Analgesics, Non-Narcotic

The dependent variable of interest was the index OIRD event. Independent variables assessed during the six months before the index date included demographic factors; the component comorbidities of the Charlson Comorbidity Index [19–21 (link)]; other selected pain- and non-pain-related health conditions [22 (link),23 (link)]; prescription medication information, including opioid active ingredient, formulation, and maximum prescribed daily morphine equivalent dose (MED) [24 ,25 ], as well as selected nonopioid medications that may impact serious adverse opioid effects (nonopioid analgesics, benzodiazepines, antidepressants, muscle relaxants, other sedatives, antipsychotics, and stimulants); and metrics indicative of health care utilization [15 (link)].
Variables in this CIP validation study were used exactly as or constructed as closely as possible to those used in the VHA development study [16 (link)]. Some modifications were required to accommodate inherent differences between the disparate data sources. For example, the CIP data lacked adequate information regarding race/ethnicity, marital status, body mass index, and motor vehicle accidents, but included additional details on the route of opioid administration. These additional routes (sublingual, buccal, nasal, and rectal) were characterized for descriptive purposes, but route was dichotomized for multivariable analysis (oral and nonoral). To facilitate clinical utility, MED was collapsed into a dichotomous variable for the CIP analysis (<100 mg/day and ≥100 mg/day). The “opioid dependence” and “substance abuse and nonopioid dependence” variables were combined into a single “substance use disorder” (SUD) variable in CIP, consistent with the updated definition in the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5) [26 ].

Patients presenting with significant pain (pain score >4) were included. Significant pain was defined as pain severe enough to limit functioning, determined to be between 5 and 10.^{6 (link),21 (link)} A recent literature review also suggests that a score between 5 and 10 generally represents moderate to severe pain.^{25 (link)}Before data analysis, we performed several transformations. Medication orders placed for each patient were grouped into 1 of the 3 categories: (1) not having received an order for an analgesic (“no medications”), (2) only nonopioid analgesics (“nonopioids”), or (3) any opioid analgesic (“opioid”). Patients in category (3) may have received additional nonopioid analgesia. Based on a list of unique medications that were ordered for patients in our cohort, 1 reviewer (WLG) classified the analgesics into opioids or nonopioids (n = 328 unique medications). This classification was based on evaluating whether an analgesic contained an opioid or not. Mixed analgesics (eg, Vicodin) were classified as containing opioid, hence grouped under the “opioid” category.
Next, we organized data from each patient encounter, sorting it chronologically by time of pain score entry, with the time of the initial pain score recording denoted as time zero. The time of each additional pain score was recorded as the time elapsed since time zero, until the predetermined cut-off value of 48 hours was reached. We had 2 reasons for using a 48-hour observation window. First, we were interested in the acute period that followed immediately after admission. As such, the 48-hour window provided opportunities to investigate a period of rapid (and significant) changes in the pain intensity due to immediate and focused treatment. Second, we specifically focused on the pain-related event that may have brought the patient to the hospital. Although using extended time periods (eg, until discharge) may be useful, it also may introduce new events (eg, surgical procedures) that can create variations in the pain trajectory.
The decision to use time of first pain measurement as time zero, instead of time of admission, was based on the need for a logical and unambiguous starting time for the pain trajectory. Other choices for time zero were administrative, based on when orders are placed, beds are found, or the ED makes a triage decision. Patients unable to give a pain assessment (eg, sedation, paralysis, or intubation) were excluded, as it would be unclear whether they were admitted with pain. Additionally, if a patient was admitted to the hospital from the ED and met our inclusion criteria, then all preceding pain scores were included for analysis. Finally, we used the discharge service to denote the patient's location of service. For example, if a patient was admitted through the ED and transferred to a medical intensive care unit then discharged from the general medicine floor unit, the location for that patient encounter was categorized as general medicine.
We removed all spurious values before the main analysis (pain scores >10, negative values; comprising <0.5% of the data set). We performed preliminary data processing using R (www.r-project.org). We used a smoothed, rolling mean fitting function provided by the ggplot2 package in R to represent the observed pain trajectories graphically. This smoothing function relies on a locally weighted scatter plot smoothing approach.⁸

Descriptive statistics were used to summarize study population characteristics. Treatment patterns over the first and second years following the index date, and for the full duration of postindex follow-up, were assessed as the proportion of patients treated with gynecologic procedures and/or prescribed pharmacologic therapies of interest that were reimbursed by insurance. Pharmacologic therapies of interest were hormonal treatments (oral and nonoral contraceptives), including intrauterine devices (IUDs, except ParaGard^®/copper IUD), estrogen, progestin, aromatase inhibitors, elagolix, danazol, leuprolide, or any luteinizing hormone-releasing hormone agonists.
Also evaluated were the use of tranexamic acid and pain medicines, including narcotic (prescribed for ≥30 days) and prescription non-narcotic analgesics. Not available for analysis were over-the-counter products not captured in medical claims and prescriptions not reimbursed by the payer. Gynecologic procedures of interest were hysterectomy, operative laparoscopy, myomectomy, oophorectomy, ablation of the endometrium and/or fibroids, excision, and salpingectomy. Finally, data were collected for pharmacologic treatments of interest (hormonal or analgesic) received by patients in the year preceding the index date.
Patients in both cohorts who underwent hysterectomy within 1 year postindex date were further stratified by age. Logistic regression models were constructed to determine factors associated with specific treatments (hysterectomy and hormonal therapy) in patients with UF-HMB and UF-only. To isolate these findings to patients who received hysterectomy due to UF, the regression analysis excluded patients with a claim for endometriosis (ICD-9 617.X or ICD-10 N80.X). This exclusion was applied because of the potential for confounding due to concomitant comorbidity. The variables included in the logistic regression were factors that could contribute to treatment decision-making and that could be captured in claims data. These were age, abnormal bleeding, anemia, fatigue, infertility, pain, prior- and post-UF diagnosis use of medications, including hormonal treatment, non-narcotic, or narcotic analgesic treatment, and inpatient or outpatient diagnosis site. Data were analyzed using SAS/STAT(r) software, version 15.1 (2016 SAS Institute Inc., Cary, NC, USA).