Orthopedics

Feature Article Supplemental Data

Predictors of Long-term Opioid Use After Elective Primary Total Shoulder Arthroplasty

Matthew J. Best, MD; Andrew B. Harris, MD; Ankit Bansal, MD; Eric Huish, DO; Uma Srikumaran, MD

Abstract

Opioids are prescribed routinely for pain after total shoulder arthroplasty (TSA). This study was designed to characterize opioid use after elective primary TSA and identify predictors of long-term postoperative opioid use. The authors used the MarketScan administrative claims database to identify 5676 adults who underwent elective primary TSA between 2010 and 2015 and had 1 year or more of continuous insurance enrollment, including prescription drug coverage, postoperatively. Long-term postoperative opioid use was defined as filling prescriptions totaling a 120-day or greater supply during the 3- to 12-month postoperative period. The authors performed univariate regression analysis with age, sex, US region, anatomic or reverse TSA, anxiety, chronic obstructive pulmonary disease, congestive heart failure, depression, diabetes, history of drug abuse, hypertension, obesity, osteoporosis, history of myocardial infarction, and current tobacco use. Variables that were significant at P<.05 were included in multivariate logistic regression. Overall, 16% of patients had long-term postoperative opioid use, which was strongly predicted by the multivariate model (area under the curve, 0.77; P<.001). The strongest predictors in the multivariate analysis were preoperative opioid use (odds ratio [OR], 4.7; 95% CI, 4.0–5.5), history of drug abuse (OR, 2.5; 95% CI, 1.3–4.9), depression (OR, 1.9; 95% CI, 1.6–2.3), anxiety (OR, 1.4; 95% CI, 1.2–1.7), surgery performed in the Western United States (OR, 1.8; 95% CI, 1.3–2.4), and reverse TSA (OR, 1.5; 95% CI, 1.2–1.8). Most patients do not have long-term opioid use after elective primary TSA. Strong predictors of long-term postoperative opioid use are preoperative opioid use, history of drug abuse, depression, anxiety, reverse TSA, and surgery performed in the Western United States. [Orthopedics. 2021;44(1):58–63.]

Abstract

Opioids are prescribed routinely for pain after total shoulder arthroplasty (TSA). This study was designed to characterize opioid use after elective primary TSA and identify predictors of long-term postoperative opioid use. The authors used the MarketScan administrative claims database to identify 5676 adults who underwent elective primary TSA between 2010 and 2015 and had 1 year or more of continuous insurance enrollment, including prescription drug coverage, postoperatively. Long-term postoperative opioid use was defined as filling prescriptions totaling a 120-day or greater supply during the 3- to 12-month postoperative period. The authors performed univariate regression analysis with age, sex, US region, anatomic or reverse TSA, anxiety, chronic obstructive pulmonary disease, congestive heart failure, depression, diabetes, history of drug abuse, hypertension, obesity, osteoporosis, history of myocardial infarction, and current tobacco use. Variables that were significant at P<.05 were included in multivariate logistic regression. Overall, 16% of patients had long-term postoperative opioid use, which was strongly predicted by the multivariate model (area under the curve, 0.77; P<.001). The strongest predictors in the multivariate analysis were preoperative opioid use (odds ratio [OR], 4.7; 95% CI, 4.0–5.5), history of drug abuse (OR, 2.5; 95% CI, 1.3–4.9), depression (OR, 1.9; 95% CI, 1.6–2.3), anxiety (OR, 1.4; 95% CI, 1.2–1.7), surgery performed in the Western United States (OR, 1.8; 95% CI, 1.3–2.4), and reverse TSA (OR, 1.5; 95% CI, 1.2–1.8). Most patients do not have long-term opioid use after elective primary TSA. Strong predictors of long-term postoperative opioid use are preoperative opioid use, history of drug abuse, depression, anxiety, reverse TSA, and surgery performed in the Western United States. [Orthopedics. 2021;44(1):58–63.]

As of 2010, 1 in 25 adults in the United States had a prescription for opioids, such as oxycodone and hydrocodone, for chronic pain.1 In 2011, opioid overdoses caused more deaths than heroin and cocaine overdoses combined.2 Kenan et al1 showed that, from 2000 to 2010, the number of opioid prescriptions written increased by 35%, and the average dosage (in morphine milligram equivalents daily [MMED]) for oxycodone and hydrocodone increased by more than 69%.

Identification of patients who are at risk for long-term opioid use after surgery is important. Long-term exposure to opioids can result in structural and functional damage to the central nervous system3 and can lead to dependence, tolerance, and a reduction in the pain threshold, which may complicate postoperative pain regimens and rehabilitation protocols.4,5 Patients who had been taking 20 or more MMED for 6 weeks or longer before undergoing total knee arthroplasty had longer hospital stays, higher rates of revision surgery for recalcitrant pain or stiffness, more arthroscopic evaluations for unexplained pain, and more referrals to chronic pain management physicians than patients who had not been taking opioids before surgery.6 Patterns of opioid use after total shoulder arthroplasty (TSA) have not been studied adequately.

The goals of this study were to characterize opioid use after elective primary TSA and identify predictors of long-term postoperative opioid use.

Materials and Methods

Data Source

This study was deemed exempt from review by the local institutional review board. The MarketScan Commercial Claims and Encounters Database includes private sector health data from approximately 100 payers and represents approximately 500 million insurance claims that have occurred since its inception. The database captures person-specific clinical use, expenditures, and enrollment across inpatient, outpatient, prescription drug, and “carve-out” services. Patients include those who are insured through employee-sponsored health insurance and their dependents, early retirees, Consolidated Omnibus Budget Reconciliation Act continuees, and Medicare-eligible retirees with employer-provided Medicare Supplemental plans. The MarketScan database links encounters to detailed patient information across episodes of care over time with the use of a unique identifier for each patient.7 Procedures are coded with Current Procedural Terminology codes, and diagnoses are coded with International Classification of Diseases, Ninth Revision (ICD-9) codes. For this study, data were available from January 1, 2010, to December 31, 2016.

Patient Selection

Patients who were 18 to 64 years and had 1 or more overnight admissions for primary TSA were identified with Current Procedural Terminology code 23472. If a patient had undergone more than 1 TSA procedure, then only the index procedure was included. To obtain an elective TSA patient population, the authors excluded patients who had a diagnosis of proximal humerus fracture (ICD-9 code 733.41) or osteonecrosis of the humerus (ICD-9 code 812.00). Only patients who had prescription drug coverage as part of their insurance plan and had continuous insurance enrollment for 3 months or more before and 1 year or more after surgery were included.

Demographic Variables and Comorbidities

Demographic variables are recorded in the MarketScan database according to information from insurance enrollment. The location of residence and the location where the surgery was performed are classified by regions as defined by the US Census Bureau.8 For the purposes of this study, the region of residence was recorded as the primary region for analysis of long-term postoperative opioid use.

Comorbidities were defined with ICD-9 and/or Current Procedural Terminology codes (Table A, available in the online version of the article). The authors identified patients who had 1 or more outpatient visits during the 3 months before surgery for any of these comorbidities or a diagnosis of any of these comorbidities during the hospital stay after TSA.

Codes Used to Identify Comorbidities

Table A:

Codes Used to Identify Comorbidities

Opioid Use

Outpatient pharmaceutical claims were identified for drugs that were classified as “opioid agonists/partial agonists” in the MarketScan database. National Drug Codes assigned by the Food and Drug Administration9 were used to identify individual drugs, formulations, and dosages. With methods that were similar to those used in previous studies, the authors classified patients who had filled opioid prescriptions 7 days or more before surgery as “using opioids preoperatively.”10,11 Prescriptions that were filled within 7 days before or 7 days after surgery were assumed to be postoperative opioid prescriptions. The MMED was calculated for each individual prescription according to the formula:

strength per unit×number of units/days’ supply×MME conversion factor=MMED.

Conversion to MMED enables comparison of opioid prescription strength across different formulations and daily doses. Long-term postoperative opioid use was defined as filling prescriptions totaling a 120-day or greater supply during the 3- to 12-month postoperative period.12 Patients who had MMED greater than the 99th percentile or less than the 1st percentile of the mean MMED were excluded because these values were attributed to data entry errors.

Statistical Analysis

Descriptive statistics were calculated for continuous variables and are reported as mean±SD unless stated otherwise. Categorical variables are expressed as number and percentage. Continuous variables were compared with Student's t tests and 1-way analysis of variance. Univariate logistic regression was performed with the primary outcome of long-term opioid use and preselected potential predictor variables. Predictors with significance of P<.10 were included in a multivariate logistic regression model controlling for comorbidities. Accuracy of the predictive value of the model was assessed with a receiver operating characteristic curve. Significance was determined at P<.05. Data were analyzed with Stata software, version 15 (StataCorp LLC).

Results

Study Population

A total of 5676 patients were included in the final cohort (Figure 1). Mean patient age was 57±5.6 years, and 42% of patients were women. Of the patients, 17% underwent reverse TSA. Mean duration of hospital stay was 1.7±1.2 days, and 81% of patients were discharged home after surgery. Additionally, 36% of patients had been taking opioids before surgery, and 79% had a prescription filled postoperatively. Patients were represented across all regions of the United States (Table 1).

Patient selection flowchart. Abbreviations: CPT, Current Procedural Terminology; ICD-9, International Classification of Diseases, Ninth Revision; TSA, total shoulder arthroplasty.

Figure 1:

Patient selection flowchart. Abbreviations: CPT, Current Procedural Terminology; ICD-9, International Classification of Diseases, Ninth Revision; TSA, total shoulder arthroplasty.

Characteristics of Patients Who Underwent Elective Primary Total Shoulder Arthroplasty in the United States, 2010–2015 (N=5676)

Table 1:

Characteristics of Patients Who Underwent Elective Primary Total Shoulder Arthroplasty in the United States, 2010–2015 (N=5676)

Initial Postoperative Opioid Prescriptions

Among the opioid prescriptions that were filled postoperatively, 2005 (45%) were for short-acting hydrocodone and 1878 (42%) were for short-acting oxycodone. These two short-acting medications accounted for most opioid prescriptions. Long-acting oxycodone, short-acting hydromorphone, codeine, and long-acting morphine were prescribed less commonly (Table 2).

Initial Postoperative Opioid Prescriptions for Patients Who Underwent Primary Total Shoulder Arthroplasty (N=4485)

Table 2:

Initial Postoperative Opioid Prescriptions for Patients Who Underwent Primary Total Shoulder Arthroplasty (N=4485)

Short-acting oxycodone was prescribed at a mean strength of 113±166 MMED. Short-acting hydrocodone was prescribed at a mean strength of 95±138 MMED for initial postoperative prescriptions.

Predictors of Long-term Postoperative Opioid Use

Univariate analysis of factors associated with long-term opioid use after TSA is shown in Table 3. The multivariate model was significant (P<.001), with an area under the receiver operating characteristic curve of 0.78, indicating fair predictive value (Figure 2).6

Univariate Odds of Long-term Postoperative Opioid Use After Total Shoulder Arthroplasty

Table 3:

Univariate Odds of Long-term Postoperative Opioid Use After Total Shoulder Arthroplasty

Receiver operating characteristic (ROC) curve of the logistic regression model used to predict postoperative opioid use after total shoulder arthroplasty.

Figure 2:

Receiver operating characteristic (ROC) curve of the logistic regression model used to predict postoperative opioid use after total shoulder arthroplasty.

After controlling for comorbidities, factors that were independently associated with long-term postoperative opioid use on multivariate analysis were preoperative opioid use (odds ratio [OR], 4.7; 95% CI, 4.0–5.5), history of drug abuse (OR, 2.5; 95% CI, 1.3–4.9), depression (OR, 1.9; 95% CI, 1.6–2.3), anxiety (OR, 1.4; 95% CI, 1.2–1.7), surgery performed in the Western United States (OR, 1.8; 95% CI, 1.4–2.3), and reverse TSA (OR, 1.5; 95% CI, 1.2–1.8). Older age was associated with lower odds of long-term opioid use (45–54 years: OR, 0.39, P=.02; 55–64 years: OR, 0.26, P<.01) (Figure 3).

Adjusted odds of long-term opioid use after total shoulder arthroplasty (TSA). Odds ratios (OR) control for the comorbidities shown in Table 3.

Figure 3:

Adjusted odds of long-term opioid use after total shoulder arthroplasty (TSA). Odds ratios (OR) control for the comorbidities shown in Table 3.

Discussion

After undergoing elective primary TSA, 16% of patients met the criteria for long-term opioid use. Patients who had been taking opioids preoperatively, had a history of drug abuse, had depression or anxiety, underwent reverse TSA, or underwent TSA in the Western United States had the highest odds of long-term postoperative opioid use.

Rates of long-term postoperative opioid use vary for different surgical procedures.13 Sun et al14 analyzed more than 640,000 opioid-naïve patients who underwent various surgical procedures and showed that the highest rate of long-term postoperative opioid use was 1.4%, occurring after total knee arthroplasty. They also found that patients undergoing total hip arthroplasty, open or laparoscopic cholecystectomy, open appendectomy, mastectomy, or cesarean delivery had greater odds of long-term opioid use compared with patients undergoing other procedures; however, they did not analyze the risk of long-term opioid use after TSA.14 Although the authors used the same definition for prolonged opioid use as in the current study, the incidence found (16%) in this study is much higher. One reason for this difference is that Sun et al14 analyzed only new cases of long-term use in opioid-naïve patients, whereas the current study included all patients with long-term postoperative use, including those who were taking opioids before surgery. In an analysis of more than 79,000 patients who were treated by different surgical specialists at 1 institution, Jiang et al15 found a mean rate of long-term (at least 3 months after surgery) postoperative opioid use of 9.2%, regardless of whether patients had been taking opioids preoperatively. The authors reported a range of rates by surgical specialty, from 4.4% for cardiac surgery to 23.8% for orthopedic surgery, but did not analyze specific procedures. Gil et al16 reported long-term opioid use in 8.3% of patients after arthroscopic shoulder surgery, which is lower than the rate of 16% reported in the current study, but this finding is not unexpected, given the focus on an arthroplasty population.

Patient-specific factors associated with prolonged postoperative opioid use are important to identify because many can be modified or treated before surgery. Factors such as preoperative opioid use are associated with long-term opioid use after bariatric surgery, organ transplant, spine surgery, and total hip and knee arthroplasty.17–21 Zarling et al21 reported that, of 367 patients who underwent total hip or knee arthroplasty, those who had been taking opioids preoperatively had higher odds of continued opioid use 1 year after surgery compared with opioid-naïve patients. Other patient factors, such as pre-operative substance abuse, also have been associated with long-term postoperative opioid use.14 Brummett et al22 stratified more than 36,000 patients according to whether they underwent “major” or “minor” surgery. They found that rates of long-term postoperative opioid use did not differ significantly by surgery category but rather that long-term use was associated with patient-specific factors, such as smoking, drug and alcohol abuse, anxiety and mood disorders, and preoperative pain disorders.22 The authors suggested that long-term opioid use was not caused by surgical pain but rather by modifiable patient-level factors.22 This finding is consistent with the current findings, which showed that drug abuse, depression, and anxiety were associated with increased odds of long-term opioid use after primary elective TSA.

Patients who underwent reverse TSA had greater odds of long-term postoperative opioid use than those who underwent anatomic TSA. One explanation for this finding may be the different indications for anatomic and reverse TSA. Because reverse TSA is indicated for patients who have rotator cuff insufficiency or cuff tear arthropathy, many of these patients may have been taking opioids for chronic rotator cuff-related pain before surgery and therefore may be at higher risk for long-term postoperative opioid use. However, logistic regression analysis controlled for confounders when making this comparison, so the higher rate of long-term use after reverse TSA shown in this study should prompt further investigation.

Rates of opioid use and opioid-related deaths vary by US region but have increased at higher rates in areas with greater poverty and unemployment.23 Regions such as Appalachia, New England, parts of the Midwest, and the West, including counties in northern California and southwestern Oregon, have higher rates of opioid use than other regions.23 Geographic differences in postoperative opioid prescribing practices also have been shown. Vail et al24 analyzed more than 73,000 patients who underwent surgery for lumbar spondylolisthesis and showed substantial geographic variation in postoperative opioid use, even after controlling for patient and procedure characteristics.24 In their study, Arizona, Idaho, Minnesota, Nevada, New York, Oklahoma, Oregon, South Carolina, Utah, and Washington showed the highest rates of postoperative opioid use.24 This finding agrees with the current study, which suggests that having surgery in the Western United States is associated with long-term opioid use after elective primary TSA and warrants further study of variations in geographic opioid prescribing patterns.

This study had several limitations. The MarketScan database includes information only for privately insured patients and does not include data for Medicare or Medicaid beneficiaries. Although the exclusion of these 2 groups limits the ability to study opioid use patterns in elderly patients and examine differences across socioeconomic classes, it produces a more homogeneous patient population for analysis. The MarketScan database does not include data on opioids that patients obtain without a prescription (eg, from friends, family, or previous supply), nor does it provide data on opioid prescriptions that were filled but not taken by the patient. The database cannot account for over-the-counter pain medications that may affect postoperative pain and the amount of opioid medication used. Additionally, the MarketScan database relies on hospital coding of diagnoses and procedures, which can be subject to coding errors. However, any coding errors that occur should be distributed evenly among patients, regardless of whether they use opioids on a long-term basis after surgery. Finally, the MarketScan database does not contain orthopedic-specific outcome measures, such as return to previous activity level or assessment of postoperative functional outcomes, but this limitation should not affect the findings on opioid use.

Conclusion

After undergoing elective primary TSA, 16% of patients had long-term opioid use. Factors associated with increased odds of long-term postoperative opioid use were preoperative opioid use, history of drug abuse, depression, anxiety, undergoing reverse TSA, or having surgery in the Western United States. Understanding risk factors for long-term opioid use after elective TSA can help in identifying modifiable factors before surgery and aid in preoperative patient counseling and postoperative pain management planning.

References

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Characteristics of Patients Who Underwent Elective Primary Total Shoulder Arthroplasty in the United States, 2010–2015 (N=5676)

CharacteristicValue
Age, mean±SD, y57±5.6
Discharged home, No. (%)4623 (81)
Female, No. (%)2395 (42)
Hospital stay, mean±SD, d1.7±1.2
Postoperative opioid use, No. (%)4484 (79)
Preoperative opioid use, No. (%)2041 (36)
Reverse total shoulder arthroplasty, No. (%)952 (17)
US region, No. (%)
  Midwest1651 (29)
  Northeast871 (15)
  South2169 (38)
  West921 (16)
  Unknown64 (1.1)
Comorbidity, No. (%)
  Anxiety1336 (24)
  Congestive heart failure179 (3.2)
  Chronic obstructive pulmonary disease1048 (18)
  Depression1771 (31)
  Diabetes mellitus, complicated203 (3.6)
  History of drug abuse43 (0.76)
  Hypertension4137 (73)
  Obesity1610 (28)
  Osteoporosis349 (6.2)
  Previous myocardial infarction191 (3.4)
  Tobacco use483 (8.5)

Initial Postoperative Opioid Prescriptions for Patients Who Underwent Primary Total Shoulder Arthroplasty (N=4485)

Opioid prescribedNo. (%)
Hydrocodone, short-acting2005 (45)
Oxycodone, short-acting1878 (42)
Oxycodone, long-acting161 (3.6)
Hydromorphone, short-acting147 (3.3)
Other146 (3.3)
Codeine101 (2.3)
Morphine, long-acting47 (1.0)

Univariate Odds of Long-term Postoperative Opioid Use After Total Shoulder Arthroplasty

VariableOdds ratio (95% CI)P
Female1.5 (1.3–1.7)<.001
Age group, y
  35–440.73 (0.33–1.6).436
  45–540.62 (0.30–1.3).178
  55–640.47 (0.23–0.94).032
Comorbidity
  Anxiety2.2 (1.9–2.6)<.001
  Congestive heart failure1.4 (0.95–1.99).096
  Chronic obstructive pulmonary disease2.1 (1.8–2.5)<.001
  Depression2.6 (2.2–2.98)<.001
  Diabetes mellitus, complicated1.9 (1.4–2.6)<.001
  Drug abuse4.8 (2.6–8.7)<.001
  Hypertension1.4 (1.2–1.7)<.001
  Past myocardial infarction1.3 (0.91–1.9).150
  Obesity1.2 (1.0–1.4).042
  Osteoporosis1.0 (0.77–1.4).831
  Preoperative opioid use4.7 (4.1–5.5)<.001
  Tobacco use1.4 (1.1–1.8).008
US region
  Northeast1.00 (referent)
  Midwest1.5 (1.2–1.97).001
  South1.5 (1.2–1.9).001
  West1.8 (1.4–2.3)<.001
Reverse total shoulder arthroplasty1.7 (1.4–2.0)<.001
Discharge location other than home1.3 (1.0–1.5).014
30-day readmission1.8 (1.2–2.9).006

Codes Used to Identify Comorbidities

ComorbidityICD-9/CPT Codes
Anxiety300.00–300.02, 309.24
Congestive heart failure398.91, 402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93, 425.4–425.9, 428
COPD416.8, 416.9, 490–505, 506.4, 508.1, 508.8
Depression296.20–296.25, 296.30–296.35, 300.4, 311
DM, complicated250.4–250.6
DM, uncomplicated250.0–250.3, 250.7
Drug abuse292.0, 292.8–292.9, 304, 305.2–305.9, 648.3
Hypertension401, 402–405, 642, 437.2
Myocardial infarction410, 411, 412, 429.7, 997.1
Obesity278.0, 649.1, 793.91, V853, V854, V8554
Osteoporosis733, V1781, V8281
Tobacco use649.0, 989.84, V15.82
Authors

The authors are from the Department of Orthopaedic Surgery, The Johns Hopkins University School of Medicine, Baltimore, Maryland.

Drs Best, Harris, Bansal, and Huish have no relevant financial relationships to disclose. Dr Srikumaran is a member of the board of Tigon Medical; is a paid consultant for Conventus, Tigon Medical, FX Shoulder USA, DePuy/Pacira, Heron, Corin, and Smith & Nephew; has received grants from OMEGA, Arthrex, DePuy, Wright, and Smith & Nephew; is on the speaker's bureau of Corin and FX Shoulder USA; has patents with Tigon Medical, Conventus, and FX Shoulder USA; and holds stock in Quantum OPS and Tigon Medical.

Correspondence should be addressed to: Uma Srikumaran, MD, Department of Orthopaedic Surgery, The Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21287 ( us@jhmi.edu).

Received: August 05, 2019
Accepted: October 27, 2019
Posted Online: October 22, 2020

10.3928/01477447-20201007-06

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