An informatics framework for SUDEP Risk Marker Identification and Risk Assessment

SUDEP 风险标记识别和风险评估的信息学框架

• 批准号: 10393043
• 负责人: Licong Cui
• 金额: $ 34.84万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-15 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10393043
• 关键词: 15 year old; Academy; Address; Adult; Affect; Algorithmic Software; Algorithms; American; Apnea; Assessment tool; Autopsy; Basic Science; Biological; Biological Markers; Brain; Brain Injuries; Caring; Categories; Cause of Death; Cessation of life; Clinical; Clinical Data; Communication; Computational algorithm; Controlled Vocabulary; Data; Data Element; Data Set; Development; Electrocardiogram; Electroencephalography; Electrophysiology (science); Enrollment; Epilepsy; Equipment and supply inventories; Europe; Functional disorder; Funding; Future; Generations; Goals; Guidelines; Incidence; Individual; Informatics; Institution; Intervention; Lead; Medical center; Methods; Modeling; Monitor; National Institute of Neurological Disorders and Stroke; Neurology; Ontology; Patient Care; Patients; Persons; Phenotype; Physiological; Plant Roots; Readability; Recommendation; Reporting; Research; Risk; Risk Assessment; Risk Factors; Risk Marker; Safety; Seizures; Semantics; Signal Transduction; Standardization; Structure; System; Techniques; Terminology; Text; Time; Tonic - clonic seizures; Unified Medical Language System; United States; United States National Institutes of Health; Vocabulary; automated algorithm; base; biomarker identification; cohort; data repository; early onset; evidence base; imaging biomarker; improved; innovation; modifiable risk; mortality; multimodal data; multimodality; post stroke; predictive marker; preventable death; success; sudden unexpected death in epilepsy; tool; years of life lost

PROJECT SUMMARY Sudden Unexpected Death in Epilepsy (SUDEP) is the leading mode of epilepsy related death. Recent estimates indicate that SUDEP is responsible for approximately 7,000 deaths each year in the United States and Europe, and is the second most common cause of the number of adult life years lost after stroke. To accelerate SUDEP research, the National Institute of Neurological Disorders and Stroke (NINDS) at the NIH- funded Center for SUDEP Research (CSR), a network of 14 institutions collaborating in a broad spectrum of basic science and clinical approaches to study possible biological mechanisms underlying this potentially preventable mortality and develop predictive biomarkers for interventions. Identification and communication of alterable SUDEP risk factors to affected patients is an important strategy to lower SUDEP incidence. However, systematic individualized assessment of SUDEP risk is currently unavailable due to a number of challenges. Often the required information is embedded in data residing in disparate, unlinked datasets and systems; there is a lack of a specific controlled vocabulary for precise extraction of SUDEP risk factor information with semantic uniformity; and the corresponding computational algorithms and tools needed for important risk marker extraction from clinical text and electrophysiological signals are yet to be fully developed. We propose to overcome these challenges by developing SURME, a SUDEP Risk Marker Extraction system for automated extraction of known and putative SUDEP risk markers from the multimodal CSR data repository (called MEDCIS) which contains over 1,600 patients enrolled from Epilepsy Monitoring Units in 7 medical centers. In Aim 1 we will develop a dedicated controlled vocabulary building on our own Epilepsy and Seizure Ontology and existing SUDEP risk guidelines and reported risk factors. We will develop an extraction pipeline, leveraging our earlier epilepsy phenotype extraction tools, for detecting risk markers from clinical text. In Aim 2 we will develop a scalable approach for detecting two significant putative physiological biomarkers from electrophysiological signals: postictal generalized EEG suppression; and root mean square differences of successive R-R intervals. In Aim 3 we will perform pilot implementation of SURME on MEDCIS for automated risk assessment using “SUDEP-7 Inventory” and “SUDEP and Seizure Safety Checklist”, as well as assessment of putative SUDEP risk factors using CSR cohort. We expect SURME and its future versions to become an invaluable SUDEP risk assessment tool as a part of standard epilepsy care. The long-term goal of this study is to create evidence-based SUDEP risk assessment tools to improve epilepsy care, with individualized risk scores and recommendations for managing modifiable risks, ultimately leading to reduced SUDEP mortality and improved epilepsy patient care.

项目摘要 癫痫（SUDEP）突然意外死亡是癫痫相关死亡的领先模式。最近的 估计表明，在美国，SUDEP每年造成约7,000人死亡 和欧洲，也是中风后丧生的成年寿命数量的第二大最常见原因。到 加速SUDEP研究，国家神经系统疾病与中风研究所（NINDS） 资助的SUDEP研究中心（CSR），这是一个由14个机构组成的网络，该机构与广泛的合作 基础科学和临床方法，用于研究这种潜在的潜在生物学机制 可预防死亡率并为干预措施开发预测性生物标志物。识别和交流 可改变的SUDEP风险因素对受影响的患者是降低SUDEP发病率的重要策略。 但是，由于多个 挑战。通常，所需的信息嵌入到位于不同的，未链接的数据集中的数据中 系统；缺乏针对SUDEP风险因素精确提取的特定受控词汇 具有语义统一的信息；以及相应的计算算法和工具 从临床文本和电生理信号中提取重要的风险标志物尚未完全开发。 我们建议通过开发SURME来克服这些挑战，这是一种SUDEP风险标记提取系统 从多模式CSR数据存储库中自动提取已知和推定的SUDEP风险标记 （称为MEDCI），其中包含1,600多名癫痫监测单元中的患者 中心。在AIM 1中，我们将在我们自己的癫痫病和癫痫发作中开发专门的受控词汇建筑 本体和现有的SUDEP风险准则和报告的风险因素。我们将开发一个提取管道， 利用我们较早的癫痫表型提取工具，以检测临床文本的风险标记。在目标2中 我们将开发一种可扩展的方法来检测从 电生理信号：邮政广泛的脑电图抑制；和根平方的差异 成功的R-R间隔。在AIM 3中，我们将在MEDCIS上执行SURME的试点 使用“ SUDEP-7库存”和“ SUDEP和扣押安全清单”的风险评估，以及 使用CSR队列评估推定的SUDEP风险因素。我们希望Surme及其未来版本 作为标准癫痫护理的一部分，成为宝贵的SUDEP风险评估工具。长期目标 这项研究是为了创建基于证据的SUDEP风险评估工具来改善癫痫护理，并使用 个性化风险评分和用于管理可修改风险的建议，最终导致减少 SUDEP死亡率和改善的癫痫患者护理。