首页|Machine Learning to Predict Successful Opioid Dose Reduction or Stabilization After Spinal Cord Stimulation
Machine Learning to Predict Successful Opioid Dose Reduction or Stabilization After Spinal Cord Stimulation
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NSTL
BACKGROUND: Spinal cord stimulation (SCS) effectively reduces opioid usage in some patients， but preoperatively， there is no objective measure to predict who will most benefit。 OBJECTIVE: To predict successful reduction or stabilization of opioid usage after SCS using machine learning models we developed and to assess if deep learning provides a significant benefit over logistic regression (LR)。 METHODS: We used the IBM MarketScan national databases to identify patients undergoing SCS from 2010 to 2015。 Our models predict surgical success as defined by opioid dose stability or reduction 1 year after SCS。 We incorporated 30 predictors， primarily regarding medication patterns and comorbidities。 Two machine learning algorithms were applied: LR with recursive feature elimination and deep neural networks (DNNs)。 To compare model performances， we used nested 5-fold cross-validation to calculate area under the receiver operating characteristic curve (AUROC)。
Syed M. Adil、Lefko T. Charalambous、Shashank Rajkumar、Andreas Seas、Pranav I. Warman、Kelly R. Murphy、Shervin Rahimpour、Beth Parente、Rajeev Dharmapurikar、Timothy W. Dunn、Shivan P. Lad
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Department of Neurosurgery, Duke University Medical Center, Durham, North Carolina, USA
NSTL
