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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要癫痫发作是颅内出血的常见病态并发症，导致脑疝，更严重的是患者的结果和死亡。虽然已经描述了癫痫发作的一些危险因素，但预测癫痫发作的能力缉获量仍然很粗略。癫痫发作预测的不准确导致预防性用药的不精确抗癫痫药物。预防性抗癫痫药物旨在预防癫痫发作、减少癫痫发作并发症，并改善患者的治疗效果。不幸的是，抗癫痫药物已经独立与更多的并发症、更差的患者预后以及更差的健康相关生活质量 (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颅内出血后发生癫痫发作以预防癫痫发作，并进一步确定哪些患者是可能受益于预防性抗癫痫药物。我们将继续成功的研究。当我们开始研究这个话题时，预防性苯妥英钠是指南推荐的。出版物暗示苯妥英会导致更多并发症和更严重的情况患者结果、指南已更改以阻止使用预防性苯妥英钠和临床医生广泛从苯妥英转换为左乙拉西坦。我们最近报道了预防性左乙拉西坦 40% 接受该治疗的患者与较差的认知功能 HRQoL 独立相关，强调目前的做法可能会导致无意的伤害，这种现状是无法维持的。的影响 HRQoL 上的癫痫发作比预防性抗癫痫药物更严重。精准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标，即改善颅内出血后患者的预后。首先，我们将在我们之前的工作基础上推导并验证用于预测癫痫发作的多维模型通过脑电图和影像学数据识别颅内出血，以确定最有可能发生颅内出血的患者受益于预防性癫痫药物。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一电子健康记录、原始数据脑电图数据和神经影像源数据。然后，我们将确定效果对癫痫高风险患者的 HRQoL 进行预防性癫痫药物治疗，并使用其他机器学习确定哪些患者最有可能从预防性抗癫痫药物中受益的技术。在根据该提案，我们将提供一个模型来预测最有可能癫痫发作的患者，以及确定哪些患者可能因预防性癫痫药物而具有更高的 HRQoL，导致有针对性的治疗和不治疗，以最大限度地提高患者的HRQoL。