Prescription Patterns and Risk Factors for Prolonged Opioid Dependence in Elective Anterior Cruciate Ligament Reconstruction in a Military Population

Ashley B Anderson; George C Balazs; Daniel I Brooks; Benjamin K Potter; Jonathan A Forsberg; Jonathan F Dickens

doi:10.1177/2325967120926489

Prescription Patterns and Risk Factors for Prolonged Opioid Dependence in Elective Anterior Cruciate Ligament Reconstruction in a Military Population

Orthop J Sports Med. 2020 Jun 29;8(6):2325967120926489. doi: 10.1177/2325967120926489. eCollection 2020 Jun.

Authors

Ashley B Anderson^{1

2}, George C Balazs³, Daniel I Brooks¹, Benjamin K Potter^{1

2}, Jonathan A Forsberg^{1

2

4}, Jonathan F Dickens^{1

2}

Affiliations

¹ Department of Surgery Division of Orthopaedics, Walter Reed National Military Medical Center, Bethesda, Maryland, USA.
² Uniformed Services University, Bethesda, Maryland, USA.
³ Naval Medical Center Portsmouth, Portsmouth, Virginia, USA.
⁴ Department of Orthopaedic Surgery, Johns Hopkins University, Baltimore, Maryland, USA.

Abstract

Background: Limited data are available regarding excessive opioid prescribing in the perioperative period after routine orthopaedic procedures in US military personnel.

Purpose: To examine the demographic profile of the patients receiving these medications and to identify potential risk factors for prolonged opioid use after anterior cruciate ligament reconstruction (ACLR) in the active duty military population.

Study design: Case-control study; Level of evidence, 3.

Methods: The Military Analysis and Reporting Tool (M2) was used to search the Military Health System Data Repository (MDR) for patients undergoing ACLR from 2012 through 2015 and specifically for active duty personnel with an arthroscopically assisted ACLR (Current Procedural Terminology [CPT] code 29888). Complete opioid prescription filling history was also obtained. This study had 2 primary outcomes: (1) use of opiate analgesics more than 90 days after surgery, representing prolonged opiate prescriptions, and (2) high levels of postoperative opiate use, defined as having filled prescriptions accounting for greater than the 95th percentile of morphine equivalents for patients in the study cohort. Data were analyzed via multivariate regression analysis to identify potential associations with the primary outcomes.

Results: A total of 9474 patients met the inclusion criteria. Median patient age was 27 years, and the sample included 1316 (14%) female and 8158 (86%) male patients. A total of 66 (0.7%) patients had a preoperative diagnosis for substance abuse; 2656 (28%) patients continued to receive opioid prescriptions more than 90 days after surgery, and 502 (5%) patients were in the top 95th percentile of all opioid users within the study cohort. Total preoperative morphine equivalents per day and total perioperative morphine equivalents per day were highly important risk factors for both outcomes, although other demographic factors such as race, sex, and age may play minor roles.

Conclusion: We identified total preoperative morphine equivalents, total perioperative morphine equivalents, sex, and race as potential predictors of prolonged opioid use after ACLR. This information may prove useful in developing a predictive model to identify at-risk patients before surgery. This could help mitigate future misuse or abuse and improve preoperative patient counseling regarding pain management expectations.

Keywords: ACL reconstruction; big data; opioid epidemic; opioid use disorders; opioids; perioperative risk management.