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Reduction of Opioid Use After Upper-Extremity Surgery through a Predictive Pain Calculator and Comprehensive Pain Plan

      Purpose

      For outpatient hand and upper-extremity surgeries, opioid prescriptions may exceed the actual need for adequate pain control. The purposes of this study were to (1) determine rates of opioid wasting and consumption after these procedures and (2) create and implement a patient-specific calculator for opioid requirements with a detailed multimodal analgesic plan to guide postoperative prescriptions.

      Methods

      Patients undergoing hand and upper-extremity surgery at a single ambulatory surgery center were recruited before (n = 305) and after (n = 221) implementation of a postoperative pain control program. On the first postoperative visit, patients were given a questionnaire regarding opioid use and pain control satisfaction. Demographic and procedural data were collected via chart review. With these data from the first cohort, we developed a patient-specific opioid calculator and pain plan that was implemented for the second cohort of patients. Bivariate analysis and multivariable regression analysis were used to determine the effect of the intervention.

      Results

      Pre-intervention data suggested that younger age; baseline opioid use; use of regional block; unemployment; procedures involving bony, tendinous, or ligamentous work (as opposed to soft tissue alone); and longer procedure time were predictive of higher opioid consumption. Pre- and post-intervention cohorts had similar age and sex distributions as well as procedure length. After the intervention, opioids prescribed decreased 63% from a mean of 32.0 ± 15.0 pills/surgery or 194.5 ± 120.2 morphine milligram equivalents (MMEs) to 11.7 ± 8.9 pills/surgery or 86.4 ± 67.2 MMEs. Opioid consumption decreased 58% from a mean of 21.7 ± 25.0 pills/surgery (137.7 ± 176.4 MMEs) to 9.3 ± 16.7 (64.4 ± 113.4 MMEs). Opioid wastage decreased 62% from 13.8 ± 13.5 pills/surgery (62.8 ± 138.0 MMEs) to 5.2 ± 10.3 (24.8 ± 89.9 MMEs). Implementation of the pain plan and calculator did not affect the odds of unsatisfactory patient-rated pain control or unplanned opioid refills.

      Conclusions

      With implementation of a comprehensive pain plan for ambulatory upper-extremity surgery, it is possible to reduce opioid prescription, consumption, and wastage rates without compromising patient satisfaction with pain control or increasing rates of unplanned pain medication refills.

      Type of study/level of evidence

      Therapeutic II.

      Key words

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      References

      1. Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying cause of death 1999–2016. CDC WONDER Online Database. Released December 2017. Available at: https://wonder.cdc.gov/mcd.html. Accessed November 5, 2019.

        • Gomes T.
        • Tadrous M.
        • Mamdani M.M.
        • Paterson J.
        • Juurlink D.N.
        The burden of opioid-related mortality in the united states.
        JAMA Network Open. 2018; 1e180217
        • Volkow N.D.
        • McLellan T.A.
        • Cotto J.H.
        • Karithanom M.
        • Weiss S.B.
        Characteristics of opioid prescriptions in 2009.
        JAMA. 2011; 305: 1299-1301
        • Substance Abuse Mental Health Services Administration
        Results from the 2013 National Survey on Drug Use and Health: Summary of National Findings. NSDUH Series H-48, HHS Pub. No. SMA 14e4863.
        Substance Abuse and Mental Health Services Administration, Rockville, MD2014: 13-4795
        • Pasero C.
        • McCaffery M.
        Orthopaedic postoperative pain management.
        J Perianesth Nurs. 2007; 22: 160-172
        • Chung F.
        • Ritchie E.
        • Su J.
        Postoperative pain in ambulatory surgery.
        Anesth Analg. 1997; 85: 808-816
        • Stanton T.
        Symposium addresses pain management in the opioid epidemic.
        AAOS Now. 2014;
        • Rodgers J.
        • Cunningham K.
        • Fitzgerald K.
        • Finnerty E.
        Opioid consumption following outpatient upper extremity surgery.
        J Hand Surg Am. 2012; 37: 645-650
        • Kim N.
        • Matzon J.L.
        • Abboudi J.
        • et al.
        A prospective evaluation of opioid utilization after upper-extremity surgical procedures: identifying consumption patterns and determining prescribing guidelines.
        J Bone Joint Surg Am. 2016; 98: e89
        • Dwyer C.L.
        • Soong M.
        • Hunter A.
        • Dashe J.
        • Tolo E.
        • Kasparyan N.G.
        Prospective evaluation of an opioid reduction protocol in hand surgery.
        J Hand Surg Am. 2018; 43: 516-522.e511
        • Bates C.
        • Laciak R.
        • Southwick A.
        • Bishoff J.
        Overprescription of postoperative narcotics: a look at postoperative pain medication delivery, consumption and disposal in urological practice.
        J Urol. 2011; 185: 551-555
        • Shrank W.H.
        Our bulging medicine cabinets—the other side of medication nonadherence.
        N Engl J Med. 2011; 364: 1591-1593
        • Stanek J.J.
        • Renslow M.A.
        • Kalliainen L.K.
        The effect of an educational program on opioid prescription patterns in hand surgery: a quality improvement program.
        J Hand Surg Am. 2015; 40: 341-346
        • Mickey R.M.
        • Greenland S.
        The impact of confounder selection criteria on effect estimation.
        Am J Epidemiol. 1989; 129: 125-137
        • Maldonado G.
        • Greenland S.
        Simulation study of confounder-selection strategies.
        Am J Epidemiol. 1993; 138: 923-936