Dataset Information

Usefulness of Intraoperative Neurophysiological Monitoring During the Clipping of Unruptured Intracranial Aneurysm: Diagnostic Efficacy and Detailed Protocol.

ABSTRACT: Background: Intraoperative neurophysiological monitoring (IONM) has been widely applied in brain vascular surgeries to reduce postoperative neurologic deficit (PND). This study aimed to investigate the effect of IONM during clipping of unruptured intracranial aneurysms (UIAs). Methods: Between January 2013 and August 2020, we enrolled 193 patients with 202 UIAs in the N group (clipping without IONM) and 319 patients with 343 UIAs in the M group (clipping with IONM). Patients in the M group were intraoperatively monitored for motor evoked potentials (MEPs) and somatosensory evoked potentials (SSEPs). Irreversible evoked potential (EP) change was defined as EP deterioration that did not recover until surgery completion. Sustained PND was defined as neurological symptoms lasting for more than one postoperative month. Results: Ten (3.1%) and 13 (6.7%) in the M and N groups, respectively, presented with PND. Compared with the N group, the M group had significantly lower occurrence rates of sustained PND [odds ratio (OR) = 0.36, 95% confidence interval (CI) = 0.13-0.98, p = 0.04], ischemic complications (OR = 0.39, 95% CI = 0.15-0.98, p = 0.04), and radiologic complications (OR = 0.40, 95% CI = 0.19-0.82, p = 0.01). Temporary clipping was an independent risk factor for ischemic complications (ICs) in the total patient group (OR = 6.18, 95% CI = 1.75-21.83, p = 0.005), but not in the M group (OR = 5.53, 95% CI = 0.76-41.92, p = 0.09). Regarding PND prediction, considering any EP changes (MEP and/or SSEP) showed the best diagnostic efficiency with a sensitivity of 0.900, specificity of 0.940, positive predictive value of 0.321, negative predictive value (NPV) of 0.997, and a negative likelihood ratio (LR) of 0.11. Conclusion: IONM application during UIA clipping can reduce PND and radiological complications. The diagnostic effectiveness of IONM, specifically the NPV and LR negative values, was optimal upon consideration of changes in any EP modality.

SUBMITTER: Park D

PROVIDER: S-EPMC7952634 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Usefulness of Intraoperative Neurophysiological Monitoring During the Clipping of Unruptured Intracranial Aneurysm: Diagnostic Efficacy and Detailed Protocol.

Park Dougho D Kim Byung Hee BH Lee Sang-Eok SE Jeong Eunhwan E Cho Kwansang K Park Ji Kang JK Choi Yeon-Ju YJ Jin Suntak S Hong Daeyoung D Kim Mun-Chul MC

Frontiers in surgery 20210226

<b>Background:</b> Intraoperative neurophysiological monitoring (IONM) has been widely applied in brain vascular surgeries to reduce postoperative neurologic deficit (PND). This study aimed to investigate the effect of IONM during clipping of unruptured intracranial aneurysms (UIAs). <b>Methods:</b> Between January 2013 and August 2020, we enrolled 193 patients with 202 UIAs in the N group (clipping without IONM) and 319 patients with 343 UIAs in the M group (clipping with IONM). Patients in the ...[more]

PMID: 33718428

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Project description:ObjectivesWe aim to evaluate the diagnostic test accuracy (DTA) of intraoperative evoked potential (EP) monitoring to detect cerebral injury during clipping of cerebral aneurysms.DesignSystematic review.Data sourcesMajor electronic databases including MEDLINE, EMBASE, LILACS.Eligibility criteriaWe included studies that reported the DTA of intraoperative EP monitoring during intracranial aneurysm clipping procedures in adult patients.Data extraction and synthesisAfter quality assessment, we performed a meta-analysis using the bivariate random effects model, and calculated the possible range of DTA point estimates using a new best-case/worst-case scenario approach to quantify the impact of rescue intervention on DTA.ResultsA total of 35 studies involving 4011 patients were included. The quality of the primary studies was modest and the heterogeneity across studies was high. The pooled sensitivity and specificity for predicting postoperative neurological deficits for the somatosensory evoked potential (SSEP) monitoring was 59% (95% CI: 39% to 76%; I2: 76%) and 86% (95% CI: 77% to 92%; I2: 94%), for motor evoked potential (MEP) monitoring was 81% (95% CI: 58% to 93%; I2: 54%) and 90% (95% CI: 86% to 93%; I2: 81%), and for combined SSEP and MEP monitoring was 92% (95% CI: 62% to 100%) and 88% (95% CI: 83% to 93%). The best-case/worst-case range for the pooled point estimates for sensitivity and specificity for SSEP was 50%-63% and 81%-100%, and for MEP was 59%-74% and 93%-100%, and for combined SSEP and MEP was 89%-94% and 83%-100%.ConclusionsDue to the modest quality and high heterogeneity of the existing primary studies, it is not possible to confidently support or refute the diagnostic value of EP monitoring in cerebral aneurysm clipping surgery. However, combined SSEP and MEP appears to provide the best DTA for predicting postoperative stroke. Contrary to popular assertion, the modest sensitivity of SSEP monitoring is not explained by the use of rescue intervention.Prospero registration numberCRD42015016884.

Project description:BACKGROUND:Intracranial aneurysms (IAs) are dangerous because of their potential to rupture. We previously found significant RNA expression differences in circulating neutrophils between patients with and without unruptured IAs and trained machine learning models to predict presence of IA using 40 neutrophil transcriptomes. Here, we aim to develop a predictive model for unruptured IA using neutrophil transcriptomes from a larger population and more robust machine learning methods. METHODS:Neutrophil RNA extracted from the blood of 134 patients (55 with IA, 79 IA-free controls) was subjected to next-generation RNA sequencing. In a randomly-selected training cohort (n?=?94), the Least Absolute Shrinkage and Selection Operator (LASSO) selected transcripts, from which we constructed prediction models via 4 well-established supervised machine-learning algorithms (K-Nearest Neighbors, Random Forest, and Support Vector Machines with Gaussian and cubic kernels). We tested the models in the remaining samples (n?=?40) and assessed model performance by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (RT-qPCR) of 9 IA-associated genes was used to verify gene expression in a subset of 49 neutrophil RNA samples. We also examined the potential influence of demographics and comorbidities on model prediction. RESULTS:Feature selection using LASSO in the training cohort identified 37 IA-associated transcripts. Models trained using these transcripts had a maximum accuracy of 90% in the testing cohort. The testing performance across all methods had an average area under ROC curve (AUC)?=?0.97, an improvement over our previous models. The Random Forest model performed best across both training and testing cohorts. RT-qPCR confirmed expression differences in 7 of 9 genes tested. Gene ontology and IPA network analyses performed on the 37 model genes reflected dysregulated inflammation, cell signaling, and apoptosis processes. In our data, demographics and comorbidities did not affect model performance. CONCLUSIONS:We improved upon our previous IA prediction models based on circulating neutrophil transcriptomes by increasing sample size and by implementing LASSO and more robust machine learning methods. Future studies are needed to validate these models in larger cohorts and further investigate effect of covariates.

Dataset Information

Usefulness of Intraoperative Neurophysiological Monitoring During the Clipping of Unruptured Intracranial Aneurysm: Diagnostic Efficacy and Detailed Protocol.

Publications

Usefulness of Intraoperative Neurophysiological Monitoring During the Clipping of Unruptured Intracranial Aneurysm: Diagnostic Efficacy and Detailed Protocol.

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