Dose Responsiveness as a Measure of Clinical Effectiveness During Neuromonitored Spine Surgery

Intraoperative neurophysiological monitoring (IONM) measures neural function and integrity during surgical procedures. Through IONM, neurodiagnostic procedures can help determine whether any nerves have become compressed or if the brain or spinal cord has any reduced vascular flow, allowing the surgical team to take immediate and corrective actions to prevent a bad outcome. Essentially, IONM acts as an early warning system for surgeons to gain reliable insight into a patient's condition during surgery, adding a layer of safety to the measures already in place that monitor cardiac and respiratory function while a patient is anesthetized. We propose an observational research study to enhance the evidence-base for IONM. A novel prospective neuromonitoring database will be implemented. In addition to the usual contingency data to assess prediction accuracy, other measures will be examined: 1. A "dose effect of injury" assessment. Three categories of injury will be recorded by physicians: (a) No New Deficit; (b) New Minor Deficit; and (c) New Severe Deficit. 2. The outcome effect of intraoperative test frequency will be evaluated (infrequent vs average vs very frequent), leading to a "dose effect of neuromonitoring" assessment. Here we hypothesize that within our prospective sample, there is a relationship between the proportion of total new neurological deficits (New Minor Deficit + New Severe Deficit) and the frequency of IONM testing. More frequent testing is associated with fewer total new neurological deficits. 3. Patient reported outcomes will be evaluated longitudinally in conjunction with clinical outcomes data. Patient interviews will be conducted by a trained research assistant aided by department technologists. Patient-reported neurological outcome assessments will be supplemented/confirmed by review of in- and outpatient surgical/neurophysiologist physician chart notes. Data from this proposed observational study will allow real-life outcomes based improvement in the field of IONM.