraceae and Enterobacteriaceae families). Detection of gut-associated bacteria was also associated with the presence of acute respiratory distress syndrome.Conclusions: Key features of the lung microbiome (bacterial burden and enrichment with gut-associated bacteria) predict outcomes in critically ill patients. The lung microbiome is an understudied source of clinical variation in critical illness and represents a novel therapeutic target for the prevention and treatment of acute respiratory failure.

Keywords: acute respiratory distress syndrome; lung injury; lung microbiome; prognosis.

Figure 1.

Lung microbiota are altered in patients with acute respiratory distress syndrome (ARDS). (A and B) Compared with patients without ARDS, patients with ARDS had increased lung bacterial burden (A) but no difference in community diversity (B). (C and D) Principal component analysis of bacterial communities (C) revealed that the community composition of lung bacteria was distinct in specimens from patients with ARDS (C), driven by members of the Pasteurellaceae and Enterobacteriaceae families (D). (E and F) Rank abundance analysis (E) identified taxa enriched in ARDS specimens (e.g., Enterobacteriaceae), and random forest analysis (F) confirmed that the Enterobacteriaceae family was the most discriminating taxonomic group between ARDS and non-ARDS specimens. Hypothesis testing was performed using the (A) Mann-Whitney U test, (B) Student’s t test, and (C and E) mvabund. Data in A and B are medians and interquartile ranges. *P ≤ 0.05 and **P = 0.002. OTUs = operational taxonomic units; PC = principal component; rRNA = ribosomal RNA.

Figure 2.

Lung microbiota predict 28-day outcomes in mechanically ventilated critically ill patients. In critically ill patients receiving mechanical ventilation, the burden of bacterial DNA detected in miniature BAL specimens was predictive of total ventilator-free days. Patients with high lung burdens of bacterial DNA were less likely to be extubated and alive than patients with low bacterial DNA burden (P = 0.008). Hypothesis testing was performed using univariate Cox proportional hazards modeling. rRNA = ribosomal RNA.

Figure 3.

Lung microbiota and 28-day outcomes in mechanically ventilated critically ill patients. (A) Community diversity of lung bacteria was highly variable among patients and did not significantly predict ventilator-free days. (B) Community composition of lung bacteria was significantly predictive of ventilator-free days (P = 0.003; mvabund). Random forest identified the gut-associated Lachnospiraceae and Enterobacteriaceae families as the strongest predictors of ventilator-free days. Hypothesis testing was performed using a Cox proportional hazards model.