Clinical StudyMachine learning for prediction of sustained opioid prescription after anterior cervical discectomy and fusion
Introduction
Total US expenditures for prescription opioid abuse exceed $78 billion dollars [1]. In 2016, 11 million US citizens misused opioids whereas drug overdose deaths increased exponentially from 1979 to 2016 [1], [2], [3], [4], [5]. The severity of the opioid crisis has led to increased scrutiny of opioid prescribing practices after surgery. Spine surgery, in particular, has been implicated as a particularly high-risk episode for sustained postoperative opioid use [6], [7].
Previous studies in spine surgery have examined large national administrative and insurance claims databases to identify trends in postoperative opioid use and risk factors for opioid use [8], [9], [10], [11], [12]. Although numerous socio-demographic and clinical characteristics have been identified as prognostic factors for sustained opioid use following spine surgery, no predictive algorithms presently exist for risk stratification of patients before an intervention.
The purpose of this analysis was to develop machine learning algorithms for preoperative prediction of sustained opioid prescription after anterior cervical discectomy and fusion (ACDF). Additional aims of this study were to provide explanations of model predictions at the individual patient level and finally to deploy these algorithms as open access applications for dissemination among the health-care community.
Section snippets
Guidelines
The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis and JMIR guidelines for Developing and Reporting Machine Learning Models in Biomedical Research were followed for this analysis [13], [14].
Data source
Institutional Review Board Approval was granted for retrospective review of electronic health records from two academic medical centers and three community hospitals. Individual patient consent was waived as the study was restricted to retrospective review of
Results
Of 2,737 patients undergoing ACDF, 270 (9.9%) patients were found to meet our criteria for sustained postoperative opioid prescription. Among the population as a whole, 1,440 (52.6%) patients were women and the median age was 51 (interquartile range=44–59) years (Table 1). Before surgery, 695 (25.4%) patients had myelopathy and 992 (36.2%) had radiculopathy. Overall, 2,028 (74.1%) patients had no opioid prescriptions in the year before surgery, 341 (12.5%) had opioid use for less than 180 days
Discussion
In this study, one-tenth of patients undergoing ACDF demonstrated sustained opioid prescription after surgery. The prospect of sustained opioid prescription following surgery appeared to bedriven by a number of factors, including preoperative opioid prescription, antidepressant use, tobacco use, and Medicaid insurance status. Among the algorithms investigated in this analysis, the stochastic gradient boosting algorithm demonstrated the best discrimination, calibration, and overall performance
Conclusions
In this investigation, one-tenth of patients undergoing ACDF demonstrated sustained prescription opioid prescription following surgery. Machine learning algorithms could be used to stratify patient risk and tailor preoperative management (eg, mental health/social work consultation and/or comanagement with pain medicine service) to reduce the potential for long-term opioid use in this population.
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FDA device/drug status: Not applicable.
Author disclosures: AVK: Nothing to disclose. PTO: Nothing to disclose. QCBST: Nothing to disclose. MLDB: Nothing to disclose. TDC: Consulting: GE Healthcare (A, paid directly to institution), NuVasive (C, paid directly to institution), K2M (A, paid directly to institution), Bio2 (A, paid directly to institution). SHH: Nothing to disclose. JM: Nothing to disclose. WCP: Nothing to disclose. AJS: Nothing to disclose. CMB: Wolters Kluwer (A), Elsevier (B). Consulting: United Health Care (B). Other Office: NASS/The Spine Journal (D). Fellowship Support: OMEGA (D, Paid directly to institution/employer). JHS: Nothing to disclose.