Big Data and Deep Learning for the Interictal-Ictal-Injury Continuum

发作间期-发作期-损伤连续体的大数据和深度学习

• 批准号: 10398908
• 负责人: Michael Brandon Westover
• 金额: $ 42.45万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10398908
• 关键词: Adverse event; Algorithms; Anesthetics; Anticonvulsants; Awareness; Big Data; Brain; Brain Injuries; Brain hemorrhage; Characteristics; Clinical; Collaborations; Communities; Computers; Coupled; Critical Care; Critical Illness; Data; Data Set; Detection; Development; Early Intervention; Electroencephalogram; Electroencephalography; Epidemic; Event; Frequencies; Goals; Growth; Hour; Human; Iatrogenesis; Injury; Intervention; Intervention Trial; Intracranial Hemorrhages; Label; Medical; Medicine; Modeling; Monitor; Neurologic; Neurological outcome; Neurology; Nomenclature; Patient Care; Patients; Pattern; Periodicity; Phenotype; Physicians; Positioning Attribute; Research; Research Personnel; Seizures; Sepsis; Standardization; Subclinical Seizures; Supervision; Telemetry; Testing; Time; Training; Uremia; Visual; Work; aggressive therapy; brain health; causal model; clinical care; clinical heterogeneity; clinically actionable; clinically significant; computer science; cost; deep learning; deep learning algorithm; disability; disability risk; improved; improved outcome; intervention effect; large datasets; learning strategy; nerve injury; neurophysiology; overtreatment; predictive modeling; preventable death; real time monitoring; tool

Project Summary/Abstract: Big Data and Deep Learning for the Interictal-Ictal-Injury Continuum Brain monitoring in critical care has grown dramatically over the past 20 years with the discovery that a large proportion of ICU patients suffer from subclinical seizures and seizure-like electrical events, collectively called “ictal-interictal-injury continuum abnormalities” (IIICAs), detectable only by electroencephalography (EEG). This growth has created a crisis in critical care: It is clear that IIICAs damage the brain and cause permanent neurologic disability. Yet detection of IIICAs by expert visual review is often delayed suggesting we need better tools for real-time monitoring, to cope with the deluge of ICU EEG data. In other cases, IIICAs appear to be harmless epiphenomena, and many worry that increased awareness of IIICAs has created an epidemic of overly-aggressive prescribing of anticonvulsant drugs leading to preventable adverse events and costs. This crisis highlights critical unmet needs for automated EEG monitoring for IIICAs, and a better understanding of which types of IIICAs cause neural injury and warrant intervention. Causes of IIICAs range widely, from primary brain injuries like hemorrhagic stroke and intracranial hemorrhage, to systemic medical illnesses like sepsis and uremia. Until recently, this massive clinical heterogeneity has been an insurmountable barrier to understanding the impact of IIICAs on neurologic outcome. However, recent advances in deep learning, coupled with the unprecedented availability of a massive dataset developed by our team over the last three years, makes it feasible for the first time to systematically study the relationship between IIICAs and neurologic outcomes. To meet the need for better monitoring tools and better models for understanding IIICAs, we will take a deep learning approach to leverage the as-yet untapped information in a massive ICU EEG dataset. We will pursue three Specific Aims: SA1: Comprehensively label all occurrences of IIICAs in a massive set of cEEG recordings, thus preparing the EEG data for training computers to detect IIICA patterns; SA2: Develop supervised DL algorithms to detect IIICAs as accurately as human experts, thus providing powerful tools for both research on IIICAs and for clinical brain monitoring; SA3: Estimate the effect of IIICAs on neurologic outcome: we will develop models to quantify effects of IIICAs on risk for disability after controlling for inciting illness and other clinical factors, and to predict effects of interventions to suppress IIICAs. This work will provide four crucial benefits to advance the field of precision critical care neurology, and by extension, our ability to provide optimal neurologic care for patients during critical illness. 1) Improved understanding of the clinical significance of seizure like IIICA states; 2) development of robust tools and algorithms for critical care brain telemetry; 3) a unique, massive, publicly available, thoroughly annotated dataset that will enable other researchers to further advance the field; and 4) a testable model that predicts which types of cEEG abnormalities warrant aggressive treatment, setting the stage for interventional trials.

项目摘要/摘要：大数据和深度学习的临时侵害连续性 在过去的20年中，重症监护中的大脑监测急剧增长，发现很大 ICU患者的比例患有亚临床癫痫发作和癫痫发作样的电气事件，统称为 “ ICTALICTICTICTICTICTARTACTAL-INJURY CONTINUUM异常”（IIICAS），仅通过脑电图（EEG）检测到。这 增长在重症监护中造成了危机：很明显，IIICAS会损害大脑并导致永久性 神经疾病。然而，通过专家视觉审查对IIICAS的检测通常会延迟表明我们需要 更好的实时监控工具，以应对ICU EEG数据的泛滥。在其他情况下，IIICAS似乎 成为无害的表皮瘤，许多担心IIICAS的意识提高了 抗惊厥药的过度侵略性处方会导致可预防的不良事件和成本。这 危机强调了对IIICAS自动脑电图监测的关键未满足需求，并有更好的理解 哪种类型的IIICA会导致神经助理和保证干预。 IIICAS的原因广泛，从脑损伤（如出血性中风）和颅内 出血，败血症和尿素等系统性医学疾病。直到最近，这个巨大的临床 异质性一直是理解IIICAS对神经系统影响的障碍 结果。但是，深度学习的最新进展，再加上空前的可用性 过去三年来，我们的团队开发的大量数据集使其首次可行 系统地研究IIICAS与神经系统结局之间的关系。 为了满足更好的监控工具和更好理解IIICAS的模型的需求，我们将采用一个 深度学习方法以利用大量ICU EEG数据集中尚未开发的信息。我们将 追求三个具体目标：SA1：全面标记IIICAS的所有事件 CEEG记录，从而为训练计算机的脑电图数据做准备以检测IIICA模式； SA2：开发 监督的DL算法与人类专家一样准确地检测IIICAS，从而提供强大的工具 对于IIICA和临床大脑监测的研究； SA3：估计IIICAS对 神经系统结局：我们将开发模型来量化IIICAS在控制后对残疾风险的影响 用于煽动疾病和其他临床因素，并预测抑制IIICAS的干预措施的影响。 这项工作将提供四个至关重要的好处，以提高精确的重症监护神经病学领域，并通过 扩展，我们在危重疾病期间为患者提供最佳神经护理的能力。 1）改进 理解像IIICA一样癫痫发作的临床意义； 2）开发强大的工具和 重症监护脑遥测算法； 3）独特的，庞大的，公开的，彻底注释的 数据集将使其他研究人员能够进一步推进该领域； 4）可预测的可测试模型 哪种类型的CEEG异常值得积极治疗，为介入试验奠定了基础。