Real World Testing and Cost-effectiveness Analysis of Subcutaneous EEG (REAL-ASE)

Research question We will investigate whether a novel method to accurately count epileptic seizures, using a minimally-invasive ultra-longterm subcutaneous EEG system (EEG-SubQ), (i) is more accurate than a patient-reported seizure diary; (ii) is feasible and acceptable to patients and clinicians; (iii) reduces impacts of epilepsy and improves quality-of-life; and (iv) provides gains to the healthcare system when rolled-out into the NHS.

Background Epilepsy is common, has high morbidity, significant mortality, and high costs. Epileptic seizures are the key symptom, substantially determining quality of life. Seizures are the direct cause of seizure-related harms and most epilepsy-related deaths.

NICE epilepsy treatment guidelines emphasise regularly assessing whether treatment is reducing the occurrence of seizures.

In clinical practice, a patient-reported seizure diary is the ubiquitous approach, but strong evidence suggests such diaries are extremely unreliable. Therefore, clinicians make treatment decisions based on deeply inaccurate seizure occurrence information. Our prior experience with EEG-SubQ suggests EEG-SubQ could provide highly reliable seizure occurrence information.

Aims and Objectives We aim to demonstrate that EEG-SubQ, linked to an AI-driven algorithm to identify seizures, will provide a feasible and reliable method to objectively document seizures.

Objectives: to undertake a multicentre cohort study in which people with epilepsy will be implanted with EEG-SubQ; to undertake a health economic assessment, based on data from the cohort; to develop a plan for NHS adoption and route to market.

Methods In an observational cohort study, we will collect the following data over 6 months: Near-continuous 2-channel EEG data from EEG-SubQ.

Estimates of times of occurrence of seizures, derived from an AI-driven algorithm applied to the EEG data from EEG-SubQ, supplemented with expert rapid review of candidate detections. Patient-reported electronic seizure diary reports of times of occurrence of seizures.

Patient-reported outcome measures collected at baseline, 2 months and 6 months: perceived Self-Mastery Over Epilepsy; Technology Acceptance Model - Fast Form; Impact of Epilepsy Scale; EQ-5D-5L; QOLIE-31-P. Client Service Receipt Inventory collected at 6 months. Clinician rating of confidence in the EEG-SubQ estimates of seizure occurrences.

Primary outcome measures will be accuracy of seizure detection of EEG-SubQ, and feasibility/acceptability of EEG-SubQ. Secondary outcomes will focus on changes in patient experience and quality of life. Both within-trial and model-based health economic analyses will be performed.

An economic decision-analytic model will be developed to consider relative cost-effectiveness of current practice compared with patient management enhanced using EEG-SubQ. Results of analysis will be presented in terms of cost per quality-adjusted life-year (QALY) gained.

Anticipated Impact and Dissemination To enhance the probability of achieving impact, we will: (1) publish in scientific journals and conference presentations; (2) undertake Policy Labs and consequent policy publication and influencing work; (3) develop a health economics case for EEG-SubQ in the NHS and subsequently engage with NHS policymakers; (4) engage with voluntary sector patient groups in epilepsy; (5) create a media work for public dissemination.

We believe this research will be key to improving seizure outcome measurement in epilepsy, thus providing new standards of patient care, improving patient outcomes, and reducing costs.