A Predictive Model for Developing Long-Term Opioid Use after Neurosurgery and Orthopedic Surgery

Blaire Bemel, Nicholas Stalter, Michelle A. Mathiason, Ratan Banik, Lisiane Pruinelli

Research output: Contribution to journalArticlepeer-review


This study aimed to identify patient characteristics that predict long-term opioid use after an orthopedic or neurosurgery procedure. Long-term opioid use was defined as opioid use for 90 or more days following the surgical procedure. A retrospective analysis was conducted of orthopedic and neurosurgery patients 18 years and older from 01/01/2011 through 12/31/2017 (n = 12,301). Characteristics included age, sex, race, length of hospital stay, body mass index, surgical procedure specialty, presence of opioid use before and after surgery, and opioid use 90 days or more after surgery. A multiple logistic regression model was used to model characteristics predictive of long-term use of opioids. In this cohort, 32.0% of patients had prescriptions for opioids 90 or more days after surgery. Statistically significant risk factors for long-term opioid use were being Caucasian, younger (18-25 years age group) or older than age 45 and being obese. People who were African American or Black, in the 25-45 years age group, underweight, and used opioids before surgery were less likely to use opioids 90 days after surgery. Nurse anesthetist awareness of predictive characteristics of long-term opioid use can lead to alternative options to prevent opioid abuse.

Original languageEnglish (US)
Pages (from-to)114-120
Number of pages7
JournalAANA Journal
Issue number2
StatePublished - Apr 2022

Bibliographical note

Publisher Copyright:
© 2022 AANA Publishing Inc.. All rights reserved.


  • Anesthesiology
  • long-term opioid use
  • neurosurgery
  • nursing informatics
  • orthopedic surgery
  • predictive characteristics
  • Humans
  • Middle Aged
  • Opioid-Related Disorders/epidemiology
  • Neurosurgery
  • Analgesics, Opioid/therapeutic use
  • Orthopedic Procedures
  • Retrospective Studies

PubMed: MeSH publication types

  • Journal Article


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