Multiparametric Prediction of Vasospasm after Subarachnoid Hemorrhage

蛛网膜下腔出血后血管痉挛的多参数预测

• 批准号: 9044336
• 负责人: Soojin Park
• 金额: $ 21.62万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-30 至 2020-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9044336
• 关键词: Affect; Blood; Blood Vessels; Blood flow; Brain; Caring; Cerebral Aneurysm; Cerebral Ischemia; Cerebrum; Classification; Clinical; Clinical Data; Coagulation Process; Coma; Complex; Computerized Medical Record; Cost Savings; Data; Detection; Development Plans; Diagnosis; Disease; Engineering; Environment; Event; Foundations; Frequencies; Funding; Future; Goals; Grant; Guidelines; Health; Healthcare; Hemorrhage; Injury; Intervention; Ischemic Penumbra; Knowledge; Laboratories; Logistic Regressions; Machine Learning; Mentorship; Methods; Mining; Modeling; Monitor; Neurologic; Odds Ratio; Outcome; Patient Care; Patient Discharge; Patient Monitoring; Patients; Pattern; Performance; Persons; Physicians; Physiological; Physiology; Predictive Value; Process; Resources; Risk; Risk Assessment; Rupture; Series; Stroke; Subarachnoid Hemorrhage; Symptoms; Techniques; Time; Time Series Analysis; Training; United States; Variant; Vasospasm; X-Ray Computed Tomography; base; brain tissue; career development; clinical decision-making; cognitive disability; cohort; cost effective; data mining; functional disability; high risk; improved; injured; instrument; knowledge base; monitoring device; multidisciplinary; pre-clinical; predictive modeling; prevent; standard of care; tool; trend

 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.

 描述（由申请人提供） 据估计，美国每 10 万人中就有 14.5 人患有蛛网膜下腔出血 (SAH)，这对医疗保健资源来说是一个沉重的负担，因为它可能导致长期的功能和认知障碍。这很大程度上是由于血管痉挛（VSP）引起的迟发性脑缺血（DCI）所致。 VSP 是指由于血管周围血液异常存在而导致的脑血管反应性狭窄。在极端情况下，严重的 VSP 会阻止血液流向脑组织，从而导致中风。 SAH 是神经重症监护病房 (NICU) 治疗的最常见疾病之一。目前，资源规划是围绕改良费希尔量表制定的，该量表根据初始脑计算机断层扫描 (CT) 上的血液量和模式来预测发生 DCI 的优势比。然而，它不允许进行进一步的个性化风险评估。前 14 天主要用于检测临床前或早期 VSP 并安排及时干预措施以防止永久性损伤。指南支持的唯一可识别临床前 VSP 的非侵入性工具是经颅多普勒 (TCD)，其灵敏度和阴性预测值范围不可靠，并且受技术人员可用性的影响。如果未在临床前发现，VSP 必须在出现症状后进行检测，这取决于 ICU 复杂的昼间环境中专业知识的质量和可用性。 电子病历 (EMR) 数据和连续生理监测仪有望为未来事件的风险分层以及实时揭示急性脑损伤患者的事件提供大量机会。将在大量潜在的歧视性数据驱动和基于知识的特征上执行有条理的特征工程方法。将使用各种定量和符号抽象技术来提取代表时间序列变量的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执行预测建模。 该项目将产生一个预测工具，提高及时性和 VSP 分类精度。它将填补对未充分利用的 EMR 和生理数据预测神经功能衰退潜力的理解的重要空白。从已收集的临床数据中生成准确、及时的预测规则将具有成本效益，并且不仅对 SAH 患者有影响，而且对任何 ICU 中的几乎所有受监测患者都有影响。