Related Experiment Video
Updated: Aug 5, 2026
Intra-Operative Neural Monitoring of Thyroid Surgery in a Porcine Model
Published on: February 11, 2019
Influence of Baseline Intraoperative Neuromonitoring (IONM) Data Quality on Neuromonitoring Performance and Surgical
Christopher K Bozorgmehr1, Leah Rakers2, Scott J Luhmann3
1Washington University School of Medicine, St. Louis, MO, USA, Barnes-Jewish Hospital, St. Louis, MO, 63110, USA.
Study Design:
Retrospective Cohort Study.
Objective:
To examine the impact of abnormal baseline intraoperative neuromonitoring (IONM) data quality on neuromonitoring alerts, surgical decision-making, and the immediate and final neurologic outcomes.
Summary Of Background Data:
The use of IONM is considered the standard of care for pediatric spine deformity surgery due to its ability to identify potential spinal cord dysfunction and facilitate remediating corrective action. However, there is little information regarding the influence of baseline data quality on IONM alerts and subsequent surgical choices and neurologic outcomes.
Methods:
An institutional neuromonitoring data base was reviewed (1992-2024) to identify all consecutive patients (0-18 y) who underwent pediatric spine deformity surgery with at least one IONM alert.
Results:
Among 5,317 consecutive cases, 223 (4.2%) had at least 1 IONM alert, and 292 (5.5%) had abnormal baseline data quality. Alerts occurred more frequently in patients with abnormal baselines (14.7% vs. 3.6%, P<0.001). Baseline abnormalities included SSEP only in 46.5%, motor only in 44.2%, and both in 9.3% of cases. The abnormal baseline modality and alert modality matched in 72.1% of cases. Abnormal baselines were associated with abnormal preoperative neurologic status (36.6% vs. 19.4%, P=0.018) but not diagnosis. Alerts occurred earlier in surgery in cases with abnormal baselines (P<0.001) and failure of data return was more common (44.2% vs. 26.7%, P=0.024). Normal compared with abnormal baseline data quality was not associated with postoperative neurologic deficits postoperatively (14.4% vs. 18.6%, P=0.495) or at final exam (3.9% vs. 2.5%, P=1.00), respectively.
Conclusion:
Abnormal IONM baseline data quality is associated with increased alert frequency, earlier alert timing, and altered intraoperative management, but not with more postoperative neurologic outcomes. These findings support the continued use of IONM in cases with poor baseline data quality when interpreted in clinical context and managed with established protocols.
