Diagnostic and therapeutic values of intraoperative electrophysiological neuromonitoring during resection of intradural extramedullary spinal tumors: a single-center retrospective cohort and meta-analysis.

AUC = area under the curve CEL = clinical evidence level EBL = estimated blood loss EEG = electroencephalography EMG = electromyography GTR = gross-total resection ID-EM = intradural extramedullary IONM = intraoperative neuromonitoring MEP = motor evoked potential NMJB = neuromuscular junction blockade NPV = negative predictive value PPV = positive predictive value SSEP = somatosensory evoked potential diagnostic value intradural extramedullary spinal tumor intraoperative neuromonitoring motor evoked potentials oncology sensitivity somatosensory evoked potentials specificity

Journal

Journal of neurosurgery. Spine
ISSN: 1547-5646
Titre abrégé: J Neurosurg Spine
Pays: United States
ID NLM: 101223545

Informations de publication

Date de publication:
01 Mar 2019
Historique:
received: 11 09 2018
accepted: 09 11 2018
entrez: 6 3 2019
pubmed: 6 3 2019
medline: 6 3 2019
Statut: aheadofprint

Résumé

OBJECTIVEWith the advent of intraoperative electrophysiological neuromonitoring (IONM), surgical outcomes of various neurosurgical pathologies, such as brain tumors and spinal deformities, have improved. However, its diagnostic and therapeutic value in resecting intradural extramedullary (ID-EM) spinal tumors has not been well documented in the literature. The objective of this study was to summarize the clinical results of IONM in patients with ID-EM spinal tumors.METHODSA retrospective patient database review identified 103 patients with ID-EM spinal tumors who underwent tumor resection with IONM (motor evoked potentials, somatosensory evoked potentials, and free-running electromyography) from January 2010 to December 2015. Patients were classified as those without any new neurological deficits at the 6-month follow-up (group A; n = 86) and those with new deficits (group B; n = 17). Baseline characteristics, clinical outcomes, and IONM findings were collected and statistically analyzed. In addition, a meta-analysis in compliance with the PRISMA guidelines was performed to estimate the overall pooled diagnostic accuracy of IONM in ID-EM spinal tumor resection.RESULTSNo intergroup differences were discovered between the groups regarding baseline characteristics and operative data. In multivariate analysis, significant IONM changes (p < 0.001) and tumor location (thoracic vs others, p = 0.018) were associated with new neurological deficits at the 6-month follow-up. In predicting these changes, IONM yielded a sensitivity of 82.4% (14/17), specificity of 90.7% (78/86), positive predictive value (PPV) of 63.6% (14/22), negative predictive value (NPV) of 96.3% (78/81), and area under the curve (AUC) of 0.893. The diagnostic value slightly decreased in patients with schwannomas (AUC = 0.875) and thoracic tumors (AUC = 0.842). Among 81 patients who did not demonstrate significant IONM changes at the end of surgery, 19 patients (23.5%) exhibited temporary intraoperative exacerbation of IONM signals, which were recovered by interruption of surgical maneuvers; none of these patients developed new neurological deficits postoperatively. Including the present study, 5 articles encompassing 323 patients were eligible for this meta-analysis, and the overall pooled diagnostic value of IONM was a sensitivity of 77.9%, a specificity of 91.1%, PPV of 56.7%, and NPV of 95.7%.CONCLUSIONSIONM for the resection of ID-EM spinal tumors is a reasonable modality to predict new postoperative neurological deficits at the 6-month follow-up. Future prospective studies are warranted to further elucidate its diagnostic and therapeutic utility.

Identifiants

pubmed: 30835707
doi: 10.3171/2018.11.SPINE181095
pii: 2018.11.SPINE181095
doi:
pii:

Types de publication

Journal Article

Langues

eng

Pagination

1-11

Auteurs

Wataru Ishida (W)

1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland.

Joshua Casaos (J)

1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland.

Arun Chandra (A)

1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland.

Adam D'Sa (A)

1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland.

Seba Ramhmdani (S)

1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland.

Alexander Perdomo-Pantoja (A)

1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland.

Nicholas Theodore (N)

1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland.

George Jallo (G)

1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland.
4Department of Neurosurgery, Johns Hopkins All Children's Hospital, St. Petersburg, Florida.

Ziya L Gokaslan (ZL)

2Department of Neurosurgery, Brown University School of Medicine, Providence, Rhode Island.

Jean-Paul Wolinsky (JP)

3Department of Neurological Surgery, Northwestern University, Chicago, Illinois; and.

Daniel M Sciubba (DM)

1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland.

Ali Bydon (A)

1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland.

Timothy F Witham (TF)

1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland.

Sheng-Fu L Lo (SL)

1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland.

Classifications MeSH