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SELECTED PROCEEDINGS FROM THE 2019 MUSCULOSKELETAL INFECTION SOCIETY MEETING (GUEST EDITOR CHARALAMPOS G. ZALAVRAS MD, PHD)

Opioid Use Disorder Is Associated with an Increased Risk of Infection after Total Joint Arthroplasty: A Large Database Study

Sodhi, Nipun MD; Anis, Hiba K. MD; Acuña, Alexander J. MD; Vakharia, Rushabh M. MD; Gold, Peter A. MD; Garbarino, Luke J. MD; Mahmood, Bilal M. MD; Ehiorobo, Joseph O. MD; Grossman, Eric L. MD; Higuera, Carlos A. MD; Roche, Martin W. MD; Mont, Michael A. MD

Author Information
Clinical Orthopaedics and Related Research: August 2020 - Volume 478 - Issue 8 - p 1752-1759
doi: 10.1097/CORR.0000000000001390
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Abstract

Introduction

Despite the ongoing opioid epidemic, opioid analgesics still are frequently prescribed to patients after orthopaedic surgery [8, 9, 12]. But opioids also are frequently given for preoperative pain relief in patients undergoing total joint arthroplasty (TJA), with as many as 39% of patients using opioids before surgery [1, 14]. Chronic preoperative opioid use has been associated with several adverse postoperative events, including decreased pain relief, higher risk of revision surgeries, prolonged length of stay, and increased mortality and morbidity [3, 11, 15, 16, 19, 24, 25, 30, 32]. Notably, one systematic review of 7356 patients undergoing TJA found that patients with preoperative opioid use had worse absolute postoperative patient-reported satisfaction than did those without preoperative opioid use [11]. Another recent database study identified preoperative opioid use to be associated with higher odds of prosthetic joint infections (odds ratio 1.55 [95% CI 1.23 to 1.94), wound complications (OR 1.40 [95% CI 1.12 to 1.76]), and revision surgery (OR 1.47 [95% CI 1.19 to 1.81]) [31]. Other studies has also found opioid use disorder to be associated with increased risks of periprosthetic joint infections (PJIs) at 2 years (adjusted OR 1.53 [95% CI 1.14 to 2.05]) [2] and at 1 year after surgery (OR 1.16 [95% CI 1.11 to 1.22]) [7]. Given these data, evaluating the relationship between opioids and specific complications can help providers more accurately forecast results for this patient population.

Considering the available studies that found an association between antecedent opioid use and infection after TJA, additional efforts are still needed to help complete the picture regarding the association of opioid use disorder and infection. The study by Wilson et al. [31] had a relatively small sample size, fewer than 10,000 patients, only evaluated in-patient data, and was restricted to a 90-day follow-up. Additionally, the study by Bell et al. [2] was also limited in that it too had a relatively small sample size of fewer than 24,000 patients. Further, Bell et al. [2] only evaluated patients at a single institution, which limits the overall demographics of patients analyzed and, therefore, the generalizability of the study. Similarly, the study by Cancienne et al. [7] was limited in the duration of its follow-up to only 1 year after surgery.

We therefore asked: Are patients with opioid use disorder who undergo (1) primary THA, (2) primary TKA, (3) revision THA, or (4) revision TKA at a higher risk of experiencing surgical site infections (SSIs) 90 days after surgery or periprosthetic joint infections (PJIs) 2 years after surgery than those who do not have opioid use disorder?

Patients and Methods

Database

We retrospectively queried the 100% Medicare Analytical Files of the PearlDiver supercomputer (PearlDiver Technologies, Fort Wayne, IN, USA) for procedures performed between January 1, 2005 and March 31, 2014. This database, one of the largest nationwide, comprehensively and longitudinally tracks patients based on all insurance claims rather than particular hospital visits, and has a low error rate (estimated to be 1.3% based on reported coding errors in the Medicare population [6, 21]). To mitigate Type 2 errors, when evaluating rare postoperative complications, such as PJI and SSI, large numbers of patients are required, which can best come from national databases. Furthermore, because these data come from a national database, the data best reflect postoperative infections in the United States and are therefore highly generalizable. Through the use of ICD-9 and CPT codes, this database can identify and provide information on more than 100 million patients with Medicare and Humana claims. Among various other metrics, the database provides information regarding patient diagnoses, complications, discharge dispositions, reimbursements, and other metrics.

Our analysis was deemed exempt from institutional review board review because all patient data available in this database are deidentified and publicly available.

Study Population

Using Boolean command operations (and, or, not, or and not), we identified a study group that consisted of all patients with a diagnosis of opioid use disorder undergoing primary or revision TJA. Patients without opioid use disorder undergoing primary or revision TJA served as controls. The database was initially queried using ICD-9 codes 81.51, 81.54, 81.53, and 81.55 to identify patients undergoing THA, TKA, revision THA, and revision TKA, respectively. Similarly, the following CPT procedural codes were used: 27130, 27447, 27487, and 27137 for THA, TKA, revision THA, and revision TKA, respectively. We queried for patients with opioid use disorder using ICD-9 diagnosis codes 304.00 to 304.02 and 305.50 to 305.52, as has been done in previously published studies [26, 27]. Patients who had a documented concomitant documented diagnosis of opioid use disorder were included in the opioid use disorder cohort. To reduce the effects of confounding, study patients were matched to controls in a 1:1 ratio by age, sex, and comorbidity burden as assessed by the Elixhauser comorbidity index (ECI). The ECI consists of the following comorbidities: congestive heart failure, cardiac arrhythmias, valvular disease, pulmonary circulation disorders, peripheral vascular disorders, hypertension-uncomplicated, hypertension-complicated, paralysis, neurological disorders, chronic pulmonary disease, diabetes-uncomplicated, diabetes-complicated, hypothyroidism, renal failure, liver disease, peptic ulcer disease, AIDS/HIV, lymphoma, metastatic cancer, solid tumor without metastasis, rheumatoid arthritis/collagen vascular diseases, coagulopathy, obesity, weight loss, fluid and electrolyte disorders, blood loss anemia, iron-deficiency anemia, alcohol abuse, drug abuse, psychoses, depression [18].When querying the database for patients, we used the parameters listed above to identify study patients, from which we also extracted the 1:1 matched control cohort. This computer-generated extraction of control patients was based on an algorithm written for the database. Control patients were not manually selected but were chosen by the PearlDiver computer to match the study cohort most closely. The query yielded 14,944 patients undergoing primary THA (opioid use disorder: n = 7472; control: n = 7472) and 23,680 patients undergoing primary TKA (opioid use disorder: n = 11,840; control: n = 11,840) with half of each of those groups consisting of patients with opioid use disorder and half without opioid use disorder. Similarly, 7592 revision TKAs (opioid use disorder: n = 3796; control: n = 3796) and 8116 revision THAs (opioid use disorder: n = 4058; control: n = 4058) were identified (Fig. 1).

Fig. 1
Fig. 1:
This STROBE diagram reflects patient selection, starting from the initial Medicare analytical files query. In this study, we included only primary and revision THAs and TKAs and filtered for patients with opioid use disorder and matched controls.

There were no differences in age, sex, or Elixhauser comorbidity index score between the opioid use disorder and control cohorts for all procedures. For the revision THA cohort, 72% of the patients were age 64 and younger, 55% were female, and the mean ECI was 12 (Table 1). For the revision TKA cohort, 74% of the patients were age 64 and under, 60% were female, and the mean ECI was 12 (Table 2). For the primary TKA cohort, 70% of the patients were age 64 and under, 68% were female, and the mean ECI was 11 (Table 3). For the primary THA cohort, 69% of the patients were age 64 and under, 57% were female, and the mean ECI was 11 (Table 4).

Table 1.
Table 1.:
Revision THA demographics (opioid use disorder = 4058; controls = 4058)
Table 2.
Table 2.:
Revision TKA demographics (opioid use disorder = 3796; controls = 3796)
Table 3.
Table 3.:
Primary TKA and opioid use disorder (11,840) and controls (11,840)
Table 4.
Table 4.:
Primary THA opioid use disorder (7472) and controls (7472)

Measured Outcomes

The measured outcomes were SSIs within 90 days and PJIs within 2 years of the index procedure. We chose 90 days for SSIs because that is the CDC-recommended period for making a diagnosis [5]. For PJIs, the 2-year postoperative time was guided by findings reported by Huotari et al. [13]. SSIs were defined based on ICD-9 and CPT codes: ICD-9-D-68100, CPT-10180, CPT-20005, CPT-26055, ICD-9-P-8604, CPT-10140. PJIs were defined based on ICD-9 code ICD-9-D-996.66.

Data Analysis

We compared baseline characteristics between the groups to verify that the opioid use disorder and control groups were matched appropriately. Patient age and sex were compared between the cohorts using Pearson’s chi-square tests, and the mean Elixhauser comorbidity index scores were compared between cohorts using Welch’s t tests. We performed a logistic regression analysis to calculate the risk of infection (SSI or PJI) in patients with opioid use disorder compared with the matched control patients without opioid use disorder. We used the t-test for Elixhauser comorbidity index because we wanted to compare the means of the scores.

A p value less than 0.05 was considered statistically significant. Statistical analyses were performed using the programming language R (R Foundation for Computation Science, Vienna, Austria).

Results

Primary THA

A higher proportion of patients with opioid use disorder who underwent primary THA developed SSI by 90 days after surgery than did patients without opioid use disorder (4% [293 of 7472] versus 3% [198 of 7472], OR 1.5 [95% CI 1.2 to 1.8]; p < 0.001). Similarly, a higher proportion of patients with opioid use disorder who underwent primary THA developed PJI by 2 years after surgery than did patients without opioid use disorder (6% [460 of 7472] versus 4% [283 of 7472], OR 1.66 [95% CI 1.43 to 1.93]; p < 0.001).

Primary TKA

A higher proportion of patients with opioid use disorder who underwent primary TKA developed SSIs by 90 days after surgery than did patients without opioid use disorder (4% [419 of 11,840] versus 2% [247 of 11,840], OR 1.7 [95% CI, 1.5 to 2.0]; p < 0.001). Similarly, a higher proportion of patients with opioid use disorder who underwent primary TKA developed PJIs by 2 years after surgery than did patients without opioid use disorder (6% [662 of 11,840] versus 4% [511 of 11,840], OR 1.3 [95% CI 1.2 to 1.5]; p < 0.001).

Revision THA

A higher proportion of patients with opioid use disorder who underwent revision THA developed SSIs by 90 days after surgery than did patients without opioid use disorder (7% [271 of 4058] versus 4% [148 of 4058], OR 1.9 [95% CI 1.5 to 2.3]; p < 0.001). Similarly, a higher proportion of patients with opioid use disorder who underwent revision THA developed PJIs by 2 years after surgery than did patients without opioid use disorder (23% [951 of 4058] versus 7% [273 of 4058], OR 4.2 [95% CI 3.7 to 4.9]; p < 0.001).

Revision TKA

A higher proportion of patients with opioid use disorder who underwent revision TKA developed SSIs by 90 days after surgery than did patients without opioid use disorder (8% [283 of 3796] versus 4% [156 of 3796], OR 1.9 [95% CI 1.5 to 2.3]; p < 0.001). Similarly, a higher proportion of patients with opioid use disorder who underwent primary TKA developed PJIs by 2 years after surgery than did patients without opioid use disorder (24% [905 of 3796] versus 6% [226 of 3796], OR 4.9 [95% CI 4.2 to 5.8]; p < 0.001).

Discussion

Because the opioid epidemic continues to be a serious public health issue, a thorough understanding of the effect of opioids on patient outcomes is crucial for targeted improvements in perioperative care. Specifically, although other studies have identified a potential association between opioid usage and postoperative infections, these studies have lacked sample size, follow-up duration, and generalizability [2, 7, 31]. We found that SSI and PJI rates were higher in patients with opioid use disorders who underwent primary and revision TJA at 90 days and 2 years, respectively, than in matched controls. Therefore, based on these data, patients who have diagnosed opioid use disorder should undergo preoperative opioid use counseling to help lower their usage before their elective surgery and mentally prepare them for expected post-surgical pain. Additionally, based on these findings, healthcare systems and/or administrators should recognize the increased associated PJI and SSI risks in patients with opioid use disorder, and enact clinical policies that reflect these associated risks. In addition, given these findings and the frequency with which opioids are prescribed for joint pain control, surgeons should be mindful when gathering a patient’s history to determine if their patient is opioid dependent. These findings should also encourage surgeons to pursue multidisciplinary approaches to help patients reduce their opioid consumption before their arthroplasty procedure. The approaches include working with pain management specialists, who can help control local pain [20], mental health professionals, who can help address psychological stressors [29], and physical therapists who can help build muscle strength potentially reducing pain [28]. The results of our study could be a function of the specific direct risks of opioids or could be due to the assortment of confounders that, taken together, seem to increase the infection risk. Surgeons should discuss these risks with patients before surgery.

Limitations

Our study has some limitations. Data extracted from a large administrative database are subject to potential coding errors. However, with a sizable study population of 54,572 patients, and a low reported coding error of 1.3% (based on reported coding errors in the Medicare population [6, 21]), this is unlikely to have a substantial effect on our findings. Additionally, although patients in the opioid use disorder cohort had documented diagnosis, we did not control for the type of medication, frequency, or duration of opioid use. Our findings should be considered along with these limitations; however, they are not disqualifying problems. Although medication type, frequency, and duration of use are important, it is more important to create an overall baseline association between opioid use disorder and infection as this study does. Additionally, these findings can still direct new strategies in patient care, as described above. Furthermore, preoperative opioid use may have been due to a more severe health issue or preoperative joint condition, which could affect the risk of adverse outcomes. Finally, we note that the database used does not provide information regarding the distribution of ECI scores; because of large numbers, we assumed a normal distribution and compared means with t-tests. We believe this is unlikely to bias the findings in any serious way.

Primary Lower Extremity Arthroplasty

The data from this study reveal that opioid use disorder is associated with an increased risk of postoperative SSI and PJI after primary TJA. One study [2] evaluated the risk of PJI in 23,754 patients undergoing THA and TKA and found the risk to be higher in patients who reported preoperative opioid use than in controls who did not use preoperative opioids. Additionally, the group found opioid use to be an independent risk factor for 2-year PJIs. Our study expands on these findings by including data from revision THA and TKA surgery, additionally evaluating SSIs, and using a study cohort comprised of patients from across the country rather than a single institution. Another study [19] analyzed 15,901 patients with preoperative opioid use disorder who underwent elective orthopaedic surgery, and after adjusting for the procedure type, comorbidity burden, various patient demographics, and hospital characteristics, opioid abuse and dependence were associated with increased adjusted odds for infections by more than 50% in TJAs. Although this study also used a large database, the reach of the database is limited to only 25% of US hospitals, and the definition used to identify postoperative infections was potentially limited. Another administrative claims database study also identified preoperative opioid use to be associated with increased SSI (hazard ratio 1.35 [95% CI 1.14 to 1.59]; p < 0.001) [4]. However, this study not only exclusively compared total hip and knee replacement but also included shoulder arthroplasty as part of their study cohort. This multicenter study allows the evaluation of the relationship between opioid use and infection in a large population with longer term follow-up and, unlike most prior studies, evaluates this relationship for each procedure type separately rather than a combined cohort for more accurate analyses.

Revision Lower Extremity Arthroplasty

Opioid use disorder was also found to be associated with increased 90-day SSI and 2-year PJI risk. An observational study by Gonzales et al. [10] reported the incidences of opioid-related adverse events were higher after revision TKA compared with after primary TKA, which is in keeping with our findings. Specifically, the authors found that 90-day respiratory complication rates, the most common complication, were 6.12% after primary TKA and 8.01% after revision TKA. Although it is known that there is a high incidence of opioid use in the revision arthroplasty patient population [22], to our knowledge, there is no prior evidence evaluating the infection risk after revision TJA with preoperative opioid use. Moreover, although there is an abundance of evidence on the effect of opioids on complications overall after primary TJA, there is comparatively little evidence for revision TJA. The lack of evidence on the effect opioids in revision arthroplasty may be due in part to the substantially smaller patient population. Nevertheless, just as with primary TJA patients, revision TJA patients should be provided with accurate information about the complication risks that are specific to their procedure to allow for a more thorough shared decision-making process preoperatively. This study is the first to show that there is an association between opioid use disorder and higher infection risk after revision TJA. Furthermore, the PJI risk with opioid use disorder was demonstrated to be higher in revision THA and TKA patients (OR 4.2 and OR 4.9, respectively) compared with primary THA and TKA (OR 1.7 and OR 1.3, respectively). Future studies to determine the cause of these associations are warranted to help mitigate infection risk with targeted interventions.

Conclusions

Based on these data, surgeons might adjust the way they manage their patients’ preoperative pain, favoring nonopioid-based strategies. Some studies have identified the advantages of opioid reduction wellness visits or use of gabapentinoids [17, 23]. Similarly, healthcare systems and/or policy makers should also consider these findings to encourage multidisciplinary approaches, such as comanagement with pain management, mental health, and physical therapy specialists, to help patients reduce their preoperative opioid consumption. Future studies should build on these associative findings to determine if a true cause-effect link exists between opioid use disorder and SSI/PJI. These studies should highlight any potential temporal or dose-response relationships.

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