Application of Machine Learning to Identify Risk Factors for Outpatient Opioid Prescriptions Following Spine Surgery

Alexander Bouterse, School of Medicine, Loma Linda University, Loma Linda, CA 92354
Andrew Cabrera, School of Medicine, Loma Linda University, Loma Linda, CA 92354
Adam Jameel, University of California Los Angeles, Los Angeles, CA, 90095
David Chung, Department of Orthopedic Surgery, Loma Linda University Medical Center, Loma Linda, CA 92354
Olumide Danisa, Division of Spine Surgery, Duke University Health, Durham, NC 27710Follow

Abstract

Introduction: Spine surgery is a common source of narcotic prescriptions and carries potential for long-term opioid dependence. As prescription opioids play a role in nearly 25% of all opioid overdose deaths in the United States, mitigating risk for prolonged postoperative opioid utilization is crucial for spine surgeons.

Purpose: The aim of this study was to employ six ML algorithms to identify clinical variables predictive of increased opioid utilization across spinal surgeries, including anterior cervical discectomy and fusion (ACDF), posterior thoracolumbar fusion (PTLF), and lumbar laminectomy.

Methods: A query of the author’s institutional database identified adult patients undergoing ACDF, PTLF, or lumbar laminectomy between 2013 and 2022. Six supervised ML algorithms, including Random Forest, Extreme Gradient Boosting, and LightGBM, were tasked with predicting additional opioid prescriptions at a patient’s first postoperative visit based on set variables. Predictive variables were evaluated for missing data and optimized. Model performance was assessed with common analytical metrics, and variable importance was quantified using permutation feature importance. Statistical analysis utilized Pearson’s

Chi-square tests for categorical and independent sample t-tests for numerical differences.

Results: The author’s query identified 3,202 patients matching selection criteria, with 841, 1,409, and 952 receiving ACDF, PTLF, and lumbar laminectomy, respectively. The ML algorithms produced an aggregate AUC of 0.743, performing most effectively for lumbar laminectomy. Random Forest and LightGBM classifieds were selected for generation of

permutation feature importance (PFI) values. Hospital length of stay was the only highly featured variable carrying statistical significance across all procedures.

Conclusion: Notable risk factors for increased postoperative opioid use were identified, including shorter hospital stays, younger age, and prolonged operative time. These findings can help identify patients at increased risk and guide strategies to mitigate opioid dependence.

Recommended Citation

Bouterse, Alexander; Cabrera, Andrew; Jameel, Adam; Chung, David; and Danisa, Olumide (2024) "Application of Machine Learning to Identify Risk Factors for Outpatient Opioid Prescriptions Following Spine Surgery," BioMedicine: Vol. 14 : Iss. 4 , Article 4.
DOI: 10.37796/2211-8039.1471