Clinical Assessment and Management of Acute Spinal Cord Injury.

The information contained in this article is suitable for clinicians practicing in the United States desiring a general overview of the assessment and management of spinal cord injury (SCI), focusing on initial care, assessment, acute management, complications, prognostication, and future research directions. SCI presents significant challenges, affecting patients physically, emotionally, and financially, with variable recovery outcomes ranging from full functionality to lifelong dependence on caregivers. Initial care aims to minimize secondary injury through thorough neurological evaluations and imaging studies to assess the severity of the injury. Acute management prioritizes stabilizing respiratory and cardiovascular functions and maintaining proper spinal cord perfusion. Patients with unstable or progressive neurological decline benefit from timely surgical intervention to optimize neurological recovery. Subacute management focuses on addressing common complications affecting the respiratory, gastrointestinal, and genitourinary systems, emphasizing a holistic, multidisciplinary approach. Prognostication is currently based on neurological assessments and imaging findings, but emerging biomarkers offer the potential to refine outcome predictions further. Additionally, novel therapeutic interventions, such as hypothermia therapy and neuroprotective medications are being explored to mitigate secondary damage and enhance recovery. This paper serves as a high-yield refresher for clinicians for the assessment and management of acute spinal cord injury during index admission.

Spinal Anesthesia for Awake Spine Surgery: A Paradigm Shift for Enhanced Recovery after Surgery.

Awake surgery has been applied for various surgical procedures with positive outcomes; however, in neurosurgery, the technique has traditionally been reserved for cranial surgery. Awake surgery for the spine (ASFS) is an alternative to general anesthesia (GA). As early studies report promising results, ASFS is progressively gaining more interest from spine surgeons. The history defining the range of adverse events facing patients undergoing GA has been well described. Adverse reactions resulting from GA can include postoperative nausea and vomiting, hemodynamic instability and cardiac complications, acute kidney injury or renal insufficiency, atelectasis, pulmonary emboli, postoperative cognitive dysfunction, or malignant hyperthermia and other direct drug reactions. For this reason, many high-risk populations who have typically been poor candidates under classifications for GA could benefit from the many advantages of ASFS. This narrative review will discuss the significant historical components related to ASFS, pertinent mechanisms of action, protocol overview, and the current trajectory of spine surgery with ASFS.

Image-Guided Navigation in Spine Surgery: From Historical Developments to Future Perspectives.

Intraoperative navigation is critical during spine surgery to ensure accurate instrumentation placement. From the early era of fluoroscopy to the current advancement in robotics, spinal navigation has continued to evolve. By understanding the variations in system protocols and their respective usage in the operating room, the surgeon can use and maximize the potential of various image guidance options more effectively. At the same time, maintaining navigation accuracy throughout the procedure is of the utmost importance, which can be confirmed intraoperatively by using an internal fiducial marker, as demonstrated herein. This technology can reduce the need for revision surgeries, minimize postoperative complications, and enhance the overall efficiency of operating rooms.

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

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.

The Use of Intraoperative Neuromonitoring for Cervical Spine Surgery: Indications, Challenges, and Advances.

Intraoperative neuromonitoring (IONM) has become an indispensable surgical adjunct in cervical spine procedures to minimize surgical complications. Understanding the historical development of IONM, indications for use, associated pitfalls, and recent developments will allow the surgeon to better utilize this important technology. While IONM has shown great promise in procedures for cervical deformity, intradural tumors, or myelopathy, routine use in all cervical spine cases with moderate pathology remains controversial. Pitfalls that need to be addressed include human error, a lack of efficient communication, variable alarm warning criteria, and a non-standardized checklist protocol. As the techniques associated with IONM technology become more robust moving forward, IONM emerges as a crucial solution to updating patient safety protocols.