Machine Learning to Predict Successful Opioid Dose Reduction or Stabilization After Spinal Cord Stimulation. Issue 2 (8th August 2022)

• Record Type: Journal Article
• Authors: Adil, Syed M.; Charalambous, Lefko T.; Rajkumar, Shashank; Seas, Andreas; Warman, Pranav I.; Murphy, Kelly R.; Rahimpour, Shervin; Parente, Beth; Dharmapurikar, Rajeev; Dunn, Timothy W.; Lad, Shivanand P.
• Is Part Of: Neurosurgery. Volume 91:Issue 2(2022)
• Page Start: 272
• Page End: 279
• Publication Date: 2022-08-08
• Subjects: Spinal cord stimulation -- Neuromodulation -- Opioids -- Machine learning -- Logistic regression -- Deep learning; Nervous system -- Surgery -- Periodicals; 617.48005
• DOI: 10.1227/neu.0000000000001969
• Languages: English
• ISSNs: 0148-396X

Abstract

Abstract : 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 SurgicalML.com . 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.