Prediction of Behavioral Improvement Through Resting-State Electroencephalography and Clinical Severity in a Randomized Controlled Trial Testing Bumetanide in Autism Spectrum Disorder.

BACKGROUND: Mechanism-based treatments such as bumetanide are being repurposed for autism spectrum disorder. We recently reported beneficial effects on repetitive behavioral symptoms that might be related to regulating excitation-inhibition (E/I) balance in the brain. Here, we tested the neurophysiological effects of bumetanide and the relationship to clinical outcome variability and investigated the potential for machine learning-based predictions of meaningful clinical improvement. METHODS: Using modified linear mixed models applied to intention-to-treat population, we analyzed E/I-sensitive electroencephalography (EEG) measures before and after 91 days of treatment in the double-blind, randomized, placebo-controlled Bumetanide in Autism Medication and Biomarker study. Resting-state EEG of 82 subjects out of 92 participants (7-15 years) were available. Alpha frequency band absolute and relative power, central frequency, long-range temporal correlations, and functional E/I ratio treatment effects were related to the Repetitive Behavior Scale-Revised (RBS-R) and the Social Responsiveness Scale 2 as clinical outcomes. RESULTS: We observed superior bumetanide effects on EEG, reflected in increased absolute and relative alpha power and functional E/I ratio and in decreased central frequency. Associations between EEG and clinical outcome change were restricted to subgroups with medium to high RBS-R improvement. Using machine learning, medium and high RBS-R improvement could be predicted by baseline RBS-R score and EEG measures with 80% and 92% accuracy, respectively. CONCLUSIONS: Bumetanide exerts neurophysiological effects related to clinical changes in more responsive subsets, in whom prediction of improvement was feasible through EEG and clinical measures.

Cost-effectiveness analysis in relation to budgetary constraints and reallocative restrictions.

BACKGROUND: Present cost-effectiveness analyses (CEAs) provide not all information necessary for decision-making. One of the factors that hamper decision-making is the difficulty in reallocating resources to new technologies. In a CEA, the incremental costs and incremental benefits of a new technology are calculated. In this article we focus on the incremental cost side. The underlying assumption in socio-economic evaluation is that resources from the substituted alternatives can be used to finance the new technology. In practice, however, not all resources are becoming available to introduce the alternative. The budgets in health care are rather fixed and shifting from one alternative to another or from one sector to another is often impossible. Even within a budget, the personnel and material resources are usually not entirely usable for the new technology, and sometimes not at all. Therefore, the present CEA outcomes might overestimate the cost-effectiveness in practice, which might influence implementation of a new technology. AIM: To optimise the usefulness of economic evaluation for health care decision-making by correcting the incremental costs of a new technology for the possible limitations in reallocating resources and adjusting budgets in health care. METHODS: Case Research. Literature, data from two completed CEAs and interviews with decision makers in the hospital setting. RESULTS: Case 1: The combined outpatient and home-treatment of psoriasis--In a CEA it was calculated that the new technology lead to much lower cost, given the same effects. The direct costs of this technology comprise personnel, material and capacity costs. Personnel and capacity are inflexible with regard to reallocation, at least in the short term. Considering these reallocative restrictions results show that the cost-savings of the combined treatment are in the short run significantly smaller than in the long run: 694 versus 6.058, respectively. Therefore, the anticipated savings, estimated are not realistic for decision makers with a short time horizon. The short-term savings amount to only 11% of the anticipated savings in the long run. Nevertheless, the combined treatment remains a cost-effective treatment. Analysing the budgetary constraints resulted in the finding that the substitution of the in-hospital treatment by the combined treatment has taken place without negative financial consequences for the hospital. Case 2: The ground bound mobile medical team--Economic arguments to implement the ground bound mobile medical team (MMT) are undecided. With respect to the budgetary constraints we find that the budget for the trauma centre is conditional upon the deployment of the ground bound MMT. Moreover, the cost of the ground bound MMT is a relatively small part of the budget for the trauma centre and therefore no hurdle to implement. CONCLUSIONS: On the basis of these findings we conclude that limitations in reallocating resources and adjusting budgets in health care may hamper the usefulness of economic evaluation for decision-making. Researching the extent of these limitations provides, together with the CEA, better information on which the decision whether a new technology should be implemented and what the expected welfare gains from such an implementation might be can be made. For this a set of checklists is developed.