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，导致脑部催眠，更糟 病人的结果和死亡。虽然已经描述了一些癫痫发作的危险因素，但可以预测的能力 癫痫发作仍然是原始的。癫痫发作预测的不准确导致预防性的不精确施用 毒剂药物。预防性抗性药物旨在防止癫痫发作，减少 并发症并改善患者的预后。不幸的是，毒药已独立 与更多的并发症，患者结局较差以及与健康相关的生活质量（HRQOL）相关， 特别是认知功能HRQOL。需要更好的方法来准确预测哪些患者可能是 在颅内出血后有癫痫发作以防止它们，然后确定哪些患者是 可能受益于预防性抗性药物。 我们将继续一项成功的研究。当时我们开始研究这个主题，预防性苯甲 由指南推荐。出版物牵涉到苯托因的并发症，更糟 患者的结果，更改了指南以阻止使用预防苯阵行和临床医生的使用 广泛地从苯妥英里切换到左乙拉西坦。我们最近报道说预防性左甲乙酰辅实 在40％的患者中，与较差的认知功能HRQOL独立相关， 强调当前的做法可能导致无意的伤害，这是一个站不住脚的现状。效果 HRQOL的癫痫发作比预防性抗性药物差。准确防止癫痫发作 预防性抗性药物的给药会有所帮助。 该提案具有两个主要目标，可以改善颅内出血后的患者预后。首先，我们 将基于我们以前的工作，以得出和验证一个多维模型，以预测之后的癫痫发作 脑外颅内出血和成像数据，以识别最有可能的患者 预防性癫痫发作药物受益。带有癫痫发作和患者记录的潜在数据库 结果提供初步数据。未来数据将从医疗系统中以电子方式抽象 使用脑电图报告中的自动化技术的单个电子健康记录，RAW 脑电图数据和神经成像源数据。然后，我们将确定 对患者HRQOL的预防性癫痫发作药物高风险癫痫发作，并使用其他机器学习 确定哪些患者最有可能受益于预防性抗性药物的技术。在 该提案的结论是，我们将提供一个模型，以预测最有可能癫痫发作的患者，并且 确定哪些患者可能具有较高的HRQOL，作为预防性癫痫药的结果， 导致靶向治疗和不治疗，以最大化患者HRQOL。