Biomarkers to Predict Outcome from Responsive Brain Stimulation for Epilepsy

预测响应性脑刺激治疗癫痫结果的生物标志物

• 批准号: 10578058
• 负责人: Kathryn Adamiak Davis
• 金额: $ 129.98万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10578058
• 关键词: Acceleration; Address; Adopted; Adoption; American; Biological Markers; Brain; Chronic; Clinic; Clinical; Clinical Data; Clinical Trials; Collaborations; Data; Data Set; Data Store; Decision Making; Development; Devices; Electroencephalography; Electrophysiology (science); Engineering; Ensure; Epilepsy; Evaluation; FDA approved; Freedom; Frequencies; Goals; Heterogeneity; Image; Implant; Individual; Industry; Industry Collaboration; Infrastructure; Institutional Review Boards; Intractable Epilepsy; Machine Learning; Medical; Metadata; Modeling; Monitor; Network-based; Operative Surgical Procedures; Outcome; Patient Selection; Patient-Focused Outcomes; Patients; Persons; Pharmaceutical Preparations; Phase; Physicians; Prior Therapy; Privatization; Prognostic Marker; Public Health; Research; Resistance; Running; Secure; Seizures; Site; Standardization; Testing; Therapeutic Effect; Time; Tonic-Clonic Epilepsy; Training; Work; analysis pipeline; biomarker discovery; biomarker signature; biomarker validation; candidate marker; clinical center; clinical decision-making; clinical research site; cloud based; computational neuroscience; data exchange; data mining; effective therapy; experience; federated data; geographically distant; hands-on learning; implantable device; implantation; improved; individual patient; industry partner; innovation; multimodal data; multimodality; nervous system disorder; neural; neuroimaging; novel; outcome prediction; participant enrollment; patient population; patient privacy; patient response; predict responsiveness; predicting response; predictive marker; predictive signature; preservation; prospective; response; screening; success; translational neuroscience; treatment response

Abstract ________________________________________________________________________________________ The current FDA-approved responsive neurostimulation (RNS) device offers a promising alternative to surgery for more than 600,000 Americans with intractable epilepsy who are not candidates for resective surgery. Unfortunately, there are no validated biomarkers to predict seizure outcomes before these devices are placed, and approximately 1/3 of patients do not benefit from RNS long-term. There is a critical need to develop biomarkers based upon clinical and electrophysiological data to determine the most effective therapy for patients with medication-resistant seizures, and to bring quantitative rigor to clinical decision making. The long-term goal of this proposal is to discover and validate a predictive biomarker signature for RNS response that can be used in epilepsy surgery decision making and broadly adopted. To achieve this goal, our overall objective is to develop this prognostic biomarker signature using machine learning applied to a carefully selected set of features and models calculated from intracranial EEG (IEEG) obtained during presurgical evaluation that incorporates qualitative clinical features. We will collaborate across centers and with industry partners via a novel federated approach, whereby each clinical site will post data in a common format to their own, private, cloud-based data store, which will be accessible to analysis pipelines run centrally from our cloud-based platform. Our central hypothesis is that biomarker signatures derived from multimodal data collected during evaluation prior to device implant can be used to predict patient response to RNS therapy. Our preliminary data, analyzing 10 RNS patients each from UCSF, NYU and UPenn, demonstrates our ability to perform the proposed research. In the R61 Phase, we will test this hypothesis retrospectively in 125 patients who underwent IEEG prior to RNS device placement at the UPenn, UCSF and NYU epilepsy centers. Our specific aims for this phase are: 1) To build a federated processing pipeline for biomarker discovery using presurgical evaluation neuroimaging, IEEG and clinical metadata, 2) To identify a predictive biomarker signature from this data. Our federated analysis framework will enable us to: (a) accelerate biomarker discovery across multiple sites and industry partners, (b) satisfy patient and industry limitations on sharing proprietary data, (c) provide a practical framework for rapid adoption across clinical centers worldwide. In the R33 phase, the biomarker signature will be validated in 100 additional patients followed longitudinally at 9 clinical sites. The proposed research is innovative because it represents a substantive departure from the status quo by rigorously analyzing multimodal patient data to predict response to RNS and guide decisions on device implantation. The proposed research is significant because it has the potential to dramatically improve the success rate of RNS for epilepsy through better patient selection. This study also puts into place a novel, versatile, federated data mining infrastructure for multi-center and industry collaboration in translational neuroscience.

抽象的 ___________________________________________________________________________________________________________________________ 当前FDA批准的反应性神经刺激（RNS）设备为 对60万名美国人的癫痫运动的手术，这些癫痫不是候选人进行手术的候选者。 不幸的是，在放置这些设备之前，没有经过验证的生物标志物可以预测癫痫发作结果， 大约1/3的患者长期无法从RN中受益。有迫切需要发展 基于临床和电生理数据的生物标志物，以确定患者最有效的治疗 抗药性癫痫发作，并将定量性严格限制到临床决策中。长期目标 该建议的是发现并验证可以使用的RNS响应的预测生物标志物签名 在癫痫手术决策中，并广泛采用。为了实现这一目标，我们的总体目标是 使用应用于精心选择的功能的机器学习来开发这种预后的生物标志物签名 以及根据术前评估中获得的颅内脑电图（IEEG）计算的模型 定性临床特征。我们将通过一个新颖的联合会进行整个中心和行业合作伙伴的合作 方法，每个临床站点都将以共同格式发布其自己的私人，基于云的数据 商店，分析管道可以从我们的基于云的平台中心运行。我们的中心 假设是从设备之前评估期间收集的多模式数据得出的生物标志物特征 植入物可用于预测患者对RNS治疗的反应。我们的初步数据，分析10名RNS患者 来自UCSF，NYU和UPENN的每个人都证明了我们执行拟议研究的能力。 在R61阶段，我们将回顾性地测试这一假设的125名患者 在UPENN，UCSF和NYU癫痫中心放置RNS设备。我们对此阶段的具体目标是： 1）建立联合处理管道，以使用术前评估神经影像学发现生物标志物发现， IEEG和临床元数据，2）从这些数据中确定预测性生物标志物签名。我们的联合分析 框架将使我们能够：（a）在多个站点和行业合作伙伴之间加速生物标志物发现，（b） 满足患者和行业共享专有数据的局限性，（c）为快速提供了一个实用的框架 全球临床中心的采用。在R33阶段，生物标志物签名将在100中进行验证 其他患者在9个临床部位进行了纵向跟踪。拟议的研究具有创新性，因为它 通过严格分析多模式患者数据来预测，代表与现状的实质性不同 对RN的响应和指导有关设备植入的决策。拟议的研究很重要，因为它 有可能通过更好的患者选择来显着提高癫痫的RN成功率。 这项研究还将用于多中心和行业的新颖，多功能的联合数据挖掘基础设施 转化神经科学的合作。