Aditya V Karhade1, Paul T Ogink1, Quirina C B S Thio1, Marike L D Broekman2, Thomas D Cha1, Stuart H Hershman1, Jianren Mao3, Wilco C Peul2, Andrew J Schoenfeld4, Christopher M Bono1, Joseph H Schwab5. 1. Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. 2. Neurosurgical Center Holland, Leiden University MC & Haaglanden MC & HAGA Teaching Hospital, Leiden, the Netherlands. 3. Divison of Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. 4. Department of Orthopedic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. 5. Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: jhschwab@mgh.harvard.edu.
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, 2000 and March 1, 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 (antidepressants, 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, antidepressant use, tobacco use, and Medicaid insurance were the most important predictors of sustained postoperative opioid prescription. CONCLUSIONS: One-tenth of patients undergoing ACDF 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.
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, 2000 and March 1, 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 (antidepressants, 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, antidepressant use, tobacco use, and Medicaid insurance were the most important predictors of sustained postoperative opioid prescription. CONCLUSIONS: One-tenth of patients undergoing ACDF 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.
Authors: Iraklis E Tseregounis; Daniel J Tancredi; Susan L Stewart; Aaron B Shev; Andrew Crawford; James J Gasper; Garen Wintemute; Brandon D L Marshall; Magdalena Cerdá; Stephen G Henry Journal: Med Care Date: 2021-12-01 Impact factor: 2.983
Authors: Ingwon Yeo; Christian Klemt; Christopher M Melnic; Meghan H Pattavina; Bruna M Castro De Oliveira; Young-Min Kwon Journal: Arch Orthop Trauma Surg Date: 2022-08-22 Impact factor: 2.928
Authors: Katherine E Pierce; Bhaveen H Kapadia; Sara Naessig; Waleed Ahmad; Shaleen Vira; Carl Paulino; Michael Gerling; Peter G Passias Journal: Int J Spine Surg Date: 2021-12
Authors: Hannah A Levy; Brian A Karamian; Jeffrey Henstenburg; Joseph Larwa; Jose A Canseco; Brett Haislup; Michael Chang; Parthik Patel; Kris E Radcliff; Barrett I Woods; Mark F Kurd; Alan S Hilibrand; Christopher K Kepler; Alexander R Vaccaro; Gregory D Schroeder Journal: J Orthop Date: 2021-06-30
Authors: Andrew Ward; Elizabeth De Souza; Daniel Miller; Ellen Wang; Eric C Sun; Nicholas Bambos; T Anthony Anderson Journal: Anesth Analg Date: 2020-10 Impact factor: 6.627