Novel methods of measuring adherence patterns reveal adherence phenotypes with distinct asthma outcomes

Jacqueline Huvanandana, Chinh D. Nguyen, Juliet M. Foster, Urs Frey, Helen K. Reddel, Cindy Thamrin

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Rationale: Poor adherence with asthma controller medication contributes to worse symptom control and increased exacerbation risk. Adherence is often expressed as the mean proportion of prescribed doses taken over a period of 6–12 months. New metrics may capture individual day-to-day variability patterns linked with distinct clinical outcomes.

Objectives: To test the hypotheses that novel time- and dose-based adherence variability metrics offer independent value to mean adherence in identifying distinct adherence patterns that are associated with symptom control (Asthma Control Test [ACT] score) and exacerbation risk, using electronically recorded medication data from a 6-month cluster randomized trial examining the effect of inhaler reminders on adherence.

Methods: Adherence metrics were calculated from the first 2 months (Months 0–2) of the study period. In addition to mean adherence (percentage prescribed puffs/day taken), we examined novel metrics, including time adherence area under the curve (T-AUC), reflecting cumulative gaps in adherence over time; entropy, reflecting disorder in the ways in which a patient changed their medication dose adherence from day to day; and standard deviation of the percentage prescribed puffs/day taken. Dominant metrics identified from factor analysis were included in hierarchical clustering analysis. We compared the resultant clusters in terms of outcomes over Months 2–6 and exacerbation risk over the entire study period.

Results: Two factors explained >65% of the total variance in adherence, primarily driven by T-AUC and entropy. Two main patient clusters based on their adherence metrics were identified: cluster 1 (high time adherence, n = 75) had better T-AUC (i.e., fewer gaps between medication-taking days) than cluster 2 (low time adherence, n = 23). Though both clusters had similar symptom control at 2 months, cluster 1 showed less subsequent decline in ACT over Months 2–6 (median [interquartile range] change in ACT score: 1 [−1 to 4] vs. −2 [−3.75 to 0.75]; P = 0.012), and had better symptom control at 6 months (ACT score: 20 [17–23] vs. 17 [15–20]; P = 0.034). There were no significant differences between the clusters in terms of proportion of exacerbators or time to exacerbation.

Conclusions: Novel metrics showed that low time adherence was associated with greater risk of decline in asthma symptom control. Adherence patterns may exhibit “memory” relevant to future clinical status, warranting validation in a larger dataset.
Original languageEnglish
Pages (from-to)933-942
Number of pages10
JournalAnnals of the American Thoracic Society
Volume19
Issue number6
DOIs
Publication statusPublished - Jun 2022
Externally publishedYes

Keywords

  • adherence
  • asthma
  • cluster analysis
  • variability
  • factor analysis

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