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Association of opioid use and peripheral artery disease

Open ArchivePublished:March 25, 2019DOI:https://doi.org/10.1016/j.jvs.2018.12.036

      Abstract

      Background

      Prescription opioids account for 40% of all U.S. opioid overdose deaths, and national efforts have intensified to reduce opioid prescriptions. Little is known about the relationship between peripheral artery disease (PAD) and high-risk opioid use. The objectives of this study were to evaluate this relationship and to assess the impact of PAD treatment on opiate use.

      Methods

      In this retrospective cohort study, the Truven Health MarketScan database (Truven Health Analytics, Ann Arbor, Mich), a deidentified national private insurance claims database, was queried to identify patients with PAD (two or more International Classification of Diseases, Ninth Revision diagnosis codes of PAD ≥2 months apart, with at least 2 years of continuous enrollment) from 2007 to 2015. Critical limb ischemia (CLI) was defined as the presence of rest pain, ulcers, or gangrene. The primary outcome was high opioid use, defined as two or more opioid prescriptions within a 1-year period. Multivariable analysis was used to determine risk factors for high opioid use.

      Results

      A total of 178,880 patients met the inclusion criteria, 35% of whom had CLI. Mean ± standard deviation follow-up time was 5.3 ± 2.1 years. An average of 24.7% of patients met the high opioid use criteria in any given calendar year, with a small but significant decline in high opioid use after 2010 (P < .01). During years of high opioid use, 5.9 ± 5.5 yearly prescriptions were filled. A new diagnosis of PAD increased high opioid use (21.7% before diagnosis vs 27.3% after diagnosis; P < .001). A diagnosis of CLI was also associated with increased high opioid use (25.4% before diagnosis vs 34.5% after diagnosis; P < .001). Multivariable analysis identified back pain (odds ratio [OR], 1.89; 95% confidence interval [CI], 1.84-1.93; P < .001) and illicit drug use (OR, 1.87; 95% CI, 1.72-2.03; P < .001) as the highest predictors of high opioid use. A diagnosis of CLI was also associated with higher risk (OR, 1.61; 95% CI, 1.57-1.64; P < .001). A total of 43,443 PAD patients (24.3%) underwent 80,816 PAD-related procedures. After exclusion of periprocedural opioid prescriptions (4.9% of all opioid prescriptions), the yearly percentage of high opioid users increased from 25.8% before treatment to 29.6% after treatment (P < .001).

      Conclusions

      Patients with PAD are at increased risk for high opioid use, with nearly one-quarter meeting described criteria. CLI and treatment for PAD additionally increase high opioid use. In addition to heightened awareness and active opioid management, our findings warrant further investigation into underlying causes and deterrents of high-risk opioid use.

      Graphical Abstract

      Keywords

      Article Highlights
      • Type of Research: Retrospective study of the Truven Health MarketScan, a national private insurance claims database
      • Key Findings: Analysis of nearly 180,000 patients found 24.7% of patients with peripheral artery disease (PAD) meeting high opioid use criteria from 2007 to 2015. A diagnosis of critical limb ischemia increased high opioid use to 34.5%, and treatment for PAD increased high opioid use to 29.6%, even after censoring prescriptions within 90 days of the procedure.
      • Take Home Message: Nearly a quarter of patients with PAD meet the high opioid use criteria in a given year, and critical limb ischemia and treatment for PAD increase the risk of high opioid use.
      Opioid-related deaths have continued to increase in the United States in recent years, and 40% of opioid-related deaths in 2016 were due to prescription opioids.
      • Dart R.C.
      • Surratt H.L.
      • Cicero T.J.
      • Parrino M.W.
      • Severtson S.G.
      • Bucher-Bartelson B.
      • et al.
      Trends in opioid analgesic abuse and mortality in the United States.
      Centers for Disease Control and Prevention
      Annual surveillance report of drug-related risks and outcomes—United States, 2017. Surveillance Special Report 1. Centers for Disease Control and Prevention, U.S. Department of Health and Human Services.
      U.S. Department of Health and Human Services
      About the Epidemic | HHS.gov.
      Although previous studies have identified a number of high-risk diagnoses associated with opioid use,
      • Hudson T.J.
      • Edlund M.J.
      • Steffick D.E.
      • Tripathi S.P.
      • Sullivan M.D.
      Epidemiology of regular prescribed opioid use: results from a national, population-based survey.
      • Fleming M.F.
      • Balousek S.L.
      • Klessig C.L.
      • Mundt M.P.
      • Brown D.D.
      Substance use disorders in a primary care sample receiving daily opioid therapy.
      little is known about the relationship between a diagnosis of peripheral artery disease (PAD) and opioid use. As decreased perfusion can lead to ischemic pain (ie, claudication or ischemic rest pain) and pain from nonhealing wounds, health care professionals may prescribe opioid medications to help alleviate symptoms. However, prescribing opioids to treat chronic symptoms may lead to high opioid use, putting patients at risk for both opioid dependence and abuse.
      • Fleming M.F.
      • Balousek S.L.
      • Klessig C.L.
      • Mundt M.P.
      • Brown D.D.
      Substance use disorders in a primary care sample receiving daily opioid therapy.
      • Dowell D.
      • Haegerich T.M.
      • Chou R.
      CDC guideline for prescribing opioids for chronic pain—United States, 2016.
      • Boscarino J.A.
      • Rukstalis M.
      • Hoffman S.N.
      • Han J.J.
      • Erlich P.M.
      • Gerhard G.S.
      • et al.
      Risk factors for drug dependence among out-patients on opioid therapy in a large US health-care system.
      As the primary goal of open or percutaneous revascularization procedures is to alleviate pain by improving blood flow, successful intervention should in theory lead to decreased opioid use. Given the current lack of literature regarding opioid prescribing patterns in patients with PAD, the primary objectives of this study were twofold: first, to evaluate the baseline relationship between PAD and opioid use and to determine whether PAD patients represent a high-risk cohort for high opiate use; and second, to assess whether treatment of PAD has an impact on opioid use.

      Methods

      The Truven Health MarketScan database (Truven Health Analytics, Ann Arbor, Mich), a deidentified, national private insurance claims database, was queried to identify patients from 2007 to 2015 with a diagnosis of PAD. This comprehensive database covers >50% of the privately insured U.S. population but excludes patients with Medicare and Medicaid coverage.
      • Hansen L.
      The Truven Health MarketScan Databases for life sciences researchers.
      Patients were determined to have PAD if they had two or more International Classification of Diseases, Ninth Revision (ICD-9) codes for PAD in the inpatient or outpatient claims records ≥2 months apart.
      • Bartels C.M.
      • Kind A.J.
      • Everett C.
      • Mell M.
      • McBride P.
      • Smith M.
      Low frequency of primary lipid screening among Medicare patients with rheumatoid arthritis.
      Other inclusion criteria included ≥2 years of continuous enrollment and enrollment in an insurance plan that submits outpatient pharmaceutical claims, approximately 40% to 50% of enrollees.
      Patients with PAD were identified by ICD-9 codes 440.20, 440.21, 440.22, 440.23, 440.24, and 440.39. Patients with critical limb ischemia (CLI) were identified with ICD-9 codes 440.22, 440.23, and 440.24, corresponding to rest pain, ulcers, and gangrene, respectively, and lower extremity wound codes 707.1, 707.10, 707.11, 707.12, 707.13, 707.14, 707.15, and 707.19. We excluded patients with only a lower extremity wound code and no corresponding PAD code to eliminate patients with lower extremity wounds of nonvascular causes.
      The primary outcome was high opioid use, defined as two or more opioid prescriptions within a 1-year period, representing a definition used in previously published studies adapted to the study time frame.
      • Sun E.C.
      • Darnall B.D.
      • Baker L.C.
      • Mackey S.
      Incidence of and risk factors for chronic opioid use among opioid-naive patients in the postoperative period.
      • Buckley J.P.
      • Cook S.F.
      • Allen J.K.
      • Kappelman M.D.
      Prevalence of chronic narcotic use among children with inflammatory bowel disease.
      Using the outpatient prescription file, opioid prescriptions were identified by the therapy class “60” classification. Prescriptions with cough/cold combination, opium, or suppository form were excluded, and prescriptions with a quantity of less than one were also excluded. Prescriptions filled within 90 days of a PAD-related procedure, identified by Current Procedural Terminology (CPT) codes for lower extremity open or percutaneous revascularization or amputation (Supplementary Table I, online only), were also excluded. CPT codes 37205 to 37208 recorded after January 1, 2011, were not counted as a PAD-related procedure as these were replaced with more specific CPT codes that specified revascularization of the lower extremities.
      Morphine equivalents (MEQs) were calculated using the outpatient pharmaceutical claims file to standardize dosing between opioid formulations. Prescription drug names, strengths, and quantities prescribed were noted and converted to MEQs according to a previously described conversion factor.
      • Svendsen K.
      • Borchgrevink P.
      • Fredheim O.
      • Hamunen K.
      • Mellbye A.
      • Dale O.
      Choosing the unit of measurement counts: the use of oral morphine equivalents in studies of opioid consumption is a useful addition to defined daily doses.
      The effect of a PAD diagnosis on high opioid use was investigated by comparing rates of high opioid use in the prediagnosis period with those in the postdiagnosis period; high opioid years before the first diagnosis of PAD were compared with high opioid years after the diagnosis. Patients with PAD diagnoses were also stratified by CLI status, and CLI patients were analyzed for high opiate use criteria as a separate subgroup. A subgroup of patients who underwent PAD-related procedures were also analyzed and compared with those who did not undergo revascularization procedures. This analysis was also separately performed for CLI patients.
      Patients' demographics were obtained from member enrollment data, and comorbidities were assessed using both outpatient and inpatient medical claims files. We evaluated comorbidities using the Charlson Comorbidity Index.
      • Deyo R.A.
      • Cherkin D.C.
      • Ciol M.A.
      Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases.
      We also specifically evaluated the presence of diagnoses previously associated with opioid use (Supplementary Table II, online only).
      • Hudson T.J.
      • Edlund M.J.
      • Steffick D.E.
      • Tripathi S.P.
      • Sullivan M.D.
      Epidemiology of regular prescribed opioid use: results from a national, population-based survey.
      • Ghate S.R.
      • Haroutiunian S.
      • Winslow R.
      • McAdam-Marx C.
      Cost and comorbidities associated with opioid abuse in managed care and Medicaid patients in the United States: a comparison of two recently published studies.
      Patients were classified into an urban setting if they resided in a metropolitan statistical area and categorized by region: Northeast, North Central, South, West, and unknown. Descriptive statistics were used to compare patients meeting high opioid use criteria during any year of follow-up with those who did not. Multivariable analysis was then performed to determine factors associated with high opioid use while adjusting for known confounders. Odds ratios (ORs) are reported with 95% confidence intervals (95% CIs). Averages are expressed as mean ± standard deviation unless otherwise specified. Data cleaning was performed using SAS Enterprise Guide (SAS Institute, Cary, NC), and statistical analysis was performed using Stata version 14 software (StataCorp LP, College Station, Tex). The Stanford University Institutional Review Board determined that this project did not meet the definition of human subjects research and exempted it from further review. This study was approved by the Stanford Institutional Review Board, and consent of the patient was waived as the database was deidentified.

      Results

      A total of 178,880 PAD patients met inclusion criteria, with a mean follow-up time of 5.3 ± 2.1 years; 63,400 patients (35%) had a diagnosis of CLI. Among PAD patients without CLI, 20,799 (18.0%) underwent a PAD-related procedure. For patients with CLI, 23,317 (36.8%) underwent a PAD-related procedure (Fig 1). After exclusion of opioid prescriptions within 90 days of a PAD-related procedure, of the 950,355 patient-years evaluated, 234,118 (24.7%) met high opioid use criteria.
      Figure thumbnail gr1
      Fig 1Number of patients meeting high opioid use criteria by year. CLI, Critical limb ischemia; PAD, peripheral artery disease.
      The number of patients meeting high opioid use criteria increased from 2007 to 2010, peaking in 2010 and then declining until 2015. This trend was statistically significant (P < .01; Fig 2, A). The distribution of MEQs per patient by calendar year follows a right skewed distribution (Fig 2, B), with the median MEQs ranging from 600 to 900 and high-end users receiving approximately 10,000 MEQs per year. Patients received an average of 5.9 ± 5.5 opioid prescriptions per year when meeting high opioid use criteria.
      Figure thumbnail gr2
      Fig 2A, Peripheral artery disease (PAD) patients meeting high opioid use criteria of two or more opioid prescriptions in a given year by year. B, Box and whiskers plot of morphine equivalents (MEQs) per patient by year. IQR, Interquartile range.
      Patients' demographics and comorbidities stratified by high opioid use are noted in Table I. High opioid users were slightly younger and more likely to be female. Patients meeting high opioid use criteria also had more comorbidities, as noted by a higher Charlson score. Other previously described risk factors for opioid use were more common in PAD patients with high opioid use, including arthritis, pain syndromes, depression, and substance use disorders. A diagnosis of CLI was more prevalent among patients in the high opioid use group. A diagnosis of chronic pain was found in 2.7% of patients not meeting high opioid use criteria and in 18.8% of patients meeting high opioid use criteria (P < .001).
      Table IComparison of patients' demographics and comorbidities for those who did and did not meet high opioid use criteria during follow-up
      Patients without high opioid use criteria (n = 87,889)Patients with high opioid use criteria (n = 90,991)Absolute differenceP value
      Age, years53.5 ± 7.753.0 ± 7.1−0.6<.001
      Female sex36,286 (41.3)40,706 (44.7)3.4<.001
      Charlson Index4.0 ± 2.65.3 ± 3.21.3<.001
       Myocardial infarction10,778 (12.3)16,742 (18.4)6.1<.001
       Congestive heart failure16,274 (18.5)26,099 (28.7)10.2<.001
       Peripheral vascular disease87,889 (100)90,991 (100)
       Cerebrovascular disease33,245 (37.9)39,266 (43.1)5.2<.001
       Dementia870 (1.0)1237 (1.4)0.4<.001
       Chronic pulmonary disease30,127 (34.3)45,737 (50.2)15.9<.001
       Rheumatologic disease4890 (5.6)9339 (10.3)4.7<.001
       Peptic ulcer disease3310 (3.8)6067 (6.7)2.9<.001
       Mild liver disease11,425 (13.0)17,918 (19.7)6.7<.001
       Diabetes44,875 (51.1)53,000 (58.2)7.1<.001
       Diabetes with chronic complications24,525 (27.9)32,913 (36.2)8.3<.001
       Hemiplegia or paraplegia2456 (2.8)4280 (4.7)1.9<.001
       Renal disease12,612 (14.4)20,682 (22.7)8.3<.001
       Any malignant disease9243 (10.5)15,182 (16.7)6.2<.001
       Moderate or severe liver disease739 (0.8)1746 (1.9)0.9<.001
       Metastatic solid tumor1224 (1.4)3888 (4.3)2.9<.001
       AIDS438 (0.5)562 (0.6)0.1<.001
      Osteoarthritis24,969 (28.4)45,085 (49.5)21.1<.001
      Joint pain42,016 (47.8)65,127 (71.5)23.7<.001
      Rheumatoid arthritis3110 (3.5)6256 (6.9)3.4<.001
      Migraine3867 (4.4)8154 (9.0)4.6<.001
      Abdominal pain33,050 (37.6)50,642 (55.6)18.0<.001
      Back pain34,122 (38.9)58,572 (64.3)25.4<.001
      Neck pain17,465 (19.9)31,971 (35.1)15.2<.001
      Tobacco use24,353 (27.7)36,044 (39.6)11.9<.001
      Alcohol use3095 (3.5)5684 (6.2)2.7<.001
      Illicit drug use819 (0.9)3397 (3.7)2.8<.001
      Depression13,914 (15.8)29,037 (31.9)16.1<.001
      CLI25,388 (28.9)38,012 (41.8)12.9<.001
      Urban74,729 (85.1)74,777 (82.1)−2.9<.001
      Region
       Northeast26,925 (30.6)17,365 (19.1)−11.6<.001
       North Central18,018 (20.5)22,895 (25.2)−4.6
       South32,678 (37.2)39,350 (43.2)6.0
       West8600 (9.8)9789 (10.7)1.0
       Unknown1618 (1.8)1642 (1.8)<.1
      AIDS, Acquired immunodeficiency syndrome; CLI, critical limb ischemia.
      Categorical variables are presented as number (%). Continuous variables are presented as mean ± standard deviation.
      Multivariable regression analysis identified dementia as the only comorbidity associated with a lower risk of high opioid use (OR, 0.77; 95% CI, 0.70-0.86; P < .001). With the exception of cerebrovascular disease, hemiplegia or paraplegia, and acquired immunodeficiency syndrome, the remaining chronic conditions were associated with an increased risk of high opioid use. Previously described high-risk conditions were also associated with high opioid use, with back pain (OR, 1.89; 95% CI, 1.85-1.94; P < .001) and illicit drug use (OR, 1.89; 95% CI, 1.73-2.06; P < .001) having the highest risk for high opioid use. CLI was also independently associated with a higher risk of high opioid use (OR, 1.62; 95% CI, 1.58-1.67; P < .001), similar to a diagnosis of osteoarthritis or tobacco use (Table II). Patients living in an urban setting (OR, 0.91; 95% CI, 0.88-0.93; P < .001) and patients living in the Northeast region (OR, 0.64; 95% CI, 0.59-0.69; P < .001) were associated with decreased risk of meeting high opioid use criteria.
      Table IIMultivariable analysis of risk factors for high opioid use
      OR95% CIP value
      Age (per 1-year increase)0.990.99-0.99<.001
      Female sex0.900.88-0.92<.001
      Charlson Index
       Myocardial infarction1.251.21-1.29<.001
       Congestive heart failure1.231.20-1.27<.001
       Peripheral vascular disease1
       Cerebrovascular disease1.021.00-1.04.08
       Dementia0.770.70-0.86<.001
       Chronic pulmonary disease1.311.28-1.34<.001
       Rheumatologic disease1.201.12-1.28<.001
       Peptic ulcer disease1.141.08-1.20<.001
       Mild liver disease1.051.02-1.08.001
       Diabetes1.061.04-1.09<.001
       Diabetes with chronic complications1.051.04-1.07<.001
       Hemiplegia or paraplegia1.031.00-1.06.052
       Renal disease1.111.09-1.13<.001
       Any malignant disease1.141.13-1.17<.001
       Moderate or severe liver disease1.071.04-1.11<.001
       Metastatic solid tumor1.151.14-1.17<.001
       AIDS1.000.98-1.03.52
      Osteoarthritis1.651.61-1.69<.001
      Joint pain1.651.61-1.69<.001
      Rheumatoid arthritis1.060.98-1.15.16
      Migraine1.371.31-1.43<.001
      Abdominal pain1.331.31-1.37<.001
      Back pain1.891.85-1.94<.001
      Neck pain1.281.25-1.31<.001
      Tobacco use1.541.51-1.58<.001
      Alcohol use1.181.12-1.24<.001
      Illicit drug use1.891.73-2.06<.001
      Depression1.591.55-1.63<.001
      CLI1.621.58-1.67<.001
      Urban vs rural0.910.88-0.93<.001
      Region compared with unknown
       Northeast0.640.59-0.69<.001
       North Central1.291.19-1.40<.001
       South1.321.22-1.43<.001
       West1.201.11-1.31<.001
      AIDS, Acquired immunodeficiency syndrome; CI, confidence interval; CLI, critical limb ischemia; OR, odds ratio.
      Analysis of the relationship between a PAD diagnosis and high opioid use demonstrated that rates of high opioid use increased from 21.7% in the years before diagnosis with PAD to 27.3% in the years after diagnosis, an absolute increase of 5.6% (Fig 3, A). High opioid use increased from 19.8% to 23.0% for patients without CLI and from 25.4% to 35.5% for patients with CLI, an absolute difference of 9.1%.
      Figure thumbnail gr3
      Fig 3A, Relationship of diagnosis and high use—all patients, peripheral artery disease (PAD) without critical limb ischemia (CLI) and CLI. B, Relationship of treatment and high opioid use—all patients, PAD without CLI and CLI.
      Treatment of PAD was found to increase rates of high opioid use, even when periprocedural opioid prescriptions were excluded. High opioid use increased from 25.8% in the years before treatment to 29.6% in the years after treatment (Fig 3, B). For patients without CLI, high opioid use increased from 22.7% before treatment to 25.9% after treatment, whereas high opioid use increased from 30.8% before treatment to 37.1% after treatment for patients with CLI. For patients who did not undergo a PAD-related procedure, the overall rate of high opioid users was 24.3%, (21.6% for patients with no CLI and 32.6% for patients with CLI).
      A total of 80,816 PAD-related procedures were performed in 43,443 patients. The median time from diagnosis to the first PAD-related procedure was 81 days (interquartile range, 26-367 days). Fifty-six percent of PAD procedures resulted in an opioid prescription within 90 days after the procedure. This percentage was the highest for open revascularization (82.7%), followed by above-ankle amputation (72.5%) and percutaneous revascularization (44.6%) (Table III). A total of 105,155 opioid prescriptions were prescribed within 90 days of PAD-related treatments, and these were not counted toward the high opioid use criteria. These treatment-related opioids accounted for only 4.9% of the 2,153,169 opioid prescriptions for all patients during follow-up.
      Table IIIPercentage of procedures with opioid prescriptions within 90 days of the procedure
      No.Procedures with opioid prescription within 90 days%
      Total procedures
      Based on procedure dates, patients may have more than one type of procedure on a given date.
      80,81645,32256.0
       Percutaneous revascularization48,79821,74544.6
       Open revascularization6732556582.7
       Above-ankle amputation30,51022,12972.5
      a Based on procedure dates, patients may have more than one type of procedure on a given date.

      Discussion

      Opiate use is highly prevalent among patients with PAD, and to our knowledge, this study is the first to describe opioid prescription patterns in patients with PAD. An average of 24.7% of PAD patients met criteria for high opioid use, after exclusion of PAD treatment-related prescriptions. Our study found that a new diagnosis of PAD increases high opioid use percentage from 21.7% to 27.3%, and this increase was even greater for patients with a new diagnosis of CLI (25.4% to 35.5%). A diagnosis of CLI was independently associated with an increased risk of high opioid use (OR, 1.61). We additionally demonstrated that PAD treatment is associated with an increase in high opioid use, as the percentage of patients meeting criteria increased from 26% before treatment to 30% after treatment, despite censoring periprocedural prescriptions. This finding should alert physicians to evaluate their opioid prescribing patterns after PAD interventions to decrease the risk for long-term use.
      Although PAD patients often have multiple other comorbidities contributing to pain and increased risk of receiving opioid prescriptions, this article highlights that PAD patients are frequently prescribed opioids, especially patients with CLI and those who undergo PAD-related treatment. PAD itself can cause pain, as decreased blood supply to the lower extremities may trigger increased pain through nociception, ischemia, and neuropathy.
      • Seretny M.
      • Colvin L.A.
      Pain management in patients with vascular disease.
      Patients may also have phantom limb pain, which may persist after major amputation. Although the primary goal of revascularization is often to improve blood supply to alleviate symptoms (especially when not performed for limb salvage), we found that high opioid use actually increased after PAD interventions. This may suggest that opioid prescriptions after PAD intervention may result in opioid dependence and long-term use, highlighting the importance of judicious perioperative prescribing practices. Chronic opioid use has been shown to increase after both minor and major surgical procedures and has been implicated as one of the many contributors to the overall opioid epidemic and the availability of prescription opioids to the U.S. population.
      • Sun E.C.
      • Darnall B.D.
      • Baker L.C.
      • Mackey S.
      Incidence of and risk factors for chronic opioid use among opioid-naive patients in the postoperative period.
      • Brummett C.M.
      • Waljee J.F.
      • Goesling J.
      • Moser S.
      • Lin P.
      • Englesbe M.J.
      • et al.
      New persistent opioid use after minor and major surgical procedures in US adults.
      Clinicians should therefore be cautious in prescribing opioids for patients with PAD, especially when multiple comorbidities are present.
      Our study found that 56.0% of PAD-related procedures have an opioid prescription within 90 days of the procedure. Further investigation is warranted to determine individual prescribing patterns after PAD interventions (eg, endovascular vs open revascularization) and to determine the risks of chronic opioid use, especially in opioid naive patients. As our results mirror national trends of decreasing rate of overall opioid prescriptions since 2010 (Fig 2, A), longer duration of opioid prescription (eg, higher quantity of pills) rather than the quantity of opioid prescriptions may be contributing to the opioid epidemic.
      • Guy G.P.
      • Zhang K.
      • Bohm M.K.
      • Losby J.
      • Lewis B.
      • Young R.
      • et al.
      Vital signs: changes in opioid prescribing in the United States, 2006-2015.
      Current efforts by the Michigan Surgical Quality Collaborative have set opioid prescribing recommendations for common general surgery procedures
      University of Michigan
      Opioid prescribing recommendations for surgery.
      after a growing body of data has suggested that surgical patients are often prescribed more pills than they use.
      • Hill M.V.
      • McMahon M.L.
      • Stucke R.S.
      • Barth R.J.
      Wide variation and excessive dosage of opioid prescriptions for common general surgical procedures.
      Similar efforts for common vascular procedures may lead to decreased opioid prescriptions for patients with PAD and CLI. As increased opioid exposure is associated with increased arterial stiffness and increased vascular age,
      • Reece A.S.
      • Hulse G.K.
      Impact of lifetime opioid exposure on arterial stiffness and vascular age: cross-sectional and longitudinal studies in men and women.
      health care providers should particularly minimize opioid prescriptions in patients with PAD.
      This study has several limitations inherent to research using insurance claims data. This data set is unable to link opioid prescriptions to a specific diagnosis, and prescriptions within 90 days of a PAD procedure may have been issued for other comorbidities or non-PAD surgical interventions. The database contains limited information about type of physician practice, and physician identification was not readily available for all prescription records. As the majority of patients hold private insurance before Medicare eligibility, there is also limited generalizability to patients older than 65 years and those with government-sponsored insurance. Finally, minor procedures, including toe amputations and wound débridements, were not analyzed and may have contributed to an increase in opioid prescriptions for patients with CLI.

      Conclusions

      Patients with PAD are at risk for high opioid use, and patients with CLI and those undergoing treatment for PAD appear to be at even greater risk. In addition to heightened awareness and active opioid management, our findings warrant further investigation into the underlying causes of this as well as methods to deter high-risk opioid use.

      Author contributions

      Conception and design: NI, LS, JS, MM
      Analysis and interpretation: NI, LS, JS, MM
      Data collection: NI, LS
      Writing the article: NI, JS
      Critical revision of the article: NI, LS, JS, MM
      Final approval of the article: NI, LS, JS, MM
      Statistical analysis: NI, LS, MM
      Obtained funding: Not applicable
      Overall responsibility: NI

      Appendix (online only).

      Supplementary Table I (online only)Current Procedural Terminology (CPT) codes for percutaneous revascularization, open revascularization, and above-ankle amputation
      Procedure typeCPT codes
      Percutaneous revascularization35450, 35452, 35454, 35456, 35459, 35470, 35472, 35473, 35474, 35481, 35482, 35483, 35485, 35491, 35492, 35493, 35495, 37184, 37185, 37186, 37205, 37206, 37207, 37208, 37220, 37221, 37222, 37223, 37224, 37225, 37226, 37227, 37228, 37229, 37230, 37231, 37232, 37233, 37234, 37235
      Open revascularization35302, 35303, 35304, 35305, 35306, 35351, 35355, 35361, 35363, 35371, 35372, 35521, 35533, 35537, 35538, 35539, 35540, 35541, 35546, 35539, 35548, 35549, 35551, 35556, 35558, 35563, 35565, 35566, 35571, 35583, 35585, 35587, 35621, 35623, 35637, 35638, 35646, 35647, 35651, 35654, 35661, 35663, 35665, 35656, 35666, 35671, 35681, 35682, 35683, 35879, 35881, 35883, 35884, 35903
      Above-ankle amputation27590, 27591, 27592, 27598, 27880, 27881, 27882, 27884, 27886, 27888, 27889, 28800, 28805, 28810, 28820, 28825
      Supplementary Table II (online only)International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10) codes for comorbidities associated with opioid use
      DiagnosisICD-9 and ICD-10 codes
      Osteoarthritis71500, 71509, 71511, 71514, 71515, 71516, 71517, 71518, 71521, 71523, 71524, 71530, 71531, 71512, 71533, 71534, 71535, 71536, 71537, 71580, 71589, 71590, 71591, 71592, 71593, 71594, 71595, 71596, 71597, 71598, 71599, M150, M159, M151, M152, M19079, M1991, M1993, M19219, M19229, M19239, M19249, M167, M175, M19279, M1993, M1990, M189, M169, M179, M158, M153, M159
      Joint pain71940, 71941, 71942, 71943, 71944, 71945, 71946, 71947, 71948, 71949, 71951, 71954, 71956, 71957, 71958, 71960, 71962, 71963, 71964, 71966, 71967, 71968, M79643, M79646
      Rheumatoid arthritis7140, M069
      Migraine34600, 34601, 34610, 34611, 34620, 34621, 34630, 34680, 34690, 34691, G43
      Abdominal pain78900, 78901 78902, 78903, 78904, 78905, 78906, 78907, 78909,R109, R1011, R1012, R1031, R1032, R1033, R1013, R1084, R1010, R102, R1030
      Back pain7212, 7213, 72142, 7218, 72190, M47815, M4716, M47817, M47819, M489, 72210, 72211, 7222, 72251, 72252, 7226, M5126, M5124, M519, M5134, M5135, M5136, M5137, 72400, 72401 72402, 72403, 72409, M4800, M4804, M48061, M48062, M4808, 7241, 7242, 7245, M546, M545, M549, M5489
      Neck pain7210, M47812, 7211, M47112, 7220, M5020, 7224, M5030, 7230, M4802, 7231, M542
      Tobacco use3051, V1582, F17200, Z87891, F17208, F17218, F17228, F17298, 29289
      Alcohol use30300, 30301, 30302, 30303, 30390, 30391, 30392, 30393, 2910, 2911, 2912, 2913, 2914, 2915, 29181, 29182, 29189, 2919, V113, 30500, 30501, 30502, 30503, F10229, F1020, F1021, F10231, F1096, F1027, F10951, F10929, F10950, F10239, F10182, F10282, F10982, F10159, F10159, F10180, F10181, F10188, F10259, F10280, F10281, F10288, F10959, F10980, F1099, Z658, F1010
      Illicit drug use30520, 30521, 30522, 30523, F1210, 30430, 30431, 30432, 30433, F1220, F1221, 30521, 30522, 30523, F1290, F4321, 30928, F4323, 311, F329, 30560, 30561, 30562, 30563, F1410, 30420, 30421, 30422, 30423, F1420, F1421, 30570, 30571, 30572, 30573, F1510, 30440, 30441, 30442, 30443, F1520, F1521, 30530, 30531, 30532, 30533, F1610, 30450, 30451, 30452, 30453, F1620, F1621, 30590, 30591, 30592, 30593, F1810, 30580, 30581, 30582, 30583, F1910, 30460, 30461, 30462, 30480, 30481, 30482, 30491, 30491, 30492, F1920, 30463, 30483, 30493, F1921, 2920, F19939
      Depression29620, 29622, 29623, 29630, 29631, 29632, 29633, 29634, 29235, F329, F321, F322, F339, F330, F331, F332, F333, F3341, 3090
      Chronic pain
      Not included in regression model.
      33820, 33822, 33828, 33829, 3384, G8921, G8922, G8928, G8929, G894
      a Not included in regression model.

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