University of Pittsburgh

A Novel Non-Invasive Method for Accurate Cardiac Output Monitoring

This invention is an innovative oscillometric arm cuff device that accurately monitors cardiac output, a critical measure of blood flow rate, without the need for invasive procedures or specialized training. It offers a non-invasive, automatic, and widely accessible alternative to current, more complex methods for hemodynamic management in patients.

Description

The invention is an automatic arm cuff device that uses a new method to monitor cardiac output. While similar to standard blood pressure cuffs, this device uniquely measures not only the air pressure inside the cuff but also the volume of air that is pumped into and out of the cuff. This additional measurement is crucial because it allows the device to determine the compliance of the cuff-arm system, which is a variable that can affect the accuracy of blood volume measurements. The device uses a basic algorithm that combines the measured cuff pressure, the unique air volume measurement, and patient anthropometric information (height, weight, arm circumference) to estimate cardiac output in L/min. Unlike a previously proposed device that estimates cardiac output during prolonged inflation, this new device can make the estimation during conventional cuff deflation, and its estimate is suitable for convenient calibration in each patient.

Applications

- Surgical and Intensive Care Patients: This device can be used for hemodynamic management of surgical and intensive care patients.
- Outpatient Therapy: The technology has potential use in outpatient settings to help determine anti-hypertensive therapy.
- Combination with Other Devices: The technology can be combined with invasive pulse contour analysis to further improve accuracy and correlation.

Advantages

- Non-invasive and automatic
- Highly Accurate
- Clinically Validated
- Convenient Calibration

Invention Readiness

A prototype exists and in vivo data has been generated from a study on 34 surgical patients, showing the method's accuracy. The accuracy of the device can be further improved by developing machine learning algorithms using a larger patient dataset, which is currently being collected. Further studies are needed to expand the patient dataset for the development of these advanced algorithms.