JP2009543609A - Method and apparatus for continuous assessment of cardiovascular parameters using arterial pressure propagation time and waveform - Google Patents

Abstract

  Receiving an input signal corresponding to an arterial pressure measurement over an interval covering at least one cardiac cycle; determining a propagation time of the input signal; determining at least one statistical moment of the input signal; Determining a cardiovascular parameter estimate using time and at least one statistical moment. A method and apparatus for determining a cardiovascular parameter. In one embodiment, the cardiovascular parameter is selected from the group consisting of arterial compliance, vascular resistance, cardiac output, and stroke volume.

Description

(Claiming priority)
This application is assigned to US Provisional Patent Application No. 60 / 830,735 assigned to the applicant (filed on July 13, 2006, entitled "METHOD AND APPARATUS FOR CONTINUOUS ASSESSMENT OF A CARDIOVASCULAR PARAMETER USPERTHERON PRATERPRENTE PRIDE Claiming priority based on WAVEFORM "), the application of which is expressly incorporated herein by reference.

(Technical field)
The present invention relates generally to systems and methods for hemodynamic monitoring. In particular, the present invention uses at least one of arterial pressure propagation time and waveform measurements to determine an individual's vascular tone, arterial compliance or resistance, stroke volume (SV), cardiac output (CO), etc. The present invention relates to a system and method for estimating cardiovascular parameters.

  Cardiac output (CO) is an important indicator not only for disease diagnosis, but also for continuous monitoring of the status of both human and animal subjects, including patients. Thus, few hospitals do not have some form of conventional device for monitoring cardiac output.

One method for measuring CO is the well-known formula:
CO = HR ^* SV, (Formula 1)
Where SV represents stroke volume and HR represents heart rate. SV is usually measured in liters and HR is usually measured in pulses per minute, although other volume and time units may be used. Equation 1 indicates that the amount of blood delivered by the heart per unit time (for example, one minute) is equal to the amount delivered per pulse (beat) multiplied by the number of pulses per unit time.

  Since HR is easily measured using a variety of equipment, the calculation of CO typically depends on the technique for estimating the SV. Conversely, SV can be determined by dividing by HR using any method that yields a direct value of CO. The estimated value of CO or SV can then be used to estimate or help estimate any parameters that can be derived from any of these values.

  One invasive method for determining CO (or equivalently SV) is to attach a flow measurement device to the catheter, then pass the catheter through the subject and manipulate the device to be in or near the subject's heart. It is. One such flow measurement device injects bolus material or energy (usually heat) at an upstream location such as the right atrium and is based on the characteristics of the injected material or energy at a downstream location such as the pulmonary artery. To determine the flow rate. Patents disclosing implementation of such invasive techniques (especially thermodilution) include:

Patent Document 1 (Newbower et al., December 2, 1980);
Patent Document 2 (Yelderman, April 2, 1985);
Patent Document 3 (McKown et al., September 8, 1992); and Patent Document 4 (McKown et al., November 18, 1997).

  Still other invasive devices are based on the known Fick method. According to this, CO is calculated as a function of oxygenation of arterial and mixed venous blood. In most cases, oxygenation is sensed using right heart catheterization. However, a system for non-invasive measurement of arterial and venous oxygenation has also been proposed, particularly using multiple wavelengths of light, but to date, it is possible to achieve satisfactory CO measurement in actual patients. Is not accurate enough.

  Invasive methods have obvious disadvantages. One such inconvenience is that cardiac catheterization is particularly important given that the subject (especially the intensive care patient) is often already hospitalized because it is in reality or a potentially serious condition. It is potentially dangerous. Invasive methods also have disadvantages that are difficult to see. One such disadvantage is that thermodilution relies on assumptions such as the uniform distribution of injected heat that affects the accuracy of the measurement, depending on the degree of achievement. Furthermore, the introduction of the device into the bloodstream can affect the value (eg, flow rate) that the device measures. Therefore, there is a long-felt need for a non-invasive (or at least as invasive as possible) and accurate CO determination method.

  One blood property that has proven particularly promising for determining CO less invasively or non-invasively is blood pressure. Most known blood pressure-based systems are pulse contouring (PCM), which calculates CO estimates from pulse-to-pulse arterial pressure waveform features. In PCM, a “windkessel” (German “air chamber”) parameters (characteristic impedance, compliance and total peripheral resistance of the aorta) are used to construct a linear or non-linear hemodynamic model of the aorta. In essence, blood flow is analogous to the current flow of a circuit in which resistance and capacitance (compliance) are connected in parallel and impedance is in series.

  The three required parameters of the model are typically compiled “anthropometric” data that is experimentally through a complex calibration process or data about the age, gender, height, weight, etc. of other patients or subjects Is determined from Patent Document 5 (Wesseling, March 28, 1995) and Patent Document 6 (Petlucelli et al., July 16, 1996) are representative systems for determining CO using a windkessel circuit model.

  Many extensions of the simple two-element windkessel model have been proposed in the hope of increasing accuracy. One such extension was developed in a 1930 paper (1) by Swiss physiologists Broemser and Ranke. In essence, the Broemser model—also known as the three-element windkessel model—adds a third element to the basic two-element windkessel model model to simulate resistance to blood flow by the aortic or pulmonary valve.

  The PCM system can monitor CO somewhat continuously without having to leave a catheter in the patient. In fact, some PCM systems function using blood pressure measurement using a finger cuff. However, one drawback of PCM systems is that they are not as accurate as the slightly simpler three-parameter models from which they are derived. In general, much higher order models are required to accurately grasp other events, such as the complex pattern of pressure wave reflections due to multiple impedance mismatches caused by arterial branching. Therefore, other improvements with varying complexity have been proposed.

  For example, the “Method and Apparatus for Measuring Cardiac Output” disclosed by Salvatore Romano in US Pat. No. 6,057,086 is invasive or non-invasive as a function of the ratio of the area under the total pressure curve to the linear combination of various impedance components. This is another attempt to improve the PCM method by estimating the SV. The Roman system relies on a series of experimentally determined numerical corrections to the mean pressure value as well as an accurate estimate of the derivative of the inherent noisy pressure function in an attempt to account for pressure reflection.

The core of several methods for estimating CO are:
CO = HR ^* (K ^* SV _est ) (Formula 2)
Where HR is the heart rate, SV _est is the estimated stroke volume, and K is a scaling factor related to arterial compliance. For example, Romano and Petrucelli depend on this formula as well as the devices disclosed in US Pat. Nos. 5,099,086 (Band et al., June 6, 2000) and U.S. Pat. .

Another formula often used to determine CO is:
CO = MAP ^* C / tau (Formula 3)
Where MAP is the mean arterial pressure, tau is the exponential pressure reduction constant, and C is the scaling factor associated with arterial compliance K as well as K. U.S. Patent No. 6,057,028 (Campbell, 26 November 2002) discloses an apparatus that uses such a formula.

  The accuracy of these methods may depend on how the scaling factors K and C are determined. In other words, an accurate estimate of compliance (or other value functionally related to compliance) may be required. For example, Langwaters (2) discusses the measurement of vascular compliance per unit length in the human aorta and correlates it with patient age and gender. The length of the aorta is determined to be proportional to the patient's weight and height. A nomogram based on this patient information is then derived and used with the information derived from the arterial pressure waveform to improve the estimation of the compliance coefficient.

  The various prior art devices identified above can each have one or more drawbacks. For example, the Band device requires external calibration with independent CO measurements to determine the coefficients associated with the vascular impedance used to calculate the CO. U.S. Patent No. 6,057,086 (Joeken et al., November 13, 2001) describes another device with the same drawbacks.

  Wesseling (Patent Document 12, March 28, 1995) attempts to determine coefficients related to vascular compliance from anthropometric data such as patient height, weight, sex, and age. This method relies on relationships determined from human nominal measurements and cannot be reliably applied to various patients.

  Romano attempts to determine a coefficient related to vascular impedance from only the arterial pressure waveform characteristics, and therefore cannot use the known relationship between patient characteristics and compliance. In other words, Romano also loses the information contained in such data by eliminating the need for system anthropometric data. In addition, Romano bases some intermediate calculations on the derivative of the pressure waveform. However, as is well known, the estimated value of such a derivative is inherently noisy. Therefore, the Romano method is not reliable.

  A system and method for more accurately and reliably estimating arterial compliance (K or C) or resistance, vascular tone, cardiovascular parameters such as tau, or values calculated from these parameters such as SV and CO is necessary.

One of the inventors of the present invention has previously published that SV can be approximated as being proportional to the arterial pressure waveform P (t), or the standard deviation of other signals that are themselves proportional to P (t). : Patent Document 13 (Luchy Roteliku et al., June 09, 2005, “Pressure based System and Method for Determining Cardiac Stroke Volume”) was published. Thus, one way to estimate SV is the relationship:
SV = Kσ (P) = Kstd (P) (Formula 4)
Is to apply.

Where K is a scaling factor from which:
CO = Kσ (P) HR = Kstd (P) HR (Formula 5)
Followed.

  This proportionality between the SV and the standard deviation of the arterial pressure waveform indicates that the pulsatility of the pressure waveform is produced by the SV of the heart to the arterial tree as a function of vascular tone (ie, vascular compliance and peripheral resistance). Based on observation. The scaling factor K in Equations 4 and 5 is an estimate of vascular tone.

In recent years, one of the inventors of the present invention has used arterial pressure waveform shapes in combination with pressure-dependent vascular compliance measurements and patient anthropometric data such as age, gender, height, weight and body surface area (BSA). It was also disclosed that characteristics can be used to reliably estimate vascular tone: Patent Document 14 (Luchy Roteliuk, June 09, 2005, “Arterial pressure-basic automatic of OF a cardiovascular parameter”). In order to quantify the shape information of the arterial pressure waveform, in addition to the newly derived pressure-weighted statistical moment, higher-order time domain statistical moments (eg, kurtosis and skewness) of the arterial pressure waveform were used. Therefore, vascular tone is calculated as a function of a combination of parameters using a multivariate regression model of the following general form:
_{K = χ (μ T1, μ} T2, ··· μ Tk, μ P1, μ P2, ··· μ Pk, C (P), BSA, Age, G ···) ( Equation 6)
Where
K is the vascular tone (calibration factor in equations 4 and 5);
X is a multiple regression statistical model;
μ _1T ... μ _kT is the 1st to kth order time domain statistical moment of the arterial pressure waveform;
μ _1P ... μ _kP is the first to kth order pressure weighted statistical moment of the arterial pressure waveform;
C (P) is the pressure-dependent vascular compliance calculated using the method proposed by Langwaters et al. 1984 (Non-Patent Document 3);
BSA is the patient's body surface area (a function of height and weight);
Age is the age of the patient;
G is the patient's gender.

The predictor set to calculate the vascular tone coefficient K using the multivariate model χ is the “true” of the test or reference population determined as a function of CO measured by thermodilution and arterial pressure. 'Related to vascular tone measurements. This creates a set of vascular tone measurements that are each a function of the component parameter of χ. Then, using known numerical methods, a multivariate approximation function is calculated that optimally associates the χ parameters with a given set of CO measurements in a predetermined sense. A polynomial multivariate fitting function is used to obtain the polynomial coefficients that give each predictor variable a value of χ. Therefore, the multivariate model has the following general form:

Where A ₁ ... _An are the coefficients of the polynomial multiple regression model and X is the model predictor:

  The method described above relies only on a single arterial pressure measurement. Its simplicity and the fact that it does not require calibration are the advantages of this method. However, due to the empirical nature of the relationship that assesses vascular tone, the accuracy of this method can be low in extreme clinical situations where the basic empirical relationship of the model is not valid. Therefore, it can be beneficial if a second independent measurement is added to the basic multiple regression model.

US Pat. No. 4,236,527 U.S. Pat. No. 4,507,974 US Pat. No. 5,146,414 US Pat. No. 5,687,733 US Pat. No. 5,400,793 US Pat. No. 5,535,753 US Pat. No. 6,758,822 US Pat. No. 6,071,244 US Pat. No. 6,348,038 US Pat. No. 6,485,431 US Pat. No. 6,315,735 US Pat. No. 5,400,793 US Patent Application Publication No. 2005/0124903 US Patent Application Publication No. 2005/0124904

“Ueber die Messung des Schlagvolumes des Herzens auf unbultigem Wegf,” Zeitung fuer Biology 90 (1930) 467-507. “The Static Elastic Properties of 45 Human Thoracic and 20 Abdominal Aortas in the Vitro and the Parameters of a New Model,” J. Biomechanics, Vol. 17, no. 6, pp. 425-435, 1984 “The Static Elastic Properties of 45 Human Thoracic and 20 Abdominal Aortas in the Vitro and the Parameters of a New Model,” J. Biomechanics, Vol. 17, no. 6, pp. 425-435, 1984 "The Static Elastic Properties of 45 Human Thoracic and 20 Abdominal Aortas in Vitro and the Parameters of a New Model," J. Biomechanics, Vol. 17, no. 6, pp. 425-435, 1984

  As indicated above, a number of non-invasive and invasive techniques have been devised for measuring SV and CO, in particular for detecting vascular compliance, peripheral resistance and vascular tone. Of course, there is a need for a system and method that is reliable, accurate and insensitive to calibration and calculation errors for estimating CO or any parameters that can be derived from or derived from CO.

  One embodiment of the present invention includes receiving an input signal corresponding to an arterial pressure measurement over an interval covering at least one cardiac cycle, determining a propagation time of the input signal, and at least one statistic of the input signal. A method for determining a cardiovascular parameter is provided that includes determining a moment and determining an estimate of the cardiovascular parameter using a propagation time and at least one statistical moment.

  One embodiment of the present invention receives an input signal corresponding to an arterial pressure measurement over an interval covering at least one cardiac cycle, determines a propagation time of the input signal, and determines at least one statistical moment of the input signal. An apparatus for determining a cardiovascular parameter is provided that includes a processing unit for determining an estimate of the cardiovascular parameter using the propagation time and at least one statistical moment.

FIG. 1 is an example of two blood pressure curves representing two different arterial pressure measurements received from a subject according to an embodiment of the present invention. FIG. 2 is an example of an electrocardiogram measurement (ECG) and blood pressure measurement received from a subject according to an embodiment of the invention. FIG. 3 is a graph showing the relationship between arterial pressure propagation time and arterial compliance according to an embodiment of the present invention. FIG. 4 is a graph showing the relationship between pulse pressure propagation time and vascular tone of a patient recovering from cardiac arrest according to an embodiment of the present invention. FIG. 5 is a graph illustrating the correlation between pulse pressure propagation time and vascular tone for different hemodynamic states of a subject according to some embodiments of the present invention. FIG. 6 is a graph showing the correlation between pulse pressure propagation time and vascular tone for different hemodynamic states of a subject according to some embodiments of the present invention. FIG. 7 illustrates CO, calculated using pulse pressure propagation time, continuous cardiac output (CCO), and thermodilution bolus measurements (of a subject's different hemodynamic states, according to some embodiments of the present invention. It is a graph which shows correlation with the CO value measured by TD-CO). FIG. 8 illustrates CO, calculated using pulse pressure propagation time, continuous cardiac output (CCO), and thermodilution bolus measurements (of a subject's different hemodynamic states, according to some embodiments of the present invention. It is a graph which shows correlation with the CO value measured by TD-CO). FIG. 9 illustrates CO, calculated using pulse pressure propagation time, continuous cardiac output (CCO), and thermodilution bolus measurements (of a subject's different hemodynamic states, according to some embodiments of the invention. It is a graph which shows correlation with the CO value measured by TD-CO). FIG. 10 is a graph illustrating the relationship between CO estimated using arterial pressure propagation time and CO estimated using an arterial pressure signal, according to some embodiments of the present invention. FIG. 11 is a block diagram illustrating an example system that may be used to perform various methods described herein, according to some embodiments of the present invention. FIG. 12 is a flow diagram illustrating a method according to an embodiment of the invention.

  Next, methods and systems for carrying out embodiments of various features of the invention will be described with reference to the drawings. The drawings and the associated descriptions are provided to illustrate embodiments of the invention and are not intended to limit the scope of the invention. Reference to “one embodiment” or “an embodiment” herein includes that a particular function, structure, or characteristic described in connection with that embodiment is included in at least one embodiment of the invention. Means. The phrases “one embodiment” or “an embodiment” in various places in the specification are not necessarily all referring to the same embodiment. Throughout the drawings, reference numbers are re-used to indicate correspondence between referenced elements.

  In a broad sense, the invention relates to cardiac values, such as values derived from SV, such as stroke volume (SV) and / or cardiac output (CO), using arterial pressure propagation time. Regarding measurement. The arterial pressure propagation time can be measured using an arterial pressure waveform or a waveform proportional to or derived from the arterial pressure, an electrocardiogram measurement value, a bioimpedance measurement value, other cardiovascular parameters, and the like. These measurements can be made with invasive, non-invasive, minimally invasive devices or combinations of devices.

  The present invention can be used on any type of subject, whether human or animal. Since the most common use of the present invention is expected to be in the case of human diagnosis, the use of the present invention for “patients” is mainly described below. This is only an example, but the term “patient” is intended to include all human and animal subjects, regardless of the situation.

FIG. 1 shows an example of two blood pressure curves representing two different arterial pressure measurements received from a subject. The upper curve represents the central arterial pressure measurement detected from the subject's aorta, and the lower curve represents the measurement detected from the subject's radial artery. The pulse pressure propagation time (t _prop ) can be measured as the elapsed time between two arterial pressure measurements.

  The rationale for using pulse pressure propagation time for hemodynamic measurements is based on the basic principles of cardiovascular biomechanics. That is, if the subject's heart transports blood through a completely rigid blood vessel, a pressure waveform should immediately appear at any distal arterial location in the subject's body when the heart contracts. However, if the subject's heart transports blood through elastic blood vessels, a waveform appears when the heart contracts, some time after the heart contracts at the location of the distal artery in the subject's body .

  The pulse pressure propagation time can be measured invasively or non-invasively at several different locations for the pressure waveform (or any other waveform associated with the pressure waveform). In the example shown in FIG. 1, the pulse pressure propagation time can be measured using two different arterial pressure measurements, for example, one reference measurement from the aorta and one peripheral measurement from the radial artery.

FIG. 2 shows an example of using an electrocardiogram signal as a reference signal for propagation time measurement. The upper curve represents an electrocardiogram (ECG) signal detected by electrodes placed near the subject's heart, and the lower curve represents arterial pressure measurements detected from the subject's peripheral blood vessels. In this example, the arterial pressure propagation time (t _prop ) can be measured using the elapsed time between the ECG signal and the peripheral arterial pressure. Similarly, transthoracic bioimpedance measurement may be used as a reference point, and propagation time can be measured as the elapsed time for a peripheral measurement derived from or proportional to arterial pressure.

  Arterial pressure propagation time provides an indirect measure of the physical (ie mechanical) properties of the blood vessel segment between the two recording sites. These properties mainly include the elastic and geometric properties of the arterial wall. Arterial wall characteristics such as thickness and lumen diameter are some of the main determinants of arterial pressure propagation time. As a result, pulse pressure propagation time depends primarily on arterial compliance.

FIG. 3 shows an example in which the pulse pressure propagation time increases with increasing arterial compliance (C). Thus, the pulse pressure propagation time (t _prop ) is a function of arterial compliance (C), ie
t _prop = f (C) (Equation 9)
Can be expressed as

  Thus, arterial pressure propagation time can be used as a simple measure for estimating arterial compliance. Propagation time may be used as a separate measure to assess the patient's vascular condition, and pulse contour cardiac output along with other parameters to account for vascular compliance, vascular resistance and vascular tone It may be used in an algorithm. In one embodiment, arterial pressure propagation time is measured using arterial pressure signals from relatively large arteries (eg, ribs, thighs, etc.) and thus the effect of peripheral resistance is minimal. Also, this measurement includes the average arterial compliance between measurement sites and may not reflect the pressure dependence of arterial compliance.

From the well-known Bramwell-Hill formula used to calculate the pulse wave velocity (PWV), the basic relationship can be derived:

Where
dP is the change in pressure;
dV is the change in volume;
p is the blood density;
V is the baseline volume.

Arterial compliance (C) can be defined as the ratio of incremental volume change (dV) resulting from incremental pressure change (dP):

Substituting equation (11) into equation (10) yields the following equation:

On the other hand, PWV is defined as follows:

In the formula, L is a blood vessel length between two recording sites, and t _prop is an arterial pressure propagation time.

Substituting Equation 13 into Equation 12 yields arterial compliance by:

If we define γ as:

Arterial compliance can be expressed as:
C = γ · t ² _prop (Equation 16)
In the equation, the scaling factor γ is a function that depends on the blood density, the effective vessel distance between the two recording sites, and the basic volume. That is, γ depends on the physical blood vessel volume and blood viscosity (ie, hematocrit, etc.) between the two recording sites.

  Based on the above equation, the arterial pressure propagation time can be used in a variety of ways.

1. Use of arterial pressure propagation time to estimate arterial compliance. The pulse pressure propagation time can be used as an input to a hemodynamic model based on the standard deviation of arterial pressure to evaluate the kinetic changes in arterial pressure caused by systolic ejection. CO can be expressed as a function of the standard deviation of arterial pressure as follows:
CO = K ^* std (P) ^* HR (Formula 17)
Where K is a scaling factor proportional to arterial compliance as indicated above, std (P) is the standard deviation of arterial pressure, and HR is the heart rate.

In addition, the following are understood:

  Where MAP is the mean arterial pressure, τ is the exponential pressure decay constant, and C is a scaling factor related to arterial compliance as well as K.

  From equations 17 and 18, the scaling factor K is a measure equal to vascular compliance. By substituting the scaling factor K of Equation 17 into the compliance given by Equation 16, CO can be calculated using the standard deviation of the arterial pressure waveform and the arterial pressure propagation time.

CO = γ · t ² _prop · std (P) · HR (Formula 19)
Where the standard deviation of arterial pressure can be calculated using the following formula:

Where n is the total number of samples, P (k) is the instantaneous pulse pressure, and P _avg is the mean arterial pressure. Mean arterial pressure can be defined as:

  FIG. 4 is a graph showing the relationship between the square of arterial pressure propagation time and the scaling factor K of a patient recovering from cardiac bypass surgery. FIG. 4 plots ten (10) average data points from ten (10) different patients. In the example of FIG. 4, the arterial pressure propagation time is calculated as the elapsed time between the ECG signal and radial artery pressure. The data shown in FIG. 4 shows that using the arterial pressure propagation time given by Equation 16, the K scaling factor of Equation 17 can be effectively estimated.

  5 and 6 are graphs showing the correlation between arterial pressure propagation time and the K scaling factor of Equation 17 for different hemodynamic states of the two subjects. Both trends correspond to animal data taken from experiments using pig animal models. These figures show the same tendency of the scaling factor K and the square of the pulse pressure propagation time. The data in FIGS. 5 and 6 show that the K or C scaling factors of Equations 17 and 18 can be effectively estimated using arterial pressure propagation time.

The scaling factor γ in Equation 19 can be determined using any given function of propagation time and pressure P (t). Therefore,
γ = Γ (t _prop , P) (Formula 22)
Where Γ is a predetermined function of propagation time and pressure used to develop a calculation method for estimating γ.

  Any known independent CO technique can be used to determine this relationship, whether it is invasive, such as thermodilution, or non-invasive, such as transesophageal echocardiography (TEE) or bioimpedance measurement. . The present invention provides a continuous trend of CO during intermittent measurements such as TD or TEE.

  Even when determining γ using invasive techniques such as catheterization, it is usually not necessary to leave the catheter in the patient during a subsequent CO-monitoring session. Furthermore, even when determining γ using catheter-based calibration techniques, measurements need not be made in or near the heart. Rather, calibration measurements can be made within the femoral artery. Thus, even when γ is determined using invasive techniques, the present invention as a whole is still minimally invasive in that any catheter procedure may be peripheral or temporary.

  As described above, instead of directly measuring arterial pressure, any other input signal proportional to blood pressure can be used. This means that calibration can be performed at any or all points of the calculation. For example, if a signal other than the arterial pressure itself is used as the input signal, the blood pressure may be calibrated before or after the value is used to calculate the standard deviation, The resulting standard deviation value can be scaled, the obtained SV value can be calibrated (eg, by setting γ appropriately), or the final function of the SV (such as CO) can be scaled. In short, the fact that the present invention may use an input signal that is sometimes different from direct measurement of arterial pressure does not limit the ability to obtain an accurate SV estimate.

In addition to blood viscosity, γ depends mainly on the physical vessel volume between the two recording sites. Of course, the effective length (L) and effective volume (V) between the two recording sites cannot be known. Vessel branching and patient-to-patient differences are two main reasons for not knowing the effective physical vessel volume between the two recording sites. However, since it is clear that this physical volume is proportional to the patient's anthropometric parameters, it can be estimated indirectly using the patient's anthropometric parameters. Anthropometric parameters are measured distance between two recording sites (l), patient weight, patient height, patient gender, patient age, patient bsa etc. various parameters, or any combination of these factors Can be derived from In one embodiment, using all anthropometric parameters, eg, distance between two recording sites (l), patient weight, patient height, patient gender, patient age, patient bsa, γ It can be calculated. Other values are preferably included in the calculation to take into account other characteristics. In one embodiment, heart rate HR (or R-wave period) may be used. Therefore,
γ = Γ _M (1, H, W, BSA, Age, G, HR) (Equation 23)
Where
l is the measured distance between the two recording sites;
H is the patient's height;
W is the patient's weight;
BSA is the patient's bsa;
Age is the age of the patient;
G is the patient's gender;
HR is the patient's heart rate;
ΓM is a multivariate model.

Predictor variables set to calculate γ using the multivariate model Γ are determined as a function of CO measured by thermodilution and arterial pressure, a “true” vascular compliance measure of the test or reference population Related to value. Thus, each of which is a function of the component parameters of the gamma _M, compliance measurements set is created. Then, using a numerical method, optimally associate multivariate approximation function in a predetermined manner to parameters of gamma _M to a given CO measurement value pairs is calculated. Using polynomial multivariate fitting function gives the value of gamma _M each predictor set, the coefficients of the polynomial are obtained. Therefore, the multivariate model has the following general equation:

Where a ₁ ... _An are the coefficients of the polynomial multiple regression model and Y is the model predictor:

Use of arterial pressure propagation time to estimate vascular tone. Vascular tone is a hemodynamic parameter used to explain the combined effects of vascular compliance and peripheral resistance. In the prior art, vascular tone was estimated using the shape characteristics of the arterial pressure waveform in combination with patient anthropometric data and other cardiovascular parameters (Roteliuk, 2005, “Arterial pressure-basic automatic determination of af. cardiovascular parameter "). The arterial pressure propagation time can also be used to estimate the vascular tone. In one embodiment, arterial pressure propagation time can be used as an independent term to the multivariate regression model to continually estimate vascular tone. In one embodiment, the vascular tone may be estimated using the arterial pressure propagation time in combination with the arterial pressure waveform shape information. Shape-sensitive higher-order arterial pressure statistical moments and pressure-weighted time moments can be used as predictors for multivariate models along with arterial pressure propagation time. Other values are preferably included in the calculation to account for other characteristics. For example, a non-linear compliance value C (P) that depends on heart rate HR (or R-wave period), body surface area BSA, and pressure calculates compliance as a polynomial function of pressure waveform and patient age and gender. Can be calculated using known methods such as those described by. Therefore,
K = χ (t _prop , μ _T1 , μ _T2 ,... Μ _Tk , μ _P1 , μ _P2 ,... Μ _Pk , C (P), BSA, Age, G...) (Equation 26)
Where
K is the vascular tone;
χ is a multiple regression statistical model;
t _prop is the arterial pressure propagation time;
μ _1T ... μ _kT is the first to k next time domain statistical moment of the arterial pressure waveform;
μ _1P ... μ _kP is the first to kth order pressure weighted statistical moment of the arterial pressure waveform;
C (P) is described in Langwaters et al. (“The Static Elastic Properties of 45 Human Thoracic and 20 Abdominal Aortas in Vitro and the Parameters of a New Model. 25”. , 1984), pressure-dependent vascular compliance;
BSA is the patient's body surface area (a function of height and weight);
Age is the age of the patient;
Gender is the gender of the patient.

  Depending on the requirements of a given implementation of the present invention, one may choose not to include skewness or kurtosis, or may include higher order moments. The use of the first four statistical moments has been found to succeed in contributing to an accurate and reliable estimation of compliance. Moreover, anthropometric parameters other than HR and BSA can be used additionally or alternatively and other methods can be used to determine C (P), which may be omitted entirely.

The exemplary method described below for calculating the current vascular tone value can be adjusted in a known manner to reflect the increased, decreased, or altered parameter sets. Once the parameter set for calculating K is collected, it can be associated with a known variable. Existing devices and methods, including invasive techniques such as thermodilution, can be used to determine the CO, HR, and SV _est of the test or reference subject population. Anthropometric data such as age, weight, BSA, height, etc. can also be recorded for each subject. This produces a set of CO measurements, each of which is a (initially unknown) function of the K component parameters. Thus, using known numerical methods, an approximation function can be calculated that optimally relates the parameter to K in a predetermined sense given the CO measurement set. One well-understood and easily calculated approximation function is a polynomial. In one embodiment, each parameter set _tprop , HR, C (P), BSA, [mu] _1P , [sigma] _P , [mu] _3P , [mu] _4P , [mu] _1T , [sigma] _T , using a standard multivariate fitting routine. Polynomial coefficients giving the values of K for μ _3T and μ _4T are obtained.

In one embodiment, K is calculated as follows:

Where

  3. The use of arterial pressure propagation to directly estimate CO is discussed below.

Pulse pressure propagation time can be used as an independent method to estimate CO. That is, as shown below, arterial pressure propagation time is independently proportional to SV:

Multiply Equation 29 by HR to estimate CO:

Using direct calibration, the scaling factor K _P may be estimated using, for example, known CO values from bolus thermodilution measurements or other gold standard CO measurements. 7-9 are measured using CO, continuous cardiac output (CCO), and intermittent thermodilution bolus measurement (ICO) calculated using the pulse pressure propagation time (COprop) shown in Equation 30. It is a graph which shows the correlation between CO value. CCO and ICO are measured using a Vigilance monitor manufactured by Edwards Lifesciences, Irvine, California. Measurements were performed on animal models of pigs with different animal hemodynamic status. These graphs experimentally show that changes in CO are related to changes in pulse pressure propagation time, and pulse pressure propagation time can be used as an independent method for estimating CO.

The scaling factor K _{P in} Equation 30 can be determined using any predetermined function of propagation time and CO or SV. This relationship can be determined using any independent CO technique, whether invasive, such as thermodilution, or non-invasive, such as transesophageal echocardiography (TEE) or bioimpedance measurement. The present invention provides a continuous trend of CO during intermittent measurements such as TD or TEE.

Even when determining K _P using invasive techniques such as catheterization, it is not necessary to leave the catheter in the patient during a subsequent CO-monitoring session. Further, even when determining K _P using catheter-based calibration techniques, measurements need not be made in or near the heart. Rather, calibration measurements can be made within the femoral artery. As such, even when K _P is determined using invasive techniques, the present invention is still minimally invasive in that any catheter procedure may be peripheral or temporary.

The approach shown in Equation 30 is that non-invasive techniques are used to measure propagation time, and CO measurement is completely non-invasive if a predetermined function or relationship is used to evaluate K _P. Is allowed to be done automatically. Non-invasive techniques for measuring propagation time include ECG, non-invasive arterial pressure measurement, bioimpedance measurement, optical pulse oximetry, Doppler ultrasound measurement, or derived from or proportional to them Including any other measurements, or combinations thereof (eg, using Doppler ultrasonic pulse velocity measurements to measure reference signals near the heart, and using bioimpedance measurements to measure peripheral signals, etc.) It is.

The scaling factor K _P mainly depends on the blood viscosity and the physical vessel distance and volume between the two recording sites. Of course, the effective length (L) and effective volume (V) between the two recording sites cannot be known. Vessel branching and patient-to-patient differences are two main reasons for not knowing the effective physical vessel volume between the two recording sites. However, since the physical volume can be proportional to the patient's anthropometric parameters, it can be estimated indirectly using the patient's anthropometric parameters. Anthropometric parameters may include various parameters such as measurement distance (L) between two recording sites, patient weight, patient height, patient gender, patient age, patient bsa, or any combination of these parameters Can be derived from In one embodiment, using all anthropometric parameters: distance between two recording sites (L), patient weight, patient height, patient gender, patient age, and patient bsa, K _P Is calculated. Therefore,
K _P = M (L, H, W, BSA, Age, G) (Formula 31)
Where
L is the measurement distance between the two recording sites;
H is the patient's height;
W is the patient's weight;
BSA is the patient's bsa;
Age is the age of the patient;
G is the patient's gender;
M is a multivariate linear regression model.

The predictor set to calculate K _P using the multivariate model M is the “true” test or reference object population CO determined as a function of propagation time, where CO is measured by thermodilution. Related to measured values. This produces a set of measurements, each of which is a function of M component parameters. A numerical method is then used to calculate a multivariate approximation function that optimally relates the parameters of M to a given set of CO measurements in a predetermined sense. A polynomial multivariate fitting function is used to obtain polynomial coefficients that give the value of M for each predictor set. Thus, the multivariate model has the following equation:

Where a ₁ ... _An are the coefficients of the polynomial multiple regression model and Y is the model predictor:

FIG. 10 is a graph showing the relationship between CO estimated using Equation 17 (CO _std on the x-axis) and CO estimated using Equation 30 (CO _prop on the y-axis) from a series of animal experiments. It is. Data show CO measurements from a total of ten (10) pigs. Three (3) selected data points from each pig are used in the graph. To cover a wide CO range, each selected data point corresponds to a different hemodynamic state in the pig, vasodilation, vasoconstriction, and blood volume reduction, respectively. The proportionality shown in FIG. 10 experimentally proves the effectiveness and reliability of using propagation time to estimate CO.

  FIG. 11 is a block diagram illustrating an example system used to perform the various methods described herein. The system may include a patient 100, a pressure transducer 201, a catheter 202, ECG electrodes 301 and 302, signal conditioning units 401 and 402, a multiplexer 403, an analog-to-digital converter 405 and a calculation unit 500. The calculation unit 500 includes a patient specific data module 501, a scaling factor module 502, a moment module 503, a standard deviation module 504, a propagation time module 505, a stroke volume module 506, a cardiac output module 507, a heart rate module 508, An input device 600, an output device 700, and a heart rate monitor 800 may be included. Each unit and module may be implemented in hardware, software, or a combination of hardware and software.

  The patient identification data module 501 is a memory module that stores patient data such as patient age, height, weight, sex, and BSA. This data may be entered using the input device 600. A scaling factor module 502 receives patient data and performs calculations to calculate a scaling compliance factor. For example, the scaling factor module 502 puts parameters into the equations given above or other equations derived by creating an approximate function that fits the test data set optimally. The scaling factor module 502 may also determine a time window [t0, tf] over which each vascular compliance, vascular tone, SV and / or CO estimate is obtained. This can be done simply to select how many of the stored continuous or discrete values are used in each calculation.

  The moment module 503 determines or estimates higher order statistical time domain moments and weighted moments of arterial pressure. Standard deviation module 504 determines or estimates the standard deviation of the arterial pressure waveform. The propagation time module 505 determines or estimates the propagation time of the arterial pressure waveform.

The scaling factor, higher order statistical moment, standard deviation, and propagation time are input to stroke volume module 506 to obtain an SV value or estimate. A heart rate monitor 800 or software routine 508 (eg, using Fourier or differential analysis) may be used to assess the patient's heart rate. The SV value or estimate and the patient's heart rate are input to the cardiac output module 507 to obtain an estimate of CO using, for example, the equation CO = SV ^* HR.

  As mentioned above, it is not necessary for the system to calculate SV or CO if these values are not intended. The same is true for vascular compliance, vascular tone and peripheral resistance. In such a case, the corresponding module is not necessary and can be omitted. For example, the present invention can be used to determine arterial compliance. Nonetheless, as FIG. 11 shows, output device 700 (eg, a monitor) so that any or all results of SV, CO, vascular compliance, vascular tone and peripheral resistance are presented to the user and deciphered. Can be displayed. Similar to input device 600, output device 700 may be generally the same as that used by other target systems.

  The invention further relates to a computer program that can be loaded into a computer unit or computing unit 500 to carry out the method of the invention. Further, various modules 501-507 can be used to perform various calculations and perform related method steps according to the present invention, and so that the present invention can be loaded and executed on various processing systems. It can be stored as computer-executable instructions on a computer-readable medium.

  While certain exemplary embodiments have been described and illustrated in the accompanying drawings, it should be understood that such embodiments are merely illustrative of the broad invention and are not limiting and are described in the preceding paragraphs. In addition, the present invention is not limited to the specific structures and settings shown and described because various other changes, combinations, omissions, modifications and substitutions are possible. It will be appreciated by those skilled in the art that various adjustments and modifications to the preferred embodiments described above can be made without departing from the scope and spirit of the invention. Therefore, it is to be understood that, within the scope of the appended claims, the invention may be practiced other than as specifically described herein.

Claims (37)

A method of determining cardiovascular parameters,
Receiving an input signal corresponding to an arterial pressure measurement over an interval covering at least one cardiac cycle;
Determining the propagation time of the input signal;
Using the propagation time to determine an estimate of the cardiovascular parameter.   The method of claim 1, further comprising determining at least one statistical moment of the input signal.   The step of determining an estimate of the cardiovascular parameter using the propagation time comprises determining an estimate of the cardiovascular parameter using the at least one statistical moment. the method of.   The at least one statistical moment of the input signal is selected from the group consisting of a standard deviation of the input signal and a statistical moment higher than second order, a kurtosis of the input signal, and a skewness of the input signal. Item 3. The method according to Item 2.   The method of claim 1, wherein the cardiovascular parameter is selected from the group consisting of arterial compliance, vascular resistance, cardiac output, and stroke volume.   Determining the propagation time of the input signal comprises determining an elapsed time between a reference signal detected near the subject's heart and a peripheral artery signal detected near the subject's artery; The method of claim 1.   The method of claim 6, wherein the reference signal is selected from the group consisting of an electrocardiogram measurement, a central aortic pressure measurement, a transthoracic bioimpedance measurement, and a Doppler ultrasound blood velocity measurement.   The peripheral arterial signal is selected from the group consisting of arterial pressure measurements, optical oxygen measurements to measure oxygen saturation of the subject's blood, peripheral bioimpedance measurements, and Doppler ultrasound blood velocity measurements; The method of claim 6.   The step of determining an estimate of the cardiovascular parameter using the propagation time also includes using a standard deviation of the input signal to determine an estimate of the cardiovascular parameter. The method described in 1.   The method of claim 1, further comprising receiving the anthropometric parameters of the subject.   11. The step of determining the cardiovascular parameter estimate using the propagation time also includes using the anthropometric parameter to determine the cardiovascular parameter estimate. the method of.   The method of claim 10, further comprising estimating an arterial compliance value using the propagation time and the anthropometric parameters.   The method of claim 12, further comprising estimating stroke volume using the arterial compliance value and a standard deviation of the input signal. Receiving a heart rate measurement of the subject;
The method of claim 13, further comprising: using the heart rate measurement and the stroke volume to estimate cardiac output.   The method of claim 14, further comprising estimating cardiac output using the arterial compliance and the standard deviation. Receiving a calibration cardiac output value;
16. The method of claim 15, further comprising: calculating a calibration constant as a quotient between the calibration cardiac output estimate and the product of the heart rate, the arterial compliance, and the standard deviation. . Estimating the arterial compliance value comprises:
Determining an approximation function for a plurality of reference measurements of arterial compliance comprising:
The approximation function is a function of the propagation time of the input signal and the anthropometric parameters;
The method of claim 12, further comprising: estimating the arterial compliance value of the subject by determining a value of the approximation function from the propagation time of the input signal and the anthropometric parameters. Calculating a propagation time component value for each of the plurality of cardiac cycles;
Calculating a composite value of the propagation time as an average of the component values of the propagation time;
The method of claim 1, further comprising using a composite value of the propagation time in calculating the estimated value of the cardiovascular parameter. An apparatus for determining cardiovascular parameters comprising:
Receiving an input signal corresponding to an arterial pressure measurement over an interval covering at least one cardiac cycle;
Determine the propagation time of the input signal;
An apparatus comprising: a processing unit for determining an estimate of the cardiovascular parameter using the propagation time.   20. The apparatus according to claim 19, wherein the processing unit determines at least one statistical moment of the input signal.   21. The apparatus of claim 20, wherein the processing unit determines an estimate of the cardiovascular parameter using the at least one statistical moment.   The at least one statistical moment of the input signal is selected from the group consisting of a statistical moment higher than second order, the kurtosis of the input signal, the skewness of the input signal, and the standard deviation of the input signal; The apparatus of claim 20.   20. The device of claim 19, wherein the cardiovascular parameter is selected from the group consisting of arterial compliance, vascular resistance, cardiac output, and stroke volume.   The processing unit determines a propagation time of the input signal by determining an elapsed time between a reference signal detected near the target heart and a peripheral artery signal detected near the target artery; The apparatus of claim 19.   25. The apparatus of claim 24, wherein the reference signal is selected from the group consisting of an electrocardiogram measurement, a central aortic pressure measurement, a transthoracic bioimpedance measurement, and a Doppler ultrasound blood velocity measurement.   The peripheral arterial signal is selected from the group consisting of arterial pressure measurements, optical oxygen measurements to measure oxygen saturation of blood of interest, peripheral bioimpedance measurements, and Doppler ultrasound blood velocity measurements. Item 25. The apparatus according to Item 24.   The apparatus of claim 19, wherein the processing unit determines an estimate of the cardiovascular parameter using a standard deviation of the input signal.   The apparatus of claim 19, wherein the processing unit receives an anthropometric parameter of a subject.   30. The apparatus of claim 28, wherein the processing unit determines an estimate of the cardiovascular parameter using the propagation time and the anthropometric parameter.   30. The apparatus of claim 28, wherein the processing unit estimates an arterial compliance value using the propagation time and the anthropometric parameters.   31. The apparatus of claim 30, further comprising estimating stroke volume using the arterial compliance value and a standard deviation of the input signal. Receiving a heart rate measurement of the subject;
32. The apparatus of claim 31, further comprising: estimating cardiac output using the heart rate measurement and the stroke volume.   35. The apparatus of claim 32, further comprising estimating cardiac output using the arterial compliance and the standard deviation. Receiving a calibration cardiac output value;
36. The method of claim 33, further comprising: calculating a calibration constant as a quotient between the calibration cardiac output estimate and the product of the heart rate, the arterial compliance, and the standard deviation. apparatus. Estimating the arterial compliance value comprises:
Determining an approximation function for a plurality of reference measurements of arterial compliance comprising:
The approximation function is a function of the propagation time of the input signal and the anthropometric parameters;
30. The apparatus of claim 29, further comprising: estimating the arterial compliance value of the subject by determining a value of the approximation function from the propagation time of the input signal and the anthropometric parameters. Calculating a propagation time component value for each of the plurality of cardiac cycles;
Calculating a composite value of the propagation time as an average of the component values of the propagation time;
20. The method of claim 19, further comprising: using a composite value of the propagation time in calculating the estimated value of the cardiovascular parameter. A machine-readable medium that provides instructions, and when executed by a processor,
Receiving an input signal corresponding to an arterial pressure measurement over an interval covering at least one cardiac cycle;
Determining the propagation time of the input signal;
Determining the cardiovascular parameter using the propagation time to determine an estimate of the cardiovascular parameter.
Machine-readable medium.

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