Utility of somatosensory- and motor-evoked potential change thresholds in surgical treatment for thoracic spinal stenosis based on different pathologies.

ABSTRACT

BACKGROUND CONTEXT Thoracic spinal stenosis (TSS) is secondary to different pathologies that differ in clinical characteristics and surgical outcomes. PURPOSE: This study aimed to determine the optimal warning thresholds for combined somatosensory-evoked potentials (SSEP) and motor-evoked potentials (MEP) for predicting postoperative neurological deterioration in surgical treatment for TSS based on different pathologies. Additionally, we explored the correlation between SSEP/MEP monitoring and postoperative spinal neurological function. STUDY SETTING: Retrospective study. PATIENT SAMPLE Two hundred and five patients. OUTCOME MEASURES: We obtained perioperative modified Japanese Orthopedic Association (mJOA) scores to assess spinal neurological function. METHODS: The data collected in this study included demographic data, intraoperative neurophysiological monitoring (IONM) signals, and perioperative neurological function assessments. To determine the optimal IONM warning threshold, a receiver operating characteristic (ROC) curve was used. Additionally, Pearson correlation analysis was conducted to determine the correlation between IONM signals and clinical neurological conditions. RESULTS: A total of 205 consecutive patients were eligible. Forty-one patients had thoracic disc herniation (TDH), 14 had ossification of the posterior longitudinal ligament (OPLL), 124 had ossification of the ligamentum flavum (OLF), and 26 had OPLL+OLF. The mean mJOA scores before surgery and 3 months after surgery were 7.0 and 7.9, respectively, resulting in a mean mJOA recovery rate (RR) of 23.1%. The average postoperative mJOA RRs for patients with TDH, OPLL, OLF, and OPLL+OLF were 24.8%, 10.4%, 26.8%, and 11.2%, respectively. Patients with OPLL+OLF exhibited a more stringent threshold for IONM changes. This included a lower amplitude cutoff value (a decrease of 49.0% in the SSEP amplitude and 57.5% in the MEP amplitude for short-term prediction) and a shorter duration of waveform change (19.5 minutes for SSEP and 22.5 minutes for MEP for short-term prediction). On the other hand, patients with TDH had more lenient IONM warning criteria (a decrease of 49.0% in SSEP amplitude and 77.5% in MEP amplitude for short-term prediction; durations of change of 25.5 minutes for SSEP and 32.5 minutes for MEP). However, OPLL patients or OLF patients had moderate and similar IONM warning thresholds. Additionally, there was a stronger correlation between the SSEP amplitude variability ratio and the JOA RR in OPLL+OLF patients, while the correlation was stronger between the MEP amplitude variability ratio and the JOA RR for the other three TSS pathologies. CONCLUSIONS: Optimal IONM change criteria for prediction vary depending on different TSS pathologies. The optimal monitoring strategy for prediction varies depending on TSS pathologies.