Precise Prediction and Treatment of Seizures After Intracranial Hemorrhage

颅内出血后癫痫发作的精确预测和治疗

• 批准号: 10658031
• 负责人: ANDREW M NAIDECH
• 金额: $ 64.3万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10658031
• 关键词: Affect; Algorithms; American; Anticonvulsants; Brain; Brain Injuries; Brain region; Cerebral hemisphere hemorrhage; Cessation of life; Clinical; Clinical Data; Clinical Trials; Complication; Data; Data Set; Databases; Dimensions; Effectiveness; Electroencephalography; Electronic Health Record; Epilepsy; Future; Guidelines; Hand; Handedness; Healthcare Systems; Heart Arrest; Hematoma; Hemorrhage; Human; Image; Intracranial Hemorrhages; Levetiracetam; Lobar; Location; Machine Learning; Methods; Modeling; Natural Language Processing; Non-Convulsive Status Epilepticus; Outcome; Patient Selection; Patient-Focused Outcomes; Patients; Pattern Recognition; Periodicity; Pharmaceutical Preparations; Phenytoin; Publications; Quality of Life Assessment; Recommendation; Reporting; Research; Risk; Risk Factors; Seizures; Source; Subarachnoid Hemorrhage; Techniques; Temporal Lobe; Testing; Time; Traumatic Brain Injury; Update; Work; clinical practice; clinical predictors; cognitive function; data warehouse; deep learning; diverse data; follow-up; functional outcomes; gradient boosting; health related quality of life; high risk; improved; inferential statistics; innovation; multidisciplinary; multimodality; neuroimaging; predictive modeling; prevent; prophylactic; prospective; side effect; targeted treatment

PROJECT SUMMARY Seizures are a common and morbid complication of intracranial hemorrhage, leading to brain herniation, worse patient outcome, and death. While a few risk factors for seizures have been described, the ability to predict seizures is still crude. The inaccuracy of seizure prediction leads to imprecise administration of prophylactic antiseizure medications. Prophylactic antiseizure medications are intended to prevent seizures, reduce complications, and improve patient outcomes. Unfortunately, antiseizure medications have been independently associated with more complications, worse patient outcomes, and worse health-related quality of life (HRQoL), particularly cognitive function HRQoL. Better methods are needed to predict precisely which patients are likely to have seizures after intracranial hemorrhage to prevent them, and, further, to determine which patients are likely to benefit from prophylactic antiseizure medications. We will continue a successful line of research. At the time we began to study this topic, prophylactic phenytoin was recommended by guidelines. After publications implicated phenytoin with more complications and worse patient outcomes, guidelines were changed to discourage the use of prophylactic phenytoin, and clinicians broadly switched from phenytoin to levetiracetam. We recently reported that prophylactic levetiracetam is independently associated with worse cognitive function HRQoL in the 40% of patients who receive it, underscoring that current practice may lead to inadvertent harm, an untenable status quo. The effects of seizures on HRQoL are worse than prophylactic antiseizure medication. Preventing seizures by precise administration of prophylactic antiseizure medication would be helpful. This proposal has two major aims that will improve patient outcomes after intracranial hemorrhage. First, we will build upon our previous work to derive and validate a multi-dimensional model for predicting seizures after intracranial hemorrhage from electroencephalography and imaging data to identify the patients most likely to benefit from prophylactic seizure medication. A prospective database with recording of seizures and patient outcomes provides preliminary data. Future data will be electronically abstracted from a health care system with a single electronic health record using automated techniques from electroencephalography reports, raw electroencephalography data, and neuroimaging source data. Then, we will determine the effect of prophylactic seizure medication on patient HRQoL at high risk for seizures, and use other machine learning techniques to determine which patients are most likely to benefit from prophylactic antiseizure medication. At the conclusion of this proposal, we will deliver a model to predict patients most likely to have seizures, and determine which patients are likely to have higher HRQoL as a results of prophylactic seizure medications, leading to targeted treatment and non-treatment to maximize patient HRQoL.

项目摘要 癫痫发作是颅内出血的常见和病态并发症，导致脑疝，更糟的是， 患者结局和死亡。虽然已经描述了癫痫发作的一些风险因素，但预测癫痫发作的能力仍然有限。 缉获量仍然很粗糙。癫痫发作预测的不准确性导致预防性给药的不准确性。 抗癫痫药物预防性抗癫痫药物旨在预防癫痫发作，减少 并发症，并改善患者的预后。不幸的是，抗癫痫药物已经独立地 与更多并发症、更差的患者结局和更差的健康相关生活质量（HRQoL）相关， 尤其是认知功能HRQoL。需要更好的方法来精确预测哪些患者可能 颅内出血后癫痫发作，以防止他们，并进一步确定哪些患者是 可能会从预防性抗癫痫药物中获益。 我们将继续进行成功的研究。当时我们开始研究这个课题，预防性苯妥英 这是指导方针建议的。在出版物涉及苯妥英钠后， 患者结局，指南被改变以阻止预防性使用苯妥英， 从苯妥英广泛转换为左乙拉西坦。我们最近报道，预防性左乙拉西坦是 在40%接受该治疗的患者中，与认知功能HRQoL恶化独立相关， 强调目前的做法可能导致无意的伤害，这是一种无法维持的现状。的影响 HRQoL的癫痫发作比预防性抗癫痫药物更差。预防癫痫发作， 给予预防性抗癫痫药物会有帮助。 该提案有两个主要目的，将改善颅内出血后的患者结局。一是 将建立在我们以前的工作，推导和验证一个多维模型，用于预测癫痫发作后， 根据脑电图和成像数据确定颅内出血患者最有可能 预防性癫痫药物的好处记录癫痫发作和患者的前瞻性数据库 结果提供了初步数据。未来的数据将从医疗保健系统中以电子方式提取 使用来自脑电图报告的自动化技术的单个电子健康记录，原始 脑电图数据和神经成像源数据。然后，我们将确定 癫痫发作高风险患者HRQoL的预防性癫痫发作药物，并使用其他机器学习 技术，以确定哪些患者最有可能受益于预防性抗癫痫药物。在 在这项提案的结论中，我们将提供一个模型来预测最有可能癫痫发作的患者， 确定哪些患者可能因预防性癫痫发作药物而具有较高的HRQoL， 导致靶向治疗和非治疗，以最大限度地提高患者的HRQoL。