OBJECTIVE Decision-making for intracranial tumor surgery requires balancing the oncological benefit against the risk for resection-related impairment. Risk estimates are commonly based on... Show moreOBJECTIVE Decision-making for intracranial tumor surgery requires balancing the oncological benefit against the risk for resection-related impairment. Risk estimates are commonly based on subjective experience and generalized num-bers from the literature, but even experienced surgeons overestimate functional outcome after surgery. Today, there is no reliable and objective way to preoperatively predict an individual patient's risk of experiencing any functional impair-ment. METHODS The authors developed a prediction model for functional impairment at 3 to 6 months after microsurgical resection, defined as a decrease in Karnofsky Performance Status of >= 10 points. Two prospective registries in Swit- zerland and Italy were used for development. External validation was performed in 7 cohorts from Sweden, Norway, Germany, Austria, and the Netherlands. Age, sex, prior surgery, tumor histology and maximum diameter, expected major brain vessel or cranial nerve manipulation, resection in eloquent areas and the posterior fossa, and surgical approach were recorded. Discrimination and calibration metrics were evaluated. RESULTS In the development (2437 patients, 48.2% male; mean age +/- SD: 55 +/- 15 years) and external validation (2427 patients, 42.4% male; mean age +/- SD: 58 +/- 13 years) cohorts, functional impairment rates were 21.5% and 28.5%, respectively. In the development cohort, area under the curve (AUC) values of 0.72 (95% CI 0.69-0.74) were observed. In the pooled external validation cohort, the AUC was 0.72 (95% CI 0.69-0.74), confirming generalizability. Calibration plots indicated fair calibration in both cohorts. The tool has been incorporated into a web-based application available at https://neurosurgery.shinyapps.io/impairment/. CONCLUSIONS Functional impairment after intracranial tumor surgery remains extraordinarily difficult to predict, al- though machine learning can help quantify risk. This externally validated prediction tool can serve as the basis for case by-case discussions and risk-to-benefit estimation of surgical treatment in the individual patient. Show less
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... Show moreRecent 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. Show less
OBJECTIVE The 6-minute walking test (6WT) is used to determine restrictions in a subject's 6-minute walking distance (6WD) due to lumbar degenerative disc disease. To facilitate simple and... Show moreOBJECTIVE The 6-minute walking test (6WT) is used to determine restrictions in a subject's 6-minute walking distance (6WD) due to lumbar degenerative disc disease. To facilitate simple and convenient patient self-measurement, a free and reliable smartphone app using Global Positioning System coordinates was previously designed. The authors aimed to determine normative values for app-based 6WD measurements.METHODS The maximum 6WD was determined three times using app-based measurement in a sample of 330 volunteers without previous spine surgery or current spine-related disability, recruited at 8 centers in 5 countries (mean subject age 44.2 years, range 16-91 years; 48.5% male; mean BMI 24.6 kg/m(2), range 16.3-40.2 kg/m(2); 67.9% working; 14.2% smokers). Subjects provided basic demographic information, including comorbidities and patient-reported outcome measures (PROMs): visual analog scale (VAS) for both low-back and lower-extremity pain, Core Outcome Measures Index (COMI), Zurich Claudication Questionnaire (ZCQ), and subjective walking distance and duration. The authors determined the test-retest reliability across three measurements (intraclass correlation coefficient [ICC], standard error of measurement [SEM], and mean 6WD [95% CI]) stratified for age and sex, and content validity (linear regression coefficients) between 6WD and PROMs.RESULTS The ICC for repeated app-based 6WD measurements was 0.89 (95% CI 0.87-0.91, p < 0.001) and the SEM was 34 meters. The overall mean 6WD was 585.9 meters (95% CI 574.7-597.0 meters), with significant differences across age categories (p < 0.001). The 6WD was on average about 32 meters less in females (570.5 vs 602.2 meters, p = 0.005). There were linear correlations between average 6WD and VAS back pain, VAS leg pain, COMI Back and COMI subscores of pain intensity and disability, ZCQ symptom severity, ZCQ physical function, and ZCQ pain and neuroischemic symptoms subscores, as well as with subjective walking distance and duration, indicating that subjects with higher pain, higher disability, and lower subjective walking capacity had significantly lower 6WD (all p < 0.001).CONCLUSIONS This study provides normative data for app-based 6WD measurements in a multicenter sample from 8 institutions and 5 countries. These values can now be used as reference to compare 6WT results and quantify objective functional impairment in patients with degenerative diseases of the spine using z-scores. The authors found a good to excellent test-retest reliability of the 6WT app, a low area of uncertainty, and high content validity of the average 6WD with commonly used PROMs. Show less
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... Show moreBackground 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. Show less
