The Utility of Multimodal Intraoperative Neuromonitoring in Spine Surgery: Case Series from a Lower-Middle-Income Country Perspective

AlMahdy, Rania A.; Wahid, M.M.; Abdel-Kader, A.; Lotfy, Mohamed; Soliman, Mohamed A.R.

doi:10.1016/j.wneu.2021.05.081

Cited by 6 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indeed, the presence of any alert during monitoring was correlated with a deterioration in neurological status at three months in 54% of patients with SSEPs, 63% of those with MEPs-DW, and 55% of those undergoing combined SSEPs-MEPs-DW monitoring. Utilizing such combined monitoring enhances the likelihood of identifying neurological lesions, thereby boosting the sensitivity of combined neuromonitoring, consistent with findings from several previous studies [ 11 , 26 , 27 , 28 ]. When an IONM alert persists until the end of surgery, a deterioration in neurological status is observed much more frequently, occurring in 78% of cases with SSEPs, 79% with MEPs-DW, and 75% of patients undergoing combined SSEPs-MEPs-DW monitoring.…”

Section: Discussionsupporting

confidence: 87%

The Impact of Neurophysiological Monitoring during Intradural Spinal Tumor Surgery

Ilhan,

Boulogne,

Morgado

et al. 2024

Cancers

View full text Add to dashboard Cite

Surgery for spinal cord tumors poses a significant challenge due to the inherent risk of neurological deterioration. Despite being performed at numerous centers, there is an ongoing debate regarding the efficacy of pre- and intraoperative neurophysiological investigations in detecting and preventing neurological lesions. This study begins by providing a comprehensive review of the neurophysiological techniques commonly employed in this context. Subsequently, we present findings from a cohort of 67 patients who underwent surgery for intradural tumors. These patients underwent preoperative and intraoperative multimodal somatosensory evoked potentials (SSEPs) and motor evoked potentials (MEPs), with clinical evaluation conducted three months postoperatively. The study aimed to evaluate the neurophysiological, clinical, and radiological factors associated with neurological outcomes. In univariate analysis, preoperative and intraoperative potential alterations, tumor size, and ependymoma-type histology were linked to the risk of worsening neurological condition. In multivariate analysis, only preoperative and intraoperative neurophysiological abnormalities remained significantly associated with such neurological deterioration. Interestingly, transient alterations in intraoperative MEPs and SSEPs did not pose a risk of neurological deterioration. The machine learning model we utilized demonstrated the possibility of predicting clinical outcome, achieving 84% accuracy.

show abstract

Section: Discussionsupporting

confidence: 87%

The Impact of Neurophysiological Monitoring during Intradural Spinal Tumor Surgery

Ilhan,

Boulogne,

Morgado

et al. 2024

Cancers

View full text Add to dashboard Cite

show abstract

“…Publications on the experience with de novo implementation of IONM programs around the world is scarce. There are some success stories including a recent case series published from Cairo University in Egypt where IONM was implemented for spine and intracranial surgeries ( 8 ). At this institution, IONM was readily adopted and continues to gain popularity.…”

Section: Barriers To Ionm Utilization In Resource-limited Areasmentioning

confidence: 99%

“…Outside of high-income countries, literature on IONM is largely non-existent. One recent study of IONM use during cervical and thoracic spine surgery in a lower-middle-income country (LMIC), showed IONM had a profound impact on patient care ( 8 ). Here, the clearest benefit of IONM proved to be in dissection of intramedullary spinal cord tumors.…”

Section: Introductionmentioning

confidence: 99%

New frontiers in intraoperative neurophysiologic monitoring: a narrative review

Oliva,

Montejano,

Simmons

et al. 2023

Ann Transl Med

View full text Add to dashboard Cite

Background and Objective Neurological insults during surgery arise from anatomic and/or physiologic perturbations. Intraoperative neurophysiologic monitoring (IONM) fills a critical role of ensuring that any neurological insults during certain surgical procedures are caught in real-time to prevent patient harm. IONM provides immediate feedback to the surgeon and anesthesiologist about the need for an intervention to prevent a neurologic deficit postoperatively. As important as it seems to have IONM available to any patient having surgery where a neurological injury is possible, the truth is that IONM is unavailable to large swaths of people around the world. This review is intended to bring attention to all of the ways IONM is critically important for a variety of surgeries and highlight the barriers preventing most patients around the world from benefiting from the technology. Expansion of IONM to benefit patients from all over the world is the new frontier. Methods We searched all English language original papers and reviews using Embase and MEDLINE/PubMed databases published from 1995 to 2022. Different combinations of the following search terms were used: intraoperative neuromonitoring, neurosurgery, low-income countries, cost, safety, and efficacy. Key Content and Findings We describe common IONM modalities used during surgery as well as explore barriers to implementation of IONM in resource-limited regions. Additionally, we describe ongoing efforts to establish IONM capabilities in new locations around the world. Conclusions In this paper, we performed a review of the literature on IONM with an emphasis on the basic understanding of clinical applications and the barriers for expansion into resource-limited settings. Finally, we provide our interpretation of “new frontiers” in IONM quite literally facilitating access to the tools and education so a hospital in Sub-Saharan Africa can incorporate IONM for their high-risk surgeries.

show abstract

“…Specifically, volatile anesthetics can affect SSEP signal amplitude and latency, prompting the need for multi-modal IONM techniques [15,18,19]. The integration of combined SSEP, TcMEPs, and EMG provides a more accurate and multi-angle viewpoint of the functional integrity of the spinal cord, which can enhance surgical precision and reduce the risk of neurological injury [20][21][22][23]. Nonetheless, the massive amount of data generated from multi-modal monitoring makes it difficult to quantify the range of variability seen from these waveforms.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Strategies to Model the Effects of Sevoflurane on Somatosensory-Evoked Potentials during Spine Surgery

Wilson,

Kumbhare,

Ronkon

et al. 2023

Diagnostics

View full text Add to dashboard Cite

In this study, a small sample of patients’ neuromonitoring data was analyzed using machine learning (ML) tools to provide proof of concept for quantifying complex signals. Intraoperative neurophysiological monitoring (IONM) is a valuable asset for monitoring the neurological status of a patient during spine surgery. Notably, this technology, when operated by neurophysiologists and surgeons familiar with proper alarm criteria, is capable of detecting neurological deficits. However, non-surgical factors, such as volatile anesthetics like sevoflurane, can negatively influence robust IONM signal generation. While sevoflurane has been shown to affect the latency and amplitude of somatosensory evoked potential (SSEP), a more complex and nuanced analysis of the SSEP waveform has not been performed. In this study, signal processing and machine learning techniques were used to more intricately characterize and predict SSEP waveform changes as a function of varying end-tidal sevoflurane concentration. With data from ten patients who underwent spinal procedures, features describing the SSEP waveforms were generated using principal component analysis (PCA), phase space curves (PSC), and time-frequency analysis (TFA). A minimum redundancy maximum relevance (MRMR) feature selection technique was then used to identify the most important SSEP features associated with changing sevoflurane concentrations. Once the features carrying the maximum amount of information about the majority of signal waveform variability were identified, ML models were used to predict future changes in SSEP waveforms. Linear regression, regression trees, support vector machines, and neural network ML models were then selected for testing. Using SSEP data from eight patients, the models were trained using a range of features selected during MRMR calculations. During the training phase of model development, the highest performing models were identified as support vector machines and regression trees. After identifying the highest performing models for each nerve group, we tested these models using the remaining two patients’ data. We compared the models’ performance metrics using the root mean square error values (RMSEs). The feasibility of the methodology described provides a general framework for the applications of machine learning strategies to further delineate the effects of surgical and non-surgical factors affecting IONM signals.

show abstract

The Utility of Multimodal Intraoperative Neuromonitoring in Spine Surgery: Case Series from a Lower-Middle-Income Country Perspective

Cited by 6 publications

References 12 publications

The Impact of Neurophysiological Monitoring during Intradural Spinal Tumor Surgery

The Impact of Neurophysiological Monitoring during Intradural Spinal Tumor Surgery

New frontiers in intraoperative neurophysiologic monitoring: a narrative review

Application of Machine Learning Strategies to Model the Effects of Sevoflurane on Somatosensory-Evoked Potentials during Spine Surgery

Contact Info

Product

Resources

About