Predicting neurological outcomes following spinal surgery: A machine learning approach using intraoperative neuromonitoring data

Background Intraoperative neuromonitoring (IONM) reduces postoperative neurological complications, but its precise value for neurological outcomes remains unclear. Machine learning offers a fast, objective, real-time approach to analyzing large IONM datasets. We developed Machine learning models combining baseline characteristics and IONM data-using motor evoked potentials (MEPs) and somatosensory evoked potentials (SSEPs), separately and combined-to predict postoperative neurological outcomes and identify key predictive features.Methods In this retrospective cohort study, 67 patients undergoing spinal surgery (2019-2023) at Haga Teaching Hospital with sufficient IONM and clinical data were analyzed. Medical records and 260 IONM features were assessed; neurological status at 3 months postoperatively was categorized in classes relative to preoperative status as "stable deficits", "intact", or "improvement". Using nested cross-validation, 4 classifiers-support vector machine, K-nearest neighbors, random forest, and extreme gradient boosting-were tested in addition to clinical data across MEP, SSEP, and combined modalities. Performance was expressed as sensitivity, specificity, accuracy, and precision.Results Extreme gradient boosting outperformed all classifiers on every metric. The combined MEP-SSEP model achieved the highest sensitivity (70.4%), specificity (88.3%), accuracy (87.1%), and best per-class scores, while the MEP model achieved the highest precision (75.6%). Key predictive features were preoperative neurological deficits (29%) and last intraoperative signal latency versus baseline (13.5%).Conclusions MEP and SSEP IONM features enhance prediction of 3-months neurological outcomes, provided preoperative status is accurately documented and incorporated. MEP features show superior predictive values compared to SSEP features when both modalities are accessible, with intraoperative signal latency change emerging as a prominent predictive IONM feature.