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.

项目概要 脑积水是一种由脑脊液 (CSF) 排水能力异常引起的致残性疾病 来自大脑，影响每 1000 名活产儿中约 1-5 名。这种使人衰弱的情况通常表现为 其本身经常头痛、癫痫发作和昏迷，如果不及时治疗，可能会导致死亡。这 缓解这种情况的标准治疗方法是脑室腹腔分流术，将脑脊液转移到远离大脑的地方 心室，从而减少多余的压力积聚。脑脊液分流系统或分流器通常很简陋 包含心室导管、阀门和引流管的系统；这项技术经历了最少的 自 20 世纪 60 年代以来的创新。然而，分流器经常会因阻塞而失败并需要进行矫正手术 和闭塞，导致美国每年进行超过 125,000 次分流修复。分流修改成本约为 美国每年为近100万受影响的美国人提供20亿美元。脑积水强加 给患者及其医疗保健提供者带来巨大的经济、生理和心理负担，强调 迫切需要改进分流故障的监测和预测方法。 使这个问题变得更加复杂的是，现有的分流故障诊断成本高昂、具有侵入性和/或有害（在前 CT 成像中的辐射暴露）。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。由于患者之间的年龄、病理、分流情况存在差异 间歇性和分流阀类型，目前缺乏有关脑脊液分流中流动动力学的数据 系统。大多数研究工作主要集中在“智能分流器”的开发上，这无意中 将完全分流故障与传感器故障耦合起来；该提案旨在提供准确和实时的监控 以无创方式进行脑脊液流动，其成功与否将直接影响 100 万人的生活质量 美国人患有脑积水，世界各地还有数百万人患有脑积水。该提案将支持开发 可穿戴传感器平台和处理算法的优化，最终形成分流的预测模型 未能减少脑积水患者的入院率并改善其生活质量。 该提案的成功将产生一个完全灵活的、软的、无线的系统，用于监测脑脊液转移（目标 1 和目标 2），从而在成人和儿童患者的长期试验中对集成系统进行验证试验 患有脑积水。这项工作的完成还将包括预测模型的生成 这使得研究人员能够通过脑室分流研究长期脑脊液流动动力学（目标 3）。最终， 我们的方法将使我们能够收集大量信息，以显着帮助医疗保健专业人员 积极治疗脑积水的破坏性症状。