Full length articleA comparison of an opioid abuse screening tool and prescription drug monitoring data in the emergency department
Introduction
Prescription opioid analgesics are used with increasing frequency for patients with pain (Kuehn, 2007). This increased use has unfortunately also exacerbated the problem of opioid medication misuse and diversion, and prescription drug overdoses have become a national epidemic (Okie, 2010; CDC, 2012). In the emergency department (ED), where pain is a common complaint, opioid prescribing has also increased markedly over the past several years (Chang et al., 2014, Mazer-Amirshahi et al., 2014).
Several screening tools have been developed to assess patients for their prescription opioid risk level (high or low risk) for aberrant medication-related behaviors in a specialty pain treatment setting context (Webster and Webster, 2005, Skinner, 1982, Butler et al., 2008). These tools are useful for clinicians in order to gauge patients' risk level for such behaviors before the prescription is written. The Centers for Disease Control and Prevention (CDC) have concluded that: “Health-care providers should only use opioid pain relievers in carefully screened and monitored patients when non-opioid pain reliever treatments are insufficient to manage pain” (CDC, 2011).
One screening tool that is commonly used in the ambulatory care (non-ED) setting is the Screener and Opioid Assessment for Patients with Pain-Revised (SOAPP-R; Butler et al., 2008). This questionnaire was derived and validated in specialty pain clinic patients and is widely used in both pain clinics and primary care practices. Despite the fact that a large percentage of ED visits are for painful conditions and that emergency physicians commonly prescribe opioids (Cantrill et al., 2012, Rupp and Delaney, 2004, Hoppe et al., 2015), screening tools are rarely used in the ED, and SOAPP-R has not been studied for emergency department (ED) patients with acute pain.
The derivation and validation of most screening tools relies on the ability to follow up patients longitudinally to determine if there are defined outcomes. Validation of the SOAPP-R, for example, employed the Aberrant Drug Behavior Index (ADBI; Butler et al., 2008), derived from interview data, physician ratings, and urine toxicology screens, in order to capture evidence of breaking pain treatment agreements, use of illegal drugs or prescription opioids not prescribed to the patient, unapproved dose increases, and early requests for refills (i.e., “losing” medication). A cutoff score of 18 on SOAPP-R showed sensitivity of 81% and specificity of 68% for detecting these behaviors.
In addition to screening instruments, 49 states have created Prescription Drug Monitoring Programs (PDMPs). These tools, implemented on a state by state basis, are online databases that lists patients' prescription histories, including the number of prescribers and pharmacies utilized (Gugelmann and Perrone, 2011, Weiner et al., 2013a, Perrone and Nelson, 2012). As longitudinal information is typically not available in the ED and some ADBI outcomes are not applicable to the ED setting, we aimed to determine if SOAPP-R was also able to detect the aberrant medication related behavior known as “doctor shopping,” or inappropriately seeking prescriptions for controlled substances from multiple prescribers, in ED patients. We explored the extent to which PDMP data might be associated with ED-administered SOAPP-R scores. We applied a previously used definition of high-risk behavior (≥4 opioid prescriptions and ≥4 providers for schedule II–V medications in the prior 12 months) as objective criteria of a high-risk patient (Weiner et al., 2013a, Weiner et al., 2013b, Weiner et al., 2015b).
The objectives of this study were to (a) determine the percentage of ED patients receiving prescriptions for opioid pain medications that meet the criteria for “high-risk for abuse potential” on the Screener and Opioid Assessment for Patients with Pain (SOAPP-R), (b) determine the percentage of patients with high-risk behavior on the state PDMP database, (c) compare the SOAPP-R with data from the PDMP for each patient, and (d) the determine psychometric properties of SOAPP-R for the ED patient population.
Section snippets
Study design and setting
This was a cross-sectional, prospective, convenience sample study of patients aged 18 and older who presented to the ED of a single urban academic Level 1 trauma center with approximately 42,000 annual visits. The study was conducted from May–August, 2013. The protocol was approved by the Institutional Review Board.
Selection of participants
Patients were identified by a trained researcher (LCH or SGW) who identified on the electronic tracking system (Medhost EDIS, Medhost, Inc., Plano TX) that the patient had a painful
Results
Ninety-three patients were approached for inclusion and 82 (88.2%) provided consent and completed the study. Patient characteristics are shown in Table 1. Mean number of total schedule II–V prescriptions was 5.9 (SD 11.6), and mean number of opioid prescriptions was 3.4 (SD 6.7). Mean number of prescribers was 2.1 (SD 3.4).
Mean SOAPP-R score was 15.9 (SD 12.8). One third (n = 27, 32.9%) were determined to be “at-risk” (score ≥18) by SOAPP-R. Of the 15.9% (n = 13) of subjects who were deemed
Discussion
Misuse of prescription opioids has had a devastating effect on society. From 1999 to 2012, the age-adjusted rates for drug-poisoning deaths involving opioid analgesics more than tripled, from 1.4 per 100,000 in 1999 to 5.1 in 2012 (Warner et al., 2014). The rate of hospital stays involving opioid overuse among adults increased more than 150% between 1993 and 2012; by 2012, there were 709,500 total opioid-related hospital stays representing a rate of 295.6 stays per 100,000 population (Owens et
Author disclosures
Role of funding source: nothing declared (there was no funding for this study)
Contributors
Study concept and design: SGW.
Acquisition of the data: SGW, LCH.
Analysis and interpretation of the data, SGW, TCG.
Drafting of the manuscript: SGW, TCG.
Critical revision of the manuscript for important intellectual content: all authors.
Statistical expertise: TCG.
Study supervision: SGW (who takes responsibility for the paper as a whole).
All authors have approved the final manuscript.
Conflict of interest
There was no funding for this project. Drs. Butler and Green have been employees of Inflexxion, Inc., the company which created the screening tool used within, but were not involved in the implementation of the tablet application or administration of the study. No additional potential financial conflicts of interest were identified.
Acknowledgement
Results of this study were presented at the Society for Academic Emergency Medicine Annual Meeting, Dallas, TX, May 2014.
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