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.

项目总结 癫痫猝死(SUDEP)是癫痫相关死亡的主要方式。近期 据估计，SUDEP每年在美国造成约7000人死亡 和欧洲，是中风后成人寿命损失的第二大常见原因。至 加速SUDEP研究，美国国立卫生研究院国家神经疾病和中风研究所(NINDS)- 资助的SUDEP研究中心(CSR)，由14个机构组成的网络，在广泛的 基础科学和临床方法来研究潜在的潜在生物学机制 可预防的死亡率，并开发干预措施的预测性生物标志物。识别和传达 改变患者的SUDEP危险因素是降低SUDEP发生率的重要策略。 然而，目前无法对SUDEP风险进行系统的个性化评估，原因是 挑战。通常，所需信息嵌入到驻留在不同的、未链接的数据集中的数据中，并且 系统；缺乏精确提取SUDEP风险因素的特定受控词汇 语义一致的信息；以及所需的相应计算算法和工具 从临床文本和电生理信号中提取重要的风险标记还没有完全开发出来。 我们建议通过开发SUDEP风险标记提取系统SURME来克服这些挑战 用于从多模式CSR数据存储库中自动提取已知和假定的SUDEP风险标记 (称为MEDCIS)，包含从7个内科癫痫监测单位登记的1600多名患者 中锋。在目标1中，我们将根据我们自己的癫痫和癫痫发作开发一个专门的受控词汇表 本体和现有的SUDEP风险指南和报告的风险因素。我们将开发一条提取管道， 利用我们早期的癫痫表型提取工具，从临床文本中检测风险标记。在AIM 2 我们将开发一种可扩展的方法来检测来自 电生理信号：发作后广泛性脑电抑制；以及 连续的R-R间期。在目标3中，我们将在MEDCIS上进行SURME的试点实施，以实现自动化 使用“SUDEP-7库存”和“SUDEP和扣押安全核对表”以及 使用企业社会责任队列评估可能的SUDEP风险因素。我们预计SURME及其未来版本将 作为标准癫痫护理的一部分，成为SUDEP风险评估的无价工具。的长期目标是 这项研究旨在创建基于证据的SUDEP风险评估工具，以改善癫痫的护理， 个性化风险评分和管理可修改风险的建议，最终降低风险 SUDEP死亡率和改善癫痫患者护理。