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Risk factors for opioid use after total shoulder arthroplasty
J Shoulder Elbow Surg (2019) -, 1–9
www.elsevier.com/locate/ymse
Risk factors for opioid use after total shoulder arthroplasty Zain M. Khazi, BSa, Yining Lu, BAb, Bhavik H. Patel, BSb, Jourdan M. Cancienne, MDb, Brian Werner, MDc, Brian Forsythe, MDb,* a
Department of Orthopedics and Rehabilitation, University of Iowa Hospitals and Clinics, Iowa City, IA, USA Division of Sports Medicine, Midwest Orthopedics at Rush, Rush University Medical Center, Chicago, IL, USA c Department of Orthopedic Surgery, University of Virginia, Charlottesville, VA, USA b
Hypothesis: The purpose was to assess opioid use before and after anatomic and reverse total shoulder arthroplasty (TSA) and determine patient factors associated with prolonged postoperative opioid use. Methods: Patients undergoing primary TSA (anatomic or reverse) were identified within the Humana database from 2007 to 2015. Patients were categorized as opioid-naive patients who did not fill a prescription prior to surgery or those who filled opioid prescriptions within 3 months preoperatively (OU); the OU cohort was subdivided into those filling opioid prescriptions within 1 month preoperatively and those filling opioid prescriptions between 1 and 3 months preoperatively. The incidence of opioid use was evaluated preoperatively and longitudinally tracked for each cohort. Multivariate analysis was used to identify factors associated with opioid use at 12 months after surgery, with statistical significance defined as P < .05. Results: Overall, 12,038 patients (5180 in OU cohort, 43%) underwent primary TSA during the study period. Opioid use declined after the first postoperative month; however, the incidence of opioid use was significantly higher in the OU cohort than in the opioid-naive cohort at 1 year (31.4% vs. 3.1%, P < .0001). Subgroup analysis revealed a similar decline in postoperative opioid use for anatomic and reverse TSA (P < .0001 for both). Multivariate analysis identified chronic preoperative opioid use (ie, filling an opioid prescription between 1 and 3 months prior to surgery) as the strongest risk factor for opioid use at 12 months after anatomic and reverse TSA (P < .0001). Conclusion: More than 40% of patients undergoing TSA received opioid medications within 3 months before surgery. Preoperative opioid use, age younger than 65 years, and fibromyalgia were independent risk factors for opioid use 1 year following anatomic and reverse TSA. Chronic preoperative opioid use conferred the highest risk of prolonged postoperative opioid use. Level of evidence: Level III; Retrospective Cohort Comparison; Large Database Analysis Ó 2019 Journal of Shoulder and Elbow Surgery Board of Trustees. All rights reserved. Keywords: Anatomic total shoulder arthroplasty; reverse total shoulder arthroplasty; opioid use; shoulder arthritis; Humana database; PearlDiver
This retrospective cohort analysis was granted exemption from the local institutional review board because of the deidentified nature of the data. *Reprint requests: Brian Forsythe, MD, Midwest Orthopaedics at Rush, Division of Sports Medicine, 1611 W Harrison St, Ste 300, Chicago, IL 60612, USA. E-mail address: [email protected] (B. Forsythe).
The use of prescription opioid medication has become increasingly challenging for the orthopedic surgeon in the past decade. Although it is the most effective method to alleviate pain in many scenarios, short-term opioid use is coupled with the danger of dependence and abuse. Whereas
1058-2746/$ - see front matter Ó 2019 Journal of Shoulder and Elbow Surgery Board of Trustees. All rights reserved. https://doi.org/10.1016/j.jse.2019.06.020
2 the number of opioid prescriptions has been decreasing since 2010, the opioid-related mortality rate showed an almost 3fold increase between 2001 and 2016.9,13 The presence of such a massive public health burden demonstrates that the epidemic of therapeutic and recreational opioid use in the United States remains significant and widespread. Chronic pain treatment remains the leading cause of opioid use, with long-term joint pain accounting for almost a third of all firsttime opioid prescriptions.10,33,36 However, the management of postsurgical pain in common orthopedic procedures has also emerged as a significant contributor to opioid prescriptions.36 In a prospective study by Rodgers et al,31 physicians found that patients were prescribed 3 times more opioids than they consumed following upper-extremity orthopedic procedures. Similar results were demonstrated by a study of opioid prescriptions after outpatient upperextremity procedures by Kim et al.18 Indeed, postsurgical opioid prescription and use have become a central focus of orthopedic research in recent years.30,34,37 Consensus guidelines for appropriate postoperative opioid consumption requires establishment of a valid and generalizable model for the course of opioid use. To inform a successful algorithm, it is imperative to have a thorough understanding of factors predicting prolonged and potentially dangerous opioid consumption. Prior studies have evaluated the utilization of opioid analgesics in common orthopedic procedures in the hand, upper extremity, and shoulder.18,20,31,37 Physicians have also investigated the impact of a preoperative diagnosis of opioid abuse or dependence on postoperative consumption in total shoulder arthroplasty (TSA).5 These studies have identified several risk factors independently associated with prolonged postoperative opioid use, including tobacco use, preoperative opioid utilization, and younger age. However, the impact of opioid use at large remains unexplored in patients undergoing anatomic TSA (aTSA) or reverse TSA (rTSA). Therefore, the purpose of this investigation was to determine preoperative and postoperative opioid use while identifying risk factors that predict prolonged postoperative opioid use following TSA. We hypothesized that patients with a history of preoperative opioid use would be associated with prolonged postoperative opioid consumption after TSA.
Z.M. Khazi et al. demographic characteristics, comorbidities, complications, and prescription medication can be retrieved using International Classification of Diseases, Ninth Revision codes (ICD-9); International Classification of Diseases, Tenth Revision codes (ICD10); Current Procedural Terminology codes; and National Drug Codes. The Humana database was used for this study to allow for longitudinal tracking of patients including monthly reports on opioid use before and after surgery.
Patient selection We conducted a retrospective review of patients undergoing primary aTSA and rTSA between 2007 and 2015 in the Humana database using ICD-9 Clinical Modification codes 81.80 and 81.88, respectively. The inclusion criteria were limited to patients who were active within the database for at least 3 months before and 12 months after TSA. Patient demographic characteristics and comorbidities were extracted for each cohort using ICD-9 and ICD-10 codes. Demographic characteristics and medical comorbidities that were compared between cohorts included age (<65 years vs. 65 years), sex, obesity (body mass index 30 kg/m2), tobacco use, diabetes mellitus, hypertension, congestive heart failure, chronic lung disease, fibromyalgia, and psychiatric conditions including major depressive disorder and generalized anxiety disorder. Patient records were subsequently queried for all doses of common oral and transdermal formulations of prescription opioids, except tramadol.16 This included hydromorphone, oxycodone, hydrocodone, fentanyl, methadone, OxyContin (Purdue Pharma, Stamford, CT, USA), propoxyphene, and morphine. Patients were categorized as opioid naive (N-OU) if they never filled opioid prescriptions prior to surgery, whereas patients who filled opioid prescriptions within 3 months prior to surgery were classified as opioid users (OU). Patients were further categorized as chronic preoperative opioid users (C-OU) if they filled an opioid prescription between 1 and 3 months prior to surgery or as acute preoperative opioid users (A-OU) if they only filled an opioid prescription within 1 month before surgery. This delineation was made to exclude patients who may have received opioid medications as part of their preoperative management. In addition, patients who filled opioid prescriptions more than 3 months prior to surgery were not analyzed in the study to avoid including patients who may have filled opioid prescriptions for an unrelated surgery, trauma, or medical condition. For the entire cohort, opioid refills were tracked monthly after the index procedure for the first postoperative year. A postoperative period of 12 months was chosen for this study to minimize bias related to opioid use owing to other conditions or subsequent, unrelated surgical procedures.
Methods
Factors associated with postoperative opioid use
Database
The primary goal of this study was to determine the rate of preoperative and postoperative opioid use in patients undergoing TSA and to identify risk factors associated with opioid use at 12 months following surgery. Subgroup analysis based on the type of procedure (aTSA and rTSA) was conducted to determine the risk of postoperative opioid use between the OU and N-OU cohorts. Independent risk factors for postoperative opioid use were analyzed for the aTSA and rTSA cohorts. Risk factors evaluated included preoperative opioid use and preoperative substance use or abuse
The Humana database (Louisville, KY, USA) was used to identify patients undergoing aTSA or rTSA using the PearlDiver patient records database (Colorado Springs, CO, USA; www. pearldiverinc.com). The PearlDiver patient records database accesses deidentified information from over 20 million private and commercially insured patients, including those with Medicare Advantage through Humana. Patient information including
Opioid use after total shoulder arthroplasty
3
disorders such as tobacco and alcohol use or abuse. Patient demographic characteristics and comorbidities including age, sex, obesity, depression or anxiety disorders, fibromyalgia, diabetes mellitus, hypertension, congestive heart failure, chronic lung disease, and glenohumeral arthritis were also evaluated.
Statistical analysis Descriptive statistics were used to report patient demographic characteristics, medical comorbidities, and monthly postoperative opioid prescription refills. Pearson c2 tests and relative risk (RR) ratios, along with 95% confidence intervals (CIs), were used to compare monthly postoperative opioid refill rates between the OU and N-OU cohorts for the overall TSA population and the aTSA and rTSA populations. Multivariate analysis was used to identify independent risk factors for postoperative opioid refills at 12 months after aTSA and rTSA, with statistical significance set at P < .05. The Hosmer-Lemeshow goodness-of-fit test was also performed for the multivariate model to assess for appropriate calibration with fitness defined as P > .1. All statistical analyses were performed using the open-source R software (R Foundation for Statistical Computing, Vienna, Austria; www.r-project.org) housed within the PearlDiver research database.
Results Patient demographic comorbidities
characteristics
and
Overall, 12,038 patients undergoing primary TSA were identified during the study period, of whom 29.8% (n ¼ 3586) were classified as N-OU patients and 43% (n ¼ 5180) were classified as OU patients, which was further stratified into 32.4% C-OU patients (n ¼ 3903) and 10.6% A-OU patients (n ¼ 1277). Most patients were female patients (n ¼ 7208, 59.9%), aged 65 years or older (n ¼ 10,259, 85.2%), and underwent aTSA (n ¼ 6589, 54.7%). Regarding medical comorbidities, 26.2% of patients (n ¼ 3148) were obese, 35.1% (n ¼ 4222) carried a diagnosis of diabetes mellitus, 83.4% (n ¼ 10,036) had hypertension, 6.9% (n ¼ 883) had congestive heart failure, 7.9% (n ¼ 946) had chronic lung disease, 18.1% (n ¼ 2173) had fibromyalgia, and 9.6% (n ¼ 1156) had depression or anxiety (Table I). Tobacco and alcohol use or abuse was noted in 13.8% of patients (n ¼ 1657) and 3.33% (n ¼ 401), respectively. N-OU patients were more frequently older (age 65 years; 90.2% vs. 79%, P < .0001) and of male sex (45.1% vs. 36.3%, P < .0001) than OU patients. Furthermore, OU patients had a significantly higher incidence of obesity, diabetes mellitus, hypertension, congestive heart failure, chronic lung disease, fibromyalgia, and history of anxiety or depression (P < .0001) than N-OU patients (Table I). The incidence of glenohumeral arthritis was significantly higher in the OU cohort because of the higher incidence of rTSAs in this cohort (rTSAs vs. aTSAs, 64.4% vs. 55%).
Monthly postoperative opioid use Postoperatively, 70.2% of patients filled opioid prescriptions within 1 month of TSA. However, the rate of opioid use decreased to 25.1% at 3 months, 18.9% at 6 months, 17.4% at 9 months, and 16.5% at 12 months postoperatively. Stratification of patients into the OU and N-OU cohorts revealed the following findings (Fig. 1): At 1 month following surgery, 80.3% of OU patients and 54.2% of N-OU patients filled opioid prescriptions. However, the opioid fill rate decreased dramatically to 46% for OU patients and 6.3% for N-OU patients at 3 months. From the sixth to twelfth postoperative months, opioid fill rates plateaued for both cohorts (OU, from 36.5% to 31.4%; N-OU, from 3.8% to 3.1%). Compared with the N-OU cohort, the OU cohort had a significantly higher rate of opioid filling in all postoperative months (P < .0001). At 1 year after surgery, the opioid fill rate was significantly higher in the OU cohort than in the N-OU cohort, with an RR of 10.25 (95% CI, 8.49-12.38; P < .0001) (Table II). Subgroup analysis of postoperative opioid use based on the type of TSA was also conducted. In the aTSA subgroup, the rate of postoperative opioid use was 70.6% at 1 month, 24.3% at 3 months, and 18.9% at 12 months (Fig. 2). When the OU and N-OU cohorts were compared, the OU cohort had a significantly higher rate of opioid filling up to 12 months after aTSA (P < .0001). At 1 year after aTSA, the RR of opioid use was significantly higher in the OU cohort than in the N-OU cohort (38% vs. 4%; RR, 9.59; 95% CI, 7.62-12.06; P < .0001) (Table III). In the rTSA subgroup, postoperative opioid use was 68.4% at 1 month, 26.3% at 3 months, and 18.7% at 12 months (Fig. 3). Compared with the N-OU cohort, the OU cohort had a significantly higher rate of opioid filling for up to 12 months after rTSA (P < .0001). At 1 year after rTSA, the OU cohort had a significantly increased risk of opioid filling compared with the N-OU cohort, with an RR of 11.84 (34.1% vs. 2.9%; 95% CI, 8.5-16.5; P < .0001) (Table III).
Risk factors for postoperative opioid use The univariate analysis identified preoperative opioid use (chronic), chronic lung disease, preoperative glenohumeral arthritis, history of a psychiatric diagnosis, age younger than 65 years, obesity, fibromyalgia, and ethanol abuse as significant risk factors associated with opioid use at 12 months following both aTSA and rTSA (Table IV). The multivariate model, which controlled for patient demographic characteristics and comorbidities, identified chronic preoperative opioid use as having the strongest independent association with opioid use 12 months after aTSA (odds ratio [OR], 10.32; 95% CI, 8.69-12.30; P < .0001) and rTSA (OR, 11.29; 95% CI, 9.24-13.88; P < .0001). Furthermore, acute preoperative opioid use (OR,
4
Z.M. Khazi et al. Table I
Characteristics of patients undergoing TSA based on preoperative opioid use
Patient factors
Overall TSA population (N ¼ 12,038), n (%)
OU cohort (n ¼ 5180), n (%)
N-OU cohort (n ¼ 3586), n (%)
P value
Age 65 yr Sex Male Female Obesity (BMI 30 kg/m2) Diabetes mellitus Hypertension Congestive heart failure Chronic lung disease History of psychiatric diagnosis Fibromyalgia Glenohumeral arthritis Preoperative substance use diagnosis Tobacco use Alcohol use or abuse
10,259 (85.2)
4092 (79)
3234 (90.2)
<.0001*
4830 7208 3148 4222 10,036 883 946 1156 2173 7761
1879 3301 1503 1977 4420 451 574 686 1141 3224
1619 1967 601 1014 2773 152 127 173 393 2329
<.0001*
(40.1) (59.9) (26.2) (35.1) (83.4) (6.9) (7.9) (9.6) (18.1)
1657 (13.8) 401 (3.3)
(36.3) (63.7) (29) (38.2) (85.3) (8.7) (11.1) (13.2) (22) (62.2)
787 (15.2) 225 (4.3)
(45.1) (54.9) (16.8) (28.3) (77.3) (4.2) (3.5) (4.8) (11) (64.9)
<.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* .0097* <.0001* <.0001*
323 (9) 65 (1.8)
TSA, total shoulder arthroplasty; OU, opioid use within 3 months prior to TSA; N-OU, opioid naive; BMI, body mass index. The P values depict differences in patient factors between the OU and N-OU cohorts. * Significant finding (P < .05).
2.54; 95% CI, 1.89-3.39; P < .0001), chronic lung disease (OR, 2.14; 95% CI, 1.62-2.82; P < .0001), age younger than 65 years (OR, 1.83; 95% CI, 1.52-2.21; P < .0001), fibromyalgia (OR, 1.45; 95% CI, 1.20-1.75; P ¼ .0001), and ethanol abuse (OR, 1.64; 95% CI, 1.05-2.56; P ¼ .0296) were independently associated with opioid use at 12 months after aTSA (Fig. 4). Similarly, acute preoperative opioid use (OR, 2.18; 95% CI, 1.58-2.98; P < .0001), preoperative glenohumeral arthritis (OR, 1.19; 95% CI, 1.00-1.42; P ¼ .0479), history of a psychiatric diagnosis (OR, 1.19; 95% CI, 1.14-1.86; P ¼ .0021), age younger than 65 years (OR, 2.57; 95% CI, 2.04-3.24; P < .0001), and fibromyalgia (OR, 1.23; 95% CI, 1.00-1.50; P ¼ .0457) were independently associated with opioid use at 12 months after rTSA (Fig. 4).
Discussion This study found that a relatively high rate of patients (43%) used opioid medications 3 months prior to TSA. It also found that opioid users were more than 10 times more likely to fill opioid prescriptions than were N-OU patients at 1 year following TSA. Most notably, the study identified chronic opioid use as the strongest predictor of prolonged opioid consumption after TSA. The findings of this study thereby help determine the natural course of postoperative opioid use after TSA by identifying significant predictors of prolonged postoperative opioid use in this population. The alarming rise in opioid use and its detrimental societal impact have put a spotlight on opioid utilization, with federal, state, and institutional agencies implementing
100 90 80 70 60 50 40 30 20 10 0 1st Month
2nd Month
3rd Month
4th Month
5th Month
6th Month
7th Month
8th Month
Opioid Naïve (N-OU)
Preoperave Opioid Use (OU)
Chronic Opioid Use (C-OU)
All TSA
Figure 1
9th Month
10th Month
11th Month
Acute Opioid Use (A-OU)
Monthly opioid use after total shoulder arthroplasty (TSA).
12th Month
Opioid use after total shoulder arthroplasty Table II
5
Risk of opioid utilization after TSA
Postoperative month
Preoperative opioid users (n ¼ 5180, 43%), n (%)
Opioid-naive patients (n ¼ 3586, 29.8%), n (%)
RR
1 2 3 4 5 6 7 8 9 10 11 12
4159 2783 2383 2114 1924 1891 1836 1787 1721 1641 1690 1629
1942 377 226 186 170 136 132 112 114 112 122 110
1.48 5.11 7.30 7.87 7.84 9.63 9.63 11.05 10.45 10.14 9.59 10.25
(80.3) (53.7) (46.0) (40.8) (37.1) (36.5) (35.4) (34.5) (33.2) (31.7) (32.6) (31.4)
(54.2) (10.5) (6.3) (5.2) (4.7) (3.8) (3.7) (3.1) (3.2) (3.1) (3.4) (3.1)
P value
95% CI Lower
Upper
1.43 4.63 6.41 6.82 6.74 8.13 8.11 9.17 8.69 8.42 8.02 8.49
1.53 5.64 8.31 9.08 9.11 11.40 11.43 13.31 12.57 12.22 11.47 12.38
<.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001*
TSA, total shoulder arthroplasty; CI, confidence interval; RR, relative risk. * Significant finding (P < .05).
policies to regulate and track opioid-prescribing practices.11,35 Orthopedic surgeons are the third-highest opioid prescribers among physicians in the United States36; therefore, research regarding trends in postoperative opioid use and risk factors associated with opioid use after surgery is emerging in the orthopedic literature. Recently, national databases have been used to identify trends and risk factors for opioid use after various orthopedic surgical procedures including spinal procedures and total joint arthroplasties.3,4,7,16,22,28 Bedard et al3 used the Humana database to investigate trends and risk factors associated with postoperative opioid use in patients undergoing total hip arthroplasty. Similarly to our study, their study found that nearly 40% of patients filled opioid prescriptions within 3 months prior to surgery. Kalakoti et al16 also used the Humana database and found that more than half of the patients filled opioid prescriptions within 3
months prior to lumbar arthrodesis. Other investigations on rotator cuff repair, anterior cruciate ligament reconstruction, and hip arthroscopy have reported similarly high incidences of preoperative opioid use.1,38,39 The use of national databases to identify trends and risk factors for postoperative opioid use following TSA has been conducted recently.5 Berglund et al5 investigated the effect of a preoperative diagnosis of opioid dependence or abuse on opioid utilization after aTSA or rTSA and found that only 2% and 3% of patients undergoing aTSA and rTSA, respectively, had a history of opioid dependence or abuse. On the other hand, our study defined preoperative opioid users as patients who filled opioid prescriptions within 3 months prior to surgery and found that about 43% of patients filled opioid prescriptions before TSA. In addition, Berglund et al found that patients with a diagnosis of opioid dependence or abuse were more than twice as
100 90 80 70 60 50 40 30 20 10 0 1st Month
2nd Month
3rd Month
4th Month
5th Month
6th Month
7th Month
8th Month
Opioid Naïve (N-OU)
Preoperave Opioid Use (OU)
Chronic Opioid Use (C-OU)
All Anatomic TSA
Figure 2
9th Month
10th Month
11th Month
Acute Opioid Use (A-OU)
Monthly opioid use after anatomic total shoulder arthroplasty (TSA).
12th Month
6
Z.M. Khazi et al. Table III
Risk of opioid utilization after TSA by procedure type
Postoperative Anatomic TSA month RR (95% CI) N-OU OU (n ¼ 2280), (n ¼ 1868), % %
P value
1 2 3 4 5 6 7 8 9 10 11 12
<.0001* 79.5 <.0001* 52.5 <.0001* 4.6 <.0001* 4.0 <.0001* 3.6 <.0001* 3.6 <.0001* 3.6 <.0001* 3.4 <.0001* 3.5 <.0001* 3.3 <.0001* 3.5 <.0001* 3.4
82.6 54.6 46.8 43.9 40.7 40.3 39.4 40.7 37.7 37.8 38.6 38.0
59.5 10.0 6.3 5.5 4.8 3.6 4.5 3.6 3.6 4.0 4.4 4.0
1.31 5.48 7.40 8.03 8.54 11.07 8.76 11.36 10.36 9.42 8.68 9.59
Reverse TSA
(1.33-1.45) (4.76-6.31) (6.18-8.86) (6.61-9.76) (6.93-10.53) (8.72-14.06) (7.06-10.86) (8.93-14.45) (8.16-13.16) (7.50-11.83) (6.99-10.78) (7.62-12.06)
RR (95% CI) N-OU OU (n ¼ 2200), (n ¼ 1216), % % 46.7 11.6 6.1 4.9 5.0 4.1 2.7 2.8 3.3 2.8 3.1 2.9
1.70 4.52 7.56 8.21 7.17 8.80 13.30 12.24 10.67 11.61 11.13 11.84
(1.60-1.82) (3.85-5.31) (6.03-9.47) (6.37-10.58) (5.60-9.25) (6.67-11.61) (9.46-18.71) (8.74-17.14) (7.82-14.55) (8.29-16.26) (8.10-15.30) (8.50-16.50)
P value
<.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001*
TSA, total shoulder arthroplasty; OU, preoperative opioid use; N-OU, opioid naive; RR, relative risk; CI, confidence interval. * Significant finding (P < .05).
likely and 3 times as likely to use opioid medications after aTSA and rTSA, respectively. In our study, patients in the OU cohort were nearly 10 times and 12 times as likely to use opioid medications at 1 year after aTSA and rTSA, respectively. These discrepancies between the 2 studies are primarily because of different definitions of preoperative opioid use. We chose to define preoperative opioid use based on prescriptions filled because ICD codes in administrative databases have been shown to have high specificity but low sensitivity in identifying comorbidities including substance abuse or dependence.6,12 Other investigators have used different databases and registries to assess the rate of preoperative opioid use and its impact on postoperative opioid use.29,32 Rao et al29 also used prescriptions filled in their definition of opioid users and found that patients who filled at least 5 opioid prescriptions before
surgery were nearly 10 times more likely to use opioids at 1 year after TSA based on a multi-institutional registry. Identifying significant risk factors for or predictors of postoperative opioid use is of paramount importance in managing opioid utilization, especially for elective procedures such as TSA. In this study, the multivariate model identified several independent risk factors for opioid use at 1 year after TSA. Chronic preoperative opioid use, defined as opioid use between 1 and 3 months prior to surgery, was the strongest predictor of continued use at 1 year after TSA. Using the same definition of chronic preoperative opioid use, Westermann et al39 identified chronic preoperative opioid use as the strongest predictor of opioid use after hip arthroscopy. In addition, our study identified that younger patients (<65 years) had a significantly higher incidence of opioid use 1 year after TSA. This finding is consistent with similar
100 90 80 70 60 50 40 30 20 10 0 1st Month
2nd Month
3rd Month
4th Month
5th Month
6th Month
7th Month
8th Month
Opioid Naïve (N-OU)
Preopera ve Opioid Use (OU)
Chronic Opioid Use (C-OU)
All Reverse TSA
Figure 3
9th Month
10th Month
11th Month
Acute Opioid Use (A-OU)
Monthly opioid use after reverse total shoulder arthroplasty (TSA).
12th Month
Opioid use after total shoulder arthroplasty Table IV
7
Univariate analysis of risk factors for opioid use 12 months after TSA
Risk factors Acute preoperative opioid use Chronic opioid use Age < 65 yr Male sex Obesity Diabetes mellitus Hypertension Congestive heart failure Chronic lung disease Glenohumeral arthritis History of psychiatric diagnosis Fibromyalgia Preoperative substance abuse disorder Ethanol abuse Tobacco use
Anatomic TSA
Reverse TSA
OR (95% CI)
P value
OR 95% CI)
P value
0.71 10.24 2.66 0.77 1.3 1.26 1.37 1.56 3.01 1.21 2.18 2
.0093* <.0001* <.0001* .0002* .0007* .0013* .0008* .0010* <.0001* .0203* <.0001* <.0001*
0.5 10.77 3.84 0.9 1.25 1.08 1.26 1.09 1.79 1.22 2.23 1.75
<.0001* <.0001* <.0001* .179 .0058* .312 .0526 .483 <.0001* .0072* <.0001* <.0001*
(0.54-0.92) (8.79-11.98) (2.27-3.11) (0.67-0.88) (1.12-1.51) (1.09-1.45) (1.14-1.65) (1.19-2.02) (1.39-3.79) (1.03-1.42) (1.76-2.68) (1.70-2.35)
2.48 (1.70-3.61) 1.36 (1.11-1.66)
<.0001* .0023*
(0.38-0.65) (9.05-12.84) (3.15-4.69) (0.77-1.05) (1.07-1.47) (0.93-1.26) (1.00-1.61) (0.85-1.40) (1.44-2.24) (1.06-1.43) (1.82-2.73) (1.47-2.07)
1.6 (1.16-2.18) 1.17 (0.96-1.41)
.0035* .119
TSA, total shoulder arthroplasty; OR, odds ratio; CI, confidence interval. * significant finding (P < .05).
reports in patients undergoing total hip or knee arthroplasties.2-4 Similarly, our study identified a preoperative diagnosis of pain syndromes such as fibromyalgia as a significant predictor of postoperative opioid use. Previous orthopedic, gynecologic, and general surgery literature has reported a significant association between a baseline diagnosis of fibromyalgia and prolonged postoperative opioid
Figure 4
use.14,15,17 However, the most notable finding of this study remains that preoperative opioid utilization status is predictive of prolonged postoperative use. Given these findings, it may be prudent for orthopedic providers to consider these specific aspects of patient history and demographic characteristics when determining postoperative pain regimens. Individualizing these prescriptions has the potential to
Multivariate analysis of independent risk factors associated with opioid use 12 months after total shoulder arthroplasty (TSA).
8 protect patients themselves from the adverse effects of opioids, including dependence. Moreover, the health system at large may benefit from adjusting these practices, as the economic burden of complications of inappropriate opioid use is quite substantial.19,25 Another consideration is the implementation of a multimodal pain-control protocol after surgery. In a prospective cohort study of 150 patients, McLaughlin et al21 found that patients who were randomized to the multimodal analgesic cohort had significantly decreased opioid consumption after TSA compared with those who received opioids and acetaminophen only. They also found that patients in the multimodal analgesic cohort had shorter hospital stays with lower pain scores.21 Ultimately, our results can help inform postoperative management as well as further endeavors to identify effective measures that reduce postoperative opioid consumption. Preoperative opioid consumption not only is a risk factor for prolonged postoperative opioid use but also has been associated with poor patient-reported outcomes (PROs) following various orthopedic procedures.8,23,24,27 Morris et al24 evaluated the impact of preoperative opioid use on PROs in 224 patients undergoing aTSA for end-stage glenohumeral osteoarthritis. They demonstrated that patients with preoperative opioid use had poorer postoperative American Shoulder and Elbow Surgeons and Western Ontario Osteoarthritis of the Shoulder index scores, as well as decreased postoperative range of motion, compared with N-OU patients.24 Morris et al23 also evaluated 68 patients undergoing rTSA for rotator cuff arthropathy and reported similar results. They demonstrated that patients who used opioids preoperatively had inferior pre- and postoperative American Shoulder and Elbow Surgeons and Western Ontario Osteoarthritis of the Shoulder index scores than NOU patients.23 We suspect that patients in the OU cohort may report poorer PROs than the N-OU cohort. Future investigations to validate such a hypothesis would further inform the development of an efficacious, safe, and costeffective postoperative pain regimen in this population. This study has limitations inherent to the use of an administrative, claims-based database that relies on accurate billing and coding. However, any inaccuracies due to coding are unlikely to significantly impact the results of this study,26 especially given the large sample size. Moreover, the retrospective nature of the study limits its conclusion to identifying associations with postoperative opioid use rather than establishing causation. In addition, filling opioid prescriptions was used as a surrogate for opioid use; therefore, it was not possible to determine the exact amount of opioid consumption. Details regarding the reason for preoperative opioid prescriptions were not available, and opioid prescriptions may in fact have been filled for use after surgery. Therefore, the multivariate model analyzed A-OU and C-OU instead of OU to address this limitation. Specific clinical and radiographic factors such as chronicity of symptoms, degree of
Z.M. Khazi et al. osteoarthritis, associated shoulder pathology, and prior shoulder surgery were also not available for analysis. Furthermore, operative factors such as operative time, patient positioning, and complexity of surgery could not be assessed with the database used. Finally, because of the lack of matching between the 2 cohorts, we were unable to control for confounders that were not explicitly assessed in this study. Despite these limitations, the large number of patients in the study provides important information on trends and risk factors for opioid use in patients undergoing TSA.
Conclusion More than 40% of patients undergoing TSA received opioid medications within 3 months before surgery. Chronic preoperative opioid use conferred the highest risk of prolonged postoperative opioid use. Age younger than 65 years, chronic lung disease, fibromyalgia, history of glenohumeral arthritis, and psychiatric diagnosis were independent risk factors for opioid use 1 year following TSA.
Disclaimer The authors, their immediate families, and any research foundations with which they are affiliated have not received any financial payments or other benefits from any commercial entity related to the subject of this article.
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9 23. Morris BJ, Laughlin MS, Elkousy HA, Gartsman GM, Edwards TB. Preoperative opioid use and outcomes after reverse shoulder arthroplasty. J Shoulder Elbow Surg 2015;24:11-6. https://doi.org/10.1016/j. jse.2014.05.002 24. Morris BJ, Sciascia AD, Jacobs CA, Edwards TB. Preoperative opioid use associated with worse outcomes after anatomic shoulder arthroplasty. J Shoulder Elbow Surg 2016;25:619-23. https://doi.org/10. 1016/j.jse.2015.09.017 25. Nicholson BD. Economic and clinical burden of opioid-induced nausea and vomiting. Postgrad Med 2017;129:111-7. https://doi.org/ 10.1080/00325481.2017.1243004 26. O’Malley KJ, Cook KF, Price MD, Wildes KR, Hurdle JF, Ashton CM. Measuring diagnoses: ICD code accuracy. Health Serv Res 2005;40(Pt 2):1620-39. https://doi.org/10.1111/j.1475-6773.2005. 00444.x 27. Pivec R, Issa K, Naziri Q, Kapadia BH, Bonutti PM, Mont MA. Opioid use prior to total hip arthroplasty leads to worse clinical outcomes. Int Orthop 2014;38:1159-65. https://doi.org/10.1007/s00264014-2298-x 28. Pugely AJ, Bedard NA, Kalakoti P, Hendrickson NR, Shillingford JN, Laratta JL, et al. Opioid use following cervical spine surgery: trends and factors associated with long-term use. Spine J 2018;18:1974-81. https://doi.org/10.1016/j.spinee.2018.03.018 29. Rao AG, Chan PH, Prentice HA, Paxton EW, Navarro RA, Dillon MT, et al. Risk factors for postoperative opioid use after elective shoulder arthroplasty. J Shoulder Elbow Surg 2018;27:1960-8. https://doi.org/ 10.1016/j.jse.2018.04.018 30. Rhon DI, Snodgrass SJ, Cleland JA, Sissel CD, Cook CE. Predictors of chronic prescription opioid use after orthopedic surgery: derivation of a clinical prediction rule. Perioper Med (Lond) 2018;7:25. https://doi. org/10.1186/s13741-018-0105-8 31. Rodgers J, Cunningham K, Fitzgerald K, Finnerty E. Opioid consumption following outpatient upper extremity surgery. J Hand Surg Am 2012;37:645-50. https://doi.org/10.1016/j.jhsa.2012.01.035 32. Rosenthal BD, Suleiman LI, Kannan A, Edelstein AI, Hsu WK, Patel AA. Risk factors for prolonged postoperative opioid use after spine surgery: a review of dispensation trends from a state-run prescription monitoring program. J Am Acad Orthop Surg 2019;27:32-8. https://doi.org/10.5435/JAAOS-D-17-00304 33. Shah A, Hayes CJ, Martin BC. Characteristics of initial prescription episodes and likelihood of long-term opioid usedUnited States, 20062015. MMWR Morb Mortal Wkly Rep 2017;66:265-9. https://doi.org/ 10.15585/mmwr.mm6610a1 34. Smith SR, Bido J, Collins JE, Yang H, Katz JN, Losina E. Impact of preoperative opioid use on total knee arthroplasty outcomes. J Bone Joint Surg Am 2017;99:803-8. https://doi.org/10.2106/JBJS.16.01200 35. Soelberg CD, Brown RE Jr, Du Vivier D, Meyer JE, Ramachandran BK. The US opioid crisis: current federal and state legal issues. Anesth Analg 2017;125:1675-81. https://doi.org/10.1213/ ANE.0000000000002403 36. Volkow ND, McLellan TA, Cotto JH, Karithanom M, Weiss SR. Characteristics of opioid prescriptions in 2009. JAMA 2011;305:1299301. https://doi.org/10.1001/jama.2011.401 37. Waljee JF, Zhong L, Hou H, Sears E, Brummett C, Chung KC. The use of opioid analgesics following common upper extremity surgical procedures: a national, population-based study. Plast Reconstr Surg 2016;137:355e-64e. https://doi.org/10.1097/01.prs.0000475788. 52446.7b 38. Westermann RW, Anthony CA, Bedard N, Glass N, Bollier M, Hettrich CM, et al. Opioid consumption after rotator cuff repair. Arthroscopy 2017;33:1467-72. https://doi.org/10.1016/j.arthro.2017. 03.016 39. Westermann RW, Mather RC III, Bedard NA, Anthony CA, Glass NA, Lynch TS, et al. Prescription opioid use before and after hip arthroscopy: a caution to prescribers. Arthroscopy 2019;35:453-60. https:// doi.org/10.1016/j.arthro.2018.08.056
www.elsevier.com/locate/ymse
Risk factors for opioid use after total shoulder arthroplasty Zain M. Khazi, BSa, Yining Lu, BAb, Bhavik H. Patel, BSb, Jourdan M. Cancienne, MDb, Brian Werner, MDc, Brian Forsythe, MDb,* a
Department of Orthopedics and Rehabilitation, University of Iowa Hospitals and Clinics, Iowa City, IA, USA Division of Sports Medicine, Midwest Orthopedics at Rush, Rush University Medical Center, Chicago, IL, USA c Department of Orthopedic Surgery, University of Virginia, Charlottesville, VA, USA b
Hypothesis: The purpose was to assess opioid use before and after anatomic and reverse total shoulder arthroplasty (TSA) and determine patient factors associated with prolonged postoperative opioid use. Methods: Patients undergoing primary TSA (anatomic or reverse) were identified within the Humana database from 2007 to 2015. Patients were categorized as opioid-naive patients who did not fill a prescription prior to surgery or those who filled opioid prescriptions within 3 months preoperatively (OU); the OU cohort was subdivided into those filling opioid prescriptions within 1 month preoperatively and those filling opioid prescriptions between 1 and 3 months preoperatively. The incidence of opioid use was evaluated preoperatively and longitudinally tracked for each cohort. Multivariate analysis was used to identify factors associated with opioid use at 12 months after surgery, with statistical significance defined as P < .05. Results: Overall, 12,038 patients (5180 in OU cohort, 43%) underwent primary TSA during the study period. Opioid use declined after the first postoperative month; however, the incidence of opioid use was significantly higher in the OU cohort than in the opioid-naive cohort at 1 year (31.4% vs. 3.1%, P < .0001). Subgroup analysis revealed a similar decline in postoperative opioid use for anatomic and reverse TSA (P < .0001 for both). Multivariate analysis identified chronic preoperative opioid use (ie, filling an opioid prescription between 1 and 3 months prior to surgery) as the strongest risk factor for opioid use at 12 months after anatomic and reverse TSA (P < .0001). Conclusion: More than 40% of patients undergoing TSA received opioid medications within 3 months before surgery. Preoperative opioid use, age younger than 65 years, and fibromyalgia were independent risk factors for opioid use 1 year following anatomic and reverse TSA. Chronic preoperative opioid use conferred the highest risk of prolonged postoperative opioid use. Level of evidence: Level III; Retrospective Cohort Comparison; Large Database Analysis Ó 2019 Journal of Shoulder and Elbow Surgery Board of Trustees. All rights reserved. Keywords: Anatomic total shoulder arthroplasty; reverse total shoulder arthroplasty; opioid use; shoulder arthritis; Humana database; PearlDiver
This retrospective cohort analysis was granted exemption from the local institutional review board because of the deidentified nature of the data. *Reprint requests: Brian Forsythe, MD, Midwest Orthopaedics at Rush, Division of Sports Medicine, 1611 W Harrison St, Ste 300, Chicago, IL 60612, USA. E-mail address: [email protected] (B. Forsythe).
The use of prescription opioid medication has become increasingly challenging for the orthopedic surgeon in the past decade. Although it is the most effective method to alleviate pain in many scenarios, short-term opioid use is coupled with the danger of dependence and abuse. Whereas
1058-2746/$ - see front matter Ó 2019 Journal of Shoulder and Elbow Surgery Board of Trustees. All rights reserved. https://doi.org/10.1016/j.jse.2019.06.020
2 the number of opioid prescriptions has been decreasing since 2010, the opioid-related mortality rate showed an almost 3fold increase between 2001 and 2016.9,13 The presence of such a massive public health burden demonstrates that the epidemic of therapeutic and recreational opioid use in the United States remains significant and widespread. Chronic pain treatment remains the leading cause of opioid use, with long-term joint pain accounting for almost a third of all firsttime opioid prescriptions.10,33,36 However, the management of postsurgical pain in common orthopedic procedures has also emerged as a significant contributor to opioid prescriptions.36 In a prospective study by Rodgers et al,31 physicians found that patients were prescribed 3 times more opioids than they consumed following upper-extremity orthopedic procedures. Similar results were demonstrated by a study of opioid prescriptions after outpatient upperextremity procedures by Kim et al.18 Indeed, postsurgical opioid prescription and use have become a central focus of orthopedic research in recent years.30,34,37 Consensus guidelines for appropriate postoperative opioid consumption requires establishment of a valid and generalizable model for the course of opioid use. To inform a successful algorithm, it is imperative to have a thorough understanding of factors predicting prolonged and potentially dangerous opioid consumption. Prior studies have evaluated the utilization of opioid analgesics in common orthopedic procedures in the hand, upper extremity, and shoulder.18,20,31,37 Physicians have also investigated the impact of a preoperative diagnosis of opioid abuse or dependence on postoperative consumption in total shoulder arthroplasty (TSA).5 These studies have identified several risk factors independently associated with prolonged postoperative opioid use, including tobacco use, preoperative opioid utilization, and younger age. However, the impact of opioid use at large remains unexplored in patients undergoing anatomic TSA (aTSA) or reverse TSA (rTSA). Therefore, the purpose of this investigation was to determine preoperative and postoperative opioid use while identifying risk factors that predict prolonged postoperative opioid use following TSA. We hypothesized that patients with a history of preoperative opioid use would be associated with prolonged postoperative opioid consumption after TSA.
Z.M. Khazi et al. demographic characteristics, comorbidities, complications, and prescription medication can be retrieved using International Classification of Diseases, Ninth Revision codes (ICD-9); International Classification of Diseases, Tenth Revision codes (ICD10); Current Procedural Terminology codes; and National Drug Codes. The Humana database was used for this study to allow for longitudinal tracking of patients including monthly reports on opioid use before and after surgery.
Patient selection We conducted a retrospective review of patients undergoing primary aTSA and rTSA between 2007 and 2015 in the Humana database using ICD-9 Clinical Modification codes 81.80 and 81.88, respectively. The inclusion criteria were limited to patients who were active within the database for at least 3 months before and 12 months after TSA. Patient demographic characteristics and comorbidities were extracted for each cohort using ICD-9 and ICD-10 codes. Demographic characteristics and medical comorbidities that were compared between cohorts included age (<65 years vs. 65 years), sex, obesity (body mass index 30 kg/m2), tobacco use, diabetes mellitus, hypertension, congestive heart failure, chronic lung disease, fibromyalgia, and psychiatric conditions including major depressive disorder and generalized anxiety disorder. Patient records were subsequently queried for all doses of common oral and transdermal formulations of prescription opioids, except tramadol.16 This included hydromorphone, oxycodone, hydrocodone, fentanyl, methadone, OxyContin (Purdue Pharma, Stamford, CT, USA), propoxyphene, and morphine. Patients were categorized as opioid naive (N-OU) if they never filled opioid prescriptions prior to surgery, whereas patients who filled opioid prescriptions within 3 months prior to surgery were classified as opioid users (OU). Patients were further categorized as chronic preoperative opioid users (C-OU) if they filled an opioid prescription between 1 and 3 months prior to surgery or as acute preoperative opioid users (A-OU) if they only filled an opioid prescription within 1 month before surgery. This delineation was made to exclude patients who may have received opioid medications as part of their preoperative management. In addition, patients who filled opioid prescriptions more than 3 months prior to surgery were not analyzed in the study to avoid including patients who may have filled opioid prescriptions for an unrelated surgery, trauma, or medical condition. For the entire cohort, opioid refills were tracked monthly after the index procedure for the first postoperative year. A postoperative period of 12 months was chosen for this study to minimize bias related to opioid use owing to other conditions or subsequent, unrelated surgical procedures.
Methods
Factors associated with postoperative opioid use
Database
The primary goal of this study was to determine the rate of preoperative and postoperative opioid use in patients undergoing TSA and to identify risk factors associated with opioid use at 12 months following surgery. Subgroup analysis based on the type of procedure (aTSA and rTSA) was conducted to determine the risk of postoperative opioid use between the OU and N-OU cohorts. Independent risk factors for postoperative opioid use were analyzed for the aTSA and rTSA cohorts. Risk factors evaluated included preoperative opioid use and preoperative substance use or abuse
The Humana database (Louisville, KY, USA) was used to identify patients undergoing aTSA or rTSA using the PearlDiver patient records database (Colorado Springs, CO, USA; www. pearldiverinc.com). The PearlDiver patient records database accesses deidentified information from over 20 million private and commercially insured patients, including those with Medicare Advantage through Humana. Patient information including
Opioid use after total shoulder arthroplasty
3
disorders such as tobacco and alcohol use or abuse. Patient demographic characteristics and comorbidities including age, sex, obesity, depression or anxiety disorders, fibromyalgia, diabetes mellitus, hypertension, congestive heart failure, chronic lung disease, and glenohumeral arthritis were also evaluated.
Statistical analysis Descriptive statistics were used to report patient demographic characteristics, medical comorbidities, and monthly postoperative opioid prescription refills. Pearson c2 tests and relative risk (RR) ratios, along with 95% confidence intervals (CIs), were used to compare monthly postoperative opioid refill rates between the OU and N-OU cohorts for the overall TSA population and the aTSA and rTSA populations. Multivariate analysis was used to identify independent risk factors for postoperative opioid refills at 12 months after aTSA and rTSA, with statistical significance set at P < .05. The Hosmer-Lemeshow goodness-of-fit test was also performed for the multivariate model to assess for appropriate calibration with fitness defined as P > .1. All statistical analyses were performed using the open-source R software (R Foundation for Statistical Computing, Vienna, Austria; www.r-project.org) housed within the PearlDiver research database.
Results Patient demographic comorbidities
characteristics
and
Overall, 12,038 patients undergoing primary TSA were identified during the study period, of whom 29.8% (n ¼ 3586) were classified as N-OU patients and 43% (n ¼ 5180) were classified as OU patients, which was further stratified into 32.4% C-OU patients (n ¼ 3903) and 10.6% A-OU patients (n ¼ 1277). Most patients were female patients (n ¼ 7208, 59.9%), aged 65 years or older (n ¼ 10,259, 85.2%), and underwent aTSA (n ¼ 6589, 54.7%). Regarding medical comorbidities, 26.2% of patients (n ¼ 3148) were obese, 35.1% (n ¼ 4222) carried a diagnosis of diabetes mellitus, 83.4% (n ¼ 10,036) had hypertension, 6.9% (n ¼ 883) had congestive heart failure, 7.9% (n ¼ 946) had chronic lung disease, 18.1% (n ¼ 2173) had fibromyalgia, and 9.6% (n ¼ 1156) had depression or anxiety (Table I). Tobacco and alcohol use or abuse was noted in 13.8% of patients (n ¼ 1657) and 3.33% (n ¼ 401), respectively. N-OU patients were more frequently older (age 65 years; 90.2% vs. 79%, P < .0001) and of male sex (45.1% vs. 36.3%, P < .0001) than OU patients. Furthermore, OU patients had a significantly higher incidence of obesity, diabetes mellitus, hypertension, congestive heart failure, chronic lung disease, fibromyalgia, and history of anxiety or depression (P < .0001) than N-OU patients (Table I). The incidence of glenohumeral arthritis was significantly higher in the OU cohort because of the higher incidence of rTSAs in this cohort (rTSAs vs. aTSAs, 64.4% vs. 55%).
Monthly postoperative opioid use Postoperatively, 70.2% of patients filled opioid prescriptions within 1 month of TSA. However, the rate of opioid use decreased to 25.1% at 3 months, 18.9% at 6 months, 17.4% at 9 months, and 16.5% at 12 months postoperatively. Stratification of patients into the OU and N-OU cohorts revealed the following findings (Fig. 1): At 1 month following surgery, 80.3% of OU patients and 54.2% of N-OU patients filled opioid prescriptions. However, the opioid fill rate decreased dramatically to 46% for OU patients and 6.3% for N-OU patients at 3 months. From the sixth to twelfth postoperative months, opioid fill rates plateaued for both cohorts (OU, from 36.5% to 31.4%; N-OU, from 3.8% to 3.1%). Compared with the N-OU cohort, the OU cohort had a significantly higher rate of opioid filling in all postoperative months (P < .0001). At 1 year after surgery, the opioid fill rate was significantly higher in the OU cohort than in the N-OU cohort, with an RR of 10.25 (95% CI, 8.49-12.38; P < .0001) (Table II). Subgroup analysis of postoperative opioid use based on the type of TSA was also conducted. In the aTSA subgroup, the rate of postoperative opioid use was 70.6% at 1 month, 24.3% at 3 months, and 18.9% at 12 months (Fig. 2). When the OU and N-OU cohorts were compared, the OU cohort had a significantly higher rate of opioid filling up to 12 months after aTSA (P < .0001). At 1 year after aTSA, the RR of opioid use was significantly higher in the OU cohort than in the N-OU cohort (38% vs. 4%; RR, 9.59; 95% CI, 7.62-12.06; P < .0001) (Table III). In the rTSA subgroup, postoperative opioid use was 68.4% at 1 month, 26.3% at 3 months, and 18.7% at 12 months (Fig. 3). Compared with the N-OU cohort, the OU cohort had a significantly higher rate of opioid filling for up to 12 months after rTSA (P < .0001). At 1 year after rTSA, the OU cohort had a significantly increased risk of opioid filling compared with the N-OU cohort, with an RR of 11.84 (34.1% vs. 2.9%; 95% CI, 8.5-16.5; P < .0001) (Table III).
Risk factors for postoperative opioid use The univariate analysis identified preoperative opioid use (chronic), chronic lung disease, preoperative glenohumeral arthritis, history of a psychiatric diagnosis, age younger than 65 years, obesity, fibromyalgia, and ethanol abuse as significant risk factors associated with opioid use at 12 months following both aTSA and rTSA (Table IV). The multivariate model, which controlled for patient demographic characteristics and comorbidities, identified chronic preoperative opioid use as having the strongest independent association with opioid use 12 months after aTSA (odds ratio [OR], 10.32; 95% CI, 8.69-12.30; P < .0001) and rTSA (OR, 11.29; 95% CI, 9.24-13.88; P < .0001). Furthermore, acute preoperative opioid use (OR,
4
Z.M. Khazi et al. Table I
Characteristics of patients undergoing TSA based on preoperative opioid use
Patient factors
Overall TSA population (N ¼ 12,038), n (%)
OU cohort (n ¼ 5180), n (%)
N-OU cohort (n ¼ 3586), n (%)
P value
Age 65 yr Sex Male Female Obesity (BMI 30 kg/m2) Diabetes mellitus Hypertension Congestive heart failure Chronic lung disease History of psychiatric diagnosis Fibromyalgia Glenohumeral arthritis Preoperative substance use diagnosis Tobacco use Alcohol use or abuse
10,259 (85.2)
4092 (79)
3234 (90.2)
<.0001*
4830 7208 3148 4222 10,036 883 946 1156 2173 7761
1879 3301 1503 1977 4420 451 574 686 1141 3224
1619 1967 601 1014 2773 152 127 173 393 2329
<.0001*
(40.1) (59.9) (26.2) (35.1) (83.4) (6.9) (7.9) (9.6) (18.1)
1657 (13.8) 401 (3.3)
(36.3) (63.7) (29) (38.2) (85.3) (8.7) (11.1) (13.2) (22) (62.2)
787 (15.2) 225 (4.3)
(45.1) (54.9) (16.8) (28.3) (77.3) (4.2) (3.5) (4.8) (11) (64.9)
<.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* .0097* <.0001* <.0001*
323 (9) 65 (1.8)
TSA, total shoulder arthroplasty; OU, opioid use within 3 months prior to TSA; N-OU, opioid naive; BMI, body mass index. The P values depict differences in patient factors between the OU and N-OU cohorts. * Significant finding (P < .05).
2.54; 95% CI, 1.89-3.39; P < .0001), chronic lung disease (OR, 2.14; 95% CI, 1.62-2.82; P < .0001), age younger than 65 years (OR, 1.83; 95% CI, 1.52-2.21; P < .0001), fibromyalgia (OR, 1.45; 95% CI, 1.20-1.75; P ¼ .0001), and ethanol abuse (OR, 1.64; 95% CI, 1.05-2.56; P ¼ .0296) were independently associated with opioid use at 12 months after aTSA (Fig. 4). Similarly, acute preoperative opioid use (OR, 2.18; 95% CI, 1.58-2.98; P < .0001), preoperative glenohumeral arthritis (OR, 1.19; 95% CI, 1.00-1.42; P ¼ .0479), history of a psychiatric diagnosis (OR, 1.19; 95% CI, 1.14-1.86; P ¼ .0021), age younger than 65 years (OR, 2.57; 95% CI, 2.04-3.24; P < .0001), and fibromyalgia (OR, 1.23; 95% CI, 1.00-1.50; P ¼ .0457) were independently associated with opioid use at 12 months after rTSA (Fig. 4).
Discussion This study found that a relatively high rate of patients (43%) used opioid medications 3 months prior to TSA. It also found that opioid users were more than 10 times more likely to fill opioid prescriptions than were N-OU patients at 1 year following TSA. Most notably, the study identified chronic opioid use as the strongest predictor of prolonged opioid consumption after TSA. The findings of this study thereby help determine the natural course of postoperative opioid use after TSA by identifying significant predictors of prolonged postoperative opioid use in this population. The alarming rise in opioid use and its detrimental societal impact have put a spotlight on opioid utilization, with federal, state, and institutional agencies implementing
100 90 80 70 60 50 40 30 20 10 0 1st Month
2nd Month
3rd Month
4th Month
5th Month
6th Month
7th Month
8th Month
Opioid Naïve (N-OU)
Preoperave Opioid Use (OU)
Chronic Opioid Use (C-OU)
All TSA
Figure 1
9th Month
10th Month
11th Month
Acute Opioid Use (A-OU)
Monthly opioid use after total shoulder arthroplasty (TSA).
12th Month
Opioid use after total shoulder arthroplasty Table II
5
Risk of opioid utilization after TSA
Postoperative month
Preoperative opioid users (n ¼ 5180, 43%), n (%)
Opioid-naive patients (n ¼ 3586, 29.8%), n (%)
RR
1 2 3 4 5 6 7 8 9 10 11 12
4159 2783 2383 2114 1924 1891 1836 1787 1721 1641 1690 1629
1942 377 226 186 170 136 132 112 114 112 122 110
1.48 5.11 7.30 7.87 7.84 9.63 9.63 11.05 10.45 10.14 9.59 10.25
(80.3) (53.7) (46.0) (40.8) (37.1) (36.5) (35.4) (34.5) (33.2) (31.7) (32.6) (31.4)
(54.2) (10.5) (6.3) (5.2) (4.7) (3.8) (3.7) (3.1) (3.2) (3.1) (3.4) (3.1)
P value
95% CI Lower
Upper
1.43 4.63 6.41 6.82 6.74 8.13 8.11 9.17 8.69 8.42 8.02 8.49
1.53 5.64 8.31 9.08 9.11 11.40 11.43 13.31 12.57 12.22 11.47 12.38
<.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001*
TSA, total shoulder arthroplasty; CI, confidence interval; RR, relative risk. * Significant finding (P < .05).
policies to regulate and track opioid-prescribing practices.11,35 Orthopedic surgeons are the third-highest opioid prescribers among physicians in the United States36; therefore, research regarding trends in postoperative opioid use and risk factors associated with opioid use after surgery is emerging in the orthopedic literature. Recently, national databases have been used to identify trends and risk factors for opioid use after various orthopedic surgical procedures including spinal procedures and total joint arthroplasties.3,4,7,16,22,28 Bedard et al3 used the Humana database to investigate trends and risk factors associated with postoperative opioid use in patients undergoing total hip arthroplasty. Similarly to our study, their study found that nearly 40% of patients filled opioid prescriptions within 3 months prior to surgery. Kalakoti et al16 also used the Humana database and found that more than half of the patients filled opioid prescriptions within 3
months prior to lumbar arthrodesis. Other investigations on rotator cuff repair, anterior cruciate ligament reconstruction, and hip arthroscopy have reported similarly high incidences of preoperative opioid use.1,38,39 The use of national databases to identify trends and risk factors for postoperative opioid use following TSA has been conducted recently.5 Berglund et al5 investigated the effect of a preoperative diagnosis of opioid dependence or abuse on opioid utilization after aTSA or rTSA and found that only 2% and 3% of patients undergoing aTSA and rTSA, respectively, had a history of opioid dependence or abuse. On the other hand, our study defined preoperative opioid users as patients who filled opioid prescriptions within 3 months prior to surgery and found that about 43% of patients filled opioid prescriptions before TSA. In addition, Berglund et al found that patients with a diagnosis of opioid dependence or abuse were more than twice as
100 90 80 70 60 50 40 30 20 10 0 1st Month
2nd Month
3rd Month
4th Month
5th Month
6th Month
7th Month
8th Month
Opioid Naïve (N-OU)
Preoperave Opioid Use (OU)
Chronic Opioid Use (C-OU)
All Anatomic TSA
Figure 2
9th Month
10th Month
11th Month
Acute Opioid Use (A-OU)
Monthly opioid use after anatomic total shoulder arthroplasty (TSA).
12th Month
6
Z.M. Khazi et al. Table III
Risk of opioid utilization after TSA by procedure type
Postoperative Anatomic TSA month RR (95% CI) N-OU OU (n ¼ 2280), (n ¼ 1868), % %
P value
1 2 3 4 5 6 7 8 9 10 11 12
<.0001* 79.5 <.0001* 52.5 <.0001* 4.6 <.0001* 4.0 <.0001* 3.6 <.0001* 3.6 <.0001* 3.6 <.0001* 3.4 <.0001* 3.5 <.0001* 3.3 <.0001* 3.5 <.0001* 3.4
82.6 54.6 46.8 43.9 40.7 40.3 39.4 40.7 37.7 37.8 38.6 38.0
59.5 10.0 6.3 5.5 4.8 3.6 4.5 3.6 3.6 4.0 4.4 4.0
1.31 5.48 7.40 8.03 8.54 11.07 8.76 11.36 10.36 9.42 8.68 9.59
Reverse TSA
(1.33-1.45) (4.76-6.31) (6.18-8.86) (6.61-9.76) (6.93-10.53) (8.72-14.06) (7.06-10.86) (8.93-14.45) (8.16-13.16) (7.50-11.83) (6.99-10.78) (7.62-12.06)
RR (95% CI) N-OU OU (n ¼ 2200), (n ¼ 1216), % % 46.7 11.6 6.1 4.9 5.0 4.1 2.7 2.8 3.3 2.8 3.1 2.9
1.70 4.52 7.56 8.21 7.17 8.80 13.30 12.24 10.67 11.61 11.13 11.84
(1.60-1.82) (3.85-5.31) (6.03-9.47) (6.37-10.58) (5.60-9.25) (6.67-11.61) (9.46-18.71) (8.74-17.14) (7.82-14.55) (8.29-16.26) (8.10-15.30) (8.50-16.50)
P value
<.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001* <.0001*
TSA, total shoulder arthroplasty; OU, preoperative opioid use; N-OU, opioid naive; RR, relative risk; CI, confidence interval. * Significant finding (P < .05).
likely and 3 times as likely to use opioid medications after aTSA and rTSA, respectively. In our study, patients in the OU cohort were nearly 10 times and 12 times as likely to use opioid medications at 1 year after aTSA and rTSA, respectively. These discrepancies between the 2 studies are primarily because of different definitions of preoperative opioid use. We chose to define preoperative opioid use based on prescriptions filled because ICD codes in administrative databases have been shown to have high specificity but low sensitivity in identifying comorbidities including substance abuse or dependence.6,12 Other investigators have used different databases and registries to assess the rate of preoperative opioid use and its impact on postoperative opioid use.29,32 Rao et al29 also used prescriptions filled in their definition of opioid users and found that patients who filled at least 5 opioid prescriptions before
surgery were nearly 10 times more likely to use opioids at 1 year after TSA based on a multi-institutional registry. Identifying significant risk factors for or predictors of postoperative opioid use is of paramount importance in managing opioid utilization, especially for elective procedures such as TSA. In this study, the multivariate model identified several independent risk factors for opioid use at 1 year after TSA. Chronic preoperative opioid use, defined as opioid use between 1 and 3 months prior to surgery, was the strongest predictor of continued use at 1 year after TSA. Using the same definition of chronic preoperative opioid use, Westermann et al39 identified chronic preoperative opioid use as the strongest predictor of opioid use after hip arthroscopy. In addition, our study identified that younger patients (<65 years) had a significantly higher incidence of opioid use 1 year after TSA. This finding is consistent with similar
100 90 80 70 60 50 40 30 20 10 0 1st Month
2nd Month
3rd Month
4th Month
5th Month
6th Month
7th Month
8th Month
Opioid Naïve (N-OU)
Preopera ve Opioid Use (OU)
Chronic Opioid Use (C-OU)
All Reverse TSA
Figure 3
9th Month
10th Month
11th Month
Acute Opioid Use (A-OU)
Monthly opioid use after reverse total shoulder arthroplasty (TSA).
12th Month
Opioid use after total shoulder arthroplasty Table IV
7
Univariate analysis of risk factors for opioid use 12 months after TSA
Risk factors Acute preoperative opioid use Chronic opioid use Age < 65 yr Male sex Obesity Diabetes mellitus Hypertension Congestive heart failure Chronic lung disease Glenohumeral arthritis History of psychiatric diagnosis Fibromyalgia Preoperative substance abuse disorder Ethanol abuse Tobacco use
Anatomic TSA
Reverse TSA
OR (95% CI)
P value
OR 95% CI)
P value
0.71 10.24 2.66 0.77 1.3 1.26 1.37 1.56 3.01 1.21 2.18 2
.0093* <.0001* <.0001* .0002* .0007* .0013* .0008* .0010* <.0001* .0203* <.0001* <.0001*
0.5 10.77 3.84 0.9 1.25 1.08 1.26 1.09 1.79 1.22 2.23 1.75
<.0001* <.0001* <.0001* .179 .0058* .312 .0526 .483 <.0001* .0072* <.0001* <.0001*
(0.54-0.92) (8.79-11.98) (2.27-3.11) (0.67-0.88) (1.12-1.51) (1.09-1.45) (1.14-1.65) (1.19-2.02) (1.39-3.79) (1.03-1.42) (1.76-2.68) (1.70-2.35)
2.48 (1.70-3.61) 1.36 (1.11-1.66)
<.0001* .0023*
(0.38-0.65) (9.05-12.84) (3.15-4.69) (0.77-1.05) (1.07-1.47) (0.93-1.26) (1.00-1.61) (0.85-1.40) (1.44-2.24) (1.06-1.43) (1.82-2.73) (1.47-2.07)
1.6 (1.16-2.18) 1.17 (0.96-1.41)
.0035* .119
TSA, total shoulder arthroplasty; OR, odds ratio; CI, confidence interval. * significant finding (P < .05).
reports in patients undergoing total hip or knee arthroplasties.2-4 Similarly, our study identified a preoperative diagnosis of pain syndromes such as fibromyalgia as a significant predictor of postoperative opioid use. Previous orthopedic, gynecologic, and general surgery literature has reported a significant association between a baseline diagnosis of fibromyalgia and prolonged postoperative opioid
Figure 4
use.14,15,17 However, the most notable finding of this study remains that preoperative opioid utilization status is predictive of prolonged postoperative use. Given these findings, it may be prudent for orthopedic providers to consider these specific aspects of patient history and demographic characteristics when determining postoperative pain regimens. Individualizing these prescriptions has the potential to
Multivariate analysis of independent risk factors associated with opioid use 12 months after total shoulder arthroplasty (TSA).
8 protect patients themselves from the adverse effects of opioids, including dependence. Moreover, the health system at large may benefit from adjusting these practices, as the economic burden of complications of inappropriate opioid use is quite substantial.19,25 Another consideration is the implementation of a multimodal pain-control protocol after surgery. In a prospective cohort study of 150 patients, McLaughlin et al21 found that patients who were randomized to the multimodal analgesic cohort had significantly decreased opioid consumption after TSA compared with those who received opioids and acetaminophen only. They also found that patients in the multimodal analgesic cohort had shorter hospital stays with lower pain scores.21 Ultimately, our results can help inform postoperative management as well as further endeavors to identify effective measures that reduce postoperative opioid consumption. Preoperative opioid consumption not only is a risk factor for prolonged postoperative opioid use but also has been associated with poor patient-reported outcomes (PROs) following various orthopedic procedures.8,23,24,27 Morris et al24 evaluated the impact of preoperative opioid use on PROs in 224 patients undergoing aTSA for end-stage glenohumeral osteoarthritis. They demonstrated that patients with preoperative opioid use had poorer postoperative American Shoulder and Elbow Surgeons and Western Ontario Osteoarthritis of the Shoulder index scores, as well as decreased postoperative range of motion, compared with N-OU patients.24 Morris et al23 also evaluated 68 patients undergoing rTSA for rotator cuff arthropathy and reported similar results. They demonstrated that patients who used opioids preoperatively had inferior pre- and postoperative American Shoulder and Elbow Surgeons and Western Ontario Osteoarthritis of the Shoulder index scores than NOU patients.23 We suspect that patients in the OU cohort may report poorer PROs than the N-OU cohort. Future investigations to validate such a hypothesis would further inform the development of an efficacious, safe, and costeffective postoperative pain regimen in this population. This study has limitations inherent to the use of an administrative, claims-based database that relies on accurate billing and coding. However, any inaccuracies due to coding are unlikely to significantly impact the results of this study,26 especially given the large sample size. Moreover, the retrospective nature of the study limits its conclusion to identifying associations with postoperative opioid use rather than establishing causation. In addition, filling opioid prescriptions was used as a surrogate for opioid use; therefore, it was not possible to determine the exact amount of opioid consumption. Details regarding the reason for preoperative opioid prescriptions were not available, and opioid prescriptions may in fact have been filled for use after surgery. Therefore, the multivariate model analyzed A-OU and C-OU instead of OU to address this limitation. Specific clinical and radiographic factors such as chronicity of symptoms, degree of
Z.M. Khazi et al. osteoarthritis, associated shoulder pathology, and prior shoulder surgery were also not available for analysis. Furthermore, operative factors such as operative time, patient positioning, and complexity of surgery could not be assessed with the database used. Finally, because of the lack of matching between the 2 cohorts, we were unable to control for confounders that were not explicitly assessed in this study. Despite these limitations, the large number of patients in the study provides important information on trends and risk factors for opioid use in patients undergoing TSA.
Conclusion More than 40% of patients undergoing TSA received opioid medications within 3 months before surgery. Chronic preoperative opioid use conferred the highest risk of prolonged postoperative opioid use. Age younger than 65 years, chronic lung disease, fibromyalgia, history of glenohumeral arthritis, and psychiatric diagnosis were independent risk factors for opioid use 1 year following TSA.
Disclaimer The authors, their immediate families, and any research foundations with which they are affiliated have not received any financial payments or other benefits from any commercial entity related to the subject of this article.
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