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Comparison of the Risks of Opioid Abuse or Dependence Between Tapentadol and Oxycodone: Results From a Cohort Study

Open AccessPublished:July 12, 2013DOI:https://doi.org/10.1016/j.jpain.2013.05.010

      Abstract

      Tapentadol may have a lower abuse risk than other opioids because it has a relatively low affinity for the mu-opioid receptor. The aim of this retrospective cohort study was to compare the risk of opioid abuse between tapentadol immediate release (IR) and oxycodone IR using 2 claims databases (Optum and MarketScan). Subjects with no recent opioid use exposed to tapentadol IR or oxycodone IR in 2010 were followed for 1 year. The outcome was the proportion of subjects who developed opioid abuse, defined as subjects with International Classification of Diseases, 9th revision, codes for opioid abuse, addiction, or dependence. The relative odds of abuse were estimated using a logistic regression model with propensity-score stratification. The estimates from the 2 databases were pooled using a random effects model. There were 13,814 subjects in Optum (11,378 exposed to oxycodone, 2,436 exposed to tapentadol) and 25,553 in MarketScan (21,728 exposed to oxycodone, 3,825 exposed to tapentadol). The risk of abuse was higher in the oxycodone group than in the tapentadol group in each database. The pooled adjusted estimate for the odds of abuse was 65% lower with tapentadol than with oxycodone (odds ratio = .35, 95% confidence interval = .21–.58). The risk of receiving an abuse diagnosis with tapentadol was lower than the risk with oxycodone. Continued monitoring is warranted because opioid desirability can change over time.

      Perspective

      This study compared the risk of receiving an opioid abuse diagnosis between tapentadol and oxycodone in 2 U.S. claims databases. The risk of receiving an abuse diagnosis was lower with tapentadol during the year of follow-up. Opioid prescribers and patients must be aware of the risk of abuse associated with all opioids.

      Key words

      The burden of pain is a significant public health problem. The Institute of Medicine reported in 2011 that chronic pain affects millions of adults in the United States, more than the total affected by heart disease, cancer, and diabetes combined,
      IOM (Institute of Medicine)
      Relieving pain in America: A blueprint for transforming prevention, care, education, and research.
      and that uncontrolled pain substantially reduces quality of life and productivity.
      IOM (Institute of Medicine)
      Relieving pain in America: A blueprint for transforming prevention, care, education, and research.
      Opioids are increasingly prescribed for the treatment of painful chronic conditions,
      • Kuehn B.M.
      Opioid prescriptions soar: increase in legitimate use as well as abuse.
      but there is growing concern about the risk of opioid abuse, diversion,
      • Compton W.M.
      • Volkow N.D.
      Major increases in opioid analgesic abuse in the United States: Concerns and strategies.
      • Kuehn B.M.
      Opioid prescriptions soar: increase in legitimate use as well as abuse.
      overdose, and death.
      CDC grand rounds: Prescription drug overdoses—A U.S. epidemic.
      • Spence D.
      The painful truth: Deaths and misuse of prescribed drugs.
      • Sullivan M.D.
      Limiting the potential harms of high-dose opioid therapy: Comment on “Opioid dose and drug-related mortality in patients with nonmalignant pain.”.
      • Volkow N.D.
      • McLellan T.A.
      Curtailing diversion and abuse of opioid analgesics without jeopardizing pain treatment.
      The mechanism of action of an opioid could influence its risk of abuse.
      • Le Merrer J.
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      • Befort K.
      • Kieffer B.L.
      Reward processing by the opioid system in the brain.
      • Zhang X.
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      Interaction and regulatory functions of mu- and delta-opioid receptors in nociceptive afferent neurons.
      • Zhang Z.
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      Synaptic mechanism for functional synergism between δ and υ opioid receptors.
      Tapentadol is an opioid with 2 mechanisms of action; it activates opioid receptors and inhibits the reuptake of norepinephrine.
      • Kress H.G.
      Tapentadol and its two mechanisms of action: Is there a new pharmacological class of centrally-acting analgesics on the horizon?.
      Tapentadol has an 18-fold lower affinity for the mu-opioid receptor than morphine.
      • Tzschentke T.M.
      • Christoph T.
      • Kogel B.
      • Schiene K.
      • Hennies H.H.
      • Englberger W.
      • Haurand M.
      • Jahnel U.
      • Cremers T.I.
      • Friderichs E.
      • De V.J.
      (–)-(1R,2R)-3-(3-dimethylamino-1-ethyl-2-methyl-propyl)-phenol hydrochloride (tapentadol HCl): A novel mu-opioid receptor agonist/norepinephrine reuptake inhibitor with broad-spectrum analgesic properties.
      Because the activation of the mu-opioid receptor is responsible for the mood alterations and the euphoria associated with opioids, the risk of abuse associated with tapentadol may be expected to be lower than with other opioids. Limited evidence from population-based studies also suggests that the risk of abuse of tapentadol may be lower than other opioids. Opioid doctor shopping, that is, obtaining opioid prescriptions from multiple prescribers,
      • Cepeda M.S.
      • Fife D.
      • Chow W.
      • Mastrogiovanni G.
      • Henderson S.C.
      Assessing opioid shopping behaviour: A large cohort study from a medication dispensing database in the US.
      • Cepeda M.S.
      • Fife D.
      • Chow W.
      • Mastrogiovanni G.
      • Henderson S.C.
      Opioid shopping behavior: How often, how soon, which drugs, and what payment method.
      which is a way in which opioids may be abused and their use diverted,
      CDC grand rounds: Prescription drug overdoses—A U.S. epidemic.
      • Peirce G.L.
      • Smith M.J.
      • Abate M.A.
      • Halverson J.
      Doctor and pharmacy shopping for controlled substances.

      Substance Abuse and Mental Health Services Administration. Results from the 2006 national survey on drug use and health: National findings. 2007. Available at: http://www.oas.samhsa.gov/nsduh/2k6nsduh/2k6Results.pdf

      is much less commonly observed in opioid-naïve subjects initially exposed to tapentadol than in opioid-naïve subjects initially exposed to oxycodone.
      • Cepeda M.S.
      • Fife D.
      • Vo L.
      • Mastrogiovanni G.
      • Yuan Y.
      Comparison of opioid doctor shopping for tapentadol and oxycodone: A cohort study.
      Similarly, data from internet monitoring, surveillance of addiction treatment centers, pharmacovigilance efforts, and surveys of college students suggest that the risk of abuse of tapentadol is lower than that of other Schedule II opioids.

      Dart R, Bucher-Bartelson B, Adams EH. Non-medical use of tapentadol immediate release by college students. American Academy of Pain Medicine (AAPM) Annual Meeting. Palm Springs, CA. February 2012. http://www.painmed.org/2012posters/abstract-271. Accessed May 16, 2013

      • Dart R.C.
      • Cicero T.J.
      • Surratt H.L.
      • Rosenblum A.
      • Bartelson B.B.
      • Adams E.H.
      Assessment of the abuse of tapentadol immediate release: The first 24 months.
      However, there are no studies that explicitly compare the risk of opioid abuse and addiction in subjects prescribed tapentadol versus oxycodone. Therefore, we sought to compare the risk of opioid abuse between tapentadol immediate release (IR) and oxycodone IR.

      Methods

      We conducted a retrospective cohort study using 2 U.S. claims databases (Optum and MarketScan), which are commonly used for pharmacoepidemiologic research. The Optum Clinformatics database represents a privately insured population and captures administrative claims primarily from the UnitedHealth Group; it has at least 36 million members with both medical and pharmacy benefits. The MarketScan Commercial Claims and Encounters database represents a privately insured population and captures administrative claims from inpatient and outpatient visits and pharmacy claims of large employers and multiple insurance plans. The data set used for this study contains more than 90 million individuals with medical and pharmacy coverage from January 2000 to January 2012.

      Inclusion Criteria

      Subjects with no recent opioid use whose first opioid exposure was to tapentadol IR or oxycodone IR in 2010 were included and observed for 1 year. Subjects with no recent opioid use were those with no opioid dispensing during the 3 months before the index date. The index date was the date of the first dispensing of tapentadol or oxycodone. Subjects were required to have been in the database for at least 3 months prior to their index date and for at least 12 months after. The codes used to identify tapentadol IR and oxycodone IR are listed in Appendix 1.
      One year of follow-up was selected because studies assessing shopping behavior suggest that 75% of the subjects who developed shopping behavior had the first event ≤261 days after first exposure with a median of 234 days.
      • Cepeda M.S.
      • Fife D.
      • Chow W.
      • Mastrogiovanni G.
      • Henderson S.C.
      Opioid shopping behavior: How often, how soon, which drugs, and what payment method.

      Exclusion Criteria

      Subjects with a history of opioid abuse, opioid addiction, or opioid dependence at any time before the index date, as well as subjects who filled a prescription for an opioid other than the indexed opioid before the index date or within the next 3 days, were excluded.

      Outcome

      The outcome of interest was incident reported diagnosis of opioid abuse, opioid addiction, or opioid dependence after the index date. The list of the International Classification of Diseases, 9th revision (ICD-9), Healthcare Common Procedure Coding System, and Current Procedural Terminology codes used is found in Table 1.
      Table 1Codes Used to Identify Opioid Abuse, Dependence, and Addiction
      CodeDescription
      305.50Opioid abuse, unspecified use
      305.51Opioid abuse, continuous use
      305.52Opioid abuse, episodic use
      304.00Opioid type dependence, unspecified use
      304.01Opioid type dependence, continuous use
      304.02Opioid type dependence, episodic use
      304.70Combinations of opioid type drug with any other drug dependence, unspecified use
      304.71Combinations of opioid type drug with any other drug dependence, continuous use
      304.72Combinations of opioid type drug with any other drug dependence, episodic use
      4306 FPatient counseled regarding psychosocial AND pharmacologic treatment options for opioid addiction

      Confounders

      To control for the effect of baseline differences between the subjects exposed to tapentadol and those exposed to oxycodone, propensity score stratification was used. Propensity score is the conditional probability of a subject's receiving a particular exposure, in this case, initial exposure to tapentadol versus oxycodone, given a set of confounders. To calculate the propensity score, the confounders were included in a logistic regression model to predict the exposure, without including the outcome.
      • Cepeda M.S.
      The use of propensity scores in pharmacoepidemiologic research.
      • Cepeda M.S.
      • Boston R.
      • Farrar J.T.
      • Strom B.L.
      Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders.
      As a result, the collection of confounders was collapsed into a single variable, the probability (propensity) of being initially exposed to tapentadol versus oxycodone. Subjects initially exposed to tapentadol and subjects initially exposed to oxycodone who have the same value of propensity score (regardless of the treatment they actually received) will have the same probability of receiving one initial treatment or the other.

      Propensity Score

      It has been shown that models that automatically select the variables to calculate the propensity score can reduce bias relative to the models that use only a predefined group of variables.
      • Myers J.A.
      • Rassen J.A.
      • Gagne J.J.
      • Huybrechts K.F.
      • Schneeweiss S.
      • Rothman K.J.
      • Joffe M.M.
      • Glynn R.J.
      Effects of adjusting for instrumental variables on bias and precision of effect estimates.
      • Rassen J.A.
      • Schneeweiss S.
      Using high-dimensional propensity scores to automate confounding control in a distributed medical product safety surveillance system.
      • Schneeweiss S.
      • Rassen J.A.
      • Glynn R.J.
      • Avorn J.
      • Mogun H.
      • Brookhart M.A.
      High-dimensional propensity score adjustment in studies of treatment effects using health care claims data.
      Therefore, we supplemented a defined set of a priori confounders with additional covariates for all medical conditions and drugs. The known confounders were age, gender, state, quarter of the year of the index date, year, time in the database before the index date, major depression, mood disorders, anxiety disorders, abuse of nonopioid medications (such as alcohol or tobacco), and use of benzodiazepines. The ICD-9 codes used to define these conditions are listed in Appendix 2. In addition, binary covariates were added for each medical condition, based on a diagnosis of the condition in the prior 3 months, as represented by the 227 unique high level group terms with the Medical Dictionary for Regulatory Activities (MedDRA) vocabulary (eg, coronary artery disorders). Eighty-two covariates were also included for each drug class, as represented by 2-digit codes within the Anatomical Therapeutic Chemical classification system (eg, diuretics) if any drug within the class was dispensing during the 3 months prior to the index date. The Observational Medical Outcomes Partnership vocabulary was used to map ICD-9 codes to MedDRA high level group terms and National Drug Codes into Anatomical Therapeutic Chemical classification.
      • DeFalco F.J.
      • Ryan P.B.
      • Cepeda M.S.
      Applying standardized drug terminologies to observational healthcare databases: A case study on opioid exposure.
      • Reich C.
      • Ryan P.B.
      • Stang P.E.
      • Rocca M.
      Evaluation of alternative standardized terminologies for medical conditions within a network of observational healthcare databases.
      • Sturmer T.
      • Rothman K.J.
      • Avorn J.
      • Glynn R.J.
      Treatment effects in the presence of unmeasured confounding: Dealing with observations in the tails of the propensity score distribution—A simulation study.
      Major depression, mood and anxiety disorders, and abuse of nonopioid medications, such as alcohol or tobacco, and pain-related diagnoses were not mapped to MedDRA concepts to allow for more specificity. Pain diagnoses were included as arthritis, back pain, fractures, headache, malignancies, musculoskeletal pain, neuropathic pain, other, reproductive system pain, visceral pain, and wound/injury using published ICD-9 groupings.
      • Seal K.H.
      • Shi Y.
      • Cohen G.
      • Cohen B.E.
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      • Krebs E.E.
      • Neylan T.C.
      Association of mental health disorders with prescription opioids and high-risk opioid use in US veterans of Iraq and Afghanistan.
      The propensity score was estimated using Bayesian logistic regression.
      Large-scale Bayesian logistic regression for text categorization.
      We used a Laplace distribution for the prior and cross validation to obtain the variance.

      Checking Balancing Properties of the Propensity Score

      To check the balancing properties of the propensity score, we tabulated the pain-related conditions and the other variables known to be associated with opioid abuse in each treatment group and calculated standardized differences of means or proportions in each of the quintiles of the propensity scores and overall. To calculate the overall standardized difference for each potential confounder, we averaged the standardized differences of the propensity score quintiles for that potential confounder. Standardized differences of less than .25 are an indication of appropriate balance.
      • Stuart E.A.
      Matching methods for causal inference: A review and a look forward.

      Outcome Model

      The relative risk of opioid abuse between tapentadol and oxycodone was estimated using a logistic regression model, with the binary indicator of incident opioid abuse diagnosis as the outcome variable and the exposure status and propensity score quintiles as covariates. The estimates from the 2 databases were then pooled using a random effects model. Odds ratios (ORs) and 95% confidence intervals (CIs) are reported. For outcomes of low frequency, as is the case with abuse, odds closely approximate risks, so we refer to the more familiar term, risk. Oxycodone was used as the referent group such that ORs <1 indicate a lower risk of an abuse diagnosis with tapentadol.

      Dose Assessment

      Daily dose of opioid at baseline was calculated and to allow comparison converted into tapentadol equivalent doses using a 5:1 conversion ratio.
      • Buynak R.
      • Shapiro D.Y.
      • Okamoto A.
      • Hove I.V.
      • Rauschkolb C.
      • Steup A.
      • Lange B.
      • Lange C.
      • Etropolski M.
      Efficacy and safety of tapentadol extended release for the management of chronic low back pain: Results of a prospective, randomized, double-blind, placebo- and active-controlled phase III study.

      Sample Size

      Approximately 1,000 subjects initially exposed to tapentadol were needed to detect a 2-fold decrease in the risk of abuse, assuming a 3% risk of abuse among those who were initially exposed to oxycodone,
      • Edlund M.J.
      • Martin B.C.
      • Fan M.Y.
      • Devries A.
      • Braden J.B.
      • Sullivan M.D.
      Risks for opioid abuse and dependence among recipients of chronic opioid therapy: Results from the TROUP study.
      with 80% power, an alpha error of 5%, and a ratio of oxycodone to tapentadol subjects of 10:1.

      Sensitivity Analyses

      We evaluated the robustness of the propensity score model by performing the analysis with and without trimming of patients with nonoverlapping propensity scores.
      • Sturmer T.
      • Rothman K.J.
      • Avorn J.
      • Glynn R.J.
      Treatment effects in the presence of unmeasured confounding: Dealing with observations in the tails of the propensity score distribution—A simulation study.
      We also performed matching as an alternative propensity score adjustment strategy to stratification. We implemented a nearest available matching algorithm with a 1:1 tapentadol to oxycodone ratio in the Optum database and, because of the larger sample size, a 1:2 match in the MarketScan database, and a propensity score difference smaller than .1. We then built a conditional logistic regression to obtain the relative risk of opioid abuse diagnosis between tapentadol and oxycodone while respecting the matches.
      The analyses were conducted using SAS, version 9.3 (SAS Institute Inc, Cary, NC). The New England Institutional Review Board determined that this study was not human subjects research and was exempt from review.

      Results

      There were 13,814 subjects from the Optum database who met the inclusion criteria (11,378 initially exposed to oxycodone, 2,436 to tapentadol) and 25,553 subjects from the MarketScan database who met the inclusion criteria (21,728 initially exposed to oxycodone, 3,825 to tapentadol). Figs 1 and 2 show the number of subjects who failed to meet each one of the inclusion criteria in each of the databases.
      Figure thumbnail gr1
      Figure 1Flow diagram in Optum database. Numbers represent subjects who failed to meet each one of the inclusion criteria. The percentages use the number of exposed patients as denominators.
      Figure thumbnail gr2
      Figure 2Flow diagram in MarketScan database. Numbers represent subjects who failed to meet each one of the inclusion criteria. The percentages use the number of exposed patients as denominators.
      In each database, subjects in the tapentadol group were older, more likely to be women, and more likely to have back pain than subjects in the oxycodone group (Table 2).
      Table 2Baseline Characteristics of Subjects Exposed to Tapentadol and Oxycodone With Standardized Differences Before and After Propensity Score Adjustment
      CharacteristicOptum DatabaseMarketScan Database
      OxycodoneTapentadolStandardized Difference Before Propensity ScoreStandardized Difference After Propensity ScoreOxycodoneTapentadolStandardized Difference Before Propensity ScoreStandardized Difference After Propensity Score
      Number of subjects113782436217283825
      Age, mean ± SD43.79 ± 17.6547.52 ± 12.90.24.0442.12 ± 15.5146.17 ± 11.59.30.07
      Women, n (%)5858 (51.49)1574 (64.61).27.0111905 (54.79)2492 (65.15)–.21.01
      Variable, n (%)
       Arthritis2935 (25.80)665 (27.30).03.105229 (24.07)942 (26.63).01.08
       Back pain1708 (15.01)552 (22.66).20.092834 (13.08)867 (22.67).25.09
       Benzodiazepine use1434 (12.60)413 (16.95).12.082934 (13.50)716 (18.72).14.11
       Drug abuse excluding opioids569 (5.00)72 (2.96)–.09.26622 (2.86)54 (1.41)–.07.06
       Mood/anxiety disorders and depression1173 (10.31)266 (10.92).02.152165 (9.96)374 (9.78)–.01.09
       Fractures849 (7.46)74 (3.04)–.18.011447 (6.66)92 (2.41)–.18–.13
       Headaches639 (5.62)133 (5.46)–.01.06970 (4.46)175 (4.58).01–.07
       Malignancy2650 (23.29)490 (20.11)–.08–.144354 (20.04)586 (15.32)–.12.00
       Musculoskeletal pain2946 (25.89)723 (29.68).08.134927 (22.68)971 (25.39).06.16
       Other229 (2.01)54 (2.22).01.01278 (1.28)40 (1.05)–.01.00
       Reproductive system pain249 (2.19)64 (2.63).02–.04458 (2.11)80 (2.09).00–.02
       Visceral pain1764 (15.50)207 (8.50)–.22.082843 (13.08)294 (7.69)–.18–.09
       Wound injury458 (4.03)37 (1.52)–.12.22827 (3.81)42 (1.10)–.12–.07
       Neuropathy302 (2.65)139 (5.71).14.05520 (2.39)139 (3.63).06.01
      Abbreviation: SD, standard deviation.
      The daily dose of opioid at baseline was slightly higher in the tapentadol group than in the oxycodone group in both databases. The median tapentadol equivalent daily dose in the tapentadol group was 300.0 mg versus 250.0 mg in the oxycodone group in the Optum database and 300.0 mg versus 214.3 mg in the MarketScan database. There was no observed difference in other opioid use between the tapentadol and oxycodone groups, and the majority of persons in each cohort had no other opioid use (25th–75th percentile, 0–2).
      The models to calculate the propensity score included 365 variables in the Optum database and 370 variables in the MarketScan database. After stratification on the propensity score, most standardized differences in baseline characteristics got smaller (Table 2), indicating that a better balance was achieved. Similarly, the standardized differences for each one of the confounders in each one of the quintiles of the propensity score were very small, especially in the first 4 propensity score categories in each database, confirming the good balance achieved with the propensity score (Appendixes 3 and 4).
      In each database, a higher percentage of subjects in the oxycodone group than in the tapentadol group received opioid abuse diagnoses. After adjustment, the risk of developing an opioid abuse diagnosis remained higher in the oxycodone group than in the tapentadol group in each database (Table 3). Overall, the risk of developing abuse diagnoses was much smaller with tapentadol than with oxycodone (pooled estimate for abuse, OR = .35, 95% CI = .21–.58; Fig 3).
      Table 3Subjects Who Developed Opioid Abuse Diagnosis in the Tapentadol and Oxycodone Groups: Unadjusted and Adjusted OR
      DatabaseOxycodone IR n (%)Tapentadol IR n (%)Unadjusted OR (95% CI)Adjusted OR

      (95% CI)
      Adjusted OR After Excluding Subjects Without Complete Propensity Score Overlap (95% CI)Adjusted OR After Matching on Propensity Score (95% CI)
      Optum
       Abuse75 (.66)7 (.29).43 (.1–.94).26 (.12–.59).28 (.12–.62).20 (.08–.54)
       No abuse11303 (99.34)2429 (99.71)
      MarketScan
       Abuse105 (.48)12 (.31).65 (.32–1.2).42 (.22–.79).45 (.23–.85).33 (.14–.79)
       No abuse21623 (99.52)3813 (99.69)
      Figure thumbnail gr3
      Figure 3Meta-analysis of the risk of opioid abuse diagnosis of tapentadol IR and oxycodone IR. Each line presents the relative estimate for abuse obtained in a database with its 95% CI. The size of the box represents the weight given to that estimate. The diamond represents the overall effect estimate. The risk of abuse with tapentadol is lower than the risk of abuse with oxycodone.
      The sensitivity analyses provided similar results to the main analyses. After excluding subjects without complete propensity score overlap, 10,111 subjects initially exposed to oxycodone and 2,434 subjects initially exposed to tapentadol in the Optum database and 19,013 subjects initially exposed to oxycodone and 3,806 subjects initially exposed to tapentadol in the MarketScan database were included in the analyses. The standardized differences in each of the quintiles of the propensity score were similarly small compared with the ones in the main analysis. As in the main analyses, the risk of developing an abuse diagnosis was much smaller in subjects exposed to tapentadol than in subjects exposed to oxycodone in each database (Table 3).
      The results after matching were similar to the main analyses as well. In the Optum database, a total of 4,018 subjects were matched, half initially exposed to tapentadol and half to oxycodone. In the MarketScan database, 5,817 subjects were matched, 1,939 initially exposed to tapentadol and 3,878 to oxycodone. The risk of developing an abuse diagnosis was much smaller with tapentadol than with oxycodone in each of the databases: OR = .20, 95% CI = .08–.54, in the Optum database and OR = .33, 95% CI = .14–.79, in the MarketScan database.

      Discussion

      The odds of receiving an abuse diagnosis among those who initiated opioid use with tapentadol IR was 65% lower than the risk of receiving an abuse diagnosis among those who initiated opioid use with oxycodone IR. The fact that the risk of receiving an abuse diagnosis with tapentadol was similarly low in the 2 claims databases and that the results remained similar in the sensitivity analyses provide confidence in the findings of the study. The relatively low affinity of tapentadol for the mu-opioid receptor may explain its lower abuse risk.
      The observed lower risk of receiving an abuse diagnosis associated with tapentadol in this study aligns with its lower risk of opioid doctor shopping behavior.
      • Cepeda M.S.
      • Fife D.
      • Vo L.
      • Mastrogiovanni G.
      • Yuan Y.
      Comparison of opioid doctor shopping for tapentadol and oxycodone: A cohort study.
      A retrospective cohort study in a longitudinal prescription database compared the risk of opioid doctor shopping behavior between opioid-naïve patients who initiated opioid use with tapentadol IR and those who initiated with oxycodone IR and found that the risk of opioid doctor shopping (subjects with overlapping opioid prescriptions written by different prescribers and filled at ≥3 pharmacies) was 72% lower in subjects exposed to tapentadol than in subjects exposed to oxycodone (OR = .28, 95% CI = .22–.35).
      • Cepeda M.S.
      • Fife D.
      • Vo L.
      • Mastrogiovanni G.
      • Yuan Y.
      Comparison of opioid doctor shopping for tapentadol and oxycodone: A cohort study.
      The relative reductions in the risks of opioid doctor shopping behavior and receiving an opioid abuse diagnosis are of similar magnitude. The results of the present study also align with data from pharmacovigilance studies, which suggest a lower risk of abuse.

      Dart R, Bucher-Bartelson B, Adams EH. Non-medical use of tapentadol immediate release by college students. American Academy of Pain Medicine (AAPM) Annual Meeting. Palm Springs, CA. February 2012. http://www.painmed.org/2012posters/abstract-271. Accessed May 16, 2013

      One of these studies included an assessment of the street price of tapentadol, which was found to be one-tenth the street price of oxycodone on a per-milligram basis,

      Surrat HL, Kurtz SP, Cicero TJ, Dart RC: Street prices of prescription opioids diverted to the illicit market. 12 A.D. Jun 9; 2012

      or half the street price of oxycodone after adjustment for potency. These overall findings provide reassurance that the lower risk of receiving an abuse diagnosis of tapentadol is neither the result of a peculiarity of a particular database nor the endpoint assessed.
      The findings of lower risks of shopping behavior and receiving an abuse diagnosis associated with tapentadol are in contrast with results of a likeability study that showed that in opioid-experienced individuals, the subjective effects of tapentadol were comparable to the subjective effects of oral hydromorphone, and with animal data studies that showed that tapentadol exhibited rewarding and reinforcing effects that were similar to the ones produced by other opioids. The different contexts in which these studies were performed, and the different types of subjects included in these studies, may contribute to the disparity of their findings.
      Tapentadol IR was launched in 2009 and has been on the U.S. market for much less time than oxycodone IR. It could be argued that there might have not been enough time for abusers to experiment with tapentadol. Data from the Researched Abuse, Diversion and Addiction-Related Surveillance, a surveillance system that monitors the abuse, misuse, and diversion of prescription opioids, suggest that abuse can be seen very soon after a new opioid is marketed.
      • Dasgupta N.
      • Bailey E.J.
      • Cicero T.
      • Inciardi J.
      • Parrino M.
      • Rosenblum A.
      • Dart R.C.
      Post-marketing surveillance of methadone and buprenorphine in the United States.
      Nonetheless, definitive proof for the lower risk of abuse of tapentadol will need to await longer experience with tapentadol because the desirability of an opioid can change over time.
      • Dasgupta N.
      • Bailey E.J.
      • Cicero T.
      • Inciardi J.
      • Parrino M.
      • Rosenblum A.
      • Dart R.C.
      Post-marketing surveillance of methadone and buprenorphine in the United States.

      Limitations

      The ICD-9 codes we used to define abuse diagnosis included codes for opioid dependence as well as abuse. Opioid dependence does not necessarily imply abuse. Opioid abuse has been defined as the use of an opioid for psychic effects or any harmful use of the opioid.
      • Zacny J.
      • Bigelow G.
      • Compton P.
      • Foley K.
      • Iguchi M.
      • Sannerud C.
      College on Problems of Drug Dependence taskforce on prescription opioid non-medical use and abuse: Position statement.
      In contrast, opioid dependence is a state of adaptation that is manifested by withdrawal syndrome, diminution of the analgesic effect over time (tolerance), or dose escalation.

      American Academy of Pain Medicine, American Pain Society, American Society of Addiction Medicine. Definitions related to the use of opioids for the treatment of pain: consensus statement. http://www.asam org/advocacy/find-a-policy-statement/view-policy-statement/public-policy-statements/2011/12/15/definitions-related-to-the-use-of-opioids-for-the-treatment-of-pain-consensus-statement 2001

      • Cepeda M.S.
      • Etropolski M.
      • Weinstein R.
      • Fife D.
      • Boston R.
      • Matcho A.
      Dose patterns in commercially insured subjects chronically exposed to opioids: A large cohort study in the United States.
      • Rinaldi R.C.
      • Steindler E.M.
      • Wilford B.B.
      • Goodwin D.
      Clarification and standardization of substance abuse terminology.
      • Zacny J.
      • Bigelow G.
      • Compton P.
      • Foley K.
      • Iguchi M.
      • Sannerud C.
      College on Problems of Drug Dependence taskforce on prescription opioid non-medical use and abuse: Position statement.
      However, many physicians use the terms “opioid abuse,” “opioid addiction,” and “opioid dependence” interchangeably, and other studies that have assessed opioid abuse combined the codes as well.
      • Edlund M.J.
      • Martin B.C.
      • Fan M.Y.
      • Devries A.
      • Braden J.B.
      • Sullivan M.D.
      Risks for opioid abuse and dependence among recipients of chronic opioid therapy: Results from the TROUP study.
      • Edlund M.J.
      • Steffick D.
      • Hudson T.
      • Harris K.M.
      • Sullivan M.
      Risk factors for clinically recognized opioid abuse and dependence among veterans using opioids for chronic non-cancer pain.
      Despite the fact that the codes for abuse and dependence are combined, opioid abuse is likely to be underascertained in claims databases. Potential reasons for underrecording of abuse include lack of recognition of the condition; reluctance to put a potentially damaging diagnosis in the patient's record, especially in the absence of certainty; and, because claims databases were developed to facilitate commercial transactions, the fact that reimbursement considerations could affect which diagnosis codes to use.
      • O'Malley K.J.
      • Cook K.F.
      • Price M.D.
      • Wildes K.R.
      • Hurdle J.F.
      • Ashton C.M.
      Measuring diagnoses: ICD code accuracy.
      The incidence of opioid abuse diagnosis in our study was .6%. Though similar to the incidence reported in other claims database studies,
      • White A.G.
      • Birnbaum H.G.
      • Schiller M.
      • Tang J.
      • Katz N.P.
      Analytic models to identify patients at risk for prescription opioid abuse.
      this abuse rate is more than 10 times lower than what has been reported in past prospective studies (range, 5–31%).
      • Martell B.A.
      • O'Connor P.G.
      • Kerns R.D.
      • Becker W.C.
      • Morales K.H.
      • Kosten T.R.
      • Fiellin D.A.
      Systematic review: Opioid treatment for chronic back pain: prevalence, efficacy, and association with addiction.
      • Turk D.C.
      • Swanson K.S.
      • Gatchel R.J.
      Predicting opioid misuse by chronic pain patients: A systematic review and literature synthesis.
      In our current study, we observed an absolute risk reduction of ≤.5%. If the true incidence of abuse is in fact 10 times higher, then the impact on the absolute risk reduction could be 10 times greater. In contrast, as long as the underreporting is similar in the 2 groups, the extent of underestimation does not bias the odds ratios reported in our study.
      The findings of this study represent a privately insured population and therefore may not generalize to other populations of interest, such as the elderly or the uninsured.
      Physicians prescribed tapentadol or oxycodone to the patients for clinical indications, and therefore patients were not randomized. We controlled for the effect of potential confounders through propensity score adjustment, which permits the inclusion of a large number of confounders. The balancing properties of the propensity score are well known, but limited to the confounders included in the models. Therefore, unobserved baseline differences cannot be ruled out and those differences could explain the results.
      In summary, subjects who initiated opioid treatment with tapentadol IR had a lower risk of receiving an opioid abuse/dependence diagnosis than subjects who initiated opioid treatment with oxycodone IR. However, the risk with tapentadol IR is not absent. Opioid prescribers and patients must be aware of the risk of abuse associated with all opioids and of changes in opioid desirability over time.

      Appendix

      Appendix 1List of Codes to Identify Tapentadol IR and Oxycodone IR
      OxycodoneTapentadol
      Oxycodone 5 MG Oral Tablet [M-Oxy]Tapentadol 50 MG Oral Tablet
      Oxycodone 20 MG/ML Oral Solution [Oxydose]Tapentadol 75 MG Oral Tablet
      Oxycodone 5 MG Oral Capsule [Oxynorm]Tapentadol 100 MG Oral Tablet
      Oxycodone 5 MG Oral Capsule [Oxyrapid]Tapentadol 100 MG Oral Tablet [Nucynta]
      Oxycodone Hydrochloride 1 MG/ML Oral SolutionTapentadol 50 MG Oral Tablet [Nucynta]
      Oxycodone Hydrochloride 1 MG/ML Oral Solution [Roxicodone]Tapentadol 75 MG Oral Tablet [Nucynta]
      Oxycodone Hydrochloride 10 MG Oral Capsule
      Oxycodone Hydrochloride 10 MG Oral Tablet
      Oxycodone Hydrochloride 10 MG Oral Tablet [Dazidox]
      Oxycodone Hydrochloride 10 MG/ML Oral Solution
      Oxycodone Hydrochloride 15 MG Oral Tablet
      Oxycodone Hydrochloride 15 MG Oral Tablet [Roxicodone]
      Oxycodone Hydrochloride 20 MG Oral Capsule
      Oxycodone Hydrochloride 20 MG Oral Tablet
      Oxycodone Hydrochloride 20 MG Oral Tablet [Dazidox]
      Oxycodone Hydrochloride 20 MG/ML Oral Solution
      Oxycodone Hydrochloride 20 MG/ML Oral Solution [ETH-Oxydose]
      Oxycodone Hydrochloride 20 MG/ML Oral Solution [Oxyfast]
      Oxycodone Hydrochloride 20 MG/ML Oral Solution [Roxicodone]
      Oxycodone Hydrochloride 30 MG Oral Tablet
      Oxycodone Hydrochloride 30 MG Oral Tablet [Roxicodone]
      Oxycodone Hydrochloride 5 MG Oral Capsule
      Oxycodone Hydrochloride 5 MG Oral Capsule [Oxy IR]
      Oxycodone Hydrochloride 5 MG Oral Tablet
      Oxycodone Hydrochloride 5 MG Oral Tablet [Endocodone]
      Oxycodone Hydrochloride 5 MG Oral Tablet [Percolone]
      Oxycodone Hydrochloride 5 MG Oral Tablet [Roxicodone]
      Oxycodone Hydrochloride 5 MG Oral Tablet [Oxecta]
      Oxycodone Hydrochloride 7.5 MG Oral Tablet
      Oxycodone Hydrochloride 7.5 MG Oral Tablet [Oxecta]
      Appendix 2List of Codes to Known Confounders: Drug Abuse Excluding Opioids and Mood and Anxiety Disorders and Depression
      Drug Abuse Excluding OpioidsICD-9Mood and Anxiety Disorders and DepressionICD-9
      Alcohol dependence syndrome303Mood disorder in conditions classified elsewhere293.83
      Acute alcoholic intoxication303.0Anxiety disorder in conditions classified elsewhere293.84
      Acute alcoholic intoxication in alcoholism, unspecified drinking behavior303.00Bipolar I disorder single manic episode296.0
      Acute alcoholic intoxication in alcoholism, continuous drinking behavior303.01Manic disorder, single episode, unspecified degree296.00
      Acute alcoholic intoxication in alcoholism, episodic drinking behavior303.02Bipolar I disorder, single manic episode, mild296.01
      Acute alcoholic intoxication in alcoholism, in remission303.03Bipolar I disorder, single manic episode, moderate296.02
      Other and unspecified alcohol dependence303.9Bipolar I disorder, single manic episode, severe, without mention of psychotic behavior296.03
      Other and unspecified alcohol dependence, unspecified drinking behavior303.90Bipolar I disorder, single manic episode, severe, specified as with psychotic behavior296.04
      Other and unspecified alcohol dependence, continuous drinking behavior303.91Bipolar I disorder, single manic episode, in partial or unspecified remission296.05
      Other and unspecified alcohol dependence, episodic drinking behavior303.92Manic disorder, single episode, in full remission296.06
      Other and unspecified alcohol dependence, in remission303.93Manic disorder recurrent episode296.1
      Drug dependence304Manic disorder, recurrent episode, unspecified degree296.10
      Sedative hypnotic or anxiolytic dependence304.1Manic disorder, recurrent episode, mild degree296.11
      Sedative, hypnotic or anxiolytic dependence, unspecified304.10Manic disorder, recurrent episode, moderate degree296.12
      Sedative, hypnotic or anxiolytic dependence, continuous304.11Manic disorder, recurrent episode, severe degree, without mention of psychotic behavior296.13
      Sedative, hypnotic or anxiolytic dependence, episodic304.12Manic disorder, recurrent episode, severe degree, specified as with psychotic behavior296.14
      Sedative, hypnotic or anxiolytic dependence, in remission304.13Manic disorder, recurrent episode, in partial or unspecified remission296.15
      Cocaine dependence304.2Manic disorder, recurrent episode, in full remission296.16
      Cocaine dependence, unspecified use304.20Major depressive disorder single episode296.2
      Cocaine dependence, continuous use304.21Major depressive disorder, single episode, unspecified degree296.20
      Cocaine dependence, episodic use304.22Major depressive disorder, single episode, mild degree296.21
      Cocaine dependence, in remission304.23Major depressive disorder, single episode, moderate degree296.22
      Cannabis dependence304.3Major depressive disorder, single episode, severe degree, without mention of psychotic behavior296.23
      Cannabis dependence, unspecified use304.30Major depressive disorder, single episode, severe degree, specified as with psychotic behavior296.24
      Cannabis dependence, continuous use304.31Major depressive disorder, single episode, in partial or unspecified remission296.25
      Cannabis dependence, episodic use304.32Major depressive disorder, single episode in full remission296.26
      Cannabis dependence, in remission304.33Major depressive disorder recurrent episode296.3
      Amphetamine and other psychostimulant dependence304.4Major depressive disorder, recurrent episode, unspecified degree296.30
      Amphetamine and other psychostimulant dependence, unspecified use304.40Major depressive disorder, recurrent episode, mild degree296.31
      Amphetamine and other psychostimulant dependence, continuous use304.41Major depressive disorder, recurrent episode, moderate degree296.32
      Amphetamine and other psychostimulant dependence, episodic use304.42Major depressive disorder, recurrent episode, severe degree, without mention of psychotic behavior296.33
      Amphetamine and other psychostimulant dependence, in remission304.43Major depressive disorder, recurrent episode, severe degree, specified as with psychotic behavior296.34
      Hallucinogen dependence304.5Major depressive disorder, recurrent episode, in partial or unspecified remission296.35
      Hallucinogen dependence, unspecified use304.50Major depressive disorder, recurrent episode, in full remission296.36
      Hallucinogen dependence, continuous use304.51Bipolar affective disorder, manic, unspecified degree296.40
      Hallucinogen dependence, episodic use304.52Bipolar I disorder, most recent episode (or current) manic, mild296.41
      Hallucinogen dependence, in remission304.53Bipolar I disorder, most recent episode (or current) manic, moderate296.42
      Other specified drug dependence304.6Bipolar I disorder, most recent episode (or current) manic, severe, without mention of psychotic behavior296.43
      Other specified drug dependence, unspecified use304.60Bipolar I disorder, most recent episode (or current) manic, severe, specified as with psychotic behavior296.44
      Other specified drug dependence, continuous use304.61Bipolar I disorder, most recent episode (or current) manic, in partial or unspecified remission296.45
      Other specified drug dependence, episodic use304.62Bipolar I disorder, most recent episode (or current) manic, in full remission296.46
      Other specified drug dependence, in remission304.63Bipolar affective disorder, depressed, unspecified degree296.50
      Combinations of drug dependence excluding opioid type drug, unspecified use304.80Bipolar I disorder, most recent episode (or current) depressed, mild296.51
      Combinations of drug dependence excluding opioid type drug, continuous use304.81Bipolar I disorder, most recent episode (or current) depressed, moderate296.52
      Combinations of drug dependence excluding opioid type drug, episodic use304.82Bipolar I disorder, most recent episode (or current) depressed, severe, without mention of psychotic behavior296.53
      Combinations of drug dependence excluding opioid type drug, in remission304.83Bipolar I disorder, most recent episode (or current) depressed, severe, specified as with psychotic behavior296.54
      Unspecified drug dependence304.9Bipolar I disorder, most recent episode (or current) depressed, in partial or unspecified remission296.55
      Unspecified drug dependence, unspecified use304.90Bipolar I disorder, most recent episode (or current) depressed, in full remission296.56
      Unspecified drug dependence, continuous use304.91Bipolar I disorder, most recent episode (or current) mixed, unspecified296.60
      Unspecified drug dependence, episodic use304.92Bipolar I disorder, most recent episode (or current) mixed, mild296.61
      Unspecified drug dependence, in remission304.93Bipolar I disorder, most recent episode (or current) mixed, moderate296.62
      Nondependent abuse of drugs305Bipolar I disorder, most recent episode (or current) mixed, severe, without mention of psychotic behavior296.63
      Alcohol abuse305.0Bipolar I disorder, most recent episode (or current) mixed, severe, specified as with psychotic behavior296.64
      Alcohol abuse, unspecified drinking behavior305.00Bipolar I disorder, most recent episode (or current) mixed, in partial or unspecified remission296.65
      Alcohol abuse, continuous drinking behavior305.01Bipolar affective disorder, mixed, in full remission296.66
      Alcohol abuse, episodic drinking behavior305.02Bipolar I disorder, most recent episode (or current) unspecified296.7
      Alcohol abuse, in remission305.03Bipolar disorder, unspecified296.80
      Tobacco use disorder305.1Atypical manic disorder296.81
      Cannabis abuse305.2Atypical depressive disorder296.82
      Cannabis abuse, unspecified use305.20Other and unspecified bipolar disorders296.89
      Cannabis abuse, continuous use305.21Unspecified episodic mood disorder296.90
      Cannabis abuse, episodic use305.22Other specified episodic mood disorder296.99
      Cannabis abuse, in remission305.23Depressive type psychosis298.0
      Hallucinogen abuse305.3Anxiety states300.0
      Hallucinogen abuse, unspecified use305.30Anxiety state, unspecified300.00
      Hallucinogen abuse, continuous use305.31Panic disorder without agoraphobia300.01
      Hallucinogen abuse, episodic use305.32Generalized anxiety disorder300.02
      Hallucinogen abuse, in remission305.33Other anxiety states300.09
      Sedative hypnotic or anxiolytic abuse305.4Agoraphobia with panic disorder300.21
      Sedative, hypnotic or anxiolytic abuse, unspecified305.40Agoraphobia without mention of panic attacks300.22
      Sedative, hypnotic or anxiolytic abuse, continuous305.41Social phobia300.23
      Sedative, hypnotic or anxiolytic abuse, episodic305.42Dysthymic disorder300.4
      Sedative, hypnotic or anxiolytic abuse, in remission305.43Affective personality disorder301.1
      Cocaine abuse305.6Affective personality disorder, unspecified301.10
      Cocaine abuse, unspecified use305.60Chronic hypomanic personality disorder301.11
      Cocaine abuse, continuous use305.61Chronic depressive personality disorder301.12
      Cocaine abuse, episodic use305.62Cyclothymic disorder301.13
      Cocaine abuse, in remission305.63Obsessive-compulsive personality disorder301.4
      Amphetamine or related acting sympathomimetic abuse, unspecified use305.70Acute reaction to stress308
      Amphetamine or related acting sympathomimetic abuse, continuous use305.71Predominant disturbance of emotions308.0
      Amphetamine or related acting sympathomimetic abuse, episodic use305.72Predominant disturbance of consciousness308.1
      Amphetamine or related acting sympathomimetic abuse, in remission305.73Predominant psychomotor disturbance308.2
      Antidepressant type abuse, unspecified use305.80Other acute reactions to stress308.3
      Antidepressant type abuse, continuous use305.81Mixed disorders as reaction to stress308.4
      Antidepressant type abuse, episodic use305.82Unspecified acute reaction to stress308.9
      Antidepressant type abuse, in remission305.83Adjustment reaction309
      Other mixed or unspecified drug abuse305.9Adjustment disorder with depressed mood309.0
      Other, mixed, or unspecified drug abuse, unspecified use305.90Adjustment reaction with prolonged depressive reaction309.1
      Other, mixed, or unspecified drug abuse, continuous use305.91Adjustment reaction with predominant disturbance of other emotions309.2
      Other, mixed, or unspecified drug abuse, episodic use305.92Separation anxiety disorder309.21
      Other, mixed, or unspecified drug abuse, in remission305.93Emancipation disorder of adolescence and early adult life309.22
      Specific academic or work inhibition309.23
      Adjustment disorder with anxiety309.24
      Adjustment disorder with mixed anxiety and depressed mood309.28
      Other adjustment reactions with predominant disturbance of other emotions309.29
      Adjustment disorder with disturbance of conduct309.3
      Adjustment disorder with mixed disturbance of emotions and conduct309.4
      Posttraumatic stress disorder309.81
      Adjustment reaction with physical symptoms309.82
      Adjustment reaction with withdrawal309.83
      Other specified adjustment reactions309.89
      Unspecified adjustment reaction309.9
      Depressive disorder, not elsewhere classified311
      Overanxious disorder specific to childhood and adolescence313.0
      Misery and unhappiness disorder specific to childhood and adolescence313.1
      Appendix 3Standardized Differences Before and After PS Adjustment in the MarketScan Database
      VariableStandardized Difference in First PS QuintileStandardized Difference in Second PS QuintileStandardized Difference in Third PS QuintileStandardized Difference in Fourth PS QuintileStandardized Difference in Fifth PS QuintileStandardized Difference Before PSOverall Standardized Difference After PS
      Age.02.10.12.13–.02.30.07
      Arthritis.02.07.01.14.16.01.08
      Back pain.13.12.12.12–.05.25.09
      Benzodiazepine use.06.05–.06.06.41.14.11
      Drug abuse excluding opioids–.01–.02.02.06.20–.07.06
      Mood/anxiety disorders and depression.00.01–.03.18.22–.01.09
      Index year (2010).03.00–.01.08.06–.08.03
      Women–.13–.02.12.21–.17–.21.01
      Fractures–.04–.15–.03–.09–.33–.18–.13
      Headaches.02–.02–.07–.07–.22.01–.07
      Malignancy.02–.09–.21.18.10–.12.00
      Musculoskeletal pain.11.11.07.18.32.06.16
      Neuropathy.03.07.04.00–.10.06.01
      Other–.01–.01.04.04–.06–.01.00
      Reproductive system pain.01.02–.01–.02–.09.00–.02
      Visceral pain–.04–.10–.03–.02–.23–.18–.09
      Wound injury–.04–.05–.03.05–.25–.12–.07
      Abbreviation: PS, propensity score.
      Appendix 4Standardized Differences Before and After PS Adjustment in the Optum Database
      VariableStandardized Difference in First PS QuintileStandardized Difference in Second PS QuintileStandardized Difference in Third PS QuintileStandardized Difference in Fourth PS QuintileStandardized Difference in Fifth PS QuintileStandardized Difference Before PSOverall Standardized Difference After PS
      Age.00.09.09.02–.01.24.04
      Arthritis.03.04.02.15.23.03.10
      Back pain.04.08.11.12.14.20.09
      Benzodiazepine use.03.00.00.19.19.12.08
      Drug abuse excluding opioids.03–.01–.11.15.70–.09.26
      Mood/anxiety disorders and depression.06–.02–.06.02.59.02.15
      Index year (2010)–.05–.01.03–.06.53–.16.06
      Women.23.00–.27–.21.32.27.01
      Fractures–.03–.10–.15.02.24–.18.01
      Headaches.03–.09–.08–.04.36–.01.06
      Malignancy.06–.15–.17–.29–.21–.08–.14
      Musculoskeletal pain.12.03.21.12.17.08.13
      Neuropathy.12.12.11–.02–.10.14.05
      Other.02.07–.02.11–.11.01.01
      Reproductive system pain.05–.04–.05–.08–.09.02–.04
      Visceral pain–.06–.08–.05.12.30–.22.08
      Wound injury–.03–.06.00–.11.77–.12.22
      Abbreviation: PS, propensity score.

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