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The demographics and comorbidities of patients with community acquired pneumonia (CAP) vary enormously but stratified treatment is difficult because aetiological studies have failed to comprehensively identify the pathogens. Our aim was to describe the bacterial microbiota of CAP and relate these to clinical characteristics in order to inform future trials of treatment stratified by co-morbidity. CAP patients were prospectively recruited at two UK hospitals. We used 16S rRNA gene sequencing to identify the dominant bacteria in sputum and compositional data analysis to determine associations with patient characteristics. We analysed sputum samples from 77 patients and found a Streptococcus sp. and a Haemophilus sp. were the most relatively abundant pathogens. The Haemophilus sp. was more likely to be dominant in patients with pre-existing lung disease, and its relative abundance was associated with qPCR levels of Haemophilus influenzae. The most abundant Streptococcus sp. was associated with qPCR levels of Streptococcus pneumoniae but dominance could not be predicted from clinical characteristics. These data suggest chronic lung disease influences the microbiota of sputum in patients with CAP. This finding could inform a trial of stratifying empirical CAP antibiotics to target Haemophilus spp. in addition to Streptococcus spp. in those with chronic lung disease.

Our aims were to address three fundamental questions relating to the symptoms of community-acquired pneumonia (CAP): Do patients completely recover from pneumonia symptoms? How long does this recovery take? Which factors influence symptomatic recovery?We prospectively recruited patients at two hospitals in Liverpool, UK, into a longitudinal, observational cohort study and modelled symptom recovery from CAP. We excluded patients with cancer, immunosuppression or advanced dementia, and those who were intubated or palliated from admission. We derived a statistical model to describe symptom patterns.We recruited 169 (52% male) adults. Multivariable analysis demonstrated that the time taken to recover to baseline was determined by the initial severity of symptoms. Severity of symptoms was associated with comorbidity and was inversely related to age. The pattern of symptom recovery was exponential and most patients' symptoms returned to baseline by 10 days.These results will inform the advice given to patients regarding the resolution of their symptoms. The recovery model described here will facilitate the use of symptom recovery as an outcome measure in future clinical trials., Competing Interests: Conflict of interest: None declared., (Copyright ©ERS 2017.)