A Predictive Model for Assessment of CSF Flow Through Ventricular Shunts

项目摘要 脑积水是由于脑脊液（CSF）引流能力异常引起的一种致残性疾病 影响到每1000个活产婴儿中的1 - 5个。这种使人衰弱的情况通常表现为 本身在频繁的头痛，癫痫发作和昏迷，与死亡作为可能的结果，如果不治疗。的 缓解这种情况的标准治疗是脑室腹腔分流术，将CSF从大脑转移 心室，从而减少过度的压力积聚。脑脊液分流系统或分流系统通常是初级的 包含脑室导管、阀和引流管的系统;该技术经历了最小的 自20世纪60年代以来的创新。然而，由于阻塞，分流器经常失败并需要矫正手术 和闭塞，导致美国每年超过125，000例分流翻修。分流翻修的费用约为 美国政府每年将为近100万受影响的美国人提供20亿美元的援助。脑积水迫使 对患者及其医疗保健提供者造成巨大的经济、生理和心理负担， 迫切需要改进监测和预测分流故障的方法。 使该问题复杂化的是，现有的分流故障诊断是昂贵的、侵入性的和/或有害的（在前分流的情况下）。 CT成像中倾向于辐射暴露）。典型的分流测试模式包括磁共振成像 (MRI)、相干断层扫描（CT）和X射线。由于患者之间在年龄、病理、分流方面的差异， 间歇性和分流阀类型，目前缺乏关于CSF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， 将完全分流器故障与传感器故障相结合;该提案旨在提供准确和实时的监测 以非侵入性方式进行CSF流动，其成功与否直接影响100万人的生活质量 患有脑积水的美国人和世界各地数百万人。该提案将支持发展- 可穿戴传感器平台和处理算法的开发，最终将产生分流的预测模型 未能减少脑积水患者的入院率和改善其生活质量。 该提案的成功将产生一个完全灵活、柔软和无线的系统，用于监测CSF分流（目标1 和目标2），导致在成人和儿科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生成预测模型 这使得研究人员能够研究通过心室分流的长期CSF流动动力学（Aim 3）。最后， 我们的方法将使我们能够收集大量的信息，以大大帮助医疗保健专业人员， 积极治疗脑积水的毁灭性症状。

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.