A data-driven typology of asthma medication adherence using cluster analysis.
A data-driven typology of asthma medication adherence using cluster analysis.
复制标题
使用聚类分析的数据驱动的哮喘药物依从性类型。
DOI：
10.1038/s41598-020-72060-0
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发表时间：
2020
影响因子：
4.6
通讯作者：
Tibble H
中科院分区：
文献类型：
--
作者：
Tibble H
哮喘预防药物的不依从性与哮喘控制不良密切相关。一维的措施，坚持可能会忽略临床上重要的模式，服药行为。我们试图构建一个数据驱动的多维类型的药物治疗不遵守儿童哮喘。我们分析了电子吸入器监测设备干预研究的数据，包括211例患者，产生35，161人-天的数据。提取了5项依从性指标：给药百分比、零剂量给药天数百分比、两次给药天数百分比、每100个研究日的治疗间歇次数和每100个研究日的治疗间歇持续时间。我们应用主成分分析的措施，随后应用k-均值来确定聚类成员。决策树确定的措施，可以预测集群分配的最高精度，增加可解释性和增加临床实用性。我们展示了使用依从性措施对三组分类的药物治疗不依从性，这简洁地描述了哮喘患者服药模式的多样性。研究期间服用处方剂量的百分比有助于最准确地预测聚类分配（样本外数据中为84%）。
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).