Novel components for hemodynamic analysis tools

Abstract

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

Description

[0001] Cross-reference

[0002] This application claims priority to U.S. Provisional Application No. 63 / 393,200, filed Jul. 28, 2022, the entire disclosure of which is incorporated herein by reference. BACKGROUND OF THE INVENTION

[0003] In a cardiac catheterization laboratory, invasive measurements of ventricular blood pressure (Pv) and arterial blood pressure (Pa) are routinely made on patients undergoing invasive evaluation. Pv measurements typically include 1) maximum systolic pressure, 2) minimum diastolic pressure, 3) end-diastolic pressure, 4) maximum rate of change of pressure over time (dP / dt), and 5) a continuous tracing of Pv versus time visually presented by existing commercial products. These measurements are used by physicians to evaluate cardiac contractile (systolic) function, relaxation (diastolic) function, and diastolic filling pressure, with the aim of identifying and treating cardiac abnormalities. SUMMARY OF THE INVENTION

[0004] In some example embodiments, methods, systems, and articles of manufacture for analyzing hemodynamic characteristics within chambers of the heart and downstream vessels of the heart may be provided, substantially as described and shown herein.

[0005] In some embodiments, a system is provided that includes at least one data processor and at least one memory storing instructions that, when executed by the at least one data processor, cause operations including: obtaining a set of time series ventricular pressure measurements; determining, from the time series ventricular pressure measurements, a set of data points that includes a rate of change of ventricular pressure over time; determining a representation indicative of a relationship between at least the set of data points and the time series ventricular pressure measurements; and determining hemodynamic characteristics within a chamber of the heart at least in part by processing the representation.

[0006] In some variations, one or more of the features disclosed herein can also be 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 set of paired relationships, the paired relationships including data points in the set of data points and corresponding time series ventricular pressure measurements. The rate of change of ventricular pressure in the rate of change of ventricular pressure is the first derivative of ventricular pressure with respect to time. The representation includes a graph associated with the relationship. The graph is a pressure loop graph. The blood flow characteristic is determined at least in part based on the loop cycle duration of the pressure loop graph. The blood flow characteristic is determined at least in part based on the border of the pressure loop graph. The border is a top border, a bottom border, a left border, or a right border. The blood flow characteristic is determined at least in part based on visual characteristics associated with a visual graph. The visual characteristics are associated with the shape of the region of the visual graph or the size of at least one region of the graph. The visual characteristics are symmetry, smoothness, the presence of a depression, a difference between two or more regions, or a tangential slope. The blood flow characteristic is determined at least in part by comparing the visual graph with a second visual graph. The treatment regimen can be based at least in part on the blood flow characteristic. The blood flow characteristic is ventricular power, ventricular resistance, or ventricular blood flow, elasticity, compliance, contractility stroke volume, or response to a modification factor. A second set of data points can be calculated, the second set of data points including the rate of change of ventricular pressure acceleration.

[0007] The a-wave diastolic pressure can be evaluated using at least in part the second set 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. The data points in the set of data points are 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, where the time difference includes the difference between the second time and the first time.

[0008] Details of one or more variations of the subject matter described herein are set forth in the accompanying drawings and the following description. Other features and advantages of the subject matter described herein will be apparent from the description and drawings, and from the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] 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, help to explain some of the principles associated with the disclosed implementations. In the drawings,

[0010] Figure 1 An example of a left ventricular pressure tracing displayed by a commercially available hemodynamic system is shown.

[0011] Figure 2 An example of a ventricular "pressure loop" generated by the disclosed method is shown.

[0012] Figure 3 A set of pressure loop variants generated by the disclosed method is shown, demonstrating the characteristics of ventricular blood pressurization.

[0013] Figure 4 The identification of the a-wave diastolic pressure by the disclosed method is shown.

[0014] Figure 5 A synchronous comparison of the loop-derived end-diastolic ventricular pressure and the maximum systolic pressure over multiple cardiac cycles is shown (left panel).

[0015] Figure 6 A comparison of pressure loops generated by the same individual at different time points is shown.

[0016] Figure 7 An example of a ventricular pressure change over time (dP / dt) versus time plot is shown.

[0017] Figure 8 An example of a standard aortic blood pressure tracing displayed by a commercially available hemodynamic system is shown.

[0018] Figure 9 An example of an arterial "pressure loop" generated by the disclosed method for multiple consecutive heartbeats is shown.

[0019] Figure 10 An arterial pressure loop variant generated by the disclosed method is shown.

[0020] Figure 11 A synchronous comparison of the arterial pressure loop area and the arterial pressure is shown.

[0021] Figure 12 A comparison of pressure loops generated by the same artery at different time points is shown.

[0022] Figure 13 The determination of the diastolic period using the minimum periodic pressure fluctuations is shown.

[0023] Figure 14 The determination of the single-exponential curve asymptote is shown.

[0024] Figure 15 Shows the determination of "corrected" τ.

[0025] Figure 16 Shows the comparison of the calculated and actual arterial pressures.

[0026] Figure 17 Shows synchronous pressure tracings from the ventricle and downstream arterial pressure sources.

[0027] Figure 18 Shows the arterial-to-ventricular pressure ratio generated by the disclosed method during valvular stenosis.

[0028] Figure 19 Shows the arterial-to-ventricular pressure ratio generated by the disclosed method during hypertrophic obstructive cardiomyopathy.

[0029] Figure 20 Shows the arterial-to-ventricular pressure ratio index generated by the disclosed method.

[0030] Figure 21 Shows a novel comparison of multiple pressure loops generated by the disclosed method.

[0031] Figure 22 Shows the general features of the disclosed method and the interface in a cardiac catheterization laboratory.

[0032] Figure 23 Shows a typical setup for collecting synchronous aortic blood pressure (Pa) and distal coronary artery pressure (Pd) during conventional FFR measurement, and the use of the disclosed method for CFRp measurement (left image).

[0033] Figure 24 Shows a typical guiding catheter and pressure wire arrangement for synchronously measuring aortic pressure (Pa) and distal pressure (Pd) from the same pressure source (tip of the guiding catheter) during pressure "equilibration" (left image).

[0034] Figure 25 Shows synchronous Pa and Pd measurements during one heartbeat following commercially available pressure "equilibration".

[0035] Figure 26 Shows a decrease in the distal coronary artery pressure measurement (Pd) compared to the guiding catheter reference measurement (Pa) in the presence of anatomical resistance between the two pressure sources.

[0036] Figure 27 Shows the synchronous beat-by-beat calculation of mean diastolic 1 - Pd / Pa and mean diastolic velocity following intracoronary adenosine injection to induce hyperemia.

[0037] Figure 28 Shows an exemplary beat-to-beat comparison of the pressure-derived formula 1 - Pd / Pa with the Doppler-derived blood velocity after adenosine-induced hyperemia and return to baseline coronary blood flow.

[0038] Figure 29 Shows an exemplary beat-to-beat comparison of the diastolic pressure-derived CFR (CFRp) with the diastolic Doppler-based CFR after adenosine-induced hyperemia and return to baseline coronary blood flow.

[0039] Figure 30 Shows a flowchart of a method according to some embodiments.

[0040] Figure 31 Depicts a block diagram showing a computing system according to some embodiments. Detailed Description

[0041] Currently available methods for assessing cardiac characteristics use time series measurements of blood pressure (ventricular or arterial pressure). These methods can be used to identify and treat cardiac abnormalities. Systems, methods, and articles of manufacture are disclosed for generating new blood pressure information to improve the detection of cardiac characteristics and the treatment of cardiac abnormalities.

[0042] The disclosed method does not merely analyze the pressure over time, but rather calculates the rate of change of pressure over time (e.g., the time derivative of pressure, or dP / dt), and evaluates this rate of change of pressure against the corresponding time series pressure measurements. The method generates a representation showing the relationship between dP / dt and the corresponding pressure measurement. The representation can use arterial pressure (Pa) or ventricular pressure (Pv). In this document, "ventricular pressure" refers to the blood pressure measured within the ventricle of the heart, and "arterial pressure" refers to the blood pressure measured within the artery of the heart. For example, the relationship can include a set of paired relationships, where a paired relationship includes a particular Pv value at a particular time and its corresponding dPv / dt. In some cases, this representation is a visual representation. In some cases, the visual representation is a graph (hereinafter referred to as a "pressure loop diagram"). 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 characteristics. dPv / dt can be calculated, for example, by calculating the pressure difference between two samples and dividing by the time difference between the two samples.

[0043] The pressure loop diagram can have several visual characteristics that indicate cardiac function. For example, the size of at least one region of the loop (or at least one region of the diagram), the shape of the loop (or at least one region of the loop or diagram), and the aspects of the top, bottom, left, and right sides of the loop can each or in combination provide information related to the normal function or impairment of the veins or arteries of the heart.

[0044] The pressure loop diagram can be analyzed in a variety of ways. Many visual features, such as the size, shape, and depressions on the surface of the loop, can be visually inspected. In some cases, mathematical models (such as statistical models or machine learning models) can be used to analyze or process the raw dP / dt values and corresponding pressure measurements through mathematical models. In some cases, machine learning models such as convolutional neural networks (CNNs) can be used to analyze visual diagrams (such as pressure loops).

[0045] Invasive or non-invasive cardiac measurement devices can be used to collect pressure data. Examples of invasive devices are intracardiac devices, such as CardioMEMS in a left ventricular assist device TM system. Examples of non-invasive cardiac measurement devices include ultrasound Doppler, magnetic resonance imaging (MRI), and / or cardiovascular sound intensity devices.

[0046] Figure 30 A flowchart of method 3000 according to some embodiments is shown.

[0047] In a first operation 3010, a set of time series pressure measurements is obtained from a subject. The set of time series pressure measurements can be ventricular pressure (Pv) measurements or arterial pressure (Pa) measurements. The time series pressure measurements can be obtained invasively or non-invasively (e.g., without contacting the subject's body). For example, the measurements can be obtained invasively through an intravascular device such as a pulmonary artery hemodynamic monitoring device or a left ventricular support device. The measurements can be obtained non-invasively using a device or apparatus that does not contact the subject's body and / or is not inserted into the body such as an ultrasound Doppler device or apparatus, magnetic resonance imaging (MRI) or apparatus, or a heart sound intensity device or apparatus. In some cases, the time series pressure measurements can be collected at least in part by measuring chamber dimensions and / or ventricular blood pressure. The time series pressure measurements can be collected over a duration that includes one or more heartbeats. In some cases, the duration can be at least one second, at least five seconds, at least 10 seconds, at least 30 seconds, at least one minute, at least 10 minutes, at least 15 minutes, at least half an hour, at least one hour, at least two hours, at least three hours, at least six hours, at least half a day, at least one day, at least one week, at least one month, at least three months, at least six months, or at least one year. In some cases, the duration can be at most five seconds, at most 10 seconds, at most 30 seconds, at most one minute, at most 10 minutes, at most 15 minutes, at most half an hour, at most one hour, at most two hours, at most three hours, at most six hours, at most half a day, at most one day, at most one week, at most one month, at most three months, at most six months, or at most one year. In some cases, the duration can be between one second and five seconds, between five seconds and 10 seconds, between 10 seconds and 30 seconds, between 30 seconds and one minute, between one minute and 10 minutes, between 10 minutes and 30 minutes, between 30 minutes and one hour, between one hour and two hours, between two hours and three hours, between three hours and six hours, between six hours and 12 hours, between 12 hours and one day, between one day and one week, between one week and one month, between one month and two months, between two months and three months, between three months and six months, or between six months and one year.

[0048] The subject can be a human subject. In some cases, the subject is a non-human animal. The subject can be a mammalian, avian, reptilian, or amphibian subject. For example, the subject can be a dog, cow, horse, pig, sheep, chicken, turkey, ostrich, mouse, cat, deer, snake, lizard, frog, monkey, ape (e.g., chimpanzee), or another animal.

[0049] In a second operation 3020, a set of data points is determined from the time series pressure measurement results, the set of data points including the rate of change of pressure over time (e.g., ventricular pressure or arterial pressure). The rate of change of pressure over time can be the first derivative of pressure with respect to time (dP / dt) and can be related to ventricular pressure (dPv / dt) or arterial pressure (dPa / dt). The time derivative of a particular pressure value can be calculated by subtracting adjacent pressure measurements in time (e.g., the most recent past pressure measurement and the most recent future pressure measurement) and dividing them by the time difference between them (e.g., by multiplying by the sampling rate).

[0050] In a third operation 3030, a representation is determined that indicates the relationship between at least the set of data points and the time series pressure measurement results. The relationship can include or comprise a set of paired relationships between the rate of change of pressure over time (dP / dt) values and the corresponding pressure values. This representation can include raw data in, for example, text format, which can be analyzed by a mathematical model. The representation can be preprocessed or compressed. In some cases, a dimensionality reduction method (e.g., an autoencoder) can be used to compress the data in order to generate a feature representation as an input to a machine learning algorithm. In some cases, the representation is a visual representation. The visual representation can be a graph. The graph can include one or more loop tracings indicating the association between the rate of change of pressure over time and the pressure values. Since blood pressure increases and decreases during the heartbeat, loop shapes can occur, resulting in multiple dP / dt values corresponding to a single pressure measurement. The average of one or more loop tracings can be determined and the average can be superimposed over the individual loop tracings. A graph including loop tracings is referred to herein as a "pressure loop graph".

[0051] In a fourth operation 3040, the blood flow characteristics within the chambers of the heart are determined by processing the representation. This processing can be performed by examining the visual features of the pressure loop graph. This processing can be performed using an image processing or computer vision system. This processing can be performed using a mathematical model. The processing can include determining the association between the visual features of the pressure loop graph and cardiac abnormalities. The characteristics indicating health of the pressure loop graph can include smoothness, the size of the loop, the number of depressions, the size of the depressions, the shape of the depressions, the tangential slope of the points on the graph, the symmetry of the graph, the area of all or part of the graph, or the position of the top, bottom, left, or right border of the graph. The characteristics of the heart that can be determined by processing the graph can include ventricular power, ventricular resistance, or ventricular blood flow, elasticity, compliance, contractility, stroke volume, or the response to a modifying factor.

[0052] In some cases, the analysis of the representation can be used to determine a treatment course for a patient with a cardiac anomaly. The treatment can include diet, exercise, surgery, a medication treatment course, the administration of intravenous (IV) fluids (e.g., saline or lactated Ringer's solution), or a combination thereof. In some cases, the analysis of the representation is used to screen potential patients. Based on the classification determined from the representation, a patient can be labeled as low risk, medium risk, or high risk. Medium or high risk cases may be escalated to appropriate medical personnel.

[0053] In some cases, the method can include collecting a second set of data points that includes the rate of change of pressure acceleration over time (e.g., d 3 P / dt 3 ). These can be the rate of change of ventricular pressure or arterial pressure over time. At least partially based on the second set of data points, the method can evaluate the a-wave diastolic pressure.

[0054] A computing device can be used to process the pressure measurement results. The computing device 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).

[0055] Ventricular blood pressurization characterization

[0056] Systems, methods, and articles of manufacture are disclosed herein for performing Pv measurements by transforming Pv versus time data (commonly presented using a "time-frequency plot") into data of the rate of change of Pv over time (dPv / dt) versus Pv. In doing so, an operator can derive multiple measurements from the raw Pv data (pre-existing data or data collected in real time) and evaluate ventricular function in a new way. Compared to the typical peaks and valleys observable from a time-frequency plot, plotting dPv / dt versus Pv can create a "pressure loop" for each cardiac cycle ( Figure 2 ). Standard measurements can be obtained from the ventricular pressure loop, including the X-axis minimum ("left border", minimum diastolic pressure), the X-axis maximum ("right border", maximum systolic pressure), the Y-axis maximum ("top border", maximum systolic dP / dt), and the Y-axis minimum ("bottom border", minimum diastolic dP / dt). As described below, multiple measurements and characteristics ( Figure 3 ) can be derived after analyzing the pressure loop.

[0057] Measurements and characteristics derived from the analysis of the pressure loop include, but are not limited to, the following:

[0058] Loop size - total area and sub - areas (e.g., upper and lower halves of the loop, quarter - loops or other sized segments), axial dimensions, or other measurements associated with the area or 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 boundaries of the loop (Equation A1).

[0059] Equation A1:

[0060] (where f(Pv) is the instantaneous dPv / dt at any Pv value)

[0061] The sub - areas of the total Pv loop can be calculated by adjusting the limits of the integral (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 between two different samples is calculated without regard to the intervening time interval, then multiplication by the sampling rate (e.g., samples per second) is performed to achieve consistency across different sampling techniques. Additionally, the Pv loop area representing ventricular function for each cardiac cycle can be used to calculate ventricular function for each period (Equation A2).

[0062] Equation A2: Pv loop area / time = heart rate * Pv loop area

[0063] Loop shape - The methods described herein can be used to determine overall shape characteristics such as "symmetry (e.g., the degree to which the portions of the loop bisected by an axis are similar or identical)", "smoothness (e.g., relative lack of indentations or sharp angles or corners)", the presence of abnormal "dips", differences between the upper - half curve / region and the lower - half curve / region, and the tangential slope of points in different quadrants of the loop. The smoothness of the observed loop segments can be compared to an expected best - fit curve, and then its correlation with the expected curve can be quantified. The Pv loop shape generated by a ventricular chamber with uncertain characteristics can be compared to a database of many loop shapes generated by ventricular chambers with known characteristics, and then the best match (and known characteristics) can be reported for the individual loop being tested. Examples of this can include 1) a loop with normal size and smoothness generated by a normal ventricle, 2) a loop with irregular smoothness generated by a ventricle damaged by coronary artery disease and ischemic cardiomyopathy, 3) a loop with regular smoothness and irregular (e.g., smaller) size generated by a ventricle damaged by global non - ischemic cardiomyopathy, and 4) a loop with irregular smoothness generated by a ventricle damaged by hypertrophic cardiomyopathy.

[0064] Loop Period Duration - The duration of each loop can be determined by dividing the total number of samples required to create a complete loop by the sampling rate. The loop duration can then be used to calculate the instantaneous heart rate for a single loop, or the average heart rate across multiple loops.

[0065] Characteristics of the Top / Bottom Borders Relative to a Reference Point - The top border represents the point of maximum blood pressure rate (i.e., peak systolic force), while the bottom border represents the point of minimum blood pressure (i.e., peak relaxation force). These points in the loop can 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., the loop center or the median X-axis pressure).

[0066] Characteristics of the Left Border - The pressure changes during diastole (which make up the left border of the pressure loop) can be characterized (e.g., "severe" or "subtle") and can help decipher hemodynamically useful time points that occur during diastole when Pv can be reported. A method for defining the exact moment before atrial contraction (also known as the pre-a wave ventricular pressure) is also proposed.

[0067] Identifying the Pre-a Wave Diastolic Pressure - The pre-a wave diastolic pressure measured within the ventricle can be a useful reporting metric because this reporting metric 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). Thus, if only ventricular pressure is measured, accurate identification of the pre-a wave diastolic pressure provides a useful surrogate for upstream atrial chamber pressure. Identification of the exact moment to capture this pre-a wave measurement from Pv data can be made. The disclosed method identifies this moment ( 3 Pv / dt 3 ) by analyzing the third derivative of Pv with respect to time (d Figure 4 ). Analyzing the third derivative of Pv with respect to time allows the disclosed method to identify the moment before peak systole (or before the major Pv upstroke) when (d 3 Pv / dt 3 ) transitions from a negative value to a positive value. Similarly, if dPv / dt represents the "speed" of pressure change over time and the second derivative (d 2 Pv / dt 2 ) represents the corresponding "acceleration," then the third derivative (d 3 Pv / dt 3 ) represents "acceleration change." Thus, the disclosed method identifies diastole (d 3 Pv / dt 3)The exact moment when it becomes uniformly positive, which represents the moment when ventricular pressure ends its peak "deceleration" towards a lower value and begins to experience a positive "acceleration change" towards a higher value. In the heart, this is the moment when the myocardium begins to contract in the atria, followed by muscle contraction in the ventricles.

[0068] The characteristics of the right border - the pressure change during maximum ventricular systolic pressure (constituting the right border of the pressure loop) can reflect the influence of pressure wave reflection and can be characterized or quantified. This pressure change also exhibits changes in maximum Pv across multiple loops.

[0069] Timing of loop characteristics - The occurrence (or timing) of different loop characteristics such as the left / right / top / bottom borders can be recorded and used to identify the occurrence of periods of systole, diastole, or other purposes.

[0070] Synchronous comparison of various parameters - A synchronous comparison of derived factors for any loop can be made. An example is the comparison of end-diastolic ventricular pressure with maximum Pv across multiple beats. This comparison reveals a relationship ( Figure 5 ) that seems similar to the classic Frank-Starling curve reflecting the relationship between end-diastolic ventricular volume and stroke volume, but is fundamentally different from it. Another example is end-diastolic pressure versus loop size. Another example is loop size versus transvalvular pressure gradient or transvalvular pressure ratio.

[0071] Comparison of parameters between different time points or different individuals - Various pressure loop measurements can be compared across different time points. Examples are the total loop area before and after a heart attack (myocardial infarction) to show deterioration of ventricular function, or the total loop area before and after a heart transplant to show improvement of ventricular function ( Figure 6 ). The efficacy of cardiac therapies, especially surgeries and medications, can be tested in this way. Loop measurements can also be compared between different individuals / populations to identify objective differences in cardiac function.

[0072] Downstream calculations using ventricular pressure loop data - The primary data within the ventricular pressure loop can be used to calculate unique secondary measurements. Important application examples of this function are how ventricular pressure loop data can be used to calculate ventricular "power", "resistance", and "blood flow" measurements. Calculation of power for the left ventricle can calculate downstream blood flow and resistance in the systemic circulation of the body, while calculation of power for the right ventricle can calculate downstream blood flow and resistance in the pulmonary circulation of the lungs.

[0073] Ventricular power: In creating a ventricular pressure loop, the disclosed method allows comparison of dPv / dt (in mmHg / sec) with Pv (in mmHg) at any point in time, rather than comparing dPv / dt with time itself. In the comparison of the disclosed method, the raw blood pressure is mathematically equivalent to the work (or stored energy) per volume of blood in joules (Equation A3).

[0074] Equation A3:

[0075] Blood pressure per time is mathematically equivalent to the power per volume of blood in watts (or energy stored per unit time) (Equation A4).

[0076] Equation A4:

[0077] Thus, the disclosed method (and corresponding systems and articles of manufacture) creates a unique pressure loop that allows analysis of ventricular power that is indexed by ventricular blood volume.

[0078] Quantification of the ventricular power metric in this way can be analyzed for different parts of the cardiac cycle (i.e., systole only, diastole only, entire cardiac cycle). Conceptually, ventricular power analysis also performs a pressure-weighted average of the power metric across different Pv values, as opposed to a local time-weighted average of the power metric across different time values (Equations A5 and Figure 6 ).

[0079] Equation A5:

[0080] (f(T) is the instantaneous dPv / dt at any T time value)

[0081] In so doing, the disclosed systems, methods, and articles of manufacture can emphasize the power metric values generated during peak systole / diastole, which 1) are generated rapidly over a relatively short period of time and are under-emphasized in time-weighted data, and 2) can more closely quantify ventricular function.

[0082] In addition, the disclosed subject matter can indicate (e.g., report, etc.) ventricular power in a variety of different ways, including but not limited to the following measurements:

[0083] · The sum of the pressure-weighted power metric values across different parts of the cardiac cycle, or simply the pressure loop area (re-written from Equation A1).

[0084] Modified Equation A1:

[0085] · The maximum value of the pressure-weighted power metric during systole (maximum dP / dt value).

[0086] · The minimum value of the pressure-weighted power index during diastole (minimum dP / dt value).

[0087] · The average value of the pressure-weighted power index across different parts of the cardiac cycle (Equation A6).

[0088] Equation A6:

[0089] · The root mean square (RMS) of the pressure-weighted power index across different parts of the cardiac cycle (Equation A7).

[0090] Equation A7:

[0091] Downstream blood flow and resistance: After the disclosed method calculates the ventricular power that establishes the index by blood volume, the disclosed method can create an estimate of downstream blood flow and resistance by using existing equations for electrical power, resistance, and flow (Equation A8).

[0092] Equation A8:

[0093] Although such equations use actual power values rather than volume-based power as generated by the disclosed method, the calculation of the downstream resistance "factor" by the disclosed method will be for creating using units of mmHg-sec or dynes sec / cm 2 (Equation A9).

[0094] Equation A9:

[0095] The calculation of the actual resistance can be achieved by including blood volume in the equation in units of mmHg-sec / m or dynes–sec / cm 5 (Equation A10).

[0096] Equation A10:

[0097] In the same way, the calculation of the downstream cardiac output or blood flow "factor" by the disclosed method will be for creating using units of sec -1 (Equation A11).

[0098] Equation A11:

[0099] The calculation of the actual blood flow can be achieved by including blood volume in the equation in units of m 3 / sec (Equation A12).

[0100] Equation A12:

[0101] The disclosed subject matter can also have the ability to transform non-invasive ventricular pressure waveforms into pressure loops. Examples of this include non-invasive ultrasound Doppler, magnetic resonance imaging, and cardiovascular sound intensity to measure ventricular blood flow velocity and calculate corresponding ventricular blood pressure for one or more cardiac cycles. The pressure waveforms derived from each of these various techniques can be transformed into pressure loops and analyzed as described above.

[0102] Hypothetically, subtle changes to the pressure loop can be created, such as using the change in Pv per sample (dPv / sample) instead of dPv / dt. Different pressure or time units can also be used. These changes will effectively change the scale of the pressure loops presented herein, and any established range of "normal" measurements will also change, but the basic concepts and principles of application will be the same.

[0103] The disclosed subject matter and its Pv pressure loops can be used by existing commercial devices (such as those that can obtain Pv measurements from the ventricles) to improve baseline function. Examples include pulmonary artery hemodynamic monitoring devices inserted through the central vein, right atrium, right ventricle, and pulmonary artery (e.g., Swan-Gantz catheter); pressure loops can be used to monitor and analyze right ventricular pressure loops. Left ventricular support devices (e.g., Impella mechanical pump) monitor aortic pressure and estimate left ventricular pressure. Modifying the Impella device to directly measure Pv or using the calculated Pv can be used to create a Pv loop to monitor left ventricular function and determine the appropriateness of upgrading / downgrading cardiac support.

[0104] Figure 1 An example of a left ventricular pressure tracing displayed by a commercially available hemodynamic system is shown. Measurements from the first beat 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 of 1392 mmHg / sec of

[0105] Figure 2 An example of a ventricular "pressure loop" generated by the disclosed method is shown. Pressure loop determination is performed for each of a plurality of consecutive heartbeats. Then the determined pressure loops are overlaid on each other (using the Figure 1 same original data source). The black line shows the average of all pressure loop curves (e.g., the average dP / dt for each Pv value). The left and right borders indicate the points of minimum diastolic pressure and maximum systolic pressure, respectively. The top and bottom border amplitudes indicate the maximum systolic dP / dt and minimum diastolic dP / dt, respectively.

[0106] Figure 3Shows a set of pressure loop variants generated by the disclosed method, demonstrating the characteristics of ventricular blood pressurization. Compared with the "tilt" in Column B (shown by the dashed double arrow connecting the maximum and minimum dP / dt points) and the loop "depression" in Column C (solid arrow), the loops in Column A exhibit "smooth" and "symmetric". In some plots (A1 to A2 and B1 to B2), the point positions of the maximum / minimum dP / dt seem to "rotate" around the pressure median (small circle), but in other plots (Row 3 and Column C) they are "shifted" away from the pressure median. Although in most plots, the areas enclosed within the upper and lower curves of each loop (above / below the X-axis) seem equal, the upper curve may seem relatively larger (plots A1 and C1) or smaller (plot C3) than the corresponding lower curve. The pressure changes during diastole (points along the left border) can be drastic (plots B1 to B2) or subtle (Row 3 and Column 3). The units used for each plot are mmHg / second (Y-axis) and mmHg (X-axis).

[0107] Figure 4 Shows the identification of the a-wave diastolic pressure by the disclosed method. The disclosed method identifies the exact moment when the third derivative of the ventricular pressure with respect to time is consistently positive before the occurrence of peak systole. This moment represents the end of the ventricular pressure "deceleration" towards the lower value peak and the start of the positive "acceleration change" towards the higher value. The ventricular pressure at this moment corresponds to the a-wave pressure, which provides useful information about cardiac function and is highly correlated with the direct measurement of the upstream atrial pressure (in the absence of intermediate valvular diseases). Figure 4 Shows an example of an electrocardiogram tracing (upper plot), left ventricular pressure tracing (middle plot), and third derivative d 3 Pv / dt 3 tracing (lower plot) synchronously obtained from the same source. The disclosed method is used to identify the exact moment when d 3 Pv / dt 3 becomes consistently positive before peak systole (circle at sample number 166), where the corresponding normal left ventricular pressure of 9.2 mmHg and electrocardiogram timing (circle) are shown on the P wave.

[0108] Figure 5 Shows a synchronous comparison (left figure) of the end-diastolic pressure (EDP: points along the left border) and the maximum systolic pressure (Pv: points along the right border) of the loop-derived ventricle across multiple cardiac cycles. The resulting relationship between the maximum Pv and the EDP is linear (right figure), and in this example has a slope greater than 1 (red reference line). This indicates that for every 1 mmHg increase in the EDP achieved, there is a >1 mmHg relative increase in the maximum Pv.

[0109] Figure 6Shows a comparison of pressure loops generated by the same individual at different time points. The smaller inner loop is associated with abnormal ventricular function, while the larger outer loop is associated with normal ventricular function.

[0110] Figure 7 Shows an example of a ventricular pressure change over time (dP / dt) versus time plot. Positive dP / dt values occur during peak systole, while negative dP / dt values occur during peak diastole. In this time-dependent plot, the period representing ventricular passive diastole (occurring from approximately 130 msec to 230 msec) between the diastolic peak and the next systolic peak has a greater weight compared to its graphical representation ([ Figure 2 left border) within the corresponding pressure loop of the disclosed method. In contrast, the pressure loop of the disclosed method (plotting dP / dt versus pressure) weights the ventricular dP / dt values uniformly across the range of ventricular pressures rather than across the range of sampling times. In doing so, peak ventricular systole / diastole is emphasized in the pressure loop analysis of the disclosed method, while end-systole / diastole is not emphasized.

[0111] Arterial blood pressurization

[0112] Arterial blood pressure (Pa) and heart rate are universal vital signs that are widely used around the world to enable clinicians to understand a patient's cardiovascular health. Elevated blood pressure defines hypertension, and decreased blood pressure defines hypotension. A significant gap between systolic blood pressure (SBP: identified by the maximum blood pressure) and diastolic blood pressure (DBP: identified by the minimum pressure) can indicate additional cardiovascular abnormalities. Both elevated and decreased heart rates can be caused by primary electrical conduction disorders of the heart or as a secondary response to different cardiovascular abnormalities. Severely decreased blood pressure or heart rate values (farther from the normal value and closer to zero) may herald cardiac death.

[0113] Invasive measurements of Pa are routinely performed on patients undergoing invasive assessment in a hospital's cardiac catheterization laboratory. Pa may also be measured continuously on those patients being monitored in an operating room, emergency room, or intensive care unit. Pa measurement is particularly useful for assessing patients suffering from a "shock" state in which the amount of blood flow from the heart to the body is insufficient to meet the body's metabolic needs. Continuous Pa recordings produce a classic pressure-versus-time waveform (see, for example, Figure 1) This classic pressure-versus-time waveform is generated by sequential heartbeats and forms the basis for conventional measurements such as SBP, DBP, and mean arterial pressure. The raw information contained within the continuous Pa waveform also provides blood pressure and heart rate vital signs that are widely used throughout the world. Pa measurements are also integrated into existing secondary measures of cardiac output (CO) and blood flow resistance (R) (Equations B1 to B5). More sophisticated systolic Pa integration (Equation B6) or Pa averaging (Equations B7 to B10) techniques have been used for various applications, including estimation of CO and quantification of heart valve stenosis.

[0114] Although the Pa waveform is widely useful for vital sign measurement and more advanced secondary calculations, the available tools for assisting clinicians in identifying and quantifying subtle variations in Pa waveform characteristics remain limited. Additionally, unrecognized Pa waveform characteristics may provide new vital signs for widespread use by clinicians throughout the world. This section describes Pa waveform analysis by the disclosed methods for generating and measuring cardiovascular vital signs.

[0115] Related equations:

[0116] Analog of Ohm's law:

[0117] Equation B1:

[0118] Equation B2:

[0119] Equation B3:

[0120] (MAP = mean arterial pressure; CVP = central venous pressure; mPAP = mean pulmonary artery pressure; LAP = left atrial pressure)

[0121] Calculation of resistance (WU):

[0122] Equation B4:

[0123] Cardiac power output (watts):

[0124] Equation B5:

[0125] Pulse contour cardiac output (PCCO):

[0126] Equation B6:

[0127] (Cal = patient-specific calibration factor; HR = heart rate; P(t) = pressure under the pressure curve; SVR = systemic vascular resistance; C(p) = compliance;

[0128] dP / dt = pressure curve shape)

[0129] Gorlin Valve Area Calculation:

[0130] Equation B7:

[0131] Equation B8:

[0132] (AVA = aortic valve area; SEP = systolic ejection period; HR = heart rate; mean gradient across the stenotic valve during antegrade flow; MVA = mitral valve area; DEP = diastolic ejection period)

[0133] Hakki Valve Area Estimation:

[0134] Equation B9: Equation B10:

[0135] The pressure loop using invasive or non-invasive Pa measurement is created by transforming traditional Pa versus time data (also known as "time-frequency plot") into Pa rate of change (dPa / dt) versus Pa data. In doing so, the operator can derive multiple measurements from the original Pa measurements (which can be pre-existing data or data collected in real time) and evaluate the characteristics of arterial pressurization in a new way. Compared to the typical peaks and valleys seen in the time-frequency plot, plotting dPa / dt versus Pa can create a "pressure loop" for each cardiac cycle ( Figure 2 ). Standard measurements can be obtained from the Pa pressure loop, including the minimum value on the X-axis ("left border", minimum diastolic pressure), the maximum value on the X-axis ("right border", maximum systolic pressure), the maximum value on the Y-axis ("top border", maximum dPa / dt), and the minimum value on the Y-axis ("bottom border", minimum dPa / dt). Novel measurements and characteristics derived from the analysis of the pressure loop ( Figure 3 ) include, but are not limited to, those listed below. In addition, each of these characteristics derived from arterial blood pressure data can be developed into potential cardiovascular "vital signs" for future use. In fact, characteristic #3 forms the basis of heart rate, while characteristics #5 to 6 form the basis of blood pressure.

[0136] Loop Size - total area and sub-areas (e.g., upper and lower halves of the loop, quarter of the loop), axial dimensions, or other quantities associated with the area or perimeter of the loop. The arterial "pressure loop" can be created by plotting dPa / dt versus Pa by the disclosed method. The area of the entire loop can generally be calculated by integrating the absolute values within the boundaries of the loop (Equation B11).

[0137] Equation B11:

[0138] (f(Pa) is the instantaneous dPa / dt at any value of Pa)

[0139] The sub - area of the total Pa loop can be calculated as follows: Adjust the limits of the integral (e.g., sum all positive dPa / dt values to calculate the area above the X - axis, or sum all negative dPa / dt values to calculate the area below the X - axis).

[0140] Additional adjustments to the integral limits can be introduced in the context of cyclic pressure fluctuations of the loop ( Figure 3 ). If the raw Pa difference between two different data samples is calculated without regard to the intervening time intervals, a multiplication by the sampling rate (per time sample) is performed to achieve cross - technology consistency. Additionally, the Pa loop area representing arterial function for each cardiac cycle can be used to calculate arterial function for each period (Equation B12).

[0141] Equation B12: Pa loop area / time = heart rate × Pa loop area

[0142] Loop shape - overall shape “symmetry”, “smoothness”, presence of abnormal “dips”, differences between the upper - half curve / region and the lower - half curve / region. The slope ranges for different quadrants of the loop can also be determined. The smoothness of the observed loop segments can be compared to an expected best - fit curve and then quantified for its correlation with the expected curve. Individual loop shapes derived from sources with uncertain characteristics can be compared to a database of many loop shapes derived from sources with known characteristics, and then the best match (and associated source characteristics) can be reported for the individual loop. Examples of this can include 1) loops with irregular smoothness indirectly generated by ventricles damaged by coronary artery disease and ischemic cardiomyopathy, 2) loops with regular smoothness indirectly generated by ventricles damaged by global non - ischemic cardiomyopathy, and 3) loops with irregular smoothness indirectly generated by ventricles damaged by hypertrophic cardiomyopathy.

[0143] Loop cycle duration - the total number of samples required to create a complete loop, which can be divided by the sampling rate to determine the duration of each loop. Then, the loop duration can be used to calculate the instantaneous heart rate for a single loop, or the average heart rate across multiple loops.

[0144] Characteristics of the top / bottom borders relative to a reference point - the top border represents the point of maximum blood pressurization rate (i.e., peak systolic force), while the bottom border represents the point of minimum blood pressurization (i.e., peak relaxation force). These points in 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., the loop center or the X - axis pressure median).

[0145] The characteristics of the left border - the pressure change during diastole (constituting the lower left quadrant of the pressure loop) can be characterized (e.g., "flat" or "stable") and contribute to deciphering the timing of diastole without periodic pressure fluctuations. This diastole timing can assist in performing additional calculations described in this supplement.

[0146] The characteristics of the right border - the pressure change during maximum arterial systolic pressure (constituting the right border of the pressure loop) can reflect the effects of pressure wave reflection and can be characterized / quantified. These can also show changes in maximum Pa across multiple loops.

[0147] The characteristics of periodic pressure fluctuations - each pressure loop can exhibit repetitive dPa / dt "dips" or fluctuations (e.g., number of occurrences, timing, frequency, or amplitude) that can be identified and characterized.

[0148] The timing of loop characteristics - the occurrence (or timing) of different loop characteristics such as the left / right / top / bottom borders can be recorded and used to identify the occurrence of systolic, diastolic, or other purposeful periods.

[0149] Synchronous comparison of various parameters - synchronous comparisons of derived factors can be made for any loop. Examples include the comparison of arterial loop size with minimum / maximum Pa across multiple heartbeats, which reveals a linear relationship ( Figure 4 ).

[0150] Comparison of parameters between different time points or different individuals - various pressure loop measurements can be compared across different time points. Examples are the total loop area before and after a heart attack (myocardial infarction) to show deteriorating heart function, or the total loop area before and after a heart transplant to show improved heart function ( Figure 5 ). This type of comparison is similar to but different from the comparison of ventricular pressure loops ( Figure 5 ). The efficacy of cardiac therapies, especially surgeries and medications, can be tested in this way. Loop measurements can also be compared between different individuals / populations to identify objective differences in heart function.

[0151] Downstream calculations using arterial pressure loop data - the primary data within the arterial pressure loop can be used to calculate unique secondary measurements. An important example of this functionality is how arterial pressure loop data can be used to estimate ventricular pressure loop data and its secondary measurements.

[0152] Estimation of ventricular pressure loop area: The characteristics of the arterial pressure loop can be used to estimate the values and characteristics of the ventricular pressure loop. In particular, the portion of the arterial pressure loop that occurs during systole can be used to estimate the corresponding portion of the ventricular pressure loop that occurs during systole (the portion of the arterial and ventricular pressure loops plotted above the X-axis). Both upper loops are similar to ovals, and the area is calculated as follows (Equations B13 and B14):

[0153] Equation B13:

[0154]

[0155] Equation B14:

[0156]

[0157] The area of the upper Pa loop can be used to estimate the area of the upper Pv loop by scaling the height and width of the upper Pa loop to match the parameters of the upper Pv loop (Equation B15).

[0158] Equation B15:

[0159]

[0160] where 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):

[0161]

[0162] This equation simplifies to Equation B16:

[0163] Equation B16:

[0164] Assuming that the area of the upper Pv loop is approximately equal to the area of the lower Pv loop, Equation B17 can be used to estimate the total Pv loop area:

[0165] Equation B17:

[0166] The unknown maximum Pv can be estimated using the maximum Pa (assuming no pressure gradient from the ventricle to the downstream artery). The unknown minimum Pv can be estimated using the pulmonary capillary wedge pressure, left atrial pressure, or the closest approximation.

[0167] Estimation of individual ventricular dP / dt and Pv values: Estimation of ventricular Pv and dP / dt values can facilitate the calculation of other downstream ventricular loop measurements. The Pv value can be estimated from the Pa value using Equation B18:

[0168] Equation B18:

[0169] Similarly, the ventricular dPv / dt value can be estimated from the arterial dPa / dt value using Equation B19:

[0170] Equation B19:

[0171] (f(Pa) is the instantaneous dPa / dt at any Pa value)

[0172] The maximum dPv / dt value can be estimated from the maximum arterial dPa / dt value using Equation B20:

[0173] Equation B20:

[0174] Estimation of ventricular power: Based on the disclosed method, the Pv and dPv / dt values are estimated from arterial pressure loop data, and then the ventricular power can be estimated using Equations A1, A6, and A7 reproduced below:

[0175] Modified Equation A1:

[0176] Equation A6:

[0177] Equation A7:

[0178] Estimation of downstream blood flow and resistance: Based on the disclosed method, the ventricular power is estimated from arterial pressure loop data, and the measurements of downstream blood flow and resistance can be calculated using Equations A9, A10, A11, and A12 reproduced below:

[0179] Equation A9:

[0180] Equation A10:

[0181] Equation A11:

[0182] Equation A12:

[0183] Calculation of arterial power, blood flow, and resistance: Although the arterial pressure loop can be used to estimate ventricular pressure loop data and the downstream power, flow, and resistance calculation results, the same measurements can also be made directly from the arterial pressure loop, as shown in Equations B21 to B27. The differences in power, blood flow, and resistance between the ventricle and the artery need to be studied in real-life scenarios and may vary in terms of the assumed limits of power, flow, and resistance (related to ventricular function) and the observed power, flow, and resistance (related to arterial function).

[0184] Equation B21:

[0185] Equation B22:

[0186] Equation B23:

[0187] Equation B24:

[0188] Equation B25:

[0189] Equation B26:

[0190] Equation B27:

[0191] The disclosed subject matter does not rely on invasive Pa measurements for its analysis. The subject matter also has the ability to transform non-invasive arterial pressure waveforms into pressure loops. Examples of this can include non-invasive ultrasound Doppler and magnetic resonance imaging to measure arterial blood velocity and calculate corresponding arterial blood pressure over one or more cardiac cycles. The resulting pressure waveforms can be transformed into pressure loops and analyzed as described. The vibrations and sounds generated by blood traveling through arteries and the derivation of arterial blood pressure waveforms can also be transformed into arterial pressure loops and analyzed as described above. It is also contemplated that non-invasive blood pressure cuff measurements can be manipulated to generate and analyze arterial blood pressure loops as described above. Regardless of how the arterial blood pressure waveforms and / or arterial pressure loops are generated as described above, the disclosed subject matter, as described above, is the subject of patent protection for both 1) the creation of arterial pressure loops and 2) the analysis thereof.

[0192] When patients are evaluated for medical care around the world, vital signs are measured from arterial blood pressure versus time data. Universal vital signs include arterial blood pressure and heart rate. As described above, the disclosed subject matter further transforms arterial blood pressure versus time data into unique dPa / dt versus Pa pressure loops that can be used not only to characterize the way arterial blood is pressurized but also to estimate measurements of ventricular blood pressurization. Each of these unique arterial and ventricular blood pressure measurements made by the disclosed subject matter can be used as a vital sign to identify a patient's disease state.

[0193] Similar to blood pressure and heart rate metrics, it is foreseeable that abnormally sized or shaped arterial pressure loops may indicate an abnormal cardiovascular state. Like other vital signs, significantly reduced pressure loop sizes that are below normal and closer to zero may also portend cardiac death. It is also anticipated that the disclosed subject matter will be able to create pressure loops and calculate pressure loop area vital signs from invasive or non-invasive blood pressure measurements (such as from indwelling arterial pressure lines or non-invasive blood pressure cuffs) respectively.

[0194] Disclosed herein is an arterial pressure loop that graphically represents the instantaneous pressure change per unit time (y = dPa / dt) versus arterial pressure (x = Pa) Figure 9)。Generally, this function converts the pressure-versus-time waveform generated by each heartbeat into a loop with a unique shape that can be analyzed individually, compared to other beats from the same patient, and / or compared to other beats from different patients. The size, shape, symmetry, and position of each graphic loop facilitate a variety of unique analyses. Examples of measurements include the left point, right point, apex, and base point along the loop that are related to DBP, SBP, maximum dPa / dt, and minimum dPa / dt, respectively. The mean center of the loop represents both the MAP and the pressure variation throughout the beat. The loop height and width can be quantified. The loop area (summed sub-areas) can be quantified. The loop area per time can be quantified. The positions of the maximum and minimum dPa / dt relative to the loop center can be quantified. The symmetry of the maximum dPa / dt with respect to the dPa / dt loop position can be characterized. The tangents along the loop can be characterized. The presence, position, frequency, and amplitude of loop artifacts can be characterized. These loops combine the derived measurements, downstream secondary measurements, and other measurements to reveal the characteristics of arterial pressurization and depressurization with each heartbeat. Additional measurements of ventricular and arterial power, as well as downstream blood flow and resistance, can also be quantified.

[0195] There are many commercially available devices that have the ability to measure or estimate arterial pressure waveforms and report such data as traditional time-frequency plots. These systems lack pressure loop analysis functionality.

[0196] Figure 8 An example of a standard aortic blood pressure tracing displayed by a commercially available hemodynamic system is shown. The total measurements include systolic blood pressure, diastolic blood pressure, and mean arterial pressure.

[0197] Figure 9 An example of an arterial "pressure loop" generated by the disclosed subject matter for multiple consecutive heartbeats is shown. The loops generated from these heartbeats are shown to overlap each other (using the Figure 8 same raw data source). The black line is the average calculated from all the loops calculated for each heartbeat. The left and right borders indicate the points of minimum diastolic blood pressure and maximum systolic blood pressure, respectively. The top and bottom border amplitudes represent the maximum systolic dP / dt and minimum diastolic dP / dt, respectively.

[0198] Figure 10Shows variants of arterial pressure loops generated by the disclosed subject matter. These variants exhibit the property of arterial blood pressurization. The original loop and the overall average (black line) are shown. The loops in Column A appear to have a "smooth" and "symmetrical" upper half curve compared to the loops in Column B that appear "tilted" and "notched". In some plots (row 1), the point locations of the maximum / minimum dPa / dt (black dots) appear to "rotate" around the pressure median (red dot), but in other plots they are "shifted" away from the pressure mean (shifted to the right in row 2 and to the left in row 3). The rotation can be clockwise (Column A) or counterclockwise (Column B). In both columns, 1) the distance of the maximum / minimum dPa / dt points from the X-axis and 2) the area of the loop halves above / below the X-axis are variable. The relatively linear portion (lower left quadrant) of each loop exhibits a "nodal-free segment" where the periodic pressure fluctuations are relatively reduced / absent.

[0199] Figure 11 Shows a synchronous comparison of arterial pressure loop area with arterial pressure. The comparison between the total pressure loop area and the maximum Pa pressure (left plot) and the lower pressure loop area and the minimum Pa pressure (right plot) is made across multiple cardiac cycles. A significant linear relationship is exhibited for each of the shown comparisons and trend lines.

[0200] Figure 12 Shows a comparison of pressure loops generated by the same artery at different time points. The smaller inner loop is associated with abnormal cardiac function, while the larger outer loop is associated with normal cardiac function, both reflecting Figure 5 the ventricular pressure loops within.

[0201] Machine learning

[0202] Pressure loop diagrams related to both ventricular pressure and arterial pressure can be analyzed by a machine learning model. The machine learning algorithm can be a neural network. The neural network can be, for example, a convolutional neural network.

[0203] The machine learning model can learn the features of the images in order to screen for heart-related abnormalities. Training data can be provided to the machine learning model, which includes multiple sets of pressure loop diagrams from various patients, showing healthy hearts and hearts with abnormalities. For example, the model can use this data to associate specific combinations of features with healthy hearts and specific combinations of features with unhealthy hearts.

[0204] A machine learning model may include a binary classifier. The binary classifier may be capable of determining whether the heart in an image is healthy or whether an abnormality is present. In some cases, the binary classifier may be used to predict the presence or absence of a particular abnormality. In some cases, the machine learning model may include a multi-class classifier that may determine whether the heart includes an abnormality or whether the heart has one of several abnormalities. In some cases, the machine learning model may include a multi-label classifier that may assign at least one abnormality label to a pressure loop plot.

[0205] Arterial pressure decay constant τ Background Art:

[0207] A method for determining a time constant is disclosed. The method includes (a) making one or more consecutive waveform recordings of arterial pressure and dividing the one or more recordings into individual heartbeat cycles; (b) for each individual heartbeat cycle, converting the arterial pressure data into a rate of change of arterial pressure over time; (c) determining a rolling standard deviation for each set of dP / dt data, revealing the diastolic period with the least change in dP / dt value; and (d) determining a set of values below a threshold to define a timing range of possible arterial pressure values for calculating the time constant.

[0208] Natural or exponential decay (where the quantity of something decreases at a rate proportional to its current amount) is commonly observed and calculated in various scientific fields. In cardiovascular medicine, observations of it include the manner in which blood pressure decreases within the ventricles and downstream arteries. The rate of pressure decay is quantified by the natural decay time constant τ (Equations C1 to C2). In this field of study, τ has previously been calculated for the purposes of: 1) quantifying ventricular chamber relaxation using continuous ventricular pressure (Pv) measurements, 2) quantifying arterial vessel compliance (Equation C3), and 3) quantifying distal venous pressure using continuous arterial pressure (Pa) measurements (Equation C4). These calculations have also been performed using non-invasive estimates of cardiovascular pressure made using ultrasound Doppler techniques.

[0209] τ has not previously been used for the purpose of evaluating other characteristics of cardiovascular function, which include vascular resistance, cardiac output, observed versus expected arterial pressor performance, or severe obstruction between the ventricular chamber and the artery (i.e., aortic valve stenosis). The disclosed subject matter aims to perform these secondary evaluations based on Pa to derive τ. While other techniques have attempted to use continuous Pa measurements to make similar evaluations of cardiovascular function (Equations B6 to B10), none of them use τ.

[0210] The disclosed subject matter also calculates τ by identifying the diastolic period with the least periodic pressure fluctuations, which is not the way τ was previously calculated. For this proprietary measurement, specifically, the distal-to-proximal pressure ratio is quantified during the diastolic "wave-free period". Calculating τ using this technique is not done with this commercially available product.

[0211] Related equations:

[0212] Equation C1:

[0213] Equation C2: P(t) = P o e -t / T

[0214] Equation C3: T = Resistance × Compliance

[0215] Equation C4: P(t) = (P o -C)e -t / T +C

[0216] Equation C5: lim t→∞ P(t) = C

[0217] The calculation of the unknown τ and the value of the decay curve asymptote "C" from Equation C4 can be performed by the disclosed subject matter by fitting an exponential curve to a set of data points. The disclosed subject matter can also perform this calculation using the following unique methods and equations.

[0218] Establish an equation (Equation C6) that contains the difference between Equation C2 using the "original" τ and Equation C4 (from the value of the "corrected" τ). For simplicity, "pressure curve 1" uses the original τ, and "pressure curve 2" uses the corrected τ. For both curves, the P(t) value at diastolic time 0 is represented by "P". For curve 1, the pressure value at time "t" is represented by P1(t). For curve 2, the pressure value at time "t" is represented by: P2(t). T R is the "original" τ (known value), T C is the "corrected" τ (to be calculated), and C is the decay curve asymptote (to be calculated).

[0219] And

[0220]

[0221] Equation C6:

[0222] For time point t A and the corresponding curve point (t A , P1(tA )) and (t A , P2(t A ))

[0223] Equation C7:

[0224] For time point t B and the corresponding curve points (t B , P1(t B )) and (t B , P2(t B ))

[0225] Equation C8:

[0226] Then C and T are determined by the following C value (generating a curve (Equation C4) that passes through the points (t0, P0), (t A , P2(t A )) and (t B , P2(t B )): Find the intersection point of Equation C7 and C8

[0227] Equation C9:

[0228] Equation C10:

[0229] The disclosed subject matter automatically performs the primary calculation of arterial τ based on the minimum periodic pressure fluctuation period, which is guided by user input settings and loop-derived diastolic limits. The automatic calculation of τ involves multiple steps ( Figure 13 ), and begins as follows: Perform one or more consecutive Pa waveform recordings and divide them into individual heartbeat cycles. This can be done by using a common start trigger (such as the R wave of an electrocardiogram or the maximum dPa / dt point). Convert the Pa data for each beat to its first derivative dP / dt. Calculate the rolling standard deviation for each set of dP / dt data, revealing the diastolic period during which the dP / dt values change minimally. The timing of the standard deviation values below a pre-specified threshold (e.g., the lowest 10% of the standard deviation values) defines the timing range for the possible Pa values used in the original τ calculation. Identify the limitations of the timing range and the corresponding Pa values to calculate τ (Equation C1). Due to changes in the data sampling rate used to create the Pa waveform, the actual time intervals rather than the number of samples should be used to calculate τ

[0230] Once the “raw” τ has been calculated, the disclosed subject matter also calculates a “corrected” τ to compensate for the non-zero decay curve asymptote (Equations C4 and C5). The process for calculating the “corrected” τ involves any of the following: 1) using a widely available method to calculate the formula for the best fit exponential curve for the data, or 2) comparing the observed Pa values to the Pa values calculated based on the “raw” τ (Equation C2)( Figure 14 ). In the latter method, the timing and pressure at the point of maximum difference between the raw τ pressure curve and the actual Pa waveform are then used to calculate the “corrected” τ and the decay curve asymptote( Figure 15 ). Using the corrected τ and decay curve asymptote values in Equation C4 produces a pressure curve that is very close to the actual diastolic Pa waveform.

[0231] The raw and corrected τ values can be used for additional secondary calculations, including 1) back-calculation of the expected mono-exponential curve during systole, and 2) calculation of cardiac output. First, the disclosed subject matter can calculate the mono-exponential pressure curves that would occur during cardiac diastole and systole and compare the actual pressures to the calculated pressures. This analysis is performed from the beginning of systole to the end of diastole for each cardiac cycle. From this setup, various comparisons can be arranged for different waveform periods, including percentage of maximum pressure, percentage of mean pressure, and / or percentage of cumulative pressure relative to systole, diastole, and / or the entire heartbeat( Figure 16 ). Second, since τ and arterial resistance are directly related (per Equation C3), τ can be used in place of the resistance in Equation B1 (and also in Equations B2, B3, and B4) to create Equation C9. Variants of this equation include calculating the gradient between the mean arterial pressure and the decay curve asymptote from Equations C3 to C8 and dividing by the corresponding corrected τ (Equation C10). Since the τ and resistance values are related but not identical, a correction factor K is applied to Equations C9 and C10 to calculate cardiac output.

[0232] Figure 13 Determination of the diastolic period using minimal periodic pressure fluctuations is shown. A single arterial pressure waveform (top, solid line) is shown and the diastolic period with minimal periodic pressure fluctuations is identified (top graph, green line). This period is identified by performing a rolling calculation of the first derivative dP / dt on the Pa waveform (bottom graph, black line). Plotting this first derivative data reveals the period of minimal periodic pressure fluctuations within diastole. A pre-specified threshold of the lowest 10% of the standard deviation values (red line) defines this period, which occurs in data samples 116 to 236.

[0233] Figure 14 Determination of the mono-exponential curve asymptote by the disclosed subject matter is shown. A curve (red line) using the “raw” τ is created and passed through fromFigure 1 Points (116, 93.8) and (236, 56.2) during the diastolic period with minimal pressure fluctuations. The calculated original τ was approximately 234. The maximum difference between the original τ curve and the observed arterial pressure (black line) occurred at sample 172, corresponding to the points (172, 73.9) and (172, 68.0) on each line respectively. These point values and the original τ were used to fill equations C7 and C8.

[0234] Figure 15 Shows the determination of "corrected" τ by the disclosed subject matter. Using the point values and the original τ from Figure 14 to fill equations C7 and C8. Solving these equations yields the corrected τ values and the single-exponential curve asymptotes (for the example beat, τ of 65 samples, 0.270 seconds or 0.0045 minutes, and asymptote 49.2 mmHg). Inserting these values into equation C4 produces a curve using the corrected τ that passes through the points (116, 93.8), (172, 68.0), (236, 56.2), and follows the observed diastolic pressure waveform (green line) more closely than the curve using the original τ (red line).

[0235] Figure 16 Shows the comparison of the calculated and actual arterial pressures by the disclosed subject matter. Examples of measurements derived from diastolic τ and back-calculated systolic pressure include the actual and calculated maximum cumulative pressure percentages and maximum pressure percentages. Different measurement arrangements include those calculated for systole only (based on the solid line), the entire beat (based on the dashed line), the original τ (red line and values), and the corrected τ (green line and values).

[0236] Analysis for blood flow obstruction

[0237] Discloses a method. The method includes: (a) determining a set of arterial-to-ventricular systolic pressure ratios (AVPR) over multiple cardiac cycles by dividing the pressure after obstruction by the pressure before obstruction to obtain values from systole; and (b) generating a plot including each of the set of arterial-to-ventricular pressure ratios.

[0238] Cardiac diseases that typically cause obstruction to blood flow from the ventricle to its downstream artery are usually evaluated. Pulmonary valve stenosis is a common cause of obstruction at the right ventricular outflow to the pulmonary artery. Aortic valve stenosis (and supravalvular and subvalvular stenosis) and hypertrophic obstructive cardiomyopathy (HOCM) are common causes of obstruction at the left ventricular outflow to the aorta ( Figure 17)。HOCM is caused by dynamic obstruction to the left ventricular outflow tract due to abnormal interference of the ventricular myocardium and the mitral valve apparatus during ventricular contraction. For pulmonary valve stenosis, abnormally elevated transvalvular pressure gradients (by invasive and non-invasive techniques) and indices of adverse right ventricular effects are used to determine the need for valve intervention and stenosis relief. For aortic valve stenosis, abnormally elevated transvalvular flow velocities and pressure gradients (by non-invasive echocardiography) and abnormally reduced aortic valve area (by both echocardiography and invasive valve studies) are used to determine the need for valve intervention and stenosis relief. For HOCM, abnormally elevated flow velocities and pressure gradients across the obstruction (by non-invasive echocardiography) and / or abnormally elevated pressure gradients (by invasive pressure measurement) are used to determine the need for obstruction intervention and relief.

[0239] In particular, aortic valve studies utilize invasive measurements from 1) right heart catheterization and 2) left heart catheterization and simultaneous pressure measurements across the stenotic valve to calculate the aortic valve area using the Gorlin equation (Equation D1) or the Hakki equation (Equation D2). Additionally, any of the aforementioned measurements can be performed on a patient in different physiological states, including baseline rest, exercise, or drug stress. Echocardiography is commonly used to assess the difference between the relatively static flow resistance due to aortic valve stenosis and the dynamic resistance due to HOCM. Invasive hemodynamic assessment of HOCM and aortic valve stenosis can only roughly distinguish between the two etiologies.

[0240] The disclosed subject matter is directed to providing an analysis of the obstruction from the ventricle to its downstream artery to quantify the degree of obstruction present, characterize the static / dynamic nature of the obstruction, and characterize the effect of the obstruction on ventricular and arterial blood pressurization. Methods for doing so are described below.

[0241] Related equations:

[0242] Equation D1 (Gorlin):

[0243] Equation D2 (Hakki):

[0244] Equation D3:

[0245] Equation D4:

[0246] Equation D5:

[0247] The disclosed subject matter analyzes ventricular-to-arterial obstruction by: 1) calculating and displaying the arterial-to-ventricular 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 located between the ventricle and its downstream artery. These obstructions include, but are not limited to, pulmonary valve stenosis, aortic valve stenosis, HOCM, and arterial narrowing. Additionally, the disclosed subject matter performs these analyses by analyzing simultaneous invasive blood pressure measurements upstream and downstream of the stenosis. These analyses can also be performed on a patient at rest at baseline and repeated during exercise or drug-induced cardiac stress. For the analysis of the disclosed subject matter, no additional invasive right heart catheterization is required.

[0248] The systolic AVPR is generated by: calculating the post-obstruction pressure (lower value) multiple times, dividing by the pre-obstruction pressure (higher value) to obtain any value that occurs during systole (identified when pre-obstruction pressure > post-obstruction pressure) (Equation D3). The AVPR calculated during each systolic period can be graphically displayed by the disclosed subject matter and compared to results from different cardiac cycles ( Figures 18 to 21 ). These repeated measurements can also be displayed for comparison over time ( Figure 4 , row 2) and / or analyzed as a whole ( Figure 4 , table). The AVPR measurements can also be compared to simultaneous ventricular and arterial pressure loop measurements ( Figure 5 ) in order to report the severity of ventricular-to-arterial obstruction as well as cardiac function, both of which contribute to the pressure gradient observed across the blood flow obstruction.

[0249] The disclosed subject matter can also use a simplified process for approximating the systolic AVPR (Equation D3). This simplified method can also be used to calculate the "resistance fraction" of ventricular-to-arterial obstruction (Equation D4). Generally, this simplified method simplifies the calculation and averaging of the instantaneous AVPR by taking advantage of: 1) the mean gradient across the obstruction and 2) the mean arterial pressure, which is typically measured by invasive and non-invasive methods. For pulmonary stenosis, the estimated AVPR will be the quotient of the mean pulmonary artery pressure (mPAP) divided by the sum of mPAP plus the mean pulmonary valve gradient. For aortic stenosis, the estimated AVPR will be the quotient of the mean arterial pressure (MAP) divided by the sum of MAP and the mean aortic valve gradient. For HOCM, the estimated AVPR will be the quotient of MAP divided by the sum of MAP and the mean left ventricular outflow tract gradient. In each of these cases, the estimated resistance fraction due to the obstruction will be the mathematical complement of the AVPR.

[0250] The present disclosure aims to protect a method in which the disclosed subject matter calculates an exact AVPR (Equation D3) and an estimated AVPR (Equation D4) and their mathematical complement known as the "resistance fraction". Further, the AVPR is a unitless value that conceptually reflects the reduction in blood flow due to an obstruction, and its "resistance fraction" complement is a unitless value that conceptually reflects the blood flow resistance caused by the obstruction as compared to other resistance sources within the same blood flow circulation. Thus, if an exact measure of blood flow or resistance is known, the AVPR and its complement can be used to calculate the absolute resistance due to the obstruction ( Figure 4 Table). The present disclosure also aims to protect such downstream calculations that result from first calculating the AVPR by the disclosed subject matter.

[0251] For the disclosed subject matter, 1) for the analysis of the disclosed subject matter, a cardiac stress test with exercise or a pharmaceutical agent is admissible but not required, 2) a variety of other functions (none of which were described in the foregoing manuscript) in addition to the arterial-to-ventricular pressure ratio calculation are performed by the disclosed subject matter, and 3) the analysis performed by the disclosed subject matter is not limited to the study of a stenotic aortic valve but extends to any other condition that causes ventricular-to-arterial obstruction (e.g., HOCM that obstructs blood flow through the left ventricular outflow tract and pulmonary valve stenosis that obstructs blood flow from the right ventricle).

[0252] The method of calculating the AVPR by the disclosed subject matter is slightly similar to but significantly different from the "fractional flow reserve" (FFR) measurement, which is performed to quantify the severity of an obstruction due to a coronary artery stenosis. The FFR is calculated as the average ratio of the coronary artery pressure distal to the obstruction to the aortic pressure proximal to the obstruction during all parts of the cardiac cycle. The FFR must also be performed during a peak blood flow state induced by a coronary vasodilator drug such as adenosine. The AVPR differs in that: 1) the location of the pressure measurement outside of the coronary arteries, 2) the ability to be measured in any physiological state including baseline rest, drug-induced peak blood flow, and exercise, 3) a specific measurement during systole rather than the entire cardiac cycle, and 4) a synchronous ventricular and arterial pressure loop analysis.

[0253] Figure 17 Synchronous pressure tracings from ventricular (1710A to 1710C) and downstream arterial (1720A to 1720C) pressure sources are shown, revealing systolic pressure gradients due to aortic valve stenosis (left graph), hypertrophic obstructive cardiomyopathy (HOCM: middle graph), and pulmonary valve stenosis (right graph). The pressure measurements from the left heart (left ventricle and aorta) and the right heart (right ventricle and pulmonary artery) are significantly different in amplitude, but both can be used by the disclosed subject matter to analyze the degree of blood flow obstruction due to valvular disease.

[0254] Figure 18 shows the arterial-to-ventricular pressure ratio generated by the disclosed subject matter during valvular stenosis. The disclosed subject matter identifies multiple cardiac cycles ( Figure 18 ) during a synchronous pressure measurement period in order to calculate and display the systolic arterial-to-ventricular pressure ratio in this example of aortic valve stenosis. The minimum pressure ratio regularly appears near the 30th sample, where a consistent degree of obstruction is observed between different cardiac cycles. Assessment of pulmonary valve stenosis can be performed similarly.

[0255] Figure 19 shows the arterial-to-ventricular pressure ratio generated by the disclosed subject matter during hypertrophic obstructive cardiomyopathy. Synchronous pressure measurement results are used to analyze multiple cardiac cycles in order to calculate and display the systolic arterial-to-ventricular pressure ratio (AVPR). Compared to the smooth curve ( Figure 19 ) generated during aortic stenosis, the analysis of hypertrophic obstructive cardiomyopathy exhibits dynamic obstruction, where a curve depression appears near the 50th sample and a variable degree of obstruction is observed between different cardiac cycles. This graphical analysis can also support finding two sources of obstruction; the first obstruction may be due to asymmetric septal hypertrophy that occurs at the 30th sample associated with a higher (and more consistent) mean AVPR, while the second obstruction may be due to presystolic anterior motion of the mitral valve leaflets that occurs at the 75th sample associated with a lower (and more consistent) mean AVPR.

[0256] Figure 20 shows the arterial-to-ventricular pressure ratio metric generated by the disclosed subject matter. The disclosed subject matter quantifies and facilitates the characterization of the arterial-to-ventricular pressure ratio (AVPR) generated from synchronous arterial and ventricular pressure measurements. The graphical display enables visualization of overlapping cardiac cycles and AVPR calculations (row 1) to assess different disease conditions, such as aortic valve stenosis (column 1), hypertrophic obstructive cardiomyopathy (HOCM; column 2), and pulmonary valve stenosis (column 3). The stability or instability of the AVPR across multiple cardiac cycles can be graphically displayed (row 2), which can be modified and tracked over time by different excitation techniques / agents. Different possible metrics include, but are not limited to, mean AVPR (e.g., minimum, maximum, mean, range, standard deviation across multiple beats), minimum AVPR across multiple beats, and the timing (table) of the minimum AVPR during systole. The AVPR can be used to perform τ calculations (e.g., from equations D1 to D4). Notably, these analyses are performed based on left heart (aorta and left ventricle, examples 1 and 2) or right heart (pulmonary artery and right ventricle, example 3) pressure measurement results.

[0257] Figure 21 Displays a novel comparison of multiple pressure loops generated by the disclosed subject matter. Multiple cardiac cycles are analyzed to generate ventricular pressure loops (2110A to 2110B) and arterial pressure loops (2120A to 2120B). Comparison of the ventricular and arterial pressure loops between the left graph (example of aortic stenosis) and the right graph (example of hypertrophic obstructive cardiomyopathy) reveals significantly different loop sizes and shapes as well as other characteristics. In some cases, AVPR data can 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 that can be displayed on a computer screen). The electronic report can include a three-dimensional (3D) graph that includes both the pressure loop and AVPR data.

[0258] Analysis of resistance fraction

[0259] Disclosed is a method for determining a resistance fraction by: (a) determining a segment gradient associated with a portion of the heart; (b) determining a total gradient associated with the portion of the heart; and (c) dividing the segment gradient by the total gradient.

[0260] Quantification of blood flow resistance between the upstream artery and the downstream vein is routinely performed as part of an invasive assessment of cardiovascular hemodynamics. Pulmonary conditions that result in pulmonary vascular disease are reflected in abnormally elevated pulmonary vascular resistance (PVR), while systemic conditions that result in systemic vascular disease within the body are reflected in abnormally decreased or elevated systemic vascular resistance (SVR).

[0261] Assessment of pulmonary disease and PVR is typically performed by performing a right heart catheterization (RHC). RHC allows measurement of cardiac output, central venous pressure (CVP), right ventricular pressure, pulmonary artery pressure (PAP), and pulmonary capillary wedge pressure (PCWP). PCWP is determined by "wedging" a small balloon within a pulmonary artery branch, thereby obstructing blood flow and allowing measurement of the pressure distal to the obstruction. A secondary calculation of PVR is routinely performed based on these RHC measurements (Equation E1). PVR is measured in Wood units, which can be multiplied by 80 and converted to dynes second / cm 5 , and is an absolute (rather than relative) measure of pulmonary blood flow resistance. An existing variant of PVR is the PVR index, which normalizes the PVR value based on the patient's body surface area (Equation E2). Disease conditions associated with abnormally elevated PVR include emphysema, pulmonary fibrosis, and pulmonary thromboembolic disease.

[0262] The assessment of systemic diseases that affect arterial blood pressure and SVR is typically performed by combining some RHC measurements (CVP and cardiac output) with additional measurements of mean arterial pressure (Equation E3). SVR is also measured in Wood units, converted to dynes seconds / cm 5 , and normalized by the patient's body surface area (Equation E4). Reduced SVR is associated with disease states such as infection and vasoplegia, while elevated SVR is associated with disease states such as hypertension, hypovolemia, and congestive heart failure.

[0263] Relevant equations:

[0264] Equation E1: Equation E2: Equation E3: Equation E4: Equation E5:

[0265]

[0266] Equation E6:

[0267] For PVR or SVR estimation, 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 provide specific examples for calculating pulmonary and systemic resistance fractions.

[0268] Equation E7: Equation E8:

[0269] mPAP = mean pulmonary artery pressure; PCWP = pulmonary capillary wedge pressure; MAP = mean arterial pressure; CVP = central venous pressure

[0270] The disclosed subject matter analyzes blood flow obstruction between the upstream artery and the downstream vein, which is uniquely different from the analysis of PVR or SVR. According to equations E1 to E4, the pressure gradient present within a vascular segment is proportional to the vascular resistance present, and gradients of different amplitudes are typically normalized against cardiac output and BSA to facilitate comparison. The disclosed subject matter uniquely calculates a "resistance fraction" that 1) is a measure of resistance through a specific part / segment of the cardiovascular flow circuit and 2) is normalized by the total resistance through the entire circuit. This calculation is derived by correlating the ratio of segmental resistance to total resistance with the ratio of pressure gradients based on equations E1 and E3 (equation E5). Thus, the "resistance fraction" calculation is the ratio of the segmental pressure gradient to its corresponding total pressure gradient (equation E6). In doing so, the disclosed subject matter normalizes the pressure gradient against other high-fidelity pressure measurements rather than other potentially problematic measures (cardiac output and body surface area). The resistance fraction calculation of the disclosed subject matter 1) provides alternative measurements of traditional PVR and SVR respectively (equations E7 and E8), 2) quantifies PVR and SVR as relatively unitless measurements, and 3) facilitates comparison of values across different cardiac output states, body sizes, and individuals. This calculation of the resistance fraction measurement result has never been described previously and is not commercially available. In addition to estimating PVR and SVR, the analysis of the disclosed subject matter can also be used to calculate the resistance fractions of other hemodynamic obstructions listed below:

[0271] Resistance fraction between the right ventricle and the left ventricle:

[0272] · Due to Pulmonary valve stenosis Resulting resistance fraction:

[0273] o Segment gradient = systolic right ventricular pressure minus systolic pulmonary artery pressure.

[0274] o Total gradient = systolic right ventricular pressure minus zero.

[0275] · Due to Pulmonary stenosis (e.g., chronic thromboembolic pulmonary disease) Resulting resistance fraction

[0276] o Segment gradient = mean Pulmonary artery pressure (mPAP) proximal to the thromboembolic disease minus mPAP distal to the thromboembolic disease.

[0277] o Total gradient = mPAP proximal to the thromboembolic disease minus zero.

[0278] · Due to between the left atrium and the left ventricle Mitral valve stenosis Resulting resistance fraction

[0279] o Segment gradient = diastolic PCWP minus diastolic left ventricular pressure.

[0280] o Total gradient = diastolic PAP minus diastolic left ventricular pressure.

[0281] · Due to Blood accumulation in the left atrium and left ventricle Resulting resistance fraction (e.g., congestive heart failure due to left ventricular dysfunction)

[0282] o Segment gradient = PCWP minus zero.

[0283] o Total gradient = mPAP minus zero.

[0284] Resistance fraction between the left ventricle and the right ventricle:

[0285] · Due to the aortic valve Membrane stenosis Resulting resistance fraction:

[0286] o Segment gradient = systolic left ventricular pressure minus systolic aortic pressure.

[0287] o Total gradient = systolic left ventricular pressure minus zero.

[0288] · Due to Hypertrophic obstructive cardiomyopathy Resulting resistance fraction:

[0289] o Segment gradient = systolic left ventricular pressure proximal to the obstruction minus systolic left ventricular outflow tract distal to the obstruction.

[0290] o Total gradient = systolic left ventricular pressure proximal to the obstruction minus zero.

[0291] · Due to Arterial stenosis (e.g., coronary artery disease, peripheral artery disease, coarctation of the aorta) Resulting resistance fraction

[0292] o Segment gradient = mean proximal arterial pressure minus mean distal arterial pressure.

[0293] o Total gradient = mean proximal arterial pressure minus zero.

[0294] · Due to Systemic Vein Stenosis (e.g., thrombotic filter device in the inferior vena cava) Resulting resistance fraction.

[0295] o Segment gradient = mean distal venous pressure minus mean proximal venous pressure.

[0296] o Total gradient = mean arterial pressure minus zero.

[0297] · Due to Tricuspid valve stenosis Resulting resistance fraction

[0298] o Segment gradient = diastolic CVP minus diastolic right ventricular pressure.

[0299] o Total gradient = diastolic arterial pressure minus diastolic right ventricular pressure.

[0300] · Due to Blood accumulation in the right atrium and right ventricle Resulting resistance fraction (e.g., congestive heart failure due to right ventricular dysfunction)

[0301] o Segment gradient = CVP minus zero.

[0302] o Total gradient = MAP minus zero.

[0303] Resistance fraction in special cases: The disclosed subject matter can calculate resistance fraction values in special cases. One example is quantifying the resistance fraction of the mitral valve during left ventricular systole, which should be closer to the value one during normal valve function and closer to zero in the case of more severe mitral regurgitation. A similar example is quantifying the resistance fraction of the tricuspid valve during right ventricular systole, which should also be closer to the value one during normal valve function and closer to zero in the case of more severe tricuspid regurgitation. The resistance fraction in these cases can be calculated from mean or peak input values and reported as peak or mean output values. The baseline left atrial pressure can be set at the level where the ventricular and atrial pressures are equal in early systole, or any other baseline level.

[0304] · Due to Mitral valve regurgitation Resulting resistance fraction

[0305] o Segment gradient = systolic left ventricular pressure minus non-baseline systolic left atrial pressure.

[0306] o Total gradient = systolic left ventricular pressure minus baseline left atrial pressure.

[0307] · Due to Tricuspid valve regurgitation Resulting resistance fraction

[0308] o Segment gradient = systolic right ventricular pressure minus non-baseline right atrial pressure.

[0309] o Total gradient = systolic right ventricular pressure minus baseline right atrial pressure.

[0310] It can be expected that the resistance fraction calculation is more reliable when 1) there are fewer sources of resistance fraction present synchronously, and 2) there is no or minimal intracardiac shunt. In the case of multiple overlapping sources of resistance fraction and significant intracardiac shunt, specific calculations beyond the scope of this disclosure may be required.

[0311] Existing products for measuring "fractional flow reserve" (FFR) in the coronary arteries utilize a similar basic principle to the disclosed subject matter, but do not perform the same analysis for several important reasons. FFR is the ratio of the maximum achievable blood flow through an epicardial coronary artery to the maximum theoretical blood flow. Pressure measurements are used as a surrogate for blood flow. FFR is calculated as the ratio of the distal pressure divided by the aortic pressure (Pd / Pa) achieved during hyperemia, and a value <0.75–0.80 identifies a significantly reduced blood flow across the vessel segment under examination. The resting Pd / Pa value without induced hyperemic blood flow is also used for the same purpose. The Pd / Pa value has no units and conceptually represents the fraction of the maximum theoretical blood flow that occurs in the presence of a coronary stenosis. Relief of the coronary stenosis would theoretically increase the FFR to its theoretical maximum of 1.0. The values used to calculate FFR or the resting Pd / Pa are different from those used by the disclosed subject matter to calculate the resistance fraction.

[0312] Conceptually, CFR represents the scaling factor by which coronary blood flow increases during hyperemia relative to the resting period. While 1 - Pd / Pa can be rearranged to (Pa - Pd) / Pa, reflecting the right - hand side expression of Equation E6, existing methods must also create a ratio of the (Pa - Pd) / Pa values that occur during hyperemia and at rest in order to produce their CFR measurement. Each of the numerator and denominator elements of this aforementioned ratio: 1) is a unique measurement of resistance rather than blood flow, 2) is additive to each other when acting as series resistance values, 3) applies to all cardiovascular regions (not just the coronary arteries), and 4) can be further adjusted when measured in the presence of ventriculo - arterial obstruction (e.g., pulmonary valve stenosis, aortic valve stenosis, HOCM), making them uniquely different from the prior art and forming the basis of the disclosed subject matter.

[0313] Analysis of Coronary Resistance

[0314] A method is disclosed that includes: (a) determining a fixed epicardial resistance; (b) determining a fixed microvascular resistance; (c) determining an adenosine - responsive microvascular resistance; (d) determining a drug - responsive epicardial resistance; (e) determining a drug - responsive microvascular resistance; and (f) determining the total resistance of blood flow at least in part by determining the sum of the fixed epicardial resistance, the fixed microvascular resistance, the adenosine - responsive microvascular resistance, the drug - responsive epicardial resistance, and the drug - responsive microvascular resistance.

[0315] The resistance to blood flow through the entire coronary artery appears in both the large - caliber proximal epicardial segments and the distal microvascular structures. While there are high - quality measurements of the resistance to blood flow due to epicardial coronary artery disease (CAD), there are no high - quality measurements of the resistance to blood flow due to microvascular CAD. This is a description of the measurement of the resistance to blood flow due to coronary artery disease in both epicardial and microvascular coronary artery segments.

[0316] The reduction in coronary blood flow due to epicardial CAD is most accurately and precisely quantified by: measuring the proximal arterial pressure (Pa) and the distal pressure (Pd) across the diseased segment at maximum hyperemia and calculating the fractional flow reserve (FFR; Equation F1). The ratio of resting non - hyperemic Pd / Pa is also used to approximate FFR using data from the entire cardiac cycle (Equation F2) and the diastolic "no - wave" period (Equation F3). Non - invasive techniques for estimating FFR have also been developed, such as using cineangiography or optical coherence tomography coronary angiography.

[0317] The reduction in coronary blood flow due to microvascular CAD is approximated by calculating the ratio of hyperemic to resting coronary blood flow, called the coronary flow reserve (CFR; Equation F4). Although CFR is generally used to quantify the extent of microvascular CAD, its value is also affected by epicardial CAD, thus making CFR a non - specific measure of microvascular CAD. In the presence of severe epicardial CAD, it is difficult to accurately identify an abnormally reduced CFR due to concurrent microvascular CAD. All techniques for measuring CFR suffer from this limitation. These CFR techniques include coronary blood velocity measurement, indicator thermodilution, non - invasive coronary blood flow imaging, and pressure - derived CFR (CFRp).

[0318] Specific measurements of microvascular CAD have previously been created based on synchronized FFR and CFR values. The first measurement is the hyperemic microvascular resistance, which is calculated as the distal epicardial pressure (Pd) divided by the mean peak blood velocity measured during hyperemia (Equation F6). The second measurement is the microvascular resistance index, which is calculated as Pd multiplied by the mean transit time of the temperature curve measured during hyperemia (Equation F7). The third measurement is the myocardial flow reserve (MFR), which is calculated as the ratio of hyperemic myocardial blood flow to resting myocardial blood flow and is quantified by non - invasive positron emission tomography or cardiac magnetic resonance techniques.

[0319] The analysis of coronary artery resistance described in this section quantifies both epicardial and microvascular coronary artery resistance in a manner different 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 coronary artery resistance sources, 3) determining the relative coronary artery resistance due to vessels that can dilate in response to the agent adenosine, 4) determining the relative coronary artery resistance due to vessels that can constrict or dilate in response to non-adenosine agents (i.e., nitroglycerin, calcium channel blockers, other types of drugs that have an effect on coronary artery resistance), 5) using both / only the results of FFR and CFR measurements to perform this type of analysis, and 6) uniquely utilizing pressure to arrive at the formula for CFR. While other commercially available devices and techniques are capable of measuring both FFR and CFR, each of these aspects of the analysis of the disclosed subject matter differentiates it from existing methods of quantifying microvascular coronary artery resistance.

[0320] Related equations:

[0321] Equation F1:

[0322] Equation F2:

[0323] Equation F3:

[0324] Equation F4:

[0325] Equation F5:

[0326] Equation F6:

[0327] Equation F7: IMR = (Pd × T mn ) 充血

[0328] Equation F8:

[0329] Equation F9: R 固定，充血 = R 固定，心外膜 + R 固定，微血管

[0330] Equation F10: R 总，静息 = R 固定，心外膜 + R 固定，微血管 + R 腺苷，微血管

[0331] Equation F11:

[0332]

[0333] CPR = 1 + R adenosine, microvasculature

[0334] Equation F12: R 腺苷，微血管 = CFR - 1

[0335] Equation F13: R 固定，微血管 = FFR

[0336] Equation F14: R 固定，心外膜 = 1 - FFR

[0337] Equation F15: R 总，静息 = CFR

[0338] When solving for MER and MMR,

[0339] ·R 总，药物前，静息 ×κ = R 总，药物后，静息

[0340] ·

[0341] ·R 总，药物后，静息 = CPR 药物后

[0342] ·R 总，药物前，静息 = (1 - FFR 药物前 ) + (FFR 药物前 ) + ARMR 药物前

[0343] ·κ[(1 - FFR 药物前 ) + (FFR 药物前 ) + ARMR 药物前 = CFR 药物后

[0344] ·R 总，药物前，静息 = (R 药物，心外膜 + R 固定，心外膜 ) + (R 药物，微血管 + R 固定，微血管 ) + ARMR 药物前

[0345] ·(R 药物，心外膜 + R 固定，心外膜 ) = κ(1 - FFR 药物前 )

[0346] .(R 药物，微血管 + R 固定，微血管 ) = κ(FFR 药物前 )

[0347] ·R 固定，心外膜 = 1 - FFR 药物后

[0348] ·R固定，微血管 = FFR 药物后

[0349] Solve for MER: (R 药物，心外膜 + R 固定，心外膜 ) = κ(1 - FFR 药物前 )

[0350] R 药物，心外膜 = κ(1 - FFR 药物前 ) - R 固定，心外膜

[0351] R 药物，心外膜 = κ(1 - FFR 药物前 ) - (1 - FFR 药物后 )

[0352] Equation F16: R 总，药物前，静息 = (R 药物，心外膜 + R 固定，心外膜 ) + (R 药物，微血管 + R 固定，微血管) + ARMR 药物前

[0353] Equation F17: R 药物，心外膜 = κ(1 - FFR 药物前 ) - (1 - FFR 药物后 )

[0354] Equation F18: R 药物，微血管 = κ(FFR 药物前 ) - FFR 药物后

[0355] Equation F19: R 总，药物 = R 药物，心外膜 + R 药物，微血管

[0356]

[0357] The disclosed subject matter conducts its analysis of coronary artery resistance by first establishing a conceptual framework for identifying multiple sources of resistance and then measuring the relative amounts of all coronary artery resistance sources. This allows the disclosed subject matter and the user to subsequently evaluate the coronary artery response to vasodilatory agents such as adenosine, nitroglycerin, calcium channel blockers, etc.

[0358] The conceptual framework for coronary artery resistance of the disclosed subject matter is that the total resistance is the sum of multiple different resistance subtypes. This concept is based on the inverse relationship between cardiovascular flow and resistance (Equations B1 and F8). At baseline resting conditions, five resistance subtypes are identified:

[0359] 1) Fixed epicardial resistance (FER)

[0360] 2) Fixed microvascular resistance (FMR)

[0361] 3) "Adenosine-responsive" microvascular resistance (ARMR)

[0362] 4) "Drug-responsive" epicardial resistance (MER)

[0363] 5) "Drug-responsive" microvascular resistance (MMR)

[0364] Epicardial resistance is defined as the resistance that appears proximal to the intracoronary pressure sensor used to measure FFR / CFRp. Microvascular resistance is defined as the resistance that occurs distal to the intracoronary pressure sensor. It is anticipated that a pressure sensor positioned within a distal epicardial artery segment will facilitate the analysis of the disclosed subject matter. Fixed resistance is the resistance remaining after administration of a vasodilatory agent. Adenosine-responsive resistance is the resistance directly affected after in vivo administration of adenosine to induce maximal coronary hyperemia. Drug-responsive resistance is the resistance directly affected after administration of a vasodilatory drug other than adenosine; this is a general term to encompass the use of various coronary vasodilators, but admittedly, the most commonly used agent during FFR / CFRp measurement is nitroglycerin.

[0365] Nitroglycerin is routinely administered prior to FFR / CFR measurement to dilate the epicardial arteries and provide a baseline hemodynamic state for comparison of different measurements. After administration of a coronary vasodilator such as nitroglycerin, it is assumed that MER and MMR are reduced to zero, and the remaining sources of coronary resistance are simplified to include three resistance subtypes:

[0366] 1. Fixed epicardial resistance (FER)

[0367] 2. Fixed microvascular resistance (FMR)

[0368] 3. "Adenosine-responsive" microvascular resistance (ARMR), by measuring FFR and CFRp before and after administration of a vasodilatory drug, all given resistance subtypes can be calculated by the disclosed subject matter. By measuring FFR and CFRp only after administration of a vasodilatory drug, only three resistance subtypes can be identified. By measuring FFR and CFRp without administration of any vasodilatory drug, the three resistance subtypes can be approximated while the effect of drug-responsive resistance remains unknown.

[0369] In the simplest clinical scenario, a vasodilator drug is administered and then FFR and CFRp are measured. During adenosine-induced hyperemic flow, ARMR transiently drops to zero and total fixed coronary resistance (TFR) equals the sum of FER and FMR (Equation F9). During resting flow, when ARMR returns to baseline levels, total coronary resistance (TCR) equals the sum of FER, FRM, and ARMR (Equation F10). The ratio of TCR / TFR can be equal to CFR (Equation F11). By defining TFR as the relative value 1, Equation F11 can be simplified and ARMR defined (Equation F12). Then, FMR and FER are equal to FFR and the complement of FFR, respectively (Equations F13 and F14). Accordingly, the first three subtypes of coronary resistance are quantified by the disclosed subject matter (Equations F12 to F14), and their sum is equal to the value of CFR (Equation F15).

[0370] In the next clinical scenario, FFR and CFRp are measured twice, once before and once after administration of a vasodilator drug. During hyperemic flow that occurs before administration of the vasodilator drug, ARMR transiently drops to zero and TFR equals the sum of FER, FMR, MER, and MMR. At baseline flow, TCR equals the sum of FER, FMR, MER, MMR, and ARMR (Equation F16). Since TCR is defined as a value that does not change, but is also equal to the value of CFR that may vary between measurements (Equations F10 and F15), a correction is used to keep TCR constant. Using the known values of FFR and CFR measured before and after drug administration, the relative values of MER, MMR, and their sum can be solved (Equations F17 to F19). This method can be used to evaluate the effects of various drugs (including nitroglycerin / nitrates, calcium channel blockers, beta blockers, and other classes of drugs) on coronary resistance. Based on the conceptual framework of the disclosed subject matter and the method for determining total coronary resistance and each of its subparts, various specific and unique measurements of microvascular resistance can be produced (table, non-exhaustive). While many different combinations of resistance measures can be reported by the disclosed subject matter, it is not yet known which of these unique results of microvascular resistance produced by the disclosed subject matter will be most useful for clinical or research purposes. Three unique measurements reported by the disclosed subject matter worthy of patent protection consideration include 1) ARMR / TFR, 2) TFR / CFR, and 3) total drug-responsive resistance.

[0371] First, the ARMR / TFR indicates the amount of adenosine-responsive resistance present relative to the amount of total fixed resistance present. A higher ARMR / TFR can indicate good vascular health due to low epicardial and microvascular CAD burden and high "ARMR reserve", while an ARMR / TFR of zero can indicate poor vascular health due to high CAD burden and / or zero ARMR reserve. Although the value of ARMR / TFR simplifies to CFR-1, the conceptual framework of the disclosed subject matter imparts highly significant meaning to this basic but novel calculation.

[0372] Second, the TFR / CFR indicates the ratio of fixed coronary resistance to resting total coronary resistance. A TFR / CFR approaching zero can indicate a low relative burden of epicardial and microvascular CAD, while a TFR / CFR of 1 indicates that all coronary resistance present at rest is due to the effects of CAD. In general, this metric can be converted / used to report the "percentage CAD burden" within the artery. Although the value of TFR / CFR simplifies to 1 / CFR, the conceptual framework of the disclosed subject matter imparts highly significant meaning to this basic but novel calculation.

[0373] Third, total drug-responsive resistance and its epicardial / microvascular sub-parts are a completely unique metric reported by the disclosed subject matter. A positive drug-responsive resistance value indicates the amount of resistance relieved (due to vasodilatory effects) by the administration of a drug, while a negative drug-responsive resistance value indicates the resistance imparted (due to vasoconstrictive effects) by the administration of a drug. The total drug-responsive resistance value indicates the total effect of the drug on coronary resistance, and the values of the sub-parts of total drug-responsive resistance indicate the regionality and symmetry of that effect. This metric can potentially be used to: 1) identify unexpected vasoconstrictive effects imparted by the administration of a drug, which may presage a dysfunctional vascular state, 2) test and compare the effects of existing vasodilator drugs on specific coronary arteries and individuals in order to customize medical therapy, and 3) test and characterize unknown effects of drugs on coronary resistance.

[0374] While the foregoing description has discussed methods of coronary artery resistance analysis of the disclosed subject matter, the means of analysis is also worthy of discussion and consideration for patent protection. Regardless of the means used, all of the described calculations can be performed using the measurements of FFR and CFR. Existing methods can measure FFR alone, measure CFR alone, or even measure FFR and CFR synchronously. However, regardless of the means used to measure FFR and CFR, subsequently using the described method to analyze the coronary artery resistance is the subject matter for patent protection. Additionally, while the disclosed subject matter uses pressure-derived CFR as its means of calculating CFRp, the distinctiveness / patentability of the specific means of measuring CFRp of the disclosed subject matter should not affect the separate consideration of the distinctiveness / patentability of the subsequent coronary artery resistance analysis of the disclosed subject matter.

[0375] Diastolic pressure-derived CFR

[0376] A method is disclosed that includes: (a) using a pressure wire to match diastolic ventricular pressure measurements and diastolic arterial pressure measurements from the same pressure source; (b) recording real-time telemetry data that includes measurements of hyperemic and baseline coronary blood flow; (c) automatically identifying the maximum and minimum diastolic 1-(ventricular pressure / arterial pressure) values in the real-time telemetry data; and (d) calculating diastolic pressure-derived coronary blood flow resistance at least in part from the maximum and minimum diastolic 1-(ventricular pressure / arterial pressure) values.

[0377] A system for analyzing hemodynamic characteristics of blood flow within arteries (coronary arteries) supplying blood to the myocardium is disclosed. Existing devices are commercially available to provide clinically important coronary blood flow measurements, including fractional flow reserve (FFR) and coronary flow reserve (CFR), but have features with significant limitations. FFR is highly useful in assessing the extent of blood flow limiting diseases within large (epicardial) coronary arteries, but does not assess the condition of distal microvasculature (microvascular architecture). In contrast, CFR is used to assess microvascular function, but does not quantify epicardial vascular disease. FFR is based on highly reproducible invasive blood pressure measurements, while CFR calculations using blood velocity or thermodilution flow rates are limited by poor reproducibility and accuracy. The disclosed system is designed to overcome these limitations by providing a single platform to collect and analyze real-time hemodynamic data and uniquely reporting synchronous FFR and CFR measurements calculated solely from invasive blood pressure measurements.

[0378] FFR is a measurement obtained by comparing simultaneous invasive blood pressure measurements taken proximal (upstream) and distal (downstream) to a coronary artery lesion and obtained during maximal coronary blood flow (hyperemia). Coronary artery disease (CAD) creates resistance to blood flow, which increases distal vessel blood velocity, decreases distal vessel blood pressure, and increases the difference between proximal and distal pressure measurements. This phenomenon is consistent with Poiseuille's law, which explains the pressure drop of an incompressible fluid flowing through a long cylindrical pipe 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 proportion of blood flow obtained in the presence of an examined coronary obstruction compared to the case of no obstruction. Thus, an FFR value of 1.0 indicates unobstructed blood flow through the examined vascular segment.

[0379] In clinical use, FFR is undoubtedly the gold standard for identifying significant coronary obstructions and guiding therapy for patients with CAD. Randomized clinical trials have demonstrated that for patients with obstructive coronary artery disease defined by FFR ≤ 0.80 (1 to 2), patient outcomes are better with FFR-guided than with coronary angiography alone, and coronary revascularization is superior to medical therapy alone. Validated FFR variants include: 1) instantaneous wave-free ratio (iFR), which specifically reports the resting Pd / Pa ratio during ventricular cardiac chamber relaxation (diastole) when coronary blood flow predominantly occurs; and 2) mean resting Pd / Pa measured indiscriminately across the entire cardiac cycle. These two measurements are recorded without inducing hyperemia. Reported FFR, iFR, and Pd / Pa are discrete decimal values ≤ 1.0.

[0380] A measurement similar to FFR is CFR. FFR compares measurements from two different source locations during the same coronary blood flow state, while CFR compares measurements from a single source location during two different coronary blood flow states (hyperemic blood flow and baseline blood flow). The CFR measurement indicates the ability of the coronary microvascular structure to maximally dilate and increase coronary blood flow. An abnormal CFR identifies a diseased microvascular structure that requires medical therapy and / or compensatory dilation in the presence of upstream obstruction. Currently, there are two ways to measure CFR: 1) using ultrasound Doppler to measure changes in blood velocity, and 2) using the thermodilution method to measure changes in blood flow rate. For individuals without angiographic evidence of CAD, a "normal" Doppler-based CFR value ≥2.0 has been shown to have high predictive accuracy for non-invasive radionuclide myocardial perfusion imaging results (3). Patients with inconsistent CFR and FFR measurements (specifically those with abnormal CFR <2.0 and normal FFR >0.80) have a greater risk of adverse cardiac events (4), highlighting the independence of CFR and FFR in measuring different CAD conditions.

[0381] Limitations of existing devices

[0382] Contemporary guidewires and microcatheters for FFR and Pd / Pa ratio measurements in a clinical setting are available from multiple manufacturers. These pressure guidewires are commonly used in contemporary cardiovascular procedures to first examine segments of diseased epicardial coronary arteries and second to deliver therapeutic balloon and stent catheters across regions of obstructive disease during subsequent percutaneous coronary intervention (PCI). Although these pressure guidewires are widely used and practically applied during routine PCI, none of these pure pressure devices provide a measurement of CFR.

[0383] Compared to traditional "workhorse" guidewires or pure pressure guidewires, Doppler-based guidewires are relatively bulky and difficult to manipulate within the coronary artery anatomy. Thus, despite regulatory approval for these uses, these pressure guidewires are impractical for routine hemodynamic studies and utilization in PCI.

[0384] In addition to these practical considerations, limitations are further encountered during traditional CFR measurements. Due to signal noise or artifacts, Doppler signals at any point in the cardiac cycle may be overestimated or underestimated due to suboptimal wire angles that are away from the blood flow direction and / or positioned against the vessel wall. Additionally, the best signal obtained at baseline blood flow may deteriorate during hyperemia and vice versa. Any of these suboptimal Doppler results will introduce errors in the CFR calculation. After injecting cold saline with a temperature-based device, a thermodilution curve is created over multiple heartbeats and used to derive coronary blood flow. Thermodilution CFR measurements can be poorly reproducible, user-dependent, and time-consuming. The technique also requires intravenous adenosine infusion (as opposed to intracoronary adenosine bolus) to induce continuous hyperemia for several minutes at a time, which not only allows multiple temperature curves to be obtained during both hyperemic and baseline blood flow states, but also prolongs the diagnostic procedure. Description of the disclosed subject matter

[0385] The disclosed system has the following two features: 1) a physical device that receives real-time patient telemetry data and user input to direct data recording; and 2) a data analysis algorithm to create a unique measurement of diastolic pressure-derived CFR (CFRp), which is then reported to the user ( Figure 22 ).

[0386] The physical device of the disclosed system receives patient telemetry data that includes real-time synchronized Pa, Pd, and electrocardiogram waveforms. The pressure data are typically obtained from commercially available devices positioned within the aorta (Pa) and the distal coronary artery (Pd) during invasive cardiovascular procedures. A fluid-filled guiding catheter positioned at the coronary ostium provides Pa measurements, while a second pressure gauge (integrated on a small wire or catheter) is advanced into the distal coronary artery segment ( Figure 2 ). These data are routinely measured every 4 to 5 milliseconds by commercially available devices. The disclosed system is directed by user input to record and automatically process the data, as discussed below. The device then exports the analyzed data to a display monitor for user review.

[0387] The data analysis capabilities of the disclosed system include: 1) baseline matching of diastolic Pa and Pd measurements from the same pressure source; 2) measurement of beat-to-beat diastolic 1 - Pd / Pa during induced hyperemia; 3) measurement of diastolic 1 - Pd / Pa at coronary blood flow baseline; and 4) calculation of diastolic CFRp. Each step is described in more detail below.

[0388] The disclosed algorithm step 1: Pressure matching

[0389] Immediately prior to performing a standard FFR measurement, it is conventional to perform baseline matching (also referred to as "equalization" or "normalization" for commercially available products) on Pa and Pd measurements obtained from the same sampling location. This is achieved by positioning the pressure wire / catheter at the tip of the guiding catheter to compare measurements obtained from the same pressure source ( Figure 24 ). Commercially available devices compare the mean Pa and Pd pressures (measured indiscriminately across various parts of the cardiac cycle) and correct one waveform mean to match the other waveform mean ( Figure 25 ). The disclosed system will specifically perform appropriate waveform correction to ensure diastolic matching, rather than doing so indiscriminately across non-specific parts of the cardiac cycle. The method of the disclosed system will also record data during this equalization process to allow for retrospective data correction. The disclosed algorithm steps 2 and 3: Measurement of beat-to-beat diastolic 1 - Pd / Pa during congestion and baseline coronary blood flow

[0390] After step 1, the user advances the pressure wire / catheter into the distal portion of the coronary artery anatomy ( Figure 23 ). In the presence of an anatomical resistance to blood flow (e.g., CAD) between the two pressure sources, a decrease in Pd is observed compared to the reference Pa ( Figure 26 ). The disclosed subject then calculates the unique value "1 - Pd / Pa" during the diastolic period of each heartbeat. This value conceptually represents the ratio of the resistance due to the epicardial vascular anatomy between the two pressure sources to the total coronary artery resistance of the entire vessel under examination (the entire epicardial vessel plus the distal microvascular structure). The minimum diastolic 1 - Pd / Pa is observed during baseline coronary blood flow (when microvascular resistance is greatest), and the maximum diastolic 1 - Pd / Pa is observed during hyperemia (when microvascular resistance is least due to adenosine-induced vasodilation) ( Figure 27 ). The ratio of the diastolic 1 - Pd / Pa that occurs during hyperemia to the baseline gain value is ≥ 1.0 and is the only calculation of diastolic CFRp reported by the disclosed system.

[0391] The process for calculating the diastolic CFRp value disclosed begins with receiving user input to start and stop the recording of real-time telemetry data to include both baseline and hyperemic coronary blood flow (e.g., data after intracoronary bolus or intravenous infusion of adenosine). Based on this recording, the disclosed system automatically calculates the aggregated diastolic 1 - Pd / Pa (e.g., mean, median, etc.) for each heartbeat. Heartbeats with significant artifacts are automatically excluded from the analysis. The maximum and minimum diastolic 1 - Pd / Pa values are automatically identified in the recording.

[0392] The disclosed system algorithm step 4: Calculation of diastolic CFRp

[0393] Once the disclosed system identifies the maximum and minimum diastolic 1-Pd / Pa values, the system automatically calculates the maximum / minimum ratio. The resulting value, diastolic CFRp, is then reported to the user by the disclosed subject matter based on the following equation: (Equation 1)

[0394] (Equation 2)

[0395] Multiple commercially available devices routinely calculate and report Pd / Pa averaged indiscriminately across various portions of the cardiac cycle. For example, a device may identify pressures obtained during the diastolic period and report an iFR measurement, which is a uniquely commercially available measurement obtained specifically without inducing a hyperemic blood flow state. The purpose of this iFR measurement is specifically as an alternative to FFR without inducing a hyperemic blood flow state.

[0396] For example, the system performs an analysis that compares to the above-described commercially available Pd / Pa, iFR, and FFR measurements. First, the disclosed subject matter measures a unique value for each heartbeat, 1-Pd / Pa (Equation 1), that other commercially available devices cannot measure. This formula can be rearranged to (Pa - Pd) / Pa and represents the instantaneous pressure gradient across the coronary artery segment under examination, which is then normalized by the Pa pressure at the time of the gradient measurement (Equation 2). Commercially available devices neither report 1-Pd / Pa nor its equivalent (Pa - Pd) / Pa measurement.

[0397] Second, the system calculates its unique measurement during the diastolic period. The iFR measurement is calculated using a fundamentally different equation (diastolic Pd / Pa only at baseline blood flow state) and assesses different hemodynamic properties (epicardial vascular obstruction) compared to CFRp (microvascular function).

[0398] Third, the system makes repeated measurements across multiple heartbeats to identify 1-Pd / Pa values during both baseline blood flow and hyperemia. While existing pressure-based metrics are obtained separately during only hyperemia or baseline blood flow, none use both of these blood flow states in their calculations. In particular, the iFR measurement differs from CFRp not only in the equation on which the measurement is based but also in its intentional use of data obtained only during the baseline coronary blood flow state.

[0399] Fourth, the diastolic CFRp results of the system are similar to, but not exactly the same as, traditional CFR results. The key difference lies in the data collected during diastole (the disclosed subject matter) versus the entire cardiac cycle (traditional CFR). Thus, the reported normal range of traditional CFR ≥ 2.0 does not apply to CFRp. For those patients without CAD, the normal range of diastolic CFRp will need to be identified independently.

[0400] Comparison of the disclosed system with other proposed forms of CFRp

[0401] Previous attempts have been made to calculate and / or validate forms of CFRp, but these attempts have been hampered by poor correlation with established traditional CFR measurements (Doppler or thermodilution methods) or adverse cardiac events. The following calculations have been previously described:

[0402] (Equation 3)

[0403] (Equation 4)

[0404] (Equation 5)

[0405] Fundamental differences are observed between these forms of CFRp (Equations 3 to 5) and those of the disclosed subject matter (Equations 1 to 2). Most notably, Equations 3 and 4 use a square root function to transform the ratio of simple pressure gradients. Equation 5 uses only the ratio of simple pressure gradients. Equations 3 and 5 use mean data from across the entire cardiac cycle, while Equation 4 uses a single maximum gradient value that may or may not occur during diastole. None of the equations use either: 1) the ratio of 1 - Pd / Pa values (or its rearranged form [Pd - Pa] / Pa), or 2) the specific use of aggregated diastolic pressure data, which are among the key differentiating components of the disclosed subject matter.

[0406] These differentiating features of the disclosed subject matter are purposeful and critical for creating a clinically useful analogue of traditional CFR. First, the beat-to-beat pressures after adenosine-induced hyperemia result in 1 - Pd / Pa and are strikingly similar to high-quality Doppler-based blood velocity tracings during diastole, but not during systole ( Figure 7 ). The present invention takes advantage of the similarity by including diastole and excluding systole in its data selection and calculations (as opposed to Equations 3 and 5). The tracings during diastole are normalized by aggregating the data during the diastolic period (as opposed to the single maximum value used in Equation 4). Second, the beat-to-beat CFR calculations derived from 1 - Pd / Pa and the Doppler velocity ratio have a very strong positive linear relationship, rather than a square root relationship (as opposed to Equations 3 and 4)( Figure 29 ).

[0407] Clinical significance

[0408] The disclosed subject matter's ability to calculate diastolic-derived CFR enables a single pressure wire / catheter to simultaneously quantify diseases affecting large caliber coronary arteries and their distal microvascular structure. This dual assessment is not possible with contemporary pure pressure devices. The disclosed subject matter aims to transform the existing "gold standard" FFR technology (which has been strongly recommended by clinical guidelines (10) for optimal patient management) into a tool that provides twice the diagnostic information currently possible. Its pressure-based design also overcomes the inherent limitations of traditional CFR, making CFRp measurements as fast, accurate, and reproducible as FFR. Thus, it can identify individuals with significant microvascular disease who may otherwise have a "normal" FFR result that risks delaying appropriate treatment and masking poor clinical outcomes. The disclosed system also eliminates the need for ultrasonic Doppler or thermodilution wires, which are suboptimal for routine clinical use, especially when transitioning from an abnormal diagnostic study to PCI. Overall, the disclosed subject matter extends the diagnostic utility of every FFR procedure performed in cardiac catheterization laboratories worldwide and provides a useful new measurement for microvascular disease.

[0409] Figure 22 Illustrates the general features of the disclosed subject matter and the interface within a cardiac catheterization laboratory.

[0410] Figure 23 Illustrates a typical setup for collecting simultaneously aortic blood pressure (Pa) and distal coronary pressure (Pd) during a traditional FFR measurement, and the use of the disclosed subject matter for CFRp measurement (left image). The angiogram shows a guiding catheter engaged with the left coronary artery and a pressure wire positioned in the distal segment of the left anterior descending artery (right image).

[0411] Figure 24 Illustrates a typical guiding catheter and pressure wire arrangement for simultaneously measuring aortic pressure (Pa) and distal pressure (Pd) from the same pressure source (the tip of the guiding catheter) during pressure "equilibration" (left image). The angiogram shows the guiding catheter engaged with the left coronary artery and the pressure wire sensor positioned at the tip of the catheter during pressure "equilibration" (right image)

[0412] Figure 25 Illustrates simultaneous Pa and Pd measurements during one heartbeat following a commercially available pressure "equilibration". The mean pressure calculated across the entire heartbeat is the same (87 mmHg), while the peak systolic and end-diastolic pressures (data samples 110 to end) are different.

[0413] Figure 26Shows that, in the presence of anatomical resistance between two pressure sources, the distal coronary artery pressure measurement result (Pd) is reduced compared to the guiding catheter reference measurement result (Pa).

[0414] Figure 27 Shows the synchronous beat-by-beat calculation of the mean diastolic 1 - Pd / Pa and the mean diastolic velocity after an intracoronary bolus injection of adenosine to induce hyperemia. The respective peaks occur during hyperemia, while the minimum values occur when coronary blood flow returns to baseline.

[0415] Figure 28 Shows an exemplary beat-by-beat comparison of the pressure-derived formula 1 - Pd / Pa (top graph) and the Doppler-derived blood velocity (bottom graph) after adenosine-induced hyperemia and return to baseline coronary blood flow. When the peak hyperemia value (2810) decreases back to baseline (2820) in a stepwise manner, the tracings are most similar during diastole of each heartbeat (approximately data sample number 100 to the end). Significant dissimilarities are observed during early systole and late systole (the peaks and valleys in the top graph).

[0416] Figure 29 Shows an exemplary beat-by-beat comparison of the diastolic pressure-derived CFR (CFRp) and the diastolic Doppler-based CFR after adenosine-induced hyperemia and return to baseline coronary blood flow. The diastolic CFR and CFRp data have a very strong positive linear correlation (R 2 = 0.991, p-value < 0.001). The deviation of the relationship slope from 1.0 may be the result of systematic offsets in the Doppler or pressure values.

[0417] Figure 31 Depicts a block diagram of a computing system 1500 that is consistent with an implementation of the present subject matter. For example, the computing system 1500 can be used to implement the analysis of pressure loops and / or any components thereof.

[0418] As Figure 31 shown, the computing system 1500 can 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 can be interconnected via a system bus 1550. The processor 1510 is capable of processing instructions for execution within the computing system 1500. Such executed instructions can implement, for example, one or more components of a model for analyzing pressure loops. In some example implementations, the processor 1510 can be a single-threaded processor. Alternatively, the processor 5110 can be a multi-threaded processor. The processor 1510 is capable of processing instructions stored in the memory 1520 and / or the storage device 1530 to display graphical information for a user interface provided via the input / output device 1540.

[0419] The memory 1520 is a computer-readable medium for storing information within the computing system 500, such as volatile or non-volatile. For example, the memory 1520 may store data structures representing a configuration object database. The storage device 1530 is capable of providing persistent storage for the computing system 1500. The storage device 1530 may be a floppy disk device, a hard disk device, an optical disk device, a magnetic tape device, a solid state device, and / or any other suitable persistent storage component. The input / output device 1540 provides input / output operations for the computing system 1500. In some example implementations, the input / output device 1540 includes a keyboard and / or a pointing device. In various implementations, the input / output device 1540 includes a display unit for displaying a graphical user interface.

[0420] According to some example implementations, the input / output device 1540 may provide input / output operations for a network device. For example, the input / output device 1540 may include an Ethernet port or other network port to communicate with one or more wired and / or wireless networks (e.g., local area network (LAN), wide area network (WAN), Internet).

[0421] In some example implementations, the computing system 1500 may be used to execute various interactive computer software applications, which may be used for organizing, analyzing, and / or storing data in various formats. Alternatively, the computing system 1500 may be used to execute any type of software application.

[0422] One or more aspects or features of the subject matter described herein may be implemented in digital electronic circuitry, integrated circuit systems, specially designed ASICs, field programmable gate arrays (FPGAs), computer hardware, firmware, software, and / or combinations thereof. These different aspects or features may be embodied in one or more computer programs executable and / or interpretable on a programmable system including at least one programmable processor, which may be coupled for special or general purpose to receive data and instructions from, and to 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. The clients and servers are typically remote from each other and typically interact via a communication network. The relationship of client to server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

[0423] These computer programs (which may also be referred to as programs, software, software applications, applications, components or code) include machine instructions for a programmable processor and can be implemented in high-level programming and / or object-oriented programming languages and / or in assembly / machine language. As used herein, the term "machine-readable medium" refers to any computer program product, device, and / or apparatus for providing machine instructions and / or data to a programmable processor, such as a magnetic disk, optical disk, memory, and programmable logic device (PLD), including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor. A machine-readable medium may non-transitorily store such machine instructions, such as in a non-transient solid-state memory or a magnetic hard disk drive or any equivalent storage medium. A machine-readable medium may alternatively or additionally store such machine instructions in a transient manner, such as in a processor cache or other random access memory associated with one or more physical processor cores that stores such machine instructions.

[0424] To provide for interaction with a user, one or more aspects or features of the subject matter described herein may be implemented on a computer having a display device (e.g., a cathode ray tube (CRT), liquid crystal display (LCD), or light emitting diode (LED) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or trackball) by which the user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with the user. For example, the feedback provided to the user may be any form of sensory feedback, such as, for example, visual feedback, auditory feedback, or tactile feedback; and input from the user may be received in any form, including acoustic, speech, or tactile input. Other possible input devices include a touch screen or other touch-sensitive device, such as a single-point or multi-point resistive or capacitive trackpad, voice recognition hardware and software, optical scanners, optical pointers, digital image capture devices and associated interpretation software, and the like.

[0425] In the foregoing description and in the claims, phrases such as "at least one of" or "one or more of" may appear before a list of elements or features. The term "and / or" may also appear in a list of two or more elements or features. Unless otherwise implicitly or explicitly contradicted by the context in which it is used, this phrase is intended to mean any element or feature individually listed, or any listed element or feature in combination with any other listed element or feature. For example, the phrases "at least one of A and B"; "one or more of A and B"; and "A and / or B" are each intended to mean "A alone, B alone, or A and B together". Similar interpretations also apply to lists including three or more items. For example, the phrases "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 and B and C together". In the foregoing and in the claims, the use of the term "based on" is intended to mean "at least partially based on", such that unlisted features or elements are also permitted.

[0426] Depending on the desired configuration, the subject matter described herein may be embodied in a system, an apparatus, a method, and / or an article of manufacture. The embodiments set forth in the foregoing description do not represent all embodiments consistent with the subject matter described herein. Rather, the embodiments are only some examples consistent with aspects of the described subject matter. Although several variations have been described in detail above, other modifications or additions are possible. Specifically, additional features and / or variations may be provided in addition to those features and / or variations set forth herein. For example, the embodiments described above may relate to various combinations and sub-combinations of the disclosed features and / or combinations and sub-combinations of several additional features disclosed above. Further, the logical flows depicted in the figures and / or described herein need not be in the particular order or sequential order shown to achieve the desired result. For example, the logical flow may include different and / or additional operations than those shown, without departing from the scope of the disclosure. One or more operations of the logical flow may be repeated and / or omitted, without departing from the scope of the disclosure. Other embodiments may be within the scope of the appended claims.

Claims

1. A method, comprising: Obtaining a set of time - series ventricular pressure measurements; Determining a set of data points from the time - series ventricular pressure measurements, the set of data points including the rate of change of ventricular pressure over time; Determining a representation indicative of the relationship between at least the set of data points and the time - series ventricular pressure measurements; And Determining hemodynamic characteristics within a chamber of the heart at least in part by processing the representation.

2. The method according to claim 1, wherein the time - series ventricular pressure measurements are collected using a device not inserted into the body of a subject.

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

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

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

7. The method according to claim 1, wherein the relationship includes a set of paired relationships, the paired relationships including data points in the set of data points and corresponding time - series ventricular pressure measurements.

8. The method according to claim 1, wherein the rate of change of ventricular pressure in the rate of change of ventricular pressure over time is the first - order derivative of ventricular pressure with respect to time.

9. The method according to claim 7, wherein the representation includes a graph associated with the relationship.

10. The method according to claim 9, wherein the graph is a pressure loop graph.

11. The method according to claim 10, wherein the hemodynamic characteristics are determined at least in part based on the loop - cycle duration of the pressure loop graph.

12. The method according to claim 10, wherein the hemodynamic characteristics are determined at least in part based on the border of the pressure loop graph.

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

14. The method according to claim 9, wherein the hemodynamic characteristics are determined at least in part based on visual characteristics associated with the graph.

15. The method according to claim 14, wherein the visual characteristics are associated with the shape of a region of the graph or the size of at least one region of the graph.

16. The method according to claim 15, wherein the visual characteristics are symmetry, smoothness, the presence of a depression, a difference between two or more regions, or a tangential slope.

17. The method according to claim 15, wherein the hemodynamic characteristics are determined at least in part by comparing the graph with a second graph.

18. The method according to claim 1, further comprising selecting a treatment option at least in part based on the hemodynamic characteristics.

19. The method according to claim 1, wherein the blood flow characteristic is ventricular power, ventricular resistance, or ventricular blood flow, elasticity, compliance, contractility, stroke volume, or response to a modification factor.

20. The method according to claim 1, further comprising calculating a second set of data points, the second set of data points including the rate of change of ventricular pressure acceleration over time.

21. The method according to claim 20, further comprising at least partially using the second set of data points to evaluate the a-wave diastolic pressure.

22. The method according to claim 1, wherein processing the representation includes using a mathematical model.

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

24. The method according to claim 23, wherein the machine learning model includes a neural network.

25. The method according to claim 1, wherein the data points in the set of data points are 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, wherein the time difference includes the difference between the second time and the first time.

26. A system, comprising: at least one processor; at least one memory including instructions that, when executed by the at least one processor, cause operations including: obtaining a set of time series ventricular pressure measurements; determining a set of data points from the time series ventricular pressure measurements, the set of data points including the rate of change of ventricular pressure over time; determining a representation indicative of the relationship between at least the set of data points and the time series ventricular pressure measurements; and determining a blood flow characteristic within a chamber of the heart at least partially by processing the representation.

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

28. The system according to 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. The system according to claim 26, wherein the time series ventricular pressure measurements are collected by an intracardiac device.

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

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

32. The system according to claim 26, wherein the relationship includes a set of paired relationships, the paired relationships including the data points in the set of data points and the corresponding time series ventricular pressure measurements.

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

34. The system according to claim 32, wherein the representation includes a graph associated with the relationship.

35. The system according to claim 34, wherein the graph is a pressure loop graph.

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

37. The system according to claim 35, wherein the blood flow characteristic is determined at least in part based on the border of the pressure loop graph.

38. The system according to claim 37, wherein the border is a top border, a bottom border, a left border, or a right border.

39. The system according to claim 34, wherein the blood flow characteristic is determined at least in part based on visual characteristics associated with the graph.

40. The system according to claim 39, wherein the visual characteristics are associated with the shape of the region of the graph or the size of at least one region of the graph.

41. The system according to claim 40, wherein the visual characteristics are symmetry, smoothness, the presence of indentations, differences between two or more regions, or tangential slope.

42. The system according to claim 40, wherein the blood flow characteristic is determined at least in part by comparing the graph with a second graph.

43. The system according to claim 26, further comprising selecting a treatment option at least in part based on the blood flow characteristic.

44. The system according to claim 26, wherein the blood flow characteristic is ventricular power, ventricular resistance, or ventricular blood flow, elasticity, compliance, contractility, stroke volume, or response to a modification factor.

45. The system according to claim 26, further comprising calculating a second set of data points, the second set of data points including the time rate of change of ventricular pressure acceleration.

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

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

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

49. The system according to claim 48, wherein the machine learning model includes a neural network.

50. The system according to claim 26, wherein the data points in the set of data points are 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, wherein the time difference includes the difference between the second time and the first time.

51. A non-transitory computer-readable medium storing instructions that, when executed by at least one data processor, result in operations including: Obtaining a set of time series ventricular pressure measurements; Determining a set of data points from the time series ventricular pressure measurements, the set of data points including the time rate of change of ventricular pressure; Determine a representation indicative of a relationship between at least the set of data points and the time series ventricular pressure measurements; and Determine hemodynamic characteristics within a chamber of the heart at least in part by processing the representation.

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

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

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

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

57. The non-transitory computer-readable medium according to claim 51, wherein the relationship includes a set of paired relationships, the paired relationships including data points from the set of data points and corresponding time series ventricular pressure measurements.

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

59. The non-transitory computer-readable medium according to claim 57, wherein the representation includes a graph associated with the relationship.

60. The non-transitory computer-readable medium according to claim 59, wherein the graph is a pressure loop graph.

61. The non-transitory computer-readable medium according to claim 60, wherein the hemodynamic characteristics are determined at least in part based on the loop cycle duration of the pressure loop graph.

62. The non-transitory computer-readable medium according to claim 60, wherein the hemodynamic characteristics are determined at least in part based on the border of the pressure loop graph.

63. The non-transitory computer-readable medium according to claim 62, wherein the border is a top border, a bottom border, a left border, or a right border.

64. The non-transitory computer-readable medium according to claim 59, wherein the hemodynamic characteristics are determined at least in part based on visual characteristics associated with the graph.

65. The non-transitory computer-readable medium according to claim 64, wherein the visual characteristics are associated with the shape of a region of the graph or the size of at least one region of the graph.

66. The non-transitory computer-readable medium according to claim 65, wherein the visual characteristics are symmetry, smoothness, the presence of a depression, a difference between two or more regions, or a tangential slope.

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

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

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

70. The non-transitory computer-readable medium according to claim 51, further comprising calculating a second set of data points, the second set of data points including the rate of change of ventricular pressure acceleration over time.

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

72. The non-transitory computer-readable medium according to claim 51, wherein processing the representation includes using a mathematical model.

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

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

75. The non-transitory computer-readable medium according to claim 51, wherein the data points in the set of data points are 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, wherein the time difference includes the difference between the second time and the first time.

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