A cardiovascular support system that quantifies cardiac function and promotes cardiac recovery.

Abstract

To provide systems, devices, and methods that use a heart pump to obtain measurements of a cardiovascular function.SOLUTION: Heart pumps can operate in parallel with and unload the heart. A system quantifies functioning of the native heart by measuring certain parameters / signals such as pressure or motor current, then calculates and displays one or more cardiovascular function metrics. These metrics, such as left ventricular end diastolic pressure (LVEDP), left ventricular pressure, and contractility, provide valuable information to a user regarding a patient's heart function and recovery states.SELECTED DRAWING: Figure 13

Description

Technical Field

[0001] Cross - reference to Related Applications This application claims the benefit of U.S. Provisional Patent Application No. 62 / 396,628, filed on September 19, 2016, the entire contents of which are incorporated herein by reference.

Background Art

[0002] Background Cardiovascular (CV) diseases are the leading cause of morbidity, mortality, and healthcare burden worldwide, with approximately 7 million cases of heart failure and even more cases of myocardial infarction in the United States alone. Acute and chronic CV conditions reduce quality of life and life expectancy. A variety of treatments for CV diseases have been developed, ranging from pharmaceuticals to mechanical devices and ultimately transplantation. Temporary heart support devices, such as assistive artificial hearts, provide hemodynamic support and promote heart recovery.

[0003] There are various types of temporary heart assist devices with different degrees of support and invasiveness, from intra - aortic balloon pumps (IABP) to extracorporeal membrane oxygenation (ECMO) devices and surgically implanted left ventricular assist devices (LVAD). These devices generally exist outside the ventricles or bypass the ventricles and do not work in parallel with or directly support cardiac function. They also do not provide clinicians with quantifiable metrics that can lead to the level of heart support required for a particular patient. Some assistive artificial hearts, such as those in the IMPELLA (registered trademark) series of devices (Abiomed, Inc., Danvers MA), can be inserted percutaneously into the heart and operate in parallel with the native heart to supplement cardiac output.

[0004] The amount of support each patient requires (e.g., the volumetric flow rate of blood pumped by the pumping device) and / or the duration of support can vary. While it has been suggested that fluctuations in motor current required to maintain rotor speed can be used to understand the placement of the pump or pump function, these proposals have not yet gone so far as to effectively process motor current data to measure cardiac function. For example, U.S. Patent No. 6,176,822 (Patent Document 1) describes measuring motor current to help with the proper positioning of the pump, and U.S. Patent No. 7,022,100 (Patent Document 2) refers to calculating blood pressure based on the relationship between the torque of the motor used to drive the rotor and the motor current. However, motor current alone provides only limited insight into the patient's overall cardiac function, and existing measures such as aortic pressure do not correlate with the patient's overall cardiac function. Therefore, there is a need to estimate cardiac function metrics more directly and quantitatively to help clinicians determine how much support the device should provide or when to discontinue the use of cardiac assist devices. [Prior art documents] [Patent Documents]

[0005] [Patent Document 1] U.S. Patent No. 6,176,822 [Patent Document 2] U.S. Patent No. 7,022,100 [Overview of the project]

[0006] overview The systems, apparatus, and methods described herein enable assistive devices present within an organ to assess the function of that organ. In particular, these systems, apparatus, and methods enable cardiac assistive devices, such as percutaneous ventricular assist devices, to be used to assess cardiac function based on measurements of device performance and one or more hemodynamic parameters. By assessing cardiac function using cardiac assistive devices, it becomes possible to adjust the degree / level of assistance provided by the assistive device (e.g., the flow rate of blood pumped by the pumping device) to the specific needs of a particular patient. For example, changes in device performance (or absolute performance of the device) can be detected, and the detected performance can be used to determine whether and to what extent the patient's heart is deteriorating or improving. Based on the detected performance, the degree of assistance is adjusted. For example, the degree of assistance can be increased when the patient's cardiac function is deteriorating, or decreased when the patient's cardiac function is recovering and returning to a baseline of normal cardiac function. This allows clinicians to encourage cardiac recovery in response to changes in cardiac function, thereby gradually withdrawing the patient from treatment. Furthermore, assessments of cardiac function, which provide a deeper understanding of cardiac function, can indicate when it is appropriate to discontinue the use of cardiac assist devices. While some embodiments presented herein concern cardiovascular assist devices implanted across the aortic valve and partially located in the left ventricle, these concepts can be applied to devices in the heart, cardiovascular system, or other areas of the body.

[0007] Furthermore, the cardiac assist device according to this specification can continuously or nearly continuously monitor and evaluate cardiac function while the device is in the patient's body. This may be advantageous over methods that estimate cardiac function only at specific time intervals. For example, continuous monitoring allows for the detection of cardiac deterioration in real time, and this detection is faster than that of prior art methods. The cardiac device can be inserted using minimally invasive procedures without damaging organs. In addition, if the cardiac assist device is already in the patient's body, cardiac function can be measured without the need to introduce additional catheters into the patient's body.

[0008] The systems, apparatus, and methods presented herein determine cardiac function parameters indicating intrinsic cardiac function from intravascular pressure measurements and pump parameters (where "parameters" may represent signals and / or operating states of the cardiovascular system and / or cardiac pump). Cardiac function can be quantified in several different ways using the apparatus and techniques presented herein, including one or more of the following: left ventricular end diastolic pressure (LVEDP), contractility, stroke volume, ejection fraction, chamber pressure, stroke work, cardiac output, cardiac power output, preload state, afterload state, heart rate, cardiac recovery, flow load state, variable volume load state, and / or cyclic flow state. In some applications, such cardiac parameters are determined in part on the hysteresis between pressure measurements (e.g., differential pressure between aortic pressure and left ventricular pressure, or aortic pressure, or other pressures measured within or inserted into a vascular structure) and motor current measurements, enabling the detection of the phase of the cardiac cycle corresponding to a given pair of pressure and current measurements. From these measurements, the user can determine important information about cardiac function and, in some cases, information about the performance of the cardiac assist device, including the occurrence of suction events.

[0009] In one aspect, the cardiac pump system includes a catheter, a motor, a rotor operably coupled to the motor, and a pump housing that at least partially surrounds the rotor such that when the motor is activated, the rotor is driven and blood is pumped through the pump housing. The cardiac pump system also includes a sensor that detects hemodynamic parameters over time, and a controller. The controller detects motor parameters over time, receives input of detected hemodynamic parameters from the sensor over time, and determines relationships between the detected hemodynamic parameters and motor parameters, such as the relationship between the hemodynamic parameters measured over time and the motor parameters measured over time. For example, the controller can store the detected motor parameters and hemodynamic parameters in memory and correlate the motor parameter and hemodynamic parameter data so that they are temporally consistent. The controller uses a polynomial best fit algorithm to characterize the relationships between the detected hemodynamic parameters and motor parameters and stores the characterized relationships in memory. For example, a controller can characterize relationships by fitting all or part of the data (e.g., some hemodynamic parameter data such as pressure measurements, and some motor parameter data such as motor current measurements) to appropriate equations such as elliptic fitting, polynomials, or Euler's equations.

[0010] In some embodiments, the motor parameter is the current supplied to the motor, the power supplied to the motor, or the motor speed. In some embodiments, the controller determines at least one cardiovascular metric by extracting an inflection point, local gradient change, or curvature change from a characterized relationship between the detected hemodynamic parameter and the motor parameter. In some embodiments, the at least one cardiovascular metric is at least one of the following: contractility, stroke volume, ejection fraction, chamber pressure, stroke work, cardiac output, cardiac power output, left ventricular pressure, preload state, afterload state, heart rate, cardiac recovery, flow load state, variable volume load state, cycle volume load state, or cycle flow state. In some embodiments, the at least one cardiovascular metric is left ventricular end-diastolic pressure (LVEDP).

[0011] In some embodiments, the hemodynamic parameter is aortic pressure and the motor parameter is current, and characterizing the relationship involves fitting an equation to at least a portion of the data representing the measured current and the pressure head calculated from the measured current and aortic pressure. In some embodiments, the controller determines the LVEDP point from the equation fitted to at least a portion of the current and pressure head data, and accesses a lookup table to determine the actual LVEDP value from the LVEDP point in the pressure head data. In some embodiments, determining the LVEDP point involves identifying an inflection point, local gradient change, or curvature change in the equation fitted to at least a portion of the current and pressure head data.

[0012] In some embodiments, the controller determines the cardiac cycle phase from the relationship between detected hemodynamic parameters and motor parameters. In some embodiments, the controller describes a hysteresis curve based on the relationship between detected hemodynamic parameters and motor parameters and selects a sample time on the hysteresis curve corresponding to the cardiac cycle phase.

[0013] In some embodiments, determining the cardiac cycle phase includes detecting that the cardiac cycle phase is in diastolic relaxation when the sample time corresponds to a segment of the hysteresis curve corresponding to an increase in pressure head; detecting that the cardiac cycle phase is in diastolic filling when the sample time corresponds to a segment of the hysteresis curve corresponding to a decrease in pressure head up to a point identified by a sharp change in slope or curvature or the identification of an inflection point that follows diastolic relaxation; or detecting that the cardiac cycle phase is in systole when the sample time corresponds to a segment of the hysteresis curve having a decrease in pressure head from an inflection point to a minimum pressure head.

[0014] In some embodiments, motor and hemodynamic parameters are detected over a portion of the cardiac cycle. In other embodiments, motor and hemodynamic parameters are detected over one or more cardiac cycles. In some embodiments, the motor maintains a substantially constant speed of the rotor during rotor operation. In some embodiments, the controller stores at least one cardiovascular metric in memory along with at least one previously determined cardiovascular metric. In some embodiments, the cardiac pump system also includes an integrated motor positioned near the distal end of the catheter adjacent to the cardiac pump.

[0015] In another aspect, the cardiac pump system includes a catheter, a motor, a rotor operably coupled to the motor, and a pump housing that at least partially surrounds the rotor so that when the motor is activated, the rotor is driven and blood is pumped through the pump housing. The cardiac pump system also includes a pressure sensor that detects aortic pressure over time, and a controller. The controller detects motor parameters over time, receives aortic pressure over time from the sensor, stores the relationship between motor parameters and aortic pressure in memory, determines a period over which an inflection point indicating LVEDP may be found, and identifies an inflection point in aortic pressure based on the determined period.

[0016] In some embodiments, determining the period during which an inflection point indicating LVEDP may be found includes identifying the period during which the received motor parameters change. In some embodiments, the controller also determines LVEDP from a dynamic curve lookup table stored in memory based on the inflection point in aortic pressure. In some embodiments, the controller receives an ECG signal, and determining the period during which an inflection point indicating LVEDP may be found includes identifying the period during which the ECG signal indicates the final cycle of diastole.

[0017] In some embodiments, the motor parameters are one of the following: motor current, change in motor current, variability of motor current, and the net integral area of ​​motor current and pressure. In some embodiments, the controller also determines the cardiac cycle phase from the relationship between the motor parameters and aortic pressure, and the cardiac cycle phase is determined using one or more of the following: ECG data, hemodynamic parameters, motor parameters and motor speed, and / or the gradient of aortic pressure. In some embodiments, the motor is configured to maintain a substantially constant speed of the rotor during rotor operation. In some embodiments, the cardiac pump further includes an integrated motor sized and configured for insertion into the patient's vascular structure.

[0018] In another aspect, a cardiac pump system includes a cardiac pump and an electronic controller. The cardiac pump includes a motor, a rotor operably coupled to the motor, and sensors for hemodynamic parameters. The controller is configured to measure motor parameters, e.g., current supplied to the motor, power supplied to the motor, or motor speed, and to measure hemodynamic parameters over time using the sensors. The controller is configured to determine and describe a hysteresis curve based on inputs representing motor parameters over time and inputs representing hemodynamic parameters, determined according to a best-fit algorithm or other appropriate processing algorithm, and to scale the fitted hysteresis curve based on measured patient cardiac parameters, e.g., aortic pressure, to determine left ventricular pressure.

[0019] In some embodiments, the controller is configured to determine at least one cardiovascular metric by extracting inflection point values ​​from a scaled hysteresis curve. In some adaptive forms, the at least one cardiovascular metric is left ventricular end-diastolic pressure. In some embodiments, determining or characterizing the hysteresis curve includes selecting a polynomial to fit the hysteresis curve and using the controller to process data representing motor parameters and hemodynamic parameters (e.g., from sensor measurements) to compute the curve. For example, data representing motor parameters and measured hemodynamic parameters may be stored in the controller as an array of data in a table in memory or a server database, and the controller may access such data tables to retrieve such data and compute the hysteresis curve. Stored data can later be accessed by the controller or by a user.

[0020] In some embodiments, the hemodynamic parameter is the pressure head. In some embodiments, at least one cardiovascular metric is at least one of the following: contractility, stroke volume, ejection fraction, chamber pressure, stroke work, cardiac output, cardiac power output, left ventricular end-diastolic pressure, preload state, afterload state, heart rate, cardiac recovery, flow load state, variable volume load state, cycle volume load state, and / or cycle flow state. In some embodiments, the motor maintains a constant rotor speed during measurement of motor parameters.

[0021] In some embodiments, the controller is further configured to determine the cardiac phase from a hysteresis curve. In some embodiments, the cardiac phase is determined using one or more of ECG data, pressure measured by a pressure sensor, motor parameters and motor speed, aortic pressure gradient, and respiratory variations. In some embodiments, determining the cardiac phase includes selecting a segment of the hysteresis curve corresponding to the sample time that corresponds to one of relaxation, contraction, ejection, and filling based on the motor parameters and measurement of the pressure head at the sample time. In some embodiments, determining the cardiac phase further includes detecting that the cardiac phase is diastole when the sample time corresponds to a segment of the hysteresis curve having a high pressure, and detecting that the cardiac phase is systole when the sample time corresponds to a segment of the hysteresis curve having a low pressure.

[0022] In another aspect, a heart pump system includes a motor, a rotor operably coupled to the motor, a pressure sensor, and a controller. The controller is configured to measure motor parameters, measure a pressure head over time using the pressure sensor, describe a hysteresis curve based on the hysteresis between the motor parameters and the pressure head over time according to a best fit algorithm, scale the applied hysteresis curve based on the measured aortic pressure to determine the left ventricular pressure, determine at least one cardiovascular metric by extracting an inflection point from the scaled hysteresis curve, and display the at least one cardiovascular metric on a display screen of the controller.

[0023] In some embodiments, at least one cardiovascular metric is left ventricular end-diastolic pressure. In some embodiments, characterizing the hysteresis curve includes selecting a polynomial for fitting the hysteresis curve. In some embodiments, at least one cardiovascular metric is at least one of contractility, stroke volume, ejection fraction, cardiac chamber pressure, stroke work, cardiac output, cardiac power output, left ventricular end-diastolic pressure, preload state, afterload state, heart rate, cardiac recovery, flow load state, variable volume load state, cardiac cycle volume load state, and / or cardiac cycle flow state. In some embodiments, the motor parameter is motor current, change in motor current, variability of motor current, or net integrated area of motor current and pressure. In some embodiments, the motor maintains a constant rotor speed during measurement of the motor parameter.

[0024] In some embodiments, the controller is further configured to determine a cardiac phase from the hysteresis curve. In some embodiments, the cardiac phase is determined using one or more of ECG data, pressure measured by a pressure sensor, motor parameter and motor speed, aortic pressure gradient, and respiratory variation. In some embodiments, determining the cardiac phase includes accessing the hysteresis curve, selecting a segment of the curve corresponding to the sample time based on the measurement of the motor parameter and pressure head and the sample time, and determining the corresponding cardiac phase of relaxation, contraction, ejection, or filling based on the segment.

[0025] In some embodiments, the motor has a diameter of less than approximately 21 French. In some embodiments, at least one cardiac metric is at least one of contractility, stroke volume, ejection fraction, chamber pressure, stroke work, cardiac output, cardiac power output, left ventricular end-diastolic pressure, preload state, afterload state, heart rate, cardiac recovery, flow load state, variable volume load state, cycle volume load state, and / or cycle flow state. In some embodiments, the controller is configured to automatically adjust the level of assistance provided by the cardiac pump when at least one cardiac metric indicates a change in the patient's cardiac state, the patient's cardiac state is defined by at least one of a change in contractility, a change in volume load, a change in preload, a change in afterload, a change in heart rate, and a change in pulse pressure. In some embodiments, the controller is configured to automate the level or method of assistance provided by the cardiac pump to enhance and improve spontaneous cardiac function, and automating the level or method of assistance includes at least one of changing the blood flow rate delivered by the cardiac pump, changing the frequency and / or amplitude of the auto-hemodynamic pulsation, and changing the rotational speed of the rotor. In some embodiments, the motor maintains a constant motor speed while measuring motor parameters.

[0026] In some embodiments, determining the cardiac phase involves accessing a plot of pressure as a function of motor parameters forming a hysteresis loop, and using measurements of motor parameters and pressure at the sample time to identify the segment of the hysteresis loop to which the sample time corresponds, where each segment corresponds to the cardiac phase. In some embodiments, the cardiac phase is determined using ECG data. In some embodiments, the cardiac phase is determined using pressure measured by a pressure sensor. In some embodiments, determining the cardiac phase further involves detecting that the cardiac phase is diastolic when the sample time corresponds to a segment of the hysteresis loop with high pressure, and detecting that the cardiac phase is systolic when the sample time corresponds to a segment of the hysteresis loop with low pressure.

[0027] In some embodiments, the controller is configured to generate a plot of pressure and motor parameter measurements where the motor parameter is the first coordinate of the plot and the pressure is the second coordinate of the plot, or to monitor the relationship between the motor parameter and the pressure system. In some embodiments, the blood pump is percutaneous. In some embodiments, the motor is implantable. In some embodiments, the cardiac pump system is configured such that the pressure sensor is positioned within the aorta when the rotor is placed within the aorta. In some embodiments, the cardiac pump system is an intravascular cardiac pump system.

[0028] In another aspect, a cardiac pump system includes a cardiac pump and a controller. The cardiac pump includes a motor, a rotor operably coupled to the motor, and a sensor for hemodynamic parameters, such as a pressure sensor. The controller is configured to measure motor parameters, measure hemodynamic parameters by the sensor, determine cardiac phase, determine at least one cardiac metric indicating cardiac function, and display at least one cardiac metric on the controller's display screen. For example, the controller may be configured to measure motor parameters such as the current supplied to the motor or the power supplied to the motor, measure pressure at the pressure sensor, determine at least one cardiac metric indicating cardiac function, and display at least one cardiac metric on the controller's display screen. The cardiac metric indicating cardiac function may be determined using a predetermined pressure-motor curve, and the determination of at least one cardiac metric may be based on hysteresis between motor parameters and pressure.

[0029] In some embodiments, the pressure measured is aortic pressure or the pressure difference between aortic pressure and left ventricular pressure. In some embodiments, at least one cardiac metric is at least one of contractility, stroke volume, ejection fraction, chamber pressure, stroke work, cardiac output, cardiac power output, left ventricular end-diastolic pressure, preload state, afterload state, heart rate, cardiac recovery, flow load state, variable volume load state, cycle volume load state, and / or cycle flow state. In some embodiments, the controller is configured to automatically adjust the level of support provided by the cardiac pump when at least one cardiac metric indicates a change in the patient's cardiac state, the patient's cardiac state is defined by at least one of a change in contractility, a change in volume load, a change in preload, a change in afterload, a change in heart rate, and a change in pulse pressure. In some embodiments, the controller is configured to automate the level or method of assistance provided by the cardiac pump to enhance and improve intrinsic cardiac function, and automating the level or method of assistance includes at least one of changing the blood flow rate pumped by the cardiac pump, changing the frequency and / or amplitude of the auto-hemodynamic pulsations, and changing the rotational speed of the rotor. In some embodiments, the motor maintains a constant motor speed while measuring motor parameters.

[0030] In some embodiments, determining the cardiac phase involves accessing a plot of pressure as a function of motor parameters forming a hysteresis loop, and using measurements of motor parameters and pressure at the sample time to determine the segment of the hysteresis loop to which the sample time corresponds, where each segment corresponds to the cardiac phase. In some embodiments, the cardiac phase is determined using ECG data. In some embodiments, the cardiac phase is determined using pressure measured by a pressure sensor. In some embodiments, determining the cardiac phase further involves detecting that the cardiac phase is diastolic when the sample time corresponds to a segment of the hysteresis loop with high pressure, and detecting that the cardiac phase is systolic when the sample time corresponds to a segment of the hysteresis loop with low pressure.

[0031] In some embodiments, the controller is configured to generate a plot of pressure and motor parameter measurements where the motor parameter is the first coordinate of the plot and the pressure is the second coordinate of the plot, or to monitor the relationship between the motor parameter and the pressure system. In some embodiments, the blood pump is percutaneous. In some embodiments, the motor is implantable. In some embodiments, the cardiac pump system is configured such that the pressure sensor is positioned within the aorta when the rotor is placed within the aorta. In some embodiments, the motor parameter is one of the following: motor current, change in motor current, variability of motor current, and the net integral area of ​​motor current and pressure. In some embodiments, the cardiac pump system is an intravascular cardiac pump system.

[0032] In another aspect, a cardiac pump system includes a cardiac pump and a controller. The cardiac pump includes a motor, a rotor operably coupled to the motor, and a pressure sensor. The controller is configured to measure motor parameters, which are the current supplied to the motor or the power supplied to the motor; measure pressure at the pressure sensor; determine the cardiac phase; determine at least one cardiac metric indicating cardiac function; determine at least one recommendation for modifications to operate the cardiac pump based on at least one cardiac metric; and display at least one recommendation on the controller's display screen. The cardiac metric indicating cardiac function is determined using a given pressure-motor curve, and the determination of at least one cardiac metric is based on hysteresis between the motor parameters and pressure.

[0033] In some embodiments, at least one recommendation includes changing the rotor rotation speed, changing the power supplied to the motor, and / or removing the cardiac pump from the patient. In some embodiments, at least one cardiac metric is at least one of contractility, stroke volume, ejection fraction, chamber dilation, chamber hypertrophy, chamber pressure, stroke work, cardiac output, cardiac power output, left ventricular end-diastolic pressure, preload state, afterload state, heart rate, and cardiac recovery. In some embodiments, the controller is configured to automatically adjust the level of assistance provided by the cardiac pump when at least one cardiac metric indicates a change in the patient's cardiac state, the patient's cardiac state is defined by at least one of a change in contractility, a change in volumetric load, a change in preload, a change in afterload, a change in heart rate, and a change in pulse pressure. In some embodiments, the controller is configured to automate the level or method of assistance provided by the cardiac pump to enhance and improve intrinsic cardiac function, and automating the level or method of assistance includes at least one of changing the blood flow rate pumped by the cardiac pump, changing the frequency and / or amplitude of the auto-hemodynamic pulsations, and changing the rotational speed of the rotor. In some embodiments, the motor maintains a constant motor speed while measuring motor parameters.

[0034] In some embodiments, determining the cardiac phase involves accessing a plot of pressure as a function of motor parameters forming a hysteresis loop, and using measurements of motor parameters and pressure at the sample time to identify the segment of the hysteresis loop to which the sample time corresponds, where each segment corresponds to the cardiac phase. In some embodiments, the cardiac phase is determined using ECG data. In some embodiments, the cardiac phase is determined using pressure measured by a pressure sensor. In some embodiments, determining the cardiac phase further involves detecting that the cardiac phase is diastolic when the sample time corresponds to a segment of the hysteresis loop with high pressure, and detecting that the cardiac phase is systolic when the sample time corresponds to a segment of the hysteresis loop with low pressure.

[0035] In some embodiments, the controller is configured to generate a plot of pressure and motor parameter measurements where the motor parameter is the first coordinate of the plot and the pressure is the second coordinate of the plot, or to monitor the relationship between the motor parameter and the pressure system. In some embodiments, the blood pump is percutaneous. In some embodiments, the motor is implantable. In some embodiments, the cardiac pump system is configured such that the pressure sensor is positioned within the aorta when the rotor is placed within the aorta. In some embodiments, the motor parameter is one of the following: motor current, change in motor current, variability of motor current, and the net integral area of ​​motor current and pressure. In some embodiments, the cardiac pump system is an intravascular cardiac pump system.

[0036] In another aspect, the cardiac pump system includes a cardiac pump and a controller. The cardiac pump includes a rotor, a motor connected to the rotor, a blood inlet, and a pressure sensor. The controller communicates with the motor and the pressure sensor. The controller is configured to measure motor parameters at sample time, measure pressure at the pressure sensor at sample time, and determine whether the blood inlet is blocked based on the motor parameter and pressure measurements at sample time, with blood inlet blockage being determined using hysteresis in the motor parameter and pressure measurement at the pressure sensor. In some embodiments, the controller is configured to display a warning parameter in response to determining that the blood inlet is blocked. [Invention 1001] Catheter and, Motor and, A rotor operably connected to the motor, The pump housing, which at least partially surrounds the rotor, is configured such that when the motor is activated, the rotor is driven and blood is pumped through the pump housing. A sensor configured to detect hemodynamic parameters over time, The motor parameters associated with the aforementioned motor are detected over time. The system receives input of the detected hemodynamic parameters from the sensor over time. Determine the relationship between the detected hemodynamic parameters and the detected motor parameters. Using a polynomial best fit algorithm, the relationship between the hemodynamic parameters and the motor parameters is characterized. The determined relationship is stored in memory. A controller configured in such a way A cardiac pump system, including the heart pump system. [Invention 1002] The cardiac pump system of the present invention 1001, wherein the motor parameter is the current supplied to the motor, the power supplied to the motor, or the motor speed. [Invention 1003] A cardiac pump system according to the present invention 1001 or 1002, wherein the controller is further configured to determine at least one cardiovascular metric by extracting an inflection point, local gradient change, or curvature change from the characterized relationship between the detected hemodynamic parameter and the motor parameter. [Invention 1004] A cardiac pump system according to any of the invention 1001 to 1003, wherein the at least one cardiovascular metric is at least one of contractility, stroke volume, ejection fraction, chamber pressure, stroke work, cardiac output, cardiac power output, left ventricular pressure, preload state, afterload state, heart rate, cardiac recovery, flow load state, variable volume load state, cycle volume load state, or cycle flow state. [Invention 1005] The cardiac pump system of the present invention 1004, wherein the at least one cardiovascular metric is left ventricular end-diastolic pressure (LVEDP). [Invention 1006] A cardiac pump system according to any one of the invention 1001 to 1005, wherein the hemodynamic parameter is aortic pressure, the motor parameter is electric current, and characterizing the relationship involves fitting an equation to at least a portion of the data representing the measured electric current and the pressure head calculated from the measured electric current and the aortic pressure. [Invention 1007] The aforementioned controller From the equation applied to at least a portion of the data representing the measured current and pressure head, the LVEDP point is determined. Access the lookup table and determine the actual LVEDP value from the LVEDP point in the pressure head data. A cardiac pump system according to any of the present invention 1001 to 1006, further configured as follows. [Invention 1008] A cardiac pump system according to any of the invention 1001 to 1007, wherein determining the LVEDP point involves identifying a change in gradient, a change in curvature, or an inflection point in the equation fitted to the current and at least a portion of the pressure head. [Invention 1009] A cardiac pump system according to any one of the present invention 1001 to 1008, wherein the controller is further configured to determine the cardiac cycle phase from the relationship between the detected hemodynamic parameter and the motor parameter. [Invention 1010] The aforementioned controller A hysteresis curve is described based on the relationship between the detected hemodynamic parameter and the motor parameter. Select the sample time on the hysteresis curve corresponding to the cardiac cycle phase. A cardiac pump system according to any of the invention 1001 to 1009, further configured as follows. [Invention 1011] Determining the cardiac cycle phase is Detecting that the cardiac cycle phase is in diastolic relaxation when the aforementioned sample time corresponds to the segment of the hysteresis curve corresponding to the pressure head increase, or Detecting that the cardiac cycle phase is in diastolic filling when the aforementioned sample time corresponds to a segment of the hysteresis curve corresponding to a decrease in pressure head up to a point identified by the identification of a sharp change in gradient or curvature or an inflection point following diastolic relaxation, or The detection of the cardiac cycle phase being in systole occurs when the aforementioned sample time corresponds to a segment of the hysteresis curve having a pressure head decrease from the inflection point to the minimum pressure head. A cardiac pump system, further comprising any of the inventions 1001 to 1010. [Invention 1012] A cardiac pump system according to any of the present invention 1001 to 1011, wherein the motor parameters and hemodynamic parameters are detected over a portion of the cardiac cycle. [Invention 1013] A cardiac pump system according to any one of the invention 1001 to 1012, wherein the motor parameters and hemodynamic parameters are detected over one or more cardiac cycles. [Invention 1014] A cardiac pump system according to any of the invention 1001 to 1013, wherein the motor is configured to maintain a substantially constant speed of the rotor while the rotor is operating. [Invention 1015] A cardiac pump system according to any of the inventions 1001 to 1015, wherein the controller is further configured to store the at least one cardiovascular metric in memory together with the at least one previously determined cardiovascular metric. [Invention 1016] A cardiac pump system according to any one of the present invention 1001 to 1016, including an integrated motor positioned near the distal end of the catheter, adjacent to the cardiac pump. [Invention 1017] Catheter and, Motor and, A rotor operably connected to the motor, The pump housing, which at least partially surrounds the rotor, is configured such that when the motor is activated, the rotor is driven and blood is pumped through the pump housing. A pressure sensor configured to detect aortic pressure over time, The motor parameters are detected over time. The aortic pressure is received over time from the aforementioned sensor. The relationship between the motor parameters and the aortic pressure is stored in memory. Determine the period during which an inflection point exhibiting LVEDP can be found. Based on the determined period, the inflection point in the aortic pressure is identified. A controller configured in such a way A cardiac pump system, including the heart pump system. [Invention 1018] A cardiac pump system according to the present invention 1017, wherein determining the period over which the inflection point exhibiting LVEDP can be found includes identifying the period over which the received motor parameters change. [Invention 1019] A cardiac pump system according to the present invention 1017 or 1018, wherein the controller is further configured to determine the LVEDP from a dynamic curve lookup table in memory based on the inflection point in the aortic pressure. [Invention 1020] A cardiac pump system according to any of the invention 1017-1019, wherein the controller is further configured to receive an ECG signal, and determining a period in which an inflection point indicating LVEDP may be found includes identifying a period in which the ECG signal indicates the final cycle of diastole. [Invention 1021] A cardiac pump system according to any of the inventions 1017 to 1020, wherein the controller is further configured to determine at least one cardiac metric from the stored differential pressure, the cardiac metric being at least one of contractility, stroke volume, ejection fraction, chamber pressure, stroke work, cardiac output, cardiac power output, left ventricular pressure, preload state, afterload state, heart rate, cardiac recovery, flow load state, variable volume load state, cycle volume load state, or cycle flow state. [Invention 1022] A cardiac pump system according to any of the inventions 1017 to 1021, wherein the motor parameter is one of the following: motor current, change in motor current, variability of motor current, and the net integral area of ​​motor current and pressure. [Invention 1023] A cardiac pump system according to any of the present invention 1017 to 1022, wherein the controller is further configured to determine the cardiac cycle phase from the relationship between the motor parameters and the aortic pressure, and the cardiac cycle phase is determined using one or more of ECG data, hemodynamic parameters, the motor parameters and motor speed, and / or the gradient of the aortic pressure. [Invention 1024] A cardiac pump system according to any of the invention 1017 to 1023, wherein the motor is configured to maintain a substantially constant rotor speed during the operation of the rotor. [Invention 1025] A cardiac pump system according to any of the invention 1017 to 1024, further comprising an integrated motor sized and configured for insertion into the vascular structure of a patient. [Brief explanation of the drawing]

[0037] The above and other objectives and benefits will become clear when the following detailed explanation is considered in conjunction with the attached drawings. In the drawings, similar reference numerals refer to the same parts throughout.

[0038] [Figure 1] This shows a prior art catheter-based intravascular cardiac pump system located within the heart. [Figure 2] This shows the LVAD cardiac pump system, a prior art system located inside the heart. [Figure 3] This document illustrates an exemplary cardiac pump system configured to estimate cardiovascular parameters according to a particular embodiment. [Figure 4] This describes a process for determining cardiac parameters indicating cardiac function, according to a specific embodiment. [Figure 5] This describes a process for calculating cardiac function metrics according to a specific embodiment. [Figure 6] This describes a process for determining LVEDP from measured motor parameter signals and sensor signals using various gating processes according to a specific embodiment. [Figure 7] This shows the process of determining LVEDP by applying a gating algorithm. [Figure 8] The plots of aortic pressure, left ventricular pressure, and motor current over time are shown. [Figure 9] This shows the process of determining LVEDP by applying a gating algorithm based on ECG data. [Figure 10] The plots of measured and algorithmically calculated LVEDP over time are shown. [Figure 11] This shows a plot of LVEDP calculated from patient data, illustrating the accuracy of LVEDP determined using the gating method. [Figure 12] This shows a plot of the pressure head as a function of motor current, based on data from a pig animal model. [Figure 13] The plot shows the pressure head as a function of motor current from a pig animal model, with a spline curve fitted to the region of the hysteresis loop. [Figure 14] This shows a plot of the pressure head as a function of the hysteresis parameter after a hysteresis gate has been applied to segment the data collected from a pig animal model. [Figure 15] The plot shows the pressure head as a function of the motor hysteresis parameter. [Figure 16] This shows a plot of the pressure head as a function of motor current before and after administration of a beta-blocker in a pig animal model. [Figure 17] The smoothed curve of the pressure head plot as a function of motor current is shown. [Figure 18A] The graph shows the pressure head as a function of motor current before and during the transition to myocardial infarction. [Figure 18B] The graph shows plots of cardiac power index and motor current as a function of the sample, measured over time before and during myocardial infarction. [Figure 19] This shows a series of plots of simulated loop data with varying contractility under a constant load. [Figure 20A] This provides an exemplary user interface for a cardiac pump controller that displays measurements over time. [Figure 20B] This document illustrates an exemplary user interface for a cardiac pump controller according to a specific embodiment. [Figure 21]This document describes a process for detecting suction in an intravascular cardiac pump and determining the cause of suction, according to a specific embodiment. [Modes for carrying out the invention]

[0039] Detailed explanation To provide an overall understanding of the systems, methods, and apparatus described herein, certain exemplary embodiments are described. Although the embodiments and features described herein are described in particular for use in connection with percutaneous cardiac pump systems, it will be understood that all components and other features outlined below may be combined with each other in any suitable manner and may be adapted and applied to other types of cardiac treatments and cardiac assist devices, including cardiac assist devices implanted using surgical incisions.

[0040] The systems, apparatus, and methods described herein enable assistive devices, which are located entirely or partially within an organ, to assess the function of that organ. In particular, these systems, apparatus, and methods enable cardiac assistive devices, such as percutaneous ventricular assist devices (VADs), to be used to assess cardiac function. For example, the state of the heart can be measured or monitored by tracking values ​​of an electromechanical controller of a VAD positioned within a patient's heart. Since the device maintains a constant rotor speed by changing the motor current in response to changes in the intracardiac pressure of the heart, continuous measurement of motor parameters and pressures, such as motor current and aortic pressure, provides a continuous, real-time, and precise assessment of cardiac function, such as left ventricular pressure. By using cardiac assistive devices to assess cardiac function, changes in cardiac function can be alerted to healthcare professionals, and the degree / level of assistance provided by the assistive device (i.e., the flow rate of blood pumped by the device) can be adjusted to the specific needs of the patient. For example, the degree of assistance can be increased when the patient's cardiac function is deteriorating, or decreased when the patient's cardiac function is recovering and returning to a baseline of normal cardiac function. This allows the device to dynamically respond to changes in cardiac function and promote cardiac recovery, enabling the patient to be gradually weaned off treatment. Furthermore, assessment of cardiac function can indicate when it is appropriate to discontinue the use of the cardiac assist device. While some embodiments presented herein concern cardiac assist devices implanted across the aortic valve and partially located in the left ventricle, these concepts can be applied to devices in the heart, cardiovascular system, or other areas of the body.

[0041] Furthermore, the cardiac assist devices of this specification can continuously or nearly continuously monitor and evaluate cardiac function while the device is in the patient's body. This may be advantageous over methods that can only estimate cardiac function at specific time intervals. For example, continuous monitoring may allow for more rapid detection of cardiac deterioration. In addition, if the cardiac assist device is already in the patient's body, cardiac function can be measured without the need to introduce an additional catheter into the patient's body.

[0042] The evaluation of cardiac function using the cardiac assist devices presented herein is made possible, at least in part, by the minimally invasive nature of the devices. Unlike some invasive cardiac assist devices that short-circuit blood from the heart, the cardiac assist devices presented herein reside within the heart and work in parallel with the patient's own ventricular function. This allows the cardiac assist devices presented herein to have sufficient sensitivity to detect the patient's own ventricular function, unlike some more invasive devices. Thus, these systems, devices, and methods enable the use of cardiac assist devices not only as support devices but also as diagnostic and prognostic tools. The cardiac assist device can essentially function as an active catheter that extracts information about cardiac function by hydraulically connecting to the heart. In some embodiments, the cardiac assist device operates at a constant level (e.g., a constant rotational speed of the rotor) while the power supplied to the assist device is measured. In certain embodiments, the rotor speed of the cardiac assist device can be varied (e.g., as a delta function, step function, or ramp function) to further examine the patient's own cardiac function.

[0043] Cardiac function parameters indicating intrinsic cardiac function can be determined from measurements of intravascular and / or ventricular pressure, as well as pump parameters / signals (where "parameters" can represent signals and / or operating states of the cardiac pump). For example, cardiac parameters can be determined from aortic pressure and pump motor current. This determination can be made using a model of the combination of the heart and cardiac pump system. One method of determining cardiac function involves the model accessing a given curve. This model may be a lookup table or a given / normalized pump performance curve or calibration curve or any other suitable model. The lookup table may include a set of curves showing that the power and pressure head required to maintain rotational speed are determined as a function of pump flow rate, as well as a set of curves relating to the determination of the pressure head and cardiac flow characteristics. For example, the lookup table may show that a particular aortic pressure and motor current correspond to a particular left ventricular end-diastolic pressure (LVEDP). Another method of determining cardiac function expresses pump performance by showing the pressure head as a function of the pump's motor current consumption, where this current consumption serves as a substitute for the pump's power or load. The relationship between motor current consumption and pressure head during the cardiac cycle describes a hysteresis curve or loop. Cardiac states and functions, including LVP and LVEDP, can be extracted from the relationship between motor current consumption and pressure head. In addition to LVEDP, or in addition to LVEDP, cardiac function can be quantified in several different ways using the cardiac assist devices presented herein. For example, cardiac function can be expressed as contractility, stroke volume, ejection fraction, chamber pressure, stroke volume, cardiac output, cardiac power output, LVEDP, preload state, afterload state, heart rate, and / or cardiac recovery.

[0044] To accurately determine these cardiac parameters, hysteresis between pressure measurements (e.g., the difference between aortic pressure and left ventricular pressure, or aortic pressure alone) and motor current measurements may be taken into account. This hysteresis can be explained by detecting the phase of the cardiac cycle corresponding to a given pair of pressure and current measurements. This can be done using at least two methods to distinguish diastolic filling from other phases of the cardiac cycle. Both methods identify key points indicating the start and end of diastolic filling. The first method uses the aortic pressure waveform and identifies key features within the curve, such as heavy beat notches, to indicate the start of diastolic filling. This method can also use the start of aortic filling to indicate the end of diastolic ventricular filling. The second method uses pressure recordings and timed ECG data to identify two key features that define diastolic filling. These features are preferably the start of the QRS complex and the end of the T wave. If the signal is noisy, detecting the peaks of the QRS complex (R wave) and the T wave may be more reliable. Furthermore, in some embodiments, the hysteresis itself between pressure measurements and motor parameter measurements can be used to determine the phase of the cardiac cycle.

[0045] The systems, apparatus, and methods presented herein also describe heart rate variability. If not described, variations in heart rate may affect the waveform resolution and, therefore, the accuracy of cardiac parameter estimation. For example, a higher heart rate at a given sampling frequency results in fewer samples per cardiac cycle. The number of samples per cardiac cycle is important for capturing key features used to describe hysteresis, such as heavy beat notches, as well as key points in pressure waveforms such as LVEDP. Too few samples may cause these features to be missed because the number of samples in the region of interest decreases. However, sensitivity to heart rate can be reduced or eliminated in some embodiments by not performing waveform analysis over a fixed period rather than per cycle. For example, in some embodiments, calculations are performed over 10-30 seconds and averaged to reduce the effects of artifacts. Such averaging is possible for some metrics, such as LVEDP, because inter-beat variability is not very high, at least within a short period (e.g., about 1 minute). By using multiple cycles, the number of samples in the region of interest can be made independent of heart rate. Furthermore, combining multiple measurements can improve the phase resolution of the cardiac cycle. Additionally, extending the sampling period can further counteract the effects of insufficient sampling.

[0046] The systems, apparatus, and methods presented herein also detect aspiration events that occur when the pump inlet is completely or partially blocked. Conventional aspiration detection systems are not sensitive enough to detect small aspiration events. In contrast, the systems, apparatus, and methods presented herein can detect slight aspiration and when aspiration occurs in the cardiac cycle. These determinations may be based on the hysteresis of the motor current-aortic pressure curve. This improved method can detect aspiration earlier and provide the user with information on how to prevent or reduce the continuation or worsening of aspiration. Furthermore, in some embodiments, the systems, methods, and apparatus can predict aspiration events by detecting undesirable cardiac cycle flow conditions that may lead to aspiration events.

[0047] Figure 1 shows an exemplary prior art cardiac assist device located within the heart 102. The heart 102 includes the left ventricle 103, the aorta 104, and the aortic valve 105. The intravascular cardiac pump system includes a catheter 106, a motor 108, a pump outlet 110, a cannula 111, a pump inlet 114, and a pressure sensor 112. The motor 108 is connected to the catheter 106 at its proximal end and to the cannula 111 at its distal end. The motor 108 also drives a rotor (not visible in the figure) which pumps blood from the pump inlet 114 through the cannula 111 to the pump outlet 110. The cannula 111 is positioned across the aortic valve 105 such that the pump inlet 114 is located within the left ventricle 103 and the pump outlet 110 is located within the aorta 104. This configuration allows the intravascular cardiac pump system 100 to pump blood from the left ventricle 103 into the aorta 104 to support cardiac output.

[0048] The intravascular cardiac pump system 100 pumps blood from the left ventricle to the aorta in parallel with the spontaneous cardiac output of the heart 102. Blood flow through a healthy heart is approximately 5 liters / min on average, and blood flow through the intravascular cardiac pump system 100 can be similar or different. For example, the flow rate through the intravascular cardiac pump system 100 may be 0.5 liters / min, 1 liter / min, 1.5 liters / min, 2 liters / min, 2.5 liters / min, 3 liters / min, 3.5 liters / min, 4 liters / min, 4.5 liters / min, 5 liters / min, greater than 5 liters / min, or other appropriate flow rates.

[0049] The motor 108 of the intravascular cardiac pump system 100 can be varied in various ways. For example, the motor 108 can be an electric motor. The rotor 108 can operate at a constant rotational speed to pump blood from the left ventricle 103 to the aorta 104. Since the load on the motor 108 differs at different stages of the cardiac cycle of the heart 102, it is generally necessary to supply various amounts of current to the motor 108 in order to operate the motor 108 to maintain a constant rotor speed. For example, when the mass flow rate of blood into the aorta 104 increases (e.g., during systole), the current required to operate the motor 108 increases. Therefore, this change in motor current can be used to characterize cardiac function, as will be further discussed in relation to the following figure. The electric motor current may be measured, or the magnetic field current may be measured. Detection of mass flow rate using motor current can be facilitated by the position of the motor 108 being aligned with the natural direction of blood flow from the left ventricle 103 to the aorta 104. Detection of mass flow rate using motor current can also be facilitated by the small size and / or low torque of motor 108. While the diameter of motor 108 in Figure 1 is approximately 4 mm, any suitable motor diameter may be used, provided that the rotor-motor mass is small enough to be affected by the inertia of pulsating blood. The rotor-motor mass may be influenced by the pulsating blood mass flow rate to produce a discernible and characteristic effect on the motor parameters. In some embodiments, the diameter of motor 108 is less than 4 mm.

[0050] In certain embodiments, one or more motor parameters other than current are measured, such as the power supplied to motor 108, the speed of motor 108, or the electromagnetic field. In some embodiments, motor 108 in Figure 1 operates at a constant speed. In some embodiments, motor 108 may be external to the patient and the rotor may be driven by an elongated mechanical transmission element such as a flexible drive shaft, drive cable, or fluid coupling.

[0051] The pressure sensor 112 of the intravascular cardiac pump system 100 can be an integrated component (as opposed to a separate diagnostic catheter) and can be configured to detect pressure at various locations on the system 100, for example, adjacent to the proximal end of the motor 108. In a particular embodiment, the pressure sensor 112 of the intravascular cardiac pump system 100 can be placed on the cannula 111, on the catheter 106, on a portion of the system 100 outside the patient's body, or at any other suitable location. The pressure sensor 112 can detect blood pressure in the aorta 104 when the intravascular cardiac pump system 100 is properly positioned within the heart 102, or, in the case of a right heart support device, can detect pressure in the inferior vena cava (IVC) or pulmonary artery. Blood pressure information can be used to properly position the intravascular cardiac pump system 100 within the heart 102. For example, the pressure sensor 112 can be used to detect whether the pump outlet is entering the left ventricle 103 through the aortic valve 105, which would result in the blood circulating within the left ventricle 103 rather than being transported from the left ventricle 103 to the aorta 104. The pressure sensor in Figure 1 detects absolute pressure at a point within the patient's vascular structure, for example, within the aorta. In other embodiments, the pressure sensor detects absolute pressure within the pulmonary artery or venous system. In other embodiments, the pressure sensor detects pressure head or delta pressure within the system, which may be equal to the aortic pressure minus the left ventricular pressure.

[0052] In addition to assisting in the placement of the intravascular cardiac pump system 100, one or more algorithms can also be applied to the data obtained by the pressure sensor 112 to detect the cardiac phase of the heart 102. For example, the data obtained by the pressure sensor 112 can be analyzed to detect a heavy beat notch indicating the start of diastolic filling. A heavy beat notch is a small downward deflection in the arterial pulsation or pressure contour immediately after the closure of the semilunar valve. This feature can be used as a marker for the end of systole or ejection period. Since the measured pressure head often contains more noise features than the measured motor current, the motor current can be used to gate periods in which heavy beat notches are likely to be identified, and then the corresponding periods in the measured pressure head can be identified and analyzed. Other features, such as changes in motor speed, the presence of an R peak in the ECG data, and changes in the curvature or local gradient of parameters over time, may also be detected as indicating LVEDP.

[0053] The intravascular cardiac pump system 100 can be inserted in various ways, including percutaneous insertion into the heart 102. For example, the intravascular cardiac pump system can be inserted into the left ventricle 103 through the femoral artery (not shown), the axillary artery (not shown), the aorta 104, and across the aortic valve 105. In certain embodiments, the intravascular cardiac pump system 100 is surgically inserted into the heart 102. In some embodiments, the intravascular cardiac pump system 100, or a similar system adapted for the right heart, is inserted into the right heart. For example, a right heart pump similar to the intravascular cardiac pump system 100 can be inserted through the inferior vena cava and extended into the pulmonary artery, bypassing the right atrium and right ventricle. In certain embodiments, the intravascular cardiac pump system 100 may be positioned to operate within the vascular system outside the heart 102 (e.g., within the aorta 104). By being minimally invasive within the vascular system, the intravascular cardiac pump system 100 has sufficient sensitivity to enable characterization of the patient's own cardiac function. In addition, surgically implanted devices such as LVADs, which will be discussed later, should also be sensitive to changes in spontaneous cardiac function, although their sensitivity is lower than that of the intravascular cardiac pump 100.

[0054] Figure 2 shows an exemplary prior art cardiac assist device 201 located outside the heart 202. The heart 202 includes the left ventricle 203 and the aorta 204. The cardiac assist device 201 includes a motor 208, an inflow conduit 207, an outflow conduit 209, a first sensor 212a, a second sensor 212b, a third sensor 212c, and a catheter 206. The inflow conduit 207 is connected to the first side of the motor 208 at a first end 213 and to the apex of the left ventricle 203 at a second end 215. The outflow conduit 209 is connected to the second side of the motor 208 at a first end 217 and to the ascending aorta 204 at a second end 219. Motor 208 also drives a rotor (not visible in the diagram) which rotates to pump blood from the apex of the left ventricle 203 through the inflow conduit 207 to the outflow conduit 209 and discharge the blood into the aorta 204. The cardiac assist device 201 is configured to pump blood from the left ventricle 203 into the ascending aorta 204 to support cardiac output.

[0055] The cardiac assist device 201 transports blood from the left ventricle 203, bypassing the aortic valve (not visible in the figure), and around the heart 202 rather than within it, through the inflow conduit 207 and outflow conduit 209 to pump blood into the aorta 204. The blood flow through the cardiac assist device 201 can deliver a flow rate similar to or greater than that of the prior art intravascular cardiac pump system 100 shown in Figure 1. The cardiac assist device 201 can be surgically implanted in a patient such that the second end 219 of the outflow conduit 209 and the second end 215 of the inflow conduit 207 are surgically implanted in the heart 202 in the ascending aorta 204 and left ventricle 203, respectively. The motor 208 can be connected to a console (not shown) located outside the patient's body via a drive line (not shown) through a catheter 206. The rotor (not shown) can operate at a constant or substantially constant speed. The power supplied to motor 208 can be monitored on a console to determine the pump flow rate or other characteristics of the pump performance.

[0056] The first sensor 212a, the second sensor 212b, and the third sensor 212c may be similar to the pressure sensor 112 in Figure 1. Sensors 212a-c may be pressure sensors used to determine the blood pressure in the aorta 204 or the blood pressure in the left ventricle 203, or they may be arranged to determine the blood pressure and blood flow through the inflow conduit 207 and outflow conduit 209. The blood pressure in the aorta 204 or the left ventricle 203 can be displayed to the user and / or used to determine the operating parameters of the cardiac assist device 201. The blood flow or blood pressure in the inflow conduit 207 and outflow conduit 209 can also be displayed to the user and used to monitor the cardiac assist device 201. The first sensor 212a may also be a power sensor of the pump motor 208 which can be used to determine the pump flow through the cardiac assist device 201.

[0057] Figure 3 shows an exemplary cardiac pump system 300 configured to estimate cardiac parameters indicating cardiac function, according to a particular embodiment. The cardiac pump system 300 may be similar to or identical to the intravascular cardiac pump system 100 in Figure 1 or the cardiac assist system 201 in Figure 2. The cardiac pump system 300 may operate within the heart, partially within the heart, outside the heart, partially outside the heart, partially outside the vascular system, or at any other suitable location within the patient's vascular system. The cardiac pump system 300 includes a cardiac pump 302 and a control system 304. All or part of the control system 304 may be in a controller unit separate from / away from the cardiac pump 302. In some embodiments, the control system 304 is located inside the cardiac pump 302. The control system 304 and cardiac pump 302 are not shown to scale.

[0058] The cardiac pump 302 may include a catheter 306, a motor 308, a rotor 310, and a pressure sensor 312. The motor 308 can be connected to the distal region of the catheter 306, or, as previously mentioned, may be located outside the patient's body and can communicate with the motor 308 via a drive shaft, drive cable, or fluid connection. The motor 308 is also connected to the rotor 310 so that the operation of the motor 308 causes the rotor 310 to rotate and pump blood. The pressure sensor 312 can be positioned along the catheter at any number of positions inserted into the patient's cardiovascular system so that the pressure sensor 312 can detect blood pressure when the cardiac pump 302 is inserted into the patient's vascular system. In embodiments where the cardiac pump 302 is an intravascular cardiac pump system 100 as shown in Figure 1, the cardiac pump 302 can be delivered to the left ventricle, and the pressure sensor 312 can sense aortic pressure when the intravascular cardiac pump 302 is properly positioned in the left ventricle. In some embodiments, the pressure sensor 312 is positioned within a cardiac chamber or blood vessel separated from the cardiac chamber of interest by a valve. For example, the pressure sensor 312 may be positioned within the aorta when the rotor 310 is positioned within the aorta, or the pressure sensor 312 may be positioned within the inferior or superior vena cava together with the rotor 310, while the outlet of the pump is in the pulmonary artery. In some embodiments, the cardiac system is configured such that the rotor 310 is positioned within the aorta when the inlet of the pump is located in the left ventricle.

[0059] The control system 304 may include a controller 322, a current sensor 314, and a cardiac parameter estimator 316. The controller 322 supplies current to the motor 308 via an electrical connection 326, for example, through one or more wires. The current supplied to the motor 308 via the electrical connection 326 is measured by the current sensor 314. The load on the motor 308 of the mechanical pump is the pressure head, i.e., the difference between the aortic pressure and the left ventricular pressure. The cardiac pump 302 receives a nominal load during steady-state operation at a given pressure head, and variations from this nominal load are a result of changes in external load conditions, such as the dynamics of left ventricular contraction. As the dynamic load conditions change, the motor current required to operate the rotor 310 at a constant or substantially constant speed changes. The motor may operate at the speed required to maintain the rotor 310 at a set speed. As a result, the motor current consumed by the motor to maintain the rotor speed can be monitored and used to understand the underlying cardiac state. The state of the heart can be more precisely quantified and understood by simultaneously monitoring the pressure head during the cardiac cycle using the pressure sensor 312 in relation to the motor current to generate a quantitative hysteresis loop of pump performance that can be visually assessed to determine changes in the state and function of the heart. The cardiac parameter estimator 316 also receives a current signal from the current sensor 314 along with the pressure signal from the pressure sensor 312. The cardiac parameter estimator 316 uses these current and pressure signals to characterize the function of the heart. The cardiac parameter estimator 316 can access a stored lookup table to obtain additional information for characterizing the function of the heart based on the pressure and current signals. For example, the cardiac parameter estimator 316 can receive aortic pressure from the pressure sensor 312, use the lookup table, and use the aortic pressure to determine the delta pressure.

[0060] The controller 322 can store current signals from the current sensor 314 and pressure signals from the pressure sensor 312 in memory or in a database on a server (not shown). The database and memory can be located outside the controller 322 or included within the controller 322. The controller 322 can store the signals as an array in the database with specific associated addresses, and can also record time along with the signals. The controller 322 can also store determined cardiac parameters, such as LVEDP, in memory for comparison with previously stored cardiac parameters. The controller 322 accesses the hysteresis curve by accessing the database address in memory. Based on the address, the controller 322 selects a first array containing multiple data points corresponding to motor parameters measured over time. The controller 322 also selects a second array containing multiple data points corresponding to pressure or other physiological parameters measured over time. The controller 322 associates the first data points corresponding to motor parameters with the second data points corresponding to physiological parameters at each point in time when measurements are taken. The controller 322 can then display the matched data points as a hysteresis curve to the user on a screen or other display. Alternatively, the controller 322 can iterate through the matched data points to calculate cardiac parameters.

[0061] The cardiac parameter estimator 316 can characterize cardiac function and determine cardiac parameters according to two different methods. In the first method, the cardiac parameter estimator 316 extracts information about cardiac function and cardiac parameters using a predetermined pressure-current curve. Using this method, the cardiac parameter estimator 316 compares the power required to maintain the rotational speed of the pump rotor 310 and the pressure head, defined as the pressure gradient at both ends of the pump, with a predetermined performance curve that shows the power and pressure head as a function of the pump flow rate, as well as a predetermined system curve (a predetermined pressure-current curve) related to the pressure head and motor current. Using the performance curve and system curve, the cardiac parameter estimator 316 characterizes the pump behavior to extract information about cardiac parameters and cardiac function.

[0062] In the second method, the cardiac parameter estimator 316 determines cardiac parameters relating to cardiac function using a best-fit algorithm. The cardiac parameter estimator 316 accesses a modified representation of pump performance by pressure head as a function of motor current consumption. Motor current consumption serves as a substitute for pump power or load. The pump load at a given rotor RPM is determined by the fluid motor torque, described by the equation τ = H·d, where the torque τ is determined by the pressure head H and the volumetric displacement d per revolution. The torque is directly related to the pump's power requirement by the following equation: TIFF0007875244000001.tif10128 In the formula, the required power (P 電力 The motor torque (τ) is the product of voltage (V) and current (I), and is related to the pump torque (τ), rotational speed (ω), and the combination of electrical and mechanical efficiency (η). Since the motor speed and efficiency are relatively constant and known, the fluid motor torque can be determined from the pump's power. The relationship between power and motor current may vary depending on the pump design, but motor current is a value that is measured in operation for most pumps. Motor current is usually directly related to torque and therefore to the pump load.

[0063] The pressure head is the load on the mechanical pump, and it is the difference between aortic pressure and left ventricular pressure, which changes throughout the cardiac cycle due to the external blood flow generated by myocardial contraction. The pump operation in the beating environment of the heart involves repeated steady-state ventricular filling and ejection. The motor current required to generate a specific RPM of the rotor depends on both the pressure head and the cardiac state, which results in a hysteresis loop as the motor receives ventricular filling during relaxation following active myocardial contraction. The resulting motor current hysteresis incorporates the effects of changes in external flow and pressure, and therefore is a complete representation of the mechanical pump's performance curve.

[0064] Traditionally, methods for measuring LVEDP have been indirect and discontinuous. One common method for measuring LVEDP involves using a Swan-Ganz catheter, which pushes an inflated balloon into the pulmonary artery through the catheter. LVEDP is then estimated by obtaining pressure in the left ventricle during diastole using the pulmonary vascular system and left atrium as a fluid column. This measurement is indirect and often involves significant measurement errors, noise, and a lack of reliability. Furthermore, the measurements are discontinuous because the balloon in the pulmonary artery cannot remain inflated. A conventional alternative method for measuring LVEDP is the use of a pressure transducer catheter inserted into the left ventricle of the heart. While this captures the entire pulsating pressure waveform over several cardiac cycles, the catheter cannot be left in the patient's body or at the bedside for extended periods. Other non-invasive methods for predicting LVEDP have been developed using Doppler echocardiography or ultrasound. Unfortunately, these methods also suffer from the same problems and cannot provide continuous pressure estimates over long periods.

[0065] LVEDP can be determined from the motor current consumption and pressure head for a given rotor speed. Assuming that the motor current fluctuations corresponding to small motor speed fluctuations at the end of the extension are linear, these fluctuations can be corrected by linear scaling according to the following equation. In formula TIFF0007875244000002.tif9128, the speed-corrected motor current (i c ) is the measured motor current (i m ) is equal to the product of the ratio of the desired fixed motor speed (ω0) and the actual motor speed (ω). This is a safe assumption because motor speed fluctuations are minimal (±0.5%). The relationship between motor current and pressure head can be characterized, for example, by fitting the equation to the data. Speed-corrected motor current (i c ) can be plotted against the measured pressure head, and then this relationship can be plotted, for example, R 2 The hysteresis loop can be fitted to a higher-order polynomial by using optimization to generate a quartic polynomial with pressure head as a function of motor current. Alternatively, any best-fit algorithm can be applied to the plot of measured pressure head and motor current to estimate the hysteresis loop. For example, the shape of the hysteresis loop can be estimated by fitting the parameter plot to an ellipse or a slanted or truncated ellipse. The equation determined by the best-fit algorithm can then be used to extract information about cardiac function; for example, LVP can be extracted from the inflection points of the hysteresis curve, and the filling, relaxation, and ejection phases can be identified. Other parameters can be determined from points on the hysteresis loop, the size or shape of the hysteresis loop, changes in the size and shape of the hysteresis loop, changes in local gradient, changes in curvature, or the area within the hysteresis loop. Furthermore, the fitting coefficients can then be used to predict LVEDP for a given corrected motor current at a given motor RPM setting. These parameters enable healthcare professionals to better understand a patient's current cardiac function and provide appropriate cardiac support.

[0066] Other cardiac parameters indicating cardiac function can also be determined by the cardiac parameter estimator 318 based on comparison of measurements with lookup tables, or from the shape and value of hysteresis loops formed from measured motor parameters and pressures during the cardiac cycle. For example, changes in contractility may be related to fluctuations in the pressure gradient (dP / dt) during cardiac contraction. Cardiac output is determined based on the flow rate of blood passing through the pump. Stroke volume is an index of left ventricular function, given by the equation SV = CO / HR, where SV is stroke volume, CO is cardiac output, and HR is heart rate. Stroke work is the work done by the ventricle to eject a certain volume of blood and can be calculated from stroke volume according to the equation SW = SV * MAP, where SW is stroke work, SV is stroke volume, and MAP is mean arterial pressure. The work of the heart is calculated by the product of stroke work and heart rate. Cardiac power output is a measure of cardiac function in watts calculated using the formula CPO = mAoP * CO / 451, where CPO is cardiac power output, mAoP is mean aortic pressure, CO is cardiac output, and 451 is a constant used to convert mmHG × L / min to watts. Ejection fraction can be calculated by dividing stroke volume by intraventricular blood volume. Other parameters such as chamber pressure, preload, afterload, cardiac recovery, flow load, variable volume load, and / or cyclic flow state can be calculated from these values ​​or determined by examining the hysteresis loop.

[0067] An active intraventricular catheter-based cardiac pump provides a means of direct, continuous LVEDP measurement during the most critical time when the device will be used. These diagnostic measurements can be obtained by leveraging parameters from the device without additional intervention. In addition, the device can also provide diagnostic metrics incorporating more time points within the cardiac cycle than a single point in time. While useful, LVEDP is limited to a single point in time across the entire cardiac cycle. More comprehensive metrics, incorporating information from the entire cardiac cycle, provide more information about the state of the heart and better represent its true condition.

[0068] The predetermined pressure-current curve can be measured using simulated circulatory loops, animal data, or clinical data. For example, pump performance limits may be defined using simulated circulatory loops (MCLs) with various contraction, preload, and afterload conditions, and biological variability and pathology may be depicted using animal models. Using MCLs for characterization and animal models for validation is an effective means of relating the performance of the cardiac pump 302 to cardiac function. Although baseline motor currents may differ between pumps, current measurements from each pump can be normalized to generate a normalized current waveform. In some embodiments, cardiac pumps are binned separately in a 30 mA range based on their current response to normalize calculations for approximate flow rates.

[0069] Cardiac phase information can significantly improve the accuracy of the cardiac parameter estimator 316 by considering the effects of hysteresis in the pressure-current curve. As will be discussed further below, the pressure-current curve may exhibit hysteresis due to the phase of the cardiac cycle. Therefore, the phase of the heart must be taken into account in order to accurately compare pressure and current data points. Otherwise, pressure and current data collected during systole may be compared with dissimilar reference pressure and current data collected, for example, during diastole, which could distort the estimation of cardiac parameters.

[0070] The cardiac parameter estimation by the cardiac parameter estimator 316 can be performed continuously or nearly continuously while the cardiac pump 302 is implanted in the heart. This can be advantageous over conventional catheter-based methods that only allow sampling of cardiac function at specific times. For example, continuous monitoring may allow for more rapid detection of cardiac deterioration. In addition, if a cardiac assist device is already in the patient's body, cardiac function can be measured without the need to introduce an additional catheter into the patient's body.

[0071] After the cardiac parameters are estimated by the cardiac parameter estimator 316, the cardiac parameters are output to the controller 322. The controller 322 then supplies control signals to drive the motor 308. In some embodiments, the controller 322 operates the motor 308 at a fixed setpoint. This setpoint may be a fixed rotational speed or flow rate. For example, the controller may supply a variable voltage to maintain a constant rotational speed of the rotor 310 by the motor 308, regardless of the preload and / or afterload. The controller 322 may also allow the user to change the rotational speed of the rotor 310, and in some embodiments, the rotational speed of the motor 308. For example, the user may select a new setpoint (e.g., by setting a new desired flow rate or rotational speed) or select a time-varying input signal (e.g., a delta function, a step function, a ramp function, or a sine curve). In some embodiments, the fixed setpoint may be the amount of energy supplied to the motor 308. In a particular embodiment, the cardiac parameters estimated by the cardiac parameter estimator 316 are displayed to the physician, who then manually adjusts the motor settings on the controller 322.

[0072] The controller 322 can adjust the setpoints sent to it based on the cardiac parameters estimated by the cardiac parameter estimator 316. For example, the degree / level of support (i.e., rotor speed, and thus the volumetric flow rate of blood pumped by the device) can be increased when cardiac function is deteriorating, or the degree of support can be decreased when cardiac function is recovering. This allows the device to dynamically respond to changes in cardiac function to promote cardiac recovery and gradually wean the patient off treatment.

[0073] Figure 4 shows a process 400 for determining cardiac parameters indicating cardiac function. Process 400 can be performed using the intravascular cardiac pump system 100 in Figure 1, the cardiac assist device 201 in Figure 2, the cardiac pump system 300 in Figure 3, or any other suitable cardiac pump. In step 402, the motor of the cardiac pump is operated. The motor may operate at a rotational speed necessary to maintain a constant or substantially constant rotational speed of the rotor. In step 404, the current supplied to the motor is measured and the motor speed is measured. The current may be measured using a current sensor (e.g., current sensor 314) or by any other suitable means. In step 406, the aortic pressure is measured. The aortic pressure may be measured by a pressure sensor connected to the cardiac pump, by a separate catheter, by a non-invasive pressure sensor, or by any other suitable sensor. The pressure sensor may be an optical pressure sensor, an electrical pressure sensor, a MEMS sensor, or any other suitable pressure sensor. In some embodiments, in addition to measuring aortic pressure, or instead, ventricular pressure is measured.

[0074] In some embodiments, additional steps may be taken after measuring the current supplied to the motor and the aortic pressure. For example, in some embodiments, the aortic pressure may be scaled by a coefficient determined from a lookup table to find the differential pressure over time. In some embodiments, the measured current and pressure data are smoothed to provide a less noisy signal.

[0075] In step 408, segments of the hysteresis loop formed by the measured current supplied to the motor and the measured aortic pressure, corresponding to the phases of the heart, are determined. Segmentation and phase estimation can function as filters for the pressure and current signals, as this allows the pressure and current signals to be compared with the pressure and current signals that occurred during the corresponding stages of the cardiac cycle. Segmentation and phase estimation may be based on the pressure information received in step 406 and may include locating a reference point in the pressure information that indicates the cardiac phase. In some embodiments, a heavy beat notch in the pressure signal is detected to indicate the start of diastolic filling. A heavy beat notch is a small downward deflection in the arterial pulsation or pressure curve immediately after the closure of the semilunar valve. This heavy beat notch can be used as a marker for the end of systole and thus the approximate start of diastole.

[0076] In some embodiments, segmentation or phase estimation is based entirely or partially on ECG data. Such ECG data may be timed with pressure recordings. Features within the ECG used to estimate the cardiac phase may be the start of the QRS complex and the end of the T wave. If the ECG signal is noisy, detecting peaks in the QRS complex (such as the R wave) and the T wave may be more reliable. The R peak of the QRS waveform can also be used to identify the timing of various parameters, such as the period during which LVEDP will be found, since the R peak corresponds to the final cycle of diastole. In phase estimation methods using either pressure signals or ECG signals, offsets from the detected features may be used to more accurately identify the filling phase, since actual filling occurs slightly before or after these identified markers. Combining both pressure-based and ECG-based methods allows for more reliable identification of the cardiac phase. The weighting between the two methods can be optimized using known filling time parameters, known left ventricular pressure, and datasets with a high signal-to-noise ratio.

[0077] In addition to ECG data, segmentation and cardiac phase estimation can also be based entirely or partially on motor parameters or motor velocity, aortic pressure gradient, respiratory variability, or any other appropriate physiological or device parameters. In some embodiments, segmentation and cardiac phase estimation are determined based on any one of these parameters or any combination of any number of these parameters.

[0078] In step 410, the hysteresis loop is mathematically described, and the LVEDP is determined based on the mathematical description. The hysteresis loop can be characterized by fitting equations to the data, for example, by describing the loop with a polynomial function based on Euler's equations describing an ellipse, and the LVEDP can be calculated using the ellipse fit. The mathematical fitting of the hysteresis loop further enables comparison of the size, shape, and area of ​​the loop or loop segments over time, as well as analysis of changes in the local gradient or curvature of loop segments to measure changes in cardiac parameters.

[0079] In some embodiments, a lookup table is referenced to determine cardiac parameters indicating cardiac function based on motor parameters, pressure, and cardiac phase. In some embodiments, the table may embody a given pressure-current curve.

[0080] In step 412, cardiac parameters are calculated. Determining cardiac parameters may include determining points on the hysteresis loop based on a mathematical fit, integrating the area of ​​sections of the hysteresis loop, or mapping measured currents and pressures to cardiac parameters using a lookup table. Sections of the hysteresis loop can be segmented based on an elliptic fit of Euler's equations and bilateral lines, as further described in relation to Figure 13, so that the ellipse consists of multiple segments, each having at least one straight side. Each segment can be translated and rotated before being integrated by a Riemann sum.

[0081] The cardiac phase information extracted from the hysteresis loop may be binary (e.g., diastole or systole) or more finely divided (e.g., systole, diastolic relaxation, and diastolic filling). The cardiac phase may be one of ejection, diastolic filling, and diastolic relaxation. The cardiac parameters determined may be contractility, stroke volume, ejection fraction, chamber pressure, stroke work, cardiac output, cardiac power output, left ventricular end-diastolic pressure (LVEDP), preload, afterload, heart rate, cardiac recovery, flow load, variable volume load, cycle volume load, and / or cycle flow state. Left ventricular end-diastolic pressure (LVEDP) is a single-point measure commonly used by physicians to assess cardiac health. LVEDP is significantly elevated in many cases of heart failure, indicating ventricular overload. This is primarily due to a shift in the Frank-Starling relationship caused by changes in the end-diastolic pressure-volume ratio (EDPVR). As a patient approaches heart failure, the Frank-Starling curve shifts downward, resulting in a lower stroke volume for a given pressure (preload). Because of this shift, LVEDP can indicate the state of the heart when all other conditions remain relatively constant at a given cardiac output. Measuring these changes in LVEDP can be useful for monitoring a patient's progression toward either heart failure or recovery, thus allowing clinicians to adjust necessary treatment accordingly.

[0082] Alternatively, if a reference table is used, the lookup table may accept pressure, motor current, and cardiac phase as its inputs. A given pressure-current curve can be measured using simulated circulatory loops, animal data, or clinical data. For example, pump performance limits may be defined using simulated circulatory loops (MCLs) with various systolic, preload, and afterload conditions, and biological variability and pathology may be depicted using animal models. Using MCLs for characterization and animal models for validation is an effective means of relating cardiac pump performance to cardiac function. Baseline motor currents may differ between pumps, but each pump can be normalized to produce a normalized current waveform.

[0083] Cardiac phase information from process 408 can significantly improve the accuracy of cardiac parameter estimation by considering the effects of hysteresis in the pressure-current curve. The pressure-current curve exhibits hysteresis due to the phase of the cardiac cycle. Therefore, the phase of the heart must be taken into account in order to accurately compare pressure and current data points. Otherwise, pressure and current data collected during systole may be compared with dissimilar reference pressure and current data collected, for example, during diastole, which could distort the estimation of cardiac parameters.

[0084] In step 414, cardiac parameters are output. Output and / or determination of cardiac parameters can be performed continuously or nearly continuously while the cardiac pump is implanted in the heart. This can be advantageous over conventional catheter-based methods that only allow sampling of cardiac function at specific times or different points in time during the cardiac cycle. For example, continuous monitoring of cardiac parameters can enable rapid detection of cardiac deterioration. Continuous monitoring of cardiac parameters can show changes in cardiac state over time by outputting continuous hysteresis parameters associated with cardiac phase, which may show differences as the cardiac state changes. In addition, if a cardiac assist device is already in the patient's body, cardiac function can be measured without the need to introduce an additional catheter into the patient's body. Cardiac parameters can be output using any suitable user interface or report, such as the user interface described later in relation to Figures 20A and 20B.

[0085] In some embodiments, the power supplied to the motor is adjusted based on cardiac parameters. The power supplied to the motor can be adjusted automatically by a controller (e.g., controller 322) or manually (e.g., by a healthcare professional). The level of support can be increased when the patient's cardiac function deteriorates, or decreased when the patient's cardiac function recovers, thereby gradually withdrawing the patient from treatment. This allows the device to dynamically respond to changes in cardiac function and promote cardiac recovery. It can also be used to intermittently adjust pumping assistance and diagnose how the heart responds, for example, if it can take over the pumping function from a cardiac pumping device.

[0086] Figure 5 shows the process for calculating cardiac function metrics and adjusting the level of support provided by the cardiovascular assisted device. In step 502, the pump controller is operated. In step 504, hysteresis parameters and motor speed are measured. Hysteresis parameters may be parameters of the cardiac pump motor (e.g., motor current or motor power). In step 506, hemodynamic parameters are measured. For example, in some embodiments, aortic pressure is measured. In step 508, a lookup table of hemodynamic parameters as a function of hysteresis parameters is investigated or referenced to determine ΔP, i.e., differential pressure. An exemplary lookup table 2011 shows having a column "Hys. para" for the stored values ​​of the hysteresis parameters and a column "ΔP" for the pressure difference between the ventricle and the aorta. In some embodiments, the table may be based on a given pressure-current curve. Cardiac parameters can be determined by mapping the measured current and pressure to cardiac parameters.

[0087] The predetermined pressure-current curve can be measured using simulated circulatory loops, animal data, or clinical data. For example, pump performance limits may be defined using simulated circulatory loops (MCLs) with various contraction, preload, and afterload conditions, and biological variability and pathology may be depicted using animal models. Using MCLs for characterization and animal models for validation is an effective means of relating cardiac pump performance to cardiac function. Baseline motor currents may differ between pumps, but each pump can be normalized to produce a normalized current waveform. In some embodiments, cardiac pumps are binned separately in a 30mA range based on their current response to normalize calculations for approximate flow rates.

[0088] In step 510, the cardiac cycle phase is determined. This determination of the cardiac cycle phase can be performed using a segmented spline curve. As discussed in relation to Figure 13, the segmented spline depicts the region of the entire hysteresis loop. The hysteresis loop can be segmented into a known number of curve-fitting splines. Each spline fitted to the curve of the hysteresis loop represents a cardiac cycle phase. For example, in a hysteresis loop fitted with three splines, the first spline may represent the diastolic relaxation phase, the second spline the diastolic filling phase, and the third spline the systolic phase. In this case, the confluence of the second and third splines is the LVEDP. Phase estimation can function as a filter for pressure and current signals, as it allows for comparison of the pressure and current signals with those generated during the corresponding stages of the cardiac cycle. Phase estimation may be based on pressure information received in step 2006 and may include locating a reference point within the pressure information that indicates the cardiac phase. In some embodiments, a heavy pulse notch in the pressure signal is detected to indicate the start of diastolic filling. A heavy pulse notch is a small downward deflection in the arterial pulse or pressure curve immediately after closure of the semilunar valve. This heavy pulse notch can be used as a marker for the end of systole and thus the approximate start of diastole.

[0089] Cardiac phase information from process 510 can significantly improve the accuracy of cardiac parameter estimation by considering the effects of hysteresis in the pressure-current curve. Since the pressure-current curve exhibits hysteresis due to the phase of the cardiac cycle, the cardiac phase must be taken into account in order to accurately compare pressure and current data points. Otherwise, pressure and current data collected during systole may be compared with dissimilar reference pressure and current data collected, for example, during diastole, which could distort the estimation of cardiac parameters.

[0090] In step 512, the cardiac chamber pressure is output. In some embodiments, the measured cardiac chamber pressure is the pressure of the left ventricle. In certain embodiments, the measured cardiac chamber pressure is the pressure of the right ventricle. In step 514, the contractility coefficient is output. The contractility score provides an indicator of cardiac function. More specifically, the contractility score represents the intrinsic strength and force of the heart's contraction during systole. Higher cardiac contractility results in a larger cardiac stroke volume. For example, moderate contractility may occur when the cardiac stroke volume is approximately 65 mL. High contractility may occur when the cardiac stroke volume exceeds 100 mL. Low contractility may occur when the cardiac stroke volume is less than 30 mL. The contractility score may be expressed numerically and / or graphically. The contractility score may be dimensionless. In step 516, the volumetric load coefficient is output. In step 518, additional state metrics are output.

[0091] In step 520, the left ventricular end-diastolic pressure (LVEDP) is determined using the cardiac chamber pressures determined in step 512. This calculation can be performed by determining the left ventricular pressure from step 512, corresponding to the end of diastole. LVEDP tends to be significantly elevated in almost all cases of acute myocardial infarction, especially in patients with heart failure. This is primarily due to a shift in the Frank-Starling relationship caused by changes in the end-diastolic pressure-to-volume ratio (EDPVR). As a patient approaches heart failure, the Frank-Starling curve shifts downward, resulting in a lower stroke volume for a given pressure. For this reason, LVEDP can indicate the state of the heart when all other conditions remain relatively constant for a given cardiac output for a patient. Measuring these changes in LVEDP can be useful for monitoring the patient's progression toward either heart failure or recovery, thus allowing clinicians to adjust necessary treatment accordingly.

[0092] In step 522, the level of support provided by the assistive device is evaluated. In some embodiments, this evaluation is performed automatically. In certain embodiments, this evaluation is performed at least partially by a healthcare professional. In some embodiments, additional information regarding hemodynamic parameters and the level of support is provided to enable a clinician to adjust the level of support to optimize the patient's outcome. In some embodiments, the level of support provided by the cardiovascular assistive device is increased or decreased by changing the power supplied to the motor, changing the motor speed, and / or changing the flow rate, or any other appropriate change that results in a change in the level of support provided by the cardiovascular assistive device. In step 526, the patient's cardiac evaluation is output. This patient's cardiac evaluation may be displayed on a user interface such as user interface 2000 in Figure 20A or 2001 in Figure 20B. In some embodiments, the evaluation is a report that may be sent to a healthcare professional. In some embodiments, recommendations on the level of support that should be provided to the patient's heart are output. In some embodiments, the evaluation is a report that may be sent to a healthcare professional. The recommendations for the level of support may be optimized to provide hemodynamic support. Recommendations for the level of support may be based on an internal algorithm or table. Recommendations for the level of support may include instructions for obtaining the recommended level of support, including changing the flow rate delivered by the pump, changing the level of autoarterial dynamism (amplitude and / or frequency) based on rapid speed changes, and / or changing the level of pump speed (motor rotation speed or rotor rotation speed) in short or long bursts to increase flow. In some embodiments, process 500 may be automatically repeated so that process 500 in Figure 5 provides closed-loop control for the cardiac assist device. Cardiac recovery can be promoted by increasing or decreasing treatment to suit the patient's needs. If the evaluation in step 526 indicates that the heart has recovered sufficiently, treatment may be terminated, or healthcare professionals may be prompted to consider terminating treatment.

[0093] Figure 6 shows the process 600 for determining LVEDP from measured motor parameter signals and sensor signals. LVEDP can be calculated according to one of several steps. In step 602, motor parameters are received over a period of time. As described herein, measured motor parameters may include motor current, power, motor speed, or torque. In step 604, input signals from sensors are received over a period of time. Signals from sensors may be any hemodynamic parameters, such as aortic pressure. In step 606, a decision is made regarding whether to use the internal gating method.

[0094] If the decision is no, process 600 proceeds to step 608 via path 607, where the received ECG input 609 is used to gate the input hemodynamic and motor parameters. The ECG input from step 609 is analyzed to identify the period of ECG data in which an inflection point indicating the final diastolic cycle is found, i.e., an R peak in the QRS waveform, indicating that the LVEDP is found within that period. The hemodynamic parameters measured during the corresponding period are then analyzed to find the point corresponding to the LVEDP. In step 610, the LVEDP is calculated from the identified point using a lookup table, and the relationship between the hemodynamic and motor parameters is characterized by determining a polynomial function fitted to the hemodynamic and motor parameters.

[0095] If the decision in step 606 is that internal gating is to be used, then process 600 proceeds to either step 612 or step 620 by following path 611 based on the desired information from the data. Either path may be used to determine LVEDP, but the path starting from step 620 can also determine additional cardiac parameters.

[0096] In step 612, the period over which the motor parameters change is identified. This period is considered a gating window, and the change in motor parameters indicates a change in cardiac phase associated with LVEDP. In some embodiments, the change in motor parameters may be a decrease in motor speed due to a change in load, an increase in motor current due to a change in load, or any other characteristic change in motor parameters as a result of cardiac changes. In step 614, the identified period, i.e., the gating window, is used to identify the corresponding period of hemodynamic parameters over which LVEDP is found. In step 616, the hemodynamic parameter input for LVEDP calculation is identified by analyzing the hemodynamic parameter data within the identified period and identifying changes in the hemodynamic parameters. In step 618, LVEDP is calculated using a lookup table and a polynomial function.

[0097] In step 620, a hysteresis loop is formed from motor parameters and sensor inputs, as well as from a polynomial algorithm that allows approximation of missing data points. The data collected from the motor parameters and sensor inputs describes the phase of the heart in the hysteresis loop. For example, if the motor parameter is motor current and the sensor input is aortic pressure, the polynomial algorithm allows determining the pressure head from the measured motor current and aortic pressure, and thus a hysteresis loop can be created from the measured motor current and calculated pressure head. In step 622, a geometric fit of an ellipse to the hysteresis loop is generated, for example, using Euler's equations to fit the hysteresis loop to an ellipse. In step 624, the data forming the hysteresis loop is analyzed with respect to the ellipse fit to determine large point deviations that indicate inflection points observed in the LVEDP values. In step 626, the LVEDP points are calculated from the determined inflection points using a lookup table and a polynomial function. For example, the inflection point may be determined by analyzing the hysteresis loop formed from the motor current and pressure head, and the LVEDP can be calculated from the pressure head data at the inflection point. The LVEDP can then be output to the user, and additional cardiac metrics may be determined to help understand the patient's cardiac function.

[0098] To determine LVEDP, cardiac cycle phase, and other parameters, the gating algorithm described above is applied to hemodynamic parameter data and pump or motor parameters. In each of the above paths to LVEDP calculation, whether the gating is internal or external, the hemodynamic parameter data points identified using the gating technique can be examined in conjunction with a lookup table to examine the dynamic LVEDP curve, and LVEDP values ​​that can be used in determining other cardiac metrics may be output. Figure 7 shows the process for determining LVEDP by applying the gating algorithm. The illustrated process shows in more detail the determination of the gating window and the application of the gating algorithm to determine LVEDP, which were described in relation to Figure 6. Gating is used to determine or isolate cardiac phase and / or left ventricular pressure (such as LVEDP). Gating can be completed by examining the device parameters and physiological parameters to locate local or maximum values.

[0099] The controller measures hysteresis parameters associated with the cardiac cycle and instrument parameters or motor parameters. The hysteresis parameters may be any cardiac hysteresis parameters discussed herein, and the instrument parameters may be any instrument parameters that change with time and pulse. In step 702, the controller generates a gating window using the input hysteresis parameters and instrument parameters. The gating algorithm includes mean and standard deviation methods to identify data points that are relevant local minima by gating the data. Local minima for instrument parameters and physiological parameters are determined independently, and the corresponding local minima data points are returned by the algorithm.

[0100] In step 704, a gating algorithm is applied to identify LVEDPs. The controller inputs local minimum data points of the instrument parameters and physiological parameters into a function that describes the relationship between hysteresis instrument parameters (e.g., motor current) and physiological parameters (e.g., aortic pressure). This function is used to determine the data points associated with LVEDPs.

[0101] In step 706, the calculated LVEDP points are used in a dynamic curve lookup table to determine the LVEDP, and in step 708, the LVEDP is output from the system. The dynamic curve lookup table may convert aortic pressure measurements in a particular cardiac cycle into differential pressures to find the LVEDP value. Figure 7 shows the LVEDP as the output of a gating algorithm, but the gating algorithm may be used in conjunction with any metric calculations described herein.

[0102] Figure 8 shows plots 800 of aortic pressure, left ventricular pressure, and motor current over time. The data from the plots in Figure 8 can be used to generate pressure-current curves for estimating cardiac parameters (e.g., left ventricular pressure) from pressure and current. Plot 800 has an x-axis 802 in units of time and a y-axis 804 in units of either pressure in mmHg or motor current in mA. Plot 800 also includes an aortic pressure signal 806, a left ventricular pressure signal 808, and a motor current signal 810. The aortic pressure signal 806 can be measured by the pressure sensor 312 in Figure 3, the pressure sensor 112 in Figure 1, or any other suitable pressure sensor. The aortic pressure signal includes a heavy beat notch 812, which can be used to mark the start of diastolic filling. The motor current signal 810 can be generated from the current sensor 314 in Figure 3 or any other suitable current sensor. The left ventricular pressure signal 808 can be generated using a dedicated catheter placed in the left ventricle, a pressure sensor attached to the inlet side of the pump, or an estimation based on pressure and current. Signals 806, 808, and 810 in plot 800 can be generated from data collected in animal models or human patients. Signals 806, 808, and 810 in plot 800 were generated from data collected in pig hearts while the pump motor was operating at 33,000 rpm.

[0103] As shown in plot 800, the motor current signal 810 changes with the cardiac phase. The load on the pump, and therefore the motor current signal 810, increases as the blood flow through the heart increases. The motor current signal 810 increases simultaneously with the increase in the left ventricular pressure signal 808 and the aortic pressure signal 806. This may seem counterintuitive since the pressure difference across the aortic valve is decreasing, but in this pump configuration, the primary determinant of the increase in current is the increased load on the motor resulting from the higher mass flow. The higher mass flow occurs during systole, which results in a higher motor current during systole. This increase in motor current is not evident in the Bernoulli relationship expressed so far, because Bernoulli's relationship is often normalized mass or velocity to describe a steady ohmic system. Unlike a typical pumping environment, the heart generates a phase-dependent dynamic load via a fluctuating mass flow to which the cardiac pump (e.g., the heat pump 302) responds. This gives rise to a phase component in the motor current signal that governs the effects from pressure changes described by Bernoulli. As a result, the motor current waveform represents cardiac cycle dynamics and can be used to extract cardiac energetics. While motor drivers may use control algorithms that adjust the motor current immediately after changes in cardiac phase, it is possible to predict the effect of such control algorithms on the motor current so that fluctuations in the motor current can still be used as an indicator of cardiac contractility and stroke volume variability.

[0104] LVEDP can be extracted from the left ventricular pressure signal 808 using the motor current signal 810. An algorithm is used to analyze the motor current signal 810 to determine the period over which the motor current signal 810 changes. For example, the motor current signal 810 drops sharply between a first time point 811 and a second time point 813. To accurately extract LVEDP, the left ventricular pressure signal 808 can be analyzed during the corresponding period. By gating the left ventricular pressure signal 808 based on the motor current signal 810, the amount of data that needs to be analyzed to find LVEDP is reduced, and noise is decreased. This gating technique, which utilizes changes in motor parameters, can be used with various motor parameters. For example, an increase in motor current indicating an increase in load can indicate a period over which LVEDP can be identified. In addition, a decrease in motor speed in response to an increase in load can also indicate a period over which LVEDP can be identified.

[0105] Figure 9 shows process 900 for determining LVEDP by applying an ECG-based gating algorithm. The illustrated process provides a more detailed explanation of the application of a gating algorithm for determining the gating window and LVEDP using ECG data, as described in relation to Figure 6.

[0106] Process 900 begins with a patient monitoring device 902, which measures and records ECG data in step 904. The patient monitoring device 902 may be external to the pump system or integrated into the pump system. The measured ECG data is sent to a pump controller 906, where the ECG data may be used to determine a gating window for identifying LVEDP. In step 908, the pump controller generates an ECG-based gating window by identifying the segment of ECG data in which the R peak of the QRS waveform, i.e., the final diastolic period, lies. This can be achieved by fitting the data to a periodicity equation and determining the data points that fall outside the equation, or by identifying points in the data corresponding to the R peak. The gate window is the period in the ECG data in which the R peak or final diastolic period is found, but this period does not need to be expressed in absolute time.

[0107] In step 910, the pump controller measures the aortic pressure, and in step 912, the pump controller measures the motor current. The ECG gating window identified in step 908, and the measured aortic pressure and motor current, are used by the pump controller in step 914 to identify LVEDP from the aortic pressure data. The controller analyzes the aortic pressure data points within the segment of aortic pressure data corresponding to the ECG gating window to determine the aortic pressure value in which LVEDP is represented. By gating the data, LVEDP points can be determined more quickly, less data needs to be analyzed, and the required processing time is reduced.

[0108] In step 916, the pump controller accesses a dynamic curve lookup table to convert the determined aortic pressure point into an actual LVEDP. The actual LVEDP can then be output from a gating algorithm for use by healthcare professionals. For example, healthcare professionals can adjust the pump speed by increasing or decreasing it based on the reported LVEDP value.

[0109] In some embodiments, cardiac cycle phase estimation is also determined entirely or partially based on ECG data. Such ECG data may be timed with pressure recordings. Features within the ECG used to estimate the cardiac phase may be the start of the QRS complex and the end of the T wave. If the ECG signal is noisy, detecting peaks in the QRS complex (such as the R wave) and the T wave may be more reliable. In phase estimation methods using either the pressure signal or the ECG signal, an offset from the detected features may be used to more accurately identify the filling phase, since the actual filling occurs slightly before or after these identified markers. The R peak of the QRS waveform may also be used to identify a period during which certain cardiac parameters, such as LVEDP, can be identified, as the R peak corresponds to the final cycle of diastole. Combining both pressure signal-based and ECG-based methods allows for more reliable identification. The weighting between the two methods can be optimized using known filling time parameters, known left ventricular pressure, and datasets with a high signal-to-noise ratio. In some embodiments, the phase estimation from the cardiac hysteresis loop corresponds to one of the following: cardiac ejection, diastolic filling, and diastolic relaxation.

[0110] Figure 10 shows plot 1000 of measured and predicted LVEDP over time by the MCL and animal models. Plot 1000 has an x-axis 1002 showing time in seconds and a y-axis showing LVEDP in mmHg. Plot 1000 includes a first waveform 406, a second waveform 1008, and a third waveform 1010. The first waveform 1006 represents LVEDP over time measured by catheter in the left ventricle. The second waveform 1008 represents LVEDP over time predicted by an algorithm developed to characterize pump performance in a simulated circulatory loop (MCL). The third waveform 1010 represents LVEDP over time predicted by an algorithm developed to characterize pump performance in a pig animal model. Inset 1012 shows the correlation between LVEDP measured in the left ventricle and LVEDP predicted by the MCL and animal models for each measurement. Inset 1012 has an x-axis 1014 showing measured LVEDP in mmHg and a y-axis 1016 showing predicted LVEDP in mmHg. Inset 1012 also includes several data points 1018 representing measured-predicted pairs. The data points 1018 in plot 1012 include unfilled points (e.g., 1020) representing pairs containing LVEDP predicted by an animal-based algorithm and star-shaped points (e.g., 1022) representing pairs containing LVEDP predicted by an MCL-based algorithm. A correlation line 1024 is provided for attention and represents a one-to-one correlation, i.e., predicted LVEDP is equal to measured LVEDP.

[0111] Pump characterization was performed in both the MCL model and in a pig animal model undergoing intervention to simulate the disease. LVEDP tracking during IVC occlusion was successful in both the animal and MCL models. The RMS error in the animal model was 0.90 mmHG. The RMS error in the MCL model was 0.35 mmHG. This suggests that pump characterization using the MCL model may be advantageous due to the presence of unidirectional versus bidirectional variance. Therefore, using the MCL for characterization and the animal model for validation may be an effective means of relating cardiac pump performance to cardiac function. Data from the MCL and animal models can be used to develop predictive algorithms and to create predetermined pressure-current curves.

[0112] Figure 11 shows a plot of LVEDP calculated from patient data, illustrating the accuracy of LVEDP determined using the gating method. Plot 1100 includes an x-axis 1102 representing the number of heartbeats and a y-axis 1104 representing the calculated LVEDP based on motor and physiological parameters. Plot 1100 includes a scatter plot 1103 of the calculated LVEDP for each heartbeat, a line 1102 showing the reported pulmonary capillary wedge pressure (PCWP), and a standard error line 1101 relative to the reported PCWP line 1102.

[0113] Plot 1100 shows the PCWP 1102 actually recorded in the patient, as well as the LVEDP 1103 calculated by retrospectively applying the algorithm to the patient data. Plot 1100 shows that the calculated LVEDP 1103 lies within the standard error line 1101 of the reported PWCP line 1102. PWCP is conventionally measured by pushing a lung catheter and balloon into an arterial branch of the pulmonary artery. The calculated LVEDP 1103 is closest to the reported PWCP 1102 at the data point taken during the patient's inspiration, which is the same point at which the wedge pressure is taken in the patient. The calculated LVEDP 1103 shown in Figure 11 is equivalent to or better than the industry standard PWCP 1102 when applied to patient data.

[0114] Figure 12 shows a scatter plot 1200 of pressure head as a function of motor current. Plot 1200 shows the effect of hysteresis on the pressure-current curve. Plot 1200 has an x-axis 1202 showing current in mA and a y-axis 1204 showing pressure head between the left ventricle and aorta in mmHg. Plot 1200 also includes several data points 1206 representing current-pressure pairs collected from a pig animal model. The data points in plot 1200 were generated while the motor was operating at 30,000 rpm. The data points 1206 roughly form a hysteresis loop. The shape of scatter plot 1200 shows that the relationship between pressure head between the left ventricle and aorta and current fluctuates throughout the entire cardiac cycle. To address hysteresis in pressure-current curves, a method of gating measurements based on cardiac phase can help improve the accuracy of cardiac parameter estimates by ensuring that sample data points are reliably compared to reference data points that occurred during the same cardiac phase (systolic or diastolic).

[0115] Figure 13 shows a scatter plot 1300 of pressure head as a function of motor current. Plot 1300 has an x-axis 1302 showing the current in mA and a y-axis 1304 showing the pressure head between the left ventricle and the aorta in mmHg. Plot 1300 also includes several data points 1306 representing current-pressure pairs. The data points 1306 form a hysteresis loop and include a segmented spline curve fitted to the hysteresis loop, indicating the determination of the phase of the cardiac cycle. The hysteresis loop is segmented into three curve-fitting splines 1309, 1311, and 1313. Each spline represents a phase of the cardiac cycle. The first spline 1309 shows a section of the hysteresis loop recorded during diastolic relaxation (isovolumetric relaxation). The second spline 1311 shows a section of the hysteresis loop recorded during diastolic filling. The third spline 1313 indicates a section of the hysteresis loop recorded during systole (ventricular contraction). The point where the second spline 1311 and the third spline 1313 merge is LVEDP 1315. A characteristic notch observed at the junction of the second spline 1311 and the third spline 1313 allows for the identification of the LVEDP 1315 point. Arrows 1307 and 1314 indicate the direction in which the cardiac cycle progresses.

[0116] At the point where LVEDP occurs within the hysteresis loop, a characteristic notch can be observed, enabling visual recognition of the point within the cardiac cycle, as well as algorithmic identification of LVEDP as an inflection point in the hysteresis loop of pressure head and motor current. At the LVEDP inflection point, the motor current changes as the left ventricle progresses from a state of diastolic filling to a state of active contraction. Determining the LVEDP inflection point from the hysteresis loop depends on the sampling rate for collecting motor and pressure parameters, and the calculation must take the sampling rate into account or extrapolate the data to accurately determine the LVEDP inflection point. Phase estimation can function as a filter for the pressure and current signals, as it allows for comparison of the pressure and current signals with those generated during corresponding stages of the cardiac cycle.

[0117] The LVEDP 1315 points can also be calculated from plot 1300 using the best-fit algorithm. The LVEDP 1315 points can be calculated from the hysteresis loop using a polynomial and the best-fit algorithm which describes the hysteresis data as an ellipse. The hysteresis loop can be estimated using an equation based on Euler's equations for steady-state fluid motion. The coefficients of the equation are calculated using multiple regression analysis from the following equation. TIFF0007875244000003.tif10128

[0118] In the equation, the coefficients are A, B, C, and D, i is the motor current, di / dt is the derivative of the motor current over time, ω is the thermodynamic work, and d 2 i / d 2 t is the second derivative of the motor current over time. The last term in the equation is optional as it is very small. The final formula describes the hysteresis loop and can be used to track changes in the loop's size and shape over time, i.e., changes in curvature or local gradient over time, as well as to extract metrics of cardiac function, including LVEDP.

[0119] According to this formula, ellipse 1321 can be fitted to the hysteresis loop using a geometric method, and LVEDP 1315 points can be detected based on the relationship of the data points to the described ellipse. Using the distance between each point on ellipse 1321 and its foci, outlier data points can be determined from the ellipse fit according to the following formula. TIFF0007875244000004.tif4128

[0120] In the formula, the value of r is the distance from a point on ellipse 1321 to each focus, and α is the length of the minor axis of ellipse 1321. The values ​​of data points outside ellipse 1321 are evaluated for their position, and by iterating over the data, the most clustered positions of the data points are determined. Clusters can be defined in various ways; for example, a cluster can be defined as at least three points that are within the range of 2 mA and 1.5 mmHg of each other.

[0121] By algorithmically determining the clusters of data points 1306 that form both ends of the hysteresis loop, a bisector 1317 can be drawn through the ellipse 1321 describing the hysteresis loop. Since the heart spends the most time in these two phases and only transiently moves between them, the majority of the measured data points 1306 are located in these two positions. A first cluster 1318 corresponding to peak relaxation and a second cluster 1319 corresponding to peak ejection are detected, and a line 1317 is drawn between the mean values ​​of the two clusters 1318 and 1319. The line 1317 divides the ellipse 1321 and the hysteresis data as a whole in half, including the first spline 1309 (diastolic relaxation), into an upper half corresponding to higher pressure overall and a lower half corresponding to higher pressure overall. Below line 1317, the lower part of ellipse 1321 includes the second spline 1311 (diastolic filling) and the third spline 1313 (systolic or ventricular contraction). The LVEDP 1315 point can be estimated from the ellipse fit of the data below line 1317 by determining the point with the largest deviation from the fitted circle or ellipse 1321. In addition, other cardiac metrics can be extracted from the data by segmenting the ellipse according to cardiac phase, and each segment can be numerically integrated by Riemann sum. Alternatively, the hysteresis loop can also be estimated using any other suitable best-fit algorithm.

[0122] Left ventricular diastolic pressure and left ventricular end-diastolic pressure (LVEDP) can be used to determine the overall state of cardiac function. LVEDP is the pressure inside the left ventricle at the end of ventricular filling and immediately before ventricular contraction. LVEDP tends to be significantly elevated in almost all cases of acute myocardial infarction, especially in patients with heart failure. This is mainly due to a shift in the Frank-Starling relationship, which describes the relationship between the cardiac contractile state and LVEDP due to changes in the end-diastolic pressure-to-volume ratio (EDPVR). As a patient approaches heart failure, the Frank-Starling curve shifts downward, resulting in a given pressure leading to a lower stroke volume. Because of this shift, LVEDP can indicate the state of the heart at a given cardiac output in a patient, provided all other conditions remain relatively constant. Measuring these changes in LVEDP can be useful for monitoring a patient's progression toward either heart failure or recovery, thus allowing clinicians to adjust necessary treatment accordingly.

[0123] After the LVEDP point is determined based on elliptic fitting, the actual LVEDP can be determined by accessing a lookup table. A given pressure-current curve can be materialized in a lookup table that accepts pressure, motor current, and cardiac phase as its inputs. Cardiac phase information may be binary (e.g., diastole or systole) or more granular (e.g., systole, diastolic relaxation, and diastolic filling). The output of the lookup table can be other parameters besides LVEDP, such as contractility, stroke volume, ejection fraction, chamber pressure, stroke work, cardiac output, cardiac power output, left ventricular end-diastolic pressure (LVEDP), preload state, afterload state, heart rate, cardiac recovery, flow load state, variable volume load state, cycle volume load state, and / or cycle flow state, or any other appropriate cardiac parameters, although the calculation of these parameters may require further inputs.

[0124] Figure 13 shows a hysteresis curve formed from data point 1306 and its bisector 1317, which illustrates the principle of the algorithm applied to the data. It is not necessary to actually create or draw a hysteresis loop to extract LVEDP data. The controller can extract LVEDP data by accessing and manipulating an array of stored data stored in memory. The controller can store the measured data in memory and characterize the relationship between the measured aortic pressure and motor parameters by fitting the data with equations that describe one parameter in relation to the other, such as elliptic fitting, Euler equations, or polynomials. The equations characterizing the relationships between data points are then used to extract information about the LVEDP points, and in some embodiments, they may also be used to extract information about additional cardiac parameters related to cardiac function.

[0125] Furthermore, to extract LVEDP data, it is not necessary to record or measure motor and hemodynamic parameters for the entire cardiac cycle. Sufficient data points must be collected to allow fitting the data points to a portion of the elliptic curve during the transition from diastolic filling to ventricular contraction phase of the cardiac cycle, and to determine the LVEDP points that fall outside this elliptic fit. Alternatively, one or more cardiac cycles can be recorded to accurately capture this portion of the curve.

[0126] In some embodiments, it may be beneficial to display the hysteresis loop formed by correlating measured motor parameters with hemodynamic parameters. The shape and size of the hysteresis loop, or changes in local gradient or curvature, may provide important details about the patient's cardiac function. These can be used, for example, by healthcare professionals to make decisions related to the patient's treatment, such as whether to increase or decrease pump assistance by changing the pump speed.

[0127] Figure 14 shows a scatter plot 1400 of pressure head as a function of hysteresis parameters after a hysteresis gate has been applied to segment the data. Plot 1400 has an x-axis 1402 representing the hysteresis parameters and a y-axis 1404 representing the pressure difference between the left ventricle and the aorta in mmHg. The data for plot 14 were collected from a pig animal model. The hysteresis parameters may be motor parameters, such as motor current expressed in mA. The hysteresis parameters may be dimensionless or normalized parameters. Plot 1400 includes data points 1406 segmented into three groups: a systolic region 1408, a diastolic filling region 1410, and a diastolic relaxation region 1412. Region 1408 corresponds to systole and includes data points 1409 that occurred during systole. Region 1410 corresponds to diastolic filling and includes data points 1411 that occurred during diastolic filling. Region 1412 corresponds to diastolic relaxation and includes data point 1413 that occurred during diastolic relaxation. Data point 1406 can be grouped into systolic region 1408, diastolic filling region 1410, and diastolic relaxation region 1412 using a cardiac phase estimator such as cardiac phase estimator 318 in Figure 3.

[0128] Plot 1400 also includes a subplot 1414 having an x-axis 1416 representing time and a y-axis 1418 representing aortic pressure. Subplot 1414 shows an aortic pressure signal 1420 with various identified reference points 1422, 1424, 1426, and 1428. Reference points 1422, 1424, 1426, and 1428 can be used to segment the aortic pressure signal 1420 into each phase of the cardiac cycle, as indicated by the systolic region 1430, the diastolic relaxation region 1432, and the diastolic filling region 1434. Using the segmentation of the aortic pressure signal into regions 1430, 1432, and 1434, data point 1406 can be segmented into the corresponding systolic region 1408, the diastolic relaxation region 1412, and the diastolic filling region 1410. By segmenting data 1406 into these regions, it becomes possible to compare similar measurements in a way that avoids bias due to inconsistencies in cardiac phase between sample and reference measurements. This makes the estimation of cardiac parameters robust to system hysteresis.

[0129] Figure 15 shows a scatter plot 1500 of pressure head as a function of motor current. Plot 1500 has an x-axis 1502 showing the motor hysteresis parameter and a y-axis 1504 showing the pressure head between the left ventricle and the aorta in mmHg. Plot 1500 includes a first hysteresis loop 1507 representing baseline hysteresis and a second hysteresis loop 1505 representing a variation of the first hysteresis loop 1507. The second hysteresis loop 1505 includes measurable parameters determined from plot 1500, including a variable hysteresis parameter 1515, a variable pressure head parameter 1517, and a variable loop width parameter 1519. The variation in the second hysteresis loop 1505 may be caused by changes in cardiac performance in response to medical events or external stimuli. The variable hysteresis parameter 1515, the variable pressure head parameter 1517, and the variable loop width parameter 1519 can describe the change between the first hysteresis loop 1507 and the second hysteresis loop 1505. The variable hysteresis parameter 1515 is measured along the x-axis 1502. The variable pressure head parameter 1517 is measured along the y-axis. The variable loop width parameter 1519 is a measure of the widest part of the hysteresis loop.

[0130] Figure 16 shows a scatter plot 1600 of pressure head as a function of motor current before and after administration of a β-blocker in a pig animal model. Plot 1600 has an x-axis 1602 showing current in mA and a y-axis 1604 showing pressure head between the left ventricle and the aorta in mmHg. Plot 1600 also includes several data points 1606 representing current-pressure pairs. The data points in plot 1600 were generated in the pig heart while the motor was operating at 30,000 rpm. The data points 1606 roughly form a first hysteresis loop 1607 and a second hysteresis loop 1605. The first hysteresis loop 1607 has three regions 1612, 1610, and 1608. The first region 1612 includes data point 1613 and shows diastolic relaxation. The second region 1610 includes data point 1611 and indicates diastolic filling. The third region 1608 includes data point 1609 and indicates systole. The first hysteresis loop 1607 was generated during normal cardiac function. The second hysteresis loop 1605 was generated after administration of a beta-blocker. The second hysteresis loop 1605 includes three regions 1616, 1618, and 1614. The first region 1616 includes data point 1615 and indicates diastolic relaxation. The second region 1618 includes data point 1617 and indicates diastolic filling. The third region 1614 includes data point 1619 and indicates systole. The shape of scatter plot 1600 shows that the relationship between the pressure head between the left ventricle and the aorta and the current fluctuates throughout the cardiac cycle, during normal function (as in hysteresis loop 1607), and after β-blocker administration (as in hysteresis loop 1605). The second hysteresis loop 1605 has a lower maximum differential pressure than the first hysteresis loop 1607. In addition, the shape of the second hysteresis loop 1605 also differs from that of the first hysteresis loop 1607. In particular, the first region 1616 of the second hysteresis loop 1605 is shifted downward and has a less distinct curve than the corresponding first region 1612 of the first hysteresis loop 1607.The third region 1614 of the second hysteresis loop 1605 is also shifted upward relative to the corresponding third region 1608 of the first hysteresis loop 1607. Furthermore, the area enclosed by the first hysteresis loop 1607 is larger than the area enclosed by the second hysteresis loop 1605. Administration of beta-blockers results in changes in cardiac contractility. A trained physician can use the shape of data point 1606 over several cardiac cycles and the area of ​​the hysteresis loop formed by the data point to determine morphological changes in the heart as a result of beta-blocker administration, or to assess the degree of heart failure.

[0131] Figure 17 shows a smooth curve derived from the scatter plot of Figure 16. Similar to scatter plot 1600 in Figure 16, plot 1701 has an x-axis 1702 showing current in mA units and a y-axis 1704 showing pressure head between the left ventricle and the aorta in mmHg units. Plot 1701 shows three curves: a baseline curve 1709, a curve showing low contractility 1705, and a curve showing high contractility 1707. The smooth curve in Figure 17 allows healthcare professionals to visualize changes in cardiac behavior, such as those observed in a low-contractility state after administration of beta-blockers, and can be used to extract meaningful changes in cardiac parameters and cardiac health. Figures 16 and 17 include hysteresis curves shown on the x-axis 1602 and 1702 of motor current in mA units, but the hysteresis curves may be plotted using any motor parameter that changes with time and pulse rate on the x-axis.

[0132] Figure 18A shows a scatter plot 1800 of pressure head as a function of motor current. Plot 1800 has an x-axis 1802 showing current in mA and a y-axis 1804 showing pressure head between the left ventricle and the aorta in mmHg. Plot 1800 also includes several data points 1806 representing current-pressure pairs. The data points 1806 form a first hysteresis loop 1808 and a second hysteresis loop 1810. The shape of scatter plot 1800 shows that the relationship between pressure head between the left ventricle and the aorta and current changes throughout the cardiac cycle, and during normal function (as in hysteresis loop 1808) and during the transition to myocardial infarction (as in hysteresis loop 1810). The first hysteresis loop 1808 shows the cardiac cycle before myocardial infarction. The second hysteresis loop 1810 shows the cardiac cycle during a transitional myocardial infarction. The area enclosed by the second hysteresis loop 1810 during myocardial infarction is smaller than the area enclosed by the first hysteresis loop 1808. A trained physician can use the shape of data points 1806 during several cardiac cycles to determine morphological changes in the heart during or after myocardial infarction.

[0133] Figure 18B shows plot 1801 of cardiac power index and motor current over a period of time. Plot 1801 has an x-axis 1803 showing the number of samples taken, a first y-axis 1805 showing the cardiac power index, and a second y-axis 1807 showing the average motor current in mA. The plot includes a first record 1812 of cardiac power index measured over the total number of samples, and a second record 1810 of motor current for the same samples. The cardiac power index is a new measure calculated from the hysteresis loop and is intended to provide physicians with information about cardiac performance. In plot 1801, the motor current 1810 remains largely constant across the samples measured. The cardiac power index 1808 is shown in small samples during a normal cardiac cycle, labeled “pre-MI” 1814. In sample number 500, the cardiac power index of 1812 decreased from approximately 3000 to approximately 2000 during myocardial infarction (labeled "MI"), indicating a decline in cardiac pumping performance. The cardiac power index is an index that trained physicians can use to monitor cardiac performance during normal cardiac cycles, as well as during and after events such as myocardial infarction.

[0134] Figure 19 shows examples of various cardiac parameters over time to illustrate the diagnostic capabilities provided by visualizing parameters. Each plot shows data generated from an animal model, illustrating changes in area index, contractility, flow load, and mean aortic pressure over time. Plot I 1900 includes an x-axis 1903 representing time in seconds, a first y-axis 1904 representing normalized indices as percentages, and a second y-axis 1905 representing mean pressure in mmHg. Plot I includes records of area index 1910 (indicating overall cardiac function), contractility index 1908, flow load 1912, and mean aortic pressure 1906 during balloon occlusion of the inferior vena cava.

[0135] Plot II 1901 includes an x-axis 1913 representing time in seconds, a first y-axis 1914 representing normalized exponents as percentages, and a second y-axis 1915 representing mean pressure in mmHg. Plot II includes records of area index 1920, contractility index 1918, flow load state 1922, and mean aortic pressure 1916 after the use of beta-blockers.

[0136] Plot III 1902 includes an x-axis 1923 representing time in seconds, a first y-axis 1924 representing normalized indices as percentages, and a second y-axis 1925 representing mean pressure in mmHg. Plot III also includes records of area index 1930, contractility index 1928, flow load condition 1932, and mean aortic pressure 1926 after the use of inotropic material.

[0137] Plots I-III in Figure 19 show various responses in various measurable cardiac parameters in response to various cardiac events. For example, the decrease in the area index 1910 in plot I precedes the decrease in the flow load index 1912, indicating a problem with the amount of blood being pumped by the heart. The decrease in the area index 1920 in plot II coincides with the decrease in the contractility index 1918, indicating that the beta-blocker administered to the animal model is affecting cardiac contractility. To show changes in contractility, flow load, and overall cardiac function, and to determine the causes of such changes, the cardiac parameters shown in plots I-III can be calculated and displayed from the hysteresis loop.

[0138] By understanding the trends in various cardiac parameters of a patient, trained healthcare professionals can better address the patient's cardiac needs. Through changes and trends in various calculated cardiac parameters, healthcare professionals can assess the patient's cardiac condition.

[0139] Figure 20A shows an exemplary user interface for a cardiac pump controller, including waveforms of cardiac function metrics over time. User interface 2000 can be used to control the intravascular cardiac pump system 100 in Figure 1, the cardiac assist device 201 in Figure 2, the cardiac pump system 300 in Figure 3, or any other suitable cardiac pump. User interface 2000 includes a pressure signal waveform 2002, a motor current waveform 2004, a cardiac state waveform 2008, and a flow rate 2006. The pressure signal waveform 2002 shows the pressure measured by a pressure sensor in the blood pump (e.g., pressure sensor 312). The pressure signal waveform 2002 can be used by healthcare professionals to properly position the intravascular cardiac pump (e.g., intravascular cardiac pump 100 in Figure 1) within the heart. The pressure signal waveform 2002 is used to verify the position of the intravascular cardiac pump by evaluating whether the waveform 2002 is an aortic waveform or a ventricular waveform. An aortic waveform indicates that the intravascular cardiac pump motor is located in the aorta. The ventricular waveform indicates that the intravascular cardiac pump motor is inserted into the ventricle in the wrong position. To the left of the waveform is a 2014 scale of the placement signal waveform. The default scaling is 0–160 mmHg. The scaling can be adjusted in 20 mmHg increments. To the right of the waveform is a display 2003 that labels the waveform, provides units of measurement, and shows the maximum, minimum, and average values ​​from the received sample.

[0140] The motor current waveform 2004 is a measure of the energy uptake of the cardiac pump motor. Energy uptake changes with motor speed and the pressure difference between the inlet and outlet regions of the cannula, which results in a fluctuating volumetric load on the rotor. When used in conjunction with an intravascular cardiac pump (such as intravascular cardiac pump 100 in Figure 1), the motor current provides information about the catheter position relative to the aortic valve. When the intravascular cardiac pump is correctly positioned, with the inlet region in the ventricle and the outlet region in the aorta, the motor current pulsates as the mass flow rate through the cardiac pump changes with the cardiac cycle. When the inlet and outlet regions are on the same side of the aortic valve, the pump inlet and outlet are located in the same cardiac chamber, resulting in a constant mass flow rate as there is no variation in differential pressure, and thus a constant motor current, which is attenuated or flat. The motor current waveform scale 2016 is displayed to the left of the waveform. The default scaling is 0 to 1000 mA. The scaling may be adjustable in 100 mA increments. To the right of the waveform is a display 2005 that labels the waveform, provides units of measurement, and shows the maximum, minimum, and average values ​​from the received sample. Pressure and motor current sensors may not be necessary for positioning surgically implanted pumps such as the cardiac assist device 201 in Figure 2, but these sensors can be used in such devices to determine yet another feature of the intrinsic cardiac function and monitor treatment.

[0141] The Cardiac Status Waveform 2008 is a display of recorded cardiac status over a period of time. Cardiac status can be expressed as a ratio of cardiac contractility to the volume of blood pumped. Cardiac status can be calculated discretely or sequentially to provide physicians with an indicator of the heart's current performance relative to its performance at other points in the patient's treatment, and can be displayed as a trend in the Cardiac Status Waveform 2008. To the left of the Cardiac Status Trend Line is the Scale 2018 of the Cardiac Status Waveform 2008. The default scaling is 1 to 100 (unitless). The scaling can be adjusted to best represent the trend of cardiac status. To the right of the Cardiac Status Waveform 2008 is Display 2007, which labels the trend line and provides additional information about the heart's performance at the present time, showing the current values ​​of contractility and volume received from the pump. Displaying this information as a trend line allows physicians to review the patient's past cardiac status history and make decisions based on the trend of cardiac status. For example, a physician can observe a decrease or increase in cardiac condition over time from a cardiac condition trend line and decide to change or continue treatment based on this observation.

[0142] The flow rate 2006 can be a target blood flow rate set by the user or an estimated actual flow rate. In some modes of the controller, the controller automatically adjusts the motor speed in response to changes in afterload to maintain the target flow rate. In some embodiments, if flow rate calculation is not possible, the controller allows the user to set a fixed motor speed indicated by the speed indicator 2008.

[0143] Figure 20B shows an exemplary user interface 2001 for a cardiac pump controller according to a particular embodiment. User interface 2001 can be used to control the intravascular cardiac pump system 100 in Figure 1, the cardiac assist device 201 in Figure 2, the cardiac pump system 300 in Figure 3, or other suitable cardiac pumps. User interface 2001 includes a pressure signal waveform 2022, a motor current waveform 2024, a flow rate 2026, a speed indicator 2028, a contractility score 2030, and a state metric score 2032. The pressure signal waveform 2022 shows the pressure measured by a pressure sensor of the blood pump (e.g., pressure sensor 312). The pressure signal waveform 2022 can be used by healthcare professionals to properly position the intravascular cardiac pump (e.g., intravascular cardiac pump 100 in Figure 1) within the heart. The pressure signal waveform 2022 is used to verify the position of the intravascular cardiac pump by evaluating whether the waveform 2022 is an aortic waveform or a ventricular waveform. An aortic waveform indicates that the intravascular cardiac pump motor is located within the aorta. A ventricular waveform indicates that the intravascular cardiac pump motor is inserted into the ventricle, which is the wrong position. To the left of the waveform is the placement signal waveform scale 2034. The default scaling is 0-160 mmHg. The scaling can be adjusted in 20 mmHg increments. To the right of the waveform is display 2033, which labels the waveform, provides units of measurement, and shows the maximum, minimum, and average values ​​from the received sample.

[0144] The motor current waveform 2024 is a measure of the energy uptake of the cardiac pump motor. Energy uptake changes with motor speed and the pressure difference between the inlet and outlet regions of the cannula, which results in a fluctuating volumetric load on the rotor. When used in conjunction with an intravascular cardiac pump (such as intravascular cardiac pump 100 in Figure 1), the motor current provides information about the catheter position relative to the aortic valve. When the intravascular cardiac pump is correctly positioned, with the inlet region in the ventricle and the outlet region in the aorta, the motor current pulsates as the mass flow rate through the cardiac pump changes with the cardiac cycle. When the inlet and outlet regions are on the same side of the aortic valve, the pump's inlet and outlet are located in the same cardiac chamber, resulting in a constant mass flow rate as there is no variation in differential pressure, and thus a constant motor current, which is attenuated or flat. The motor current waveform scale 2036 is displayed to the left of the waveform. The default scaling is 0-1000mA. The scaling may be adjustable in 100mA increments. To the right of the waveform is a display 2025 that labels the waveform, provides units of measurement, and shows the maximum and minimum values ​​as well as the average value from the received sample. Pressure sensors and motor current sensors may not be necessary for positioning surgically implanted pumps such as the cardiac assist device 201 in Figure 2, but these sensors can be used in such devices to determine yet another feature of the intrinsic cardiac function and monitor treatment.

[0145] The flow rate 2026 can be a target flow rate set by the user or an estimated actual flow rate. In some modes of the controller, the controller automatically adjusts the motor speed in response to changes in afterload to maintain the target flow rate. In some embodiments, if flow rate calculation is not possible, the controller allows the user to set a fixed motor speed indicated by the speed indicator 2028.

[0146] The contractility score 2030 provides an indicator of cardiac function. More specifically, the contractility score represents the intrinsic strength and force of the heart's contraction during systole. Higher cardiac contractility results in a larger stroke volume. For example, moderate contractility may occur when the cardiac stroke volume is approximately 65 mL. High contractility may occur when the cardiac stroke volume exceeds 100 mL. Low contractility may occur when the cardiac stroke volume is less than 30 mL. The contractility score can be expressed numerically and / or graphically. The contractility score may be dimensionless. Changes in contractility can be determined from the variation in the pressure gradient during cardiac contraction (dP / dt). The state metric score 2032 also provides an indicator of cardiac function. The state metric score may be an indicator of volumetric load, cardiac pressure, or another metric of cardiac function.

[0147] The locations in Figures 20A and 20B, the depiction of metrics on the controller, and the identification and number of metrics and recommendations are intended as examples only. The number of metrics and indicators, the location of the same metrics and indicators on the console, and the metrics displayed may differ from those shown herein. The metrics displayed to the user may be any other appropriate metrics derived from hysteresis parameters associated with cardiac assist devices placed on or in relation to the patient's organs or parts thereof, defined by any or all of the aforementioned cardiac-related parameters, trends over time, and specific thresholds, such as contractility, stroke volume, ejection fraction, chamber pressure, stroke work, cardiac output, cardiac power output, LVEDP, preload state, afterload state, flow load state, variable volume load state, cycle volume load state, cycle flow state, heart rate, and / or cardiac recovery, or any other appropriate metrics derived from any hysteresis parameters associated with cardiac assist devices placed on or in relation to the patient's organs or parts thereof.

[0148] Figure 21 shows a process for detecting aspiration within an intravascular cardiac pump and determining its cause. Aspiration occurs when the inlet of the cardiac assist device is obstructed (e.g., by a valve leaflet or other anatomical structure) or when blood volume or ventricular preload is reduced to less than the output of the selected pump speed. Preventing aspiration allows the intravascular cardiac assist device to operate safely at higher flow rates. Conventional aspiration detection techniques are insufficient in detecting slight aspiration, determining when aspiration occurs in the cardiac cycle, and detecting undesirable cardiac cycle flow conditions that may lead to aspiration events. Process 2100 can detect aspiration earlier than conventional methods and can provide the user with information on how to prevent the aspiration from continuing or worsening.

[0149] In step 2102, pressure is detected from the cardiac assist device. In step 2104, rotor speed and motor current are detected. In step 2106, the cardiac cycle phase is determined. Phase estimation can function as a filter for the pressure and current signals, as it allows the pressure and current signals to be compared with the pressure and current signals generated during corresponding stages of the cardiac cycle. Phase estimation may be based on the pressure information received in step 2102 and may include locating a reference point in the pressure information that indicates the cardiac phase. In some embodiments, a heavy beat notch in the pressure signal is detected to indicate the start of diastolic filling. A heavy beat notch is a small downward deflection in the arterial pulsation or pressure curve immediately after closure of the semilunar valve. This heavy beat notch can be used as a marker for the end of systole and thus the approximate start of diastole.

[0150] In some embodiments, phase estimation is based entirely or partially on ECG data. Such ECG data may be timed with pressure recordings. Features within the ECG used to estimate the cardiac phase may be the start of the QRS complex and the end of the T wave. If the ECG signal is noisy, detecting peaks in the QRS complex (e.g., the R wave) and the T wave may be more reliable. In phase estimation methods using either the pressure signal or the ECG signal, an offset from the detected features may be used to more accurately identify the filling phase, since the actual filling occurs slightly before or after these identified markers. Combining both pressure-based and ECG-based methods allows for more reliable identification. The weighting between the two methods can be optimized using known filling time parameters, known left ventricular pressure, and datasets with a high signal-to-noise ratio.

[0151] In step 2108, a predetermined pressure curve is referenced to determine cardiac parameters indicating suction. In some embodiments, the table may be based on a predetermined pressure-current curve. Cardiac parameters can be determined by mapping measured current and pressure to cardiac parameters. The reference table may be a lookup table that accepts pressure, motor current, and cardiac phase as its inputs. Cardiac phase information may be binary (e.g., diastolic or systolic) or more granular (e.g., systolic, diastolic relaxation, and diastolic filling). In step 2110, the suction event is detected. The suction event can be detected by determining a deviation from a normal predetermined pressure-current curve. The deviation may indicate an anomalously low mass flow rate relative to the corresponding aortic pressure and cardiac phase. In some embodiments, the suction event is detected by changes in motor parameters and the hysteresis loop of the pressure head. An early sign of the suction phenomenon is the collapse of the hysteresis loop. As the volumetric load decreases, the loop collapses, indicating that the suction event has begun.

[0152] In step 2112, the time within the cardiac cycle at which the aspiration event occurs is determined. For example, it may be determined whether the aspiration event occurs during systole or diastole. The method for stopping one or more aspiration events may depend on whether the aspiration event occurs during systole or diastole. In step 2114, a volumetric load coefficient is determined. Based on the volumetric load coefficient and the determination of when the aspiration occurs in the cardiac cycle, the root cause of the aspiration is determined. For example, the root cause may be aspiration to the valve leaflets. In step 2118, the user is provided with corrective actions to address the aspiration event. For example, the user may be prompted to reposition the cardiac assist device within the heart. In some embodiments, when the onset of an aspiration event is detected, an early detection warning of possible aspiration events is activated.

[0153] In some embodiments, conditions leading to an aspiration event can be detected, for example, by detecting a decrease in the volumetric load on the pump. The pump's chamber blood volume is detected using hysteresis in motor parameter measurements and pressure measurements at a pressure sensor and can be compared to a set level of pump assistance to determine whether the chamber blood volume has decreased significantly. When an aspiration event occurs, a significant decrease in chamber blood volume is likely to be occurring, and if a decrease is detected, an early warning or a prompt to take action to prevent the aspiration event from continuing can be provided. In some embodiments, the action is automated. In some embodiments, the action is recommended. In some embodiments, the automated or recommended action is to reduce the level of assistance provided by the pump to match the volumetric state (e.g., reducing the rotor speed). [Examples]

[0154] Exemplary example 1: The IMPELLA® percutaneous cardiac pump (Abiomed, Inc., Danvers, Massachusetts) was implanted in a simulated circulatory loop (MCL) consisting of the ventricle and aorta, and pressure was measured over the entire period. While motor current was recorded, IMPELLA® operated at various performance levels and MCL hydrodynamic profiles. A predictive algorithm for LVP was generated using the pump's characterization. Performance was validated in anesthetized pigs using the implanted IMPELLA®. Ischemia-like events or hemorrhagic shock-like events were induced by balloon occlusion of the left anterior descending coronary artery or the inferior vena cava, respectively. Motor current and pressure signals from the IMPELLA® pump in the pulmonary artery, left ventricle, and aorta were recorded simultaneously.

[0155] In minimal ventricular support, significant changes occurred within 4 minutes following ischemia and shock. Instability and shock were reflected in changes in the motor current waveform. Characterization from both MCL (RMS error approximately 0.3 mmHg) and pig (RMS error approximately 0.9 mmHg) predicted left ventricular pressure (LVP) during hemorrhagic shock. In contrast, in maximal ventricular support, there was no hemodynamic deterioration, and the motor current remained unchanged beyond 20 minutes after occlusion.

[0156] The results demonstrate a coupling between the heart and device function. Without sufficient support, cardiac performance deteriorates, leading to hemodynamic collapse, which was tracked by the LVP algorithm. The algorithm's success is attributed to the use of MCL and porcine models under development. MCL defined the limits of pump performance, while animals demonstrated biological variability and pathology. This unified approach—using MCL for characterization and animals for validation—could be an effective means of defining the performance of any device.

[0157] The above are merely illustrative examples of the principles of this disclosure, and the apparatus may be carried out in ways other than those described, which are presented for illustrative purposes only and not limitation. Although the apparatus disclosed herein is shown for use in percutaneous insertion of a cardiac pump, it should be understood that it may be applicable to apparatus for other uses.

[0158] Those skilled in the art will likely come up with variations and modifications after reviewing this disclosure. The features disclosed may be implemented in any combination and partial combination (including multiple dependent and partial combinations) with one or more other features described herein. The various features described or illustrated above, including any combination thereof, may be combined or integrated in other systems. Furthermore, certain features may be omitted or not implemented.

[0159] In general, the subjects and functional operating modes described herein can be implemented as digital electronic circuits, or as computer software, firmware, or hardware including the structures disclosed herein and their structural equivalents, or as a combination of one or more of these. The subjects described herein can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a computer-readable medium for a data processing device to execute or to control the operation of a data processing device. The computer-readable medium can be a machine-readable storage device, a machine-readable storage board, a memory device, a composition affecting machine-readable propagating signals, or a combination of one or more of these. The term “data processing device” encompasses all devices and machines for processing data, including, for example, a programmable processor, a computer, or multiple processors or computers. In addition to hardware, a device may include code that generates an execution environment for the computer program in question, such as code constituting processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of these. A propagating signal is an artificially generated signal, such as a machine-generated electrical, optical, or electromagnetic signal created to encode information for transmission to a suitable receiving device.

[0160] Computer programs (also known as programs, software, software applications, scripts, or code) can be written in any form of programming language, including compiled and interpreted languages, and can be deployed in any form, including standalone programs, modules, components, subroutines, or other units suitable for use in a computing environment. Computer programs do not necessarily correspond to files in a file system. A program can be part of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple interconnected files (e.g., a file containing one or more modules, subprograms, or parts of code). Computer programs can be deployed to run on a single computer, located in one place, or distributed across multiple sites and interconnected by a communication network.

[0161] The processes and logic flows described herein can be executed by one or more programmable processors that run one or more computer programs that perform their function by acting on input data to produce outputs. Alternatively, the processes and logic flows can also be executed by dedicated logic circuits such as FPGAs (Field-Programmable Gate Arrays) or ASICs (Application-Specific Integrated Circuits), and the device can be implemented as a dedicated logic circuit.

[0162] Processors suitable for executing computer programs include, for example, both general-purpose and dedicated microprocessors, and any one or more processors in any type of digital computer. Generally, a processor receives instructions and data from read-only memory, random-access memory, or both. Essential elements of a computer are a processor for executing instructions and one or more storage devices for storing instructions and data. Generally, a computer includes one or more mass storage devices for storing data, such as magnetic, magneto-optical disks, or optical disks, or is operablely coupled to receive data from them, transfer data to them, or both. However, a computer does not necessarily have to have such devices.

[0163] Examples of changes, substitutions, and modifications can be seen by those skilled in the art and may be made without departing from the scope of the information disclosed herein. All references cited herein are incorporated herein by reference in their entirety and form part of this application.

Claims

1. Motor and, A rotor operably connected to the motor, The pump housing, which at least partially surrounds the rotor, is configured such that when the motor is activated, the rotor is driven and blood is pumped through the pump housing. A sensor configured to detect aortic pressure over time, The motor parameters associated with the aforementioned motor are detected over time. The aortic pressure is received over time as input from the aforementioned sensor. Determine the mathematical relationship between the aortic pressure and the motor parameters. The cardiac cycle phase is determined from the mathematical relationship between the aortic pressure and the motor parameters. A hysteresis curve is obtained based on the mathematical relationship between the aortic pressure and the motor parameters. Select the sample time on the hysteresis curve corresponding to the cardiac cycle phase. A controller configured in such a way A cardiac pump system, including the heart pump system.

2. The cardiac pump system according to claim 1, wherein the motor parameter is at least one of the current supplied to the motor, the power supplied to the motor, or the motor speed.

3. The cardiac pump system according to claim 1, wherein the controller is configured to determine at least one cardiovascular metric by extracting an inflection point, local gradient change, or curvature change from the mathematical relationship between the aortic pressure and the motor parameters.

4. The cardiac pump system according to claim 3, wherein the at least one cardiovascular metric is at least one of contractility, stroke volume, ejection fraction, chamber pressure, stroke work, cardiac output, cardiac power output, left ventricular end-diastolic pressure (LVEDP), preload state, afterload state, heart rate, cardiac recovery, flow load state, variable volume load state, cycle volume load state, and cycle flow state.

5. The cardiac pump system according to claim 4, wherein the at least one cardiovascular metric is the LVEDP.

6. The cardiac pump system according to claim 1, comprising a catheter having a distal region operably connected to the motor.

7. The cardiac pump system according to claim 1, wherein the controller is configured to determine the mathematical relationship between the aortic pressure and the motor parameters using a polynomial best fit algorithm.

8. The cardiac pump system according to claim 7, wherein the motor parameters include the current supplied to the motor, and determining the mathematical relationship involves fitting an equation to at least a portion of the data representing the current and the pressure head calculated from the current and the aortic pressure.

9. The aforementioned controller Using the equation fitted to at least a portion of the data representing the current and the pressure head, the LVEDP point is determined. Access the lookup table and determine the actual LVEDP value from the LVEDP point in the pressure head. The cardiac pump system according to claim 8, configured as described above.

10. The cardiac pump system according to claim 9, wherein determining the LVEDP point includes identifying a change in gradient, a change in curvature, or an inflection point in the equation fitted to at least a portion of the data representing the current and the pressure head.

11. The cardiac pump system according to claim 1, wherein the controller is configured to detect the motor parameters, and the sensor is configured to detect the aortic pressure over a portion of the cardiac cycle.

12. The cardiac pump system according to claim 1, wherein the controller is configured to detect the motor parameters, and the sensor is configured to detect the aortic pressure over one or more cardiac cycles.

13. The cardiac pump system according to claim 1, wherein the motor is configured to maintain a substantially constant speed of the rotor while the rotor is operating.

14. The cardiac pump system according to claim 3, wherein the controller is further configured to store the at least one cardiovascular metric in memory together with the at least one previously determined cardiovascular metric.

15. The cardiac pump system according to claim 1, wherein the controller is configured to adjust the operation of the cardiac pump system to change the speed of the rotor based on the mathematical relationship.

16. The aforementioned controller The indicators of the aforementioned mathematical relationship are displayed, Upon receiving a request for adjustment of the operation of the aforementioned cardiac pump system, Based on the above requirements, the operation of the motor is adjusted to drive the heart pump system. The cardiac pump system according to claim 1, configured as follows.

17. The cardiac pump system according to claim 16, wherein the motor is an integrated motor sized and configured for insertion into the patient's vascular structure by a catheter.

18. Determining the cardiac cycle phase is When the aforementioned sample time corresponds to the segment of the hysteresis curve corresponding to the pressure head increase, it is determined that the cardiac cycle phase is in diastolic relaxation, or Determining that the cardiac cycle phase is in diastolic filling when the aforementioned sample time corresponds to a segment of the hysteresis curve corresponding to a decrease in pressure head up to a point identified by a sharp change in gradient or curvature or an inflection point following diastolic relaxation, or The cardiac cycle phase is determined to be systolic when the aforementioned sample time corresponds to a segment of the hysteresis curve having a pressure head decrease from the inflection point to the minimum pressure head. The cardiac pump system according to claim 1, further comprising:

19. The cardiac pump system according to claim 1, further comprising a pump inlet, wherein the controller is further configured to determine pump inlet occlusion based on the hysteresis curve.

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