A data-driven typology of asthma medication adherence using cluster analysis.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
14 09 2020
14 09 2020
Historique:
received:
11
10
2019
accepted:
20
08
2020
entrez:
15
9
2020
pubmed:
16
9
2020
medline:
13
2
2021
Statut:
epublish
Résumé
Asthma preventer medication non-adherence is strongly associated with poor asthma control. One-dimensional measures of adherence may ignore clinically important patterns of medication-taking behavior. We sought to construct a data-driven multi-dimensional typology of medication non-adherence in children with asthma. We analyzed data from an intervention study of electronic inhaler monitoring devices, comprising 211 patients yielding 35,161 person-days of data. Five adherence measures were extracted: the percentage of doses taken, the percentage of days on which zero doses were taken, the percentage of days on which both doses were taken, the number of treatment intermissions per 100 study days, and the duration of treatment intermissions per 100 study days. We applied principal component analysis on the measures and subsequently applied k-means to determine cluster membership. Decision trees identified the measure that could predict cluster assignment with the highest accuracy, increasing interpretability and increasing clinical utility. We demonstrate the use of adherence measures towards a three-group categorization of medication non-adherence, which succinctly describes the diversity of patient medication taking patterns in asthma. The percentage of prescribed doses taken during the study contributed to the prediction of cluster assignment most accurately (84% in out-of-sample data).
Identifiants
pubmed: 32929109
doi: 10.1038/s41598-020-72060-0
pii: 10.1038/s41598-020-72060-0
pmc: PMC7490405
doi:
Substances chimiques
Anti-Asthmatic Agents
0
Types de publication
Journal Article
Randomized Controlled Trial
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
14999Subventions
Organisme : Medical Research Council
Pays : United Kingdom
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