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Figure.
Rate of Postoperative Opioid Overdose in Entire Cohort and Stratified by Preoperative Daily Morphine Equivalent Dose
Rate of Postoperative Opioid Overdose in Entire Cohort and Stratified by Preoperative Daily Morphine Equivalent Dose

Error bars indicate 95% CIs.

aOverall opioid overdose rate per 100 000 operations within 30 days of hospital discharge.

Table.  
Characteristics and Procedure Types of Surgical Patients With vs Without Postoperative Opioid Overdose Within 30 Days of Surgical Discharge
Characteristics and Procedure Types of Surgical Patients With vs Without Postoperative Opioid Overdose Within 30 Days of Surgical Discharge
1.
Wilson  EB.  Probable inference, the law of succession, and statistical inference.  J Am Stat Assoc. 1927;22(158):209-212. doi:10.1080/01621459.1927.10502953Google ScholarCrossref
2.
Elzey  MJ, Barden  SM, Edwards  ES.  Patient characteristics and outcomes in unintentional, non-fatal prescription opioid overdoses: a systematic review.  Pain Physician. 2016;19(4):215-228.PubMedGoogle Scholar
3.
Dunn  KM, Saunders  KW, Rutter  CM,  et al.  Opioid prescriptions for chronic pain and overdose: a cohort study.  Ann Intern Med. 2010;152(2):85-92. doi:10.7326/0003-4819-152-2-201001190-00006PubMedGoogle ScholarCrossref
4.
Rowe  C, Vittinghoff  E, Santos  G-M, Behar  E, Turner  C, Coffin  PO.  Performance measures of diagnostic codes for detecting opioid overdose in the emergency department.  Acad Emerg Med. 2017;24(4):475-483. PubMedGoogle ScholarCrossref
Research Letter
August 7, 2018

Opioid Overdose After Surgical Discharge

Author Affiliations
  • 1Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital, Boston, Massachusetts
  • 2Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
JAMA. 2018;320(5):502-504. doi:10.1001/jama.2018.6933

Despite ongoing concerns regarding the prescription opioid epidemic, these medications remain the mainstay of postsurgical pain management. The period after surgical admission represents a potentially vulnerable time when a patient may be using opioids for the first time in an unsupervised setting or require an escalation of their chronic opioid dose. The objective of this study was to determine the frequency of opioid overdose after surgical discharge.

Methods

Data for the study were obtained from the Clinformatics Data Mart (Optum), which includes claims data from a nationwide commercial insurer for approximately 13.5 million beneficiaries yearly. Individuals in the database are representative of patients younger than 65 years with slightly more patients in the south and slightly less in the northeast, which is a function of the market share of the insurer. The use of this deidentified database for research was approved by the Partners institutional review board with a waiver of informed consent.

Adult patients undergoing 1 of 22 prespecified common surgical procedures from 2004 to 2015 and who filled an opioid prescription postoperatively within 3 days of discharge were included in the cohort. Patients with a diagnostic code indicating opioid overdose during the 6 months prior to the operation were excluded.

Patients were defined as having an overdose if they had a hospitalization or emergency department visit with an International Classification of Diseases, Ninth Revision, code indicating an opioid overdose (965.00, 965.02, 965.09, E850.1, E850.2). Overdoses were assessed from the day the opioid prescription was filled until 30 days after discharge. We also examined the frequency of overdose from 31 to 60 days and from 61 to 90 days after surgical discharge to understand the evolution of risk over time. Data were collected on the daily morphine equivalents for all opioids dispensed in the 90 days prior to the procedure. The Wilson Score method1 was used to calculate 95% CIs and comparisons were made using the χ2 and McNemar tests with a 2-sided P value less than .05 to indicate significance. Analyses were conducted in R (R Foundation), version 3.4.1.

Results

The cohort consisted of 1 305 715 patients undergoing an operation who met all inclusion criteria (Table). The mean age was 48.2 years (SD, 15.7) and 68.8% were women. Of the total patient sample, 134 were found to have an opioid overdose within 30 days of surgical discharge (0.01%). The frequency of overdose decreased over time after the operation with 10.3 overdoses (95% CI, 8.7-12.2) per 100 000 surgeries within 30 days after surgical discharge and 3.2 overdoses (95% CI, 2.3-4.3) per 100 000 surgeries within 61 to 90 days after surgical discharge (P < .001) (Figure). The frequency of overdoses increased with increasing amounts of preoperative opioid use with 2.8 overdoses (95% CI, 1.9-4.1) per 100 000 surgeries for opioid-naive patients within 30 days of discharge and 142.5 overdoses (95% CI, 100.4-202.2) per 100 000 surgeries for patients taking more than 100 mg of daily morphine equivalents (P < .001). Overdose rates varied by surgical procedure, with the highest rates observed in patients undergoing lower extremity amputation and spinal fusion.

Discussion

This study demonstrated that opioid overdose after surgical discharge was rare. Patients were at risk of experiencing an overdose after leaving the hospital, especially in the first month. Furthermore, patients using high quantities of opioids preoperatively were at a heightened risk compared with those not receiving high-dose opioid therapy prior to the operation.

Limitations of this study include a lack of information on fatal opioid overdoses outside of the hospital, although this proportion is likely to be small.2,3 Additionally, the overdose codes that were used favored specificity over sensitivity, which may mean that the rates may underestimate the true incidence.4 Patients could have used diverted or nonprescribed opioids, which would not have been captured in the database. The study did not address the initiation of opioid-use disorders after the operation, which is an important consideration when assessing the safety of these medications. The last year included in the study was 2015 and thus the results may not be reflective of current patterns or practice. The use of a commercial insurance database limits generalizability to other populations especially those older than 65 years.

Section Editor: Jody W. Zylke, MD, Deputy Editor.
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Article Information

Accepted for Publication: May 3, 2018.

Corresponding Author: Karim Ladha, MD, MSc, Department of Medicine, Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital, Harvard Medical School, 1620 Tremont St, Ste 3030, Boston, MA 02120 (karim.ladha@uhn.ca).

Author Contributions: Drs Ladha and Bateman had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Ladha, Gagne, Huybrechts, Bateman.

Acquisition, analysis, or interpretation of data: Ladha, Gagne, Patorno, Rathmell, Wang, Bateman.

Drafting of the manuscript: Ladha, Bateman.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Ladha, Wang.

Obtained funding: Bateman.

Administrative, technical, or material support: Huybrechts, Rathmell, Wang, Bateman.

Supervision: Gagne, Patorno, Bateman.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Gagne reported grants from Eli Lilly and Novartis Pharmaceuticals and personal fees from Aetion and Optum. Dr Wang reported being principal investigator on grants from Novartis, Boehringer Ingelheim, and Johnson & Johnson to her institution and consulting for Aetion. No other disclosures were reported.

Funding/Support: This work was supported by grant K08HD075831 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health (Dr Bateman), grant K01MH099141 from the National Institute of Mental Health (Dr Huybrechts), and grant K08AG055670 from the National Institute on Aging (Dr Patorno).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: We thank Jun Liu, MD, MS (Brigham and Women’s Hospital), for assisting with statistical analysis. She did not receive compensation for her contributions.

Disclaimer: This content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

References
1.
Wilson  EB.  Probable inference, the law of succession, and statistical inference.  J Am Stat Assoc. 1927;22(158):209-212. doi:10.1080/01621459.1927.10502953Google ScholarCrossref
2.
Elzey  MJ, Barden  SM, Edwards  ES.  Patient characteristics and outcomes in unintentional, non-fatal prescription opioid overdoses: a systematic review.  Pain Physician. 2016;19(4):215-228.PubMedGoogle Scholar
3.
Dunn  KM, Saunders  KW, Rutter  CM,  et al.  Opioid prescriptions for chronic pain and overdose: a cohort study.  Ann Intern Med. 2010;152(2):85-92. doi:10.7326/0003-4819-152-2-201001190-00006PubMedGoogle ScholarCrossref
4.
Rowe  C, Vittinghoff  E, Santos  G-M, Behar  E, Turner  C, Coffin  PO.  Performance measures of diagnostic codes for detecting opioid overdose in the emergency department.  Acad Emerg Med. 2017;24(4):475-483. PubMedGoogle ScholarCrossref
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