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.

项目摘要/摘要 需要多参数血液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，允许检测频繁的低血压和血流动力学不稳定性，诊断 选择适当的治疗和滴定干预措施的原因（例如，目标定向治疗）。 但是，这三个血液动力学变量的测量当前需要多个设备 具有侵入性，手动或专业的。虽然振荡臂袖带是非侵入性的，但 自动化和标准，它仅通过A估算测得的袖口压力波形的BP 在临床范围内无法保持准确性的人群平均算法。总体 该项目的目标是扩展无处不在的手臂袖口设备，以便准确且方便 参数血液动力学通过智能算法监测。具体目的是：（1）建造一个手臂 用于记录袖口压力波形的袖口装置； （2）同时获取患者数据 和算法培训的参考设备； （3）开发和合并算法以准确 根据训练数据，从袖口压力波形中计算BP，CO和EF； （4）到 验证实时智能袖带，以防止患者的可靠参考测量值。设备将 开发以控制袖口压力并结合自定义算法。袖口压力 通过设备和参考BP，CO和EF通过动脉和肺动脉导管的波形 并将超声心动图记录在许多手术和 重症监护患者。这些培训数据将进行分析以完善或适应以前的生理学 算法并研究潜在的卓越机器学习算法，以最佳估计 三个血液动力学变量。最终算法将用于实时设备， 并且将在临床期间针对相同的参考测量值测试集成系统 干预措施，但来自新患者。特定目标的实现将是 转化项目将智能袖口带到患者护理和研究项目以扩展 设备功能，包括增加自动临床决策支持。最终，这些努力 可能有助于改善患者的结果并在短期内降低医疗保健费用。