Current state of global neurosurgery activity amongst European neurosurgeons

BACKGROUND: The expanding field of global neurosurgery calls for a committed neurosurgical community to advocate for universal access to timely, safe, and affordable neurosurgical care for everyone, everywhere. This study aims to (i) assess the current state of global neurosurgery activity amongst European neurosurgeons and (ii) identify barriers to involvement in global neurosurgery initiatives. METHODS: Cross-sectional study through dissemination of a web-based survey, from September 2019 to January 2020, to collect data from European neurosurgeons at various career stages. Descriptive analysis was conducted on respondent data. RESULTS: Three hundred and ten neurosurgeons from 40 European countries responded. 53.5% regularly follow global neurosurgery developments. 29.4% had travelled abroad with a global neurosurgery collaborative, with 23.2% planning a future trip. Respondents from high income European countries predominantly travelled to Africa (41.6%) or Asia (34.4%), whereas, respondents from middle income European countries frequently traversed Europe (63.2%) and North America (47.4). Cost implications (66.5%) were the most common barrier to global neurosurgery activity, followed by interference with current practice (45.8%), family duties (35.2%), difficulties obtaining humanitarian leave (27.7%) and lack of international partners (27.4%). 86.8% would incorporate a global neurosurgery period within training programmes. CONCLUSIONS: European neurosurgeons are interested in engaging in global neurosurgery partnerships, and several sustainable programmes focused on local capacity building, education and research have been established over the last decade. However, individual and system barriers to engagement persist. We provide insight into these to allow development of tailored mechanisms to overcome such barriers, enabling European neurosurgeons to advocate for the Global Surgery 2030 goals

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 months postoperatively was defined as a reduction from baseline of at least 15 points for ODI, 2.2 points for COMI, or 2 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