Goldenholz Daniel M, Karoly Philippa J, Viana Pedro F, Nurse Ewan, Loddenkemper Tobias, Schulze-Bonhage Andreas, Vieluf Solveig, Bruno Elisa, Nasseri Mona, Richardson Mark P, Brinkmann Benjamin H, Westover M Brandon
Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA.
Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
Epilepsia. 2024 Apr;65(4):1017-1028. doi: 10.1111/epi.17917. Epub 2024 Feb 17.
Epilepsy management employs self-reported seizure diaries, despite evidence of seizure underreporting. Wearable and implantable seizure detection devices are now becoming more widely available. There are no clear guidelines about what levels of accuracy are sufficient. This study aimed to simulate clinical use cases and identify the necessary level of accuracy for each.
Using a realistic seizure simulator (CHOCOLATES), a ground truth was produced, which was then sampled to generate signals from simulated seizure detectors of various capabilities. Five use cases were evaluated: (1) randomized clinical trials (RCTs), (2) medication adjustment in clinic, (3) injury prevention, (4) sudden unexpected death in epilepsy (SUDEP) prevention, and (5) treatment of seizure clusters. We considered sensitivity (0%-100%), false alarm rate (FAR; 0-2/day), and device type (external wearable vs. implant) in each scenario.
The RCT case was efficient for a wide range of wearable parameters, though implantable devices were preferred. Lower accuracy wearables resulted in subtle changes in the distribution of patients enrolled in RCTs, and therefore higher sensitivity and lower FAR values were preferred. In the clinic case, a wide range of sensitivity, FAR, and device type yielded similar results. For injury prevention, SUDEP prevention, and seizure cluster treatment, each scenario required high sensitivity and yet was minimally influenced by FAR.
The choice of use case is paramount in determining acceptable accuracy levels for a wearable seizure detection device. We offer simulation results for determining and verifying utility for specific use case and specific wearable parameters.
尽管有证据表明癫痫发作报告存在漏报情况,但癫痫管理仍采用自我报告的发作日记。可穿戴和植入式癫痫发作检测设备现在越来越普遍。对于何种准确度水平足够,尚无明确指南。本研究旨在模拟临床用例并确定每个用例所需的准确度水平。
使用逼真的癫痫发作模拟器(CHOCOLATES)生成真实情况,然后对其进行采样,以生成来自各种功能的模拟癫痫发作检测器的信号。评估了五个用例:(1)随机临床试验(RCT),(2)临床药物调整,(3)预防伤害,(4)癫痫性猝死(SUDEP)预防,以及(5)癫痫发作丛集的治疗。我们在每种情况下考虑了灵敏度(0%-100%)、误报率(FAR;0-2次/天)和设备类型(外部可穿戴设备与植入式设备)。
对于广泛的可穿戴参数,RCT用例是有效的,不过植入式设备更受青睐。准确度较低的可穿戴设备会导致RCT招募患者的分布发生细微变化,因此较高的灵敏度和较低的FAR值更受青睐。在临床用例中,广泛的灵敏度、FAR和设备类型产生了相似的结果。对于预防伤害、预防SUDEP和治疗癫痫发作丛集,每种情况都需要高灵敏度,且受FAR的影响最小。
在用例的选择对于确定可穿戴癫痫发作检测设备可接受的准确度水平至关重要。我们提供了模拟结果,用于确定和验证特定用例和特定可穿戴参数的效用。