Neurosurgery
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Extended postoperative hospital stays are associated with numerous clinical risks and increased economic cost. Accurate preoperative prediction of extended length of stay (LOS) can facilitate targeted interventions to mitigate clinical harm and resource utilization. ⋯ Machine learning algorithms accurately predict extended LOS across single-center and national patient cohorts and characterize key preoperative drivers of increased LOS after cervical spine surgery.
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Spinal cord stimulation (SCS) effectively reduces opioid usage in some patients, but preoperatively, there is no objective measure to predict who will most benefit. ⋯ 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.