Multiparametric Prediction of Vasospasm after Subarachnoid Hemorrhage

 描述（由申请人提供） 在美国，蛛网膜下腔出血（SAH）影响估计每10万人中有14.5人，并且是卫生保健资源的重大负担，因为它可以导致长期的功能和认知障碍。其中大部分是由于血管痉挛（VSP）引起的迟发性脑缺血（DCI）。VSP是指由于血管周围异常存在血液而导致的脑血管反应性狭窄。在极端情况下，严重的VSP阻止血液流向脑组织，导致中风。 SAH是神经重症监护病房（NICU）中治疗的最常见疾病之一。目前，资源规划是围绕修改后的Fisher量表编写的，该量表根据初始脑计算机断层扫描（CT）上的血液体积和模式预测发生DCI的比值比。然而，它不允许进一步的个性化风险评估。前14天用于检测临床前或早期VSP，并安排及时干预以防止永久性损伤。指南支持的唯一一种可能识别临床前VSP的无创工具是经颅多普勒（TCD），其灵敏度和阴性预测值范围不可靠，并且取决于技术人员的可用性。如果未在临床前识别，则必须在出现症状后立即检测VSP，然后取决于ICU复杂和昼夜环境中专业知识的质量和可用性。 有希望的是，电子病历（EMR）数据和连续的生理监测器提供了丰富的机会，为未来的事件进行风险分层，以及揭示急性脑损伤患者的实时事件。一个有条不紊的方法，功能工程将执行一个大的一套潜在的歧视性数据驱动和知识为基础的功能。将使用各种定量和符号抽象技术提取代表时间序列变量变化和趋势的元特征。将使用朴素贝叶斯、逻辑回归和支持向量机进行预测建模。 该项目将产生一个预测工具，提高及时性， VSP分类精度。它将填补一个重要的空白，在理解未充分利用的EMR和生理数据的潜力，以预测神经功能衰退。从已经收集的临床数据中生成准确和及时的预测规则将具有成本效益，并且不仅对SAH患者而且对任何ICU中的几乎任何监测患者都具有影响。

 DESCRIPTION (provided by applicant) Subarachnoid Hemorrhage (SAH) affects an estimated 14.5 per 100,000 persons in the United States, and is a substantial burden on health care resources, because it can cause long-term functional and cognitive disability. Much of this is due to delayed cerebral ischemia (DCI) from vasospasm (VSP). VSP refers to the reactive narrowing of cerebral blood vessels due the unusual presence of blood surrounding the vessel. In its extreme, severe VSP precludes blood flow to brain tissue, resulting in stroke. SAH is one of the most common disease entities treated in the Neurointensive Care Unit (NICU). Currently, resource planning is scripted around the Modified Fisher Scale, which predicts the odds ratio of developing DCI based on the volume and pattern of blood on initial brain computed tomography (CT). It does not, however, allow for further individualized risk assessments. The first 14 days are occupied by efforts to detect preclinical or early VSP and arrange timely interventions to prevent permanent injury. The only noninvasive tool supported by guidelines to potentially identify preclinical VSP is the transcrania Doppler (TCD), which has an unreliable range of sensitivity and negative predictive values, and is at the mercy of technician availability. If not identified preclinically, VSP must be detected once it is symptomatic and is then dependent on quality and availability of expertise in the complex and diurnal environment of the ICU. Promisingly, electronic medical record (EMR) data and continuous physiology monitors offer abundant opportunities to risk stratify for future events as well as reveal events in real-time in the acutely brain injured patient. A methodical approach to feature engineering will be performed over a large set of potentially discriminatory data-driven and knowledge-based features. Meta-features representing variations and trends in time series variables will be extracted using a variety of quantitative and symbolic abstraction techniques. Predictive modeling will be performed using Naïve Bayes, Logistic Regression, and Support Vector Machine. This project will result in a prediction tool that improves timeliness and precision in VSP classification. It will fill an important gap in the understanding of the potentia of underutilized EMR and physiological data to predict neurological decline. Generating accurate and timely prediction rules from already collected clinical data would be cost effective and have implications not only for SAH patients, but also for almost any monitored patient in any ICU.