A Predictive Model for Assessment of CSF Flow Through Ventricular Shunts

项目摘要 脑积水是一种由脑脊液(Csf)排出能力异常引起的致残性疾病。 来自大脑，每1000名活产儿中约有1-5人受到影响。这种衰弱的情况通常表现为 经常头痛、癫痫发作和昏迷，如果不治疗，可能会死亡。这个 缓解这种情况的标准护理是脑室腹膜分流术，它将脑脊液从大脑中转移出去。 脑室，从而减少多余的压力积聚。脑脊液分流系统或分流装置通常很简陋 包含脑室导管、瓣膜和引流管的系统；这项技术经历了最少的 自20世纪60年代以来的创新。然而，由于梗阻，分流手术经常失败，需要进行矫正手术。 和闭塞，导致美国每年超过12.5万次分流修正。分流修正成本约为 美国每年为近100万受影响的美国人提供20亿美元。脑积水强加于 给患者和他们的医疗保健提供者带来巨大的经济、生理和心理负担，强调 迫切需要改进分流失效的监测和预测方法。 使这一问题更加复杂的是，现有的分路故障诊断昂贵、有侵入性和/或有害(在前 CT成像中的倾向辐射暴露)。典型的分流检测方式包括磁共振成像 (MRI)、相干断层扫描(CT)和X光。由于患者在年龄、病理、分流方面的差异- 暂时性和分流阀类型，目前缺乏关于脑脊液分流的流动动力学的数据 系统。大多数的研究工作主要集中在“智能分流装置”的开发上，它在不经意间 夫妇完全分流故障到传感器故障；这一建议寻求提供准确和实时的监测 无创脑脊液血流监测，其成功与否将直接影响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。这项提议将支持以下发展- 一种可穿戴式传感器平台的开发和处理算法将最终形成分流的预测模型 未能减少入院人数，提高脑积水患者的生活质量。 这一提议的成功将产生一个完全灵活、柔软和无线的系统，用于监控脑脊液分流(目标1 以及目标2)，导致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研究通过脑室分流的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丰富的信息，以显著帮助医疗保健专业人员在 积极主动地治疗脑积水的破坏性症状。

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.