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

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.