A closed-loop gravity infusion control device

项目摘要 这项工作将开发一种创新的输液设备，利用新的电子设备和传感器 生产改善静脉(IV)输液输送的医疗设备的能力。IV 输液是全世界实施的最常见的医疗保健程序之一； 据估计，80%的住院患者接受了一些静脉注射治疗。输液也越来越多。 在医院以外的环境、家庭、长期护理设施、激增环境和 其他备用现场护理(ASC)环境。尤其是ASC的注入，是一个关键的 医疗保健趋势，因为它们通过消除患者等障碍来增加获得医疗服务的机会 交通，免疫受损患者的病原体暴露，以及住院费用。而当 输液是医疗保健提供的关键基础设施，但提供它的技术却不是 跟上该部门不断变化的需求。这项建议建立在一项经过临床验证的 输液监控系统，DripAsset输液率监测器，以创建一个闭环系统， 数字连接输液平台，即使是潜在的复杂药物也能输注 急性和ASC环境下的制度。这项技术实现了重力输液输送 带有自动流量调节的系统，我们称之为DripAssistControl，将使用低成本 传感器可以精确测量液滴中的液体体积。这是一项实验研究， 将在电子计算机中收集一组预定的因变量和自变量的数据。 将收集大量的数据集，传统的统计分析和机器都将收集到这些数据集 将应用学习方法。我们的研究将利用丰富的信息证实 出现在现有初步台式系统的输出中，以建立新的数据处理 测量液滴体积的方法。一旦改进，这些方法将被优化为一个 闭环式流量控制系统，将开发成成型器并进行测试 在具有现实约束和可变性的临床环境中。我们有三个目标 工作。目标1：建立两个可广泛识别滴水量的台式系统 通过一种新的检测过程对输液器进行评级。目标2：优化我们的感知和 执行算法以达到临床可接受的精密度和准确度。目标3：实施闭环系统 控制系统集成和临床前开发工作以实现工作原型 可测试可用性和已建立的法规标准的设备。我们相信我们的 工作可以通过引入新的器械和 传感器和生产一种更简单的输液输送装置，与精确度和 价格昂贵的泵台的精度。

Project Summary This work will develop an innovative infusion device that leverages new electronics and sensor capabilities to produce a medical device that improves intravenous (IV) infusion delivery. IV infusions are among most common healthcare procedures administered worldwide; an estimated 80% of hospital patients receive some IV therapy. Infusions are also increasingly delivered outside of hospital settings, in homes, long-term care facilities, surge settings and other alternate site care (ASC) environments. ASC infusions, in particular, are a critical healthcare trend because they increase access to care by eliminating obstacles such as patient transportation, pathogen exposure for the immunocompromised, and inpatient costs. While infusion is critical infrastructure for healthcare delivery, the technologies to deliver it have not kept up with changing needs in the sector. This proposal builds on a clinically-validated infusion monitoring system, the DripAssist Infusion Rate Monitor, to create a closed-loop, digitally-connected infusion platform capable of administering even potentially complex drug regimes in both acute and ASC settings. This technology-enabled gravity infusion delivery system with automatic flow adjustment, what we call DripAssist Control, will use low cost sensors to accurately measure the volume of fluid in a drop. This is an experimental study that will collect data in silico across a predetermined set of dependent and independent variables. Large data sets will be collected to which both conventional statistical analysis and machine learning methods will be applied. Our research will utilize rich information confirmed to be present in outputs of an existing preliminary benchtop system to establish new data processing methods for measuring drop volume. Once refined, these methods will be optimized into a closed-loop flow control system, which will be developed into a form and fit device and tested within clinical settings with realistic constraints and variability. We have three Aims in this work. Aim 1: establishing two benchtop systems that can broadly identify the drip volume rating of an infusion set through a novel detection process. Aim 2: optimize our sensing and actuating algorithms to clinically acceptable precision and accuracy. Aim 3: perform closed-loop control system integration and preclinical development work to attain a working prototype device that can be tested for usability and established regulatory standards. We believe our work can create a bold shift in clinical practice by introducing novel instrumentation and sensors and producing a simpler infusion delivery device that matches the precision and accuracy of expensive pump platforms.