Defining key deprescribing measures from electronic health data: A multisite data harmonization project.

Sascha Dublin, Ladia Albertson-Junkans, Thanh Phuong Pham Nguyen, Juliessa M Pavon, S Nicole Hastings, Matthew L Maciejewski, Allison Willis, Lindsay Zepel, Sean Hennessy, Kathleen B Albers, Danielle Mowery, Amy G Clark, Sunil Thomas, Michael A Steinman, Cynthia M Boyd, Elizabeth A Bayliss
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Abstract

Background: Stopping or reducing risky or unneeded medications ("deprescribing") could improve older adults' health. Electronic health data can support observational and intervention studies of deprescribing, but there are no standardized measures for key variables, and healthcare systems have differing data types and availability. We developed definitions for chronic medication use and discontinuation based on electronic health data and applied them in a case study of benzodiazepines and Z-drugs in five diverse US healthcare systems.

Methods: We conducted a retrospective cohort study of adults age 65+ from 2017 to 2019 with chronic benzodiazepine or Z-drug use. We determined whether sites had access to medication orders and/or dispensings. We developed definitions for chronic use and discontinuation using both data types. Discontinuation definitions were based on (1) gaps in medication availability during follow-up or (2) not having medication available at a fixed time point. We examined the impact of varying the gap length and requiring a 30-day period without orders/dispensings ("halo") around the fixed time point. We compared results derived from orders versus dispensings at one site.

Results: Approximately 1.6%-2.6% of older adults had chronic benzodiazepine/Z-drug use (total N = 6775, ranging from 431 to 2122 across sites). Depending on the definition and site, the proportion discontinuing use during 12 months ranged from 6% to 49%. Requiring a longer gap or a 30-day "halo" resulted in lower estimates. At one site, only 56% of those with chronic use defined from orders also qualified based on dispensings, and the discontinuation rate at 180 days was 20% from orders versus 32% from dispensings.

Conclusions: Requiring a gap of ≥90 days or a "halo" around a time point may more accurately capture discontinuation than using a shorter gap or no halo. Orders data underestimate discontinuation compared to dispensings. Work is needed to adapt these definitions for other drug classes and settings.

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从电子健康数据中定义关键的去处方化措施:多站点数据协调项目。
背景:停止或减少有风险或不需要的药物("停药")可以改善老年人的健康。电子健康数据可支持对停药的观察和干预研究,但目前还没有针对关键变量的标准化测量方法,而且医疗保健系统的数据类型和可用性也各不相同。我们根据电子健康数据制定了长期用药和停药的定义,并将其应用于美国五个不同医疗系统中苯二氮卓类药物和 Z 类药物的案例研究中:我们对 2017 年至 2019 年期间长期使用苯二氮卓类药物或 Z 类药物的 65 岁以上成年人进行了一项回顾性队列研究。我们确定了医疗机构是否能够获取用药订单和/或配药情况。我们利用这两种数据类型制定了慢性用药和停药的定义。停药定义的依据是:(1) 随访期间的药物供应间隙或 (2) 在固定时间点没有药物供应。我们研究了不同间隙长度的影响,以及要求固定时间点周围 30 天内无订单/配药("光环")的影响。我们比较了一个地点的订单和配药结果:约有 1.6%-2.6% 的老年人长期服用苯二氮卓/Z 类药物(总人数 = 6775 人,不同地点的人数从 431 到 2122 不等)。根据不同的定义和地点,12 个月内停止使用的比例从 6% 到 49% 不等。要求更长的间隔期或 30 天的 "光环 "会导致较低的估计值。在一个研究机构中,只有 56% 的长期用药者根据订单也符合配药条件,180 天的停药率为 20%(订单)和 32%(配药):结论:要求时间间隔≥90 天或时间点周围有 "光环 "可能比使用较短的时间间隔或没有光环更能准确地捕捉到停药情况。与配药相比,订单数据低估了停药情况。还需要努力调整这些定义,使其适用于其他药物类别和环境。
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