“Take up to eight tablets per day”: Incorporating free‐text medication instructions into a transparent and reproducible process for preparing drug exposure data for pharmacoepidemiology

Abstract

Purpose: 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.Methods: 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.Results: 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

Machine learning identifies risk factors associated with long-term opioid use in fibromyalgia patients newly initiated on an opioid

“…Electronic prescription data were prepared using the framework provided in our previous work. 33 Data processing and cleaning were conducted in Stata V. 13.1 (StataCorp LLC, College Station, Texas). Patients were integral collaborators in the design of the research questions and will continue to be actively engaged throughout the dissemination plans of our research.…”

“…Daily prescription data were prepared using a drug preparation algorithm published previously. 33 MME per day was calculated multiplying the daily prescription dose with the corresponding analgesic ratio as outlined by the CDC 24 34 and was categorised as low: <50 MME/ day; medium: 50-119 MME/day; high: 120-199 MME/ day and very high: ≥200 MME/day. 24 The patient's body mass index (BMI) was calculated based on the closest weight and height measurements to the index date for each individual.…”

Prescribing of antipsychotics for people diagnosed with severe mental illness in UK primary care: A 20-year investigation of who receives treatment, with which agents, and at what doses

“…Free-text dosage instructions (e.g., “take five tablets per day”) were converted to numerical quantities using a text-mining algorithm implemented in the R package doseminer . 22 To enable comparison across agents, calculated doses were then converted to olanzapine equivalents according to the Defined Daily Dose (DDD) method 23 using chlorpromazineR 24 (cariprazine and droperidol were not reported in the DDD method, 23 equivalence formulae for these antipsychotics came from references 25 and 26 , respectively). In the case of multiple prescriptions issued on the same date, we considered up to three unique prescriptions of each antipsychotic prescribed on a given date (>3 unique prescriptions of one medication was considered potentially erroneous).…”

Postoperative opioids administered to inpatients with major or orthopaedic surgery: A retrospective cohort study using data from hospital electronic prescribing systems

“…Additionally, most studies focusing on primary care opioid prescribing do not provide information on whether the patient was administered the medication in hospital, leading to some exposure misclassification. Hospital data provides opportunity to assess administered use of such medications, especially when a considerable proportion of opioids may be prescribed on an as required basis [ 12 ].…”