Edwards Lifesciences Corporation

Other

Baxter International Inc.

Luchy D Roteliuk

Feras Hatib

Russell Carl McKown

Doug Meyer, Jr

Weldon Dean West

Charles R Mooney

Luong N Phan

Jeffrey Pearce

Douglas Meyer, Jr

Robert A Pecor

David J Evans

Timothy C Mills

Mark A Konno

Clifford Currier

Howard Taymor-Luria

Erin Glines

Ives De Jonghe

Frederic Michard

John Frazier

Morgan Mckeown

Doug Patton

Shane Doorish

Russell Mckown

A method and apparatus for determining a cardiovascular parameter including receiving an input signal corresponding to an arterial blood pressure measurement over an interval that covers at least one cardiac cycle, determining a propagation time of the input signal, determining at least one statistical moment of the input signal, and determining an estimate of the cardiovascular parameter using the propagation time and the at least one statistical moment.

Method and apparatus for continuous assessment of a cardiovascular parameter using the arterial pulse pressure propagation time and waveform

A cardiovascular parameter such as cardiac output is estimated from a current pressure waveform data set without needing to directly measure blood flow or arterial compliance. The general shape of an input flow waveform over one beat-to-beat cycle is assumed (or computed), and then the parameters of a flow-to-pressure model, if not pre-determined, are determined using system identification techniques. In one embodiment, the parameters thus determined are used to estimate a current peripheral resistance, which is used not only to compute an estimate of the cardiovascular parameter, but also to adjust the shape of the input flow waveform assumed during at least one subsequent beat-to-beat cycle. Another embodiment does not require computation of the peripheral resistance and still another embodiment computes a flow estimate from an optimized identification of the parameters defining the assumed input flow waveform.

Pulse contour method and apparatus for continuous assessment of a cardiovascular parameter

Methods for detecting parameters in cardiac output related waveforms are described. The methods include methods for detecting individual heart beat cycles in a cardiac output related waveform, methods for detecting an error in an assigned starting point for an individual heart beat cycle in a cardiac output related waveform, methods for detecting a dichrotic notch for an individual heart beat cycle in a cardiac output related waveform, and methods for detecting an error in an assigned dichrotic notch for an individual heart beat cycle in a cardiac output related waveform. The identification of these parameters is important for a clinician as these parameters form the basis for the calculation of many other cardiac output related parameters.

Detection of parameters in cardiac output related waveforms

A medical blood pressure transducer that provides an identifier to a monitor that conveys characteristics of the transducer. The monitor can use the information to decide whether to function, or in calibration of the system. The transducer may be part of a disposable blood pressure monitoring system, and may include two transducers closely-spaced to provide two separate but identical outputs. In this way, the transducer may be connected to both a patient monitor and a cardiac output monitor at the same time measurements from a single line can be simultaneously supplied to two separate monitoring devices (for example a patient monitor and a cardiac output monitor). The identifier for the transducer may be circuitry, specifically a resistance/capacitance (RC) combination that possesses a characteristic time constant.

Disposable blood pressure transducer and monitor interface

One or more cardiovascular parameters is estimated as a function of the arterial pressure waveform, in particular, using at least one statistical moment of a discrete representation pressure waveform having an order greater than one. Arterial compliance, the exponential pressure decay constant, vascular resistance, cardiac output, and stroke volume are examples of cardiovascular parameters that can be estimated using various aspects of the invention. In one embodiment of the invention, not only are the first four moments (mean, standard deviation, skewness, and kurtosis) of the pressure waveform used to estimate the cardiovascular parameter(s) of interest, but also heart rate, statistical moments of a set of pressure-weighted time values, and certain anthropometric patient measurements such as age, sex, body surface area, etc.

Arterial pressure-based, automatic determination of a cardiovascular parameter

Cardiac stroke volume (SV) of a subject is estimated as a function of a value derived from a measured arterial pressure waveform. The value may be the standard deviation, or a function of the difference between maximum and minimum pressure values, or a function of either the maximum value of the first time derivative or the absolute value of the minimum of the first time derivative of the pressure waveform, or both, or a function of the magnitude of one or more spectral components of the pressure waveform at a frequency corresponding to the heart rate. Cardiac output is then estimated as the product of the subject's heart rate and SV, scaled by a calibration constant. Arterial pressure may be measured invasively or non-invasively.

Pressure-based system and method for determining cardiac stroke volume

Sensor case

One or more cardiovascular parameters is estimated as a function of the arterial pressure waveform, in particular, using at least one statistical moment of pressure waveform having an order greater than one. Arterial compliance, the exponential pressure decay constant, vascular resistance, cardiac output, and stroke volume are examples of cardiovascular parameters that can be estimated using various aspects of the invention. In one embodiment of the invention, not only are the first four moments (mean, standard deviation, skewness, and kurtosis) of the pressure waveform used to estimate the cardiovascular parameter(s) of interest, but also heart rate, statistical moments of a set of pressure-weighted time values, and certain anthropometric patient measurements such as age, sex, body surface area, etc.

Ventricular stroke volume variation (SVV) is estimated as a function of the standard deviation of arterial blood pressure value measured over each of at least two cardiac cycles, preferably over each of the cardiac cycles in a computation interval covering a full respiratory cycle. In one embodiment, maximum and minimum standard deviation values are determined over the computation interval. SVV is then estimated proportional to the ratio of the difference between the maximum and minimum standard deviation values and the mean of the standard deviation values. In another embodiment, SVV is then estimated proportional to the ratio of the standard deviation of the standard deviation values and the mean standard deviation over the entire computation interval. A pre-processing arrangement for improving reliability of estimates of more general cardiac or hemodynamic parameters is also disclosed and involves smoothing with an approximating function, and sampling and low-pass filtering at an adjustable rate.

Real-time measurement of ventricular stroke volume variations by continuous arterial pulse contour analysis

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Lake Forest, CA, United States of America

Luchy D Roteliuk