A Predictive Model for Assessment of CSF Flow Through Ventricular Shunts

评估脑脊液流经心室分流的预测模型

• 批准号: 10220896
• 负责人: Hany Mohamed Arafa
• 金额: $ 4.21万
• 依托单位: NORTHWESTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10220896
• 关键词: Accelerometer; Adult; Affect; Age; Algorithms; American; Brain; Catheters; Cerebral Ventricles; Cerebrospinal Fluid; Cessation of life; Childhood; Clinical; Clinical Trials; Collaborations; Collection; Coma; Consumption; Couples; Creation of ventriculo-peritoneal shunt; Data; Detection; Development; Devices; Diagnostic; Drainage procedure; Electronics; Epidermis; Evaluation; Failure; Feedback; Formulation; Generations; Geometry; Headache; Health Care Costs; Health Personnel; Health Professional; Hospitalization; Hospitals; Hydrocephalus; Image; Implant; Institutional Review Boards; Left; Live Birth; Logistic Regressions; Longitudinal Studies; Magnetic Resonance Imaging; Measures; Mechanics; Medical Imaging; Methodology; Methods; Modality; Modeling; Monitor; Obstruction; Operative Surgical Procedures; Outcome; Pathology; Patients; Pediatric Hospitals; Physicians; Physiological; Protocols documentation; Quality of life; Radiation exposure; Research; Research Personnel; Roentgen Rays; Seizures; Series; Shunt Device; Skin; Symptoms; System; Technology; Testing; Time; Tracer; Ultrasonography; United States; Universities; Validation; Ventricular; Wireless Technology; Work; cerebrospinal fluid flow; cost; design; diagnosis standard; experience; experimental study; flexibility; implantation; improved; in vivo; innovation; insight; miniaturize; mortality; novel; patient variability; pediatric patients; prediction algorithm; predictive modeling; pressure; psychologic; real time monitoring; regression algorithm; sensor; standard of care; success; tomography; wearable sensor technology

Project Summary Hydrocephalus is a crippling condition which caused by an aberrant draining capacity of cerebrospinal fluid (CSF) from the brain, affecting about 1-5 of every 1000 live births. This debilitating condition commonly manifests itself in frequent headaches, seizures, and comas, with death as a likely outcome when left untreated. The standard of care to alleviate this condition is ventriculoperitoneal shunting which diverts CSF away from the brain ventricles, thereby reducing excess pressure build-up. CSF diversion systems or shunts are typically rudimentary systems which contain a ventricular catheter, valve, and drainage tubing; this technology has experienced minimal innovation since the 1960s. However, shunts regularly fail and require correction surgeries due to obstructions and occlusions, leading to over 125,000 shunt revisions in the United States annually. Shunt revisions cost about 2 billion dollars in the United States annually for the nearly 1 million affected Americans. Hydrocephalus imposes a huge financial, physiological, and psychological burden on patients and their health care providers, emphasizing the urgent need to improve methods of monitoring and prediction of shunt failure. To compound this issue, existing shunt failure diagnostics are costly, invasive, and/or harmful (in the case of ex- tended radiation exposure in CT imaging). Typical shunt testing modalities include Magnetic Resonance Imaging (MRI), Coherence Tomography (CT), and X-rays. Due to patient-to-patient variability in age, pathology, shunt in- termittency, and shunt valve type, there is currently a lack of data with regards to flow dynamics in CSF diversion systems. Most research efforts have primarily focused on the development of ‘smart shunts’ which inadvertently couples complete shunt failure to sensor failure; this proposal seeks to provide accurate and real-time monitoring of CSF flow in a noninvasive manner, the success of which could directly affect the quality of life of 1 million Americans suffering with hydrocephalus and millions more around the world. This proposal will support the devel- opment of a wearable sensor platform and processing algorithm that will culminate in a predictive model of shunt failure to reduce hospital admissions and improve the quality of life for patients with hydrocephalus. Success of this proposal will yield a fully flexible, soft, and wireless system which monitors CSF diversion (Aim 1 and Aim 2), leading to a validation trial of the integrated system in long term trials of both adult and pediatric patients suffering with hydrocephalus. The completion of this work will also include the generation of a predictive model which allows researchers to study long term CSF flow dynamics through ventricular shunts (Aim 3). Ultimately, our methodology will enable us to collect a wealth of information to significantly aid healthcare professionals in the proactive treatment of the devastating symptoms of hydrocephalus.

项目摘要 脑积水是一种哭泣的条件，是由脑脊液（CSF）的异常排出能力引起的 从大脑中，每1000个活产中约有1-5个。这种衰弱的状况通常表现出来 在未经治疗的情况下，其本身会经常出现在标题，癫痫发作和昏迷中，可能是死亡。这 减轻这种情况的护理标准是脑室旋转的分流，将CSF从大脑转移 心室，从而减少了过量的压力。 CSF转移系统或分流通常是基本的 包含心室导管，阀门和排水管的系统；这项技术的经历很少 自1960年代以来的创新。但是，分流会定期失败，需要因异议而进行更正手术 和闭塞，导致美国年度超过125,000分的修订。分流修订费用 美国每年有近100万受影响的美国人在美国20亿美元。不可能的脑积水 对患者及其医疗保健提供者的巨大财务，身体和心理燃烧，强调 迫切需要改善分流故障的监视和预测方法。 为了使这个问题更加复杂，现有的分流故障诊断是昂贵，侵入性和/或有害的（在事实的情况下 在CT成像中倾向于辐射暴露）。典型的分流测试方式包括磁共振成像 （MRI），相干断层扫描（CT）和X射线。由于年龄，病理学的患者到患者的变异性，分流 死亡和分流阀类型，目前缺乏有关CSF转移流动动态的数据 系统。大多数研究工作主要集中在“智能分流”的发展上 夫妻完全分流失败传感器故障；该建议旨在提供准确，实时的监控 CSF以非侵入性方式流动，其成功可能直接影响100万的生活质量 美国人患有脑积水，世界上有数百万。该建议将支持开发 选项的可穿戴传感器平台和处理算法，该算法将在分流的预测模型中达到顶峰 无法减少住院和改善脑积水患者的生活质量。 该提案的成功将产生完全灵活，柔软和无线的系统，可监视CSF转移（AIM 1 AIM 2），导致成人和儿科患者长期试验中综合系统的验证试验 患有脑积水。这项工作的完成还将包括生成预测模型 这使研究人员能够通过心室分流研究长期的CSF流动动力学（AIM 3）。最终， 我们的方法将使我们能够收集大量信息，以极大地帮助医疗保健专业人员 积极治疗脑积水的毁灭性症状。