Eosinophilic COPD appears to be a distinct patient subgroup with an increased corticosteroid response. Eosinophilic COPD has been labelled as part of the asthma COPD overlap syndrome (ACOS). We compared the clinical characteristics of eosinophilic COPD patients (without any clinical history of asthma) and COPD patients with a childhood history of asthma. COPD patients with asthma were characterised by more allergies and more exacerbations, but less eosinophilic inflammation. While terms such as “ACOS” are used to “lump” patients together, we report distinct differences between eosinophilic COPD and COPD patients with asthma, and propose that these groups should be split rather than lumped.

BackgroundHaemophilus influenzae is commonly isolated from the airways of COPD patients. Antibiotic treatment may cause the emergence of resistant H. influenzae strains, particularly ampicillin-resistant strains, including β-lactamase-negative ampicillin resistance (BLNAR) strains. Genetic identification using ftsI sequencing is the optimum method for identifying mutations within BLNAR strains. The prevalence of BLNAR in COPD patients during the stable state has not been reported. We investigated the antibiotic resistance patterns of H. influenzae present in the sputum of stable COPD patients, focusing on ampicillin resistance; the prevalence of enzyme and non-enzyme-mediated ampicillin resistance was determined. A subset of patients was followed up longitudinally to study H. influenzae strain switching and antibiotic sensitivity changes.Patients and methodsSputum sampling was performed in 61 COPD patients, with 42 samples obtained at baseline; H. influenzae was detected by polymerase chain reaction in 28 samples. In all, 45 patients completed the follow-up for 2 years; 24 H. influenzae isolates were obtained.ResultsDisk diffusion showed the highest antibiotic resistance in the penicillin antibiotic group (eg, 67% for ampicillin) and macrolides (eg, 46% for erythromycin), whereas all isolates were susceptible to quinolones. Of the 16 isolates resistant to ampicillin, 9 (56%) were β-lactamase positive. The β-lactamase-negative isolates were further investigated; none of these fulfilled the phenotypic BLNAR classification criteria of ampicillin minimum inhibitory concentration >1 µg/mL, and only one demonstrated an ftsI mutation. Frequent H. influenzae strain switching was confirmed using multilocus sequence typing and was associated with changes in the antibiotic sensitivity pattern.ConclusionWe observed an overidentification of ampicillin resistance by disk diffusion. The majority of ampicillin resistance was due to enzyme production. H. influenzae strain changes during the stable state may be associated with a change in antibiotic sensitivity; this has implications for empirical antibiotic prescribing.

Effective diagnosis and treatment of bloodstream infections are often hampered by a lack of time-critical information from blood cultures. Molecular techniques aimed at the detection of circulating pathogen DNA have the potential to dramatically improve the timeliness of infection diagnosis. Our aim in this study was to establish a rapid, low-cost PCR approach using high-resolution melting analysis to identify a syndromic panel of 21 pathogens responsible for most bloodstream bacterial infections encountered in critical care environments. A broad-range, real-time PCR technique that combines primers for molecular Gram classification and high-resolution melting analysis in a single run was established. The differentiation of bacterial species was achieved using a multiparameter, decision-tree approach that was based on Gram type, grouping according to melting temperature, and sequential comparisons of melting profiles against multiple reference organisms. A preliminary validation study was undertaken by blinded analysis of 53 consecutive bloodstream isolates from a clinical microbiology laboratory. Fifty isolates contained organisms that were present in the panel, and 96% of these were identified correctly at the genus or species level. A correct Gram classification was reported for all 53 isolates. This technique shows promise as a cost-effective tool for the timely identification of bloodstream pathogens, allowing clinicians to make informed decisions on appropriate antibiotic therapies at an earlier stage.