Machine Learning for Prediction of Sustained Opioid Prescription After Anterior Cervical Discectomy and Fusion

Publication date: Available online 30 January 2019

Source: The Spine Journal

Author(s): Aditya V. Karhade, Paul T. Ogink, Quirina C.B.S. Thio, Marike L.D. Broekman, Thomas D. Cha, Stuart H. Hershman, Jianren Mao, Wilco C. Peul, Andrew J. Schoenfeld, Christopher M. Bono, Joseph H. Schwab

Abstract

BACKGROUND CONTEXT

The severity of the opioid epidemic has increased scrutiny of opioid prescribing practices. Spine surgery is a high-risk episode for sustained postoperative opioid prescription.

PURPOSE

To develop machine learning algorithms for preoperative prediction of sustained opioid prescription after anterior cervical discectomy and fusion (ACDF).

STUDY DESIGN/SETTING

Retrospective, case-control study at two academic medical centers and three community hospitals

PATIENT SAMPLE

Electronic health records were queried for adult patients undergoing ACDF for degenerative disorders between January 1^st, 2000 and March 1^st, 2018.

OUTCOME MEASURES

Sustained postoperative opioid prescription was defined as uninterrupted filing of prescription opioid extending to at least 90-180 days after surgery.

METHODS

Five machine learning models were developed to predict postoperative opioid prescription and assessed for overall performance.

RESULTS

Of 2,737 patients undergoing ACDF, 270 (9.9%) demonstrated sustained opioid prescription. Variables identified for prediction of sustained opioid prescription were male sex, multilevel surgery, myelopathy, tobacco use, insurance status (Medicaid, Medicare), duration of preoperative opioid use, and medications (anti-depressants, benzodiazepines, beta-2-agonist, angiotensin-converting enzyme-inhibitors, gabapentin). The stochastic gradient boosting algorithm achieved the best performance with c-statistic = 0.81 and good calibration. Global explanations of the model demonstrated that preoperative opioid duration, anti-depressant use, tobacco use, and Medicaid insurance were the most important predictors of sustained postoperative opioid prescription.

CONCLUSION

One-tenth of patients undergoing anterior cervical discectomy and fusion demonstrated sustained opioid prescription following surgery. Machine learning algorithms could be used to preoperatively stratify risk these patients, possibly enabling early intervention to reduce the potential for long-term opioid use in this population.

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