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Study Design: Case report and narrative review., Objective: To explore the therapeutic role of surgical and nonsurgical treatment of diaphragmatic paralysis secondary to spinal cord and nerve root compression., Summary of Background Data: Phrenic nerve dysfunction due to central or neuroforaminal stenosis is a rare yet unappreciated etiology of diaphragmatic paralysis and chronic dyspnea. Surgical spine decompression, diaphragmatic pacing, and intensive physiotherapy are potential treatment options with varying degrees of evidence., Methods: The case of a 70-year-old male with progressive dyspnea, reduced hemi-diaphragmatic excursion, and C3-C7 stenosis, who underwent a microscopic foraminotomy is discussed. Literature review (MEDLINE, PubMed, Google Scholar) identified 19 similar reports and discussed alternative treatments and outcomes., Results and Conclusions: Phrenic nerve root decompression and improvement in neuromonitoring signals were observed intraoperatively. The patient's postoperative course was uncomplicated, and after 15 months, he experienced significant symptomatic improvement and minor improvement in hemi-diaphragmatic paralysis and pulmonary function tests. All case reports of patients treated with spinal decompression showed symptomatic and/or functional improvement, while one of the 2 patients treated with physiotherapy showed improvement. More studies are needed to further describe the course and outcomes of these interventions, but early identification and spinal decompression can be an effective treatment., Ocebm Level of Evidence: Level-4., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Background: The frequency of anterior cervical discectomy and fusion (ACDF) has increased up to 400% since 2011, underscoring the need to preoperatively anticipate adverse postoperative outcomes given the procedure's expanding use. Our study aims to accomplish two goals: firstly, to develop a suite of explainable machine learning (ML) models capable of predicting adverse postoperative outcomes following ACDF surgery, and secondly, to embed these models in a user-friendly web application, demonstrating their potential utility., Methods: We utilized data from the National Surgical Quality Improvement Program database to identify patients who underwent ACDF surgery. The outcomes of interest were four short-term postoperative adverse outcomes: prolonged length of stay (LOS), non-home discharges, 30-day readmissions, and major complications. We utilized five ML algorithms - TabPFN, TabNET, XGBoost, LightGBM, and Random Forest - coupled with the Optuna optimization library for hyperparameter tuning. To bolster the interpretability of our models, we employed SHapley Additive exPlanations (SHAP) for evaluating predictor variables' relative importance and used partial dependence plots to illustrate the impact of individual variables on the predictions generated by our top-performing models. We visualized model performance using receiver operating characteristic (ROC) curves and precision-recall curves (PRC). Quantitative metrics calculated were the area under the ROC curve (AUROC), balanced accuracy, weighted area under the PRC (AUPRC), weighted precision, and weighted recall. Models with the highest AUROC values were selected for inclusion in a web application., Results: The analysis included 57,760 patients for prolonged LOS [11.1% with prolonged LOS], 57,780 for non-home discharges [3.3% non-home discharges], 57,790 for 30-day readmissions [2.9% readmitted], and 57,800 for major complications [1.4% with major complications]. The top-performing models, which were the ones built with the Random Forest algorithm, yielded mean AUROCs of 0.776, 0.846, 0.775, and 0.747 for predicting prolonged LOS, non-home discharges, readmissions, and complications, respectively., Conclusions: Our study employs advanced ML methodologies to enhance the prediction of adverse postoperative outcomes following ACDF. We designed an accessible web application to integrate these models into clinical practice. Our findings affirm that ML tools serve as vital supplements in risk stratification, facilitating the prediction of diverse outcomes and enhancing patient counseling for ACDF., (© 2024. The Author(s).)

Background: Degenerative cervical myelopathy (DCM) is a common and disabling condition. Early effective treatment is limited by late diagnosis. Conventional descriptions of DCM focus on motor and sensory limb disability, however, recent work suggests the true impact is much broader. This study aimed to characterise the symptomatic presentation of DCM from the perspective of people with DCM and determine whether any of the reported symptoms, or groups of symptoms, were associated with early diagnosis., Methods: An internet survey was developed, using an established list of patient-reported effects. Participants (N = 171) were recruited from an online community of people with DCM. Respondents selected their current symptoms and primary presenting symptom. The relationship of symptoms and their relationship to time to diagnosis were explored. This included symptoms not commonly measured today, termed 'non-conventional' symptoms., Results: All listed symptoms were experienced by >10% of respondents, with poor balance being the most commonly reported (84.2%). Non-conventional symptoms accounted for 39.7% of symptomatic burden. 55.4% of the symptoms were reported as an initial symptom, with neck pain the most common (13.5%). Non-conventional symptoms accounted for 11.1% of initial symptoms. 79.5% of the respondents were diagnosed late (>6 months). Heavy legs was the only initial symptom associated with early diagnosis., Conclusions: A comprehensive description of the self-reported effects of DCM has been established, including the prevalence of symptoms at disease presentation. The experience of DCM is broader than suggested by conventional descriptions and further exploration of non-conventional symptoms may support earlier diagnosis., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: CFM has declared that no competing interests exist. RY has declared that no competing interests exist. ZCM has declared that no competing interests exist. MGF currently serves as an academic editor at PLOS ONE. RRP has declared that no competing interests exist. JM has declared that no competing interests exist. KM has declared that no competing interests exist. MRNK has declared that no competing interests exist. BMD is supported by NIHR POLYFIX DCM and NIHR Clinical Doctoral Research Fellowship grants. BMD is a founder of MoveMed (a digital therapeutics platform which develops assessments and treatments using software). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2023 Munro et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objective: Spinal epidural abscess may require prompt surgical intervention. Ventral cervical abscesses pose a particular challenge regarding the approach for surgical evacuation. The aim of this article was to describe the technical nuances of a posterior transpedicular cervical approach for evacuation of a ventral epidural abscess., Methods: After a standard laminectomy, a foraminotomy was performed to identify the exiting nerve root. Then the medial aspect of the pedicle below the nerve was drilled. This allowed the insertion of a dissector to reach the ventral epidural space and drain the contents in conjunction with suction and irrigation. The posterolateral aspect of the superior endplate of the respective vertebra could be further drilled at this point, allowing access to the disc space with minimal retraction of the exiting nerve root., Results: Two patients underwent emergent evacuation of a ventral epidural abscess in the cervical spine using this technique. Radiographic and clinical improvement was evident after evacuation of the abscesses in both cases., Conclusions: Access to the ventral epidural space is feasible using a transpedicular approach in the cervical spine for evacuation of an epidural abscess., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Vertebral artery injuries as a result of blunt trauma can result in vertebrobasilar strokes. Typical treatment of such an injury includes early anticoagulation to prevent cerebral ischemic events due to vessel occlusion or embolism. We present a case of cervical fracture-dislocation injury and compression/occlusion of the right vertebral artery, which spontaneously resolved following surgical reduction and fusion. Postoperative cerebral angiography showed no evidence of vertebral artery stenosis, and systemic anticoagulation was discontinued. This case shows that vertebral artery occlusion can resolve spontaneously after fracture reduction, and cerebral angiography should play a role in assessing these complicated traumatic injuries., Competing Interests: Competing interests: None declared., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.)

Study Design: A retrospective machine learning (ML) classification study for prognostic modeling after anterior cervical corpectomy (ACC). Purpose: To evaluate the effectiveness of ML in predicting ACC outcomes and develop an accessible, user-friendly tool for this purpose. Overview of Literature: Based on our literature review, no study has examined the capability of ML algorithms to predict major short-term ACC outcomes, such as prolonged length of hospital stay (LOS), non-home discharge, and major complications. Methods: The American College of Surgeons' National Surgical Quality Improvement Program database was used to identify patients who underwent ACC. Prolonged LOS, non-home discharges, and major complications were assessed as the outcomes of interest. ML models were developed with the TabPFN algorithm and integrated into an open-access website to predict these outcomes. Results: The models for predicting prolonged LOS, non-home discharges, and major complications demonstrated mean areas under the receiver operating characteristic curve (AUROC) of 0.802, 0.816, and 0.702, respectively. These findings highlight the discriminatory capacities of the models: fair (AUROC >0.7) for differentiating patients with major complications from those without, and good (AUROC >0.8) for distinguishing between those with and without prolonged LOS and non-home discharges. According to the SHapley Additive Explanations analysis, single- versus multiple-level surgery, age, body mass index, preoperative hematocrit, and American Society of Anesthesiologists physical status repetitively emerged as the most important variables for each outcome. Conclusions: This study has considerably enhanced the prediction of postoperative results after ACC surgery by implementing advanced ML techniques. A major contribution is the creation of an accessible web application, highlighting the practical value of the developed models. Our findings imply that ML can serve as an invaluable supplementary tool to stratify patient risk for this procedure and can predict diverse postoperative adverse outcomes. [ABSTRACT FROM AUTHOR]