Smart Cuff: Multi-Parameter Hemodynamic Monitoring via a Single Convenient Device

智能袖带：通过单个便捷设备进行多参数血流动力学监测

• 批准号: 10583061
• 负责人: RAMAKRISHNA MUKKAMALA
• 金额: $ 71.7万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-10 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10583061
• 关键词: Achievement; Algorithms; Automated Clinical Decision Support; Blood Pressure; Blood Pressure Monitors; Blood flow; Cardiac Output; Cardiovascular system; Caring; Catheters; Clinical; Collection; Complex; Computer software; Custom; Data; Detection; Devices; Diagnosis; Diastolic blood pressure; EFRAC; Echocardiography; Enhancement Technology; Goals; Health Care Costs; Hospital Costs; Hospitals; Hypotension; Intensive Care; Intervention; Left Ventricular Ejection Fraction; Liquid substance; Machine Learning; Manuals; Measurement; Measures; Medical center; Monitor; Operative Surgical Procedures; Outcome; Patient Care; Patient-Focused Outcomes; Patients; Physiological; Population; Precision therapeutics; Pulmonary artery structure; Pulse Pressure; Real-Time Systems; Research Project Grants; Surgical Management; Systems Integration; Technology; Testing; Time; Titrations; Training; Translating; Universities; Variant; algorithm training; arm; clinical care; cohort; commercialization; heart function; hemodynamics; improved; innovation; intelligent algorithm; machine learning algorithm; monitoring device; novel; pressure; real time monitoring

PROJECT SUMMARY/ABSTRACT Multi-parameter hemodynamic monitoring is needed to manage surgical and intensive care patients. Monitoring blood pressure (BP), cardiac output (CO), and left ventricular ejection fraction (EF), in particular, permits detection of frequent hypotension and hemodynamic instability, diagnosis of the cause for selecting appropriate therapy, and titration of interventions (e.g., goal-directed therapy). However, measurement of these three hemodynamic variables currently requires multiple devices that are invasive, manual, or specialized. While the oscillometric arm cuff device is non-invasive, automated, and standard, it only estimates BP from the measured cuff pressure waveform via a population average algorithm that does not maintain accuracy over the clinical range. The overall goal of this project is to extend the ubiquitous arm cuff device for accurate and convenient multi- parameter hemodynamic monitoring via smart algorithms. The specific aims are: (1) to build an arm cuff device for recording cuff pressure waveforms; (2) to simultaneously acquire patient data with this and reference devices for algorithm training; (3) to develop and incorporate algorithms for accurately computing BP, CO, and EF from the cuff pressure waveform based on the training data; and (4) to validate the real-time Smart Cuff against reliable reference measurements in patients. The device will be developed to control the cuff pressure and incorporate custom algorithms. The cuff pressure waveform via the device and reference BP, CO, and EF via arterial and pulmonary artery catheters and echocardiography will be recorded before and after clinical interventions in many surgical and intensive care patients. These training data will be analyzed to refine or adapt previous physiologic algorithms and to investigate potentially superior machine learning algorithms for best estimation of the three hemodynamic variables. The final algorithms will be implemented for a real-time device, and the integrated system will be tested against the same reference measurements during clinical interventions but from new patients. Achievement of the specific aims will be followed by a translational project to bring the Smart Cuff to patient care and a research project to extend the device capabilities including addition of automated clinical decision support. Ultimately, these efforts may help in improving patient outcomes and reducing healthcare costs in the near-term.

项目概要/摘要 需要多参数血流动力学监测来管理手术和重症监护患者。 监测血压 (BP)、心输出量 (CO) 和左心室射血分数 (EF) 特别是，可以检测频繁的低血压和血流动力学不稳定，诊断 选择适当治疗的原因以及干预措施的调整（例如目标导向治疗）。 然而，这三个血流动力学变量的测量目前需要多个设备 侵入性、手动或专门的。虽然示波臂套装置是非侵入性的， 自动化且标准，它仅通过测量的袖带压力波形来估计血压 总体平均算法不能保持临床范围内的准确性。整体 该项目的目标是扩展无处不在的袖带装置，以实现准确、方便的多用途 通过智能算法监测参数血流动力学。具体目标是：（1）打造手臂 用于记录袖带压力波形的袖带装置； (2) 同时获取患者数据 以及算法训练的参考设备； (3) 开发并整合准确的算法 根据训练数据从袖带压力波形计算 BP、CO 和 EF； (4) 至 根据患者可靠的参考测量结果验证实时智能袖带。该设备将 被开发用于控制袖带压力并结合定制算法。袖带压力 通过设备获取波形，通过动脉和肺动脉导管获取参考 BP、CO 和 EF 在许多手术和临床干预之前和之后都会记录超声心动图 重症监护患者。这些训练数据将被分析以完善或调整之前的生理数据 算法并研究潜在的卓越机器学习算法，以实现最佳估计 三个血流动力学变量。最终的算法将在实时设备上实现， 集成系统将在临床期间根据相同的参考测量进行测试 干预措施但来自新患者。实现具体目标后将 将智能袖带带入患者护理的转化项目和扩展智能袖带的研究项目 设备功能包括添加自动化临床决策支持。最终，这些努力 可能有助于在短期内改善患者的治疗效果并降低医疗费用。