Loss of intraoperative neurological monitoring signals during flexed prone positioning on a hinged open frame during surgery for kyphoscoliosis correction: case report

Graham, Randall B.; Cotton, Mathew; Koht, Antoun; Koski, Tyler R.

doi:10.3171/2018.1.spine17811

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…It is also important to highlight the risk for neurological complications when increasing kyphosis using the hinged surgical table, particularly in patients with severe kyphosis and stenosis. 18 In the present study, we focused on patients undergoing lumbar fusion surgery and used free-running electromyograms for all patients. Although we had no significant neurophysiological changes in this series, it is reasonable to consider monitoring somatosensory and motor-evoked potentials for patients undergoing thoracic fusion with the hinged surgical table.…”

Section: Discussionmentioning

confidence: 99%

Use of an Open-Frame Hinged Surgical Table to Restore Segmental Lumbar Lordosis After Posterior Column Osteotomy

et al. 2020

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Section: Discussionmentioning

confidence: 99%

Use of an Open-Frame Hinged Surgical Table to Restore Segmental Lumbar Lordosis After Posterior Column Osteotomy

et al. 2020

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“…Using specific stimulation paradigms designed for optimal data collection during a spine operation, IONM technicians inspect incoming stimulation data for any signal alteration from baseline, which may indicate a neurologic insult from surgical manipulation, non-surgical factors, or not uncommonly, from mechanical artifacts. An example of a non-surgical factor can be seen during patient positioning, which if carried out improperly, can trigger detectable signal alteration that allows the surgeon to immediately re-position the patient to avert any potential deficit [5,6]. Nevertheless, distinguishing surgical from non-surgical factors or mechanical artifacts leading to signal alteration is of paramount importance for proper troubleshooting of IONM waveform disturbances.…”

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

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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.

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“…C omplex spine deformity correction surgery carries a considerable risk for medical and neurological adverse effects. 1 The overall prevalence of adverse effects is 50%−60% due to the very high physiological trespass, underlying comorbidities in older patients and challenging physiology, in addition to common risks of spine and spinal cord surgery. 2 Furthermore, the average prevalence of postoperative spinal cord injury (SCI) is 9%.…”

mentioning

confidence: 99%

Comprehensive Perioperative Approach to Complex Spine Deformity Management

Camargo

Abode-Iyamah²,

Shah³

et al. 2021

Clinical Spine Surgery: A Spine Publication

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Objective: Study perioperative strategies for optimizing neuroprotection in complex spine deformity correction surgery. Methods: We report the case of a patient with severe lumbar dextroscoliosis, thoracolumbar junction hyperkyphosis with a 40-degree Cobb angle levoconvex scoliosis who underwent spinal deformity correction with loss of neuromonitoring during surgery. We performed a literature review on perioperative management of complex spine deformity. Results: A 50-year-old man presented with lumbar pain and right L4 radiculopathy. Surgical intervention for deformity correction and decompression was indicated with T4−L4 posterior instrumentation L2/L3 and L3/L4 transforaminal lumbar interbody fusion. Surgery was aborted due to the loss of neuromonitoring. Postsurgery, the patient had left sensory deficit and the neurocritical care team clinically suspected and deduced the anatomic location of the spinal cord compression. Magnetic resonance imaging confirmed a T10−T11 hyperintensity suggestive of cord ischemia due to osteophyte compressing the spinal cord. The patient underwent a second corrective surgery with no intraoperative events and has no long-term neurological sequela. Conclusions: This case illustrates that a comprehensive perioperative approach and individualized risk factor assessment is useful in complex spine deformity surgery. Further research is needed to determine how this individualized comprehensive approach can lead to intraoperative and postoperative countermeasures that improved spine surgery outcomes. Level of Evidence: Level V.

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Loss of intraoperative neurological monitoring signals during flexed prone positioning on a hinged open frame during surgery for kyphoscoliosis correction: case report

Cited by 8 publications

References 23 publications

Use of an Open-Frame Hinged Surgical Table to Restore Segmental Lumbar Lordosis After Posterior Column Osteotomy

Use of an Open-Frame Hinged Surgical Table to Restore Segmental Lumbar Lordosis After Posterior Column Osteotomy

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

Comprehensive Perioperative Approach to Complex Spine Deformity Management

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