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. 2024 Mar;14(3_suppl):105S-149S.
doi: 10.1177/21925682231196514.

Accuracy of Intraoperative Neuromonitoring in the Diagnosis of Intraoperative Neurological Decline in the Setting of Spinal Surgery-A Systematic Review and Meta-Analysis

Mohammed Ali Alvi  1 , Brian K Kwon  2   3 , Nader Hejrati  4 , Lindsay A Tetreault  5 , Nathan Evaniew  6 , Andrea C Skelly  7 , Michael G Fehlings  1   4   8
Affiliations

Accuracy of Intraoperative Neuromonitoring in the Diagnosis of Intraoperative Neurological Decline in the Setting of Spinal Surgery-A Systematic Review and Meta-Analysis

Mohammed Ali Alvi et al. Global Spine J. 2024 Mar.

Abstract

Study design: Systematic review and meta-analysis.

Objectives: In an effort to prevent intraoperative neurological injury during spine surgery, the use of intraoperative neurophysiological monitoring (IONM) has increased significantly in recent years. Using IONM, spinal cord function can be evaluated intraoperatively by recording signals from specific nerve roots, motor tracts, and sensory tracts. We performed a systematic review and meta-analysis of diagnostic test accuracy (DTA) studies to evaluate the efficacy of IONM among patients undergoing spine surgery for any indication.

Methods: The current systematic review and meta-analysis was performed using the Preferred Reporting Items for a Systematic Review and Meta-analysis statement for Diagnostic Test Accuracy Studies (PRISMA-DTA) and was registered on PROSPERO. A comprehensive search was performed using MEDLINE, EMBASE and SCOPUS for all studies assessing the diagnostic accuracy of neuromonitoring, including somatosensory evoked potential (SSEP), motor evoked potential (MEP) and electromyography (EMG), either on their own or in combination (multimodal). Studies were included if they reported raw numbers for True Positives (TP), False Negatives (FN), False Positives (FP) and True Negative (TN) either in a 2 × 2 contingency table or in text, and if they used postoperative neurologic exam as a reference standard. Pooled sensitivity and specificity were calculated to evaluate the overall efficacy of each modality type using a bivariate model adapted by Reitsma et al, for all spine surgeries and for individual disease groups and regions of spine. The risk of bias (ROB) of included studies was assessed using the quality assessment tool for diagnostic accuracy studies (QUADAS-2).

Results: A total of 163 studies were included; 52 of these studies with 16,310 patients reported data for SSEP, 68 studies with 71,144 patients reported data for MEP, 16 studies with 7888 patients reported data for EMG and 69 studies with 17,968 patients reported data for multimodal monitoring. The overall sensitivity, specificity, DOR and AUC for SSEP were 71.4% (95% CI 54.8-83.7), 97.1% (95% CI 95.3-98.3), 41.9 (95% CI 24.1-73.1) and .899, respectively; for MEP, these were 90.2% (95% CI 86.2-93.1), 96% (95% CI 94.3-97.2), 103.25 (95% CI 69.98-152.34) and .927; for EMG, these were 48.3% (95% CI 31.4-65.6), 92.9% (95% CI 84.4-96.9), 11.2 (95% CI 4.84-25.97) and .773; for multimodal, these were found to be 83.5% (95% CI 81-85.7), 93.8% (95% CI 90.6-95.9), 60 (95% CI 35.6-101.3) and .895, respectively. Using the QUADAS-2 ROB analysis, of the 52 studies reporting on SSEP, 13 (25%) were high-risk, 10 (19.2%) had some concerns and 29 (55.8%) were low-risk; for MEP, 8 (11.7%) were high-risk, 21 had some concerns and 39 (57.3%) were low-risk; for EMG, 4 (25%) were high-risk, 3 (18.75%) had some concerns and 9 (56.25%) were low-risk; for multimodal, 14 (20.3%) were high-risk, 13 (18.8%) had some concerns and 42 (60.7%) were low-risk.

Conclusions: These results indicate that all neuromonitoring modalities have diagnostic utility in successfully detecting impending or incident intraoperative neurologic injuries among patients undergoing spine surgery for any condition, although it is clear that the accuracy of each modality differs.PROSPERO Registration Number: CRD42023384158.

Keywords: intraoperative neurological injury; neuro; spinal cord injury; trauma.

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Conflict of interest statement

Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
PRISMA-DTA Flowchart for selection of studies.
Figure 2.
Figure 2.
Forest plot for sensitivity of SSEP.
Figure 3.
Figure 3.
Forest plot for specificity of SSEP.
Figure 4.
Figure 4.
Overall sROC plot for SSEP.
Figure 5.
Figure 5.
Forest plot for sensitivity of MEP.
Figure 6.
Figure 6.
Forest plot for specificity of MEP.
Figure 7.
Figure 7.
Overall sROC plot for MEP.
Figure 8.
Figure 8.
Forest plot for sensitivity of EMG.
Figure 9.
Figure 9.
Forest plot for specificity of EMG.
Figure 10.
Figure 10.
Overall sROC plot for EMG.
Figure 11.
Figure 11.
Forest plot for sensitivity of Multimodal Neuromonitoring.
Figure 12.
Figure 12.
Forest plot for specificity of Multimodal Neuromonitoring.
Figure 13.
Figure 13.
Overall sROC plot for Multimodal Neuromonitoring.
Figure 14.
Figure 14.
Funnel plot for Assessment of Publication Bias for SSEP.
Figure 15.
Figure 15.
Funnel plot for Assessment of Publication Bias for MEP.
Figure 16.
Figure 16.
Funnel plot for Assessment of Publication Bias for EMG.
Figure 17.
Figure 17.
Funnel plot for Assessment of Publication Bias for Multimodal Neuromonitoring.
Figure 18.
Figure 18.
QUADAS-2 risk of bias traffic light plot for SSEP.
Figure 19.
Figure 19.
QUADAS-2 risk of bias summary plot for SSEP.
Figure 20.
Figure 20.
QUADAS-2 risk of bias traffic light plot for MEP.
Figure 21.
Figure 21.
QUADAS-2 risk of bias summary plot for MEP.
Figure 22.
Figure 22.
QUADAS-2 risk of bias traffic light plot for EMG.
Figure 23.
Figure 23.
QUADAS-2 risk of bias summary plot for EMG.
Figure 24.
Figure 24.
QUADAS-2 risk of bias traffic light plot for multimodal neuromonitoring.
Figure 25.
Figure 25.
QUADAS-2 risk of bias summary plot for multimodal neuromonitoring.
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