CARDIAC MONITORING SYSTEM

A cardiac monitoring system includes a display, a blood pressure sensor configured to sense a blood pressure of an artery of a patient, an analog-to-digital converter (ADC) configured to convert a blood pressure signal from the blood pressure sensor to digital blood pressure data, a hardware processor, and a software code. The hardware processor executes the software code to identify cardiovascular metrics of the patient, including one or more of an aortic impedance and an aortic compliance of the patient, based on the blood pressure data, and to determine a stroke volume (SV) of the patient using a subset of the cardiovascular metrics including the aortic impedance and/or aortic compliance, as well as an additive factor. The additive factor is based on a meta-parameter including a weighted sum of combinatorial parameters, each combinatorial parameter including another subset of the cardiovascular metrics.

BACKGROUND

Cardiac monitoring can be an important part of patient care in the hospital setting, particularly for patients who are seriously ill or undergoing surgery. Many cardiac parameters of significant clinical importance can be derived from central arterial blood pressure waveforms obtained through continuous or periodic blood pressure monitoring. Examples of such significant cardiac parameters include cardiac output, stroke volume, and stroke volume variation, to name a few. Accurately calculating those cardiac parameters can be of critical importance for clinicians responsible for making treatment decisions.

For patients in a normal hemodynamic condition, the relationship between arterial blood pressure measured peripherally and central arterial blood pressure is understood, so that blood pressure measurements taken at the femoral or radial arteries, for example, can be used to quickly and accurately determine cardiac output and stroke volume, as well as other cardiac parameters of interest. However, for patients in a hyperdynamic condition, peripheral arterial blood pressure and central arterial blood pressure may be decoupled, rendering conventional solutions for determining cardiac parameters based on peripheral arterial blood pressure measurements unreliable.

SUMMARY

There are provided systems and methods for performing cardiac monitoring, substantially as shown in and/or described in connection with at least one of the figures, and as set forth more completely in the claims.

DETAILED DESCRIPTION

As stated above, cardiac monitoring can be an important part of patient care in the hospital setting, particularly for patients who are seriously ill or undergoing surgery. Many cardiac parameters of significant clinical importance can be derived from central arterial blood pressure waveforms obtained through continuous or periodic blood pressure monitoring. Examples of such significant cardiac parameters include cardiac output, stroke volume, and stroke volume variation, to name a few. Accurately calculating those cardiac parameters can be of critical importance for clinicians responsible for making treatment decisions.

As further stated above, for patients in a normal hemodynamic condition, the relationship between arterial blood pressure measured peripherally and central arterial blood pressure is understood, so that blood pressure measurements taken at the femoral or radial arteries, for example, can be used to quickly and accurately determine cardiac output and stroke volume, as well as other cardiac parameters of interest. However, for patients in a hyperdynamic condition, peripheral arterial blood pressure and central arterial blood pressure may be decoupled, rendering conventional solutions for determining cardiac parameters based on peripheral arterial blood pressure measurements unreliable.

The present disclosure provides systems and methods for performing cardiac monitoring that overcomes the deficiencies of conventional approaches when applied to patients in a hyperdynamic condition. By identifying multiple cardiovascular metrics of a patient based on blood pressure data for the patient, and using a first subset of those cardiovascular metrics supplemented by an additive factor, the present solution enables reliable cardiac monitoring for patients in a clinical setting. Moreover, by determining the additive factor based on a meta-parameter including a weighted sum of combinatorial parameters that include a second subset of the cardiovascular metrics, the present solution advantageously further enables the reliable determination of cardiac output and/or stroke volume for patients whether in a normal hemodynamic or hyperdynamic condition.

It is noted that, as defined for the purposes of the present disclosure, the expression “normal hemodynamic condition” refers to a patient condition in which peripheral arterial blood pressure corresponds to central arterial blood pressure in a way that is known or predictable in the conventional art. As a result, for patients in a normal hemodynamic condition, peripheral arterial blood pressure measurements may be used to reliably calculate central arterial blood pressure, as well as cardiac parameters such as stroke volume and cardiac output that are determined based on central arterial blood pressure.

By contrast, and as further defined for the purposes of the present disclosure, the expression “hyperdynamic condition” refers to a patient condition in which the normal correspondence between peripheral arterial blood pressure and central arterial blood pressure cannot be relied upon. In other words, in a hyperdynamic condition, central arterial blood pressure and peripheral arterial blood pressure are decoupled. Consequently, for patients in a hyperdynamic condition, peripheral arterial blood pressure measurements cannot be reliably used to calculate central arterial blood pressure, nor to determine cardiac parameters such as stroke volume and cardiac output using conventional techniques.

FIG. 1shows a diagram of exemplary cardiac monitoring system100, according to one implementation. Cardiac monitoring system100includes hardware processor102, system memory104, and blood pressure sensor120coupled to analog-to-digital converter (ADC)106. As shown inFIG. 1, system memory104stores software code110including cardiovascular metrics112. As further shown inFIG. 1, cardiac monitoring system100also includes display124providing user interface126, digital-to-analog converter (DAC)108, and sensory alarm128. Display124may take the form of a liquid crystal display (LCD), a light-emitting diode (LED) display, an organic light-emitting diode (OLED) display, or another suitable display screen that performs a physical transformation of signals to light.

Referring toFIG. 2A,FIG. 2Ashows diagram200A comparing central arterial blood pressure waveform246ato peripheral femoral arterial blood pressure waveform244a1and peripheral radial arterial blood pressure244a2for a patient when in a normal hemodynamic condition.FIG. 2B, shows diagram200B comparing central arterial blood pressure waveform246bto peripheral femoral arterial blood pressure waveform244b1and peripheral radial arterial blood pressure244b2for a patient when in a hyperdynamic condition.

As shown inFIG. 2A, in a normal hemodynamic condition, the systolic peaks of peripheral femoral arterial blood pressure waveform244a1and peripheral radial arterial blood pressure244a2are substantially higher than the systolic peaks of central arterial blood pressure waveform246a.By contrast, in a hyperdynamic condition, those relationships may be inverted, such that the systolic peaks of peripheral femoral arterial blood pressure waveform244a1and peripheral radial arterial blood pressure244a2are substantially lower than the systolic peaks of central arterial blood pressure waveform246a.Thus, a hyperdynamic condition may result in a sudden and/or significant reduction in peripheral arterial blood pressure of a patient.

Referring toFIG. 3A,FIG. 3Ashows diagram300A comparing distribution362of systolic areas of arterial blood pressure waveforms measured in patients in a normal hemodynamic condition to distribution364of systolic areas of arterial blood pressure waveforms measured in patients in a hyperdynamic condition. As may be seen fromFIG. 3A, a hyperdynamic condition is associated with a significant shift in systolic area, as well as with a significant change in the standard deviation of distribution364relative to distribution362. Thus, a hyperdynamic condition may result in a sudden and/or significant shift in average systolic area, and/or by a sudden and/or significant increase in the standard deviation of the systolic area distribution.

FIG. 3Bshows diagram300B comparing distribution366of systolic time of arterial blood pressure waveforms measured in patients in a normal hemodynamic condition to distribution368of systolic time of arterial blood pressure waveforms measured in patients in a hyperdynamic condition. As may be seen fromFIG. 3B, a hyperdynamic condition is associated with a significant shift in systolic time, as well as with a significant change in the standard deviation of distribution368relative to distribution366. Thus, a hyperdynamic condition may result in a sudden and/or significant shift in average systolic time, and/or by a sudden and/or significant decrease in the standard deviation of the systolic time distribution.

Turning back toFIG. 1, cardiac monitoring system100may be implemented within a patient care environment such as an intensive care unit (ICU) or operating room (OR), for example. As shown inFIG. 1, in addition to cardiac monitoring system100, the patient care environment includes patient130, and healthcare worker140(hereinafter “user140”) trained to utilize cardiac monitoring system100via user interface126. User interface126is configured to receive inputs142from user140, and can output cardiac status data and/or provide sensory alarm128.

Blood pressure sensor120is shown in an exemplary implementation inFIG. 1, and is attached to patient130. It is noted that blood pressure sensor120may be an invasive or non-invasive sensor attached to patient130. In one implementation, as represented inFIG. 1, blood pressure sensor120may be attached non-invasively so as to sense a peripheral arterial blood pressure of patient130, such as arterial blood pressure measured at a wrist or finger of patient130. Although not explicitly shown inFIG. 1, in other implementations, blood pressure sensor120may be attached non-invasively to measure a peripheral arterial blood pressure at an ankle or toe of patient130.

Alternatively, in some implementations, blood pressure sensor120may be attached invasively or non-invasively to measure a peripheral arterial blood pressure at a wrist of patient130(i.e., radial blood pressure), or a peripheral arterial blood pressure at a thigh of patient130(i.e., femoral blood pressure). Blood pressure signal122received by ADC106of cardiac monitoring system100from blood pressure sensor120may include signals corresponding to the arterial blood pressure of patient130, and may include an arterial blood pressure waveform of patient130.

According to the exemplary implementation shown inFIG. 1, system processor102is configured to utilize ADC106to convert blood pressure signal122to blood pressure data144in digital form, and to identify multiple cardiovascular metrics112based on digital blood pressure data144. System processor102is further configured to determine the stroke volume (hereinafter “SV”) of patient130using a subset of cardiovascular metrics112and an additive factor. As described in greater detail below, the additive factor is based on a meta-parameter including a weighted sum of combinatorial parameters, each combinatorial parameter including another subset of cardiovascular metrics112.

Moreover, system processor102may be further configured to execute software code110to invoke sensory alarm128if and it is determined that patient130is experiencing a cardiovascular crisis or that such a crisis is imminent. In various implementations, sensory alarm128may include one or more of a visual alarm, an audible alarm, and a haptic alarm. For example, when implemented to provide a visual alarm, sensory alarm128may be invoked as flashing and/or colored graphics shown by user interface126on display124. When implemented to provide an audible alarm, sensory alarm128may be invoked as any suitable warning sound, such as a siren or repeated tone. Moreover, when implemented to provide a haptic alarm, sensory alarm128may cause one or more components of system100to vibrate or otherwise deliver a physical impulse perceptible to user140.

FIG. 4Ashows an exemplary implementation for sensing peripheral arterial blood pressure non-invasively at an extremity of a patient. Cardiac monitoring system400A, inFIG. 4A, includes ADC406and software code410. As shown byFIG. 4A, the arterial blood pressure of patient430is sensed non-invasively at finger432of patient430using blood pressure sensing cuff420a.Also shown inFIG. 4Aare blood pressure signal422received by ADC406of cardiac monitoring system400A from blood pressure sensing cuff420a,digital blood pressure data444converted from blood pressure signal422by ADC406, and cardiovascular metrics412identified based on digital blood pressure data444by software code410.

Patient430, blood pressure signal422, and digital blood pressure data444correspond respectively in general to patient130, blood pressure signal122, and digital blood pressure data144, inFIG. 1, and those corresponding features may share any of the characteristics attributed to either corresponding feature by the present disclosure. Moreover, cardiac monitoring system400A including blood pressure sensing cuff420a,ADC406, and software code410including cardiovascular metrics412, inFIG. 4A, corresponds in general to cardiac monitoring system100including blood pressure sensor120, ADC106, and software code110including cardiovascular metrics112, inFIG. 1, and those corresponding features may share any of the characteristics attributed to either corresponding feature by the present disclosure. In other words, although not explicitly shown inFIG. 4A, cardiac monitoring system400A includes features corresponding respectively to hardware processor102, DAC108, display124, user interface126, and sensory alarm128.

According to the implementation shown inFIG. 4A, blood pressure sensing cuff420ais designed to sense a peripheral arterial blood pressure of patient130/430non-invasively at finger432of patient130/430. Moreover, as shown inFIG. 4A, blood pressure sensing cuff420amay take the form of a small, lightweight, and comfortable blood pressure sensor suitable for extended wear by patient130/430. It is noted that although blood pressure sensing cuff420ais shown as a finger cuff, inFIG. 4A, in other implementations, blood pressure sensing cuff420amay be suitably adapted as a wrist, ankle, or toe cuff for attachment to patient130/430.

It is further noted that the advantageous extended wear capability described above for blood pressure sensing cuff420awhen implemented as a finger cuff may also be attributed to wrist, ankle, and toe cuff implementations. As a result, blood pressure sensing cuff420amay be configured to provide substantially continuous beat-to-beat monitoring of the peripheral arterial blood pressure of patient130/430over an extended period of time, such as minutes or hours, for example.

FIG. 4Bshows an exemplary implementation for performing minimally invasive detection of peripheral arterial blood pressure of a patient. As shown byFIG. 4B, the radial arterial blood pressure of patient130/430is detected via minimally invasive blood pressure sensor420b.It is noted that the features shown inFIG. 4Band identified by reference numbers identical to those shown inFIG. 4Acorrespond respectively to those previously described features, and may share any of the characteristics attributed to them above. It is further noted that blood pressure sensor420bcorresponds in general to blood pressure sensor120, inFIG. 1, and those corresponding features may share any of the characteristics attributed to either corresponding feature by the present disclosure.

According to the implementation shown inFIG. 4B, blood pressure sensor420bis designed to sense a peripheral arterial blood pressure of patient130/430in a minimally invasive manner For example, blood pressure sensor420bmay be attached to patient130/430via a radial arterial catheter inserted into an arm of patient130/430. Alternatively, and although not explicitly represented inFIG. 4B, in another implementation, blood pressure sensor420bmay be attached to patient130/430via a femoral arterial catheter inserted into a leg of patient130/430. Like non-invasive blood pressure sensing cuff420a,inFIG. 4A, minimally invasive blood pressure sensor420b,inFIG. 4B, may be configured to provide substantially continuous beat-to-beat monitoring of the peripheral arterial blood pressure of patient130/430over an extended period of time, such as minutes or hours.

FIG. 5shows diagram500depicting transformation of digital blood pressure data544converted by ADC506from blood pressure signal522to central arterial blood pressure waveform546, according to one implementation. Also shown inFIG. 5are patient530, blood pressure sensor520, and software code310. Blood pressure sensor320, blood pressure signal322, ADC306, digital blood pressure data544, and software code510correspond respectively in general to blood pressure sensor120/420a/420b,blood pressure signal122/422, ADC106/406, digital blood pressure data144/444, and software code110/410, inFIGS. 1, 4A, and 4B, and those corresponding features may share the characteristics attributed to any corresponding feature by the present disclosure.

Thus, blood pressure signal522and digital blood pressure data544correspond to a peripheral arterial blood pressure waveform of patient130/430/530detected using blood pressure sensor120/420a/420b/520. As shown inFIG. 5, digital blood pressure data144/444/544, converted from blood pressure signal122/422/522by ADC106/406/506, may be transformed to central arterial blood pressure waveform546corresponding to an aortic blood pressure waveform or a brachial blood pressure waveform of patient130/430/530. As further shown byFIG. 5, such a transformation may be performed by software code110/410/510through application of a transfer function to digital blood pressure data144/444/544. That is to say, application of such a transfer function may be performed by software code110/410/510, executed by hardware processor102.

Example implementations of the present disclosure will be further described below with reference toFIGS. 6 and 7.FIG. 6presents flowchart650outlining an exemplary method for use by a system to perform cardiac monitoring.FIG. 7shows a trace of a central arterial blood pressure waveform including exemplary cardiovascular metrics.

Referring toFIG. 6in combination withFIGS. 1, 4A, 4B, and 5, flowchart650begins with converting blood pressure signal122/422/522corresponding to a blood pressure of an artery of patient130/430/530to blood pressure data144/444/544in digital form (action652). As discussed above, digital blood pressure data144/444/544may be converted from blood pressure signal122/422/522sensed using blood pressure sensor120/420a/420b/520by ADC106/406/506of cardiac monitoring system100/400A/400B. In one implementation, for example, blood pressure sensor120/420a/420b/520may be used to sense a peripheral arterial blood pressure of patient130/430/530, and to generate blood pressure signal122/422/522as an analog signal corresponding to that peripheral arterial blood pressure. In such an implementation, ADC106/406/506can be used to convert blood pressure signal122/422/522into blood pressure data144/444/544in digital form.

Flowchart650continues with identifying cardiovascular metrics112/412of patient130/430/530based on digital blood pressure data144/444/544of patient130/430/530(action654). As noted above by reference toFIG. 5, digital blood pressure data144/444/544may be transformed to central arterial blood pressure waveform546of patient130/430/530by software code110/410/510, executed by hardware processor102.

Referring to trace700of central arterial blood pressure waveform746, inFIG. 7, it is noted that central arterial blood pressure waveform746corresponds in general to central arterial blood pressure waveform546, inFIG. 5, and those corresponding features may share any of the characteristics attributed to either corresponding feature by the present disclosure. As shown inFIG. 7, central arterial blood pressure waveform546/746may be used to identify various cardiovascular metrics important for performing cardiac monitoring.

For example, trace700shows cardiovascular metrics712including exemplary cardiovascular metrics712a,712b,712c,and712d,corresponding respectively to the start of a heartbeat, the maximum systolic blood pressure marking the end of systolic rise, the presence of the dicrotic notch marking the end of systolic decay, and the diastole of the heartbeat of patient130/430/530. Also included among cardiovascular metrics712is exemplary slope712eof central arterial blood pressure waveform546/746. It is noted that slope712eis merely representative of multiple slopes that may be measured at multiple locations along central arterial blood pressure waveform546/746. It is further noted that cardiovascular metrics712correspond in general to cardiovascular metrics112/412, inFIGS. 1, 4A, and 4B. Consequently, those corresponding features may share the characteristics attributed to any corresponding feature by the present disclosure.

In addition to cardiovascular metrics712a,712b,712c,and712d,the behavior of central arterial blood pressure waveform546/746during the intervals between those events may also be used as cardiovascular metrics for use in cardiac monitoring. For example, the interval between the start of the heartbeat at cardiovascular metric712aand the maximum systolic pressure at cardiovascular metric712bmarks the duration of the systolic rise (hereinafter “systolic rise712a-712b”). The systolic decay of central arterial blood pressure waveform546/746is marked by the interval between the maximum systolic pressure at cardiovascular metric712band the dicrotic notch at cardiovascular metric712c(hereinafter “systolic decay712b-712c”). Together, systolic rise712a-712band systolic decay712b-712cmark the entire systolic phase (hereinafter “systolic phase712a-712c”), while the interval between the dicrotic notch at cardiovascular metric712cand the diastole at cardiovascular metric712dmark the diastolic phase of central arterial blood pressure waveform546/746(hereinafter “diastolic phase712c-712d”).

Also of potential clinical interest is the behavior of central arterial blood pressure waveform546/746in the interval from the maximum systolic pressure at cardiovascular metric712bto the diastole at cardiovascular metric712d(hereinafter “interval712b-712d”), as well as the behavior of central arterial blood pressure waveform546/746from the start of the heartbeat at cardiovascular metric712ato the diastole at cardiovascular metric712d(hereinafter “heartbeat interval712a-712d”). The behavior of central arterial blood pressure waveform546/746during intervals: 1) systolic rise712a-712b,2) systolic decay712b-712c,3) systolic phase712a-712c,4) diastolic phase712c-712d,5) interval712b-712d,and 6) heartbeat interval712a-712dmay be identified by measuring the area under the curve of central arterial blood pressure waveform546/746and the standard deviation of central arterial blood pressure waveform546/746in each of those intervals, for example. The respective areas and standard deviations measured for intervals 1, 2, 3, 4, 5, and 6 above may serve as additional cardiac parameters for use in cardiac monitoring.

It is noted that the standard deviation of central arterial waveform546/746across heartbeat interval712a-712, i.e., interval6above, will hereinafter be represented by the lower case Greek letter sigma as “σ.” It is further noted that the area under the curve of central arterial blood and referred to as the pulsatile systolic area in the art will hereinafter be referred to as “PSA.”

In addition to the exemplary cardiovascular metrics described above by reference to trace700, cardiovascular metrics312/412/712may include many other metrics. Some examples of those other cardiovascular metrics include the input impedance of the aorta (hereinafter “aortic impedance” or “Z”), as well as a compliance factor corresponding to the compliance of the aorta (hereinafter “aortic compliance” or “C”). It is noted that aortic impedance “Z” and aortic compliance “C” can be determined using various techniques known in the art. For example, a technique for determining Z and/or C using a non-linear model and based on at least one measurement of blood pressure is disclosed in U.S. patent application Ser. No. 13/896,873 titled “Method, a System and a Computer Program Product for Determining a Beat-to-Beat Stroke Volume and/or a Cardiac Output”, filed on May 17, 2013 and published as U.S. Patent Application Publication Number 2014/0163401 A1 on Jun. 12, 2014. This patent application is hereby incorporated fully by reference into the present application.

Additional examples of cardiovascular metrics include the pulse rate (hereinafter “PR”), mean arterial blood pressure (hereinafter “MAP”), skewness of the arterial blood pressure (hereinafter “SKU”), and kurtosis of the arterial blood pressure (hereinafter “KURT”). Yet other additional examples of cardiovascular metrics include the whole systolic area (hereinafter “SYS_AREA”), the systolic time (hereinafter “SYS_T”), and the diastolic time (hereinafter “DIA_T”) of arterial blood pressure waveform546/746of patient130/430/530. Identification of cardiovascular metrics112/412/712may be performed by software code110/410/510, executed by hardware processor102.

Flowchart650can conclude with determining the SV of patient130/430/530when in the hyperdynamic condition using a first subset of cardiovascular metrics112/412/712and an additive factor that is based on a meta-parameter including a weighted sum of combinatorial parameters, each combinatorial parameter including a second subset of cardiovascular metrics112/412/712(action656). For example, the SV of patient130/430/530can be determined using the cardiovascular metrics PSA and Z discussed above as follows:

Where the additive factor is represented by Ψ. Thus, in one implementation, the first subset of cardiovascular metrics112/412/712includes PSA and Z (i.e., pulsatile systolic area and impedance of the aorta, respectively).

The additive factor Ψ may be expressed as a function of a meta-parameter represented by Φ, wherein:

Where σ is the standard deviation of central arterial blood pressure waveform546/746, the symbol * indicates multiplication, MAP is the mean arterial blood pressure of patient130/430/530, and I is an integer less than 10, such as 5, for example. Thus, in one implementation, additive factor Ψ includes a numerator including meta-parameter Φ, and a denominator including the MAP of patient130/430/530. Moreover, as shown by Equation 2, in some implementations the numerator of additive factor Ψ is a product of meta-parameter Φ and σ.

Thus, substituting Equation 2 into Equation 1, in one implementation:

As stated above, the meta-parameter Φ includes a weighted sum of combinatorial parameters, each combinatorial parameter including a second subset of cardiovascular metrics112/412/712. In one exemplary implementation, the second subset of cardiovascular metrics112/412/712may include the following enumerated subset of 10 metrics:1. σ/102. 600/PR3. MAP/1004. SKU5. KURT+36. (100*C)/BSA7. BSA8. SYS_AREA/10009. SYS_T*1010. DIA_T*10

It is noted that the cardiovascular metrics included in the combinatorial parameters of meta-parameter Φ may be directly proportional to or inversely proportional to one or more of the cardiovascular metrics identified above prior to the introduction of Equation 1. For example, metric 7 above is directly proportional to the BSA of patient130/430/530, while metric 6 is inversely proportional to the BSA of patient130/430/530.

The combinatorial parameters included in Φ may be obtained from the enumerated subset of cardiovascular metrics112/412/712listed above though use of the operator O on that subset. The operator O may be expressed in tabular form as:

Where each row represents a combinatorial parameter. The first three columns of O are an index to the cardiovascular metrics enumerated above, and the last three columns are the exponential power of each cardiovascular metric as it appears in the combinatorial parameter, with the fourth column being the power of the cardiovascular metrics listed in the first column, the fifth column being the power of the cardiovascular metrics listed in the second column, and the sixth column being the power of the cardiovascular metrics listed in the third column.

As a specific example, in one implementation, O may be expressed as:

Equation 5 identifies 4 combinatorial parameters, P1, P2, P3, and P4, corresponding respectively to the first through fourth rows of O. Referring to Equation 5 in combination with the enumerated list of cardiovascular metrics above, P1, P2, P3, and P4may be expressed as:

That is to say, the cardiovascular metrics included in one or more of the combinatorial parameters may be raised to an exponential power greater than 1, to a negative exponential power, or to an exponential power more negative than −1 (e.g., −2).

Meta-parameter Φ may be expressed in terms of P1, P2, P3, and P4as:

Where C0and the Cnare coefficients that may be determined experimentally. Determination of the SV of patient130/430/530using the first subset of cardiovascular metrics112/412/712and additive factor Ψ based on meta-parameter Φ may be performed by software code110/410/510, executed by hardware processor102. Moreover, in some implementations, hardware processor102is configured to execute software code110/410/510to determine the SV of patient130/430/530based on the first subset of cardiovascular metrics112/412/712, additive factor Ψ, and using a pulse contour method, as known in the art and disclosed in U.S. patent application Ser. No. 13/896,873 incorporated by reference above.

In some implementations, the method outlined by flowchart650can include invoking, by software code110/410/510executed by hardware processor102, sensory alarm128when and if and it is determined that patient130/430/530is experiencing a cardiovascular crisis or that such a crisis is imminent. As shown inFIG. 3, for example, software code110/410/510may be configured to output a cardiac status data to display324. As further shown inFIG. 1, in some implementations, the output of software code110/410/510may be processed using DAC108to convert digital signals into analog signals for presentation via display124and user interface126.

In addition, in some implementations, the present method may include determining, by software code110/410/510executed by hardware processor102, the cardiac output (hereinafter “CO”) of patient130/430/530based on the SV. For example, and as known in the art, the CO of a patient in a normal hemodynamic condition may be determined based on the SV of the patient as:

Where PR is the pulse rate of the patient.

Consequently, the CO of patient130/430/530may be obtained through substitution of Equation 3 into Equation 7 as:

Thus, the present disclosure provides systems and methods for performing cardiac monitoring that overcome the deficiencies of conventional approaches when applied to patients in a hyperdynamic condition. By identifying multiple cardiovascular metrics including an aortic impedance and/or aortic compliance of a patient based on blood pressure data for the patient, and using a first subset of those cardiovascular metrics supplemented by an additive factor, the present solution enables reliable cardiac monitoring for patients in a clinical setting. Moreover, by determining the additive factor based on a meta-parameter including a weighted sum of combinatorial parameters that include a second subset of the cardiovascular metrics, the present solution advantageously further enables the reliable determination of CO and/or SV for patients whether in a normal hemodynamic or hyperdynamic condition.