Utility of intraoperative neurophysiological monitoring in detecting motor and sensory nerve injuries in pediatric high-grade spondylolisthesis
Notice bibliographique
Résumé
BACKGROUND CONTEXT: Intraoperative neuromonitoring (IONM) during surgical correction of spinal deformity has been shown to reduce iatrogenic injury in pediatric and adult populations. Although motor-evoked potentials (MEP), somatosensory-evoked potentials (SSEP), and electromyography (EMG) have been shown to be highly sensitive and specific in detecting spinal cord and nerve root injuries, their utility in detecting motor and sensory nerve root injury in pediatric high-grade spondylolisthesis (HGS) remains unknown. PURPOSE: We aim to assess the diagnostic accuracy and therapeutic impact of unimodal and multimodal IONM in the surgical management of HGS. STUDY DESIGN/SETTING: Retrospective cohort study. PATIENT SAMPLE: Pediatric patients undergoing posterior spinal fusion (PSF) for treatment of HGS. OUTCOME MEASURES: Data on patient demographics, spinopelvic and spondylolisthesis parameters, and the presence of pre-and postoperative neurological deficits were collected. METHODS: Intraoperative MEP, SSEP, and EMG alerts were recorded. Alert criteria were defined as a change in amplitude of more than 50% for MEP and/or SSEP, with or without change in latency, and more than 10 seconds of sustained EMG activity. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated for each modality and the combination of MEP and SSEP. The 95% confidence intervals (CIs) were calculated using the exact (Clopper-Pearson) method. RESULTS: Fifty-four pediatric patients with HGS undergoing PSF between 2003 and 2021 in a single tertiary center were included. Seventy-two percent (39/54) of patients were female; the average age of patients was 13.7±2.3 years. The sensitivity of MEP in detecting new postoperative neurologic deficit was 92.3% (95% CI [64.0-99.8]), SSEP 77.8% (95% CI [40.0-97.2]), EMG 69.2% (95% CI [38.6-90.9]), and combination MEP and SSEP 100% (95% CI [73.5-100]). The specificity of MEP was 80.0% (95% CI [64.4-91.0]), SSEP 95.1% (95% CI [83.5-99.4]), EMG 65.9% (95% CI [49.4-79.9]), and combination MEP and SSEP 82.9% (95% CI [67.9-92.9]). The accuracy of SSEP was 92.0% (95% CI [80.8%-97.8%]), and the combination of MEP and SSEP was 86.8% (95% CI [74.7%-94.5%]). Twelve (22.2%) patients had a new motor or sensory deficit diagnosed immediately postoperatively. Nine patients made a full recovery, and 3 had some neurologic deficit on final follow-up. CONCLUSION: Unimodal IONM using SSEP and MEP alone were accurate in diagnosing sensory and motor nerve root injuries, respectively. The diagnostic accuracy in predicting motor and sensory nerve injuries in pediatric HGS improved further with the use of multimodal IONM (combining MEP and SEP). We recommend the utilization of multimodal IONM in all HGS PSF surgeries.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».