Systems and methods for monitoring autoregulation

Abstract

The system includes a first sensor for continuously measuring the patient's renal blood flow signal. A second sensor continuously measures the patient's arterial pressure signal. A blood flow monitor is in communication with the first and second sensors. The blood flow monitor includes a system memory storing monitoring software code and a processor. The processor is configured to execute the monitoring software code to estimate the patient's renal blood flow rate from the renal blood flow signal and to monitor changes in the renal blood flow rate over time. The processor is also configured to execute the monitoring software code to monitor changes in the arterial pressure signal over time and to evaluate a mathematical relationship between changes in the arterial pressure signal and changes in the renal blood flow rate.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Application No. 63 / 479,264, entitled "SYSTEM AND METHOD FOR MONITORING AUTOREGULATION," filed January 10, 2023, the disclosure of which is incorporated herein by reference in its entirety.

[0002] The present disclosure relates to monitoring perfusion and blood flow in organs of a patient, and more particularly to medical devices and methods for measuring and / or monitoring autoregulation. [Background technology]

[0003] Autoregulation is the ability of an organ to locally regulate blood flow through it. Failure or loss of autoregulation is a risk factor for organ damage and is often a sign of a breakdown in the patient's body's compensatory circulatory processes. Different organs exhibit varying degrees of autoregulatory behavior. The kidneys and brain are two organs with high blood flow and are the two most tightly autoregulated organs in the human body. The goals of proper autoregulation are very different between the brain and the kidneys. The goal of brain autoregulation is to maintain adequate oxygen to the brain. The goal of renal autoregulation is to achieve adequate tubular and glomerular flow.

[0004] The myogenic response and tubuloglomerular feedback (TGF) response are two mechanisms governing renal autoregulation. The myogenic response occurs in the afferent arteriole and is a fast, ballistic response to buffer sudden increases in blood pressure, such as during cardiac systole. The myogenic response is triggered by hoop stress in the afferent arteriole and is a purely mechanical, protective response. The TGF response is a slow, closed-loop response that modulates renal blood flow in response to salt concentration in the distal tubule. In animal studies, the lower limit of renal autoregulation is much higher than that of cerebral autoregulation: 70 mmHg versus 30 mmHg. Summary of the Invention [Problem to be solved by the invention]

[0005] Multiple factors (e.g., arterial stiffening that occurs with aging) can alter the characteristics of vasoreactivity, and these factors can alter a patient's associated autoregulatory characteristics. Therefore, the range of autoregulation of blood flow due to changing blood pressure can vary between and within patients and cannot be assumed to be constant. Methods and devices for determining whether a particular patient's autoregulation is functioning and the potential range for managing blood pressure variability would be of great assistance to clinicians. Devices and methods for monitoring autoregulation are needed that are an improvement over those known in the prior art, including those that identify and consider factors that may confound the determination or measurement of autoregulation. [Means for solving the problem]

[0006] A method for continuously monitoring a patient's kidneys during a surgical procedure, medical treatment, or medical observation includes continuously measuring the patient's renal blood flow signal with a first sensor attached to the patient. The first sensor is in communication with a blood flow monitor. A processor of the blood flow monitor estimates the patient's renal blood flow rate from the renal blood flow signal. The processor also monitors changes in the renal blood flow rate over time. The patient's arterial pressure signal is continuously measured with a second sensor. The second sensor is in communication with the blood flow monitor. The processor also monitors changes in the arterial pressure signal over time and evaluates a mathematical relationship between the arterial pressure change signal and changes in the renal blood flow rate.

[0007] The system includes a first sensor configured to continuously measure a patient's renal blood flow signal during a surgical procedure, medical treatment, or medical observation. A second sensor configured to continuously measure the patient's arterial pressure signal during a surgical procedure, medical treatment, or medical observation. A blood flow monitor is in communication with the first sensor and the second sensor. The blood flow monitor includes a system memory storing monitoring software code and a processor. The processor is configured to execute the monitoring software code to estimate the patient's renal blood flow rate from the renal blood flow signal and to monitor changes in the renal blood flow rate over time. The processor is also configured to execute the monitoring software code to monitor changes in the arterial pressure signal over time and to evaluate a mathematical relationship between changes in the arterial pressure signal and changes in the renal blood flow rate.

[0008] A method for continuously monitoring a patient's kidneys during a surgical procedure, medical treatment, or medical observation includes continuously measuring a Doppler flow signal of the patient's renal blood flow with an ultrasound transducer probe. The ultrasound transducer probe is attached to the patient's abdomen in a stationary position and is in communication with a processor of a blood flow monitor. The processor monitors changes in the renal blood flow over time. A hemodynamic pressure sensor continuously measures the patient's arterial pressure signal. The hemodynamic pressure sensor is in communication with the blood flow monitor. The processor monitors changes in the arterial pressure signal over time and evaluates a mathematical relationship between the changes in the arterial pressure signal and the changes in the renal blood flow. The processor determines an autoregulation profile of the patient's renal blood flow based on the mathematical relationship between the changes in the arterial pressure signal and the changes in the renal blood flow.

[0009] The system includes an ultrasound transducer probe with a two-dimensional array of transducer elements configured to continuously measure a patient's renal blood flow Doppler flow signal during a surgical procedure, medical procedure, or medical observation. An adhesive patch is connected to the ultrasound transducer probe and configured to attach the ultrasound transducer probe to the patient and maintain contact between the patient and the ultrasound transducer probe without an operator. The system further includes a hemodynamic pressure sensor configured to continuously measure the patient's arterial pressure signal during the surgical procedure, medical procedure, or medical observation. A blood flow monitor is in communication with the ultrasound transducer probe and the hemodynamic pressure sensor. The blood flow monitor includes a system memory storing monitoring software code and a processor. The processor is configured to execute the monitoring software code to determine changes in the patient's renal blood flow from the renal blood flow Doppler flow signal and to monitor the changes in renal blood flow over time. The processor is also configured to execute the monitoring software code to monitor changes in the arterial pressure signal over time and to evaluate a mathematical relationship between the changes in the arterial pressure signal and the changes in renal blood flow. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a schematic diagram illustrating an exemplary monitoring system including a blood flow monitor, an ultrasound transducer probe attached to the patient's abdomen by an adhesive patch, and a hemodynamic pressure sensor connected to the patient for sensing hemodynamic data representative of the patient's arterial pressure. [Figure 2] 2 is another schematic diagram illustrating the blood flow monitor of FIG. 1 connected to an ultrasound transducer probe having a two-dimensional array of transducer elements. [Figure 3]1 is a schematic diagram of an ultrasound transducer probe attached to a patient's abdomen by an adhesive patch for monitoring renal flow in the patient's kidneys. [Figure 4A] 1 is another schematic diagram of an ultrasound transducer probe attached to a patient's abdomen by an adhesive patch for monitoring the patient's kidneys. [Figure 4B] 1 is another schematic diagram of an ultrasound transducer probe attached to a patient's abdomen by an adhesive patch for monitoring the patient's kidneys. [Figure 5] FIG. 1 is a perspective view of an exemplary minimally invasive hemodynamic pressure sensor for sensing hemodynamic data representative of a patient's arterial pressure. [Figure 6] FIG. 1 is a perspective view of an exemplary non-invasive hemodynamic pressure sensor for sensing hemodynamic data representative of a patient's arterial pressure. [Figure 7] 1 is a graphical representation of a time domain method for determining the mathematical relationship between a patient's mean arterial pressure (MAP) and renal blood flow in the patient's kidneys. [Figure 8] 1 is a graphical representation of a frequency domain method for determining the mathematical relationship between a patient's mean arterial pressure (MAP) and renal blood flow in the patient's kidneys. [Figure 9] 1 is a chart of correlation versus patient MAP. [Figure 10] 1 is a plot of the correlation between a patient's renal blood flow rate and change in MAP versus the patient's MAP. [Figure 11] FIG. 1 is a block diagram of a method for determining a patient's renal flow autoregulation profile. [Figure 12] 2 is a chart from an experiment demonstrating the monitoring system of FIG. 1. [Figure 13] 13 is a plot of renal blood flow versus MAP for test subjects from the experiment of FIG. 12. [Figure 14] 13 is a chart of correlation versus MAP for test subjects from the experiment of FIG. 12. [Figure 15]FIG. 1 is a block diagram of a method for continuously monitoring a patient's renal blood flow autoregulation profile during surgery, medical treatment, or medical observation due to the risk of acute kidney injury by a blood flow monitor. DETAILED DESCRIPTION OF THE INVENTION

[0011] The present disclosure is directed to a monitoring system and method for monitoring blood flow in a patient's abdominal organ (e.g., kidney) in real time during surgery, medical treatment, or medical observation. The monitoring system includes a blood flow monitor, an ultrasound transducer probe, and a hemodynamic pressure sensor. The monitoring system also includes an adhesive patch capable of attaching the ultrasound transducer probe to the patient and maintaining the ultrasound transducer probe attached to the patient throughout the patient's surgery, medical treatment, or medical observation without assistance from an ultrasound operator. The blood flow monitor determines a renal blood flow autoregulation index of the patient's kidney based on information received by the blood flow monitor from the ultrasound transducer probe and the hemodynamic pressure sensor. The index is determined as a function of time and as a function of blood pressure. The blood flow monitor determines a renal blood flow autoregulation profile of the patient's kidney based on the autoregulation index information received by the blood flow monitor from the ultrasound transducer probe and the hemodynamic pressure sensor. The patient's autoregulation index and renal blood flow autoregulation profile can be continuously updated and output to a display during a surgical procedure, medical procedure, or medical observation, allowing medical personnel to be apprised of the patient's renal blood flow autoregulation profile in real time. The monitoring system is described in detail below with reference to Figures 1-15.

[0012] FIG. 1 is a schematic diagram of a patient 10 and a monitoring system 11 that continuously monitors organ blood flow in the patient 10 during a surgical procedure, medical treatment, or medical observation. As shown in the example of FIG. 1, the monitoring system 11 may include a blood flow monitor 12, an ultrasound transducer probe 14, an adhesive patch 15, an ultrasound front-end (UFE) circuit 16, a hemodynamic pressure sensor 17, a radial catheter 18, a system processor 19, a system memory 20 with software code 22, a probe cable 24, a first analog-to-digital (ADC) converter 26, a second analog-to-digital (ADC) converter 27, and a display 28. The software code 22 may include a transducer probe control module 30 and an autoregulation (AR) monitoring module 32. The display 28 may include a user interface 29, a first plot 33, a second plot 34, a third plot 35, an autoregulation index value 36, and an injury score indicator 37. Monitoring system 11 may also include input devices 38 and output devices 39. FIG. 1 also shows abdomen 40 of patient 10, along with kidneys 42L and 42R, liver 44, and spleen 46. In the example of FIG. 1, monitoring system 11 is monitoring renal blood flow of kidney 42L of patient 10. In other examples, monitoring system 11 may be used to monitor hepatic blood flow of liver 44, to monitor peritoneal blood flow of spleen 46, pancreas (not shown), and stomach (not shown) of patient 10, and / or to monitor portal vein blood flow from the stomach of patient 10. Thus, blood flow monitor 12 may be adapted as an organ blood flow monitor 12 for any organ of patient 10.

[0013] Blood flow monitor 12 can be an integrated hardware unit including, for example, system processor 19, system memory 20, display 28, UFE circuitry 16, first ADC 26, and second ADC 27. In other examples, any one or more components and / or described functionality of the organ blood flow monitor can be distributed among multiple hardware units. For example, in some examples, display 28 can be a separate display device that is remote from blood flow monitor 12 and operably coupled to blood flow monitor 12 as output device 39. While generally shown and described as an integrated hardware unit in the example of FIG. 1, it should be understood that blood flow monitor 12 can include any combination of devices and components electrically, communicatively, or otherwise operably connected to perform the functionality attributed to blood flow monitor 12 herein. Input device 38 can be connected to blood flow monitor 12 to enable a user to input data and / or commands into blood flow monitor 12. Non-limiting examples of input device 38 include a keyboard, touchpad, and / or other device by which a user can input data and / or commands into blood flow monitor 12. Input device 38 can also include a port configured to communicate with an external input device via a hardwired or wireless connection.

[0014] The ultrasound transducer probe 14 is the first sensor of the monitoring system 11. The ultrasound transducer probe 14 can be attached or secured to the patient 10 by an adhesive patch 15. In the example of FIG. 1 , the ultrasound transducer probe 14 is positioned on the abdomen 40 of the patient 10 over at least a portion of the kidney 42L. The adhesive patch 15 can include a sheet of structural material (e.g., fabric or flexible plastic) with a layer of bonding adhesive deposited on its surface. The adhesive patch 15 can be bonded or mechanically connected to the ultrasound transducer probe 14 or to a frame (not shown) connected to the base of the ultrasound transducer probe 14 and can extend outward from the ultrasound transducer probe 14 along the surface of the abdomen 40 of the patient 10. In another example, the adhesive patch 15 can be placed over the ultrasound transducer probe 14 to attach the ultrasound transducer probe 14 to the abdomen 40 of the patient 10. The adhesive patch 15 maintains the ultrasound transducer probe 14 attached to the patient 10 and fixed in place throughout the duration of the surgery, medical procedure, or medical observation of the patient 10. Because the adhesive patch 15 keeps the ultrasound transducer probe 14 stationary and in contact with the patient 10, an ultrasound operator or technician is not required to maintain the ultrasound transducer probe 14 in place during the surgery, medical procedure, or medical observation. A coupling layer (not shown) comprising couplant material can be positioned between the skin of the patient 10 and the ultrasound transducer probe 14. The coupling layer enables transmission of ultrasound energy between the skin of the patient 10 and the ultrasound transducer probe 14.

[0015] In the example of FIG. 1 , the ultrasound transducer probe 14 detects and continuously senses the Doppler flow signal of the renal blood flow of the kidney 42L during a surgical procedure, medical treatment, or medical observation of the patient 10. As used herein, the term “continuously” means that the ultrasound transducer probe 14 senses the Doppler flow signal of the renal blood flow of the kidney 42L during a monitoring time period and collects patient data periodically, where periodically is frequently enough that the periodicity can be considered clinically continuous. For example, the ultrasound transducer probe 14 may sample the Doppler flow signal of the renal blood flow of the kidney 42L every 10 seconds or less (<10 seconds) and may be configured to sample data more frequently (e.g., every 2 seconds or less). The present disclosure is not limited to any particular device configuration or sampling rate.

[0016] The ultrasound transducer probe 14 can be operably connected to the blood flow monitor 12 by a cable 24. Via the cable 24, the ultrasound transducer probe 14 can receive electrical signals from the UFE circuit 16 of the blood flow monitor 12 and can relay ultrasound signals received from the patient 10 to the blood flow monitor 12 for extraction of a Doppler flow signal of the renal blood flow in the kidney 42L. In another example, the UFE circuit 16 combined with the ultrasound transducer probe 14 can be battery-powered and can include a receiver for wirelessly receiving commands from the blood flow monitor 12. The combined UFE circuit 16 and ultrasound transducer probe 14 can also include a transmitter for wirelessly transmitting the Doppler flow signal of the renal blood flow in the kidney 42L to the blood flow monitor 12 for analysis. In some examples, the combined ultrasound transducer probe 14 and UFE circuit 16 provide the Doppler flow signal to the blood flow monitor 12 as an analog signal, which is converted by the first ADC 26 into digital hemodynamic data representative of renal blood flow in the kidney 42L. In other examples, the combined ultrasound transducer probe 14 and UFE circuit 16 can provide the sensed Doppler flow signal to the blood flow monitor 12 in digital form, in which case the blood flow monitor 12 can not include or utilize the first ADC 26. In yet other examples, the ultrasound transducer probe 14 can provide the Doppler flow signal of renal blood flow in the kidney 42L to the blood flow monitor 12 as an analog signal, which is analyzed by the blood flow monitor 12 in its analog form.

[0017] Hemodynamic pressure sensor 17 is a second sensor in monitoring system 11. In the example of FIG. 1 , hemodynamic pressure sensor 17 is a minimally invasive hemodynamic pressure sensor attached to patient 10 via a radial catheter 18 inserted into the arm of patient 10. In other examples, hemodynamic pressure sensor 17 can be attached to patient 10 via a femoral artery catheter inserted into the leg of patient 10, or hemodynamic pressure sensor 17 can be non-invasively placed on an extremity of patient 10 (e.g., the wrist, arm, finger, ankle, toe, or other extremity of patient 10). Hemodynamic pressure sensor 17 continuously senses hemodynamic data representing the arterial pressure of patient 10 during surgery, medical treatment, or medical observation of patient 10. As used herein, the term "continuously" means that hemodynamic pressure sensor 17 senses and collects patient data periodically during the monitoring time period, where periodically is sufficiently frequent that it can be considered clinically continuous. For example, hemodynamic pressure sensor 17 may sample the hemodynamic data waveform representing the arterial pressure of patient 10 at a rate of at least 10 Hz, at least 20 Hz, at least 60 Hz, at least 100 Hz, or at least 200 Hz. In another example, hemodynamic pressure sensor 17 may sample an average of the hemodynamic data signal representing the arterial pressure of patient 10 over a window of time (e.g., every 10 seconds or less (<10 seconds)). Hemodynamic pressure sensor 17 may sample an average of the hemodynamic data signal representing the arterial pressure of patient 10 more frequently (e.g., every 2 seconds or less). In another example, hemodynamic pressure sensor 17 may sample an average of the hemodynamic data signal representing the arterial pressure of patient 10 over a rolling window of time. The present disclosure is not limited to any particular device configuration or sampling rate.

[0018] The hemodynamic pressure sensor 17 is operatively connected to the blood flow monitor 12 (e.g., electrically and / or communicatively connected via a wired or wireless connection, or both) and provides sensed hemodynamic data to the blood flow monitor 12. In some examples, the hemodynamic pressure sensor 17 provides the sensed hemodynamic data representative of the arterial pressure of the patient 10 to the blood flow monitor 12 as an analog signal, which is converted by the second ADC 27 into digital hemodynamic data representative of the arterial pressure of the patient 10. In other examples, the hemodynamic pressure sensor 17 can provide the sensed hemodynamic data representative of the arterial pressure of the patient 10 to the blood flow monitor 12 in digital form, in which case the blood flow monitor 12 can not include or utilize the second ADC 27. In yet other examples, the hemodynamic pressure sensor 17 can provide the hemodynamic data representative of the arterial pressure of the patient 10 to the blood flow monitor 12 as an analog signal, which is analyzed by the blood flow monitor 12 in its analog form.

[0019] The system memory 20 can be configured to store information within the blood flow monitor 12 during operation. The system memory 20 is described in some examples as a computer-readable storage medium. In some examples, the computer-readable storage medium can include a non-transitory medium. The term "non-transitory" can indicate that the storage medium is not embodied in a carrier wave or propagated signal. In certain examples, the non-transitory storage medium can store data that may change over time (e.g., in RAM or cache). The system memory 20 can include both volatile and non-volatile computer-readable memory. Examples of volatile memory can include random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), and other forms of volatile memory. Examples of non-volatile memory can include, for example, a magnetic hard disk, an optical disk, flash memory, or electrically programmable memory (EPROM) or electrically erasable and programmable (EEPROM) memory.

[0020] As shown in FIG. 1 , the system memory 20 of the blood flow monitor 12 may store software code 22 that forms a monitoring model of the blood flow monitor 12. The software code 22 may include a transducer probe control module 30 for controlling and commanding the ultrasound transducer probe 14. The transducer probe control module 30 includes a beamformer, as discussed in more detail below with reference to FIG. 2 , that keeps the ultrasound transducer probe 14 focused on the renal blood flow of the kidney 42L and enables the ultrasound transducer probe 14 to continuously sense and communicate Doppler flow signals of the renal blood flow to the blood flow monitor 12 throughout the surgical procedure, medical treatment, or medical observation of the patient 10. The software code 22 may also include an AR monitoring module 32, which includes monitoring software code for continuously monitoring a renal blood flow Doppler flow signal DF and continuously monitoring the arterial pressure of the patient 10 during a surgical procedure, medical procedure, or medical observation of the patient 10, and determining a renal blood flow autoregulation profile of the kidney 42L. The renal blood flow autoregulation profile of the kidney 42L is based on a calculated mathematical relationship between the renal blood flow of the kidney 42L and the arterial pressure of the patient 10, as will be discussed in more detail below. The AR monitoring module 32 may also include code for determining an acute kidney injury (AKI) risk score of the patient 10 from the renal blood flow autoregulation profile of the kidney 42L. The AKI risk score represents the probability that the kidney 42L is experiencing or approaching acute kidney injury.When the monitoring system 11 is used to monitor organs other than the kidneys 42L and 42R of the patient 10, the AR monitoring module 32 can be adapted to determine an autoregulation profile and a real-time organ injury risk score from the arterial pressure of the patient 10 and the Doppler flow signals of the organ blood flow of the organ being monitored (e.g., the liver 44).

[0021] The system processor 19 is a hardware processor configured to execute software code 22 that implements the transducer probe control module 30 and the AR monitoring module 32. Examples of the system processor 19 may include any one or more of a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other equivalent discrete or integrated logic circuitry.

[0022] The display 28 provides a user interface 29, which includes control elements that enable user interaction with the blood flow monitor 12 and / or other components of the monitoring system 11. The display 28 is in communication with the system processor 19 and is configured to provide a first plot 33, a second plot 34, and a third plot 35. The first plot 33 can be a plot of the renal blood flow Doppler flow signal of the kidney 42L over time, a plot of the renal blood flow rate determined from the renal blood flow Doppler flow signal over time, or a plot of the change in the renal blood flow rate of the kidney 42L over time. The second plot 34 can be a plot of the arterial pressure of the patient 10 over time or a plot of the change in the arterial pressure of the patient 10 over time. The third plot 35 may be a plot over time of a calculated mathematical relationship between renal blood flow in the kidney 42L and arterial pressure in the patient 10, forming an autoregulation profile of renal blood flow in the kidney 42L (e.g., such as that shown in plot 114 of FIG. 12 ). In another example, the third plot 35 may include a plot of the calculated mathematical relationship versus arterial pressure in the patient 10, with each data point color-coded to represent time. In addition to showing plots 33, 34, and 35, the display 28 may also provide an audible representation of any of the plots 33, 34, and 35 via a speaker, or may simply display the numerical values ​​of the plots 33, 34, and 35 (e.g., via a table).

[0023] The display 28 also displays an autoregulation index value 36 and an injury score indicator 37, as shown in FIG. 1 . The autoregulation index value 36 is a representation of the real-time value or state of the patient's 10 autoregulation profile based on a calculated mathematical relationship between the renal blood flow of the kidney 42L and the arterial pressure of the patient 10. As discussed in more detail below with reference to FIGS. 7-10 , the calculated mathematical relationship between the renal blood flow of the kidney 42L and the arterial pressure of the patient 10 can be a correlation or coherence between the renal blood flow rate of the kidney 42L and the arterial pressure of the patient 10. The autoregulation index value 36 is the inverse of the correlation or coherence between the renal blood flow rate of the kidney 42L and the arterial pressure of the patient 10. The injury score indicator 37 is a representation of the patient's 10 real-time AKI risk score determined from the autoregulation index value by the system processor 19 and the AR monitoring module 32. The display 28 may also include a sensory alarm to alert a healthcare professional when the renal blood flow autoregulation index value 36 of the kidney 42L approaches a lower autoregulation limit or an upper autoregulation limit. As discussed in more detail below, the lower autoregulation limit is the mean arterial pressure (MAP) value below which autoregulation of the renal blood flow of the kidney 42L is impaired. The upper autoregulation limit is the MAP value above which autoregulation of the renal blood flow of the kidney 42L is impaired. The sensory alarm may also include a sensory alarm that alerts a healthcare professional when the patient 10's real-time AKI risk score approaches or exceeds a predetermined threshold. The sensory alarm may be implemented as one or more of a visual alarm, an audible alarm, a tactile alarm, or other type of sensory alarm.For example, the sensory alarm can be evoked as any combination of a flashing and / or colored graphic shown by the user interface 29 on the display 28, an audible alarm (e.g., a siren or repeating voice), and a tactile alarm configured to cause the blood flow monitor 12 to vibrate or otherwise deliver a perceptible physical stimulus to the medical professional.

[0024] Display 28 can be a liquid crystal display (LCD), a light-emitting diode (LED) display, an organic light-emitting diode (OLED) display, or other display device suitable for providing information to a user in graphical form. User interface 29 can include graphical and / or physical control elements that enable user input to interact with blood flow monitor 12 and / or other components of monitoring system 11. In some examples, user interface 29 can take the form of a graphical user interface (GUI), which presents graphical control elements presented, for example, on a touch-sensitive and / or pressure-sensitive display screen of display 28. In such examples, user input can be received in the form of gestural input (e.g., touch gestures, scrolling gestures, zoom gestures, or other gestural inputs, etc.). In certain examples, user interface 29 can take the form of and / or include physical control elements (e.g., physical buttons, keys, knobs, or other physical control elements configured to receive user input to interact with components of monitoring system 11, etc.). The user interface 29 may include a speaker that enables the blood flow monitor 12 to generate an audible alarm.

[0025] In operation of monitoring system 11, before a surgical procedure, medical procedure, or medical observation begins, a medical worker connects hemodynamic pressure sensor 17 to patient 10. In the example of FIG. 1 , the medical worker connects hemodynamic pressure sensor 17 to patient 10 by first inserting a radial catheter 18 into the arm of patient 10 and then connecting hemodynamic pressure sensor 17 to the radial catheter 18. In another example, the medical worker may connect hemodynamic pressure sensor 17 to patient 10 by first inserting a femoral artery catheter into the leg of patient 10 and then connecting hemodynamic pressure sensor 17 to the femoral artery catheter. In another example, the medical worker may non-invasively connect hemodynamic pressure sensor 17 to an extremity of patient 10 (e.g., a wrist, arm, finger, ankle, toe, or other extremity of patient 10). When the hemodynamic pressure sensor 17 is connected to the patient 10, the hemodynamic pressure sensor senses hemodynamic data representative of the arterial pressure of the patient 10 and communicates the hemodynamic data (e.g., as analog sensor data) to the blood flow monitor 12. The second ADC 27 converts the analog hemodynamic data into digital hemodynamic data representative of the arterial pressure of the patient 10. The system processor 19 of the blood flow monitor 12 receives the hemodynamic data representative of the arterial pressure of the patient 10 and processes the hemodynamic data representative of the arterial pressure through the AR monitoring module 32.

[0026] Before the surgical procedure, medical treatment, or medical observation begins, the medical practitioner also places the ultrasound transducer probe 14 on the abdomen 40 of the patient 10. The medical practitioner uses the ultrasound transducer probe 14 to locate the Doppler flow signal of the renal blood flow in the kidney 42L. The ultrasound transducer probe 14 is capable of generating an audible representation of the Doppler flow signal to assist the medical practitioner in locating the Doppler flow signal of the renal blood flow in the kidney 42L. Once the medical practitioner locates the Doppler flow signal of the renal blood flow in the kidney 42L, the medical practitioner attaches and secures the ultrasound transducer probe 14 to the patient 10 with an adhesive patch 15. The adhesive patch 15 maintains the ultrasound transducer probe 14 in constant contact with the patient 10, preventing the ultrasound transducer probe 14 from shifting position on the patient 10 and losing the Doppler flow signal of the renal blood flow in the kidney 42L during the surgical procedure, medical treatment, or medical observation. The ultrasound transducer probe 14 relays the received ultrasound signals to the blood flow monitor 12 via a cable 24 or wirelessly. In the case of wireless transmission, the ultrasound transducer probe 14 includes a UFE circuit 16. A system processor 19 of the blood flow monitor 12 receives the Doppler flow signals and processes them sequentially or simultaneously through a transducer probe control module 30 and an AR monitoring module 32.

[0027] The system processor 19 executes the monitoring software code of the AR monitoring module 32 to continuously monitor the renal blood flow Doppler flow signal sensed by the ultrasound transducer probe 14 and the arterial pressure of the patient 10 sensed by the hemodynamic pressure sensor 17 throughout the duration of the surgical procedure, medical procedure, or medical observation of the patient 10. The system processor 19 also executes the monitoring software code of the AR monitoring module 32 to calculate a mathematical relationship between the renal blood flow of the kidney 42L and the arterial pressure of the patient 10 and use the mathematical relationship to generate an autoregulation profile of the renal blood flow of the kidney 42L. The system processor 19 also executes the AR monitoring module 32 to continuously monitor the autoregulation profile of the renal blood flow of the kidney 42L and estimate an AKI risk score of the kidney 42L of the patient 10 from the autoregulation value.

[0028] The system processor 19 outputs information to the display 28 to generate a first plot 33, a second plot 34, and a third plot 35. The first plot 33 can be a plot of the Doppler flow signal of the renal blood flow of the kidney 42L over time, a plot of the renal blood flow rate determined by the system processor 19 from the Doppler flow signal of the renal blood flow over time, or a plot of the change in the renal blood flow rate of the kidney 42L over time. The second plot 34 can be a plot of the arterial pressure of the patient 10 over time, or a plot of the change in the arterial pressure of the patient 10 over time. The third plot 35 can be a plot of a calculated mathematical relationship between the renal blood flow of the kidney 42L and the arterial pressure of the patient 10 over time, determined by the system processor 19 from the renal blood flow of the kidney 42L and the arterial pressure of the patient 10, or a plot of the autoregulation profile of the renal blood flow of the kidney 42L. The system processor 19 also outputs an autoregulation index value 36 and an injury score indicator 37. As previously discussed, the autoregulation index value 36 is a representation of the real-time value or state of the renal blood flow autoregulation profile of the kidney 42L, and the injury score indicator 37 is a representation of the real-time AKI risk score of the patient 10.

[0029] As the surgical procedure, medical treatment, or medical observation of patient 10 progresses, system processor 19 continues to receive Doppler flow signals from ultrasound transducer probe 14, continue to receive hemodynamic data representative of arterial pressure in patient 10, continue to calculate the mathematical relationship between renal blood flow in kidney 42L and arterial pressure in patient 10, continue to output plots 33, 34, and 35 on display 28, continue to output autoregulation index value 36 on display 28, and continue to output injury score indicator 37 on display 28. If autoregulation index value 36 changes toward an undesirable threshold (e.g., indicating a trend toward a lower autoregulation limit or an upper autoregulation limit), system processor 19 and display 28 can alert medical personnel so that medical personnel can act to restore normal autoregulation of renal blood flow in kidney 42L. For example, a medical professional may administer a medication or fluid to increase the arterial pressure of patient 10 and / or maintain autoregulation index value 36 above the lower autoregulation limit. In another example, a medical professional may administer a medication or take action to reduce the arterial pressure of patient 10 to decrease and / or maintain autoregulation index value 36 below the upper autoregulation limit.

[0030] Similarly, if the real-time AKI risk score for the kidney 42L changes toward an undesirable threshold or at an undesirable rate, the system processor 19 and display 28 can alert a medical professional so that the medical professional can take action to increase renal perfusion to prevent or minimize AKI to the kidney 42L. For example, the medical professional can administer a medication or fluid that increases renal blood flow and perfusion to the kidney 42L or improves autoregulation of renal blood flow to the kidney 42L. At the completion of the surgery, medical procedure, or medical observation, the system processor 19 and AR monitoring module 32 can estimate a final AKI risk score for the kidney 42L and output the final AKI risk score to the display 28. If the final AKI risk score for kidney 42L indicates that kidney 42L is at high risk for AKI, medical personnel can take immediate action to treat kidney 42L without having to wait for biomarkers to appear in blood and urine samples from patient 10. Biomarkers indicative of AKI can take hours or days to appear in blood and urine samples from patient 10. Monitoring system 11 allows medical personnel to quickly determine whether patient 10 needs to be treated for AKI of kidney 42L.

[0031] If the kidney 42L of the patient 10 moves within the abdomen 40 of the patient 10 during surgery, medical procedure, or medical observation, the transducer probe control module 30 will detect a change in the Doppler flow signal and respond by adjusting the location of the focal point of the set of beams for scanning the abdomen 40 of the patient 10 to relocate the Doppler flow signal of the renal blood flow of the kidney 42L. As discussed below with reference to Figures 2-5, the blood flow monitor 12 can include a beamformer that can steer the beam signals produced by the array of transducer elements of the ultrasound transducer probe 14.

[0032] FIG. 2 is another schematic diagram of the blood flow monitor 12. As shown in FIG. 2, the blood flow monitor 12 can include a beamformer 48, and the ultrasound transducer probe 14 can include an array 50 of transducer elements 52. Each transducer element 52 in the array 50 can include a piezoelectric material (e.g., lead zirconate titanate, etc.) capable of transmitting and detecting ultrasound pulses. The array 50 of transducer elements 52 in the ultrasound transducer probe 14 can form a two-dimensional phased array having a probe length PL and a probe width PW. As a phased array, each transducer element 52 in the array 50 can generate pulses independently of the other transducer elements 52 in the array 50.

[0033] In the example of FIG. 2 , the beamformer 48 drives the array 50 of transducer elements 52 via the system processor 19 and UFE circuitry 16. The beamformer 48 functions as a transducer probe controller with flow signal tracking software code that controls when each transducer element 52 in the array 50 emits an ultrasound pulse. The beamformer 48 can determine the timing and pattern in which each transducer element 52 emits a pulse, enabling the array 50 to form one or more ultrasound beams and to sweep or steer the one or more ultrasound beams without physically moving the position of the ultrasound transducer probe 14 on the patient 10. The beamformer 48 can be a software sub-module of the transducer probe control module 30, which can be executed by the system processor 19 and control the activation of the transducer elements 52 in the array 50. In other examples, the beamformer 48 may be a hardware component separate from the system processor 19 and system memory 20, and may comprise memory and software separate from the software code 22 that cooperates with the system processor 19 to control activation of the transducer elements 52 of the array 50. In the example of FIG. 2, the beamformer 48 is housed within the blood flow monitor 12 as part of the transducer probe control module 30 of the software code 22 executed by the system processor 19. In other examples, the beamformer 48 may be housed completely or partially within the casing of the ultrasound transducer probe 14 as a separate hardware and software unit that cooperates with the system processor 19.Housing the beamformer 48 in the same unit as the blood flow monitor 12 (whether as part of the software code 22 or as an add-on hardware component) can reduce the overall size and thickness of the ultrasound transducer probe 14. The ultrasound transducer probe 14 can have a relatively thin and flat profile, with a thickness that is less than the width or diameter of the ultrasound transducer probe 14. Attaching the ultrasound transducer probe 14 to the patient 10 with an adhesive patch 15 is easier and safer when the ultrasound transducer probe 14 has a thin and flat profile.

[0034] 3 is another schematic diagram of an ultrasound transducer probe 14 attached to the abdomen 40 of a patient 10 by an adhesive patch 15 over a kidney 42L. The Doppler flow signal of the kidney 42L can be measured either from the renal artery RA as blood enters the kidney 42L from the aorta of the patient 10 via the renal artery, or from the renal vein RV as blood exits the kidney 42L via the renal vein RV into the vena cava of the patient 10. The ultrasound transducer probe 14 generates an transmitted signal OW that travels into the abdomen 40 of the patient 10. Due to the physical properties of Doppler, the Doppler signal BW of the blood flow in the renal artery RA is "blue-shifted" as the blood flow in the renal artery RA is moving toward the ultrasound transducer probe 14, and therefore appears to have a shorter wavelength than the transmitted signal of the ultrasound transducer probe 14. The Doppler signal RW of the blood flow in the renal vein RV is "redshifted" because the blood flow in the renal vein RV is moving away from the ultrasound transducer, and therefore appears to have a longer wavelength than the transmitted signal of the ultrasound transducer probe 14. Because the Doppler signal BW is blueshifted and the Doppler signal RW is redshifted, the blood flow monitor 12 can easily distinguish renal artery blood flow from renal vein blood flow. In a human subject, the renal artery RA and renal vein RV are close together and aligned parallel to one another, allowing the beamformer 48 to position the beam to simultaneously capture both the arterial and venous flow of the kidney 42L.

[0035] Figures 4A and 4B will be discussed simultaneously. Figure 4A is another schematic diagram of an ultrasound transducer probe 14 attached to the abdomen 40 of a patient 10 by an adhesive patch 15 over a kidney 42L. Figure 4B is also a schematic diagram of an ultrasound transducer probe 14 attached to the abdomen 40 of a patient 10 by an adhesive patch 15 over a kidney 42L. In the example of Figures 4A and 4B, the ultrasound transducer probe 14 is attached to the surface of the abdomen 40 by adhesive patches 15 over the kidney 42L and over at least some of the ribs 54a, 54b, and 54c of the patient 10.

[0036] The ultrasound transducer probe 14 may include a probe length PL, probe width PW (shown in FIG. 2), or diameter large enough to enable the array 50 of transducer elements 52 of the ultrasound transducer probe 14 to cover one or more acoustic windows in the patient 10. An acoustic window of the patient 10 is defined as an area of ​​the patient 10 where ultrasound transmission is substantially unattenuated compared to its immediate surroundings. For example, the array 50 of transducer elements 52 of the ultrasound transducer probe 14 may be sized in length or width to extend across at least two intercostal spaces of the patient 10. For example, in FIG. 4A , the array 50 of transducer elements 52 of the ultrasound transducer probe 14 is positioned over a first acoustic window W1 (formed by the intercostal space between ribs 54a and 54b) and a second acoustic window W2 (formed by the intercostal space between ribs 54b and 54c). In the example of FIG. 4A, the beamformer 48 (shown in FIG. 2) selectively activates transducer elements 52 in the array 50 to steer signal beams 56a and 56b into the abdomen 40 through the first acoustic window W1 and / or the second acoustic window W2, avoiding the ribs 54a, 54b, and 54c. In the example of FIG. 4B, the ultrasound transducer probe 14 is positioned slightly higher on the abdomen 40 of the patient 10 compared to the example of FIG. 4A. However, the probe length PL or probe width PW of the ultrasound transducer probe 14 is long enough that the ultrasound transducer probe 14 still has access to the first acoustic window W1 and can still scan and steer the signal beams 56a and 56b into the abdomen 40 through the first acoustic window W1. Regardless of where the ultrasound transducer probe 14 is placed on the ribs 54a, 54b, and 54c, the ribs 54a, 54b, and 54c do not obstruct a direct view of the kidney 42L from the array 50 of the ultrasound transducer probe 14.

[0037] When the ultrasound transducer probe 14 is initially placed on the patient 10, the beamformer 48 controls the transducer elements 52 in the array 50 to electronically beam scan the abdomen 40 to find and sense Doppler flow signals. The beamformer 48 also controls the transducer elements 52 in the array 50 to track scan the abdomen 40 to track the Doppler flow signals of the renal blood flow over time. The beamformer 48 beam scans and / or track scans the Doppler flow signals of the renal blood flow in the kidney 42L of the patient 10 by sequentially emitting signal beams 56 a and 56 b from the array 50 of transducer elements 52 and by focusing each of the beams 56 a and 56 b at different locations. The signal beams 56 a and 56 b track the Doppler flow signals relative to the array 50 of transducer elements 52. If the kidney 42L, renal artery RA, and / or renal vein RV shift within the abdomen 40, the Doppler flow signal of the renal blood flow may be altered and the signal strength may decrease. When this occurs, the beamformer 48 may emit signal beams 56a and 56b (and possibly more signal beams) to scan and sweep around the abdomen 40. In one example, the beamformer 48 uses signal beams 56a and 56b to track the center of the renal blood flow where the Doppler flow signal is strongest and adjusts signal beams 56a and 56b to follow the center of the renal blood flow as the center moves and changes position. While the beamformer 48 tracks and scans the Doppler flow signal to increase signal strength, the system processor 19 may stop calculating the mathematical relationship between the renal blood flow in the kidney 42L and the arterial pressure of the patient 10 until the signal strength of the Doppler flow signal increases.

[0038] To enable the ultrasound transducer probe 14 to measure the Doppler flow signal of renal blood flow in the kidney 42L, the ultrasound transducer probe 14 can have a low center frequency between 0.5 MHz and 4.0 MHz. A center frequency between 0.5 MHz and 4.0 MHz allows the ultrasound transducer probe 14 to penetrate 15 cm or more into the patient 10, which is deep enough to measure renal blood flow. This depth also allows the ultrasound transducer probe 14 to measure hepatic blood flow, peritoneal blood flow, portal vein blood flow, and mesenteric blood flow. The monitoring system 11 does not use the ultrasound transducer probe 14 for high-resolution imaging of the kidney 42L. Therefore, the ultrasound transducer probe 14 can have a lower transducer element count than ultrasound transducer probes used for ultrasound imaging. Reducing the transducer element count of the array 50 of transducer elements 52 increases the signal-to-noise ratio (SNR) of the Doppler flow signal of renal blood flow sensed by the ultrasound transducer probe 14. Various embodiments of the hemodynamic pressure sensor 17 are discussed in more detail with reference to FIGS.

[0039] Figure 5 is a perspective view of a hemodynamic pressure sensor 17 that may be attached to a patient 10 to sense hemodynamic data representative of the arterial pressure of the patient 10. The hemodynamic pressure sensor 17 (shown in Figure 5) is one example of a minimally invasive hemodynamic pressure sensor that may be attached to the patient 10 via a radial catheter 18 inserted into the arm of the patient 10, as shown in Figure 1. In another example, the hemodynamic pressure sensor 17 may be attached to the patient 10 via a femoral artery catheter inserted into the leg of the patient 10.

[0040] 5 , hemodynamic pressure sensor 17 includes a housing 58, a fluid input port 60, a catheter-side fluid port 62, and an input / output (I / O) cable 64. Fluid input port 60 is configured to be connected to a fluid source (e.g., a saline bag or other fluid input source) via tubing or other hydraulic connection. Catheter-side fluid port 62 is configured to be connected to a catheter (e.g., a radial catheter 18 or a femoral artery catheter) inserted into an arm (i.e., a radial catheter 18) or a leg (i.e., a femoral artery catheter) of patient 10 via tubing or other hydraulic connection. I / O cable 64 connects hemodynamic pressure sensor 17 to blood flow monitor 12, for example, via one or more of the I / O connectors. The housing 58 of the hemodynamic pressure sensor 17 encloses one or more pressure transducers, communication circuitry, processing circuitry, and corresponding electronic components to sense fluid pressure corresponding to the arterial pressure of the patient 10 which is transmitted to the blood flow monitor 12 via an I / O cable 64.

[0041] In operation, a column of fluid (e.g., saline solution) is introduced from a fluid source (e.g., a saline bag) through hemodynamic pressure sensor 17, via fluid input port 60, to catheter fluid port 62, and toward a catheter inserted into patient 10. Arterial pressure is transmitted through the fluid column to a pressure sensor positioned within housing 58, which senses the pressure of the fluid column. Hemodynamic pressure sensor 17 converts the sensed pressure of the fluid column into an electrical signal via a pressure transducer and outputs a corresponding electrical signal via I / O cable 64 to blood flow monitor 12. Hemodynamic pressure sensor 17 thus transmits analog sensor data (or a digital representation of the analog sensor data) representing substantially continuous beat-to-beat monitoring of arterial pressure in patient 10 to blood flow monitor 12.

[0042] FIG. 6 is a perspective view of an alternative example of a hemodynamic pressure sensor 17 for sensing hemodynamic data representative of arterial pressure in patient 10. The hemodynamic pressure sensor 17 illustrated in FIG. 6 is one example of a non-invasive hemodynamic pressure sensor that may be attached to patient 10 via one or more finger cuffs to sense data representative of the arterial pressure in patient 10. As illustrated in FIG. 6, the hemodynamic pressure sensor 17 includes an inflatable finger cuff 66 and a heart reference sensor 68. The inflatable finger cuff 66 includes an inflatable blood pressure bladder configured to inflate and deflate as controlled by a pressure controller (not shown) pneumatically connected to the inflatable finger cuff 66. The inflatable finger cuff 66 also includes an optical (e.g., infrared) transmitter and an optical receiver, which are electrically connected to the pressure controller (not shown), to measure the changing volume of the artery below the cuff on the finger.

[0043] In operation, the pressure controller continuously adjusts the pressure within the finger cuff to maintain a constant arterial volume within the finger (i.e., the arterial unloaded volume) as measured via the optical transmitter and optical receiver of the inflatable finger cuff 66. The pressure applied by the pressure controller to continuously maintain the unloaded volume represents the blood pressure within the finger and is communicated by the pressure controller to the blood flow monitor 12 shown in FIG. 1. The heart reference sensor 68 measures the difference in hydrostatic pressure height between the level at which the finger is maintained and the reference level for pressure measurement (which is typically the heart level). Thus, the hemodynamic pressure sensor 17 transmits hemodynamic data representing substantially continuous beat-to-beat monitoring of the arterial pressure of the patient 10. As discussed below with reference to FIGS. 7-10, the hemodynamic pressure sensor 17 transmits the hemodynamic data to the system processor 19, which calculates the mathematical relationship between the arterial pressure of the patient 10 and the renal blood flow in the kidney 42L of the patient 10.

[0044] FIG. 7 is a graphical representation of a method 70 for determining the mathematical relationship between arterial pressure of a patient 10 and renal blood flow in a kidney 42L of the patient 10 in the time domain. The method 70 in FIG. 7 is illustrated by a first data plot 72, a second data plot 74, and a correlation plot 76. The first data plot 72 represents the change in mean arterial pressure (MAP) of the patient 10 over time, which is determined by the system processor 19 from hemodynamic data sensed in real time by the hemodynamic pressure sensor 17. The system processor 19 can output the first data plot 72 as a second plot 34 on the display 28 (shown in FIGS. 1 and 2 ). The second data plot 74 represents the change in renal blood flow rate in the kidney 42L over time, which is estimated by the system processor 19 from a renal blood flow Doppler flow signal sensed in real time by the ultrasound transducer probe 14. The system processor 19 can estimate the renal blood flow rate from the renal blood flow Doppler flow signal by using the flow velocity of the Doppler flow signal and the mean cross-sectional area of ​​the renal artery RA and / or renal vein RV of the patient 10. The system processor 19 can output the second data plot 74 to the display 28 as the first plot 33 (shown in FIGS. 1 and 2). In other examples, the renal blood flow rate can be determined from the Doppler flow signal by using a flow velocity signal, by using a peak flow velocity signal, and / or by using a renal blood flow relative change signal.

[0045] Correlation plot 76 represents the calculated mathematical relationship determined and evaluated by system processor 19 between changes in MAP and changes in renal blood flow rate in kidney 42L over time. The calculated mathematical relationship shown in FIG. 7 is the correlation or non-correlation between changes in MAP and changes in renal blood flow rate in kidney 42L. System processor 19 can use the computed Pearson correlation coefficient over a rolling window of time to determine the correlation or non-correlation between changes in arterial pressure and changes in renal blood flow rate in kidney 42L. System processor 19 and AR monitoring module 32 (shown in FIGS. 1 and 2) can use Equation 1 below to determine the Pearson correlation coefficient between changes in MAP and changes in renal blood flow rate in kidney 42L.

[0046]

number

[0047] where r is the correlation coefficient between the change in MAP and the change in renal blood flow rate in the kidney 42L, and x i is the real-time value of MAP, and [equation 2] is the moving average of the value of MAP over a rolling time window. i where σ is the real-time value of the renal blood flow rate of kidney 42L estimated by system processor 19, and σ is the running average of the renal blood flow rate values ​​of kidney 42L over a rolling time window.

[0048]

number

[0049]

number

[0050] When the system processor 19 and the AR monitoring module 32 determine the correlation coefficient between the change in MAP and the change in renal blood flow rate of the kidney 42L, the system processor 19 can generate a correlation plot 76 and output the correlation plot 76 to the display 28 as a third plot 35 (shown in FIGS. 1 and 2 ). The system processor 19 and the AR monitoring module 32 use the correlation coefficient between the change in MAP and the change in renal blood flow rate of the kidney 42L to generate a renal autoregulation value 78. The renal autoregulation value 78 is a real-time value or state of autoregulation of the patient 10. When the correlation coefficient between the change in MAP and the change in renal blood flow rate of the kidney 42L is high (e.g., approaching a value of 1), the renal autoregulation value 78 is low, indicating impaired renal blood flow autoregulation of the kidney 42L. A low correlation coefficient (e.g., below 0.5) between changes in MAP and changes in renal blood flow rate of kidney 42L indicates high renal autoregulation value 78 or normal renal blood flow autoregulation of kidney 42L. System processor 19 can output renal autoregulation value 78 to display 28 as autoregulation index value 36 shown in Figures 1 and 2.

[0051] In other examples, the system processor 19 and the AR monitoring module 32 may use mathematical correlations or tools other than the Pearson correlation coefficient to determine a calculated mathematical relationship between changes in arterial pressure and changes in renal blood flow rate of the kidney 42L over the course of a patient's surgery, medical treatment, or medical observation. For example, as shown in FIG. 8, the system processor 19 and the AR monitoring module 32 may use a coherence function computed over a pre-specified frequency range and computed from parameters of the transfer function of the MAP signal and the transfer function of the renal blood flow rate. A Fourier transform may be used as a transfer function to convert the patient's 10 MAP signal (shown in the first data plot 72 of FIG. 7) from the time domain to the frequency domain, as represented by the first data plot 172 in FIG. 8. The system processor 19 may output the first data plot 172 as the second plot 34 on the display 28 (shown in FIGS. 1 and 2). Similarly, a Fourier transform can be used as a transfer function to convert the renal blood flow rate (shown in second data plot 74 of FIG. 7) from the time domain to the frequency domain, as represented by second data plot 174 in FIG. 8. System processor 19 can output second data plot 174 to display 28 as first plot 33 (shown in FIGS. 1 and 2).

[0052] The system processor 19 and the AR monitoring module 32 input the transformed renal blood flow rate of the kidney 42L and the transformed MAP of the patient 10 into a coherence function to generate a coherence coefficient between the transformed renal blood flow rate of the kidney 42L and the transformed MAP of the patient 10, as represented by coherence plot 176 in FIG. 8. The system processor 19 can output the coherence plot 176 to the display 28 as the third plot 35 (shown in FIGS. 1 and 2). Similar to the correlation coefficient described with reference to FIG. 7, the system processor 19 and the AR monitoring module 32 can use the coherence coefficient to generate a renal autoregulation value 78. The renal autoregulation value 78 is a real-time value or state of autoregulation of the patient 10. When the coherence coefficient between changes in MAP and changes in renal blood flow rate of kidney 42L is high (e.g., approaching a value of 1), the renal autoregulation value 78 is low, or indicates impaired renal blood flow autoregulation of kidney 42L. When the correlation coefficient between changes in MAP and changes in renal blood flow rate of kidney 42L is low (e.g., below a predetermined coherence threshold), the renal autoregulation value 78 is high, or indicates normal renal blood flow autoregulation of kidney 42L. System processor 19 can output renal autoregulation value 78 to display 28 as autoregulation index value 36 shown in FIGS. 1 and 2. As discussed below with reference to Figures 9 and 10, the system processor 19 and AR monitoring module 32 use correlation and / or coherence coefficients between changes in MAP and changes in renal blood flow rate of kidney 42L to determine and monitor the autoregulation profile of the monitored patient 10.

[0053] FIG. 9 is a chart with the X-axis divided into MAP bins in 5 mmHg increments and the correlation coefficient from FIG. 7 set as the Y-axis. As the system processor 19 determines and monitors the correlation coefficient between the renal blood flow rate of the kidney 42L and the MAP of the patient 10 over time, the system processor 19 can generate the chart of FIG. 9 by classifying the correlation coefficient values ​​into the MAP bins of the chart of FIG. 9 and generating an autoregulation profile or index for the patient 10. The system processor 19 will classify the correlation coefficient values ​​into the MAP bins of the chart of FIG. 9 only when the ultrasound transducer probe 14 and the beamformer 48 have steady, stable readings of the Doppler flow signal of the renal blood flow of the kidney 42L. The system processor 19 will not add the correlation coefficient values ​​to the MAP bins of the chart of FIG. 9 when the beamformer 48 and the ultrasound transducer probe 14 are searching for a Doppler flow signal, or when the Doppler flow signal is unreliable, or when patient movement causes artifacts that disrupt the Doppler flow signal. Similarly, if hemodynamic pressure sensor 17 produces an unreliable reading of patient 10's arterial pressure, system processor 19 will not add a correlation coefficient value to the MAP bin of the chart of FIG. 9 until hemodynamic pressure sensor 17 can regain a reliable reading of patient 10's arterial pressure.

[0054] The chart of Figure 9 includes a correlation threshold line 80 at a correlation coefficient value of approximately 0.5. When the correlation coefficient for any given MAP value is above the correlation threshold line 80, the autoregulation of renal blood flow of the kidney 42L can be described as passive, and as the correlation coefficient approaches a value of 1, the autoregulation of renal blood flow of the kidney 42L becomes more passive. When the autoregulation of renal blood flow of the kidney 42L is passive, the autoregulation of renal blood flow of the kidney 42L is impaired, and the renal blood flow rate of the kidney 42L varies with the MAP of the patient 10. Thus, when the correlation coefficient indicates that changes in the renal blood flow rate of the kidney 42L are correlated with the MAP of the patient 10, the autoregulation of renal blood flow of the kidney 42L is impaired.

[0055] When the correlation coefficient for any given MAP value falls below the correlation threshold line 80, the renal blood flow autoregulation of the kidney 42L is functioning substantially normally. When the renal blood flow autoregulation of the kidney 42L is functioning normally, the renal blood flow rate of the kidney 42L is independent of the MAP of the patient 10. Thus, changes in the renal blood flow rate of the kidney 42L do not correlate with changes in the MAP of the patient 10 when the renal blood flow autoregulation of the kidney 42L is functioning normally. As the correlation coefficient approaches zero, the degree to which the renal blood flow autoregulation of the kidney 42L is functioning normally increases. The correlation threshold line 80 of the present disclosure is not limited to a value of 0.5 or any particular value. The value of the correlation threshold line 80 can be based on empirical data and can vary depending on factors such as the characteristics of the patient 10 (e.g., age, health, smoking habits, etc.).

[0056] The lower limit of autoregulation (LLA) line indicates the lower threshold for the patient's 10 MAP at which normal autoregulation of renal blood flow in the kidney 42L occurs. Generally, when the patient 10 has a MAP value above the LLA line, autoregulation of renal blood flow in the kidney 42L is normal. When the patient 10 has a MAP value below the LLA line, autoregulation of renal blood flow in the kidney 42L is impaired. In the example of FIG. 9 , the chart shows that when the patient's 10 MAP falls below 50 mmHg, the correlation coefficient between changes in renal blood flow rate in the kidney 42L and changes in the patient's 10 MAP exceeds the correlation threshold line 80. Thus, the LLA line for renal blood flow in the example of FIG. 9 is 50 mmHg. The system processor 19 can set an alarm in the blood flow monitor 12 so that the blood flow monitor alerts medical personnel when the patient's 10 MAP approaches or falls below the LLA line. If the alarm is activated, a medical professional can be alerted that the patient's 10 MAP is below the LLA line and that autoregulation of renal blood flow in the kidney 42L is likely impaired. The medical professional can respond to the alarm by taking measures to raise the patient's 10 MAP above the LLA line (e.g., administering fluids or medications to the patient 10). Over time, as the system processor 19 continues to monitor the patient's 10 MAP, monitor changes in renal blood flow rate, and determine and add real-time values ​​for the correlation coefficient to the chart of FIG. 9 , the position of the LLA line may shift relative to the X-axis. The system processor 19 can update the alarms of the blood flow monitor 12 to track changes in the LLA line. The blood flow monitor 12 can also include a hypotension prediction algorithm as part of the AR monitoring module 32 and use the LLA line as a threshold or definition for hypotension in the patient 10.

[0057] FIG. 10 is a plot of the correlation coefficient between changes in renal blood flow rate of kidney 42L and changes in MAP of patient 10 versus MAP of patient 10. The Y-axis represents the value of the correlation coefficient, and the X-axis represents the value of MAP of patient 10 at the time the correlation coefficient was determined by system processor 19. Similar to the chart of FIG. 9, the plot of FIG. 10 shows an LLA line and a correlation threshold line 80. The plot of FIG. 10 further includes an upper autoregulation limit (ULA) line for renal blood flow of kidney 42L, which is currently at approximately 105 mmHg. When patient 10's MAP is between the LLA line and the ULA line, changes in renal blood flow rate of kidney 42L are not correlated with changes in MAP of patient 10. When the patient's 10 MAP is between the LLA line and the ULA line, renal blood flow autoregulation of the kidney 42L is functioning normally because changes in renal blood flow rate of the kidney 42L are not correlated with changes in the patient's 10 MAP. As discussed above with reference to FIG. 9 , when the patient's 10 MAP is below the LLA line, renal blood flow autoregulation of the kidney 42L is impaired. Renal blood flow autoregulation of the kidney 42L is also impaired when the patient's 10 MAP is above the ULA line. When the patient's 10 MAP is above the ULA line, renal blood flow autoregulation of the kidney 42L is passive, and changes in renal blood flow rate of the kidney 42L are correlated with changes in the patient's 10 MAP.

[0058] System processor 19 can set a first alarm in blood flow monitor 12 to cause blood flow monitor 12 to alert medical personnel if patient 10's MAP approaches or falls below the LLA line. System processor 19 can set a second alarm in blood flow monitor 12 if patient 10's MAP approaches or exceeds the ULA line. If the first alarm is activated, medical personnel can be alerted that patient 10's MAP is below or approaching the LLA line and that renal blood flow autoregulation of kidney 42L is likely impaired. Medical personnel can respond to the alarm by taking measures to raise patient 10's MAP above the LLA line (e.g., administering fluids or medication to patient 10). If the second alarm is activated, medical personnel can be alerted that the patient's 10 MAP is above or approaching the ULA line and that autoregulation of renal blood flow of kidney 42L is likely impaired. Medical personnel can respond to the alarm by taking measures to reduce the patient's 10 MAP below the ULA line.

[0059] System processor 19 can output the plot of FIG. 10 on display 28 and color-code the plot to assist medical personnel in identifying when renal blood flow autoregulation of kidney 42L is functioning or impaired. For example, the area of ​​the plot of FIG. 10 above the ULA line and below the LLA line relative to the X-axis can be shaded red, while the area of ​​the plot between the ULA and LLA lines can be shaded green. Over time, as system processor 19 continues to monitor patient 10's MAP, monitor changes in renal blood flow rate, and determine and add real-time values ​​for the correlation coefficient to the plot of FIG. 10, the position of the LLA line and / or the position of the ULA line can shift relative to the X-axis. System processor 19 can update the first and second alarms of blood flow monitor 12 to track changes in the position of the LLA or ULA line.

[0060] FIG. 11 is a block diagram of a method 82 for determining the autoregulation profile of renal blood flow of a kidney 42L of a patient 10. A medical professional performs a first step 84 of the method 82 by placing a noninvasive ultrasound transducer probe 14 and a continuous hemodynamic pressure sensor 17 on the patient 10. A second step 86 of the method 82 is to measure paired values ​​of the arterial pressure of the patient 10 and the renal blood flow of the kidney 42L over time using the ultrasound transducer probe 14 and the hemodynamic pressure sensor 17. The system processor 19 performs a third step 88 of the method 82 by placing the paired values ​​of the arterial pressure of the patient 10 and the renal blood flow of the kidney 42L into a first array. The first array is a first data buffer in the system processor 19 and / or system memory 20 that stores information regarding the paired values ​​of the arterial pressure of the patient 10 and the renal blood flow of the kidney 42L for future processing by the system processor 19.

[0061] A fourth step 90 of method 82 is a query performed by system processor 19. The query in fourth step 90 is whether the first array is full. If the first array is not full, then system processor 19 returns to step 86 and repeats steps 86-90 until the first array is full. If the first array is full, system processor 19 proceeds to a fifth step 92 of method 82. In fifth step 92 of method 82, system processor 19 calculates the mean arterial pressure (MAP) and a mathematical relationship between the arterial pressure values ​​and the renal blood flow values ​​in the first array. As discussed above, the calculated mathematical relationship between the arterial pressure values ​​and the renal blood flow values ​​can be a correlation (or coherence) between changes in renal blood flow rate in kidney 42L and changes in MAP in patient 10. In a sixth step 94 of method 82, system processor 19 pairs the correlation (or coherence) value with a MAP value and outputs the paired correlation (or coherence) and MAP values ​​to display 28.

[0062] In a seventh step 96 of method 82, system processor 19 places the paired correlation (or coherence) and MAP values ​​into a second array. The second array is a second data buffer in system processor 19 and / or system memory 20 that stores information about the paired correlation (or coherence) and MAP values ​​for future processing by system processor 19. System processor 19 performs an eighth step 98 of method 82 by determining whether there are sufficient values ​​in the second array to estimate an upper autoregulation limit or a lower autoregulation limit. If system processor 19 determines that the second array does not contain sufficient values ​​to estimate an upper autoregulation limit or a lower autoregulation limit, the system processor proceeds to a ninth step 99 by removing the oldest pair of paired values ​​for arterial pressure of patient 10 and renal blood flow of kidney 42L in the first array. After removing the oldest pair of paired values ​​for arterial pressure of patient 10 and renal blood flow of kidney 42L in the first array, the system processor proceeds to a second step 86 and repeats steps 86-98. If the system processor 19 determines that the second array is full, the system processor proceeds to a tenth step 100.

[0063] System processor 19 performs tenth step 100 of method 82 by estimating an upper autoregulation limit and / or a lower autoregulation limit. System processor 19 may fit the upper autoregulation limit and / or the lower autoregulation limit to a Lassen curve. In eleventh step 102 of method 82, system processor 19 may output the upper autoregulation limit and / or the lower autoregulation limit to display 28. Twelfth step 104 of method 82 is a query for system processor 19. Twelfth step 104 queries whether the monitoring session of kidney 42 of patient 10 is complete. If the answer is "no," then system processor proceeds to ninth step 99 by removing the oldest pair of paired values ​​for arterial pressure of patient 10 and renal blood flow of kidney 42L in the first array. After removing the oldest pair of paired values ​​for arterial pressure of patient 10 and renal blood flow of kidney 42L in the first array, the system processor proceeds to a second step 86 and repeats steps 86-104. If the system processor 19 determines that the kidney 42 monitoring session is complete, then the medical professional will perform a thirteenth step 106 of method 82 by removing the ultrasound transducer probe 14 and hemodynamic pressure sensor 17 from the patient 10.

[0064] FIG. 12 is a chart from an experiment demonstrating the monitoring system 11. The chart shows four plots. A first plot 108 is a plot of the mean arterial pressure (MAP) of a test subject (pig) over time as measured by the hemodynamic pressure sensor 17. The first plot 108 also includes a lower limit of autoregulation (LLA) line for the test subject's renal blood flow. The LLA line in the first plot 108 is determined by plotting the test subject's renal blood flow against the test subject's MAP for each time sample, as shown in plot 116 of FIG. 13, and fitting two lines through the data points and assigning the inflection point between the two lines as the location of the LLA line. The LLA line indicates the test subject's lower threshold of MAP, at which normal autoregulation of renal blood flow in the kidney 42L occurs. When the test subject has a MAP value above the LLA line, renal blood flow autoregulation in the kidney 42L is normal, and there is a low correlation between renal blood flow rate and the test subject's MAP. When patient 10 has a MAP value below the LLA line, autoregulation of renal blood flow in kidney 42L is impaired and the correlation between renal blood flow rate and the test subject's MAP is high (approaching 1).

[0065] A second plot 110 is a plot of the test subject's renal blood flow rate over time as measured by an invasive flow probe surgically implanted around the test subject's renal arteries to provide a baseline measurement of renal blood flow. A third plot 112 is a plot of the test subject's renal blood flow rate over time as measured by the ultrasound transducer probe 14 of the monitoring system 11. The third plot 112 shows that the non-invasive ultrasound transducer probe 14 of the monitoring system 11 in this experiment was able to identify changes in the test subject's renal blood flow in a manner similar to the invasive transonic flow probe surgically implanted around the test subject's renal arteries.

[0066] The fourth plot 114 includes a first line representing the correlation between the change in the test subject's MAP as measured by the invasive flow probe and the change in the test subject's renal blood flow rate. The fourth plot 114 also includes a second line representing the correlation between the change in the test subject's MAP as measured by the ultrasound transducer probe 14 of the monitoring system 11 and the change in the test subject's renal blood flow rate. A balloon catheter was inserted into the test subject's inferior vena cava. During the experiment, the balloon catheter was inflated and deflated several times, causing a decrease and an increase in the test subject's MAP.

[0067] When the balloon catheter was inflated, the test subject's MAP decreased below the LLA line, causing the correlation line in fourth plot 114 to increase, indicating a correlation between changes in MAP and changes in the test subject's renal blood flow rate. The existence of a correlation between changes in MAP and changes in the test subject's renal blood flow rate indicates that the test subject's renal blood flow autoregulation was impaired. When the balloon catheter was deflated, the test subject's MAP increased above the LLA line, causing the correlation line in fourth plot 114 to decrease, indicating a non-correlation between changes in MAP and changes in the test subject's renal blood flow rate. The existence of a non-correlation between changes in MAP and changes in the test subject's renal blood flow rate indicates that the test subject's renal blood flow autoregulation was functioning. As shown in the fourth plot 114, the monitoring system 11 in this experiment was able to identify correlations or non-correlations between changes in MAP and changes in the test subject's renal blood flow rate, and was able to determine whether the subject's renal blood flow autoregulation was impaired or functional.

[0068] FIG. 13 shows a plot 116 of the test subject's renal blood flow versus MAP from the experiment discussed with reference to FIG. 12 , which is the test subject's physiological flow autoregulation functionality. As shown in plot 116 of FIG. 13 , when the test subject's MAP is below the LLA line, the test subject's renal blood flow autoregulation is impaired and passive, such that the renal blood flow rate tends to track changes in the test subject's MAP. When the test subject's MAP is above the LLA line, the test subject's renal blood flow autoregulation is functional, and the test subject's renal blood flow rate no longer correlates with changes in the test subject's MAP.

[0069] FIG. 14 shows a chart 118 of the correlation versus the test subject's MAP from the experiment of FIG. 12. Chart 118 in FIG. 14 is similar to the chart in FIG. 9. Chart 118 shows the correlation between the change in the test subject's MAP and the change in the test subject's renal blood flow rate versus the test subject's MAP. Chart 118 compares the correlation determined by system processor 19 using renal blood flow data collected by ultrasound transducer probe 14 with the correlation determined by system processor 19 using renal blood flow data collected by an invasive flow probe surgically implanted around the test subject's renal artery. Both sets of data show that the test subject's LLA line was at approximately 50 mmHg. When the test subject's MAP was below 50 mmHg, the test subject's renal blood flow autoregulation was impaired, and a correlation existed between the test subject's MAP and the test subject's renal blood flow rate. When the test subject's MAP was above 50 mmHg, the test subject's renal blood flow autoregulation was functioning, and there was no correlation between the test subject's MAP and the test subject's renal blood flow rate.

[0070] FIG. 15 is a block diagram of a method 120 for operating the monitoring system 11 shown in FIGS. 1-2 to continuously monitor renal blood flow autoregulation of the kidney 42L of a patient 10 during a surgical procedure, medical treatment, or medical observation. Renal blood flow autoregulation of the kidney 42L is defined as the ability of the renal artery and renal vein to dilate and constrict in response to dynamic perfusion pressure changes to maintain renal blood flow sufficient for the needs of the kidney 42L. Changes in blood flow in the kidney 42L are largely uncorrelated with changes in the patient's 10 blood pressure.

[0071] As discussed above with reference to Figures 2-14, the monitoring system 11 uses the renal blood flow rate of the kidney 42L estimated from the Doppler flow signal and the patient's 10 MAP to determine an autoregulation profile of the kidney 42L of the patient 10. The system processor 19 monitors time-domain and / or frequency-domain changes in both the patient's 10 renal blood flow rate and the patient's 10 MAP. The system processor 19 evaluates the changes in renal blood flow rate and the changes in the patient's 10 MAP relative to each other to determine an autoregulation profile of the kidney 42L of the patient 10. If the system processor 19 determines a non-correlation between the changes in the patient's 10 renal blood flow rate and the changes in the patient's 10 MAP, then the system processor 19 determines that the autoregulation profile of the kidney 42L is active and functioning properly. If the system processor 19 determines that a correlation exists between the changes in the patient's 10 renal blood flow rate and the changes in the patient's 10 MAP, then the system processor 19 determines that the autoregulation profile of the kidney 42L is impaired. The Pearson correlation coefficient is an example of a time-domain correlation that the system processor 19 can use over a rolling time window to monitor renal blood flow and the MAP of the patient 10 for autoregulation. The coherence function (sometimes referred to as the magnitude-squared coherence function) is an example of a frequency-domain correlation that the system processor 19 can use to monitor renal blood flow and the MAP of the patient 10 for autoregulation.

[0072] With the ultrasound transducer probe 14 attached to the patient 10 and sensing a Doppler flow signal of renal blood flow to the kidney 42L, and the pressure sensor 70 attached to the patient 10 and sensing hemodynamic data representative of the patient's MAP, the system processor 19 executes the AR monitoring module 32 to perform a first step 122 of the method 120. In the first step 122, the system processor 19 executes the AR monitoring module 32, analyzes the Doppler flow signal of renal blood flow and the patient's MAP, determines an autoregulation profile of renal blood flow to the kidney 42L, and establishes the LLA line and the ULA line of the autoregulation profile. Using the LLA line and the ULA line, the system processor 19 can determine when autoregulation of renal blood flow to the kidney 42L is functioning or impaired based on the value of the patient's MAP. Impaired autoregulation of renal blood flow to the kidney 42L over time may indicate injury to the kidney 42L.

[0073] In a second step 124 of method 120, system processor 19 executes AR monitoring module 32 to continuously monitor the Doppler flow signal of renal blood flow to kidney 42L during the surgical procedure, medical treatment, or medical observation of patient 10. As part of second step 124, system processor 19 may output the renal blood flow autoregulation profile to display 28. In the example of FIG. 15 , second step 124 of method 120 further includes sub-step 125. In sub-step 125, system processor 19 executes AR monitoring module 32 to collect a cumulative sum of time during the surgical procedure, medical treatment, or medical observation of patient 10 during which the autoregulation profile indicates that renal blood flow autoregulation of kidney 42L is impaired.

[0074] In a third step 126 of method 120, system processor 19 executes AR monitoring module 32 to estimate a real-time AKI risk score for patient 10 from the renal blood flow autoregulation profile. System processor 19 and AR monitoring module 32 use the cumulative sum of time that renal blood flow autoregulation was impaired to estimate the real-time AKI risk score for patient 10. In a fourth step 128 of method 120, system processor 19 outputs the real-time AKI risk score for patient 10 to display 28. The real-time AKI risk score can be shown on display 28 as a plot showing how the patient's 10 real-time AKI risk score changes over time, and / or the real-time AKI risk score can be shown as the current value of injury score indicator 37. The real-time AKI risk score is recorded by system processor 19 into system memory 20. When estimating the next iteration of the real-time AKI risk score for the patient 10, the system processor 19 can use the recorded AKI risk score in the system memory 20 as part of the estimation of the next iteration of the real-time AKI risk score for the patient 10. Thus, over time, the real-time AKI risk score for the patient 10 is based on both real-time information from the renal blood flow autoregulation profile of the kidney 42L, plus cumulative historical information from the renal blood flow autoregulation profile of the kidney 42L.

[0075] As the patient 10 undergoes surgery, medical treatment, or medical observation, the system processor 19 and AR monitoring module 32 continue to repeat the second step 124, the third step 126, and the fourth step 128 of the method 120 to continuously update and display the patient 10's real-time AKI risk score. Whenever the monitoring system 11 indicates that renal blood flow autoregulation of the kidney 42L is impaired, the monitoring system 11 can activate a warning or alarm to notify medical personnel so that the medical personnel can take action to compensate for the impaired autoregulation or to restore renal blood flow autoregulation. At the end of the patient 10's surgery, medical treatment, or medical observation, the system processor 19 can execute the AR monitoring module 32 to perform the fifth step 129 and estimate a final AKI risk score for the patient 10's kidney 42L. The system processor 19 can determine a final AKI risk score for the patient 10 based on the real-time AKI risk score values ​​tracked throughout the patient's 10 surgery, medical procedure, or medical observation and recorded in the system memory 20. After estimating the final AKI risk score for the kidney 42L, the system processor 19 performs a sixth step 130 of the method 120 by outputting the final AKI risk score to the display 28. Based on the value of the final AKI risk score, a medical professional can estimate whether the patient's 10 kidney 42L was injured during the surgery, medical procedure, or medical observation and can recommend that the patient 10 seek treatment for the kidney 42L.

[0076] The treatment techniques, methods, steps, etc. described or suggested herein or in the references incorporated herein may be performed on live animals or non-living simulations. Any of the various systems, devices, apparatus, etc. in the present disclosure may be sterilized (e.g., by heat, radiation, ethylene oxide, hydrogen peroxide, etc.) to ensure they are safe for use by patients, and the methods herein may include sterilization (e.g., by heat, radiation, ethylene oxide, hydrogen peroxide, etc.) of the associated systems, devices, apparatus, etc.

[0077] Discussion of Possible Embodiments The following is a non-exclusive description of possible embodiments of the present invention.

[0078] A method for continuously monitoring a patient's kidneys during a surgical procedure, medical treatment, or medical observation includes continuously measuring the patient's renal blood flow signal with a first sensor attached to the patient. The first sensor is in communication with a blood flow monitor. A processor of the blood flow monitor estimates the patient's renal blood flow rate from the renal blood flow signal. The processor also monitors changes in the renal blood flow rate over time. The patient's arterial pressure signal is continuously measured with a second sensor. The second sensor is in communication with the blood flow monitor. The processor also monitors changes in the arterial pressure signal over time and evaluates a mathematical relationship between the arterial pressure change signal and changes in the renal blood flow rate.

[0079] The method of the preceding paragraph may additionally and / or alternatively optionally include any one or more of the following features, configurations, and / or additional components in the paragraphs below.

[0080] In an embodiment of the foregoing method, the method further includes determining, by the processor, an autoregulation profile of the patient's renal blood flow based on a mathematical relationship between changes in the arterial pressure signal and changes in renal blood flow rate.

[0081] In an embodiment of the foregoing method, the method further includes outputting in real time to a display in communication with the processor the representation of the renal blood flow rate over time, the representation of the arterial pressure signal over time, and / or the representation of the mathematical relationship between changes in the arterial pressure signal and changes in the renal blood flow rate over time.

[0082] In an embodiment of the foregoing method, the method further includes the step of continuously monitoring, by the processor, the mathematical relationship between changes in the arterial pressure signal and changes in the renal blood flow rate over time during the patient's surgery, medical treatment, or medical observation.

[0083] In an embodiment of the aforementioned method, the method further includes evaluating, by the processor, a correlation coefficient representing a correlation or non-correlation between changes in arterial pressure and changes in renal blood flow rate, and the processor determines a renal blood flow autoregulation profile of the patient based on the correlation coefficient.

[0084] In an embodiment of the foregoing method, the processor evaluates the correlation coefficient in the time domain.

[0085] In an embodiment of the foregoing method, the processor evaluates the correlation coefficient using a Pearson correlation coefficient computed over a rolling window of time.

[0086] In an embodiment of the foregoing method, the processor evaluates the correlation coefficient in the frequency domain.

[0087] In an embodiment of the foregoing method, the processor evaluates the correlation coefficient using a coherence function computed over a pre-specified frequency range.

[0088] In an embodiment of the foregoing method, the processor evaluates the correlation coefficient using a coherence function computed from parameters of the transfer function of the arterial pressure signal and the transfer function of the renal blood flow rate.

[0089] In an embodiment of the foregoing method, the method further includes setting, by the processor, a correlation threshold defining a boundary above which the correlation coefficient represents a correlation between changes in arterial pressure and changes in renal blood flow rate, and below which the correlation coefficient represents a non-correlation between changes in arterial pressure and changes in renal blood flow rate.

[0090] In an embodiment of the aforementioned method, the method further includes estimating, by the processor, a lower limit of autoregulation (LLA), the LLA being the patient's arterial pressure value below which the correlation coefficient is consistently above the correlation threshold.

[0091] In an embodiment of the aforementioned method, the method further includes estimating, by the processor, an upper limit of autoregulation (ULA), the ULA being the patient's arterial pressure value above which the correlation coefficient is consistently above the correlation threshold.

[0092] In an embodiment of the foregoing method, the method further includes setting, by the processor, at least one alarm in the blood flow monitor, the at least one alarm being activated in response to the hemodynamic pressure sensor sensing that the patient's arterial pressure rises above the ULA or falls below the LLA.

[0093] In an embodiment of the foregoing method, the method further includes estimating, by a processor of the blood flow monitor, a real-time acute kidney injury risk score for the patient from the patient's autoregulation profile and the predetermined threshold, and outputting, in real time, a representation of the patient's real-time acute kidney injury risk score over time on a display.

[0094] In an embodiment of the foregoing method, the predetermined threshold includes at least one of an LLA and a ULA.

[0095] In an embodiment of the foregoing method, the method further includes setting, by the processor, a hypotension threshold and / or definition for a hypotension prediction algorithm of the blood flow monitor based on the patient's autoregulation profile.

[0096] In embodiments of the foregoing methods, the hypotension threshold and / or definition for the hypotension prediction algorithm of the blood flow monitor includes at least one of an LLA and a ULA.

[0097] In an embodiment of the foregoing method, the method further includes collecting, by the blood flow monitor, a cumulative sum of times over which changes in the arterial pressure signal and changes in renal blood flow rate correlate; estimating, by a processor of the blood flow monitor, a real-time acute kidney injury risk score for the patient from the cumulative sum of times over which changes in the arterial pressure signal and changes in renal blood flow rate correlate; and outputting, in real time, a representation of the patient's real-time acute kidney injury risk score over time on a display.

[0098] In embodiments of the aforementioned method, continuously measuring the patient's renal blood flow signal with a first sensor attached to the patient includes sampling the waveform of the renal blood flow signal at a rate of at least 10 Hz, at least 20 Hz, at least 60 Hz, at least 100 Hz, or at least 200 Hz; sampling the renal blood flow signal every cardiac cycle of the patient; sampling an average of the renal blood flow signal over a window of time; and / or sampling an average of the renal blood flow signal over a rolling window of time.

[0099] In embodiments of the foregoing methods, the renal blood flow signal is a relative change in renal blood flow, a flow velocity of renal blood flow, and / or a peak flow velocity of renal blood flow.

[0100] In an embodiment of the foregoing method, the first sensor includes an ultrasound transducer probe attached to the patient's abdomen in a stationary position, and the signal of the patient's renal blood flow is a Doppler flow signal.

[0101] In an embodiment of the foregoing method, the method further includes the steps of positioning an ultrasound transducer probe over the patient's abdomen, attaching the ultrasound transducer probe to the patient's abdomen with an adhesive patch, and maintaining contact between the ultrasound transducer probe and the patient without an ultrasound operator; and scanning the patient's abdomen with the ultrasound transducer probe to identify a location of a Doppler flow signal of the patient's renal blood flow.

[0102] In an embodiment of the foregoing method, the method further includes executing beamformer software code by a processor to track scan the Doppler flow signals of the patient's renal blood flow with a two-dimensional phased array of transducer elements of an ultrasound transducer probe to continuously sense the Doppler flow signals of the patient's renal blood flow during surgery, medical procedure, or medical observation without an ultrasound operator.

[0103] In an embodiment of the aforementioned method, the method further includes the steps of executing beamformer software code by a processor to emit a set of sequential beams from the array of transducer elements and track a center of renal blood flow relative to the array of transducer elements; focusing, by the processor executing the beamformer software code, each beam from the set of sequential beams at a different location; and adjusting, by the processor executing the beamformer software code, the position of the set of sequential beams over the center of renal blood flow to maintain a Doppler flow signal of the patient's renal blood flow.

[0104] In an embodiment of the foregoing method, the second sensor includes a hemodynamic pressure sensor attached to the patient by a radial catheter.

[0105] In an embodiment of the foregoing method, the second sensor includes a hemodynamic pressure sensor attached to the patient by a femoral artery catheter.

[0106] In an embodiment of the foregoing method, the second sensor includes a non-invasive hemodynamic pressure sensor.

[0107] In an embodiment of the foregoing method, the method further includes setting, by the processor, a blood pressure alarm in the blood flow monitor based on the patient's autoregulation profile.

[0108] The system includes a first sensor configured to continuously measure a patient's renal blood flow signal during a surgical procedure, medical treatment, or medical observation. A second sensor configured to continuously measure the patient's arterial pressure signal during a surgical procedure, medical treatment, or medical observation. A blood flow monitor is in communication with the first sensor and the second sensor. The blood flow monitor includes a system memory storing monitoring software code and a processor. The processor is configured to execute the monitoring software code to estimate the patient's renal blood flow rate from the renal blood flow signal and to monitor changes in the renal blood flow rate over time. The processor is also configured to execute the monitoring software code to monitor changes in the arterial pressure signal over time and to evaluate a mathematical relationship between changes in the arterial pressure signal and changes in the renal blood flow rate.

[0109] The system of the preceding paragraph may additionally and / or alternatively optionally include any one or more of the following features, configurations, and / or additional components in the paragraphs below:

[0110] In an embodiment of the aforementioned system, the processor is configured to execute monitoring software code and determine the patient's renal blood flow autoregulation profile based on the mathematical relationship between changes in the arterial pressure signal and changes in renal blood flow rate.

[0111] In an embodiment of the foregoing system, the renal blood flow signal comprises a Doppler flow signal, and the first sensor comprises an ultrasound transducer probe including a two-dimensional array of transducer elements configured to continuously measure the Doppler flow signal of the patient's renal blood flow during surgery, medical procedure, or medical observation.

[0112] In an embodiment of the aforementioned system, the two-dimensional array of transducer elements of the ultrasound transducer probe comprises a phased array of transducer elements.

[0113] In an embodiment of the system described above, the system memory stores probe control software code comprising beamformer software code, and the processor is configured to execute the beamformer software code to track scan the Doppler flow signals of the patient's renal blood flow by emitting multiple ultrasound beams from the phased array of transducer elements and tracking the Doppler flow signals of the patient's renal blood flow relative to the phased array of transducer elements.

[0114] In an embodiment of the system described above, the second sensor includes a hemodynamic pressure sensor connected to the radial catheter.

[0115] In an embodiment of the system described above, the second sensor includes a hemodynamic pressure sensor connected to the femoral artery catheter.

[0116] In an embodiment of the foregoing system, the second sensor includes a non-invasive hemodynamic pressure sensor.

[0117] In embodiments of the foregoing system, the system further includes a display in communication with the processor for receiving and showing a representation of the renal blood flow rate over time, a representation of the arterial pressure signal over time, and / or a representation of the mathematical relationship between changes in the arterial pressure signal and changes in the renal blood flow rate over time.

[0118] In embodiments of the foregoing systems, the processor is configured to execute monitoring software code that causes the processor to continuously monitor the patient's renal blood flow autoregulation profile over time during the patient's surgery, medical procedure, or medical observation.

[0119] In embodiments of the aforementioned system, the processor is configured to execute the monitoring software code to evaluate a correlation or non-correlation between changes in arterial pressure and changes in renal blood flow rate, and to determine a renal blood flow autoregulation profile for the patient based on the correlation or non-correlation between changes in arterial pressure and changes in renal blood flow rate.

[0120] In embodiments of the aforementioned systems, the processor is configured to execute monitoring software code to assess correlation or non-correlation between changes in arterial pressure and changes in renal blood flow rate in the time domain.

[0121] In embodiments of the aforementioned systems, the processor is configured to execute the monitoring software code and to assess the correlation or non-correlation between changes in arterial pressure and changes in renal blood flow rate using a Pearson correlation coefficient computed over a rolling window of time.

[0122] In embodiments of the aforementioned systems, the processor is configured to execute monitoring software code to assess correlation or non-correlation between changes in arterial pressure and changes in renal blood flow rate in the frequency domain.

[0123] In embodiments of the aforementioned system, the processor is configured to execute monitoring software code and to assess correlation or non-correlation between changes in arterial pressure and changes in renal blood flow rate using a computed coherence function over a pre-specified frequency range.

[0124] In an embodiment of the system described above, the processor is configured to execute the monitoring software code and to evaluate the correlation or non-correlation between changes in arterial pressure and changes in renal blood flow rate using a coherence function computed from parameters of the transfer function of the arterial pressure signal and the transfer function of the renal blood flow rate.

[0125] In embodiments of the foregoing system, the processor is configured to execute monitoring software code to estimate the patient's real-time acute kidney injury risk score from the patient's autoregulation profile and the predetermined threshold, and to output a representation of the patient's real-time acute kidney injury risk score over time on the display in real time.

[0126] In embodiments of the aforementioned system, the processor is configured to execute the monitoring software code to collect a cumulative sum of correlated time periods of changes in the arterial pressure signal and changes in renal blood flow rate, to estimate the patient's real-time acute kidney injury risk score from the cumulative sum of correlated time periods of changes in the arterial pressure signal and changes in renal blood flow rate, and to output a representation of the patient's real-time acute kidney injury risk score over time to a display in real time.

[0127] In embodiments of the foregoing systems, the processor is configured to execute monitoring software code and set blood pressure alarms in the blood flow monitor based on the patient's autoregulation profile.

[0128] In embodiments of the aforementioned systems, the processor is configured to execute the monitoring software code and set hypotension thresholds and / or definitions for the hypotension prediction algorithm of the blood flow monitor based on the patient's autoregulation profile.

[0129] A method for continuously monitoring a patient's kidneys during a surgical procedure, medical treatment, or medical observation includes continuously measuring a Doppler flow signal of the patient's renal blood flow with an ultrasound transducer probe. The ultrasound transducer probe is attached to the patient's abdomen in a stationary position and is in communication with a processor of a blood flow monitor. The processor monitors changes in the renal blood flow over time. A hemodynamic pressure sensor continuously measures the patient's arterial pressure signal. The hemodynamic pressure sensor is in communication with the blood flow monitor. The processor monitors changes in the arterial pressure signal over time and evaluates a mathematical relationship between the changes in the arterial pressure signal and the changes in the renal blood flow. The processor determines an autoregulation profile of the patient's renal blood flow based on the mathematical relationship between the changes in the arterial pressure signal and the changes in the renal blood flow.

[0130] A method for continuously monitoring a patient's kidneys during a surgical procedure, medical treatment, or medical observation includes continuously measuring a Doppler flow signal of the patient's renal blood flow with an ultrasound transducer probe. The ultrasound transducer probe is attached to the patient's abdomen in a stationary position and is in communication with a processor of a blood flow monitor. The processor monitors changes in the renal blood flow over time. A hemodynamic pressure sensor continuously measures the patient's arterial pressure signal. The hemodynamic pressure sensor is in communication with the blood flow monitor. The processor monitors changes in the arterial pressure signal over time and evaluates a mathematical relationship between the changes in the arterial pressure signal and the changes in the renal blood flow. The processor determines an autoregulation profile of the patient's renal blood flow based on the mathematical relationship between the changes in the arterial pressure signal and the changes in the renal blood flow.

[0131] The method of the preceding paragraph may additionally and / or alternatively optionally include any one or more of the following features, configurations, and / or additional components in the paragraphs below.

[0132] In an embodiment of the foregoing method, the processor is configured to execute the monitoring software code and determine an autoregulation profile of the patient's renal blood flow based on a mathematical relationship between changes in the arterial pressure signal and changes in renal blood flow.

[0133] While the present invention has been described with reference to exemplary embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is not intended that the invention be limited to the particular embodiments disclosed, but rather that the invention will include all embodiments falling within the scope of the appended claims. [Explanation of symbols]

[0134] 10 patients 11 Monitoring System 12 Blood Flow Monitor 14 Ultrasonic transducer probe 15 adhesive patches 16 Ultrasonic Front-End (UFE) Circuit 17 Hemodynamic pressure sensor 18 Radial catheter 19 System Processor 20 System Memory 22 Software Code 24 Probe cable 26 First Analog-to-Digital Converter (ADC) 27 Second Analog-to-Digital Converter (ADC) 28 Display 29 User Interface 30 Transducer Probe Control Module 32 Autoregulation (AR) Monitoring Module 33 First Plot 34 Second Plot 35 The Third Plot 36 Autoregulation Index Values 37 Injury Score Indicator 38 Input Devices 39 Output Devices 40 Abdomen 42L kidney 42R Kidney 44 Liver 46 Spleen 48 Beamformer 50 Array 52 Transducer Element 54a, 54b, 54c ribs 56a, 56b signal beam 58 Housing 60 fluid input port 62 Catheter fluid port 64 Input / Output (I / O) Cables 66 Finger Cuff 68 Heart Reference Sensor 72 First Data Plot 74 Second Data Plot 76 Correlation Plot 78 Renal Autoregulation 80 Correlation Threshold Lines 108 First Plot 110 Second Plot 112 The Third Plot 114 Fourth Plot 116 Plots 118 Charts 172 First Data Plot 174 Second Data Plot 176 Coherence Plot BW Doppler signal of blood flow in the renal artery RA LLA Autoregulation Lower Limit OW Outgoing Signal PL probe length PW Probe width RA renal artery RV renal vein RW Doppler signal of blood flow in the renal vein RV ULA Auto-Regulation Upper Limit W1 First acoustic window W2 Second acoustic window

Claims

1. 1. A method for continuously monitoring a patient's kidneys during surgery, medical treatment, or medical observation, comprising: continuously measuring a signal of the patient's renal blood flow with a first sensor attached to the patient, the first sensor being in communication with a blood flow monitor; estimating, by a processor of the blood flow monitor, a renal blood flow rate of the patient from the renal blood flow signal; monitoring, by the processor, changes in the rate of renal blood flow over time; continuously measuring the patient's arterial pressure signal with a second sensor, the second sensor in communication with the blood flow monitor; monitoring, by the processor, changes in the arterial pressure signal over time; evaluating, by the processor, a mathematical relationship between the change in the arterial pressure signal and the change in the rate of renal blood flow; A method comprising:

2. 2. The method of claim 1, further comprising determining, by the processor, an autoregulation profile of the patient's renal blood flow based on the mathematical relationship between the change in the arterial pressure signal and the change in the rate of renal blood flow.

3. 3. The method of claim 1, further comprising outputting in real time to a display in communication with the processor a representation of the rate of renal blood flow over time, a representation of the arterial pressure signal over time, and / or a representation of the mathematical relationship between the change in the arterial pressure signal and the change in the rate of renal blood flow over time.

4. 4. The method of claim 1, further comprising the step of continuously monitoring, by the processor, the mathematical relationship between the changes in the arterial pressure signal and the changes in the rate of renal blood flow over time during the surgical procedure, medical treatment, or medical observation of the patient.

5. evaluating, by the processor, a correlation coefficient representing a correlation or non-correlation between the change in the arterial pressure and the change in the rate of renal blood flow; 5. The method of claim 1, wherein the processor determines the autoregulation profile of the renal blood flow of the patient based on the correlation coefficient.

6. The method of claim 5 , wherein the processor evaluates the correlation coefficient in the time domain.

7. The method of claim 6 , wherein the processor evaluates the correlation coefficient using a Pearson correlation coefficient computed over a rolling window of time.

8. The method of claim 5 , wherein the processor evaluates the correlation coefficient in the frequency domain.

9. The method of claim 8 , wherein the processor evaluates the correlation coefficient using a coherence function computed over a pre-specified frequency range.

10. 9. The method of claim 8, wherein the processor evaluates the correlation coefficient using a coherence function computed from parameters of a transfer function of the arterial pressure signal and a transfer function of the flow rate of the renal blood flow.

11. 11. The method of claim 5, further comprising the step of setting, by the processor, a correlation threshold defining a boundary above which the correlation coefficient represents a correlation between the changes in the arterial pressure and the changes in the rate of renal blood flow, and below which the correlation coefficient represents a non-correlation between the changes in the arterial pressure and the changes in the rate of renal blood flow.

12. 12. The method of claim 11, further comprising estimating, by the processor, a lower limit of autoregulation (LLA), the LLA being the patient's arterial pressure value below which the correlation coefficient is consistently above the correlation threshold.

13. 13. The method of claim 12, further comprising estimating, by the processor, an upper limit of autoregulation (ULA), the ULA being the arterial pressure value of the patient above which the correlation coefficient is consistently above the correlation threshold.

14. 14. The method of claim 13, further comprising setting, by the processor, at least one alarm in the blood flow monitor, the at least one alarm being activated in response to the hemodynamic pressure sensor sensing that the patient's arterial pressure rises above the ULA or falls below the LLA.

15. estimating, by the processor of the blood flow monitor, a real-time acute kidney injury risk score for the patient from the autoregulation profile and a predetermined threshold for the patient; outputting in real time on the display a representation of the real-time acute kidney injury risk score for the patient over time; 15. The method of claim 14, further comprising:

16. The method of claim 15 , wherein the predetermined threshold comprises at least one of the LLA and the ULA.

17. 17. The method of any one of claims 12 to 16, further comprising the step of setting, by the processor, a hypotension threshold and / or definition for a hypotension prediction algorithm of the blood flow monitor based on the autoregulation profile of the patient.

18. 18. The method of claim 17, wherein the hypotension threshold and / or the definition for the hypotension prediction algorithm of the blood flow monitor includes at least one of the LLA and the ULA.

19. collecting, with the blood flow monitor, a cumulative sum of the time over which the change in the arterial pressure signal and the change in the rate of the renal blood flow correlate; estimating, by the processor of the blood flow monitor, a real-time acute kidney injury risk score for the patient from the cumulative sum of the time periods over which the changes in the arterial pressure signal and the changes in the renal blood flow rate correlate; outputting in real time on the display a representation of the real-time acute kidney injury risk score for the patient over time; 19. The method of any one of claims 3 to 18, further comprising:

20. continuously measuring the signal of the renal blood flow of the patient with the first sensor attached to the patient, sampling the waveform of the signal of renal blood flow at a rate of at least 10 Hz, at least 20 Hz, at least 60 Hz, at least 100 Hz, or at least 200 Hz; sampling the signal of the renal blood flow every cardiac cycle of the patient; sampling an average of the signal of the renal blood flow over a window of time; and / or sampling an average of the signal of the renal blood flow over a rolling window of time; 20. The method of any one of claims 1 to 19, comprising:

21. 21. The method of any one of claims 1 to 20, wherein the signal of the renal blood flow is a relative change in the renal blood flow, a flow velocity of the renal blood flow, and / or a peak flow velocity of the renal blood flow.

22. 22. The method of any one of claims 1 to 21, wherein the first sensor includes an ultrasound transducer probe attached to the patient's abdomen in a stationary position, and the signal of the patient's renal blood flow is a Doppler flow signal.

23. positioning the ultrasound transducer probe over the abdomen of the patient and attaching the ultrasound transducer probe to the abdomen of the patient with an adhesive patch, and maintaining contact between the ultrasound transducer probe and the patient without an ultrasound operator; scanning the abdomen of the patient with the ultrasound transducer probe to locate the Doppler flow signal of the renal blood flow of the patient; 23. The method of claim 22, further comprising:

24. 24. The method of claim 23, further comprising the step of executing beamformer software code by the processor to track scan the Doppler flow signals of the renal blood flow of the patient with a two-dimensional phased array of transducer elements of the ultrasound transducer probe, and continuously sensing the Doppler flow signals of the renal blood flow of the patient during the surgery, medical procedure, or medical observation without an ultrasound operator.

25. executing, by the processor, the beamformer software code to emit a set of sequential beams from the array of transducer elements and track the center of renal blood flow relative to the array of transducer elements; focusing, by the processor executing the beamformer software code, each beam from the set of sequential beams to a different location; adjusting, by the processor executing the beamformer software code, the position of the set of sequential beams over the center of the renal blood flow to maintain the Doppler flow signal of the renal blood flow of the patient; 25. The method of claim 24, further comprising:

26. 26. The method of any one of claims 1 to 25, wherein the second sensor comprises a hemodynamic pressure sensor attached to the patient by a radial catheter.

27. 26. The method of any one of claims 1 to 25, wherein the second sensor comprises a hemodynamic pressure sensor attached to the patient by a femoral artery catheter.

28. 26. The method of any one of claims 1 to 25, wherein the second sensor comprises a non-invasive hemodynamic pressure sensor.

29. 29. The method of any one of claims 1 to 28, further comprising the step of setting, by the processor, a blood pressure alarm in the blood flow monitor based on the autoregulation profile of the patient.

30. a first sensor configured to continuously measure a renal blood flow signal of a patient during a surgical procedure, medical treatment, or medical observation; a second sensor configured to continuously measure the patient's arterial pressure signal during the surgical procedure, the medical procedure, or the medical observation; and a blood flow monitor in communication with the first sensor and the second sensor; Including, The blood flow monitor a system memory for storing monitoring software code; a processor, the processor executing the monitoring software code; estimating a renal blood flow rate of the patient from the renal blood flow signal; monitoring changes in the renal blood flow rate over time; monitoring changes in the arterial pressure signal over time; and evaluating a mathematical relationship between the change in the arterial pressure signal and the change in the rate of renal blood flow; a processor, comprising: Including, the system.

31. the processor executes the monitoring software code; 31. The system of claim 30, configured to determine an autoregulation profile of the patient's renal blood flow based on the mathematical relationship between the change in the arterial pressure signal and the change in the rate of the renal blood flow.

32. 32. The system of claim 30 or 31, wherein the signal of the renal blood flow comprises a Doppler flow signal, and the first sensor comprises an ultrasound transducer probe including a two-dimensional array of transducer elements configured to continuously measure the Doppler flow signal of the renal blood flow of the patient during the surgical procedure, the medical procedure, or the medical observation.

33. 33. The system of claim 32, wherein the two-dimensional array of transducer elements of the ultrasound transducer probe comprises a phased array of transducer elements.

34. the system memory storing probe control software code comprising beamformer software code, the processor executing the beamformer software code; 34. The system of claim 33, configured to track scan the Doppler flow signal of the renal blood flow of the patient by emitting multiple ultrasound beams from the phased array of transducer elements and tracking the Doppler flow signal of the renal blood flow of the patient relative to the phased array of transducer elements.

35. 35. The system of any one of claims 30 to 34, wherein the second sensor includes a hemodynamic pressure sensor connected to a radial catheter.

36. 35. The system of any one of claims 30 to 34, wherein the second sensor includes a hemodynamic pressure sensor connected to a femoral artery catheter.

37. 35. The system of any one of claims 30 to 34, wherein the second sensor includes a non-invasive hemodynamic pressure sensor.

38. 38. The system of any one of claims 30 to 37, further comprising a display in communication with the processor for receiving and showing a representation of the rate of renal blood flow over time, a representation of the arterial pressure signal over time, and / or a representation of the mathematical relationship between the changes in the arterial pressure signal and the changes in the rate of renal blood flow over time.

39. the processor executes the monitoring software code; 39. The system of any one of claims 30 to 38, wherein the processor is configured to continuously monitor the autoregulation profile of the renal blood flow of the patient over time during the surgical procedure, medical treatment, or medical observation of the patient.

40. the processor executes the monitoring software code; assessing the correlation or non-correlation between the change in arterial pressure and the change in renal blood flow rate; and 40. The system of any one of claims 30 to 39, configured to determine the autoregulation profile of the patient's renal blood flow based on the correlation or non-correlation between the changes in the arterial pressure and the changes in the rate of renal blood flow.

41. the processor executes the monitoring software code; 41. The system of any one of claims 30 to 40, configured to assess the correlation or non-correlation between the changes in the arterial pressure and the changes in the renal blood flow rate in the time domain.

42. the processor executes the monitoring software code; 42. The system of claim 41, configured to assess the correlation or non-correlation between the change in arterial pressure and the change in renal blood flow rate using a Pearson correlation coefficient computed over a rolling window of time.

43. the processor executes the monitoring software code; 41. The system of any one of claims 30 to 40, configured to assess the correlation or the non-correlation between the changes in the arterial pressure and the changes in the rate of the renal blood flow in the frequency domain.

44. the processor executes the monitoring software code; 44. The system of claim 43, configured to assess the correlation or the non-correlation between the changes in the arterial pressure and the changes in the rate of the renal blood flow using a coherence function computed over a pre-specified frequency range.

45. the processor executes the monitoring software code; 44. The system of claim 43, configured to assess the correlation or non-correlation between the change in the arterial pressure and the change in the renal blood flow rate using a coherence function computed from parameters of a transfer function of the arterial pressure signal and a transfer function of the renal blood flow rate.

46. the processor executes the monitoring software code; estimating a real-time acute kidney injury risk score for the patient from the autoregulatory profile and a predetermined threshold for the patient; and outputting a representation of the real-time acute kidney injury risk score of the patient over time on the display in real time; 46. ​​The system of any one of claims 38 to 45, comprising:

47. the processor executes the monitoring software code; collecting a cumulative sum over time over which the change in the arterial pressure signal and the change in the rate of renal blood flow correlate; estimating a real-time acute kidney injury risk score for the patient from the cumulative sum of the time over which the change in the arterial pressure signal and the change in the rate of the renal blood flow correlate; and outputting a representation of the real-time acute kidney injury risk score of the patient over time on the display in real time; 46. ​​The system of any one of claims 38 to 45, comprising:

48. the processor executes the monitoring software code; setting a blood pressure alarm on the blood flow monitor based on the autoregulation profile of the patient; 48. The system of any one of claims 30 to 47, comprising:

49. the processor executes the monitoring software code; 49. The system of any one of claims 30 to 48, configured to set hypotension thresholds and / or definitions for the hypotension prediction algorithm of the blood flow monitor based on the autoregulation profile of the patient.

50. 1. A method for continuously monitoring a patient's kidneys during surgery, medical treatment, or medical observation, comprising: continuously measuring a Doppler flow signal of the patient's renal blood flow with an ultrasound transducer probe attached to the patient's abdomen in a stationary position and in communication with a blood flow monitor processor; monitoring, by the processor, changes in the renal blood flow over time; continuously measuring the patient's arterial pressure signal with a hemodynamic pressure sensor, the hemodynamic pressure sensor in communication with the blood flow monitor; monitoring, by the processor, changes in the arterial pressure signal over time; evaluating, by the processor, a mathematical relationship between the change in the arterial pressure signal and the change in the renal blood flow; determining, by the processor, an autoregulation profile of the patient's renal blood flow based on the mathematical relationship between the changes in the arterial pressure signal and the changes in the renal blood flow; A method comprising:

51. an ultrasound transducer probe including a two-dimensional array of transducer elements configured to continuously measure Doppler flow signals of a patient's renal blood flow during a surgical procedure, medical treatment, or medical observation; an adhesive patch connected to the ultrasound transducer probe and configured to attach the ultrasound transducer probe to the patient and maintain contact between the patient and the ultrasound transducer probe without an operator; a hemodynamic pressure sensor configured to continuously measure an arterial pressure signal of the patient during the surgical procedure, the medical procedure, or the medical observation; a blood flow monitor in communication with the ultrasound transducer probe and the hemodynamic pressure sensor; Including, The blood flow monitor a system memory for storing monitoring software code; a processor, the processor executing the monitoring software code; determining a change in the renal blood flow of the patient from the Doppler flow signal of the renal blood flow; monitoring the change in renal blood flow over time; monitoring changes in the arterial pressure signal over time; and evaluating a mathematical relationship between the change in the arterial pressure signal and the change in the renal blood flow; a processor, comprising: Including, the system.

52. the processor executes the monitoring software code; 52. The system of claim 51, configured to determine an autoregulation profile of the patient's renal blood flow based on the mathematical relationship between the changes in the arterial pressure signal and the changes in the renal blood flow.

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