Division of Overdose Prevention, National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA, United States.
Pain. 2023 Dec 1;164(12):2675-2683. doi: 10.1097/j.pain.0000000000002994. Epub 2023 Jul 27.
Opioid prescribing varies widely, and prescribed opioid dosages for an individual can fluctuate over time. Patterns in daily opioid dosage among patients prescribed long-term opioid therapy have not been previously examined. This study uses a novel application of time-series cluster analysis to characterize and visualize daily opioid dosage trajectories and associated demographic characteristics of patients newly initiated on long-term opioid therapy. We used 2018 to 2019 data from the IQVIA Longitudinal Prescription (LRx) all-payer pharmacy database, which covers 92% of retail pharmacy prescriptions dispensed in the United States. We identified a cohort of 277,967 patients newly initiated on long-term opioid therapy during 2018. Patients were stratified into 4 categories based on their mean daily dosage during a 90-day baseline period (<50, 50-89, 90-149, and ≥150 morphine milligram equivalent [MME]) and followed for a 270-day follow-up period. Time-series cluster analysis identified 2 clusters for each of the 3 baseline dosage categories <150 MME and 3 clusters for the baseline dosage category ≥150 MME. One cluster in each baseline dosage category comprised opioid dosage trajectories with decreases in dosage at the end of the follow-up period (80.7%, 98.7%, 98.7%, and 99.0%, respectively), discontinuation (58.5%, 80.0%, 79.3%, and 81.7%, respectively), and rapid tapering (50.8%, 85.8%, 87.5%, and 92.9%, respectively). These findings indicate multiple clusters of patients newly initiated on long-term opioid therapy who experience discontinuation and rapid tapering and highlight potential areas for clinician training to advance evidence-based guideline-concordant opioid prescribing, including strategies to minimize sudden dosage changes, discontinuation, or rapid tapering, and the importance of shared decision-making.
阿片类药物的处方差异很大,个体的阿片类药物剂量也会随时间波动。先前尚未研究过长期阿片类药物治疗患者的日常阿片类药物剂量模式。本研究使用时间序列聚类分析的新方法来描述和可视化新开始长期阿片类药物治疗的患者的日常阿片类药物剂量轨迹以及相关人口统计学特征。我们使用了 2018 年至 2019 年来自 IQVIA 纵向处方 (LRx) 所有支付方药房数据库的数据,该数据库涵盖了美国 92%的零售药房配药。我们确定了 2018 年期间新开始长期阿片类药物治疗的 277967 名患者队列。根据他们在 90 天基线期内的平均每日剂量(<50、50-89、90-149 和≥150 吗啡毫克当量 [MME]),患者分为 4 类,并进行了 270 天的随访。时间序列聚类分析为每个基线剂量类别<150 MME 确定了 2 个聚类,为基线剂量类别≥150 MME 确定了 3 个聚类。每个基线剂量类别中的一个聚类包括在随访期末剂量减少的阿片类药物剂量轨迹(分别为 80.7%、98.7%、98.7%和 99.0%)、停药(分别为 58.5%、80.0%、79.3%和 81.7%)和快速减量(分别为 50.8%、85.8%、87.5%和 92.9%)。这些发现表明,新开始长期阿片类药物治疗的患者存在多个停药和快速减量的聚类,并强调了为临床医生培训提供潜在领域,以推进基于证据的指南一致的阿片类药物处方,包括策略以最小化突然的剂量变化、停药或快速减量,以及共同决策的重要性。