Interval change in fractional exhaled nitric oxide (FENO) reflects short-term change in adherence following electronic inhaler reminders.

BACKGROUND: In asthma, suppression of fractional exhaled nitric oxide (FENO) is a marker of adherence in the short-term. The usefulness of FENO to indicate change in adherence in the longer term is unknown. The aims of this study were to determine the relationship between changes in adherence and corresponding changes in FENO over short (1 week) and long-term (3 month) periods. METHODS: After establishing initial ICS adherence using electronic inhaler monitor (EIM) devices, reminders were switched on for 1 week ('short-term') to optimize adherence. Reminders were then switched off and patients followed up after 3 months ('long-term'). FENO was measured at the start and end of each period. Using linear regression, we analyzed change in FENO in relation to change in adherence. RESULTS: Forty-two patients contributed complete data for analysis. In the short-term, change in adherence was independently associated with change in FENO (ß = -0.36, p = 0.036) even after adjusting for initial adherence and ICS dose. The higher the initial FENO, the greater the decline in FENO with improved adherence. This relationship between change in adherence and change in FENO was not observed in the long-term. CONCLUSION: Change in adherence over 1 week following the use of EIM reminders independently predicted change in FENO. This relationship was not maintained at 3 months.

Asthma phenotypes in a multi-ethnic Asian cohort.

BACKGROUND: Identification of asthma phenotypes facilitates our understanding of asthma pathobiologies. Phenotypes observed in homogenous Asian cohorts have distinct differences from those described in Caucasian cohorts, suggesting that ethnicity may influence phenotypic expression. Phenotypic clusters in a multi-ethnic Southeast Asian cohort have not been described before, and direct comparisons of these clusters within a single study may reveal how ethnicity affects phenotypic expression. METHODS: Six hundred and thirty adult asthma patients from two healthcare institutions in Singapore were randomly assigned in a 2:1 fashion to a test and validation cohort. Latent class analysis was performed on both cohorts using age of asthma onset, sex, ethnicity, smoking status, body mass index, lung function, blood eosinophil count, asthma control test score, and exacerbation frequency as input variables. Phenotypic clusters between the test and validation cohorts were compared RESULTS: Three clusters were identified in both the test and validation cohorts, with corresponding clusters of each cohort sharing similar characteristics. Ethnic representation and asthma control were significantly different between clusters. Cluster one comprised Chinese females with late-onset asthma and the best asthma control. Cluster two comprised non-Chinese females with obesity and the worst asthma control. Cluster three was multi-ethnic with the greatest proportion of atopic patients. CONCLUSION: We identified three phenotypic clusters in our multi-ethnic Southeast Asian population, with distinct differences in ethnicity which may be attributable to inherent differences in baseline characteristics among ethnic groups.

A new approach to the classification and management of airways diseases: identification of treatable traits.

This review outlines a new, personalized approach for the classification and management of airway diseases. The current approach to airways disease is, we believe, no longer fit for purpose. It is impractical, overgeneralizes complex and heterogeneous conditions and results in management that is imprecise and outcomes that are worse than they could be. Importantly, the assumptions we make when applying a diagnostic label have impeded new drug discovery and will continue to do so unless we change our approach. This review suggests a new mechanism-based approach where the emphasis is on identification of key causal mechanisms and targeted intervention with treatment based on possession of the relevant mechanism rather than an arbitrary label. We highlight several treatable traits and suggest how they can be identified and managed in different healthcare settings.