Noninvasive EM image guidance in shunt surgery reduces poor shunt placement, resulting in a significant decrease in the early shunt revision rate.

The success rate of ETVs in adults is comparable, if not better, than in children. In addition to the well-defined role of ETV in the treatment of hydrocephalus caused by tumors and aqueduct stenosis, ETV may also have a role in the management of CM-I, LOVA, persistent shunt infection, and IVH resistant to other CSF diversion procedures.

ObjectivesExternal ventricular drain (EVD) insertion is a common neurosurgical procedure. EVD-related infection (ERI) is a major complication that can lead to morbidity and mortality. In this study, we aimed to establish a national ERI rate in the UK and Ireland and determine key factors influencing the infection risk.MethodsA prospective multicentre cohort study of EVD insertions in 21 neurosurgical units was performed over 6 months. The primary outcome measure was 30-day ERI. A Cox regression model was used for multivariate analysis to calculate HR.ResultsA total of 495 EVD catheters were inserted into 452 patients with EVDs remaining in situ for 4700 days (median 8 days; IQR 4–13). Of the catheters inserted, 188 (38%) were antibiotic-impregnated, 161 (32.5%) were plain and 146 (29.5%) were silver-bearing. A total of 46 ERIs occurred giving an infection risk of 9.3%. Cox regression analysis demonstrated that factors independently associated with increased infection risk included duration of EVD placement for ≥8 days (HR=2.47 (1.12–5.45); p=0.03), regular sampling (daily sampling (HR=4.73 (1.28–17.42), p=0.02) and alternate day sampling (HR=5.28 (2.25–12.38); p<0.01). There was no association between catheter type or tunnelling distance and ERI.ConclusionsIn the UK and Ireland, the ERI rate was 9.3% during the study period. The study demonstrated that EVDs left in situ for ≥8 days and those sampled more frequently were associated with a higher risk of infection. Importantly, the study showed no significant difference in ERI risk between different catheter types.

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted ‘gold-standard’ subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy.

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