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Published on: May 10, 2021
Persistent Opioid Use Following Resection of Oral Cavity Squamous Cell Carcinoma: Incidence and Risk Factors
Cameron C Lee1, Sahil Gandotra1, Aaron Bliss1
1Department of Oral-Maxillofacial Surgery, University of Maryland Medical Center, Baltimore, Maryland, USA.
Persistent opioid use after head and neck cancer surgery is common, affecting 18.8% of patients. Preoperative opioid use is a significant predictor, suggesting early risk assessment is crucial for managing post-surgical pain.
Area of Science:
- Oncology
- Pain Management
- Surgical Outcomes
Background:
- Persistent opioid use following head and neck cancer surgery is an increasing clinical concern.
- Limited data exists on the incidence and risk factors for this issue.
- Oral cavity squamous cell carcinoma (OCSCC) represents a significant subset of head and neck cancers.
Purpose of the Study:
- To determine the incidence of persistent opioid use after primary resection for OCSCC.
- To identify independent predictors of persistent opioid use in this patient population.
- To inform strategies for early risk stratification and improved patient outcomes.
Main Methods:
- Retrospective cohort study of 430 patients undergoing OCSCC resection (2016-2022).
- Preoperative opioid use categorized as naïve, intermittent, or chronic via prescription drug monitoring data.
- Persistent use defined by specific prescription filling criteria (120-365 days postoperatively); regression analysis used for predictor identification.
Main Results:
- 18.8% of patients exhibited persistent opioid use post-surgery.
- Independent predictors included chronic preoperative opioid use, G-tube dependence at 12 months, chronic pain diagnosis, and cancer recurrence.
- The peak incidence of persistent opioid use occurred between 120 and 180 days postoperatively.
Conclusions:
- Persistent opioid use is a frequent complication after oral cancer resection.
- Preoperative opioid use is a strong, independent predictor of persistent postoperative use.
- Implementing early risk stratification models is recommended to mitigate this complication.
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