Determining the clinical utility of BioEP: a multisite, prospective, study

我们的建议集中在癫痫：一种严重的神经系统疾病。癫痫的特点是倾向于反复发作，自发性发作。由于癫痫发作似乎是随机发生的，因此癫痫诊断非常具有挑战性。脑电图（EEG）是一个重要的临床工具，揭示异常，表明癫痫。然而，大约70%的脑电图记录没有提供癫痫的临床指征。因此，诊断延误经常超过一年，误诊率估计为30%。据估计，这种误诊每年给NHS带来的损失超过3亿英镑。为了应对这一挑战，神经内科医生开发了一种计算机癫痫生物标志物：BioEP。BioEP的工作原理是将常规采集的EEG整合到大脑的计算机模型中。使用先进的机器学习算法对该模型进行模拟，提供了一个风险评分，显示癫痫发作的容易程度。对于目前被认为在临床上无贡献的EEG，BioEP风险评分提供了有价值的额外诊断信息。支持BioEP的研究得到了《柳叶刀神经病学》的认可（作为2015年癫痫的突破性研究），癫痫基金会（通过2017年的商业化奖）和《自然》（Neuronostics是2021年SpinOff奖的最后四名之一）。通过Innovate UK和NIHR的资助，Neuronostics开发了一个由BioEP提供支持的诊断决策支持工具原型，它可以实现半自动化的特征提取和报告编写。神经诊断学最近完成了一项回顾性研究（使用已知和准确诊断的人的数据），以验证BioEP的性能。这是同类研究中规模最大的一项研究，我们证明BioEP可以在明显正常的EEG中揭示癫痫的易感性，其水平超过目前的临床实践。在目前的项目中，我们将进行一项前瞻性研究，以证明BioEP风险评分的临床实用性。我们将进行一项多中心研究，疑似癫痫患者将通过正常护理途径，但在一半的情况下，我们将额外提供BioEP风险评分。我们将比较各组的工作诊断时间、诊断类型以及临床医生对决策的信心，这将使我们能够了解BioEP对诊断时间、决策的准确性和客观性以及对决策的信心等关键临床结局的影响。

Our proposal is focussed on Epilepsy: a serious neurological condition. Epilepsy is characterised by the tendency to have repeated, spontaneous, seizures. Since seizures occur seemingly at random, epilepsy diagnosis is very challenging. The electroencephalogram (EEG) is an important clinical tool for revealing abnormalities that indicate epilepsy. However, around 70% of recorded EEGs provide no clinical indication of epilepsy. Consequently, diagnostic delays regularly exceed a year and the rate of misdiagnosis is estimated at 30%. The cost of this misdiagnosis has been estimated at over £300M annually to the NHS.To address this challenge, Neuronostics have developed a computer Biomarker ofEpilepsy: BioEP. BioEP works by integrating routinely acquired EEG into a computer model of the brain. Simulations of this model, interrogated using advanced machine learning algorithms, provide a risk score showing how easy it is for seizures to occur. For EEGs currently considered clinically non-contributory, the BioEP risk score provides valuable additional diagnostic information. Research underpinning BioEP has been recognised by the Lancet Neurology (as a breakthrough study in epilepsy in 2015), the Epilepsy Foundation (through a commercialisation prize in 2017) and Nature (Neuronostics was one of the final four in the SpinOff Prize 2021).Through Innovate UK and NIHR funding, Neuronostics have developed a prototype of a diagnostic decision support tool powered by BioEP, that enables semi-automated feature extraction and report writing. Neuronostics have recently completed a retrospective study (using data from people with a known and accurate diagnosis) to validate the performance of the BioEP. The largest study of its kind, we demonstrated that BioEP can reveal susceptibility to epilepsy in apparently normal EEGs at a level that out performs current clinical practice.In the current project, we will undertake a prospective study to demonstrate the clinical utility of the BioEP risk score. We will conduct a multi-site study where people with suspected epilepsy will go through the normal care pathway, but in half the cases we will additionally provide the BioEP risk score. We will compare the time to working diagnosis, the types of diagnosis made, and the confidence of the clinician in the decision made in each arm. This will enable us to understand the impact BioEP has on the key clinical outcomes of time to diagnosis, accuracy and objectivity of decision making, as well as confidence in the decision reached.