An individual segmented trajectory approach for identifying opioid use patterns using longitudinal dispensing data.
An individual segmented trajectory approach for identifying opioid use patterns using longitudinal dispensing data.
复制标题
使用纵向配药数据识别阿片类药物使用模式的单独分段轨迹方法。
DOI：
10.1002/pds.5708
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发表时间：
2024
影响因子：
2.6
通讯作者：
Glanz,JasonM
中科院分区：
文献类型：
--
作者：
Xu,Stanley;Narwaney,KomalJ;Nguyen,AnhP;Binswanger,IngridA;McClure,DavidL;Glanz,JasonM
目的本研究的目的是利用电子阿片类药物配药数据开发一种单独的分段轨迹方法来识别阿片类药物的使用模式。由此产生的阿片类药物使用模式可用于检查阿片类药物使用与药物过量之间的关联。方法我们回顾性地收集了一组在 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，这些成员年龄在 18 岁及以上，并加入了美国三个医疗保健系统之一。我们开发了一种单独的分段轨迹分析，用于通过扫描随访来识别各种阿片类药物使用模式，并根据用变异系数和毫克吗啡当量水平趋势测量的变异性发现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 日期间参加 LTOT 的 31 名 865 名成员中，58.3% 为女性，平均年龄为55.4 年（STD = 15.4）。研究人群进行了 152 557 人年的随访，每人每次入组的平均随访时间为 4.4 年（STD = 3.4）。这种新颖的方法识别出多达 13 种不同的模式，包括 88 756 次“稳定”模式（42.1%），平均随访时间为 11.2 个月；29 140 次“增加”模式（13.8%），平均随访时间为 6.0 个月；13 201 次剂量减少≤10% (6.3%)，平均随访 10.4 个月；7286 次减少 11%–20% 剂量 (3.5%)，平均随访 5.3 个月；4457 次减少 21%–30% 剂量 (2.1%)，平均随访 4.0 个月；9903 次减少 21%–30% 剂量，平均随访 4.0 个月。平均随访 2.6 个月，剂量减少超过 30% (4.7%)。结论开发了一种新方法，利用每个人的纵向配药数据识别 13 种不同的阿片类药物使用模式，这些模式可用于检查这些模式持续期间的过量风险。
PurposeThe 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.MethodsWe 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.ResultsAmong 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.ConclusionsA 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.