Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK.
NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK.
Pharmacoepidemiol Drug Saf. 2023 Jun;32(6):651-660. doi: 10.1002/pds.5595. Epub 2023 Feb 11.
Routinely collected prescription data provides drug exposure information for pharmacoepidemiology, informing start/stop dates and dosage. Prescribing information includes structured data and unstructured free-text instructions, which can include inherent variability, such as "one to two tablets up to four times a day". Preparing drug exposure data from raw prescriptions to a research ready dataset is rarely fully reported, yet assumptions have considerable implications for pharmacoepidemiology. This may have bigger consequences for "pro re nata" (PRN) drugs. Our aim was, using a worked example of opioids and fracture risk, to examine the impact of incorporating narrative prescribing instructions and subsequent drug preparation assumptions on adverse event rates.
R-packages for extracting free-text medication prescription instructions in a structured form (doseminer) and an algorithm for transparently processing drug exposure information (drugprepr) were developed. Clinical Practice Research Datalink GOLD was used to define a cohort of adult new opioid users without prior cancer. A retrospective cohort study was performed using data between January 1, 2017 and July 31, 2018. We tested the impact of varying drug preparation assumptions by estimating the risk of opioids on fracture risk using Cox proportional hazards models.
During the study window, 60 394 patients were identified with 190 754 opioid prescriptions. Free-text prescribing instruction variability, where there was flexibility in the number of tablets to be administered, was present in 42% prescriptions. Variations in the decisions made during preparing raw data for analysis led to marked differences impacting the event number (n = 303-415) and person years of drug exposure (5619-9832). The distribution of hazard ratios as a function of the decisions ranged from 2.71 (95% CI: 2.31, 3.18) to 3.24 (2.76, 3.82).
Assumptions made during the drug preparation process, especially for those with variability in prescription instructions, can impact results of subsequent risk estimates. The developed R packages can improve transparency related to drug preparation assumptions, in line with best practice advocated by international pharmacoepidemiology guidelines.
常规收集的处方数据可为药物流行病学提供药物暴露信息，告知起始/停止日期和剂量。处方信息包括结构化数据和非结构化的自由文本说明，其中可能包含固有变异性，例如“一次一到两片，每日四到六次”。从原始处方中准备药物暴露数据到研究就绪的数据集的过程很少得到全面报告，但假设对药物流行病学有很大影响。对于“按需”（PRN）药物，这可能会产生更大的影响。我们的目的是，通过一个关于阿片类药物和骨折风险的实例研究，来检查纳入叙述性处方说明和随后的药物准备假设对不良事件发生率的影响。
开发了用于以结构化形式提取自由文本药物处方说明的 R 包（doseminer）和用于透明处理药物暴露信息的算法（drugprepr）。使用临床实践研究数据链接 GOLD 定义了一个没有既往癌症的成年新阿片类药物使用者队列。在 2017 年 1 月 1 日至 2018 年 7 月 31 日期间进行了回顾性队列研究。我们通过使用 Cox 比例风险模型估计阿片类药物对骨折风险的影响，测试了不同药物准备假设的影响。
在研究期间，共确定了 60394 名患者，共开出了 190754 份阿片类药物处方。42%的处方中存在药物服用片数灵活的自由文本处方说明变异性。在为分析准备原始数据的过程中所做决策的差异，导致了对事件数量（n=303-415）和药物暴露的人年数（5619-9832）的显著影响。作为决策函数的危险比分布范围为 2.71（95%CI：2.31，3.18）至 3.24（2.76，3.82）。
在药物准备过程中做出的假设，特别是对于那些处方说明具有可变性的药物，会影响随后风险估计的结果。所开发的 R 包可以提高与药物准备假设相关的透明度，符合国际药物流行病学指南所倡导的最佳实践。
