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DOI 10.1111/jgs.19280

Медицинская библиотека · Journal of the American Geriatrics Society · 2024

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

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

СтатьяМногоцентровое исследованиеПоддержка исследования: вне правительства СШАПоддержка NIH: внешняя программаПеревод в очереди

Аннотация

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.

Для цитирования: Dublin Sascha, Albertson-Junkans Ladia, Pham Nguyen Thanh Phuong, Pavon Juliessa M, Hastings S Nicole, Maciejewski Matthew L, Willis Allison, Zepel Lindsay, Hennessy Sean, Albers Kathleen B, Mowery Danielle, Clark Amy G, Thomas Sunil, Steinman Michael A, Boyd Cynthia M, Bayliss Elizabeth A. Defining key deprescribing measures from electronic health data: A multisite data harmonization project.. Journal of the American Geriatrics Society. 2024. DOI: 10.1111/jgs.19280.

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