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ObjectiveNovel and minimally invasive neurotechnologies offer the potential to reduce the burden of epilepsy while avoiding the risks of conventional resective surgery. Few neurotechnologies have been tested in randomized controlled trials with pediatric populations, leaving clinicians to face decisions about whether to recommend these treatments with insufficient evidence about the relevant risks and benefits. This study specifically explores the preferences of clinicians for treating pediatric drug-resistant epilepsy (DRE) with novel neurotechnologies.MethodsA discrete-choice experiment (DCE) was designed to elicit the preferences of clinicians with experience in treating children with DRE using novel neurotechnological interventions. The preferences for six key attributes used when making treatment decisions (chances of clinically significant improvement in seizures, major and minor risks from intervention, availability of evidence, financial burden for the family, and access to the intervention) were estimated using a conditional logit model. The estimates from this model were then used to predict the adoption of existing novel neurotechnological interventions.ResultsSixty-eight clinicians completed the survey: 33 neurosurgeons, 28 neurologists, and 7 other clinicians. Most clinicians were working in the United States (74%), and the remainder (26%) in Canada. All attributes, apart from the nearest location with access to the intervention, influenced preferences significantly. The chance of clinically significant improvement in seizures was the most positive influence on clinician preferences, but low-quality evidence and a higher risk of major complications could offset these preferences. Of the existing neurotechnological interventions, vagus nerve stimulation was predicted to have the highest likelihood of adoption; deep brain stimulation had the lowest likelihood of adoption.SignificanceThe preferences of clinicians are drive primarily by the likelihood of achieving seizure freedom for their patients, but preferences for an intervention are largely eradicated if only low quality of evidence supporting the intervention is available. Until better evidence supporting the use of potentially effective, novel neurotechnologies becomes available, clinicians are likely to prefer more established treatments.

This qualitative study investigated factors that guide caregiver decision making and ethical trade-offs for advanced neurotechnologies used to treat children with drug-resistant epilepsy. Caregivers with affected children were recruited to semi-structured focus groups or interviews at one of 4 major epilepsy centers in Eastern and Western Canada and the USA (n = 22). Discussions were transcribed and qualitative analytic methods applied to examine values and priorities (eg, risks, benefits, adherence, invasiveness, reversibility) of caregivers pertaining to novel technologies to treat drug-resistant epilepsy. Discussions revealed 3 major thematic branches for decision making: (1) features of the intervention-risks and benefits, with an emphasis on an aversion to perceived invasiveness; (2) decision drivers-trust in the clinical team, treatment costs; and (3) quality of available information about neurotechnological options. Overall, caregivers' definition of treatment success is more expansive than seizure freedom. The full involvement of their values and priorities must be considered in the decision-making process.

This qualitative study investigated factors that guide physicians' choices for minimally invasive and neuromodulatory interventions as alternatives to conventional surgery or medical management for pediatric drug-resistant epilepsy. North American physicians were recruited to one of 4 focus groups at national conferences. Discussions were analyzed using qualitative content analysis. A pragmatic neuroethics framework was applied to interpret results. Discussions revealed 2 major thematic branches: (1) clinical decision making and (2) ethical considerations. Under clinical decision making, physicians emphasized scientific evidence and patient candidacy when assessing neurotechnologies for patients. Ongoing seizures without intervention was important for safety and neurodevelopment. Under ethical considerations, resource allocation, among other financial considerations for technology adoption, were considerable sources of pressure on decision making. Access to neurotechnology was a salient theme differentiating Canadian and American contexts. When assessing novel neurotechnological interventions for pediatric drug-resistant epilepsy, physicians balance clinical and ethical factors to guide decision making and best practice.

Purpose: This Hydrocephalus Clinical Research Network (HCRN) study had two aims: (1) to compare the predictive performance of the original ETV Success Score (ETVSS) using logistic regression modeling with other newer machine learning models and (2) to assess whether inclusion of imaging variables improves prediction performance using machine learning models. Methods: We identified children undergoing first-time ETV for hydrocephalus that were enrolled prospectively at HCRN sites between 200 and 2020. The primary outcome was ETV success 6 months after index surgery. The cohort was randomly divided into training (70%) and testing (30%) datasets. The classic ETVSS variables were used for logistic regression and machine learning models. Predictive performance of each model was evaluated on the testing dataset using area under the receiver operating characteristic curve (AUROC). Results: There were 752 patients that underwent first time ETV, of which 185 patients (24.6%) experienced ETV failure within 6 months. For aim 1, using the classic ETVSS variables, machine learning models did not outperform logistic regression with AUROC 0.60 (95% CI: 0.52–0.69) for Naïve Bayes (highest machine learning model performance) and 0.68 (95% CI: 0.60–0.76) for logistic regression. After inclusion of imaging features (aim 2), machine learning model prediction improved but remained no better than the above logistic regression with the highest AUROC of 0.67 (95% CI: 0.59–0.75) attained using Naïve Bayes architecture compared to 0.68 (95% CI: 0.59–0.76) for logistic regression. Conclusions: This contemporary multicenter observational cohort study demonstrated that machine learning modeling strategies did not improve performance of the ETVSS model over logistic regression. [ABSTRACT FROM AUTHOR]

Shunt infections are a common complication when treating hydrocephalus by cerebrospinal fluid (CSF) shunt placement. The source of infecting pathogens is not well understood. One hypothesis, which we explored here, is that microorganisms persist chronically in the host long before a symptomatic infection occurs and may be detectable in surgical events preceding infection. A cohort of 13 patients was selected, for which CSF samples were available from an infection episode and from a previous surgery event, which was either an initial shunt placement or a revision. Microbiota were analyzed both directly from CSF and from isolates cultured from CSF on aerobic and anaerobic media. The detection and identification of bacteria was done with high throughput DNA sequencing methods and mass spectrometry. The presence of bacteria was confirmed in 4 infection samples, of which 2 were after initial placement and 2 after revision surgery. Taxonomic identification was consistent with clinical microbiology laboratory results. Bacteria were not detected in any of the CSF samples collected at the time of the previous surgical events. While our findings do not provide direct evidence for long-term persistence of pathogens, they suggest the need for consideration of additional source material, such as biofilm and environmental swabs, and/or the use of more sensitive and specific analytical methods. [ABSTRACT FROM AUTHOR]

Objective: To characterize the microbiota of the cerebrospinal fluid (CSF) from children with hydrocephalus at the time of initial surgical intervention. Study design: CSF was obtained at initial surgical intervention. One aliquot was stored in skim milk-tryptone-glucose-glycerol (STGG) medium and the second was unprocessed; both were then stored at –70°C. Bacterial growth for CSF samples stored in STGG were subsequently characterized using aerobic and anaerobic culture on blood agar and MALDI-TOF sequencing. All unprocessed CSF samples underwent 16S quantitative polymerase chain reaction (qPCR) sequencing, and a subset underwent standard clinical microbiological culture. CSF with culture growth (either after storage in STGG or standard clinical) were further analyzed using whole-genome amplification sequencing (WGAS). Results: 11/66 (17%) samples stored in STGG and 1/36 (3%) that underwent standard clinical microbiological culture demonstrated bacterial growth. Of the organisms present, 8 were common skin flora and 4 were potential pathogens; only 1 was also qPCR positive. WGAS findings and STGG culture findings were concordant for only 1 sample, identifying Staphylococcus epidermidis. No significant difference in time to second surgical intervention was observed between the STGG culture-positive and negative groups. Conclusion(s): Using high sensitivity methods, we detected the presence of bacteria in a subset of CSF samples at the time of first surgery. Therefore, the true presence of bacteria in CSF of children with hydrocephalus cannot be ruled out, though our findings may suggest these bacteria are contaminants or false positives of the detection methods. Regardless of origin, the detection of microbiota in the CSF of these children may not have any clinical significance. [ABSTRACT FROM AUTHOR]