Association of Medical Comorbidities With Objective Functional Impairment in Lumbar Degenerative Disc Disease

STUDY DESIGN Analysis of a prospective 2-center database. OBJECTIVES Medical comorbidities co-determine clinical outcome. Objective functional impairment (OFI) provides a supplementary dimension of patient assessment. We set out to study whether comorbidities are associated with the presence and degree of OFI in this patient population. METHODS Patients with degenerative diseases of the spine preoperatively performed the timed-up-and-go (TUG) test and a battery of questionnaires. Comorbidities were quantified using the Charlson Comorbidity Index (CCI) and the American Society of Anesthesiology (ASA) grading. Crude and adjusted linear regression models were fitted. RESULTS Of 375 included patients, 97 (25.9%) presented at least some degree of medical comorbidity according to the CCI, and 312 (83.2%) according to ASA grading. In the univariate analysis, the CCI was inconsistently associated with OFI. Only patients with low-grade CCI comorbidity displayed significantly higher TUG test times (p = 0.004). In the multivariable analysis, this effect persisted for patients with CCI = 1 (p = 0.030). Regarding ASA grade, patients with ASA = 3 exhibited significantly increased TUG test times (p = 0.003) and t-scores (p = 0.015). This effect disappeared after multivariable adjustment (p = 0.786 and p = 0.969). In addition, subjective functional impairment according to ODI, and EQ5D index was moderately associated with comorbidities according to ASA (all p < 0.05). CONCLUSION The degree of medical comorbidities appears only weakly and inconsistently associated with OFI in patients scheduled for degenerative lumbar spine surgery, especially after controlling for potential confounders. TUG testing may be valid even in patients with relatively severe comorbidities who are able to complete the test

Global adoption of robotic technology into neurosurgical practice and research

Recent technological advancements have led to the development and implementation of robotic surgery in several specialties, including neurosurgery. Our aim was to carry out a worldwide survey among neurosurgeons to assess the adoption of and attitude toward robotic technology in the neurosurgical operating room and to identify factors associated with use of robotic technology. The online survey was made up of nine or ten compulsory questions and was distributed via the European Association of the Neurosurgical Societies (EANS) and the Congress of Neurological Surgeons (CNS) in February and March 2018. From a total of 7280 neurosurgeons who were sent the survey, we received 406 answers, corresponding to a response rate of 5.6%, mostly from Europe and North America. Overall, 197 neurosurgeons (48.5%) reported having used robotic technology in clinical practice. The highest rates of adoption of robotics were observed for Europe (54%) and North America (51%). Apart from geographical region, only age under 30, female gender, and absence of a non-academic setting were significantly associated with clinical use of robotics. The Mazor family (32%) and ROSA (26%) robots were most commonly reported among robot users. Our study provides a worldwide overview of neurosurgical adoption of robotic technology. Almost half of the surveyed neurosurgeons reported having clinical experience with at least one robotic system. Ongoing and future trials should aim to clarify superiority or non-inferiority of neurosurgical robotic applications and balance these potential benefits with considerations on acquisition and maintenance costs

Medical Student Interest and Recruitment in Neurosurgery

Objective: Recent years have witnessed an increase in articles describing factors influencing medical student recruitment in neurosurgery, such as undergraduate preparation, impact of research experience, and selection into residency programs. In this study, we provide a comprehensive review of the literature addressing the relationship of medical students within neurosurgery. Methods: A search of the literature was conducted on the PubMed/MEDLINE database to October 2018 to screen for studies on medical student interest and recruitment in neurosurgery. Articles were screened for eligibility and reviewed for inclusion and their findings critically discussed. Results: Sixty-nine articles were included. Most research on the relationship of medical students with neurosurgery was conducted in the United States and United Kingdom. Data analysis was categorized into 2 groups: educational and noneducational factors. Eight areas of interest were identified: baseline undergraduate education, early research involvement, attitude toward neuroscience, mentoring, existence of a gender gap, residency program requirements, availability of educational resources, and networking opportunities. Conclusions: Our study bridges the gap of fragmented knowledge on medical student involvement in neurosurgery with the aim of optimizing existing approaches. We suggest that medical institutions outside the United States and United Kingdom should implement university-based interest groups to stimulate student interest, with reinforced participation of faculty for leading educational initiatives and collaborative research. We advocate the creation of national and international associations to support medical students in approaching neurosurgery early in their education

Development and external validation of clinical prediction models for pituitary surgery

Introduction: Gross total resection (GTR), Biochemical Remission (BR) and restitution of a priorly disrupted hypothalamus pituitary axis (new improvement, IMP) are important factors in pituitary adenoma (PA) resection surgery. Prediction of these metrics using simple and preoperatively available data might help improve patient care and contribute to a more personalized medicine. Research question: This study aims to develop machine learning models predicting GTR, BR, and IMP in PA resection surgery, using preoperatively available data. Material and methods: With data from patients undergoing endoscopic transsphenoidal surgery for PAs machine learning models for prediction of GTR, BR and IMP were developed and externally validated. Development was carried out on a registry from Bologna, Italy while external validation was conducted using patient data from Zurich, Switzerland. Results: The model development cohort consisted of 1203 patients. GTR was achieved in 207 (17.2%, 945 (78.6%) missing), BR in 173 (14.4%, 992 (82.5%) missing) and IMP in 208 (17.3%, 167 (13.9%) missing) cases. In the external validation cohort 206 patients were included and GTR was achieved in 121 (58.7%, 32 (15.5%) missing), BR in 46 (22.3%, 145 (70.4%) missing) and IMP in 42 (20.4%, 7 (3.4%) missing) cases. The AUC at external validation amounted to 0.72 (95% CI: 0.63–0.80) for GTR, 0.69 (0.52–0.83) for BR, as well as 0.82 (0.76–0.89) for IMP. Discussion and conclusion: All models showed adequate generalizability, performing similarly in training and external validation, confirming the possible potentials of machine learning in helping to adapt surgical therapy to the individual patient

Multicenter external validation of the Zurich Pituitary Score

Purpose: Recently, the Zurich Pituitary Score (ZPS) has been proposed as a new quantitative preoperative classification scheme for predicting gross total resection (GTR), extent of resection (EOR), and residual tumor volume (RV) in endoscopic pituitary surgery. We evaluated the external validity of the ZPS. Methods: In three reference centers for pituitary surgery, the ZPS was applied and correlated to GTR, EOR, and RV. Furthermore, its inter-rater agreement was assessed. Results: A total of 485 patients (53% male; age, 53.8 \ub1 15.7) were included. ZPS grades I, II, III, and IV were observed in 110 (23%), 270 (56%), 64 (13%), and 41 (8%) patients, respectively. GTR was achieved in 358 (74%) cases, with mean EOR of 87.6% \ub1 20.3% and RV of 1.42 \ub1 2.80&nbsp;cm3. With increasing ZPS grade, strongly significant decreasing trends for GTR (I, 92%; II, 77%; III, 67%; IV, 15%; p &lt; 0.001) and EOR (I, 93.8%; II, 89.9%; III, 88.1%; IV, 75.4%; p &lt; 0.001) were found. Similarly, RV increased steadily ([cm3] I, 0.16; II, 0.61; III, 2.01; IV, 3.84; p &lt; 0.001). We observed intraclass correlation coefficients of 0.837 (95% CI, 0.804\u20130.865) for intercarotid distance and 0.964 (95% CI, 0.956\u20130.970) for adenoma diameter, and Cohen\u2019s kappa of 0.972 (95% CI, 0.952\u20130.992) for the ZPS grades. Conclusions: Application of the ZPS in three external cohorts was successful. The ZPS generalized well in terms of GTR, EOR, and RV; demonstrated excellent inter-rater agreement; and can safely and effectively be applied as a quantitative classification of adenomas with relevance to surgical outcome

Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction

Machine learning (ML) is a rapidly rising research tool in biomedical sciences whose applications include segmentation, classification, disease detection, and outcome prediction. With respect to traditional statistical methods, ML algorithms have the potential to learn and improve their predictive performance when fed with large data sets without the need of being specifically programmed. In recent years, this technology has been increasingly applied for tackling clinical issues in intracranial aneurysm (IA) research. Several studies attempted to provide reliable models for enhanced aneurysm detection. Convolutional neural networks trained with variable degrees of human interaction on data from diverse imaging modalities showed high sensitivity in aneurysm detection tasks, also outperforming expert image analysis. Algorithms were also shown to differentiate ruptured from unruptured IAs, with however limited clinical relevance. For prediction of rupture and stability assessment, ML was preliminarily shown to achieve better performance compared to conventional statistical methods and existing risk scores. ML-based complication and functional outcome prediction in the event of SAH have been more extensively reported, in contrast with periprocedural outcome investigation in unruptured IA patients. ML has the potential to be a game changer in IA patient management. Currently clinical translation of experimental results is limited

Machine learning in neurosurgery: a global survey

Background: Recent technological advances have led to the development and implementation of machine learning (ML) in various disciplines, including neurosurgery. Our goal was to conduct a comprehensive survey of neurosurgeons to assess the acceptance of and attitudes toward ML in neurosurgical practice and to identify factors associated with its use. Methods: The online survey consisted of nine or ten mandatory questions and was distributed in February and March 2019 through the European Association of Neurosurgical Societies (EANS) and the Congress of Neurosurgeons (CNS). Results: Out of 7280 neurosurgeons who received the survey, we received 362 responses, with a response rate of 5%, mainly in Europe and North America. In total, 103 neurosurgeons (28.5%) reported using ML in their clinical practice, and 31.1% in research. Adoption rates of ML were relatively evenly distributed, with 25.6% for North America, 30.9% for Europe, 33.3% for Latin America and the Middle East, 44.4% for Asia and Pacific and 100% for Africa with only two responses. No predictors of clinical ML use were identified, although academic settings and subspecialties neuro-oncology, functional, trauma and epilepsy predicted use of ML in research. The most common applications were for predicting outcomes and complications, as well as interpretation of imaging. Conclusions: This report provides a global overview of the neurosurgical applications of ML. A relevant proportion of the surveyed neurosurgeons reported clinical experience with ML algorithms. Future studies should aim to clarify the role and potential benefits of ML in neurosurgery and to reconcile these potential advantages with bioethical considerations

FUSE-ML: development and external validation of a clinical prediction model for mid-term outcomes after lumbar spinal fusion for degenerative disease

Background: Indications and outcomes in lumbar spinal fusion for degenerative disease are notoriously heterogenous. Selected subsets of patients show remarkable benefit. However, their objective identification is often difficult. Decision-making may be improved with reliable prediction of long-term outcomes for each individual patient, improving patient selection and avoiding ineffective procedures. Methods: Clinical prediction models for long-term functional impairment [Oswestry Disability Index (ODI) or Core Outcome Measures Index (COMI)], back pain, and leg pain after lumbar fusion for degenerative disease were developed. Achievement of the minimum clinically important difference at 12&nbsp;months postoperatively was defined as a reduction from baseline of at least 15&nbsp;points for ODI, 2.2&nbsp;points for COMI, or 2&nbsp;points for pain severity. Results: Models were developed and integrated into a web-app (https://neurosurgery.shinyapps.io/fuseml/) based on a multinational cohort [N = 817; 42.7% male; mean (SD) age: 61.19 (12.36) years]. At external validation [N = 298; 35.6% male; mean (SD) age: 59.73 (12.64) years], areas under the curves for functional impairment [0.67, 95% confidence interval (CI): 0.59–0.74], back pain (0.72, 95%CI: 0.64–0.79), and leg pain (0.64, 95%CI: 0.54–0.73) demonstrated moderate ability to identify patients who are likely to benefit from surgery. Models demonstrated fair calibration of the predicted probabilities. Conclusions: Outcomes after lumbar spinal fusion for degenerative disease remain difficult to predict. Although assistive clinical prediction models can help in quantifying potential benefits of surgery and the externally validated FUSE-ML tool may aid in individualized risk–benefit estimation, truly impacting clinical practice in the era of “personalized medicine” necessitates more robust tools in this patient population