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Efficacy of evoked potential monitoring for predicting postoperative motor status in internal carotid artery aneurysm surgeries

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This study aimed to investigate the efficacy of intraoperative motor evoked potential (MEP) and somatosensory evoked potential (SSEP) monitoring for predicting postoperative motor deficits (PMDs) in patients with internal carotid… Click to show full abstract

This study aimed to investigate the efficacy of intraoperative motor evoked potential (MEP) and somatosensory evoked potential (SSEP) monitoring for predicting postoperative motor deficits (PMDs) in patients with internal carotid artery (ICA) aneurysms. The data for 138 patients with ICA aneurysms who underwent surgical clipping as well as their intraoperative neuromonitoring data were retrospectively reviewed. The efficacy of MEP/SSEP changes for predicting PMDs was assessed using binary logistic regression analysis. Subsequently, receiver operating characteristic curve analysis was used to obtain a supplementary critical value of the MEP/SSEP deterioration duration. The sensitivity and specificity of MEP changes for predicting PMDs were 0.824 and 0.843, respectively. For SSEP changes, the sensitivity and specificity were 0.529 and 0.959, respectively. MEP and SSEP changes were identified as independent predictors for short-term (p = 0.002 and 0.011, respectively) and long-term PMDs (p = 0.040 and 0.006, respectively). The supplementary critical value for MEP deterioration duration for predicting PMDs was 14 min (p = 0.007, AUC = 0.805). For SSEP, the value was 14.5 min (p = 0.042, AUC = 0.875). The MEP/SSEP changes adjusted by those optimal values were also identified as independent predictors for short-term (p < 0.001 and p = 0.005, respectively) and long-term PMDs (p = 0.019 and 0.003, respectively). Intraoperative MEP and SSEP deterioration durations are effective in predicting PMDs in patients with ICA aneurysms.

Keywords: mep; evoked potential; monitoring; motor; ssep; mep ssep

Journal Title: Journal of Clinical Monitoring and Computing
Year Published: 2021

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