https://orcid.org/0000-0002-7546-1966
Figures
Abstract
Objective
Whether short-term, low-potency opioid prescriptions for acute pain lead to future at-risk opioid use remains controversial and inadequately characterized. Our objective was to measure the association between emergency department (ED) opioid analgesic exposure after a physical, trauma-related event and subsequent opioid use. We hypothesized ED opioid analgesic exposure is associated with subsequent at-risk opioid use.
Methods
Participants were enrolled in AURORA, a prospective cohort study of adult patients in 29 U.S., urban EDs receiving care for a traumatic event. Exclusion criteria were hospital admission, persons reporting any non-medical opioid use (e.g., opioids without prescription or taking more than prescribed for euphoria) in the 30 days before enrollment, and missing or incomplete data regarding opioid exposure or pain. We used multivariable logistic regression to assess the relationship between ED opioid exposure and at-risk opioid use, defined as any self-reported non-medical opioid use after initial ED encounter or prescription opioid use at 3-months.
Results
Of 1441 subjects completing 3-month follow-up, 872 participants were included for analysis. At-risk opioid use occurred within 3 months in 33/620 (5.3%, CI: 3.7,7.4) participants without ED opioid analgesic exposure; 4/16 (25.0%, CI: 8.3, 52.6) with ED opioid prescription only; 17/146 (11.6%, CI: 7.1, 18.3) with ED opioid administration only; 12/90 (13.3%, CI: 7.4, 22.5) with both. Controlling for clinical factors, adjusted odds ratios (aORs) for at-risk opioid use after ED opioid exposure were: ED prescription only: 4.9 (95% CI 1.4, 17.4); ED administration for analgesia only: 2.0 (CI 1.0, 3.8); both: 2.8 (CI 1.2, 6.5).
Conclusions
ED opioids were associated with subsequent at-risk opioid use within three months in a geographically diverse cohort of adult trauma patients. This supports need for prospective studies focused on the long-term consequences of ED opioid analgesic exposure to estimate individual risk and guide therapeutic decision-making.
Citation: Punches BE, Stolz U, Freiermuth CE, Ancona RM, McLean SA, House SL, et al. (2022) Predicting at-risk opioid use three months after ed visit for trauma: Results from the AURORA study. PLoS ONE 17(9): e0273378. https://doi.org/10.1371/journal.pone.0273378
Editor: Vijayaprakash Suppiah, University of South Australia, AUSTRALIA
Received: February 17, 2022; Accepted: August 7, 2022; Published: September 23, 2022
Copyright: © 2022 Punches et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data that support the findings of this study are openly available at the NIMH National Data Archive at https://nda.nih.gov/edit_collection.html?id=2526, reference number 2526.
Funding: This manuscript was in part funded by NIDA K08 DA049948. The parent study from which data was obtained for this analysis was funded by NIMH U01MH110925, the US Army Medical Research and Material Command, The One Mind Foundation, and The Mayday Fund. Verily Life Sciences and Mindstrong Health provided hardware and software used to perform study assessments. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: Conflict of Interest: In the last three years Dr. Clifford has received research funding from the NSF, NIH and LifeBell AI, and unrestricted donations from AliveCor, Amazon Research, the Center for Discovery, the Gordon and Betty Moore Foundation, MathWorks, Microsoft Research, the Gates Foundation, Google, One Mind Foundation, and Samsung Research. Dr Clifford has financial interest in AliveCor, and receives unrestricted funding from the company. He also is the CTO of MindChild Medical and CSO of LifeBell AI and has ownership in both companies. These relationships are unconnected to the current work. In the past three years, Dr. Germine has served on the Scientific Advisory Board of Sage Bionetworks, for which she received a small honorarium. She also receives grant support from NIH. Dr. Rauch reports grants from NIH during the conduct of the study; personal fees from SOBP (Society of Biological Psychiatry) paid role as secretary, other from Oxford University Press royalties, other from APP (American Psychiatric Publishing Inc.) royalties, other from VA (Veterans Administration) per diem for oversight committee, and other from Community Psychiatry paid board service, including equity outside the submitted work; and Leadership roles on Board or Council for SOBP, ADAA (Anxiety and Depression Association of America), and NNDC (National Network of Depression Centers). Sophia Sheikh has received funding from the Florida Medical Malpractice Joint Underwriter’s Association Dr. Alvin E. Smith Safety of Healthcare Services Grant; Allergan Foundation; the NIH/NIA-funded Jacksonville Aging Studies Center (JAX-ASCENT; R33AG05654); and the Substance Abuse and Mental Health Services Administration (1H79TI083101-01); and the Florida Blue Foundation. Christopher Jones has been an investigator on studies funded by Roche Diagnostics, AstraZeneca, Janssen, and Hologic Inc, for which my department has received research funding. No direct conflicts related to this paper, and no ongoing conflicts. Dr. Joormann receives consulting payments from Janssen Pharmaceuticals. Over the past 3 years, Dr. Pizzagalli has received consulting fees from Albright Stonebridge Group, BlackThorn Therapeutics, Boehringer Ingelheim, Compass Pathway, Concert Pharmaceuticals, Engrail Therapeutics, Neurocrine Biosciences, Otsuka Pharmaceuticals, and Takeda Pharmaceuticals; one honorarium from Alkermes, and research funding from NIMH, Dana Foundation, Brain and Behavior Research Foundation, and Millennium Pharmaceuticals. In addition, he has received stock options from BlackThorn Therapeutics. J Elliott reports support from the National Institutes of Health (NIH) through Grant Numbers R01HD079076 & R03HD094577: Eunice Kennedy Shriver National Institute of Child Health & Human Development; National Center for Medical Rehabilitation Research. He also reports funding from New South Wales Health, Spinal Cord Injury Award (2020-2025) and consulting fees (< $15,000 per annum) from Orofacial Therapeutics, LLC. In the past 3 years, Dr. Kessler was a consultant for Datastat, Inc., Holmusk, RallyPoint Networks, Inc., and Sage Pharmaceuticals. He has stock options in Mirah, PYM, and Roga Sciences. Dr. Ressler has received consulting income from Alkermes and Takeda, research support from NIH, Alkermes, Genomind and Brainsway, and he has served on advisory boards for Takeda, Resilience Therapeutics, Janssen and Verily/Google.
Introduction
The opioid crisis continues despite substantial efforts to date [1, 2]. Opioid use disorder (OUD), with consequences including overdose, injection drug use, and impaired consciousness, is a massive contributor to morbidity, mortality, and economic burden [3–6]. Thus far, response to the opioid epidemic has predominantly focused on law enforcement and secondary/tertiary prevention, such as expanded OUD treatment [7–11] and overdose reversal [12], with reduced attention to primary prevention beyond opioid prescribing reductions [13–17].
Emergency departments (EDs) commonly encounter patients in pain [18–20], and EDs are a recognized source of opioid exposure [21, 22]. An initial opioid exposure is a necessary, if not sufficient, antecedent to OUD [23]. Moreover, it is accepted that widespread increases in opioid prescription lead to observed increases in opioid overdose [24–26]. Yet, whether this association is causal and the relative contribution of prescription opioid use to later OUD are poorly understood [21, 22, 27], particularly for short-term, low dose exposures in episodic, unscheduled care settings treating acute pain, such as the ED [16, 28, 29]. Further complicating this narrative, self-reported sources of early opioid exposure by individuals with OUD are subject to recall bias and case-control study designs cannot be used to estimate exposure risk for individuals not yet suffering from OUD [21, 30, 31]. Retrospective reports associating duration and dosage of initial opioid therapy with later long-term use [27, 32] do not assess non-medical use or otherwise distinguish at the time of follow-up whether opioids are for new painful conditions, chronic pain, or OUD.
Our objective was to use existing data from a multi-center, prospective, observational study of posttraumatic neuropsychiatric sequelae to evaluate the degree to which an analgesic opioid exposure in the ED contributes to at-risk opioid use. We hypothesized that opioid exposure during the initial ED encounter for a traumatic event would be associated with at-risk opioid use within three months.
Materials and methods
Study design and setting
This study was a secondary analysis of data collected during the AURORA (Advancing Understanding of RecOvery afteR traumA) study. AURORA collected a wide array of psychological and biobehavioral data from adult patients recruited from a geographically diverse sample of 29 urban, U.S. emergency departments (EDs) who presented within 72 hours of a physical trauma [33]. Detailed elsewhere [33], participants provided written informed consent and completed baseline surveys in the ED and completed follow-up surveys at 2-weeks, 8-weeks, and 3-months after the initial visit. AURORA participants were: a) 18–65 years old, b) able to speak and read English, c) without cognitive impairment, d) able to use their own smart phone for >1 year post-enrollment, and e) discharged home or hospitalized for fewer than three days. Patients were excluded for solid organ injury > Grade 1 as defined by the American Association for the Surgery of Trauma (AAST), significant hemorrhage, requiring a chest tube or operation with anesthesia, or receiving greater than 20 morphine milligram of opioid medication daily prior to enrollment [34]. Occupational, self-inflicted, and injuries related to domestic violence were also excluded. The study was centrally approved by the Institutional Review Board at UNC Chapel Hill (IRB#17–0703), and all participants provided written informed consent.
Participant selection
This analysis included AURORA participants enrolled after September 2017 who completed the 3-month follow-up assessment by October 2019. We additionally excluded from analysis those reporting any non-medical opioid use in the 30 days before enrollment and those with missing or incomplete opioid use/exposure responses or pain scores.
Main outcomes/measures
We developed a composite definition using surrogate markers of interest to identify a group of patients spanning from high to potential concern. The primary outcome was “at-risk opioid use” defined as the composite outcome of 1) any non-medical opioid use after the initial ED visit (at 2-week, 8-week, or 3-month follow-up), or 2) opioid prescription use at 3-month follow-up. Non-medical opioid use was defined by affirmative response to the survey question “heroin, any opioids without a prescription, or taking more than prescribed for euphoria” [35, 36] This definition of “at-risk” use depends on a simplifying assumption that 1) any non-medical opioid use is problematic, and 2) pain at three months would generally be due to the original traumatic event with a transition to chronic pain and that 3) ongoing opioid exposure for chronic pain (3 months or greater) is at-risk for disordered opioid use [35, 37, 38]. Most traumatic injuries have healed to the degree possible absent additional complications by three months, and long-term prescription opioid use is associated with negative outcomes [3, 4, 6, 38, 39].
Exposure was measured as opioid administration for analgesia only during the ED visit, a prescription for opioids at ED discharge, or both at study enrollment. Covariates included self-reported patient gender, sex at birth, age, race/ethnicity, pain score at baseline and at 3-month follow-up, prescription opioid use in the 30 days prior to enrollment, marital status, employment status, income, injury severity score at baseline, and self-reported history of opioid use disorder.
Primary data analysis
Descriptive statistics were used to summarize and assess participant selection characteristics. Summary data are reported as percentages, percentages with 95% confidence intervals (CI), medians with interquartile range (IQR), and means with 95% CI. Crude (unadjusted) odds ratios (cORs) and adjusted ORs (aORs) are presented with 95% CI to assess statistical significance.
We first conducted univariable analyses to quantify the association between at-risk opioid use and ED opioid exposure (none, ED prescription only, ED administration for analgesia only, ED administration and prescription), as well as a wide array of potential confounders of this association and possible risk factors for at-risk opioid use leveraging literature and expert opinion [29, 40, 41]. We then used multivariable logistic regression to further characterize the relationship between ED opioid exposure and at-risk opioid use, accounting for potential confounders and risk factors. All variables from the univariable analysis with a (p≤0.10) association via Fisher’s exact test for categorical data, Student’s t-test for parametric data, or Kruskal-Wallis test for non-parametric continuous data were included in an initial multivariable model. ED opioid exposure was kept in all models regardless of p-value. We used backward elimination to remove covariates with a p-value >0.05 starting with the covariate with the highest p-value based on a likelihood ratio test. All excluded covariates were re-introduced one at a time to assess confounding between ED opioid exposure and at-risk opioid use. Variables that resulted in a change in the regression coefficient of ≥10% were considered significant and included in the final model. After identifying the preliminary final model, goodness-of-fit, discrimination, and diagnostic statistics were calculated. The assumption of a linear relationship between the outcome and continuous variables in the logit (log-odds) scale was tested using fractional polynomials and graphic analyses. We also examined for clinically plausible interactions (i.e., effect modification) between ED opioid exposure and previous prescription that might affect the relationship between ED opioid exposure and at-risk opioid use; however, we found no evidence of significant effect modification. We conducted sensitivity analyses to assess the robustness of primary analysis results. Potential outliers and overly influential observations identified via diagnostic statistics were checked for miscoding and removed as part of these sensitivity analyses.
Results
Study flow and participant characteristics
There were 1441 patients available for analysis in the AURORA 3-month follow-up cohort, and subsequent exclusion criteria are outlined in Fig 1. We excluded 569 participants for the following reasons: 1) missing the primary outcome at two weeks, eight weeks, or three months (n = 198), 2) missing ED opioid exposure data during enrollment visit (n = 139), 3) missing age, sex, race, history of opioid prescription use in the 30 days prior to enrollment, injury severity score, ED pain score, or reported pain at 3-month follow-up (n = 106), 4) reported non-medical opioid use prior to enrollment (n = 59), and 5) hospitalized at conclusion of ED encounter (n = 67). Participants’ demographic characteristics, medical history, and ED opioid exposure are stratified by at-risk opioid use versus no at-risk use in Table 1. Of the 872 participants with complete data in the analysis, 54% were Black/African American, 67% female, and median age was 34 years.
Primary outcome
Of 872 subjects included in the primary analysis, at-risk opioid use was reported by 66 (7.6%) individuals by the 3-month follow-up. In comparison to type of ED opioid exposure, at-risk opioid use was reported by: 33/620 (5.3%, CI: 3.7, 7.5) without an ED opioid analgesic exposure, 4/16 (25%, CI: 8.3, 52.6) with ED opioid prescription only, 17/146 (11.6%, CI: 7.1, 18.3) with in-ED opioid administration only, and 12/90 (13.3%, CI: 7.4, 22.5) with both ED administration and opioid prescription at discharge (Table 1).
Multi-variable analysis
Compared to no ED opioid exposure, the aOR for at-risk opioid use was 4.9 (CI 1.4, 17.4) for ED opioid prescription only, 2.0 (CI 1.0, 3.8) for ED administered opioids only, and 2.8 (CI 1.2, 6.5) for both ED opioid administration and prescription at discharge, when controlling for patient age, prescription opioid use prior to enrollment, pain at initial ED visit, moderate or severe pain at three months, race/ethnicity, marital status, injury severity score, and self-reported history of OUD (Table 2). Other patient characteristics (e.g., income, education, employment status, gender) were not associated with at-risk opioid use in the multivariable model (Table 2). The aOR for at-risk opioid use did not differ significantly for ED opioid prescriptions only compared to either ED-administration only (aOR 2.0, CI 0.5, 7.2) or ED-administration plus prescription at discharge (aOR 1.7, CI 0.4, 6.5). Combining all ED opioid exposure categories suggested exposure to any opioid (prescription at discharge or in-ED administration, or both) during the ED visit was associated with a more than doubling of the odds of at-risk opioid use by the 3-month follow-up period (aOR 2.2, CI 1.3, 3.75). Sensitivity analyses were consistent with primary findings.
Discussion
Exposure to opioids after a traumatic event was associated with increased at-risk opioid use within three months in a geographically diverse cohort of patients who experienced trauma. While the study was observational, data were collected prospectively, and the associations of prescription opioid exposure and at-risk opioid use persisted after controlling for patient and clinical factors. Not surprisingly, ED opioid administration and prescribing was relatively common for these trauma patients, with 12% receiving an opioid prescription at ED discharge, and 29% receiving a prescription, in-ED administration, or both. These percentages equate to millions of exposures annually nationwide. Every year, approximately 35 million ED visits result from injury in the US, so even a small effect from prescription opioid exposure would have significant ramifications for individual and public health [42]. If even a small proportion of those exposures are causally related and avoidable, there is an urgent need to develop, target, and deploy efficacious interventions.
Our findings align with previous studies associating the strength and duration of initial opioid prescription with later use [21, 27, 32, 43, 44]. This study is unique in at least four respects. First, we considered both in ED administration and prescription at discharge. The hypothesis that administration in the ED without ongoing prescription exposure could influence long-term opioid-related outcomes contributes to our understanding of the development of OUD. Second, we assessed for and excluded individuals with self-reported previous non-medical opioid use from the cohort. As such, the cohort selected helps isolate new from ongoing at-risk opioid use. Third, we were able to control for patient and clinical factors that affect the association between exposure and outcome. Finally, we measured non-medical use as part of the outcome, as opposed to only continued usage. We do acknowledge that inclusion of opioid prescriptions at three months suffers from the same limitations as other retrospective longitudinal studies of prescription history. However, our use of a 3-month time horizon increases the likelihood that opioid use at three months was a continuation from the index event rather than a new and unrelated (and thus less concerning) short-term exposure.
When controlling for the effects of prior opioid exposure, opioid prescription in the 30 days prior to enrollment was associated with later at-risk opioid use (aOR 3.11, CI 1.13, 8.57). We do not know if this association was due to misclassification (i.e., undisclosed/undiagnosed opioid use disorder), a direct cause of increased risk, or a marker of pain or opioid response predisposing to at-risk use. It is easy to hypothesize that treatment for acute pain can be a causal event along the trajectory from initial exposure to later at-risk use even if not a triggering event when occurring as a first exposure.
Even if short-term low-potency opioid exposure is causally associated with later long-term opioid use, or the development of OUD, it does not mean that initial opioid exposure is necessarily avoidable. All therapy in medicine is associated with potential risks and benefits, and the potential for later harm must be balanced against the potential for unrelieved short-term suffering. Prospective study of opioid exposure for acute pain is necessary so that patients and providers can accurately estimate individualized risk to guide therapeutic decision-making. It is important to realize that scientific developments in this area could simultaneously support both expansions and reductions in opioid therapy depending on the individual patient and the circumstance.
Limitations
While this study capitalized on the availability of a prospective, multicenter cohort, results should be understood in context with important limitations. Most notably, the generalizability of this analysis was limited to measures and sample size available from the parent study which was not specifically designed to assess opioid exposure or long-term opioid use. Bias may have been introduced by our exclusion of a large number of participants with missing follow-up data and well as the voluntary aspect of research participation. These exclusions may limit the number of individuals with later at-risk opioid use due to stigma and the self-reported nature of the survey follow-up. Additionally, due to sample size limitations, we used a composite outcome that did not fully resolve limitations of prior studies in which the reasons for later prescription opioid use are uncharacterized. Our ability to assess causality is additionally limited by its observational design. Finally, due to the categorical nature of of both prior opioid prescription use and later opioid use, we were unable to reduce time-based confounding in our model.
Conclusions
Exposure to opioids from an ED visit was associated with increased odds of at-risk opioid use within three months among trauma patients when controlling for age, gender, race/ethnicity, prescription opioid use prior to enrollment, pain, injury severity score, and self-reported history of OUD. These results support the need for prospective study focused on the long-term consequences of ED opioid analgesic exposure to guide therapeutic decision-making.
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