一种基于个体分段轨迹的方法，用于使用纵向配药数据识别阿片类药物使用模式。
An individual segmented trajectory approach for identifying opioid use patterns using longitudinal dispensing data.
机构信息
Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California, USA.
Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, California, USA.
出版信息
Pharmacoepidemiol Drug Saf. 2024 Jan;33(1):e5708. doi: 10.1002/pds.5708. Epub 2023 Oct 10.
PURPOSE
The aim of this study is to use electronic opioid dispensing data to develop an individual segmented trajectory approach for identifying opioid use patterns. The resulting opioid use patterns can be used for examining the association between opioid use and drug overdose.
METHODS
We retrospectively assembled a cohort of members on long-term opioid therapy (LTOT) between January 1, 2006 and June 30, 2019 who were 18 years and older and enrolled in one of three health care systems in the US. We have developed an individual segmented trajectory analysis for identifying various opioid use patterns by scanning over the follow-up and finding distinct opioid use patterns based on variability measured with coefficient of variation and trends of milligram morphine equivalents levels.
RESULTS
Among 31, 865 members who were on LTOT between January 1, 2006 and June 30, 2019, 58.3% were female, and the average age was 55.4 years (STD = 15.4). The study population had 152 557 person-years of follow-up, with an average follow-up of 4.4 years per enrollment per person (STD = 3.4). This novel approach identified up to 13 distinct patterns including 88 756 episodes of "stable" pattern (42.1%) with an average follow-up of 11.2 months, 29 140 episodes of "increasing" pattern (13.8%) with an average follow-up of 6.0 months, 13 201 episodes of ≤10% dose reduction (6.3%) with an average follow-up of 10.4 months, 7286 episodes of 11%-20% dose reduction (3.5%) with an average follow-up of 5.3 months, 4457 episodes of 21%-30% dose reduction (2.1%) with an average follow-up of 4.0 months, and 9903 episodes of >30% dose reduction (4.7%) with an average follow-up of 2.6 months.
CONCLUSIONS
A novel approach was developed to identify 13 distinct opioid use patterns using each individual's longitudinal dispensing data and these patterns can be used in examining overdose risk during the time that these patterns are ongoing.
目的
本研究旨在利用电子阿片类药物配药数据，开发一种个体分段轨迹方法来识别阿片类药物使用模式。由此产生的阿片类药物使用模式可用于研究阿片类药物使用与药物过量之间的关联。
方法
我们回顾性地组建了一个 2006 年 1 月 1 日至 2019 年 6 月 30 日期间接受长期阿片类药物治疗（LTOT）的成员队列，这些成员年龄在 18 岁及以上，并且参加了美国三个医疗保健系统中的一个。我们已经开发了一种个体分段轨迹分析方法，通过扫描随访过程来识别各种阿片类药物使用模式，并根据变异系数测量的变异性和毫克吗啡当量水平的趋势来确定不同的阿片类药物使用模式。
结果
在 2006 年 1 月 1 日至 2019 年 6 月 30 日期间接受 LTOT 的 31865 名成员中，有 58.3%为女性，平均年龄为 55.4 岁（标准差=15.4）。研究人群的随访时间为 152557 人年，平均每个入组者的随访时间为 4.4 年（标准差=3.4）。这种新方法确定了多达 13 种不同的模式，包括 88756 例“稳定”模式（42.1%），平均随访时间为 11.2 个月，29140 例“递增”模式（13.8%），平均随访时间为 6.0 个月，13201 例剂量减少≤10%（6.3%），平均随访时间为 10.4 个月，7286 例剂量减少 11%-20%（3.5%），平均随访时间为 5.3 个月，4457 例剂量减少 21%-30%（2.1%），平均随访时间为 4.0 个月，9903 例剂量减少>30%（4.7%），平均随访时间为 2.6 个月。
结论
我们开发了一种新方法，利用每个个体的纵向配药数据来识别 13 种不同的阿片类药物使用模式，这些模式可用于研究在这些模式持续期间的药物过量风险。
Zotero 插件