Determining the risk of spinal pathology progression in neurofibromatosis type 1 patients - a national tertiary neurofibromatosis type 1 centre study.

BACKGROUND: Neurofibromatosis type 1 (NF1) gives rise to a variety of spinal pathologies that include dural ectasia (DE), vertebral malalignments (VMA), spinal deformities (SD), syrinx, meningoceles, spinal nerve root tumours (SNRT), and spinal plexiform tumours (SPT). The relationship between these and the progression of these pathologies has not been explored before in detail and this paper aims to address this. METHODS: Data was retrospectively collected from adult NF1 multi-disciplinary team meetings from 2016 to 2022 involving a total of 593 patients with 20 distinct predictor variables. Data were analyzed utilizing; Chi-Square tests, binary logistic regression, and Kaplan-Meier analysis. RESULTS: SNRT (19.9%), SD (18.6%), and (17.7%) of VMA had the highest rates of progression. SD was significantly associated (p < 0.02) with the presence and progression of all spinal pathologies except for SPT. Statistically significant predictors of SD progression included the presence of DVA, VMA, syrinx, meningocele, and SNRT. Kaplan-Meier analysis revealed no statistically significant difference between the times to progression for SD (85 days), SNRT (1196 days), and VMA (2243 days). CONCLUSION: This paper explores for the first time in detail, the progression of various spinal pathologies in NF1. The presence and progression of SD is a key factor that correlated with the progression of different spinal pathologies. Early identification of SD may help support clinical decision-making and guide radiological follow-up protocols and treatment.

Predicting neurosurgical referral outcomes in patients with chronic subdural hematomas using machine learning algorithms - A multi-center feasibility study.

Background: Chronic subdural hematoma (CSDH) incidence and referral rates to neurosurgery are increasing. Accurate and automated evidence-based referral decision-support tools that can triage referrals are required. Our objective was to explore the feasibility of machine learning (ML) algorithms in predicting the outcome of a CSDH referral made to neurosurgery and to examine their reliability on external validation. Methods: Multicenter retrospective case series conducted from 2015 to 2020, analyzing all CSDH patient referrals at two neurosurgical centers in the United Kingdom. 10 independent predictor variables were analyzed to predict the binary outcome of either accepting (for surgical treatment) or rejecting the CSDH referral with the aim of conservative management. 5 ML algorithms were developed and externally tested to determine the most reliable model for deployment. Results: 1500 referrals in the internal cohort were analyzed, with 70% being rejected referrals. On a holdout set of 450 patients, the artificial neural network demonstrated an accuracy of 96.222% (94.444-97.778), an area under the receiver operating curve (AUC) of 0.951 (0.927-0.973) and a brier score loss of 0.037 (0.022-0.056). On a 1713 external validation patient cohort, the model demonstrated an AUC of 0.896 (0.878-0.912) and an accuracy of 92.294% (90.952-93.520). This model is publicly deployed: https://medmlanalytics.com/neural-analysis-model/. Conclusion: ML models can accurately predict referral outcomes and can potentially be used in clinical practice as CSDH referral decision making support tools. The growing demand in healthcare, combined with increasing digitization of health records raises the opportunity for ML algorithms to be used for decision making in complex clinical scenarios.