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.

摘要