A novel component of the hemodynamic analysis tool

Abstract

In some embodiments, the method includes obtaining a series of time-series ventricular pressure measurements, determining a series of data points comprising a ventricular pressure time rate of change from the time-series ventricular pressure measurements, determining a representation indicative of a relationship between at least the series of data points and the time-series ventricular pressure measurements, and determining, at least in part, a characteristic of blood flow in a chamber of the heart by processing the representation. Related systems and articles of manufacture are also disclosed.

Description

[Background technology]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Application No. 63 / 393,200, filed July 28, 2022, which is incorporated by reference herein in its entirety.

[0002] Invasive measurements of ventricular blood pressure (Pv) and arterial blood pressure (Pa) are routinely performed in patients undergoing invasive evaluation in a cardiac catheterization laboratory. Pv measurements typically include: 1) peak systolic pressure, 2) peak diastolic pressure, 3) end-diastolic pressure, 4) maximum rate of pressure change per time (dP / dt), and 5) a continuous tracing visually displayed as Pv versus time in existing commercial products. These measurements are used by physicians to assess cardiac contractile (systolic) function, relaxation (diastolic) function, and diastolic filling pressure, and to identify and treat cardiac abnormalities. Summary of the Invention

[0003] In some exemplary embodiments, methods, systems, and articles of manufacture may be provided for analyzing characteristics of blood flow within a cardiac chamber and downstream blood vessels, substantially as described and illustrated herein.

[0004] In some embodiments, a system is provided that includes at least one data processor and at least one memory that stores instructions that, when executed by the at least one data processor, cause operations to include obtaining a series of time-series ventricular pressure measurements; determining a series of data points comprising a ventricular pressure time rate of change from the time-series ventricular pressure measurements; determining a representation that indicates a relationship between at least the series of data points and the time-series ventricular pressure measurements; and determining, at least in part, characteristics of blood flow in a chamber of the heart by processing the representation.

[0005] In some variations, one or more features disclosed herein can be similarly implemented, including one or more of the following features: The time-series ventricular pressure measurements are collected using a device not inserted into the subject's body. The device is a non-invasive ultrasound Doppler device, a magnetic resonance imaging device, and / or a heart sound intensity device. The time-series ventricular pressure measurements are collected by an intracardiac device. The intracardiac device is a pulmonary artery hemodynamic monitoring catheter or a left ventricular support device. The time-series ventricular pressure measurements are collected, at least in part, by measuring chamber dimensions and ventricular blood pressure. The relationship includes a series of pairwise relationships, the series of pairwise relationships including a series of data points and corresponding data points of the time-series ventricular pressure measurements. The ventricular pressure time rate of change is the first derivative of the ventricular pressure with respect to time. The representation includes a plot related to the relationship. The plot is a pressure loop plot. The blood flow characteristic is determined based, at least in part, on a loop cycle duration of the pressure loop plot. The blood flow characteristic is determined at least in part based on a boundary of the pressure loop plot. The boundary can be an upper boundary, a lower boundary, a left boundary, or a right boundary. The blood flow characteristic is determined at least in part based on a visual characteristic associated with the visual plot. The visual characteristic can be related to the shape of an area of the visual plot or the size of at least one area of the plot. The visual characteristic can be symmetry, smoothness, the presence of dips, differences between two or more areas, or tangential slope. The blood flow characteristic can be determined at least in part by comparing the visual plot with a second visual plot. The treatment regimen can be based at least in part on the blood flow characteristic. The blood flow characteristic can be ventricular power, ventricular resistance, or ventricular blood flow, elasticity, compliance, contractile force, stroke volume, or response to a variable. A second series of data points can be calculated, including a time rate of change of ventricular pressure acceleration.

[0006] The a-wave pre-diastolic pressure can be estimated at least in part using a second series of data points. Processing the representation includes using a mathematical model. The mathematical model is a statistical model or a machine learning model. The machine learning model includes a neural network. A data point of the series of data points is determined by (a) determining a pressure difference by subtracting a first pressure value associated with a first time from a second pressure value associated with a second time, and (b) dividing the pressure difference by a time difference, the time difference comprising the difference between the second time and the first time.

[0007] The details of one or more variations of the subject matter described herein are set forth in the accompanying drawings and the detailed description that follows. Other features and advantages of the subject matter described herein will be apparent from the detailed description and drawings, and from the claims.

[0008] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate certain aspects of the subject matter disclosed herein and, together with the detailed description, serve to explain some of the principles associated with the disclosed embodiments. [Brief explanation of the drawings]

[0009] [Figure 1] 1 shows an example of a left ventricular pressure tracing displayed on a commercially available hemodynamic system. [Figure 2] 1 shows an example of a ventricular "pressure loop" generated by the disclosed method. [Figure 3] 1 shows a series of pressure loop deformations generated by the disclosed method, illustrating the characteristics of ventricular blood pressurization. [Figure 4] 1 shows the identification of a-wave pre-diastolic pressure by the disclosed method. [Figure 5] shows a simultaneous comparison of ventricular end-diastolic pressure versus peak systolic pressure from loops across multiple cardiac cycles (left graph). [Figure 6] A comparison of pressure loops generated from the same individual at different times is shown. [Figure 7]1 shows an example of a plot of ventricular pressure change per time (dP / dt) versus time. [Figure 8] 1 shows an example of a typical aortic blood pressure trace displayed by a commercially available hemodynamic system. [Figure 9] An example of an arterial "pressure loop" produced by the disclosed method is shown, the method being performed over a number of consecutive heartbeats. [Figure 10] 10 shows a variation of an arterial pressure loop produced by the disclosed method. [Figure 11] A simultaneous comparison of arterial pressure loop area versus arterial pressure is shown. [Figure 12] A comparison of pressure loops generated from the same artery at different times is shown. [Figure 13] Demonstrates diastolic determination with minimal cyclic pressure fluctuations [Figure 14] Determining the asymptotics of a monoexponential curve is shown. [Figure 15] The "corrected" Tau determination is shown. [Figure 16] 1 shows a comparison of calculated versus actual arterial pressure. [Figure 17] Simultaneous pressure tracings from the ventricle and a downstream arterial pressure source are shown. [Figure 18] 1 shows the arterial to ventricular pressure ratio produced by the disclosed method during valve stenosis. [Figure 19] 1 shows the arterial to ventricular pressure ratio generated by the disclosed method during obstructive hypertrophic obstructive cardiomyopathy. [Figure 20] 1 shows an arterial-to-ventricular pressure ratio measure produced by the disclosed method. [Figure 21] 1 shows a novel comparison of multiple pressure loops generated by the disclosed method. [Figure 22] 1 illustrates the general characteristics of the disclosed method and interface in a cardiac catheterization laboratory. [Figure 23]A typical setup for simultaneously collecting aortic blood pressure (Pa) and distal coronary artery pressure (Pd) during conventional FFR measurement, as well as the use of the disclosed method for CFRp measurement, is shown (left). [Figure 24] A typical guide catheter and pressure wire arrangement for simultaneously measuring aortic pressure (Pa) and distal pressure (Pd) from the same pressure source (tip of the guide catheter) during pressure "equalization" is shown (left). [Figure 25] Simultaneous measurement of Pa and Pd during one heartbeat immediately after commercial pressure "equalization" is shown. [Figure 26] It has been shown that when an anatomical resistance exists between the two pressure sources, the distal coronary artery pressure measurement (Pd) is reduced compared to the guide catheter reference measurement (Pa). [Figure 27] Figure 1 shows simultaneous beat-by-beat calculation of mean diastolic 1-Pd / Pa and mean diastolic velocity after intracoronary bolus administration of adenosine to induce hyperemia. [Figure 28] 1 shows an exemplary beat-by-beat comparison of pressure-derived 1-Pd / Pa versus Doppler-derived blood flow velocity after adenosine-induced hyperemia and return to baseline coronary flow. [Figure 29] 1 shows an exemplary beat-by-beat comparison of diastolic pressure-derived CFR (CFRp) versus diastolic Doppler-based CFR after adenosine-induced hyperemia and return to baseline coronary flow. [Figure 30] 1 illustrates a flow diagram of a method according to some embodiments. [Figure 31] 1 shows a block diagram illustrating a computing system according to some embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0010] Currently available methods for assessing cardiac characteristics use time-series measurements of blood pressure (ventricular or arterial pressure). These methods may be useful for identifying and treating cardiac abnormalities. Disclosed are systems, methods, and articles of manufacture that generate novel blood pressure information for improved detection of cardiac characteristics to improve treatment of cardiac abnormalities.

[0011] Instead of simply analyzing pressure over time, the disclosed method calculates the time rate of change of pressure (e.g., the time derivative of pressure, or dP / dt) and evaluates this against the corresponding time series measurements of pressure. The method generates a representation showing the relationship between dP / dt and the corresponding pressure measurements. This representation can use arterial pressure (Pa) or ventricular pressure (Pv). Here, "ventricular pressure" refers to blood pressure measured in the ventricles of the heart, and "arterial pressure" refers to blood pressure measured in the arteries of the heart. For example, this relationship can include a series of relationships, where the relationship includes a particular Pv value at a particular time and the corresponding dPv / dt. In some cases, this representation is a visual representation. In some cases, the visual representation is a plot (hereinafter referred to as a "pressure loop plot"). The type of pressure used (Pa or Pv) can determine important characteristics of the representation. For example, Pa and Pv can generate different types of pressure loops with different visual representations, and dPv / dt can be calculated, for example, by calculating the difference in pressure between two samples and dividing by the time difference between the two samples.

[0012] The pressure loop plot has several visual characteristics that are indicative of cardiac function. For example, the size of at least a region of the loop (or at least a region of the plot), the shape of the loop (or the loop, or at least a region of the plot), and the upper, lower, left, and right sides of the loop can each, or in combination, provide information related to the proper functioning or impairment of the veins or arteries of the heart.

[0013] Pressure loop plots can be analyzed in multiple ways. Many visual features, such as the size, shape, and depressions on the surface of the loop, can be visually inspected. In some cases, the raw dP / dt values and corresponding pressure measurements are analyzed or processed using a mathematical model, such as a statistical model or a machine learning model. In some cases, the visual plot (e.g., pressure loop) can be analyzed using a machine learning model, such as a convolutional neural network (CNN).

[0014] Pressure data can be collected using invasive or non-invasive cardiac measurement devices. An example of an invasive device is an intracardiac device such as the CardioMEMS® system left ventricular assist device. Examples of non-invasive cardiac measurement devices include ultrasound Doppler, magnetic resonance imaging (MRI), and / or cardiovascular acoustic intensity devices.

[0015] FIG. 30 shows a flow diagram of a method 3000 according to some embodiments.

[0016] In a first operation 3010, a series of time-series pressure measurements is obtained from a subject. The series of time-series pressure measurements may be ventricular pressure (Pv) measurements or arterial pressure (Pa) measurements. The time-series pressure measurements may be obtained invasively or non-invasively (e.g., without contacting the subject's body). For example, the measurements may be obtained invasively by an intracardiac device, such as a pulmonary artery hemodynamic monitoring device or a left ventricular support device. The measurements may be obtained non-invasively using a device or instrument that does not contact and / or is not inserted into the subject's body, such as an ultrasound Doppler device or instrument, a magnetic resonance imaging (MRI) device or instrument, or a heart sound intensity device or instrument. In some cases, the time-series pressure measurements are collected at least in part by measuring chamber dimensions and / or ventricular blood pressure. The time-series pressure measurements may be collected over a duration that includes one or more heartbeats. In some cases, the duration is at least 1 second, at least 5 seconds, at least 10 seconds, at least 30 seconds, at least 1 minute, at least 10 minutes, at least 15 minutes, at least 30 minutes, at least 1 hour, at least 2 hours, at least 3 hours, at least 6 hours, at least half a day, at least 1 day, at least 1 week, at least 1 month, at least 3 months, at least 6 months, or at least 1 year. In some cases, the duration is at most 5 seconds, at most 10 seconds, at most 30 seconds, at most 1 minute, at most 10 minutes, at most 15 minutes, at most 30 minutes, at most 1 hour, at most 2 hours, at most 3 hours, at most 6 hours, at most half a day, at most 1 day, at most 1 week, at most 1 month, at most 3 months, at most 6 months, or at most 1 year. In some cases, the duration is between 1 second and 5 seconds, between 5 seconds and 10 seconds, between 10 seconds and 30 seconds, between 30 seconds and 1 minute, between 1 minute and 10 minutes, between 10 minutes and 30 minutes, between 30 minutes and 1 hour, between 1 hour and 2 hours, between 2 hours and 3 hours, between 3 hours and 6 hours, between 6 hours and 12 hours, between 12 hours and 1 day, between 1 day and 1 week, between 1 week and 1 month, between 1 month and 2 months, between 2 months and 3 months, between 3 months and 6 months, or between 6 months and 1 year.

[0017] The subject may be a human subject. In some cases, the subject may be a non-human animal. The subject may be a mammal, bird, reptile, or amphibian subject. For example, the subject may be a dog, cow, horse, pig, sheep, chicken, turkey, ostrich, mouse, cat, deer, snake, lizard, frog, monkey, ape (e.g., chimpanzee), or other animal.

[0018] In a second operation 3020, a series of data points containing the time rate of change of pressure (e.g., ventricular pressure or arterial pressure) is determined from the time series of pressure measurements. The time rate of change may be the first derivative of pressure with respect to time (dP / dt) and may be related to ventricular pressure (dPv / dt) or arterial pressure (dPa / dt). The time derivative for a particular pressure value may be calculated by subtracting pressure measurements adjacent in time (e.g., those immediately past and immediately future) and dividing by the time difference between them (e.g., by multiplying by the sampling rate).

[0019] In a third operation 3030, a representation is determined that shows a relationship between at least the series of data and the time-series pressure measurements. This relationship can include or incorporate a series of pairwise relationships between pressure time rate of change (dP / dt) values and corresponding pressure values. This representation can include raw data in text format, for example, or can be analyzed by a mathematical model. This representation can be preprocessed or compressed. In some cases, dimensionality reduction methods (e.g., autoencoders) can be used to compress the data to generate feature representations as input to machine learning algorithms. In some cases, the representation is a visual representation. The visual representation can be a plot. The plot can include one or more loop traces that show the relationship between the pressure time rate of change and pressure values. Loop shapes occur because blood pressure rises and falls during a heartbeat, resulting in multiple dP / dt values corresponding to a single pressure measurement. One or more loop traces can be averaged, and the average value can be overlaid on each individual loop trace. A plot including loop traces is referred to herein as a "pressure loop plot."

[0020] In a fourth operation 3040, characteristics of blood flow within the cardiac chambers are determined by processing the representation. Processing can be performed by examining visual features of the pressure loop plot. This processing can be performed using an image processing system or a computer vision system. This processing can be performed using a mathematical model. Processing includes determining an association between visual features of the pressure loop plot and cardiac abnormalities. Characteristics of a pressure loop plot indicative of health can include smoothness, loop size, number of dips, dip size, dip shape, tangential slope of points on the plot, symmetry of the plot, area of all or part of the plot, or location of the upper, lower, left, and right boundaries of the plot. Cardiac characteristics that can be determined by processing the plot include ventricular power, ventricular resistance, or ventricular blood flow, elasticity, compliance, contractile force, stroke volume, or response to modifiers.

[0021] In some cases, analysis of the representations may be used to determine a course of treatment for a patient with a cardiac abnormality. Treatment may include diet, exercise, surgery, medication, intravenous fluids (e.g., saline or lactated Ringer's solution), or a combination thereof. In some cases, analysis of the representations is used to screen potential patients. Based on the classification determined from the representations, patients may be designated as either low-risk, medium-risk, or high-risk. Medium-risk or high-risk cases may be escalated to appropriate medical personnel.

[0022] In some instances, the method may include collecting a second series of data points comprising a time rate of change of pressure acceleration (e.g., dP / dt). These may be the time rate of change of ventricular pressure or arterial pressure. Based at least in part on the second series of data points, the method may estimate a-wave pre-diastolic pressure.

[0023] The processing of the pressure measurements can be performed using a computing device, which can be, for example, a laptop computer, a desktop computer, a tablet computer, a smartphone, a graphics processing unit (GPU), or a personal digital assistant (PDA).

[0024] Ventricular blood pressure characteristics Disclosed herein are systems, methods, and articles of manufacture for performing Pv measurements by converting Pv versus time data (often shown using a "time-frequency plot") into rate-of-change Pv per time (dPv / dt vs. Pv) data. Doing so allows operators to derive multiple measurements from raw Pv data (either pre-existing data or data collected in real time) to assess ventricular function in novel ways. Plotting dPv / dt versus Pv generates a "pressure loop" for each cardiac cycle, compared to the typical peaks and valleys observable from a time-frequency plot ( FIG. 2 ). Typical measurements can be obtained from the ventricular pressure loop, including the x-axis minimum ("left boundary," diastolic pressure minimum), x-axis maximum ("right boundary," systolic pressure maximum), y-axis maximum ("upper boundary," systolic pressure maximum dP / dt), and y-axis minimum ("lower boundary," diastolic pressure minimum dP / dt). Following analysis of the pressure loop (FIG. 3), several measurements and characteristics can be derived, as described below.

[0025] Measurements and characteristics derived from analysis of the pressure loop include, but are not limited to:

[0026] Loop size—total area and partial area (e.g., upper and lower half of the loop, quarter loop, or other sized portion), axial dimension, or other dimension related to the area, length, or width of at least a portion of the pressure loop. Conceptually, the disclosed ventricular "pressure loop" method can plot dPv / dt versus Pv. The area of the entire loop can be calculated by integrating the absolute value within the loop boundary (Equation A1).

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[0027] By adjusting the range of integration, the partial area of the entire Pv loop can be calculated (e.g., summing all positive dPv / dt values to calculate the area above the X-axis, or summing all negative dPv / dt values to calculate the area below the X-axis). If the raw Pv difference is calculated between two different samples regardless of the time interval, then multiplication by the sampling rate (e.g., samples per second) is performed to maintain consistency between different sampling techniques. Additionally, the Pv loop area, which represents ventricular function over each cardiac cycle, can be used to calculate ventricular function over time (Equation A2). Formula A2: Pv loop area / time = heart rate * Pv loop area

[0028] Loop shape—global shape characteristics such as symmetry (e.g., the degree to which the portions of the loop bisected by the axis are similar or identical), smoothness (e.g., the relative absence of dips and sharp angles or corners), the presence of abnormal dips, differences in the curves / areas of the upper and lower halves, and tangent slopes of points in different quadrants of the loop—can be determined using the methods described herein. The smoothness of the observed loop segments can be compared to an expected best-fit curve, and correlation with the expected curve can then be quantified. Pv loop shapes generated in ventricular chambers with uncertain characteristics can be compared to a database of numerous loop shapes generated in ventricular chambers with known characteristics, and the best match (and known characteristics) is then reported for each loop tested. Examples of this include: 1) loops with normal size and smoothness resulting from a normal ventricle; 2) loops with irregular smoothness resulting from a ventricle impaired by coronary artery disease and ischemic cardiomyopathy; 3) loops with regular smoothness and irregular (e.g., smaller) size resulting from a ventricle impaired by systemic non-ischemic cardiomyopathy; and 4) loops with irregular smoothness resulting from a ventricle impaired by hypertrophic cardiomyopathy.

[0029] Loop Cycle Duration - The total number of samples required to generate a complete loop can be divided by the sampling rate to determine the duration of each loop. The loop duration can then be used to calculate the instantaneous heart rate for a single loop, or the average heart rate for multiple loops.

[0030] Characterization of the upper / lower boundary lines relative to a reference point - the upper boundary line represents the point of maximum blood pressurization rate (i.e., peak contractile force), and the lower boundary line represents the point of minimum blood pressurization rate (i.e., peak relaxation force). These points within a loop may be consistent across multiple pressure loops for a particular subject and can be observed to be "aligned," "rotated," and / or "shifted" around a reference point (e.g., loop center, or median X-axis pressure).

[0031] Characterization of the left border—The pressure change during diastole (which constitutes the left border of the pressure loop) can be characterized (e.g., "dramatic" or "minor") and helps decipher the hemodynamically useful time points during diastole where Pv occurs. Methods have also been proposed to define the precise moment before atrial contraction (also known as pre-a-wave ventricular pressure).

[0032] Identification of pre-a-wave diastolic pressure—Pre-a-wave diastolic pressure measured intravenously can be a useful reporting measure because it provides useful information about cardiac function and is highly correlated with more direct measurements of upstream atrial pressure (in the absence of intervening valvular disease). Therefore, if only ventricular pressure is measured, then accurate identification of pre-a-wave diastolic pressure provides a useful proxy for upstream atrial chamber pressure. From Pv data, identification of the precise moment to capture this pre-a-wave measurement can be performed. The disclosed method identifies this moment by analyzing the third derivative of the Pv versus time function (d3Pv / dt3) (Figure 4). By analyzing the third derivative of Pv versus time, the disclosed method can identify the moment before the systolic peak (or before the Pv upstroke becomes dominant) when (d3Pv / dt3) transitions from a negative to a positive value. Similarly, if dPv / dt represents the "velocity" of pressure change over time, and the second derivative (dPv / dt) represents the corresponding "acceleration," then the third derivative (dPv / dt) represents the "change in acceleration." Thus, the disclosed method identifies the exact moment when diastole (dPv / dt) is consistently positive, which represents the moment when ventricular pressure ends its peak "deceleration" to a lower value and begins its positive "acceleration change" to a higher value. In the heart, this is the moment when the myocardium begins contraction in the atria, followed by the onset of muscle contraction in the ventricles.

[0033] Characterization of the right border - Pressure changes during peak ventricular systolic pressure (which constitutes the right border of the pressure loop) are believed to reflect the effects of pressure wave reflection and can be characterized and quantified. It also shows the variation of maximum Pv across multiple loops.

[0034] Timing of Loop Characteristics—The occurrence (or timing) of distinct loop features, such as left / right / upper / lower borders, can be recorded and used to identify the occurrence of systole, diastole, or other periods of interest.

[0035] Simultaneous comparison of various parameters—simultaneous comparison of any factor derived from the loops can be performed. One example is a comparison of ventricular end-diastolic pressure versus maximum Pv over multiple beats. This comparison revealed a relationship that appears similar to, but is fundamentally different from, the classic Frank-Starling curve (FIG. 5), reflecting the relationship between ventricular end-diastolic volume and stroke volume. Another example is end-diastolic pressure versus loop size. Another example is loop size versus transvalvular pressure gradient or transvalvular pressure ratio.

[0036] Comparison of parameters across different time points or between different individuals—Various pressure loop measurements can be compared across different time points, such as total loop area before and after a heart attack (myocardial infarction) to indicate deterioration of ventricular function, or total loop area before and after a heart transplant to indicate improvement of ventricular function (Figure 6). The effectiveness of cardiac treatments, particularly procedures and pharmacological agents, can be tested in this manner. Loop measurements can also be compared between different individuals / populations to identify objective differences in cardiac function.

[0037] Downstream Calculations Using Ventricular Pressure Loop Data - The primary data in the ventricular pressure loop can be used to calculate unique secondary measurements. An important application of this functionality is how the ventricular pressure loop data can be used to calculate ventricular "power," "resistance," and "flow" measurements. Left ventricular power calculations can calculate downstream flow and resistance within the body's systemic circulation, while right ventricular power calculations can calculate downstream flow and resistance within the lung's pulmonary circulation.

[0038] In generating a ventricular power:ventricular pressure loop, the disclosed method allows for a comparison of dPv / dt (in mmHg / sec) versus Pv (in mmHg) at any point in time, rather than a comparison of dPv / dt versus time itself. In the disclosed method comparison, raw blood pressure is mathematically equivalent to work done (or energy stored) in joules per volume of blood (Equation A3).

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[0039] Blood pressure per unit of time is mathematically equivalent to power in watts (or energy stored per unit of time) per volume of blood (Equation A4).

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[0040] Thus, the disclosed method (and corresponding system and article or manufacture) creates a unique pressure loop that allows for analysis of ventricular power as a measure of ventricular blood volume.

[0041] Quantification of the ventricular power index by this method can be analyzed for different portions of the cardiac cycle (i.e., systole only, diastole only, or the entire cardiac cycle). Conceptually, ventricular power analysis also performs a pressure-weighted average of the power index over different Pv values, as opposed to the native time-weighted average of the power index over different time values (Equation A5, and Figure 6).

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[0042] In doing so, the disclosed systems, methods, and articles of manufacture can emphasize power indices generated during peak systole / diastole, which 1) are generated quickly within a relatively short time period and are not overemphasized in time-weighted data, and 2) can more closely quantify ventricular function.

[0043] Additionally, the disclosed subject matter can indicate (eg, report, etc.) ventricular power in a number of different ways, including but not limited to the following measurements: · The sum of the pressure-weighted power index values over different parts of the cardiac cycle, or simply the pressure loop area (adapted from Equation A1).

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[0044] Downstream Blood Flow and Resistance: Following the disclosed calculations indexed by blood volume by the disclosed method, the disclosed method can generate estimates of downstream blood flow and resistance by using existing equations for power, resistance, and flow (Equation A8).

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[0045] Although such a formula uses actual power values rather than power indexed by volume as produced by the disclosed method, the downstream resistance "coefficient" calculation by the disclosed method results in a formula (Equation A9) having units of mmHg-seconds or dyne-seconds / cm.

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[0046] Calculation of the actual resistance can be achieved by including blood volume, in units of mmHg-sec / m, or dyne-sec / cm, in the equation (Equation A10).

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[0047] Similarly, the downstream cardiac output, or blood flow "factor" calculation according to the disclosed method is a formula (Equation A11) with units sec-1.

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[0048] Calculation of the actual blood flow rate can be achieved by including the blood volume, in units of m3 / sec, in equation (Equation A12).

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[0049] The disclosed subject matter may also have the ability to convert non-invasive ventricular pressure waveforms into pressure loops. Examples of this include non-invasive Doppler ultrasound, magnetic resonance imaging, and cardiovascular acoustic intensity techniques, which measure ventricular blood flow velocity over one or more cardiac cycles and calculate the corresponding ventricular blood pressure. The pressure waveforms derived from each of these various techniques may be converted into pressure loops and analyzed as described above.

[0050] Subtle variations of the pressure loop can be made, such as using change in Pv per sample (dPv / sample) rather than dPv / dt. Different units of pressure and time can also be used. These variations effectively change the scale of the pressure loop shown here and the range established for "normal" measurements, but the basic concept and principles of application remain the same.

[0051] The disclosed subject matter and its Pv pressure loop may be used with existing commercially available devices, such as those capable of obtaining Pv measurements from the ventricles, to improve baseline function. Examples include pulmonary artery hemodynamic monitoring devices (e.g., Swan-Ganz catheters) inserted through a central vein, right atrium, right ventricle, or pulmonary artery, which may monitor and analyze right ventricular pressure loops using the pressure loop. Left ventricular assist devices (e.g., Impella mechanical pumps) monitor aortic pressure and estimate left ventricular pressure. Modifications to the Impella device to directly measure Pv or use calculated Pv to generate Pv loops can monitor left ventricular function and determine the appropriateness of titrating or tapering cardiac support.

[0052] Figure 1 shows an example of a left ventricular pressure tracing displayed on a commercially available hemodynamic system. Measurements from the first heartbeat include a maximum systolic pressure of 146 mmHg, a minimum diastolic pressure of 0 mmHg, an end-diastolic pressure of 6 mmHg, and a maximum

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[0053] FIG. 2 shows an example of a ventricular "pressure loop" generated by the disclosed method. Pressure loop determinations are performed for each of a number of consecutive heartbeats. The determined pressure loops are then overlaid on one another (using the same raw data source as in FIG. 1). The black line indicates the average of all pressure loop curves (e.g., the average dP / dt for each Pv value). The left and right boundary lines indicate the minimum diastolic pressure and maximum systolic pressure, respectively. The upper and lower boundary line amplitudes indicate the maximum systolic dP / dt and minimum diastolic dP / dt, respectively.

[0054] Figure 3 shows a series of pressure loop variations generated by the disclosed method, illustrating the characteristics of ventricular blood pressurization. "Smooth" and "symmetric" loops are shown in column A, while "diagonal" loops (indicated by dashed double arrows connecting the maximum and minimum dP / dt points) are shown in column B, and loop "dip" (indicated by solid arrows) are shown in column C. The maximum / minimum dP / dt point locations appear to "rotate" around the median pressure (small circle) in some panels (A1-2 and B1-2) and "shift" from the median pressure in other panels (third row and column C). The areas included by the upper and lower curves (above / below the x-axis) of each loop appear equal in most panels, although the upper curves may appear relatively larger (panels A1 and C1) or smaller (panel C3) than the corresponding lower curves. Pressure changes during diastole (points along the left border) can be dramatic (Panels B1-2) or subtle (third row and third column). The units for each graph are mmHg / sec (y-axis) and mmHg (x-axis).

[0055] FIG. 4 illustrates the identification of pre-a-wave diastolic pressure using the disclosed method. The disclosed method identifies the exact moment before peak systole when the third derivative of the ventricular pressure versus time function is consistently positive. This moment represents the time when ventricular pressure ends its peak "deceleration" to a lower value and begins its positive "acceleration" change to a higher value. The ventricular pressure at this moment corresponds to the pre-a-wave pressure, provides useful information about cardiac function, and (in the absence of intervening valvular disease) is highly correlated with direct measurements of upstream atrial pressure. FIG. 4 shows an example of an electrocardiogram trace (top), a left ventricular pressure trace (middle), and a third derivative d3Pv / dt3 trace (bottom) acquired simultaneously from the same source. The exact moment when d3Pv / dt3 is consistently positive before peak systole is identified by the disclosed method (circled sample number 166), and the corresponding normal left ventricular pressure of 9.2 mmHg and electrocardiogram timing on the P wave are shown (circled).

[0056] Figure 5 shows a simultaneous comparison of ventricular end-diastolic pressure (EDP; points along the left border) and peak systolic pressure (Pv; points along the right border) from loops spanning multiple cardiac cycles (left graph). The resulting relationship between peak Pv and EDP is linear (right graph), with a slope greater than 1 in this example (red reference line). This suggests that for every 1 mmHg increase in EDP, there is a relative increase in peak Pv of more than 1 mmHg.

[0057] Figure 6 shows a comparison of pressure loops generated from the same individual at different time points: the smaller inner loop is associated with abnormal ventricular function, and the larger outer loop is associated with normal ventricular function.

[0058] FIG. 7 shows an example of a plot of ventricular pressure change per unit time (dP / dt) versus time. Positive dP / dt values occur during peak systole, while negative dP / dt values occur during peak diastole. In this time-dependent plot, the time between the diastolic peak and the next systolic peak (occurring from 130 ms to 230 ms), representing passive relaxation of the ventricle, is given greater weight compared to the graphical representation in the corresponding pressure loop of the disclosed method (FIG. 2, left border). In comparison, the pressure loop of the disclosed method (plot of dP / dt versus pressure) weights ventricular dP / dt values equally across the range of ventricular pressures, rather than across the range of sampling times. This results in an emphasis on the peaks of ventricular systole / diastole and a de-emphasis on the end of systole / diastole in the pressure loop analysis of the disclosed method.

[0059] Arterial blood pressure Arterial blood pressure (Pa) and heart rate are ubiquitous vital signs used worldwide to inform clinicians of a patient's cardiovascular health. Elevated blood pressure defines hypertension, while decreased blood pressure defines hypotension. A significant dissociation between systolic blood pressure (SBP, identified by the systolic blood pressure) and diastolic blood pressure (DBP, identified by the diastolic blood pressure) may indicate additional cardiovascular abnormalities. Both elevated and depressed heart rates may result from primary electrical conduction disorders in the heart or may occur as secondary responses to different cardiovascular abnormalities. A further significant decrease in blood pressure or heart rate from normal, approaching zero, indicates cardiac death.

[0060] Invasive measurements of Pa are routinely performed on patients undergoing invasive evaluation in hospital cardiac catheterization laboratories. Pa can also be measured continuously for patients being monitored in operating rooms, emergency rooms, and intensive care units. Pa measurements are particularly useful for evaluating patients in a state of "shock" (a condition in which the blood flow provided by the heart to the body is insufficient to meet the body's metabolic needs). Continuous Pa recordings generate the classic pressure-versus-time waveform (see, e.g., Figure 1) resulting from successive heartbeats and form the basis for routine measurements such as SBP, DBP, and mean arterial pressure. Continuous recordings generate the classic pressure-versus-time waveform (see, e.g., Figure 1) resulting from successive heartbeats and form the basis for routine measurements such as SBP, DBP, and mean arterial pressure. The raw information contained within the continuous Pa waveform also provides vital signs of blood pressure and heart rate, which are used worldwide. Pa measurements are also integrated into existing secondary measurements of cardiac output (CO) and flow resistance (R) (Equations B1-5). The more complex systolic Pa integral method (Equation B6), or Pa average method (Equations B7-10), is used for a variety of applications, including estimation of CO and quantification of cardiac valve stenosis.

[0061] Despite the widespread utility of Pa waveforms for vital sign measurement and more advanced secondary calculations, there are limited tools available to assist clinicians in identifying and quantifying subtle changes in Pa waveforms. Furthermore, unrecognized Pa waveform characteristics could provide new vital signs to clinicians worldwide. This section describes the Pa waveform analysis performed by the disclosed method to generate and measure cardiovascular vital signs.

[0062] Related equations: Analogy of Ohm's Law:

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[0063] Pressure loops using invasive or noninvasive Pa measurements are generated by converting traditional Pa versus time data (also called "time-frequency plots") into Pa rate of change (dPa / dt) versus Pa data. This allows operators to derive multiple measurements from raw Pa measurements (either pre-existing data or data collected in real time) and characterize arterial pressurization in novel ways. Plotting dPa / dt versus Pa creates a "pressure loop" for each cardiac cycle, comparable to the typical peaks and valleys seen in a time-frequency plot (Figure 2). Standard measurements can be derived from the Pa pressure loop, including the x-axis minimum ("left border," minimum diastolic pressure), x-axis maximum ("right border," maximum systolic pressure), y-axis maximum ("upper border," maximum dPa / dt), and y-axis minimum ("lower border," minimum dPa / dt). Novel measurements and characteristics derived from analysis of pressure loops (Figure 3) include, but are not limited to, the following: Additionally, each of these characteristics derived from arterial blood pressure data may be developed into cardiovascular "vital signs" for future use. Indeed, characteristic #3 forms the basis for heart rate, and characteristics #5-6 form the basis for blood pressure.

[0064] Loop size - total area and partial area (e.g., top and bottom half of the loop, quarter of the loop), axial dimension, or other quantity related to the area or circumference of the loop. Arterial "pressure loops" can be generated by the disclosed method by plotting dPa / dt vs. Pa. The total loop area can generally be calculated by integrating the absolute value within the loop boundary (Equation B11).

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[0065] The partial area of the total Pa loop can be calculated by adjusting the limits of the integration (e.g., the sum of all positive dPa / dt values to calculate the area above the X-axis, or the sum of all negative dPa / dt values to calculate the area below the X-axis).

[0066] For periodic pressure fluctuations in the loop, an additional adjustment to the limits of integration can be introduced (Figure 3). When calculating the raw Pa difference between two different data samples, regardless of the time interval, it is multiplied by the sampling rate (number of samples per time) to maintain consistency across techniques. Additionally, the Pa loop area, which represents arterial function over a cardiac cycle, can be used to calculate arterial function over a time period (Equation B12). Formula B12: Pa loop area / time = heart rate x Pa loop area

[0067] Loop shape—the "symmetry" and "smoothness" of the overall shape, the presence or absence of abnormal "dimples," and the difference between the curves / regions of the upper and lower halves. The range of slope in different quadrants of the loop can also be determined. The smoothness of the observed loop segments can be compared to a predicted best-fit curve, and the correlation with the predicted curve can be quantified. Individual loop shapes derived from sources with uncertain characteristics can be compared against a database of numerous loop shapes derived from sources with known characteristics, and the best match (and associated source characteristics) for each loop is then reported. Examples of this include: 1) loops with irregular smoothness resulting indirectly from ventricles affected by coronary artery disease and ischemic cardiomyopathy; 2) loops with regular smoothness resulting indirectly from ventricles affected by systemic non-ischemic cardiomyopathy; and 3) loops with irregular smoothness resulting indirectly from ventricles affected by hypertrophic cardiomyopathy.

[0068] Loop Cycle Duration - The total number of samples required to generate a complete loop is divided by the sampling rate to determine the duration of each loop. The loop duration can then be used to calculate the instantaneous heart rate for a single loop, or the average heart rate for multiple loops.

[0069] Characterization of the upper / lower boundary lines relative to a reference point—the upper boundary line represents the point of maximum blood pressure (i.e., peak contractile force), while the lower boundary line represents the point of minimum blood pressure (i.e., peak relaxation force). These points within the loop are consistent across multiple pressure loops and can be observed to be "aligned," "rotated," and / or "shifted" around a reference point (e.g., loop center, or median X-axis pressure).

[0070] Left boundary characteristics - Pressure changes during diastole (which constitute the left lower quadrant of the pressure loop) can be characterized (e.g., "flat" or "stable") and help decipher the timing of diastole when there are no cyclic pressure fluctuations. This timing of diastole can be useful for additional calculations described in this supplement.

[0071] Right border characteristics - Pressure changes during peak arterial systolic pressure (which constitutes the right border of the pressure loop) may reflect the effects of pressure wave reflections and can be characterized / quantified. They can also show variations in maximum Pa across multiple loops.

[0072] Characteristics of periodic pressure fluctuations - Each pressure loop can exhibit recurring dPa / dt "dip" or fluctuations that can be identified and characterized (eg, number of occurrences, timing, frequency, amplitude).

[0073] Timing of Loop Characteristics—The occurrence (or timing) of distinct loop features, such as left / right / upper / lower borders, can be recorded and used to identify the occurrence of systole, diastole, or other periods of interest.

[0074] Simultaneous comparison of various parameters - Simultaneous comparison of any loop-derived factor can be performed, such as comparing arterial loop size vs. min / max Pa over multiple heartbeats, which revealed a linear relationship (Figure 4).

[0075] Comparison of parameters at different time points or between different individuals—Various pressure loop measurements can be compared across different time points, such as total loop area before and after a heart attack (myocardial infarction) to indicate deterioration of cardiac function, or total loop area before and after a heart transplant to indicate improvement of cardiac function (Figure 5). This type of comparison is similar to, but distinct from, the comparison of ventricular pressure loops (Figure 5). The effectiveness of cardiac treatments, particularly procedures and pharmacological agents, can be tested in this manner. Loop measurements can also be compared between different individuals / populations to identify objective differences in cardiac function.

[0076] Downstream calculations using arterial pressure loop data - Primary data in the arterial pressure loop can be used to calculate unique secondary measurements. An important application of this functionality is using arterial pressure loop data to estimate ventricular pressure loop data and its secondary measurements.

[0077] Ventricular Pressure Loop Area Estimation: Characteristics of the arterial pressure loop can be used to estimate the value and characteristics of the ventricular pressure loop. In particular, the portion of the arterial pressure loop occurring during systole can be used to estimate the corresponding portion of the ventricular pressure loop occurring during systole (portions of the arterial and ventricular pressure loops graphed above the X-axis). Both upper loops resemble ellipses in the following area calculations (Equations B13 and B14):

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[0078] The area above the Pa loop can be used to estimate the area above the Pv loop by scaling the height and width of the Pa loop to the parameters of the Pv loop (Equation B15).

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[0079] If the scaling factor "s" is equal to the width of the ventricular pressure loop (max Pv - min Pv) divided by the width of the arterial pressure loop (max Pa - min Pa):

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[0080] This equation simplifies to equation B16.

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[0081] Assuming that the area above the Pv loop is approximately equal to the area below the Pv loop, the total loop area of Pv can be estimated using equation B17.

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[0082] The unknown maximum Pv can be estimated using maximum Pa (assuming no pressure gradient from the ventricle to downstream arteries). The unknown minimum Pv can be estimated using pulmonary capillary wedge pressure, left atrial pressure, or the closest approximation.

[0083] Estimation of Individual Ventricular dP / dt and Pv Values: Estimating the ventricular Pv and dP / dt values facilitates the calculation of other downstream ventricular loop measurements. Pv values can be estimated from Pa values using Equation B18.

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[0084] Similarly, the ventricular dPv / dt value can be estimated from the arterial dPa / dt value using equation B19.

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[0085] The maximum dPv / dt value can be estimated from the maximum arterial dPa / dt value using equation B20.

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[0086] Estimation of Ventricular Power: Following the estimation of Pv, dPv / dt values from the arterial pressure loop data in the disclosed method, ventricular power can be estimated using equations A1, A6, and A7, reproduced below.

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[0087] Estimation of Downstream Blood Flow and Resistance: Following estimation of ventricular power from arterial pressure loop data by the disclosed method, measurements of downstream blood flow and resistance can be calculated using equations A9, A10, A11, and A12, reproduced below:

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[0088] Calculation of Arterial Power, Flow, and Resistance: While the arterial pressure loop can be used to estimate the ventricular pressure loop data and the downstream power, flow, and resistance calculations, the same measurements can also be performed directly from the arterial pressure loop, as shown in equations B21-B27. Discrepancies between ventricular and arterial power, flow, and resistance need to be studied in real-life situations, which may differ in terms of hypothetical limits on power, flow, and resistance (related to ventricular function) versus observed power, flow, and resistance (related to arterial function).

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[0089] The disclosed subject matter does not rely on invasive Pa measurements to perform its analysis. It also functions to convert a noninvasive arterial pressure waveform into a pressure loop. Examples of this may include using noninvasive Doppler ultrasound and magnetic resonance imaging to measure arterial blood velocity over one or more cardiac cycles and calculate the corresponding arterial blood pressure. The derived pressure waveform may be converted into a pressure loop and analyzed as described above. Vibrations and sounds emanating from blood passing through an artery, as well as a derived arterial blood pressure waveform, may also be converted into an arterial pressure loop and analyzed as described above. It is also contemplated that a noninvasive blood pressure cutoff measurement may be manipulated to generate an arterial blood pressure loop and analyzed as described above. Regardless of the means for generating an arterial blood pressure waveform and / or arterial pressure loop as described above, both 1) the generation of the arterial pressure loop and 2) the analysis as described above of the disclosed subject matter are subject to patent protection.

[0090] When patients undergo medical evaluations around the world, vital signs are measured from arterial blood pressure versus time data. Ubiquitous vital signs include arterial blood pressure and heart rate. As noted above, the disclosed subject matter further converts arterial blood pressure versus time data into a unique dPa / dt versus Pa pressure loop, which can be used to not only characterize how arterial blood is pressurized, but also to estimate a measure of ventricular blood pressurization. Each of these unique arterial and ventricular blood pressure measurements performed by the disclosed subject matter can be used as a vital sign to identify disease states in a patient.

[0091] It is anticipated that, similar to blood pressure and heart rate metrics, an arterial pressure loop of abnormal size or shape may indicate an abnormal cardiovascular condition. As with other vital signs, a pressure loop magnitude that is significantly reduced from normal values and approaches zero may also indicate cardiac death. It is also anticipated that the disclosed subject matter can generate pressure loops and calculate vital signs in the pressure loop area from either invasive or non-invasive measurements of blood pressure, such as from an indwelling arterial pressure line or a non-invasive blood pressure cuff, respectively.

[0092] Disclosed herein is an arterial pressure loop (FIG. 9) that graphs the instantaneous change in pressure per unit time (y=dPa / dt) versus arterial pressure (x=Pa). In general, this function converts the pressure versus time waveform generated from each heartbeat into a uniquely shaped loop that can be analyzed alone, compared to other beats from the same patient, and / or compared to other beats from different patients. The size, shape, symmetry, and location of each graphed loop facilitates multiple unique analyses. Example measurements include left, right, top, and bottom points along the loop, which correlate to DBP, SBP, maximum dPa / dt, and minimum dPa / dt, respectively. The mean center of the loop represents both MAP and the change in pressure across the beat. The height and width of the loop can be quantified. Loop area (total and subtotal) area can be quantified. Loop area per time can be quantified. Maximum and minimum dPa / dt locations relative to loop center can be quantified. Symmetry of maximum dPa / dt vs. dPa / dt loop location can be characterized. Tangents along the loop can be characterized. Presence, location, frequency, and magnitude of loop artifacts can be characterized. Combining these loop-derived measurements, downstream secondary measurements, and other measurements characterizes the arterial compression and decompression forces associated with each heartbeat. Additionally, ventricular and arterial power, as well as downstream blood flow and resistance measurements, can also be quantified.

[0093] There are many commercially available devices capable of measuring or estimating arterial pressure waveforms and reporting such data as traditional time-frequency plots. These systems lack pressure loop analysis capabilities.

[0094] Figure 8 shows an example of a typical aortic blood pressure trace displayed by a commercially available hemodynamic system. Global measurements include systolic, diastolic, and mean arterial pressure.

[0095] FIG. 9 shows an example of an arterial "pressure loop" generated by the disclosed subject matter, performed over a number of consecutive heartbeats. The loops generated from these heartbeats are shown overlaid on top of each other (using the same raw data source as FIG. 8). The black line is the average calculated from all the loops calculated for each heartbeat. The left and right boundary lines indicate the minimum diastolic pressure and maximum systolic pressure, respectively. The amplitudes of the upper and lower boundary lines represent the maximum systolic dP / dt and minimum diastolic dP / dt, respectively.

[0096] FIG. 10 shows deformations of arterial pressure loops generated by the disclosed subject matter. These deformations demonstrate the characteristics of arterial blood pressure. The raw loops and the overall average (black line) are shown. The loops in column A have "smooth" and "symmetric" upper half curves, while the loops in column B appear "diagonal" and "notched" in comparison. The location of the max / min dPa / dt (black dots) points appears to be "rotated" around the median pressure (red dots) in some panels (first row), but is "shifted" from the mean pressure in other panels (second row, rightward shift; third row, leftward shift). The rotation occurs either clockwise (column A) or counterclockwise (column B). In both columns, 1) the distance of the max / min dPa / dt points from the x-axis and 2) the area of the half of the loop above / below the x-axis vary. The relatively straight portion of each loop (lower left quadrant) represents a "wave-free segment" where periodic pressure fluctuations are relatively reduced / eliminated.

[0097] Figure 11 shows a simultaneous comparison of arterial pressure loop area versus arterial pressure. Comparisons between total pressure loop area versus maximum Pa pressure (left panel) and low pressure loop area versus minimum Pa pressure (right panel) are performed over multiple cardiac cycles. A significant linear relationship is demonstrated for each comparison, and trendlines are also shown.

[0098] Figure 12 shows a comparison of pressure loops generated from the same artery at different times. The smaller inner loop is associated with abnormal cardiac function, and the larger outer loop is associated with normal cardiac function, both of which mirror the ventricular pressure loops of Figure 5.

[0099] Machine Learning The pressure loop plots relating to both ventricular and arterial pressures can be analyzed by a machine learning model. The machine learning algorithm can be a neural network, such as a convolutional neural network.

[0100] The machine learning model can learn image characteristics to screen for cardiac-related abnormalities. The machine learning model is provided with training data consisting of various sets of pressure loop plots from various patients showing healthy hearts and hearts with abnormalities. The model can use this data to, for example, associate certain combinations of features with healthy hearts and certain combinations of features with unhealthy hearts.

[0101] The machine learning model can include a binary classifier. The binary classifier can determine whether the heart in the image is healthy or whether an abnormality is present. In some cases, the binary classifier can be used to predict the presence or absence of a particular abnormality. In some cases, the machine learning model can include a multi-class classifier that can determine whether the heart contains an abnormality or whether the heart has one of several abnormalities. In some cases, the machine learning model can include a multi-label classifier that can assign at least one abnormality label to the pressure loop plot.

[0102] Arterial pressure decay constant Tau background: A method for determining a time constant is disclosed that includes (a) taking one or more continuous waveform recordings of arterial pressure and dividing the one or more recordings into individual cardiac cycles, (b) for each individual cardiac cycle, converting the arterial pressure data into a time rate of change of arterial pressure, (c) determining a rolling standard deviation for each set of dP / dt data to identify periods of diastole where variability in dP / dt values is minimal, and (d) determining a set of sub-threshold values to define a timing range of arterial pressure values that can be used to calculate the time constant.

[0103] Natural decay, or exponential decay, is the decrease in the amount of a substance at a rate proportional to its current volume. It is commonly observed and calculated in various scientific fields. In cardiovascular medicine, this observation involves the fall in blood pressure within the ventricle and downstream arteries. The rate of pressure decay is quantified by the natural decay time constant, Tau (Equations C1-C2). Research in this field has previously calculated Tau for the following purposes: 1) quantifying ventricular chamber relaxation using continuous measurements of ventricular pressure (Pv), 2) quantifying arterial vascular compliance (Equation C3), and 3) quantifying distal venous pressure using continuous measurements of arterial pressure (Pa) (Equation C4). These calculations have also been implemented in noninvasive cardiovascular pressure estimation using ultrasound Doppler techniques.

[0104] Tau has not previously been used to assess other features of cardiovascular function, including vascular resistance, cardiac output, observed and predicted arterial pressurization performance, or severe obstruction between the ventricular chamber and the artery (i.e., aortic stenosis). The disclosed subject matter aims to perform these secondary assessments based on Pa-derived Tau. While other techniques have attempted to perform similar assessments of cardiovascular function using continuous Pa measurements (Equations B6-10), none use Tau.

[0105] The disclosed subject matter also calculates Tau in a manner that identifies the diastolic time period when cyclical pressure fluctuations are minimal, which is not a method of calculating Tau traditionally. This unique measurement quantifies the ratio of distal to proximal pressure, specifically during the "quiet period" of diastole. Calculating Tau using this technique is not implemented in this commercially available product. Related equations:

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[0106] Calculation of the unknown Tau value and decay curve asymptote "C" value from equation C4 can be performed by the disclosed subject matter by fitting an exponential curve to a series of data points. The disclosed subject matter can also perform this calculation using the following specific method and equations:

[0107] An equation is established (Equation C6) that incorporates the difference between Equation C2, which uses "raw" Tau, and Equation C4 (the value from "corrected" Tau). For simplicity, "Pressure Curve 1" uses raw Tau, and "Pressure Curve 2" uses corrected Tau. The P(t) value at diastolic time 0 is represented by "P" for both curves. The pressure value at time "t" for Curve 1 is represented by P1(t). The pressure value at time "t" for Curve 2 is P2(t). TR is represented by "raw" Tau (known value), TC is represented by "corrected" Tau (calculated), and C is the asymptote of the decay curve (calculated).

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[0108] The disclosed subject matter automatically performs a first-order calculation of arterial Tau based on the period of minimum cycle-to-cycle pressure variation, guided by user-input settings and loop-derived diastolic limits. The automatic calculation of Tau involves multiple steps (FIG. 13) and begins by taking one or more continuous waveform recordings and dividing them into individual cardiac cycles. This can be performed using a common starting trigger, such as the R wave on an electrocardiogram or the point of maximum dPa / dt, and the Pa data for each heartbeat is converted to its first derivative, dP / dt. For each series of dP / dt data, a rolling standard deviation is calculated to identify the diastolic period with the least variation in dP / dt values. The timing of standard deviation values below a pre-specified threshold (e.g., a minimum of 10% of the standard deviation value) then defines the timing range of Pa values that may be used in the raw Tau calculation. The limits of the timing range and the corresponding Pa values are identified, and Tau is calculated (Equation C1). Due to variations in the data sampling rate used to generate the Pa waveform, the actual time interval, rather than the number of samples, should be used to calculate Tau.

[0109] Once "raw" Tau is calculated, the disclosed subject matter also calculates a "corrected" Tau to correct for non-zero decay curve asymptotics (Equations C4 and C5). The process of calculating a "corrected" Tau involves either 1) calculating the exponential curve equation that best fits the data using widely available methods, or 2) comparing the observed Pa value with the calculated Pa value based on "raw" Tau (Equation C2) (FIG. 14). The latter method uses the timing and pressure at which the difference between the raw Tau pressure curve and the actual Pa waveform is greatest to calculate a "corrected" Tau and decay curve asymptotics (FIG. 15). Using the corrected Tau and decay curve asymptotics in Equation C4 produces a pressure curve that closely approximates the actual diastolic Pa waveform.

[0110] The raw Tau and corrected Tau values can be used for additional secondary calculations, including 1) backward calculation of the expected monoexponential curve during systole and 2) calculation of cardiac output. First, the disclosed subject matter can calculate the monoexponential pressure curve that would occur during diastole and systole of the heart and compare the actual and calculated pressures. This analysis is performed from the start of systole to the end of diastole for each cardiac cycle. From this setup, various comparisons can be made for different waveform periods, including percent maximum pressure, percent mean pressure, and / or percent cumulative pressure versus systole, diastole, and / or the entire heartbeat (FIG. 16). Second, because Tau and arterial resistance are directly related (according to Equation C3), Tau can be used instead of resistance in Equation B1 (and similarly in Equations B2, B3, and B4) to generate Equation C9. A variation of this equation involves calculating the slope between the mean arterial pressure and the asymptotes of the decay curve from Equations C3-C8 and dividing by the corresponding corrected Tau (Equation C10). Because the Tau and resistance values are related but not identical, a correction factor K is applied to equations C9 and C10 to calculate cardiac output.

[0111] Figure 13 shows a single arterial pressure waveform (top panel, full line) illustrating the determination of the diastolic period with minimal cyclic pressure fluctuations (top panel, green line). This period is identified by performing a rolling calculation on the first derivative dP / dt of the Pa waveform (bottom panel, black line). Plotting this first derivative data reveals the period of minimal cyclic pressure fluctuations within the diastolic period. A pre-specified minimum 10% standard deviation threshold (red line) defines this period, occurring between data samples 116 and 236.

[0112] FIG. 14 illustrates the determination of the asymptotics of a mono-exponential curve in accordance with the disclosed subject matter. A curve using "raw" Tau (red line) is generated and passes through points (116, 93.8) and (236, 56.2) within the diastolic phase with minimal pressure fluctuations from FIG. 1. The calculated value of raw Tau is approximately 234. The maximum difference between the raw Tau curve and the observed arterial pressure (black line) occurs at sample number 172, which correlates with points (172, 73.9) and (172, 68.0) on each line, respectively. The values of these points, and raw Tau, are used as inputs into equations C7 and C8.

[0113] FIG. 15 illustrates the determination of a "corrected" Tau in accordance with the disclosed subject matter. Equations C7 and C8 are entered using the point values from FIG. 14 and raw Tau. Solving these equations yields the corrected Tau value and the asymptote of the monoexponential curve (e.g., for a heart rate of 65 samples, 0.270 seconds, or 0.0045 minutes, the asymptote is 49.2 mmHg). Inserting these values into equation C4 produces a curve using corrected Tau that passes through the points (116, 93.8), (172, 68.0), and (236, 56.2), which more closely follows the observed diastolic pressure waveform (green line) than the curve using raw Tau (red line).

[0114] 16 shows a comparison of calculated and measured arterial pressure values according to the disclosed subject matter. Examples of measurements derived from diastolic Tau and back-calculated systolic pressure include actual and calculated percent maximum cumulative pressure, and percent maximum pressure. Various measurement permutations include systole only (based on the solid line), all beats (based on the dotted line), raw Tau (red line and values), and corrected Tau (green line and values). Analysis of blood flow disorders

[0115] Disclosed is a method that includes (a) determining a series of arterial-ventricular systolic pressure ratios (AVPRs) by dividing post-occlusion pressure by pre-occlusion pressure for values resulting from systole over a plurality of cardiac cycles, and (b) generating a plot including each of the series of arterial-ventricular pressure ratios.

[0116] Cardiac disorders that cause impaired blood flow from the ventricles to downstream arteries are commonly evaluated. Pulmonary valve stenosis is a common cause of right ventricular outflow obstruction to the pulmonary artery. Aortic valve stenosis (and supravalvular and subvalvular stenosis) and hypertrophic obstructive cardiomyopathy (HOCM) are common causes of outflow obstruction from the left ventricle to the aorta (Figure 17). HOCM is caused by dynamic obstruction of left ventricular outflow due to abnormal interference between the ventricular muscle and the mitral valve apparatus during ventricular contraction. In pulmonary valve stenosis, abnormally elevated transvalvular pressure gradients (by invasive and noninvasive techniques) and measures of detrimental effects on the right ventricle are used to determine the need for valvular intervention and stenosis relief. In aortic valve stenosis, abnormally elevated transvalvular flow velocities and pressure gradients (by noninvasive echocardiography) and abnormally reduced aortic valve area (by both echocardiography and invasive valve testing) are used to determine the need for valvular intervention and stenosis relief. In HOCM, elevated flow velocity and pressure gradients across the obstruction (by noninvasive echocardiography) and / or elevated pressure gradients (by invasive pressure measurements) are used to determine the need for intervention and relief of the obstruction.

[0117] In particular, aortic valve testing utilizes invasive measurements via 1) right heart catheterization or 2) left heart catheterization with simultaneous measurement of pressure across the stenotic valve to calculate aortic valve area using the Gorlin equation (Equation D1) or the Hakki equation (Equation D2). Furthermore, these measurements can be performed on patients in different physiological states, such as baseline rest, exercise, or pharmacological stress. The distinction between the relatively static flow resistance due to aortic stenosis versus the dynamic flow resistance due to HOCM is typically assessed using echocardiography. Invasive hemodynamic assessment of HOCM versus aortic stenosis can only roughly distinguish between the two etiologies.

[0118] The disclosed subject matter aims to provide an analysis of occlusion from a ventricle to its downstream artery, quantify the degree of occlusion present, characterize the static / dynamic nature of the occlusion, and characterize the effect on ventricular and arterial blood pressure, as described below.

[0119] Related equations:

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[0120] The disclosed subject matter performs analysis of ventricular-arterial obstructions by: 1) calculating and displaying the arterial-ventricular systolic pressure ratio (AVPR) across the obstruction; 2) characterizing beat-to-beat pressure ratio measurements; 3) characterizing ventricular blood pressurization; and 4) characterizing arterial blood pressurization. These functions can be performed for any type of flow obstruction between a ventricle and its downstream artery. These obstructions include, but are not limited to, pulmonary stenosis, aortic stenosis, HOCM, and arterial stenosis. Furthermore, the disclosed subject matter performs these analyses by analyzing simultaneous invasive blood pressure measurements upstream and downstream of the stenosis. These analyses can be performed at a patient's baseline at rest and repeatedly during cardiac stress due to exercise or medication. The disclosed subject matter's analyses do not require an additional invasive right heart catheterization.

[0121] Systolic AVPR is created by multiple calculations of the post-occlusion pressure (lower value) divided by the pre-occlusion pressure (higher value) for all values occurring during systole (identified when pre-occlusion pressure > post-occlusion pressure) (Equation D3). The AVPR calculated for each systole can be displayed graphically by the disclosed subject matter and compared across different cardiac cycles (Figures 18-21). These repeated measurements can also be displayed for comparison over time (Figure 4, row 2) and / or analyzed together (Figure 4, table). AVPR measurements can also be compared with simultaneous measurements of ventricular and arterial pressure loops (Figure 5) to report on the severity of the ventricle-to-arterial occlusion and cardiac function (both of which contribute to the pressure gradient observed across the blood flow occlusion).

[0122] The disclosed subject matter can also use a shorthand notation to approximate systolic AVPR (Equation D3). This shorthand can also be used to calculate the "partial resistance" of the ventricle-to-artery occlusion (Equation D4). Overall, this shorthand simplifies the calculation and averaging of instantaneous AVPR by utilizing 1) the average gradient across the occlusion and 2) the mean arterial pressure, which is commonly measured by invasive and non-invasive methods. In the case of pulmonary stenosis, the estimated AVPR is the quotient of the mean pulmonary artery pressure (mPAP) divided by the sum of the mPAP and the mean pulmonary valve gradient. In the case of aortic stenosis, the estimated AVPR is the quotient of the mean arterial pressure (MAP) divided by the sum of the MAP and the mean aortic valve gradient. In the case of HOCM, the estimated AVPR is the quotient of the MAP divided by the sum of the MAP and the mean left ventricular outflow tract gradient. In each of these situations, the estimated partial resistance due to the occlusion is the mathematical complement of the AVPR.

[0123] The present disclosure aims to protect the method by which the disclosed subject matter calculates both the exact AVPR (Equation D3) and the estimated AVPR (Equation D4), as well as their mathematical complement, known as "partial resistance." Furthermore, AVPR is a unitless value that conceptually reflects the reduction in blood flow due to an occlusion, and its complement, "partial resistance," is a unitless value that conceptually reflects the resistance to flow due to an occlusion compared to other sources of resistance within the same blood flow circuit. Thus, if an accurate measurement of blood flow, or resistance, is known, AVPR and its complement can be used to calculate the absolute resistance due to an occlusion (see Table in Figure 4). The present disclosure also aims to protect such downstream calculations that would result from initially calculating AVPR by the disclosed subject matter.

[0124] With respect to the disclosed subject matter, 1) cardiac stress testing using exercise or drugs is permissible but not required for the disclosed subject matter's analysis, 2) in addition to calculating the arterial-ventricular pressure ratio, several other functions (none of which are described in the preceding sentence) are performed by the disclosed subject matter, and 3) analysis by the disclosed subject matter is not limited to the study of stenotic aortic valves, but extends to any other condition that causes a blockage from the ventricle to the artery (e.g., HOCM, which blocks blood flow through the left ventricular outflow tract, or pulmonary valve stenosis, which blocks blood flow from the right ventricle).

[0125] The disclosed subject matter's method for calculating AVPR is distantly similar to, but significantly different from, "fractional flow reserve" (FFR) measurements, which are performed to quantify the severity of obstruction due to coronary artery stenosis. FFR is calculated throughout all parts of the cardiac cycle as the average ratio of post-occlusion distal coronary artery pressure divided by pre-occlusion proximal aortic pressure. FFR must also be performed during peak flow conditions, such as those induced by coronary vasodilators such as adenosine. AVPR differs in: 1) pressure measurement location outside the coronary arteries; 2) it can be measured during any physiological state (including baseline rest, drug-induced peak flow, and exercise); 3) it is measured specifically during systole rather than the entire cardiac cycle; and 4) simultaneous analysis of ventricular and arterial pressure loops.

[0126] 17 shows simultaneous pressure tracings from ventricular (1710A-C) and downstream arterial (1720A-C) pressure sources, revealing systolic pressure gradients due to aortic stenosis (left chart), hypertrophic obstructive cardiomyopathy (HOCM; center chart), and pulmonary stenosis (right chart). Pressure measurements from the left heart (left ventricle and aorta) and right heart (right ventricle and pulmonary artery) vary significantly in amplitude, but both can be used in the disclosed subject matter to analyze the degree of blood flow obstruction due to valvular disease.

[0127] FIG. 18 illustrates the arterial to ventricular pressure ratio generated by the disclosed subject matter during valve stenosis. The disclosed subject matter identifies multiple cardiac cycles during simultaneous pressure measurements (FIG. 18) to calculate and display the systolic arterial pressure to ventricular pressure ratio in this example of aortic valve stenosis. Minimum pressure ratios occur regularly around the 30th sample, and a consistent degree of obstruction is observed between different cardiac cycles. Assessment of pulmonary valve stenosis can be performed similarly.

[0128] FIG. 19 shows the arterial-ventricular pressure ratio generated by the disclosed subject matter during obstructive hypertrophic cardiomyopathy. Multiple cardiac cycles are analyzed using simultaneous pressure measurements to calculate and display the systolic arterial-ventricular pressure ratio (AVPR). Compared to the smooth curve generated during aortic stenosis (FIG. 19), the analysis of obstructive hypertrophic cardiomyopathy shows a dip in the curve occurring around the 50th sample and dynamic obstruction with varying degrees of obstruction observed during different cardiac cycles. This graphical analysis may support the finding of two sources of obstruction: the first is due to asymmetric septal hypertrophy occurring at the 30th sample, when the mean AVPR is higher (and more consistent), while the second is due to systolic anterior motion of the mitral valve leaflets, when the mean AVPR is lower (and less consistent).

[0129] FIG. 20 illustrates an arterial-ventricular pressure ratio index generated by the disclosed subject matter. The disclosed subject matter quantifies and facilitates characterization of the arterial-ventricular pressure ratio (AVPR), which is generated from simultaneous measurements of arterial and ventricular pressures. A graphical display visualizes overlapping cardiac cycles and calculated AVPR values (column 1) to assess various pathologies, such as aortic stenosis (column 1), hypertrophic obstructive cardiomyopathy (HOCM; column 2), and pulmonary stenosis (column 3). The stability or instability of AVPR over multiple cardiac cycles can be displayed graphically (row 2), altered by various provocative procedures / drugs, and tracked over time. Different possible measures include, but are not limited to, mean AVPR (e.g., minimum, maximum, mean, range, standard deviation over multiple beats), minimum AVPR over multiple beats, and timing of minimum AVPR within systole (table). Calculation of Tau (e.g., from Equations D1-4) can be performed using AVPR. Note that these analyses are performed from either left heart (aorta and left ventricle, Examples 1 and 2) or right heart (pulmonary artery and right ventricle, Example 3) pressure measurements.

[0130] FIG. 21 shows a novel comparison of multiple pressure loops generated by the disclosed subject matter. Multiple cardiac cycles are analyzed to generate ventricular pressure loops (2110A-B) and arterial pressure loops (2120A-B). Comparing the ventricular and arterial pressure loops between the left chart (example of aortic stenosis) and the right chart (example of hypertrophic obstructive cardiomyopathy) reveals significant differences in loop size and shape compared to other characteristics. In some instances, AVPR data can also be combined with pressure loop data. The combination of AVPR and pressure loop data can be displayed together in an electronic report (e.g., as a visual object in a graphical user interface displayable on a computer screen). The electronic report consists of a three-dimensional (3D) plot that includes both pressure loop and AVPR data.

[0131] Partial Resistance Analysis A method for determining partial resistance is disclosed by: a) determining a segmental gradient associated with a portion of the heart; (b) determining a total gradient associated with the portion of the heart; and (c) dividing the segmental gradient by the total gradient.

[0132] Quantification of the resistance to blood flow between upstream arteries and downstream veins is routinely performed as part of invasive assessments of cardiovascular hemodynamics. Pulmonary conditions leading to pulmonary vascular disease are reflected by abnormally elevated pulmonary vascular resistance (PVR), while systemic conditions leading to systemic vascular disease within the body are reflected by abnormally elevated or decreased systemic vascular resistance (SVR).

[0133] Assessment of pulmonary disease and PVR is commonly performed by right heart catheterization (RHC). RHC allows for the measurement of cardiac output, central venous pressure (CVP), right ventricular pressure, pulmonary artery pressure (PAP), and pulmonary capillary wedge pressure (PCWP). PCWP involves placing a small balloon "wedge" into a pulmonary artery branch, thereby occluding blood flow and allowing measurement of pressure distal to the obstruction. A secondary calculation of PVR is routinely performed from these RHC measurements (Equation E1). PVR is measured in Wood's units, which can be converted to dyne-seconds / cm5 by multiplying by 80, providing an absolute (rather than relative) measure of pulmonary blood flow resistance. An existing variation of PVR is the PVR index, which normalizes the PVR value according to the patient's body surface area (Equation E2). Conditions associated with abnormally elevated PVR include emphysema, pulmonary fibrosis, and pulmonary thromboembolism.

[0134] Assessment of systemic diseases affecting arterial blood pressure and SVR is commonly performed by combining some of the RHC measurements (CVP and cardiac output) with an additional measurement of mean arterial pressure (Equation E3). SVR is also measured in Wood's units, converted to dyne-seconds / cm5, and normalized to the patient's body surface area (Equation E4). Decreased SVR is associated with conditions such as infection and vascular pain, while elevated SVR is associated with conditions such as hypertension, hypovolemia, and congestive heart failure. Related equations:

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[0135] To estimate PVR or SVR, Pa measurements are obtained from the proximal pulmonary artery or aorta, while Pd measurements are obtained from the pulmonary vein (PCWP) or systemic vein (CVP), respectively. Equations E7 and E8 show specific examples of the calculation of pulmonary and systemic partial resistances.

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[0136] The disclosed subject matter performs an analysis of blood flow obstruction between an upstream artery and a downstream vein, which is uniquely different from PVR or SVR. According to Equations E1-E4, the pressure gradient present within a vascular segment is directly proportional to the vascular resistance present, but gradients of different amplitudes are typically normalized to cardiac output and BSA to facilitate cross-comparison. The disclosed subject matter uniquely calculates "partial resistance," which is 1) a measure of resistance through a particular portion / segment of the cardiovascular flow path and 2) normalized by the total resistance through the entire circulation. This calculation is derived by relating the ratio of segmental resistance and total resistance to the ratio of pressure gradients based on Equations E1 and E3 (Equation E5). The calculation of "partial resistance" is therefore the ratio of a segmental pressure gradient and its corresponding total pressure gradient (Equation E6). In doing so, the disclosed subject matter normalizes the pressure gradient to other high-fidelity pressure measurements rather than other potentially problematic measurements (cardiac output and body surface area). The partial resistance calculation of the disclosed subject matter 1) provides a surrogate measurement for traditional PVR and SVR (Equations E7 and E8), respectively; 2) quantifies PVR and SVR as relative, unitless measurements; and 3) facilitates comparison of values across different cardiac output states, body sizes, and individuals. Calculation of such partial resistance measurements has not previously been described or commercially available. In addition to estimating PVR and SVR, the analysis of the disclosed subject matter can also be used to calculate the partial resistance of other hemodynamic obstructions, such as those listed below.

[0137] Partial resistance between the right and left ventricles: Partial resistance due to pulmonary valve stenosis: Segmental gradient = systolic right ventricular pressure minus systolic pulmonary artery pressure Total gradient = systolic right ventricular pressure minus zero Partial resistance due to pulmonary artery stenosis (chronic pulmonary thromboembolism, etc.) Segment gradient = mean pulmonary artery pressure (mPAP) proximal to the thromboembolism minus mPAP distal to the thromboembolism Total gradient = mPAP proximal to thromboembolism minus zero Partial resistance due to mitral valve stenosis between the left atrium and left ventricle Segmental gradient = diastolic PCWP - diastolic left ventricular pressure Total gradient = Diastolic PAP minus diastolic left ventricular pressure Partial resistance due to blood pooling in the left atrium and left ventricle (e.g., congestive heart failure due to left ventricular dysfunction) Segment gradient = PCWP-0 Total gradient = mPAP minus zero

[0138] Partial resistance between the left and right ventricles: Partial resistance due to aortic stenosis: Segmental gradient = systolic left ventricular pressure minus systolic aortic pressure Total gradient = systolic left ventricular pressure minus zero Partial resistance due to hypertrophic obstructive cardiomyopathy: Segmental gradient = systolic left ventricular pressure proximal to the obstruction minus systolic left ventricular outflow tract pressure distal to the obstruction Total gradient = systolic left ventricular pressure proximal to the obstruction minus zero Partial resistance due to arterial stenosis (e.g., coronary artery disease, peripheral artery disease, aortic infarction) Segment gradient = mean proximal arterial pressure minus mean distal arterial pressure Total gradient = mean proximal arterial pressure minus zero Partial resistance due to systemic venous stenosis (e.g., thrombus in the inferior vena cava filtering function) Segment gradient = mean distal venous pressure minus mean proximal venous pressure Total gradient = mean arterial pressure minus zero Partial resistance due to tricuspid valve stenosis Segmental gradient = Diastolic CVP minus diastolic right ventricular pressure Total gradient = diastolic arterial pressure minus diastolic right ventricular pressure Partial resistance due to blood pooling in the right atrium and right ventricle (e.g., congestive heart failure due to right ventricular dysfunction) Segment gradient = CVP-0 Total gradient = MAP-0

[0139] Fractional Resistance in Special Situations: The disclosed subject matter can calculate fractional resistance values under special circumstances. One example is quantifying the fractional resistance of the mitral valve during left ventricular contraction; with normal valve function, this value should be close to 1, and with severe mitral regurgitation, it should be close to zero. A similar example is quantifying the fractional resistance of the tricuspid valve during right ventricular contraction; with normal valve function, this value should be close to 1, and with severe tricuspid regurgitation, it should be close to zero. Fractional resistance in these situations can be calculated from average or peak input power and reported as peak or average output power. A baseline left atrial pressure can be established as the level at which ventricular and atrial pressures are equal during early systole, or some other baseline level. Partial resistance due to mitral regurgitation Segmental gradient = systolic left ventricular pressure minus non-baseline systolic left atrial pressure Total gradient = systolic left ventricular pressure minus baseline left atrial pressure Partial resistance due to tricuspid valve insufficiency Segmental gradient = systolic right ventricular pressure minus non-baseline right atrial pressure Total gradient = systolic right ventricular pressure minus baseline right atrial pressure

[0140] It is expected that calculation of partial resistance will be more reliable when 1) there are fewer simultaneous sources of partial resistance and 2) there is no or minimal intracardiac shunting. When multiple sources of partial resistance overlap and there is significant intracardiac shunting, specialized calculations beyond the scope of this disclosure may be required.

[0141] Existing products that measure coronary "fractional flow reserve" (FFR) share common underlying principles with the disclosed subject matter but do not perform the same analysis for several important reasons. FFR is the ratio of achievable maximum blood flow to theoretical maximum blood flow through an epicardial coronary artery. Using pressure measurements as a surrogate for blood flow, FFR is calculated as the ratio of distal pressure divided by the aortic pressure achieved during hyperemia (Pd / Pa), with values <0.75-0.80 indicating significantly reduced blood flow through the examined vessel segment. Resting Pd / Pa values, without inducing hyperemic blood flow, have also been used for the same purpose. Pd / Pa values are unitless and conceptually represent the fraction of theoretical maximum blood flow that occurs in the presence of coronary artery stenosis. If the coronary artery stenosis is relieved, the FFR theoretically improves to a theoretical maximum of 1.0. The values used to calculate FFR, or resting Pd / Pa, are not identical to the values used by the disclosed subject matter to calculate fractional resistance.

[0142] Conceptually, CFR represents the factor by which coronary blood flow increases during hyperemia relative to rest. While 1-Pd / Pa can be rearranged to reflect the right-hand side of Equation E6 as (Pa-Pd) / Pa, existing methods must also generate a ratio of the (Pa-Pd) / Pa values occurring during hyperemia and rest to generate a measure of CFR. The individual numerator and denominator elements of this stated ratio 1) are inherent measures of resistance rather than flow, 2) are additive when acting as a series of resistance values, 3) are applicable within all cardiovascular territories (not just the coronary arteries), and 4) are subject to further adjustment when measured in the presence of ventricle-to-arterial obstruction (e.g., pulmonary stenosis, aortic stenosis, HOCM), which makes it uniquely distinct from existing techniques and forms the basis of the disclosed subject matter. Coronary artery resistance analysis

[0143] Disclosed is a method that includes: (a) determining fixed epicardial resistance; (b) determining fixed microvascular resistance; (c) determining adenosine-responsive microvascular resistance; (d) determining drug-responsive epicardial resistance; (e) determining drug-responsive microvascular resistance; and f) determining total resistance to blood flow at least in part by determining the sum of fixed epicardial resistance, fixed microvascular resistance, adenosine-responsive microvascular resistance, drug-responsive epicardial resistance, and drug-responsive microvascular resistance.

[0144] Resistance to blood flow through the entire coronary artery occurs in both the large-caliber proximal epicardial segments and the distal microvessels. High-quality measurements of flow resistance due to epicardial coronary artery disease (CAD) exist, but high-quality measurements of flow resistance due to microvascular CAD do not. This paper describes the measurement of flow resistance due to coronary artery disease in both epicardial and microvascular coronary artery segments.

[0145] Quantification of coronary blood flow reduction due to epicardial CAD is most accurately and precisely performed by measuring the proximal arterial pressure (Pa) and distal arterial pressure (Pd) at the lesion site during peak hyperemia and calculating the fractional flow reserve (FFR, Equation F1). FFR can also be approximated using the ratio of Pd / Pa at rest, non-hyperemic times, using data from the entire cardiac cycle (Equation F2), as well as the "waveless" period of diastole (Equation F3). Noninvasive methods for estimating FFR, such as those using cineangiography or coherence tomography, have also been developed.

[0146] The reduction in coronary blood flow due to microvascular CAD can be approximately quantified by calculating the ratio of hyperemic coronary blood flow to resting coronary blood flow, known as coronary flow reserve (CFR, Equation F4). Although CFR is commonly used to quantify the degree of microvascular CAD, its value is also affected by epicardial CAD, making CFR a nonspecific measure of microvascular CAD. In the presence of severe epicardial CAD, abnormally reduced CFR due to concomitant microvascular CAD is difficult to accurately identify. All techniques for measuring CFR suffer from this limitation. These CFR techniques include coronary flow velocimetry, indicator thermodilution, noninvasive coronary perfusion imaging, and pressure-derived CFR (CFRp).

[0147] Specific measures of microvascular CAD have previously been developed based on simultaneous measurements of FFR and CFR. The first measure is hyperemic microvascular resistance, calculated as distal epicardial pressure (Pd) divided by the mean peak blood flow velocity measured during hyperemia (Equation F6). The second measure is an index of microvascular resistance, calculated as Pd multiplied by the mean temperature curve transit time measured during hyperemia (Equation F7). The third measure is myocardial flow reserve (MFR), calculated as the ratio of hyperemic myocardial blood flow divided by resting myocardial blood flow, and quantified by noninvasive positron emission tomography or cardiac magnetic resonance imaging.

[0148] The analysis of coronary artery resistance described in this section quantifies both epicardial and microvascular coronary resistance in a manner distinct from all previously described techniques by: 1) establishing a conceptual framework for identifying multiple sources of coronary artery resistance; 2) measuring the relative amounts of all sources of coronary artery resistance; 3) determining the relative amount of coronary artery resistance due to blood vessels that can dilate in response to the pharmacological agent adenosine; 4) determining the relative amount of coronary artery resistance due to blood vessels that can contract or dilate in response to non-adenosine pharmacological agents (i.e., nitroglycerin, calcium channel blockers, and other types of drugs that affect coronary artery resistance); 5) using both / only FFR and CFR measurements for this type of analysis; and 6) uniquely utilizing pressure-derived CFR to quantify both epicardial and microvascular coronary resistance. While other commercially available devices and techniques can measure both FFR and CFR, each of these aspects of the analysis of the disclosed subject matter distinguishes it from existing methods of quantifying microvascular coronary resistance.

[0149] Related equations:

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[0150] The disclosed subject matter first establishes a conceptual framework for identifying multiple resistance sources, and then performs an analysis of coronary artery resistance by measuring the relative abundance of all coronary artery resistance sources, thereby enabling the disclosed subject matter, and users, to assess coronary artery response to vasodilatory pharmacological agents such as adenosine, nitroglycerin, and calcium channel blockers.

[0151] The conceptual framework of coronary artery resistance in the disclosed subject matter is that total resistance is the sum of several different resistance subtypes. This concept is based on the inverse relationship between flow and resistance in the cardiovascular system (Equations B1 and F8). At baseline resting conditions, five resistance subtypes are identified: 1) Fixed epicardial resistance (FER) 2) Fixed microvascular resistance (FMR) 3) "Adenosine-Responsive" Microvascular Resistance (ARMR) 4) "Drug-Responsive" Epicardial Resistance (MER) 5) "Pharmacotherapy-responsive" microvascular resistance (MMR)

[0152] Epicardial resistance is defined as resistance occurring proximal to the intracoronary pressure sensor used in measuring FFR / CFRp. Microvascular resistance is defined as resistance occurring distal to the intracoronary pressure sensor. Placing a pressure sensor within a distal epicardial artery segment is expected to be beneficial for analysis of the disclosed subject matter. Fixed resistance refers to the resistance remaining after administration of a vasodilatory pharmacological agent. Adenosine-responsive resistance refers to the resistance directly affected after in vivo administration of adenosine to induce maximal coronary flow enhancement. Drug-responsive resistance refers to the resistance directed after administration of a vasodilator other than adenosine. While this is a general term that encompasses the use of various coronary vasodilators, it is recognized that the most commonly used agent during FFR / CFRp measurements is nitroglycerin.

[0153] Nitroglycerin is routinely administered prior to FFR / CFR measurements to dilate epicardial arteries and provide a baseline hemodynamic profile for comparing different measurements. After administration of a coronary vasodilator such as nitroglycerin, the MER and MMR are assumed to be zero, and the remaining sources of coronary resistance are simplified to include three resistance subtypes: 1. Fixed epicardial resistance (FER) 2. Fixed microvascular resistance (FMR) 3. "Adenosine-responsive" microvascular resistance (ARMR). By measuring FFR and CFRp before and after administering a vasodilator, all resistance subtypes can be calculated by the disclosed subject matter. By measuring FFR and CFRp only after administering a vasodilator, only the three resistance subtypes can be identified. By measuring FFR and CFRp without administering a vasodilator, the three resistance subtypes can be estimated, while the impact of drug-responsive resistance remains unclear.

[0154] In the simplest clinical scenario, a vasodilator is administered, and then FFR and CFRp are measured. During adenosine-induced hyperemia, ARMR temporarily decreases to zero, and total fixed coronary resistance (TFR) is equal to the sum of FER and FMR (Equation F9). During resting blood flow, when ARMR returns to baseline levels, total coronary resistance (TCR) is equal to the sum of FER, FRM, and ARMR (Equation F10). The ratio of TCR / TFR is equal to CFR (Equation F11). By defining TFR as a relative value of 1, Equation F11 can be simplified to define ARMR (Equation F12). FMR and FER are equal to FFR and the complement of FFR, respectively (Equations F13 and F14). From here, the first three subtypes of coronary artery resistance are quantified by the disclosed subject matter (Equations F12-F14), and their sum equals the value of CFR (Equation F15). In the following clinical scenario, FFR and CFRp are measured twice: once before and once after administration of a vasodilator. During hyperemic flow before the vasodilator is administered, ARMR temporarily decreases to zero, and TFR equals the sum of FER, FMR, MER, and MMR. At baseline blood flow, TCR equals the sum of FER, FMR, MER, MMR, and ARMR (Equation F16). Because TCR is defined as a constant value but also equals the CFR value, which can vary between measurements (Equations F10 and F15), a correction is used to keep TCR constant. Using known values of FFR and CFR measured both before and after drug administration, the relative values of MER, MMR, and their sum can be analyzed (Equations F17–F19). The effects of various drugs on coronary resistance, including nitroglycerin / nitrates, calcium channel blockers, beta-blockers, and other classes of drugs, can be assessed in this manner. Based on the conceptual framework of the disclosed subject matter and the method for determining total coronary artery resistance and its components, various specific and unique measurements of microvascular resistance can be generated (Table, not exhaustive). Many different combinations of resistance measurements may be reported by the disclosed subject matter, and it is unclear at this time which of these unique measurements of microvascular resistance generated by the disclosed subject matter will be most useful for clinical or research purposes. Three unique measurements reported by the disclosed subject matter that merit consideration for patent protection include: 1) ARMR / TFR, 2) TFR / CFR, and 3) total drug responsive resistance.

[0155] First, ARMR / TFR indicates the amount of adenosine-responsive resistance relative to the total fixed resistance present. A high ARMR / TFR indicates good vascular health due to low epicardial and microvascular CAD burden and a high "ARMR reserve," while a zero ARMR / TFR indicates poor vascular health due to high CAD burden and zero ARMR reserve. While the numerical value of ARMR / TFR simplifies to CFR-1, the conceptual framework of the disclosed subject matter provides significant implications for this basic yet novel calculation.

[0156] TFR / CFR then represents the ratio of fixed coronary resistance to total coronary resistance at rest. A TFR / CFR close to 0 indicates a relatively low burden of epicardial and microvascular CAD, while a TFR / CFR of 1 indicates that all coronary resistance present at rest is due to the influence of CAD. In general, this measure can be converted / used to report the "CAD burden" within an artery. While the numerical value of TFR / CFR is simplified to 1 / CFR, the conceptual framework of the disclosed subject matter provides significant implications for this basic yet novel calculation.

[0157] Third, total drug-responsive resistance and its epicardial / microvascular subpart are completely unique metrics reported by the disclosed subject matter. Positive drug-responsive resistance values indicate the amount of resistance relieved by drug administration (via vasodilatory effects), while negative drug-responsive resistance values indicate the resistance imparted by drug administration (via vasoconstrictor effects). The total drug-responsive resistance value indicates the overall effect of a drug on coronary artery resistance, while its subpart values indicate the regionality and symmetry of this effect. This measure could potentially be used to 1) identify unexpected vasoconstrictor effects of drug administration that may indicate vascular dysfunction, 2) test and compare the effects of existing vasodilators on specific coronary arteries and individuals for the purpose of adjusting drug therapy, and 3) test and characterize unknown effects of drugs on coronary artery resistance.

[0158] While the foregoing discussion discusses the disclosed subject matter's method for analyzing coronary artery resistance, the means of analysis are also worthy of discussion and consideration for patent protection. All of the described calculations can be performed in any manner using measurements of FFR and CFR. Existing methods can measure FFR alone, CFR alone, or both FFR and CFR simultaneously. However, regardless of the means for measuring FFR and CFR, subsequent use of the described method to analyze coronary artery resistance is eligible for patent protection. Furthermore, while the disclosed subject matter uses pressure-derived CFR as a means for calculating CFRp, the uniqueness / patentability of the disclosed subject matter's particular means for measuring CFRp should not affect other concerns regarding the uniqueness / patentability of the disclosed subject matter's subsequent coronary artery resistance analysis.

[0159] Diastolic blood pressure-derived CFR Disclosed is a method including: (a) matching diastolic ventricular pressure measurements and diastolic arterial pressure measurements from the same pressure source using a pressure wire; (b) recording real-time telemetry data including measurements of hyperemia and baseline coronary blood flow; (c) automatically identifying maximum and minimum diastolic l-(ventricular pressure / arterial pressure) values in the real-time telemetry data; and (d) calculating, at least in part, diastolic pressure-derived coronary flow resistance from the maximum and minimum diastolic l-(ventricular pressure / arterial pressure) values.

[0160] A system is disclosed for analyzing blood flow characteristics within arteries supplying the myocardium (coronary arteries). While existing devices capable of measuring clinically relevant coronary blood flow, such as fractional flow reserve (FFR) and coronary flow reserve (CFR), are commercially available, they suffer from significant limitations. While FFR is highly useful for assessing the extent of flow-limiting disease within the large coronary arteries (epicardium), it is unable to assess the status of the distal, smaller blood vessels (microvasculature). Conversely, CFR is used to assess microvascular function but does not quantify epicardial vascular disease. While FFR is based on highly reproducible invasive blood pressure measurements, CFR calculations using blood flow velocity and thermodilution flow rates are limited by poor reproducibility and accuracy. The disclosed system is designed to overcome these limitations by providing a single platform for collecting and analyzing real-time hemodynamic data and uniquely reporting simultaneous measurements of FFR and CFR calculated solely from invasive blood pressure measurements.

[0161] FFR is a measurement obtained by comparing simultaneously obtained invasive blood pressure measurements proximal (upstream) and distal (downstream) to a coronary artery disease site during maximal coronary blood flow (hyperemia). Coronary artery disease (CAD) causes resistance to blood flow, thereby increasing distal blood flow velocity and decreasing distal blood pressure, increasing the difference between proximal and distal pressure measurements. This phenomenon is consistent with Poiseuille's law, which describes the pressure loss of an incompressible fluid flowing through a long, cylindrical aortic body of constant cross-section. FFR is calculated as the ratio of distal pressure (Pd) to proximal aortic pressure (Pa) during hyperemia and conceptually represents the fraction of blood flow achieved in the presence of a coronary artery occlusion compared to the absence of occlusion. Thus, an FFR value of 1.0 indicates unimpeded blood flow through the examined vessel segment.

[0162] In clinical practice, FFR is the gold standard for identifying critical coronary artery blockages and guiding treatment in patients with CAD. Randomized clinical trials have demonstrated superior patient outcomes with FFR guidance compared with coronary angiography alone and superior coronary revascularization compared with medical therapy alone in patients with obstructive coronary artery disease defined by an FFR of ≤0.80 (1-2). Validated variants of FFR include: 1) the instantaneous waveless ratio (iFR), which reports the resting Pd / Pa ratio during ventricular relaxation (diastole), when coronary blood flow primarily occurs, and 2) the average resting Pd / Pa measured randomly over the entire cardiac cycle. Both measurements are recorded without inducing hyperemia. Reported FFR, iFR, and Pd / Pa are discrete decimal values less than or equal to 1.0.

[0163] A similar measurement to FFR is CFR. While FFR compares measurements from two different pressure source locations under the same coronary flow state, CFR compares measurements from a single pressure source location under two different coronary flow states (hyperemic flow and baseline flow). CFR measurements indicate the ability of coronary microvasculature to maximally dilate and increase coronary blood flow. Abnormal CFR identifies pathological microvasculature, warranting medical treatment and / or compensatory dilation if an upstream blockage is present. Currently, there are two methods for measuring CFR: 1) changes in blood flow velocity using Doppler ultrasound and 2) changes in blood flow volume using thermodilution. A "normal" Doppler-based CFR value of ≥ 2.0 has been shown to have high predictive accuracy with noninvasive nuclear myocardial perfusion imaging in individuals without angiographic evidence of CAD (3). Patients with discordant CFR and FFR measurements, especially those with an abnormal CFR <2.0 and a normal FFR >0.80, have a higher risk of adverse cardiac events (4), highlighting the independence of CFR and FFR in determining CAD status.

[0164] Limitations of existing equipment Modern guidewires and microcatheters are available from multiple manufacturers for measuring FFR and Pd / Pa ratios in clinical settings. These pressure wires are commonly used in modern cardiovascular procedures, first to probe diseased segments of epicardial coronary arteries and second to deliver therapeutic balloons or stent catheters across obstructive lesions during subsequent percutaneous coronary intervention (PCI). Despite their widespread use and practical application in routine PCI, none of these pressure-only devices provide a measurement of CFR.

[0165] Compared to traditional "workhorse" or pressure-only wires, Doppler-based wires are relatively cumbersome and difficult to maneuver within the coronary anatomy, making them impractical for routine hemodynamic and PCl applications, despite their approval for these uses.

[0166] In addition to these practical considerations, further limitations arise during conventional CFR measurements. The Doppler signal at any point in the cardiac cycle can be overestimated due to signal noise and artifacts, or underestimated due to wire angle away from the flow direction and / or suboptimal wire position relative to the vessel wall. Furthermore, an optimal signal obtained with baseline flow can deteriorate during hyperemia, and vice versa. Both of these suboptimal Doppler measurements result in CFR calculation errors. After injecting cold saline through a temperature-based device, thermodilution curves are generated over multiple heartbeats and used to derive coronary blood flow. Thermodilution CFR measurements are less reproducible, user-dependent, and time-consuming. This procedure also requires intravenous injection of adenosine (rather than intracoronary injection of an adenosine bolus) to induce sustained hyperemia for several minutes at a time, which not only limits the acquisition of multiple temperature curves during both hyperemic and baseline flow conditions, but can also prolong the diagnostic procedure. DESCRIPTION OF THE DISCLOSED SUBJECT MATTERS

[0167] The disclosed system features both 1) a physical device that receives real-time patient telemetry data and user input to guide data recording, and 2) a data analysis algorithm that creates a unique measure of diastolic pressure-derived CFR (CFRp) that is reported to the user (Figure 22).

[0168] The physical device of the disclosed system receives real-time, simultaneous patient telemetry data, including Pa, Pd, and ECG waveforms. Pressure data are typically obtained from commercially available devices placed in the aorta (Pa) and distal coronary arteries (Pd) during invasive cardiovascular procedures. A fluid-filled guide catheter placed at the coronary artery ostium provides Pa measurements, while a second manometer (small wire or integrated into the catheter) is advanced to the distal coronary artery segment (Figure 2). These data are routinely measured every 4–5 milliseconds by commercially available equipment. The disclosed system is guided by user input to record and automatically process the data described below. The device exports the analyzed data to a display monitor for user review.

[0169] The data analysis functions of the disclosed system include: 1) baseline matching of diastolic Pa and Pd measurements from the same pressure source, 2) beat-by-beat measurement of diastolic 1-Pd / Pa during hyperemia induction, 3) measurement of diastolic 1-Pd / Pa at baseline coronary flow, and 4) calculation of diastolic CFRp. Each step is described in more detail below.

[0170] Disclosed Algorithm Step 1: Pressure Matching Baseline matching of Pa and Pd measurements obtained from the same sampling location (also called "equalization" or "normalization" in commercially available products) is routinely performed immediately prior to performing a standard FFR measurement. This is accomplished by placing a pressure wire / catheter at the tip of the guide catheter and comparing measurements obtained from the same pressure source (Figure 24). Commercially available devices compare average Pa and Pd pressures (measured indiscriminately over various portions of the cardiac cycle) and correct the average value of one waveform to match the average value of the other waveform (Figure 25). The disclosed system specifically performs appropriate waveform correction to ensure diastolic pressure matching, rather than indiscriminately over an unspecified portion of the cardiac cycle. The disclosed system's method also records data during this equalization process, allowing for retrospective data correction.

[0171] Disclosed Algorithm Steps 2 and 3: Measurement of pulsatile diastolic 1-Pd / Pa during hyperemia and baseline coronary flow Following step 1, the pressure wire / catheter is advanced by the user into the distal portion of the coronary anatomy (FIG. 23). If an anatomical resistance to blood flow exists between the two pressure sources (e.g., CAD), a decrease in Pd is observed compared to the reference Pa (FIG. 26). The disclosed subject matter then calculates a specific value, "1-Pd / Pa," during diastole for each heartbeat. This value conceptually represents the proportion of resistance due to the epicardial vascular anatomy between the two pressure sources compared to the total coronary resistance of the entire vessel examined (total epicardial vessels + distal microvessels). Minimum diastolic 1-Pd / Pa is observed during baseline coronary flow (when microvascular resistance is greatest), and maximum diastolic 1-Pd / Pa is observed during hyperemia (when microvascular resistance is lowest due to adenosine-induced vasodilation) (FIG. 27). The ratio of diastolic 1-Pd / Pa occurring during hyperemia to baseline yields a value of ≈1.0, which is the inherent calculated value of diastolic CFRp reported by the disclosed system.

[0172] The disclosed process for calculating diastolic CFRp values begins by accepting user input to start and stop recording real-time telemetry data, including both baseline and hyperemic coronary blood flow (e.g., data after an intracoronary bolus or intravenous infusion of adenosine). From this recording, the disclosed system automatically calculates an aggregated diastolic 1-Pd / Pa (e.g., mean, median, or other) value for each heartbeat. Beats with significant artifact are automatically excluded from analysis. Maximum and minimum diastolic 1-Pd / Pa values are automatically identified within the recording.

[0173] Disclosed System Algorithm Step 4: Calculate Diastolic CFRp Once the disclosed system identifies the maximum and minimum diastolic 1-Pd / Pa values, it automatically calculates the maximum / minimum ratio. The resulting value, diastolic CFRp, is then reported by the disclosed subject matter to the user based on the following formula:

number

number

[0174] Several commercially available devices routinely calculate and report Pd / Pa averaged indiscriminately over various portions of the cardiac cycle. For example, a device can identify pressures obtained during diastole and report an iFR measurement, which is a commercially available, unique measurement obtained without specifically inducing a hypervascular flow state. The purpose of this iFR measurement is to serve as a surrogate for FFR, specifically without inducing a hypervascular flow state.

[0175] For example, the present system performs analysis in comparison with the commercially available Pd / Pa, iFR, and FFR measurements described above. First, the disclosed subject matter measures a unique value for each heartbeat, 1-Pd / Pa (Equation 1), which other commercially available devices do not measure. This equation can be rearranged as (Pa-Pd) / Pa and represents the instantaneous pressure gradient across the examined coronary artery segment, which is then normalized to the Pa pressure at the time of the gradient measurement (Equation 2). Commercially available devices do not report 1-Pd / Pa or its equivalent (PaPd) / Pa) measurement.

[0176] Second, the system calculates its own intrinsic measurement during diastole. The iFR measurement is calculated using a fundamentally different formula (diastolic Pd / Pa at baseline flow conditions only) compared to CFRp (microvascular function) and assesses a different hemodynamic characteristic (epicardial vascular occlusion).

[0177] Third, the system performs repeated measurements over multiple heartbeats to identify 1-Pd / Pa values during both baseline flow and hyperemia. Existing pressure-based measurements are acquired either during hyperemia or baseline flow alone, but none utilize both flow states in their calculations. Notably, iFR measurements differ from CFRp not only in their underlying equations but also in their intentional use of data obtained only at baseline coronary flow states.

[0178] Fourth, the diastolic CFRp measurement in this system is similar to, but not identical to, conventional CFR measurements. The key difference is in the data collected during diastole (disclosed subject matter) versus the data collected throughout the cardiac cycle (conventional CFR). Therefore, the normal range reported in conventional CFR 2.0 does not apply to CFRp. The normal range for diastolic CFRp in individuals without CAD must be independently determined. Comparison of the disclosed system against other proposed versions of the CFRp

[0179] Previous attempts to calculate and / or validate CFRp have been hampered by poor correlation with established traditional CFR measurements (Doppler or thermodilution) or adverse cardiac events. The following calculation has been previously described:

number

number

number

[0180] Fundamental differences are observed between these versions of CFRp (Equations 3-5) and that of the disclosed subject matter (Equations 1-2). Equations 3 and 4, in particular, use a square root function to convert the ratio of simple pressure gradients. Equation 5 uses the ratio of simple pressure gradients only. Equations 3 and 5 use average data over the entire cardiac cycle, while Equation 4 uses a single maximum gradient value that may or may not occur in diastole. None of the equations use either 1) the ratio of 1-Pd / Pa values (or the rearranged version [Pd-Pa] / Pa), or 2) the dedicated use of aggregated diastolic pressure data, which is one of the key characterizing elements of the disclosed subject matter.

[0181] These distinctive features of the disclosed subject matter are intentional and crucial for generating clinically useful analogs of conventional CFR. First, the beat-to-beat pressure-derived 1-Pd / Pa and high-quality Doppler-based blood flow velocity traces after adenosine-induced hyperemia are remarkably similar during diastole and dissimilar during systole (FIG. 7). The present invention exploits this similarity by including diastole and excluding systole in data selection and calculations (contrary to Equations 3 and 5). Trace variability during diastole is normalized by aggregating data during diastole (contrary to the single maximum value used in Equation 4). Second, the beat-to-beat CFR calculations derived from 1-Pd / Pa and Doppler velocity ratio have a very strong positive linear relationship (FIG. 29), rather than a square root (contrary to Equations 3 and 4).

[0182] Clinical information The disclosed subject matter's ability to calculate diastolic pressure-derived CFR allows for the simultaneous quantification of disease affecting both large-caliber coronary arteries and their distal microvasculature with a single pressure wire / catheter. This dual assessment is not possible with current pressure-only devices. The disclosed subject matter aims to transform existing "golf-standard" FFR technology, transforming this technology, already highly recommended in clinical guidelines for optimal patient management (10), into a tool that provides twice the diagnostic information currently available. Its pressure-based design also overcomes the inherent limitations of traditional CFR, making CFRp measurements as rapid, accurate, and reproducible as FFR. As a result, patients with significant microvascular disease who would otherwise have "normal" FFR results could be identified, potentially delaying appropriate treatment or masking the risk of adverse clinical outcomes. The disclosed system also eliminates the need for ultrasound Doppler or thermodilution guidewires, which are suboptimal for routine clinical use, particularly when transitioning from abnormal diagnostic studies to PC1. Overall, this disclosed subject matter expands the diagnostic utility of all FFR procedures performed in cardiac catheterization laboratories worldwide and provides a useful new measure of microvascular disease.

[0183] FIG. 22 illustrates the general features of the disclosed subject matter and interfaces within a cardiac catheterization laboratory.

[0184] 23 shows a typical setup for simultaneously collecting aortic blood pressure (Pa) and distal coronary artery pressure (Pd) during conventional FFR measurement, as well as the use of the disclosed subject matter for CFRp measurement (left panel). An angiogram shows a guide catheter inserted into the left coronary artery and a pressure wire positioned in the distal segment of the left anterior descending artery (right panel).

[0185] Figure 24 shows a typical guide catheter and pressure wire placement for simultaneous measurement of aortic pressure (Pa) and distal pressure (Pd) from the same pressure source (tip of the guide catheter) during pressure "equalization" (left). The angiogram shows the guide catheter inserted into the left coronary artery and the pressure wire sensor positioned at the catheter tip during pressure "equalization" (right).

[0186] Figure 25 shows simultaneous measurements of Pa and Pd during one heartbeat immediately after commercial pressure "equalization." The mean pressure calculated over the entire heartbeat is identical (87 mmHg), while the peak systolic pressure and end-diastolic pressure (from data sample 110 to the end) are not identical.

[0187] FIG. 26 shows that when an anatomical resistance exists between the two pressure sources, the distal intracoronary pressure measurement (Pd) decreases relative to the guide catheter reference measurement (Pa).

[0188] Figure 27 shows simultaneous beat-by-beat calculations of mean diastolic 1-Pd / Pa and mean diastolic velocity after intracoronary bolus administration of adenosine to induce hyperemia. The peak value of each occurs at the time of hyperemia, and the minimum value occurs when coronary blood flow returns to baseline.

[0189] Figure 28 shows an exemplary beat-by-beat comparison of pressure-derived 1-Pd / Pa (top graph) and Doppler-derived blood flow velocity (bottom graph) after adenosine-induced hyperemia and return to baseline coronary flow. As peak hyperemia values (2810) gradually decrease to baseline (2820), the traces are most similar in the diastolic phase of each beat (approximately data sample number 100 to the end). Significant differences are observed in early and late systole (peaks and troughs in the top graph).

[0190] Figure 29 shows an exemplary beat-by-beat comparison of diastolic pressure-derived CFR (CFRp) versus diastolic Doppler-based CFR after adenosine-induced hyperemia and return to baseline coronary flow. The diastolic CFR and CFRp data have a very strong positive linear correlation (R = 0.991, P < 0.001). The deviation of the slope of the relationship from 1.0 may be the result of systematic bias in the Doppler or pressure values.

[0191] 31 shows a block diagram illustrating a computing system 1500 consistent with embodiments of the present subject matter. For example, the computing system 1500 can be used to implement analysis of the pressure loop plot and / or any components therein.

[0192] As shown in FIG. 31 , computing system 1500 may include a processor 1510, a memory 1520, a storage device 1530, and an input / output device 1540. The processor 1510, the memory 1520, the storage device 1530, and the input / output device 1540 may be interconnected via a system bus 1550. The processor 1510 may process instructions for execution within the computing system 1500. Such executing instructions may implement, for example, one or more components of a model for analyzing a pressure loop plot. In some exemplary implementations, the processor 1510 may be a single-threaded processor. Alternatively, the processor 1510 may be a multi-threaded processor. The processor 1510 may process instructions stored in the memory 1520 and / or the storage device 1530 to display graphical information of a user interface provided via the input / output device 1540.

[0193] Memory 1520 is a computer-readable medium, such as a volatile or non-volatile medium, that stores information within computing system 500. Memory 1520 may store data structures, such as those representing a configuration object database. Storage device 1530 may provide persistent storage for computing device 1500. Storage device 1530 may be a floppy disk drive, a hard disk drive, an optical disk drive, a tape drive, a solid-state drive, and / or any other suitable persistent storage means. Input / output device 1540 provides input / output operations to computing system 1500. In some exemplary implementations, input / output device 1540 includes a keyboard and / or a pointing device. In various implementations, input / output device 1540 includes a display unit for displaying a graphical user interface.

[0194] According to some exemplary embodiments, input / output devices 1540 may provide input / output capabilities for network devices. For example, input / output devices 1540 may include an Ethernet port or other networking port for communicating with one or more wired and / or wireless networks (e.g., a local area network (LAN), a wide area network (WAN), the Internet).

[0195] In some exemplary implementations, computing system 1500 may be used to run a variety of interactive computer software applications that may be used to organize, analyze, and / or store various types of data, or may be used to run any type of software application.

[0196] One or more aspects or features of the subject matter described herein may be implemented in digital electronic circuitry, integrated circuits, specially designed ASICs, field programmable gate array (FPGA) computer hardware, firmware, software, and / or combinations thereof. These various aspects or features may include implementation in one or more computer programs executable and / or interpretable on a programmable system including at least one programmable processor, which may be special-purpose or general-purpose, coupled to receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device. The programmable system, or computing system, may include clients and servers. Clients and servers are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

[0197] These computer programs (also referred to as programs, software, software applications, or code) include machine instructions for a programmable processor and may be implemented in a high-level procedural and / or object-oriented programming language and / or assembly / machine language. As used herein, the term “machine-readable medium” refers to any computer program product, apparatus, and / or device (e.g., magnetic disk, optical disk, memory, programmable logic device (PLD)) used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and / or data to a programmable processor. A machine-readable medium may store such machine instructions non-transitoryly, such as, for example, a non-transitory solid-state memory, a magnetic hard drive, or a comparable storage medium. Alternatively, or additionally, a machine-readable medium may store such machine instructions in a transitory manner, such as, for example, a processor cache or other random access memory associated with one or more physical processor cores.

[0198] To provide for user interaction, one or more aspects or features of the subject matter described herein can be implemented on a computer having a display device, such as a cathode ray tube (CRT), liquid crystal display (LCD), or light-emitting diode (LED) monitor, for displaying information to a user, and a keyboard and pointing device, such as a mouse or trackball, by which the user can provide input to the computer. Other types of devices can also be used to provide for user interaction. For example, feedback provided to the user can be any form of sensory feedback, such as visual feedback, auditory feedback, tactile feedback, etc., and input from the user can be received in any form, such as acoustic input, voice input, tactile input, etc. Other possible input devices include other touch-sensitive devices, such as touchscreens, single- or multi-point resistive or capacitive trackpads, voice recognition hardware and software, optical scanners, optical pointers, digital image capture devices, and associated interpretation software.

[0199] Above and in the claims, phrases such as "at least one of" or "one or more of" may appear before a sequential listing of elements or characteristics. The term "and / or" may also appear within a list of two or more elements or characteristics. Such phrases are intended to refer to any listed element or characteristic individually, or any listed element or characteristic in combination with any other listed element or characteristic, unless otherwise implicitly or explicitly contradicted by the context in which they are used. For example, the phrases "at least one of A and B," "one or more of A and B," and "A and / or B" are intended to mean "A alone, B alone, or A and B together," respectively. A similar interpretation is also intended for lists containing more than two items. For example, "at least one of A, B, and C," "one or more of A, B, and C," and "A, B, and / or C" are each intended to mean "A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B, and C together." Use of the term "based on" above and in the claims is intended to mean "based at least in part on," allowing for unrecited features or elements.

[0200] The subject matter described herein may be embodied as a system, apparatus, method, and / or article, depending on the desired configuration. The embodiments set forth in the above description do not represent all implementations consistent with the subject matter described herein. Instead, they are merely some examples consistent with aspects related to the described subject matter. While several variations have been described in detail above, other modifications or additions are possible. In particular, additional features and / or variations may be provided in addition to those described herein. For example, the embodiments described above may be directed to various combinations and subcombinations of the disclosed features and / or combinations and subcombinations of certain additional features disclosed above. Furthermore, the logical flows illustrated in the accompanying drawings and / or described herein do not necessarily require the particular order shown or sequence to achieve desired results. For example, the logical flows may include different and / or additional operations than those illustrated without departing from the scope of the present disclosure. One or more operations in the logical flows may be repeated and / or omitted without departing from the scope of the present disclosure. Other implementations may be within the scope of the following claims.

Claims

1. 1. A method comprising: obtaining a series of time-series ventricular pressure measurements; determining a series of data points comprising a ventricular pressure time rate of change from said time series of ventricular pressure measurements; determining a representation of a relationship between the at least a series of data points and the time series of ventricular pressure measurements; and determining, at least in part, characteristics of blood flow within a chamber of the heart by processing the representation. method.

2. The method of claim 1 , wherein the time series of ventricular pressure measurements are collected using a device that is not inserted into the subject's body.

3. The method of claim 2 , wherein the device is a non-invasive ultrasound Doppler device, a magnetic resonance imaging device, and / or a heart sound intensity device.

4. The method of claim 1 , wherein the time-series ventricular pressure measurements are collected by an intracardiac device.

5. 5. The method of claim 4, wherein the intracardiac device is a pulmonary artery hemodynamic monitoring catheter or a left ventricular support device.

6. The method of claim 4 , wherein the time series of ventricular pressure measurements are collected at least in part by measuring chamber dimensions and ventricular blood pressure.

7. The method of claim 1 , wherein the relationship comprises a series of pairwise relationships, the pairwise relationships comprising data points of the series of data points and corresponding time-series ventricular pressure measurements.

8. 2. The method of claim 1, wherein the ventricular pressure time rate of change is a first derivative of the ventricular pressure with respect to time.

9. The method of claim 7 , wherein the representation comprises a plot relating to the relationship.

10. The method of claim 9 , wherein the plot is a pressure loop plot.

11. The method of claim 10 , wherein the blood flow characteristic is determined based at least in part on a loop cycle duration of the pressure loop plot.

12. The method of claim 10 , wherein the blood flow characteristics are determined based at least in part on boundaries of the pressure loop plot.

13. The method of claim 12 , wherein the border is a top border, a bottom border, a left border, or a right border.

14. The method of claim 9 , wherein the characteristics of the blood flow are determined based at least in part on visual characteristics associated with the plot.

15. The method of claim 14 , wherein the visual characteristic relates to a shape of a region of the plot or a size of at least one region of the plot.

16. The method of claim 15, wherein the visual characteristic is symmetry, smoothness, the presence of depressions, differences between two or more regions, or tangential slope.

17. The method of claim 15 , wherein the characteristic of the blood flow is determined at least in part by comparing the plot to a second plot.

18. The method of claim 1 , further comprising selecting a treatment regimen based at least in part on the characteristics of the blood flow.

19. 2. The method of claim 1, wherein the blood flow characteristic is ventricular power, ventricular resistance, or ventricular blood flow, elasticity, compliance, contractile stroke volume, or response to a changing factor.

20. 10. The method of claim 1, further comprising calculating a second series of data points comprising a time rate of change of acceleration of ventricular pressure.

21. 21. The method of claim 20, further comprising assessing a-wave pre-diastolic pressure at least in part using the second series of data points.

22. The method of claim 1 , wherein processing the representation comprises using a mathematical model.

23. 23. The method of claim 22, wherein the mathematical model is a statistical model or a machine learning model.

24. 24. The method of claim 23, wherein the machine learning model comprises a neural network.

25. 2. The method of claim 1, wherein a data point in the series of data points is determined by (a) determining a pressure difference by subtracting a first pressure value associated with a first time from a second pressure value associated with a second time, and (b) dividing the pressure difference by a time difference, the time difference comprising the difference between the second time and the first time.

26. 1. A system comprising: at least one processor; When executed by the at least one processor, obtaining a series of time-series ventricular pressure measurements; determining a series of data points comprising a ventricular pressure time rate of change from said time series of ventricular pressure measurements; determining a representation of a relationship between the at least a series of data points and the time series of ventricular pressure measurements; determining, at least in part, a characteristic of blood flow within a chamber of the heart by processing the representation. at least one memory; system.

27. 27. The system of claim 26, wherein the time series of ventricular pressure measurements are collected using a device that is not inserted into the subject's body.

28. 28. The system of claim 27, wherein the device is a non-invasive ultrasound Doppler device, a magnetic resonance imaging device, and / or a heart sound intensity device.

29. 27. The system of claim 26, wherein the time series of ventricular pressure measurements are collected by an intracardiac device.

30. 30. The system of claim 29, wherein the intracardiac device is a pulmonary artery hemodynamic monitoring catheter or a left ventricular support device.

31. 31. The system of claim 30, wherein the time series of ventricular pressure measurements are collected at least in part by measuring chamber dimensions and ventricular blood pressure.

32. 27. The system of claim 26, wherein the relationship comprises a series of pairwise relationships, the pairwise relationships comprising data points of the series of data points and corresponding time-series ventricular pressure measurements.

33. 27. The system of claim 26, wherein the ventricular pressure time rate of change is a first derivative of the ventricular pressure with respect to time.

34. 33. The system of claim 32, wherein the representation comprises a plot related to the relationship.

35. 35. The system of claim 34, wherein the plot is a pressure loop plot.

36. 36. The system of claim 35, wherein the blood flow characteristic is determined based at least in part on a loop cycle duration of the pressure loop plot.

37. 36. The system of claim 35, wherein the blood flow characteristics are determined based at least in part on boundaries of the pressure loop plot.

38. 38. The system of claim 37, wherein the boundary line is a top boundary line, a bottom boundary line, a left boundary line, or a right boundary line.

39. 35. The system of claim 34, wherein the characteristic of the blood flow is determined based at least in part on a visual characteristic associated with the plot.

40. 40. The system of claim 39, wherein the visual characteristic relates to a shape of a region of the plot or a size of at least one region of the plot.

41. 41. The system of claim 40, wherein the visual characteristic is symmetry, smoothness, the presence of depressions, differences between two or more regions, or tangential slope.

42. 41. The system of claim 40, wherein the characteristic of blood flow is determined at least in part by comparing the plot to a second plot.

43. 27. The system of claim 26, further comprising selecting a treatment regimen based at least in part on the characteristics of the blood flow.

44. 27. The system of claim 26, wherein the characteristic of blood flow is ventricular power, ventricular resistance, or ventricular blood flow, elasticity, compliance, contractile stroke volume, or response to a changing factor.

45. 27. The system of claim 26, further comprising calculating a second series of data points comprising a time rate of change of ventricular pressure acceleration.

46. 46. The system of claim 45, further comprising assessing a-wave pre-diastolic pressure at least in part using the second series of data points.

47. 27. The system of claim 26, wherein processing the representation includes using a mathematical model.

48. 27. The system of claim 26, wherein the mathematical model is a statistical model or a machine learning model.

49. 49. The system of claim 48, wherein the machine learning model comprises a neural network.

50. 27. The system of claim 26, wherein a data point in the series of data points is determined by (a) determining a pressure difference by subtracting a first pressure value associated with a first time from a second pressure value associated with a second time, and (b) dividing the pressure difference by a time difference, the time difference comprising the difference between the second time and the first time.

51. When executed by at least one data processor: obtaining a series of time-series ventricular pressure measurements; determining a series of data points comprising a ventricular pressure time rate of change from said time series of ventricular pressure measurements; determining a representation of a relationship between the at least a series of data points and the time series of ventricular pressure measurements; and determining, at least in part, a characteristic of blood flow within a chamber of the heart by processing the representation. Non-transitory computer-readable medium.

52. 52. The non-transitory computer-readable medium of claim 51, wherein the time-series ventricular pressure measurements are collected using a device that is not inserted into the subject's body.

53. 53. The non-transitory computer readable medium of claim 52, wherein the device is a non-invasive ultrasound Doppler device, a magnetic resonance imaging device, and / or a heart sound intensity device.

54. 52. The non-transitory computer-readable medium of claim 51, wherein the time-series ventricular pressure measurements are collected by an intracardiac device.

55. 55. The non-transitory computer readable medium of claim 54, wherein the intracardiac device is a pulmonary artery hemodynamic monitoring catheter or a left ventricular support device.

56. 55. The non-transitory computer-readable medium of claim 54, wherein the time series of ventricular pressure measurements are collected, at least in part, by measuring chamber dimensions and ventricular blood pressure.

57. 52. The non-transitory computer-readable medium of claim 51, wherein the relationship comprises a series of pairwise relationships, the pairwise relationships comprising a data point of the series of data points and a corresponding time-series ventricular pressure measurement value.

58. 52. The non-transitory computer-readable medium of claim 51, wherein the ventricular pressure time rate of change is a first derivative of the ventricular pressure with respect to time.

59. 58. The non-transitory computer-readable medium of claim 57, wherein the representation comprises a plot related to the relationship.

60. 60. The non-transitory computer readable medium of claim 59, wherein the plot is a pressure loop plot.

61. 61. The non-transitory computer-readable medium of claim 60, wherein the characteristic of blood flow is determined based at least in part on a loop cycle duration of the pressure loop plot.

62. 61. The non-transitory computer-readable medium of claim 60, wherein the characteristics of blood flow are determined based at least in part on boundaries of a pressure loop plot.

63. 63. The non-transitory computer-readable medium of claim 62, wherein the boundary line is a top boundary line, a bottom boundary line, a left boundary line, or a right boundary line.

64. 60. The non-transitory computer-readable medium of claim 59, wherein the characteristic of the blood flow is determined based at least in part on a visual characteristic associated with the plot.

65. 65. The non-transitory computer-readable medium of claim 64, wherein the visual characteristic relates to a shape of a region of the plot or a size of at least one region of the plot.

66. 66. The non-transitory computer-readable medium of claim 65, wherein the visual characteristic is symmetry, smoothness, the presence of depressions, differences between two or more regions, or tangential slope.

67. 66. The non-transitory computer-readable medium of claim 65, wherein the characteristic of blood flow is determined at least in part by comparing the plot to a second plot.

68. 52. The non-transitory computer-readable medium of claim 51, further comprising selecting a treatment regimen based at least in part on the characteristics of the blood flow.

69. 52. The non-transitory computer readable medium of claim 51, wherein the characteristic of blood flow is ventricular power, ventricular resistance or ventricular blood flow, elasticity, compliance, contractile stroke volume, or response to a changing factor.

70. 52. The non-transitory computer readable medium of claim 51, further comprising calculating a second series of data points comprising a time rate of change of ventricular pressure acceleration.

71. 71. The non-transitory computer readable medium of claim 70, further comprising assessing a-wave pre-diastolic pressure using at least in part the second series of data points.

72. 52. The non-transitory computer-readable medium of claim 51, wherein processing the representation comprises using a mathematical model.

73. 73. The non-transitory computer-readable medium of claim 72, wherein the mathematical model is a statistical model or a machine learning model.

74. 74. The non-transitory computer-readable medium of claim 73, wherein the machine learning model comprises a neural network.

75. 52. The non-transitory computer-readable medium of claim 51 , wherein a data point in the series of data points is determined by (a) determining a pressure differential by subtracting a first pressure value associated with a first time from a second pressure value associated with a second time, and (b) dividing the pressure differential by a time difference, the time difference comprising the difference between the second time and the first time.

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