Machine Learning to Predict Successful Opioid Dose Reduction or Stabilization After Spinal Cord Stimulation.

Journal Article (Journal Article)

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). RESULTS: The final cohort included 7022 patients, of whom 66.9% had successful surgery. Our 5-variable LR performed comparably with the full 30-variable version (AUROC difference <0.01). The DNN and 5-variable LR models demonstrated similar AUROCs of 0.740 (95% CI, 0.727-0.753) and 0.737 (95% CI, 0.728-0.746) ( P = .25), respectively. The simplified model can be accessed at . CONCLUSION: We present the first machine learning-based models for predicting reduction or stabilization of opioid usage after SCS. The DNN and 5-variable LR models demonstrated comparable performances, with the latter revealing significant associations with patients' pre-SCS pharmacologic patterns. This simplified, interpretable LR model may augment patient and surgeon decision making regarding SCS.

Full Text

Duke Authors

Cited Authors

  • Adil, SM; Charalambous, LT; Rajkumar, S; Seas, A; Warman, PI; Murphy, KR; Rahimpour, S; Parente, B; Dharmapurikar, R; Dunn, TW; Lad, SP

Published Date

  • August 1, 2022

Published In

Volume / Issue

  • 91 / 2

Start / End Page

  • 272 - 279

PubMed ID

  • 35384918

Electronic International Standard Serial Number (EISSN)

  • 1524-4040

Digital Object Identifier (DOI)

  • 10.1227/neu.0000000000001969


  • eng

Conference Location

  • United States