Precise Prediction and Treatment of Seizures After Intracranial Hemorrhage

项目总结 癫痫发作是颅内出血的常见和病态并发症，会导致脑突出，情况更糟。 病人结局和死亡。虽然已经描述了癫痫发作的几个风险因素，但预测的能力 癫痫发作仍然很粗暴。癫痫发作预测的不准确导致预防性用药的不准确 抗癫痫药物。预防性抗癫痫药物旨在预防癫痫发作，减少 减少并发症，改善患者预后。不幸的是，抗癫痫药物一直是独立的 伴随着更多的并发症，更糟糕的患者结局，以及更差的健康相关生活质量(HRQOL)， 尤其是认知功能的HRQL。需要更好的方法来准确预测哪些患者有可能 在颅内出血后发生癫痫以防止癫痫发作，并进一步确定哪些患者 可能受益于预防性的抗癫痫药物。 我们将继续一条成功的研究路线。在我们开始研究这个课题的时候，预防性的苯妥英 是由指南推荐的。在发表了涉及苯妥英的更多并发症和更糟糕的文章后 患者结局、指南被更改以不鼓励使用预防性苯妥英钠，以及临床医生 从苯妥英广泛改为左乙拉西坦。我们最近报道了预防性左乙拉西坦是 在接受治疗的患者中，40%的患者独立地与认知功能较差的HRQOL相关， 强调目前的做法可能会导致无意中的伤害，这是一种难以为继的现状。的影响 HRQL上的癫痫发作比预防性抗癫痫药物更糟糕。用精准的方法防止癫痫发作 给予预防性抗癫痫药物将是有帮助的。 这项提议有两个主要目标，将改善颅内出血后的患者预后。首先，我们 将以我们之前的工作为基础，推导和验证用于预测癫痫发作的多维模型 从脑电和影像数据中找出最有可能发生颅内出血的患者 从预防癫痫的药物中受益。一个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。未来的数据将以电子方式从医疗保健系统中提取出来 使用来自脑电报告的自动化技术的单个电子健康记录，RAW 脑电数据和神经影像来源数据。然后，我们将确定 对癫痫高风险患者的HRQOL预防性用药，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… 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发生癫痫的患者，以及 确定哪些患者可能由于预防性癫痫药物治疗而具有更高的HRQOL， 导致靶向治疗和非治疗，以最大限度地提高患者的HRQOL。

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.