System and method for monitoring self-adjustment

Abstract

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

Description

[0001] Cross-references to related applications

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 479,264, filed January 10, 2023, entitled “SYSTEM AND METHOD FOR MONITORING AUTOREGULATION,” the disclosure of which is incorporated herein by reference in its entirety. Background Technology

[0003] This disclosure relates to monitoring the perfusion and blood flow of a patient's organs, and more specifically, to medical devices and methods for measuring and / or monitoring autoregulation.

[0004] Autoregulation is an organ's ability to regulate local blood flow. Failure or loss of autoregulation is a risk factor for organ damage and is often a sign of the collapse of the body's compensatory circulatory processes. Different organs exhibit varying degrees of autoregulation. The kidneys and brain are two high-blood-flow organs and also two of the most strictly autoregulated organs in the body. The goals of adequate autoregulation differ significantly between the brain and kidneys. The goal of brain autoregulation is to maintain sufficient oxygenation in the brain. The goal of kidney autoregulation is to achieve adequate tubular and glomerular blood flow.

[0005] Myogenic responses and tubuloglomerular feedback (TGF) responses are the two dominant mechanisms of renal autoregulation. Myogenic responses occur in the afferent arterioles and are rapid and intense, mitigating systolic and similar blood pressure spikes. Triggered by circumferential stress in the afferent arterioles, myogenic responses are purely mechanical and protective. TGF responses are slow, closed-loop responses that regulate renal blood flow in response to salt concentration in the distal tubules. In animal studies, the lower limit of renal autoregulation is much higher than that of brain autoregulation: 70 mmHg for the former and 30 mmHg for the latter.

[0006] Multiple factors (e.g., age-related arteriosclerosis) alter the characteristics of vascular reactivity, which in turn alter a patient's relevant autoregulatory characteristics. Therefore, the range of blood flow autoregulation induced by blood pressure changes can vary between and within different patients and cannot be assumed to be a constant. Methods and devices for determining whether a particular patient's autoregulatory function is adequate and for managing the potential range of blood pressure fluctuations would be of great benefit to clinicians. There is a need for devices and methods for monitoring autoregulation that are improvements over existing technologies, including the ability to identify and interpret factors that may affect the determination or measurement of autoregulation. Summary of the Invention

[0007] A method for continuously monitoring a patient's kidneys during surgery, a medical procedure, or a medical observation includes continuously measuring a signal of a patient's renal blood flow using 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 evaluates a flow rate of the patient's renal blood flow from the signal of the renal blood flow. The processor also monitors changes in the flow rate of the renal blood flow over time. An arterial pressure signal of the patient is continuously measured by 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 changes in the arterial pressure signal and changes in the flow rate of the renal blood flow.

[0008] A system includes a first sensor configured to continuously measure a signal of a patient's renal blood flow during surgery, a medical procedure, or a medical observation. A second sensor configured to continuously measure an arterial pressure signal of the patient during surgery, a medical procedure, or a medical observation. A blood flow monitor is in communication with the first sensor and the second sensor. The blood flow monitor includes a processor and a system memory storing monitoring software code. The processor is configured to execute the monitoring software code to evaluate a flow rate of the patient's renal blood flow from the signal of the renal blood flow and to monitor changes in the flow rate of the 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 changes in the arterial pressure signal and changes in the flow rate of the renal blood flow.

[0009] A method for continuously monitoring a patient's kidneys during surgery, a medical procedure, or a medical observation includes continuously measuring a Doppler flow signal of a patient's renal blood flow using an ultrasound transducer probe. The ultrasound transducer probe is affixed to the patient's abdomen at a fixed location and is in communication with a processor of a blood flow monitor. The processor monitors changes in the renal blood flow over time. An arterial pressure signal of the patient is continuously measured by a hemodynamic pressure sensor. 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 changes in the arterial pressure signal and changes in the renal blood flow. The processor determines an autoregulation curve of the patient's renal blood flow based on the mathematical relationship between changes in the arterial pressure signal and changes in the renal blood flow.

[0010] A system includes an ultrasound transducer probe having a two-dimensional array of transducer elements configured to continuously measure a Doppler flow signal of renal blood flow of a patient during a surgery, medical procedure, or medical observation. An adhesive patch is connected to the ultrasound transducer probe and is configured to affix the ultrasound transducer probe to the patient and maintain contact between the patient and the ultrasound transducer probe without requiring an operator. The system also includes a hemodynamic pressure sensor configured to continuously measure an arterial pressure signal of the patient during the surgery, 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 processor and a system memory storing monitoring software code. The processor is configured to execute the monitoring software code to determine a change in the renal blood flow of the patient from the Doppler flow signal of the renal blood flow and monitor the change in the renal blood flow over time. The processor is also configured to execute the monitoring software code to monitor the change in the arterial pressure signal over time and assess a mathematical relationship between the change in the arterial pressure signal and the change in the renal blood flow. BRIEF DESCRIPTION OF DRAWINGS

[0011] Figure 1 is a schematic diagram illustrating an example monitoring system having a blood flow monitor, an ultrasound transducer probe affixed to a patient's abdomen by an adhesive patch, and a hemodynamic pressure sensor connected to the patient for sensing hemodynamic data indicative of arterial pressure of the patient.

[0012] Figure 2 is a schematic diagram illustrating Figure 1 is another schematic diagram of a blood flow monitor connected to an ultrasound transducer probe having a two-dimensional array of transducer elements.

[0013] Figure 3 is a schematic diagram of an ultrasound transducer probe affixed to a patient's abdomen by an adhesive patch for monitoring renal flow of a kidney of the patient.

[0014] Figure 4A is a schematic diagram of an ultrasound transducer probe affixed to a patient's abdomen by an adhesive patch for monitoring a kidney of the patient.

[0015] Figure 4B is another schematic diagram of an ultrasound transducer probe affixed to a patient's abdomen by an adhesive patch for monitoring a kidney of the patient.

[0016] Figure 5 is a perspective view of an example minimally invasive hemodynamic pressure sensor for sensing hemodynamic data indicative of arterial pressure of a patient.

[0017] Figure 6This is a perspective view of an exemplary non-invasive hemodynamic pressure sensor used to sense hemodynamic data representing a patient's arterial pressure.

[0018] Figure 7 This is a schematic representation of a time-domain method used to determine the mathematical relationship between a patient's mean arterial pressure (MAP) and renal blood flow in the patient's kidneys.

[0019] Figure 8 This is a schematic diagram of a frequency domain method used to determine the mathematical relationship between a patient's mean arterial pressure (MAP) and renal blood flow in the patient's kidneys.

[0020] Figure 9 It is a graph showing the correlation between patients and MAP.

[0021] Figure 10 This is a graph showing the correlation between changes in renal blood flow rate and mean arterial pressure (MAP) and the patient's mean arterial pressure (MAP).

[0022] Figure 11 This is a block diagram of a method for determining the autoregulation curve of renal blood flow in a patient.

[0023] Figure 12 It comes from a demo Figure 1 The diagrams of the experiment of the monitoring system.

[0024] Figure 13 It comes from Figure 12 The curves of renal blood flow versus MAP in the experimental test subjects.

[0025] Figure 14 It comes from Figure 12 The graph shows the MAP and correlation of the test subjects in the experiment.

[0026] Figure 15 This is a block diagram of a method for continuously monitoring the autoregulation curve of renal blood flow in patients during surgery, medical procedures, or medical observation using a blood flow monitor to assess the risk of acute kidney injury. Detailed Implementation

[0027] The present disclosure relates to a monitoring system and method for monitoring blood flow to abdominal organs, such as kidneys, of a patient in real-time during a surgery, medical procedure, 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 that can attach the ultrasound transducer probe to the patient and keep the ultrasound transducer probe attached to the patient during the surgery, medical procedure, or medical observation of the patient without assistance from an ultrasound operator. The blood flow monitor determines an auto-regulation index of renal blood flow to the kidneys of the patient 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 an auto-regulation curve of renal blood flow to the kidneys of the patient based on the auto-regulation index information received from the ultrasound transducer probe and the hemodynamic pressure sensor. The auto-regulation index and the auto-regulation curve of renal blood flow to the patient can be continuously updated and output to a display during the surgery, medical procedure, or medical observation so that medical personnel can have real-time knowledge of the auto-regulation curve of renal blood flow to the patient. Reference will be made to the following drawings Figures 1 to 15 The monitoring system is described in detail.

[0028] Figure 1 is a schematic view of a patient 10 and a monitoring system 11 that continuously monitors organ blood flow to the patient 10 during a surgery, medical procedure, or medical observation. As shown in the example of Figure 1 The monitoring system 11 can 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 artery catheter 18, a system processor 19, a system memory 20 with software code 22, a probe cable(s) 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 can include a transducer probe control module 30 and an auto-regulation (AR) monitoring module 32. The display 28 can include a user interface 29, a first graph 33, a second graph 34, a third graph 35, an auto-regulation index value 36, and an injury score indicator 37. The monitoring system 11 can also include input device(s) 38 and output device(s) 39. Figure 1 The abdomen 40 of the patient 10 is also shown along with the kidneys 42L and 42R, the liver 44, and the spleen 46. The radial artery 48 is shown in the wrist 50 of the patient 10. Figure 1In the example of FIG. 1, the monitoring system 11 is monitoring renal blood flow of the kidney 42L of the patient 10. In other examples, the monitoring system 11 can be used to monitor hepatic blood flow of the liver 44, celiac blood flow of the spleen 46, pancreas (not shown), and stomach (not shown) of the patient 10, and / or portal blood flow of the stomach of the patient 10. Thus, the blood flow monitor 12 can be adapted as an organ blood flow monitor 12 for any organ of the patient 10.

[0029] The blood flow monitor 12 can be, for example, an integrated hardware unit that includes the system processor 19, the system memory 20, the display 28, the UFE circuit 16, the first ADC 26, and the second ADC 27. In other examples, any one or more components of the organ blood flow monitor and / or the described functionality can be distributed among multiple hardware units. For example, in some examples, the display 28 can be a separate display device that is remote from the blood flow monitor 12 and is operably coupled with the blood flow monitor 12 as the output device 39. Generally, although the blood flow monitor 12 is shown in the example of FIG. 1 as an integrated hardware unit, it should be understood that the blood flow monitor 12 can include any combination of devices and components that are electrically, communicatively, or otherwise operably connected to perform the functions attributed herein to the blood flow monitor 12. Figure 1 The input device(s) 38 can be connected to the blood flow monitor 12 such that a user can input data and / or commands into the blood flow monitor 12. Non-limiting examples of the input device(s) 38 include a keyboard, a touchpad, and / or other devices through which a user can input data and / or commands into the blood flow monitor 12. The input device(s) 38 can also include a port configured to communicate with an external input device via a hardware or wireless connection.

[0030] The ultrasound transducer probe 14 is a first sensor of the monitoring system 11. The ultrasound transducer probe 14 can be adhered or affixed to the patient 10 by an adhesive patch 15. In the example of FIG. 1, the ultrasound transducer probe 14 is adhered to the patient 10 on the patient’s back. In other examples, the ultrasound transducer probe 14 can be adhered to the patient 10 on the patient’s abdomen, on the patient’s front, or in any other location on the patient 10. Figure 1In 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, such as fabric or flexible plastic, and a layer of adhesive deposited on a surface of the sheet of structural material. The adhesive patch 15 can be adhered or mechanically coupled to the ultrasound transducer probe 14, or to a frame (not shown) coupled to a base of the ultrasound transducer probe 14, and can extend outward from the ultrasound transducer probe 14 along a surface of the abdomen 40 of the patient 10. In other examples, the adhesive patch 15 can be placed on the ultrasound transducer probe 14 to affix the ultrasound transducer probe 14 to the abdomen 40 of the patient 10. The adhesive patch 15 holds the ultrasound transducer probe 14 affixed to the patient 10 and in place for the duration of the procedure, medical treatment, or medical observation of the patient 10. Because the adhesive patch 15 holds the ultrasound transducer probe 14 immobile and in contact with the patient 10, an ultrasound operator or technician is not required to hold the ultrasound transducer probe 14 in place during the procedure, medical treatment, or medical observation. A coupling layer (not shown) having a coupling agent material can be placed 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.

[0031] In Figure 1 In the example of FIG. 1, the ultrasound transducer probe 14 detects and continuously senses Doppler flow signals of the renal blood flow of the kidney 42L during the procedure, medical treatment, or medical observation of the patient 10. The term "continuously" as used herein means that the ultrasound transducer probe 14 senses Doppler flow signals of the renal blood flow of the kidney 42L and collects patient data on a periodic basis during a monitoring period, the periodic basis being frequent enough that the periodic basis can be considered clinically continuous. For example, the ultrasound transducer probe 14 can sample Doppler flow signals of the renal blood flow of the kidney 42L every ten seconds or less (<10 seconds), and can be configured to sample data more frequently, for example, every two seconds or less. The present disclosure is not limited to any particular device settings or sampling rates.

[0032] The ultrasound transducer probe 14 can be operatively connected to the blood flow monitor 12 by cable(s) 24. Through the cable(s) 24, the ultrasound transducer probe 14 can receive electrical signals from the UFE circuit 16 of the blood flow monitor 12 and forward received ultrasound signals from the patient 10 to the blood flow monitor 12 for extraction of the Doppler flow signal of the renal blood flow of the kidney 42L. In other examples, the UFE circuit 16 used in combination with the ultrasound transducer probe 14 can be powered by a battery and can contain a receiver for wirelessly receiving commands from the blood flow monitor 12. The combined UFE circuit 16 and ultrasound transducer probe 14 can also contain a transmitter for wirelessly communicating the Doppler flow signal of the renal blood flow of 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 to digital hemodynamic data representing the renal blood flow of 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 use the first ADC 26. In other examples, the ultrasound transducer probe 14 can provide the Doppler flow signal of the renal blood flow of 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.

[0033] The hemodynamic pressure sensor 17 is a second sensor of the monitoring system 11. In Figure 1In the example of FIG. 1, the hemodynamic pressure sensor 17 is a minimally invasive hemodynamic pressure sensor that is attached to the patient 10 via a radial artery catheter 18 inserted into the arm of the patient 10. In other examples, the hemodynamic pressure sensor 17 can be attached to the patient 10 via a femoral artery catheter inserted into the leg of the patient 10, or the hemodynamic pressure sensor 17 can be placed non-invasively on a limb of the patient 10, such as a wrist, arm, finger, ankle, toe, or other limb of the patient 10. The hemodynamic pressure sensor 17 continuously senses hemodynamic data representative of the arterial pressure of the patient 10 during a surgical, medical procedure, or medical observation of the patient 10. As used herein, the term “continuous” means that the hemodynamic pressure sensor 17 senses and collects patient data on a periodic basis that is sufficiently frequent so as to be considered clinically continuous. For example, the hemodynamic pressure sensor 17 can sample a waveform of hemodynamic data representative of the arterial pressure of the 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 other examples, the hemodynamic pressure sensor 17 can sample an average value of the hemodynamic data signal representative of the arterial pressure of the patient 10 over a time window, such as every ten seconds or less (<10 seconds). The hemodynamic pressure sensor 17 can sample an average value of the hemodynamic data signal representative of the arterial pressure of the patient 10 more frequently, such as every two seconds or less. In other examples, an average value of the hemodynamic data signal representative of the arterial pressure of the patient 10 can be sampled over a rolling time window. The present disclosure is not limited to any particular device settings or sampling rates.

[0034] The hemodynamic pressure sensor 17 is operably connected to the blood flow monitor 12 (e.g., electrically and / or communicatively connected by a wired or wireless connection) to provide sensed hemodynamic data to the blood flow monitor 12. In some examples, the hemodynamic pressure sensor 17 provides sensed hemodynamic data representative of the arterial pressure of the patient 10 to the blood flow monitor 12 as an analog signal that is converted to digital hemodynamic data representative of the arterial pressure of the patient 10 by the second ADC 27. In other examples, the hemodynamic pressure sensor 17 can provide 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 use the second ADC 27. In other examples, the hemodynamic pressure sensor 17 can provide hemodynamic data representative of the arterial pressure of the patient 10 to the blood flow monitor 12 as an analog signal that is analyzed by the blood flow monitor 12 in its analog form.

[0035] System memory 20 can be configured to store information within blood flow monitor 12 during operation. In some examples, system memory 20 is described as a computer-readable storage medium. In some examples, the computer-readable storage medium may include a non-transitory medium. The term "non-transitory" may mean that the storage medium is not embodied in a carrier wave or propagating signal. In some examples, a non-transitory storage medium may store data that can change over time (e.g., in RAM or a cache). System memory 20 may include volatile and non-volatile computer-readable memory. Examples of volatile memory may 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 may include, for example, magnetic hard disks, optical disks, flash memory, or electrically programmable memory (EPROM) or electrically erasable programmable memory (EEPROM).

[0036] like Figure 1 As shown, the system memory 20 of the blood flow monitor 12 can store software code 22, which forms the 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 (see below for reference) Figure 2 (Discussed in more detail) This includes a beamformer that keeps the ultrasound transducer probe 14 aimed at the renal blood flow of the kidney 42L, allowing the ultrasound transducer probe 14 to continuously sense the Doppler flow signal of the renal blood flow throughout the patient 10's surgery, medical procedure, or medical observation and transmit it to the blood flow monitor 12. The software code 22 may also include an AR monitoring module 32 containing monitoring software code for continuously monitoring the Doppler flow signal DF of the renal blood flow during the patient 10's surgery, medical procedure, or medical observation, and for continuously monitoring the patient 10's arterial pressure to determine an autoregulation curve of the renal blood flow of the kidney 42L. The autoregulation curve of the renal blood flow of the kidney 42L (discussed in more detail below) is based on a calculated mathematical relationship between the renal blood flow of the kidney 42L and the patient 10's arterial pressure. The AR monitoring module 32 may also include code for determining an acute kidney injury (AKI) risk score for the patient 10 based on the autoregulation curve of the renal blood flow of the kidney 42L. The AKI risk score indicates the probability that kidney 42L is experiencing or is about to experience acute kidney injury. When the monitoring system 11 is used to monitor organs other than kidneys 42L and 42R of patient 10, the AR monitoring module 32 can be adapted to determine an autoregulation curve and a real-time organ injury risk score based on the patient 10's arterial pressure and the Doppler flow signal of the organ being monitored (such as liver 44).

[0037] 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 can 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

[0038] The display 28 provides a user interface 29 that includes control elements that enable a user to interact 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 graph 33, a second graph 34, and a third graph 35. The first graph 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 flow rate of the renal blood flow determined from the Doppler flow signal of the renal blood flow over time, or a plot of the change in the flow rate of the renal blood flow of the kidney 42L over time. The second graph 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 graph 35 can be a plot of the calculated mathematical relationship between the renal blood flow of the kidney 42L and the arterial pressure of the patient 10 over time that forms the autoregulation curve of the renal blood flow of the kidney 42L, as shown in the graph 114 in Figure 12 In other examples, the third graph 35 can also include a corresponding plot of the calculated mathematical relationship and the arterial pressure of the patient 10, with each data point color-coded to represent time. In addition to displaying the graphs 33, 34, and 35, the display 28 can also provide an audible representation of any of the graphs 33, 34, and 35 via a speaker, or simply display the numerical values of the graphs 33, 34, and 35 (such as through a table).

[0039] As shown in Figure 1 The display 28 can also display an autoregulation index value 36 and an injury score indicator 37, as shown in Figures 7-10As discussed in greater detail, 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 flow rate of the renal blood flow of the kidney 42L and the arterial pressure of the patient 10. The auto-regulation index value 36 is the inverse of the correlation or coherence between the flow rate of the renal blood flow of the kidney 42L and the arterial pressure of the patient 10. The injury score indicator 37 represents the real-time AKI risk score of the patient 10 determined by the system processor 19 and the AR monitoring module 32 from the auto-regulation index value. The display 28 can also include a sensory alert for alerting medical personnel when the auto-regulation index value 36 of the renal blood flow of the kidney 42L approaches a lower limit of auto-regulation or an upper limit of auto-regulation. As discussed in greater detail below, the lower limit of auto-regulation is a mean arterial pressure (MAP) value below which auto-regulation of the renal blood flow of the kidney 42L becomes impaired. The upper limit of auto-regulation is a MAP value above which auto-regulation of the renal blood flow of the kidney 42L becomes impaired. The sensory alert can also alert medical personnel when the real-time AKI risk score of the patient 10 approaches or exceeds a predetermined threshold. The sensory alert can be implemented as one or more of a visual alert, an audible alert, a tactile alert, or other types of sensory alerts. For example, the sensory alert can be invoked as any combination of a flashing and / or colored graphic displayed on the display 28 through the user interface 29, a warning sound such as a siren or a repeating tone, and a tactile alert configured to cause the blood flow monitor 12 to vibrate or otherwise deliver a physical impulse perceptible to medical personnel.

[0040] The 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. The user interface 29 can include graphical and / or physical control elements that enable user input to interact with the blood flow monitor 12 and / or other components of the monitoring system 11. In some examples, the user interface 29 can take the form of a graphical user interface (GUI) that presents graphical control elements, e.g., that are presented on a touch-sensitive and / or pressure-sensitive display screen of the display 28. In these examples, user input can be received in the form of gesture inputs, such as touch gestures, scroll gestures, zoom gestures, or other gesture inputs. In certain examples, the user interface 29 can take the form of and / or include physical control elements, such as physical buttons, keys, knobs, or other physical control elements configured to receive user input to interact with components of the monitoring system 11. The user interface 29 can include a speaker that enables the blood flow monitor 12 to produce audible alerts.

[0041] In operation of the monitoring system 11, prior to the start of a surgery, medical procedure, or medical observation, a medical professional connects the hemodynamic pressure sensor 17 to the patient 10. In Figure 1 In an example, the medical professional first inserts the radial artery catheter 18 into the arm of the patient 10, and then connects the hemodynamic pressure sensor 17 to the radial artery catheter 18, thereby connecting the hemodynamic pressure sensor 17 to the patient 10. In other examples, the medical professional can first insert a femoral artery catheter into the leg of the patient 10, and then connect the hemodynamic pressure sensor 17 to the femoral artery catheter, thereby connecting the hemodynamic pressure sensor 17 to the patient 10. In other examples, the medical professional can non-invasively connect the hemodynamic pressure sensor 17 to a limb of the patient 10, such as a wrist, arm, finger, ankle, toe, or other limb of the patient 10. Once 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 to 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.

[0042] Prior to the start of the surgery, medical procedure, or medical observation, the medical professional also places the ultrasound transducer probe 14 on the abdomen 40 of the patient 10. The medical professional uses the ultrasound transducer probe 14 to locate the Doppler flow signal of the renal blood flow of the kidney 42L. The ultrasound transducer probe 14 can generate an audible representation of the Doppler flow signal to assist the medical professional in locating the Doppler flow signal of the renal blood flow of the kidney 42L. Once the medical professional locates the Doppler flow signal of the renal blood flow of the kidney 42L, the medical professional adheres and secures the ultrasound transducer probe 14 to the patient 10 using the adhesive patch 15. The adhesive patch 15 maintains the ultrasound transducer probe 14 in continuous contact with the patient 10, such that the ultrasound transducer probe 14 does not shift on the patient 10 during the surgery, medical procedure, or medical observation, and the Doppler flow signal of the renal blood flow of the kidney 42L is not lost. The ultrasound transducer probe 14 transmits the received ultrasound signals to the blood flow monitor 12 through the cable(s) 24 or wirelessly. In the case of wireless transmission, the ultrasound transducer probe 14 includes the UFE circuit 16. The system processor 19 of the blood flow monitor 12 receives the Doppler flow signal, and processes the Doppler flow signal sequentially or simultaneously through the transducer probe control module 30 and the AR monitoring module 32.

[0043] The system processor 19 can execute the monitoring software code of the AR monitoring module 32 to continuously monitor the Doppler flow signal of the renal blood flow sensed by the ultrasound transducer probe 14 and continuously monitor the arterial pressure of the patient 10 sensed by the hemodynamic pressure sensor 17 throughout the entire surgery, 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 the mathematical relationship between the renal blood flow of the kidney 42L and the arterial pressure of the patient 10 and generate the auto-regulation curve of the renal blood flow of the kidney 42 using the mathematical relationship. The system processor 19 also executes the AR monitoring module 32 to continuously monitor the auto-regulation curve of the renal blood flow of the kidney 42L and assess the AKI risk score of the kidney 42L of the patient 10 according to the auto-regulation value.

[0044] The system processor 19 outputs information to the display 28 to generate the first graph 33, the second graph 34, and the third graph 35. The first graph 33 can be a graph of the Doppler flow signal of the renal blood flow of the kidney 42L over time, a graph of the flow rate of the renal blood flow determined by the system processor 19 from the Doppler flow signal of the renal blood flow, or a graph of the change in the flow rate of the renal blood flow of the kidney 42L over time. The second graph 34 can be a graph of the arterial pressure of the patient 10 over time or a graph of the change in the arterial pressure of the patient 10 over time. The third graph 35 can be a graph of the calculated mathematical relationship between the renal blood flow of the kidney 42L and the arterial pressure of the patient 10 over time or a graph of the auto-regulation curve of the renal blood flow of the kidney 42L determined by the system processor 19 from the renal blood flow of the kidney 42L and the arterial pressure of the patient 10. The system processor 19 also outputs the auto-regulation index value 36 and the injury score indicator 37. As previously described, the auto-regulation index value 36 represents the real-time value or status of the auto-regulation curve of the renal blood flow of the kidney 42L and the injury score indicator 37 represents the real-time AKI risk score of the patient 10.

[0045] As the patient 10's surgery, medical procedures, or medical observation progresses, the system processor 19 continues to receive Doppler flow signals from the ultrasound transducer probe 14, continues to receive hemodynamic data representing the patient 10's arterial pressure, continues to calculate the mathematical relationship between renal blood flow in the kidney 42L and the patient 10's arterial pressure, continues to output graphs 33, 34, and 35 to the display 28, continues to output the autoregulation index value 36 to the display 28, and continues to output the injury score index 37 to the display 28. If the autoregulation index value 36 changes toward an undesirable threshold, such as tending toward the lower or upper limit of autoregulation, the system processor 19 and the display 28 can alert medical personnel so that they can take action to restore normal autoregulation of renal blood flow in the kidney 42L. For example, medical personnel can administer medications or fluids that increase the patient 10's arterial pressure to raise and / or maintain the autoregulation index value 36 above the lower limit of autoregulation. In another example, medical personnel may administer medication or take measures to lower the patient’s arterial pressure 10, thereby reducing and / or maintaining the autoregulation index value 36 below the upper limit of autoregulation.

[0046] Similarly, if the real-time AKI risk score of kidney 42L changes toward an unexpected threshold or at an unexpected rate, the system processor 19 and display 28 can alert medical personnel so that they can take action to increase renal perfusion and prevent or minimize AKI in kidney 42L. For example, medical personnel can administer medications or fluids that increase renal blood flow and perfusion in kidney 42L or improve the autoregulation of renal blood flow in kidney 42L. At the end of surgery, medical procedure, or medical observation, the system processor 19 and AR monitoring module 32 can assess the final AKI risk score of kidney 42L and output the final AKI risk score to display 28. If the final AKI risk score of kidney 42L indicates a high risk of AKI, medical personnel can immediately take measures to treat kidney 42L without waiting for biomarkers to appear in the blood and urine samples of patient 10. Biomarkers indicating AKI may take hours or days to appear in the blood and urine samples of patient 10. With the help of monitoring system 11, medical staff can quickly determine whether patient 10 needs treatment for AKI in kidney 42L.

[0047] If the kidney 42L of patient 10 moves within the abdomen 40 of patient 10 during surgery, medical procedures, or medical observation, the transducer probe control module 30 will detect changes in the Doppler flow signal and respond by adjusting the focusing position of the beam group scanning the abdomen 40 of patient 10 to reposition the Doppler flow signal of the renal blood flow in the kidney 42L. (See below for reference.) Figures 2 to 5The blood flow monitor 12 may include a beamformer that can manipulate beam signals generated by the transducer element array of the ultrasonic transducer probe 14.

[0048] Figure 2 This is another schematic diagram of the blood flow monitor 12. (See diagram below.) Figure 2 As shown, the blood flow monitor 12 may include a beamformer 48, and the ultrasonic transducer probe 14 may include an array 50 of transducer elements 52. Each transducer element 52 in the array 50 may include a piezoelectric material, such as lead zirconate titanate, capable of emitting and detecting ultrasonic pulses. The array 50 of transducer elements 52 of the ultrasonic transducer probe 14 may form a two-dimensional phased array with a probe length of PL and a probe width of PW. As a phased array, each transducer element 52 in the array 50 may emit pulses individually relative to the other transducer elements 52 in the array 50.

[0049] exist Figure 2 In one example, beamformer 48 drives an array 50 of transducer elements 52 via system processor 19 and UFE circuitry 16. Beamformer 48 acts as a transducer probe controller, having flow signal tracking software code for controlling the timing of ultrasound pulses emitted by each transducer element 52 in array 50. Beamformer 48 can be set to the timing and pattern of pulses emitted by each transducer element 52, allowing array 50 to form one or more ultrasound beams and to sweep or manipulate one or more ultrasound beams without physically moving the ultrasound transducer probe 14 on the patient 10. Beamformer 48 can be a software submodule of transducer probe control module 30, which can be executed by system processor 19 to control the activation of transducer elements 52 in array 50. In other examples, beamformer 48 can be a hardware component separate from system processor 19 and system memory 20, having memory and software separate from software code 22, which coordinates with system processor 19 to control the activation of transducer elements 52 in array 50. Figure 2In the example of FIG. 1, 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 can be housed entirely or partially within the housing of the ultrasound transducer probe 14 as a separate hardware and software unit that coordinates 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 additional hardware component, can reduce the overall size and thickness of the ultrasound transducer probe 14. The profile of the ultrasound transducer probe 14 can be relatively thin and flat, with a thickness that is less than the width or diameter of the ultrasound transducer probe 14. When the ultrasound transducer probe 14 has a thin and flat profile, it is easier and more secure to attach the ultrasound transducer probe 14 to the patient 10 with the adhesive patch 30.

[0050] Figure 3 is another illustration of the ultrasound transducer probe 14 attached to the abdomen 40 of the patient 10 over the kidneys 42L with the adhesive patch 15.

[0051] Figure 4A and Figure 4B will be discussed concurrently. Figure 4A is another illustration of the ultrasound transducer probe 14 attached to the abdomen 40 of the patient 10 over the kidneys 42L with the adhesive patch 15. Figure 4BAlso shown is another view of the ultrasound transducer probe 14, which is attached to the abdomen 40 of the patient 10 by the adhesive patch 15, over the kidney 42L. In this example, the ultrasound transducer probe 14 is positioned over the first acoustic window Wl (formed by the intercostal space between the rib 54a and the rib 54b) and the second acoustic window W2 (formed by the intercostal space between the rib 54b and the rib 54c). Figure 4A In this example, the ultrasound transducer probe 14 is attached to the surface of the abdomen 40 by the adhesive patch 15, over the kidney 42L and over at least some of the ribs 54a, 54b, and 54c of the patient 10. Figure 4B In this example, the ultrasound transducer probe 14 is attached to the surface of the abdomen 40 by the adhesive patch 15, over the kidney 42L and over at least some of the ribs 54a, 54b, and 54c of the patient 10.

[0052] The ultrasound transducer probe 14 can have a probe length PL, a probe width PW (as shown in Figure 2 ), or a diameter that is large enough so that the array 50 of transducer elements 52 of the ultrasound transducer probe 14 can cover one or more acoustic windows of the patient 10. An acoustic window of the patient 10 is defined as an area of the patient 10 where ultrasound wave transmission is not significantly attenuated compared to the surrounding environment. For example, the length or width of the array 50 of transducer elements 52 of the ultrasound transducer probe 14 can be designed to extend over at least two intercostal spaces of the patient 10. For example, in Figure 4A , the array 50 of transducer elements 52 of the ultrasound transducer probe 14 is positioned over a first acoustic window Wl (formed by the intercostal space between the rib 54a and the rib 54b) and a second acoustic window W2 (formed by the intercostal space between the rib 54b and the rib 54c). In Figure 4A , the beamformer 48 (as shown in Figure 2 ) can selectively activate the transducer elements 52 in the array 50 to steer the signal beams 56a and 56b through the first acoustic window Wl and / or the second acoustic window W2 into the abdomen 40, thereby avoiding the ribs 54a, 54b, and 54c. In Figure 4B , the ultrasound transducer probe 14 is positioned slightly higher on the abdomen 40 of the patient 10 compared to the example of Figure 4A . However, the probe length PL or the probe width PW of the ultrasound transducer probe 14 is long enough so that the ultrasound transducer probe 14 can still access the first acoustic window Wl and can still scan and steer the signal beams 56a and 56b through the first acoustic window Wl into the abdomen 40. Regardless of where the ultrasound transducer probe 14 is placed over the ribs 54a, 54b, and 54c, the ribs 54a, 54b, and 54c do not block the direct view of the kidney 42L by the array 50 of the ultrasound transducer probe 14.

[0053] When the ultrasound transducer probe 14 is first 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 renal blood flow over time. The beamformer 48 beam scans and / or track scans the Doppler flow signals of renal blood flow of the kidneys 42L of the patient 10 by sequentially transmitting signal beams 56a and 56b from the array 50 of transducer elements 52 and focusing each beam 56a and 56b at a different location. The signal beams 56a and 56b track the Doppler flow signals relative to the array 50 of transducer elements 52. If the kidneys 42L, the renal arteries RA, and / or the renal veins RV shift within the abdomen 40, the Doppler flow signals of renal blood flow can be altered and the signal strength diminished. If this occurs, the beamformer 48 can transmit the 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 the signal beams 56a and 56b to track the center of the strongest Doppler flow signals of renal blood flow and adjusts the signal beams 56a and 56b to follow the center of the renal blood flow as the center moves and changes location. While the beamformer 48 track scans the Doppler flow signals to increase the signal strength, the system processor 19 can stop calculating the mathematical relationship between the renal blood flow of the kidneys 42L and the arterial pressure of the patient 10 until the signal strength of the Doppler flow signals is increased.

[0054] To enable the ultrasound transducer probe 14 to measure the Doppler flow signals of renal blood flow of the kidneys 42L, the ultrasound transducer probe 14 can have a low center frequency between 0.5 MHz and 4.0 MHz. With the center frequency between 0.5 MHz and 4.0 MHz, the ultrasound transducer probe 14 can penetrate more than 15 centimeters into the patient 10, which is a sufficient depth to measure renal blood flow. This depth also enables the ultrasound transducer probe 14 to measure hepatic blood flow, abdominal cavity blood flow, portal vein blood flow, and mesenteric blood flow. The monitoring system 11 does not use the ultrasound transducer probe 14 to high resolution image the kidneys 42L. Therefore, the ultrasound transducer probe 14 can have a lower number of transducer elements than an ultrasound transducer probe used for ultrasound imaging. Reducing the number of transducer elements of the array 50 of transducer elements 52 increases the signal-to-noise ratio (SNR) of the Doppler flow signals of renal blood flow sensed by the ultrasound transducer probe 14. Referring to Figure 5 and Figure 6 Various embodiments of the hemodynamic pressure sensor 17 will be discussed in more detail.

[0055] Figure 5is a perspective view of a hemodynamic pressure sensor 17 that can be attached to a patient for sensing hemodynamic data representative of arterial pressure of the patient 10. Figure 5 The illustrated hemodynamic pressure sensor 17 is one example of a minimally-invasive hemodynamic pressure sensor that can be attached to the patient 10 by way of a radial artery catheter 18 inserted into an arm of the patient 10, as Figure 1 illustrated. In other examples, the hemodynamic pressure sensor 17 can be attached to the patient 10 by way of a femoral artery catheter inserted into a leg of the patient 10.

[0056] As Figure 5 illustrated, the 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. The fluid input port 60 is configured to be connected to a fluid source, such as a saline bag or other fluid input source, by way of tubing or other hydraulic connection. The catheter-side fluid port 62 is configured to be connected to a catheter (e.g., the radial artery catheter 18 or the femoral artery catheter) inserted into an arm (i.e., the radial artery catheter 18) or a leg (i.e., the femoral artery catheter) of the patient 10 via tubing or other hydraulic connection. The I / O cable 64 connects the hemodynamic pressure sensor 17 to the blood flow monitor 12 by way of, for example, one or more I / O connectors. The housing 58 of the hemodynamic pressure sensor 17 encloses one or more pressure sensors, communication circuitry, processing circuitry, and corresponding electronic components for sensing fluid pressure corresponding to arterial pressure of the patient 10, which is transmitted to the blood flow monitor 12 by way of the I / O cable 64.

[0057] In operation, a fluid column (e.g., physiological saline) is introduced from a fluid source (e.g., a saline bag) through the hemodynamic pressure sensor 17 via the fluid input port 60 to the catheter-side fluid port 62, which flows to a catheter inserted into the patient 10. Arterial pressure is transmitted through the fluid column to pressure sensors located within the housing 58, which are used to sense pressure of the fluid column. The hemodynamic pressure sensor 17 converts the sensed fluid column pressure to electrical signals via pressure transducers and outputs corresponding electrical signals to the blood flow monitor 12 via the I / O cable 64. Thus, the hemodynamic pressure sensor 17 transmits analog sensor data (or a digital representation of analog sensor data) representative of continuous beat-by-beat monitoring of arterial pressure of the patient 10 to the blood flow monitor 12.

[0058] Figure 6 is a perspective view of an alternative example of a hemodynamic pressure sensor 17 for sensing hemodynamic data representative of arterial pressure of the patient 10. Figure 6The illustrated hemodynamic pressure sensor 17 is an example of a non-invasive hemodynamic pressure sensor that can be attached to the patient 10 via one or more finger cuffs to sense data indicative of arterial pressure of the patient 10. As Figure 6 As illustrated, the hemodynamic pressure sensor 17 includes an inflatable finger cuff 66 and a cardiac reference sensor 68. The inflatable finger cuff 66 includes an inflatable blood pressure bladder that is configured to be inflated and deflated by a pressure controller (not shown) that is pneumatically connected to the inflatable finger cuff 66. The inflatable finger cuff 66 also includes optical (e.g., infrared) emitters and optical receivers that are electrically connected to the pressure controller (not shown) for measuring volume changes in the finger artery under the finger cuff.

[0059] In operation, the pressure controller continuously adjusts the pressure within the finger cuff to maintain a constant volume of the finger artery (i.e., the unloaded volume of the artery) that is measured via the optical emitters and optical receivers of the inflatable finger cuff 66. The pressure applied by the pressure controller to continuously maintain the unloaded volume is indicative of the blood pressure within the finger and is transmitted by the pressure controller to the Figure 1 illustrated blood flow monitor 12. The cardiac reference sensor 68 measures the hydrostatic pressure height difference between the finger placement level and the pressure measurement reference level (typically the heart level). Thus, the hemodynamic pressure sensor 17 transmits hemodynamic data that is indicative of substantially continuous beat-to-beat monitoring of the arterial pressure of the patient 10. As described below with reference to Figures 7 to 10 The hemodynamic pressure sensor 17 transmits the hemodynamic data to the system processor 19, which calculates a mathematical relationship between the arterial pressure of the patient 10 and the renal blood flow of the kidney 42L of the patient 10.

[0060] Figure 7 is a schematic illustration of a method 70 for determining a mathematical relationship between the arterial pressure of the patient 10 and the renal blood flow of the kidney 42L of the patient 10 in the time domain. Figure 7 The method 70 in is described by a first data plot 72, a second data plot 74, and a correlation plot 76. The first data plot 72 is indicative of the mean arterial pressure (MAP) of the patient 10 as a function of time, which is determined by the system processor 19 from the hemodynamic data sensed in real-time by the hemodynamic pressure sensor 17. The system processor 19 can output the first data plot 72 as the second data plot 34 to the display 28 (as described above with reference to Figure 1 and Figure 2(As shown). The second data curve 74 represents the change in renal blood flow rate over time in the kidney 42L, which is assessed by the system processor 19 based on the Doppler flow signal of renal blood flow sensed in real time by the ultrasound transducer probe 14. The system processor 19 can assess the renal blood flow rate based on the Doppler flow signal velocity and the average 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 curve 74 to the display 28 as the first curve 33 (as shown). Figure 1 and Figure 2 (As shown). In other examples, the renal blood flow velocity can be determined from the Doppler flow signal using the flow velocity signal, the peak flow velocity signal, and / or the relative change signal of renal blood flow.

[0061] Correlation curve 76 shows the calculated mathematical relationship over time between the MAP change determined and evaluated by system processor 19 and the rate change of renal blood flow in kidney 42L. For example... Figure 7 The mathematical relationship calculated shows the correlation or lack thereof between changes in arterial pressure (MAP) and changes in renal blood flow rate in the 42L renal tract. System processor 19 can use the Pearson correlation coefficient calculated within a rolling time window to determine the correlation or lack thereof between changes in arterial pressure and changes in renal blood flow rate in the 42L renal tract. System processor 19 and AR monitoring module 32 (e.g.) Figure 1 and Figure 2 (As shown) The Pearson correlation coefficient between changes in MAP and changes in renal blood flow rate in the kidney (42L) can be determined using the following formula 1:

[0062] Formula 1:

[0063] Where r is the correlation coefficient between changes in MAP and changes in renal blood flow rate in the 42L renal tract, x i It is the real-time value of MAP, and It is a moving average of the MAP values ​​over a rolling time window. The variable y i It is a real-time value of the renal blood flow rate of the kidney 42L assessed by the system processor 19, and It is the moving average of the renal blood flow rate of 42L in the kidney over a rolling time window.

[0064] When the system processor 19 and AR monitoring module 32 determine the correlation coefficient between MAP changes and renal blood flow rate changes in the kidney 42L, the system processor 19 can generate a correlation curve 76, and the correlation curve 76 can be used as a third curve 35 (e.g., Figure 1 and Figure 2The system processor 19 and AR monitoring module 32 use the correlation coefficient between the changes in MAP and the changes in flow rate of renal blood flow of the kidney 42L to generate a renal autoregulation value 78. The renal autoregulation value 78 is a real-time value or status of autoregulation of the patient 10. When the correlation coefficient between the changes in MAP and the changes in flow rate of renal blood flow of the kidney 42L is high (e.g., close to a value of 1), then the renal autoregulation value 78 is low or indicates that autoregulation of the renal blood flow of the kidney 42L is impaired. When the correlation coefficient between the changes in MAP and the changes in flow rate of renal blood flow of the kidney 42L is low (e.g., below 0.5), then the renal autoregulation value 78 is high or indicates that autoregulation of the renal blood flow of the kidney 42L is normal. The system processor 19 can output the renal autoregulation value 78 to the display 28 as the autoregulation index value 36 as shown in FIGS. 1-3. Figure 1 and Figure 2 The system processor 19 and AR monitoring module 32 use the correlation coefficient between the changes in MAP and the changes in flow rate of renal blood flow of the kidney 42L to generate a renal autoregulation value 78. The renal autoregulation value 78 is a real-time value or status of autoregulation of the patient 10. When the correlation coefficient between the changes in MAP and the changes in flow rate of renal blood flow of the kidney 42L is high (e.g., close to a value of 1), then the renal autoregulation value 78 is low or indicates that autoregulation of the renal blood flow of the kidney 42L is impaired. When the correlation coefficient between the changes in MAP and the changes in flow rate of renal blood flow of the kidney 42L is low (e.g., below 0.5), then the renal autoregulation value 78 is high or indicates that autoregulation of the renal blood flow of the kidney 42L is normal. The system processor 19 can output the renal autoregulation value 78 to the display 28 as the autoregulation index value 36 as shown in FIGS. 1-3.

[0065] In other examples, the system processor 19 and AR monitoring module 32 can use mathematical correlations or tools other than the Pearson correlation coefficient to determine the calculated mathematical relationship between the changes in arterial pressure and the changes in flow rate of renal blood flow of the kidney 42L during a surgical, medical procedure, or patient medical observation. For example, as shown in FIGS. 1-3, the system processor 19 and AR monitoring module 32 can use a coherence function that is calculated over a pre-specified frequency range and derived from the parameters of the transfer function of the MAP signal and the transfer function of the flow rate of renal blood flow. The Fourier transform can be used as the transfer function to convert the MAP signal of the patient 10 (as shown in the first data plot 72 in FIG. 1) from the time domain to the frequency domain, as represented by the first data plot 172 in FIG. 2. The system processor 19 can output the first data plot 172 to the display 28 as the second data plot 34 (as shown in FIGS. 3 and 4). Figure 8 Figure 7 Figure 8 Figure 1 and Figure 2 Figure 7 Figure 8 Figure 1 and Figure 2

[0066] ​​​​​​​The system processor 19 and the AR monitoring module 32 input the converted flow rate of the renal blood flow of the kidney 42L and the converted MAP of the patient 10 into a coherence function to generate a coherence coefficient between the converted flow rate of the renal blood flow of the kidney 42L and the converted MAP of the patient 10, as represented by the coherence plot 176 in Figure 8 The system processor 19 can output the coherence plot 176 as the third plot 35 (as shown in Figure 1 and Figure 2 to the display 28. Similar to the correlation coefficient described with reference to Figure 7 The system processor 19 and the AR monitoring module 32 can utilize the coherence coefficient to generate a renal autoregulation value 78. The renal autoregulation value 78 is a real-time value or status of the autoregulation of the patient 10. When the coherence coefficient between the MAP changes and the flow rate changes of the renal blood flow of the kidney 42L is high (e.g., close to a value of 1), the renal autoregulation value 78 is low or indicates that the autoregulation of the renal blood flow of the kidney 42L is impaired. When the correlation coefficient between the MAP changes and the flow rate changes of the renal blood flow of the kidney 42L is low (e.g., below a predetermined coherence threshold), the renal autoregulation value 78 is high or indicates that the autoregulation function of the renal blood flow of the kidney 42L is normal. The system processor 19 can output the renal autoregulation value 78 as an autoregulation index value 36 (as shown in Figure 1 and Figure 2 to the display 28. As described below with reference to Figure 9 and Figure 10 The system processor 19 and the AR monitoring module 32 use the correlation coefficient and / or the coherence coefficient between the MAP changes and the flow rate changes of the renal blood flow of the kidney 42L to determine and monitor the autoregulation curve of the patient 10.

[0067] Figure 9 is a plot where the X-axis is divided into MAP bins of 5 mmHg increments and Figure 7 the correlation coefficient in Figure 9 is set as the Y-axis. When the system processor 19 determines and monitors the correlation coefficient between the flow rate of the renal blood flow of the kidney 42L and the MAP of the patient 10 over time, the system processor 19 can generate the autoregulation curve or index of the patient 10 by classifying the values of the correlation coefficient into the MAP bins of the plot of Figure 9 to generate the plot of Figure 9The MAP interval of the graph. 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 interfering with the Doppler flow signal, the system processor 19 will not add the value of the correlation coefficient. Figure 9 The MAP interval of the graph. Similarly, if the arterial pressure reading of patient 10 generated by hemodynamic pressure sensor 17 is unreliable, system processor 19 will not add the value of the correlation coefficient to the graph. Figure 9 Within the MAP range of the graph, until the hemodynamic pressure sensor 17 is able to reliably obtain a reading of the patient 10's arterial pressure.

[0068] Figure 9 The chart includes a correlation threshold line 80 where the correlation coefficient value is 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 in the 42L kidney can be described as passive, and the passivity of the autoregulation of renal blood flow in the 42L kidney increases as the correlation coefficient approaches 1. When the autoregulation of renal blood flow in the 42L kidney is passive, the autoregulation of renal blood flow in the 42L kidney is impaired, and the flow rate of renal blood flow in the 42L kidney fluctuates with the MAP of patient 10. Therefore, when the correlation coefficient indicates that the flow rate change of renal blood flow in the 42L kidney is correlated with the MAP of patient 10, the autoregulation of renal blood flow in the 42L kidney is impaired.

[0069] When the correlation coefficient for any given MAP value is below the correlation threshold 80, the autoregulation of renal blood flow in the 42L renal tract is essentially normal. When the autoregulation of renal blood flow in the 42L renal tract is normal, the flow rate of renal blood flow in the 42L renal tract is independent of the MAP of patient 10. Therefore, when the autoregulation of renal blood flow in the 42L renal tract is normal, changes in the flow rate of renal blood flow in the 42L renal tract are not correlated with changes in the MAP of patient 10. As the correlation coefficient approaches zero, the degree of normality of the autoregulation of renal blood flow in the 42L renal tract increases. The correlation threshold 80 in this disclosure is not limited to a value of 0.5 or any specific value. The value of the correlation threshold 80 may be based on empirical data and may vary depending on factors such as characteristics of patient 10 (e.g., age, health status, smoking habits, etc.).

[0070] The lower limit of autoregulation (LLA) line marks the lower threshold of renal arterial pressure (MAP) in patient 10, at which normal autoregulation of renal blood flow in the 42L renal tract occurs. Typically, when patient 10's MAP value is above the LLA line, autoregulation of renal blood flow in the 42L renal tract is normal. When patient 10's MAP value is below the LLA line, autoregulation of renal blood flow in the 42L renal tract is impaired. Figure 9In the example of FIG. 6, the graph shows that the correlation coefficient between the change in renal blood flow rate of the kidney 42L and the change in MAP of the patient 10 is above the correlation threshold line 80 when the MAP of the patient 10 is below 50 mmHg. Thus, Figure 9 In the example, the LLA line for the renal blood flow of the kidney 42L is 50 mmHg. The system processor 19 can set an alarm in the blood flow monitor 12 such that when the MAP of the patient 10 approaches or falls below the LLA line, the blood flow monitor alerts medical personnel. If the alarm is activated, the medical personnel can be alerted that the MAP of the patient 10 is below the LLA line and that the autoregulation of the renal blood flow of the kidney 42L can be impaired. The medical personnel can take measures (such as administering fluids or medication to the patient 10) to address the alarm to raise the MAP of the patient 10 above the LLA line. Over time, as the system processor 19 continues to monitor the MAP of the patient 10, continues to monitor the change in the renal blood flow rate, and continues to determine the real-time value of the correlation coefficient and add the value to the graph of FIG. 6, the LLA line can shift relative to the X-axis. Figure 9 In the graph of FIG. 6, the position of the LLA line can shift relative to the X-axis. The system processor 19 can update the alarm of the blood flow monitor 12 to track any changes in the LLA line. The blood flow monitor 12 can also incorporate a hypotension prediction algorithm as part of the AR monitoring module 32 and can use the LLA line as a threshold or definition of hypotension for the patient 10.

[0071] Figure 10 FIG. 7 is a graph of the correlation coefficient between the change in renal blood flow rate of the kidney 42L and the change in MAP of the patient 10. The Y-axis represents the correlation coefficient value and the X-axis represents the MAP value of the patient 10 when the system processor 19 determines the correlation coefficient. Similar to the graph in FIG. 6, Figure 9 FIG. 8 is a graph of the correlation coefficient between the change in renal blood flow rate of the kidney 42L and the change in MAP of the patient 10. The Y-axis represents the correlation coefficient value and the X-axis represents the MAP value of the patient 10 when the system processor 19 determines the correlation coefficient. Similar to the graph in FIG. 6, Figure 10 FIG. 9 is a graph of the correlation coefficient between the change in renal blood flow rate of the kidney 42L and the change in MAP of the patient 10. The Y-axis represents the correlation coefficient value and the X-axis represents the MAP value of the patient 10 when the system processor 19 determines the correlation coefficient. Similar to the graph in FIG. 6, Figure 10 FIG. 9 is a graph of the correlation coefficient between the change in renal blood flow rate of the kidney 42L and the change in MAP of the patient 10. The Y-axis represents the correlation coefficient value and the X-axis represents the MAP value of the patient 10 when the system processor 19 determines the correlation coefficient. Similar to the graph in FIG. 6, Figure 9As the MAP of the patient 10 falls below the LLA line, the autoregulation of the renal blood flow of the kidney 42L is impaired. As the MAP of the patient 10 rises above the ULA line, the autoregulation of the renal blood flow of the kidney 42L is also impaired. As the MAP of the patient 10 rises above the ULA line, the autoregulation of the renal blood flow of the kidney 42L becomes passive regulation, and the changes in the flow rate of the renal blood flow of the kidney 42L are related to the changes in the MAP of the patient 10.

[0072] The system processor 19 can set a first alarm in the blood flow monitor 12 such that the blood flow monitor 12 will alert medical personnel when the MAP of the patient 10 approaches or falls below the LLA line. The system processor 19 can set a second alarm in the blood flow monitor 12 when the MAP of the patient 10 approaches or exceeds the ULA line. If the first alarm is activated, the medical personnel can be alerted that the MAP of the patient 10 is below or approaching the LLA line, and the autoregulation of the renal blood flow of the kidney 42L can be impaired. The medical personnel can take measures, such as administering fluids or medications to the patient 10, to raise the MAP of the patient 10 above the LLA line to address the alarm. If the second alarm is activated, the medical personnel can be alerted that the MAP of the patient 10 is above or approaching the ULA line, and the autoregulation of the renal blood flow of the kidney 42L can be impaired. The medical personnel can take measures to lower the MAP of the patient 10 below the ULA line to address the alarm.

[0073] The system processor 19 can output the plot of Figure 10 to the display 28 and color-code the plot to help the medical personnel identify when the autoregulation of the renal blood flow of the kidney 42L is normal or impaired. For example, the area of the plot of Figure 10 above the ULA line and below the LLA line relative to the X-axis can be colored red, and the area of the plot between the ULA line and the LLA line can be colored green. As time progresses, as the system processor 19 continues to monitor the MAP of the patient 10, continues to monitor the changes in the flow rate of the renal blood flow, and continues to determine and add real-time values of the correlation coefficient to the plot of Figure 10 , the position of the LLA line and / or the position of the ULA line can shift relative to the X-axis. The system processor 19 can update the first alarm and the second alarm of the blood flow monitor 12 to track any changes in the position of the LLA line or the ULA line.

[0074] Figure 11is a block diagram of a method 82 for determining a self-regulating curve of renal blood flow of a kidney 42L of a patient 10. The first step 84 of the method 82 is performed by a medical professional placing a non-invasive ultrasound transducer probe 14 and a continuous hemodynamic pressure sensor 17 on the patient 10. The second step 86 of the method 82 is using the ultrasound transducer probe 14 and the hemodynamic pressure sensor 17 to time measure paired values of arterial pressure of the patient 10 and renal blood flow of the kidney 42L. The third step 88 of the method 82 is performed by the system processor 19 by placing the paired values of arterial pressure of the patient 10 and renal blood flow of the kidney 42L into a first array. The first array is a first data buffer of the system processor 19 and / or the system memory 20 for storing paired value information of arterial pressure of the patient 10 and renal blood flow of the kidney 42L for subsequent processing by the system processor 19.

[0075] The fourth step 90 of the method 82 is a query by the system processor 19. The query of the fourth step 90 is whether the first array is full. If the first array is not full, the system processor 19 returns to step 86 and repeats steps 86-90 until the first array is full. If the first array is full, the system processor 19 will proceed to the fifth step 92 of the method 82. In the fifth step 92 of the method 82, the system processor 19 calculates a mean arterial pressure (MAP) and a mathematical relationship between the arterial pressure values and the renal blood flow values in the first array. As described above, the calculated mathematical relationship between the arterial pressure values and the renal blood flow values can be a correlation (or coherence) between a change in flow rate of the renal blood flow of the kidney 42L and a change in the MAP of the patient 10. In the sixth step 94 of the method 82, the system processor 19 pairs the correlation (or coherence) values with the MAP values and outputs the paired correlation (or coherence) values and MAP values to the display 28.

[0076] In a seventh step 96 of the method 82, the system processor 19 places the paired correlation (or coherence) value and the MAP value into a second array. The second array is a second data buffer of the system processor 19 and / or the system memory 20 for storing information of the paired correlation (or coherence) value and the MAP value for future processing by the system processor 19. The system processor 19 performs an eighth step 98 of the method 82 by determining whether there are enough values in the second array to evaluate an upper limit of autoregulation or a lower limit of autoregulation. If the system processor 19 determines that the second array does not contain enough values to evaluate an upper limit of autoregulation or a lower limit of autoregulation, the system processor continues to perform a ninth step 99 by removing the oldest paired value of the arterial pressure of the patient 10 and the renal blood flow of the kidney 42L in the first array. After removing the oldest paired value of the arterial pressure of the patient 10 and the renal blood flow of the kidney 42L in the first array, the system processor continues to perform the second step 86 and repeats steps 86-98. If the system processor 19 determines that the second array is full, the system processor continues to a tenth step 100.

[0077] The system processor 19 performs the tenth step 100 of the method 82 by evaluating the upper limit of autoregulation and / or the lower limit of autoregulation. The system processor 19 can fit the upper limit of autoregulation and / or the lower limit of autoregulation to a Lassen curve. In an eleventh step 102 of the method 82, the system processor 19 can output the upper limit of autoregulation and / or the lower limit of autoregulation to the display 28. A twelfth step 104 of the method 82 is a query of the system processor 19. The twelfth step 104 queries whether the monitoring session of the kidney 42 of the patient 10 has been completed. If the answer is no, the system processor continues to perform the ninth step 99 by removing the oldest paired value of the arterial pressure of the patient 10 and the renal blood flow of the kidney 42L in the first array. After removing the oldest paired value of the arterial pressure of the patient 10 and the renal blood flow of the kidney 42L in the first array, the system processor continues to perform the second step 86 and repeats steps 86-104. If the system processor 19 determines that the monitoring session of the kidney 42 has been completed, the medical professional will perform a thirteenth step 106 of the method 82 by removing the ultrasound transducer probe 14 and the hemodynamic pressure sensor 17 from the patient 10.

[0078] Figure 12 is a graph demonstrating an experiment of the monitoring system 11. The graph shows four plots. The first plot 108 is a plot of the mean arterial pressure (MAP) of a test subject (pig) measured by the hemodynamic pressure sensor 17 over time. The first plot 108 also includes a lower limit of autoregulation (LLA) line of the test subject's renal blood flow. As Figure 13The LLA line in the first plot 108 is determined by plotting the renal blood flow of the test subject against the MAP of the test subject for each time sample and fitting two lines through the data points and designating the inflection point between the two lines as the location of the LLA line, as shown in plot 116. The LLA line marks the lower limit threshold of the MAP of the test subject at which normal autoregulation of the renal blood flow of the kidney 42L occurs. When the MAP value of the test subject is above the LLA line, the autoregulation of the renal blood flow of the kidney 42L is normal and the correlation between the flow rate of the renal blood flow of the test subject and the MAP is low. When the MAP value of the patient 10 is below the LLA line, the autoregulation of the renal blood flow of the kidney 42L is impaired and the correlation between the flow rate of the renal blood flow of the test subject and the MAP is high (close to 1).

[0079] The second plot 110 is a plot of the flow rate of the renal blood flow of the test subject over time as measured by an invasive flow probe surgically implanted around the renal artery of the test subject for providing a reference measurement of the renal blood flow. The third plot 112 is a plot of the flow rate of the renal blood flow of the test subject 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 renal blood flow of the test subject in a similar manner as the invasive ultrasound flow probe surgically implanted around the renal artery of the test subject.

[0080] The first line of the fourth plot 114 represents the correlation between the changes in the MAP of the test subject and the changes in the flow rate of the renal blood flow of the test subject as measured by the invasive flow probe. The second line of the fourth plot 114 represents the correlation between the changes in the MAP of the test subject and the changes in the flow rate of the renal blood flow of the test subject as measured by the ultrasound transducer probe 14 of the monitoring system 11. The balloon catheter was inserted into the inferior vena cava of the test subject. The balloon catheter was inflated and deflated multiple times during the experiment, causing the MAP of the test subject to decrease and increase.

[0081] When the balloon catheter is inflated, the test subject's MAP falls below the LLA line, which causes the correlation line of the fourth graph 114 to rise and indicates a correlation between the test subject's MAP changes and the flow rate changes of the renal blood flow. The correlation between the test subject's MAP changes and the flow rate changes of the renal blood flow indicates that the test subject's autoregulation of the renal blood flow is impaired. When the balloon catheter is deflated, the test subject's MAP rises above the LLA line, which causes the correlation line of the fourth graph 114 to fall and indicates a lack of correlation between the test subject's MAP changes and the flow rate changes of the renal blood flow. The lack of correlation between the test subject's MAP changes and the flow rate changes of the renal blood flow indicates that the test subject's autoregulation of the renal blood flow is functioning normally. As shown in the fourth graph 114, the monitoring system 11 in this experiment is able to identify the correlation or lack of correlation between the test subject's MAP changes and the flow rate changes of the renal blood flow and determine whether the test subject's autoregulation of the renal blood flow is impaired or functioning normally.

[0082] Figure 13 The graph 116 of the renal blood flow of the test subject versus the MAP in the experiment discussed in Figure 12 The graph 116 of the renal blood flow of the test subject versus the MAP in the experiment discussed in Figure 13 As shown in the graph 116 in the experiment discussed in

[0083] Figure 14 The graph 118 of the correlation and the MAP of the test subject in the experiment. Figure 12 The graph 118 of the correlation and the MAP of the test subject in the experiment. Figure 14 The graph 118 in the experiment discussed in Figure 9The graph in FIG. 118 is similar. Graph 118 shows the correlation between the change in MAP of the test subject versus the change in flow rate of the renal blood flow of the test subject versus the MAP of the test subject. Graph 118 compares the correlation determined by the system processor 19 using the renal blood flow rate data collected by the ultrasound transducer probe 14 versus the correlation determined by the system processor 19 using the renal blood flow rate data collected by the invasive flow probe surgically implanted around the renal artery of the test subject. Both sets of data show that the LLA line of the test subject is approximately 50 mmHg. When the MAP of the test subject is below 50 mmHg, the autoregulation of the renal blood flow of the test subject is impaired, and there is a correlation between the MAP of the test subject and the flow rate of the renal blood flow of the test subject. When the MAP of the test subject is above 50 mmHg, the autoregulation of the renal blood flow of the test subject is functioning normally, and there is no correlation between the MAP of the test subject and the flow rate of the renal blood flow of the test subject.

[0084] Figure 15 is a block diagram of a method 120 for operating Figures 1-2 The monitoring system 11 shown in FIG. 118 is used to continuously monitor the autoregulation of the renal blood flow of the kidney 42L of the patient 10 during a surgery, medical procedure, or medical observation. The autoregulation of the renal blood flow of the kidney 42L is defined as the ability of the renal artery and the renal vein to dilate and constrict in response to dynamic perfusion pressure changes to maintain sufficient renal blood flow to meet the needs of the kidney 42L. Changes in the blood flow of the kidney 42L are substantially independent of changes in the blood pressure of the patient 10.

[0085] As described above with reference to Figures 2-14 The monitoring system 11 uses the flow rate of the renal blood flow of the kidney 42L evaluated from the Doppler flow signal and the MAP of the patient 10 to determine the autoregulation curve of the kidney 42L of the patient 10. The system processor 19 monitors the changes in the renal blood flow rate and the MAP of the patient 10 in the time domain and / or in the frequency domain. The system processor 19 evaluates the changes in the renal blood flow rate and the changes in the MAP relative to each other to determine the autoregulation curve of the kidney 42L of the patient 10. If the system processor 19 determines that there is no correlation between the changes in the renal blood flow rate and the changes in the MAP of the patient 10, then the system processor 19 determines that the autoregulation curve of the kidney 42L is active and functioning normally. If the system processor 19 determines that there is a correlation between the changes in the renal blood flow rate and the changes in the MAP of the patient 10, then the system processor 19 determines that the autoregulation curve 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 to monitor the renal blood flow rate and the MAP of the patient 10 for autoregulation in a rolling time window. 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 the renal blood flow rate and the MAP of the patient 10 for autoregulation.

[0086] Once the ultrasound transducer probe 14 is attached to the patient 10 and is sensing the Doppler flow signal of renal blood flow in the kidney 42L, and the pressure sensor 70 is attached to the patient 10 and is sensing hemodynamic data representing the patient 10's mean arterial pressure (MAP), the system processor 19 executes the AR monitoring module 32 to perform the first step 122 of method 120. In the first step 122, the system processor 19 executes the AR monitoring module 32 to analyze the Doppler flow signal and MAP of the patient 10's renal blood flow to determine the autoregulation curve of renal blood flow in the kidney 42L and establish the LLA and ULA lines of the autoregulation curve. By using the LLA and ULA lines, the system processor 19 can determine, based on the patient 10's MAP value, when the autoregulation of renal blood flow in the kidney 42L is functioning normally or impaired. Impaired autoregulation of renal blood flow in the kidney 42L over time can indicate damage to the kidney 42L.

[0087] In the 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 during surgery, medical procedures, or medical observation of patient 10, for the purpose of calculating the autoregulation curve of renal blood flow in kidney 42L. As part of the second step 124, system processor 19 may output the autoregulation curve of renal blood flow to display 28. Figure 15 In the example, the second step 124 of method 120 further includes a sub-step 125. In sub-step 125, system processor 19 executes AR monitoring module 32 to collect the cumulative running sum of time during which autoregulation of renal blood flow in the kidney 42L, as indicated by autoregulation curves, is impaired during surgery, medical procedures, or medical observation of patient 10.

[0088] In a third step 126 of the method 120, the system processor 19 executes the AR monitoring module 32 to evaluate a real-time AKI risk score for the patient 10 according to the self-regulation curve of renal blood flow. The system processor 19 and the AR monitoring module 32 use the cumulative sum of the time of self-regulation impairment of renal blood flow to evaluate the real-time AKI risk score for the patient 10. In a fourth step 128 of the method 120, the system processor 19 outputs the real-time AKI risk score for the patient 10 to the display 28. The real-time AKI risk score can be displayed on the display 28 as a graph showing how the real-time AKI risk score for the patient 10 changes over time, and / or the real-time AKI risk score can be displayed as a current value in the impairment score indicator 37. The real-time AKI risk score is recorded by the system processor 19 into the system memory 20. In the next iteration of evaluating the real-time AKI risk score for the patient 10, the system processor 19 can use the AKI risk score(s) recorded in the system memory 20 as part of the evaluation 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 real-time information of the self-regulation curve of renal blood flow of the kidney 42L and cumulative historical information of the self-regulation curve of renal blood flow of the kidney 42L.

[0089] As the surgery, medical procedure, or medical observation of the patient 10 progresses, the system processor 19 and the 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 real-time AKI risk score for the patient 10. Each time the monitoring system 11 indicates that the self-regulation of renal blood flow of the kidney 42L is impaired, the monitoring system 11 alerts or alarms the medical staff to be aware so that the medical staff can take measures to compensate for the impaired self-regulation or take measures to restore the self-regulation of renal blood flow. At the end of the surgery, medical procedure, or medical observation of the patient 10, the system processor 19 can execute the AR monitoring module 32 to perform a fifth step 129 to evaluate a final AKI risk score for the kidney 42L of the patient 10. The system processor 19 can determine the final AKI risk score for the patient 10 based on the value of the real-time AKI risk score that is tracked and recorded to the system memory 20 throughout the surgery, medical procedure, or medical observation of the patient 10. After evaluating 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, the medical staff can evaluate whether the kidney 42L of the patient 10 was injured during the surgery, medical procedure, or medical observation and can recommend that the patient 10 seek treatment for the kidney 42L.

[0090] The treatment techniques, methods, steps, etc. described or suggested herein or in references incorporated herein can be performed on living animals or non-living analogs. Any of the various systems, devices, apparatuses, etc. in the present disclosure can be sterilized (e.g., using heat, radiation, ethylene oxide, hydrogen peroxide, etc.) to ensure that they are safe for use with patients, and the methods herein can include sterilizing the associated systems, devices, apparatuses, etc. (e.g., using heat, radiation, ethylene oxide, hydrogen peroxide, etc.).

[0091] Discussion of Possible Embodiments

[0092] The following is a non-exclusive description of possible embodiments of the invention.

[0093] A method for continuously monitoring a patient's kidneys during a surgery, medical procedure, or medical observation includes continuously measuring a patient's renal blood flow signal using 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 evaluates a flow rate of the patient's renal blood flow from the renal blood flow signal. The processor also monitors changes in the flow rate of the renal blood flow over time. A second sensor continuously measures an arterial pressure signal of the patient. 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 changes in the arterial pressure signal and changes in the flow rate of the renal blood flow.

[0094] The method of the preceding paragraph can optionally, additionally, and / or alternatively include one or more of the following features, configurations, and / or additional components in the following paragraphs.

[0095] In embodiments of the above method, the method further includes determining, by the processor, an autoregulation curve of the patient's renal blood flow based on the mathematical relationship between changes in the arterial pressure signal and changes in the flow rate of the renal blood flow.

[0096] In embodiments of the above method, the method further includes outputting, by the processor, to a display in communication with the processor, a representation of the flow rate of the renal blood flow 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 flow rate of the renal blood flow over time in real time.

[0097] In embodiments of the above method, the method further includes continuously monitoring, by the processor, the mathematical relationship between changes in the arterial pressure signal and changes in the flow rate of the renal blood flow over time during the surgery, medical procedure, or medical observation of the patient.

[0098] In embodiments of the above method, the method further includes evaluating, by the processor, a correlation coefficient representing a correlation or lack of correlation between changes in the arterial pressure and changes in the flow rate of the renal blood flow; wherein the processor determines the autoregulation curve of the patient's renal blood flow based on the correlation coefficient.

[0099] In embodiments of the above method, the processor evaluates the correlation coefficient in the time domain.

[0100] In embodiments of the above method, the processor evaluates the correlation coefficient using a Pearson correlation coefficient calculated over a rolling time window.

[0101] In embodiments of the above method, the processor evaluates the correlation coefficient in the frequency domain.

[0102] In embodiments of the above method, the processor evaluates the correlation coefficient using a coherence function calculated over a pre-specified frequency range.

[0103] In embodiments of the above method, the processor evaluates the correlation coefficient using a coherence function calculated from parameters of a transfer function of the arterial pressure signal and a transfer function of the flow rate of the renal blood flow.

[0104] In embodiments of the above method, the method further comprises setting, by the processor, a correlation threshold that delineates a boundary above which a correlation coefficient indicates a correlation between changes in the arterial pressure and changes in the flow rate of the renal blood flow and below which a correlation coefficient indicates an absence of correlation between changes in the arterial pressure and changes in the flow rate of the renal blood flow.

[0105] In embodiments of the above method, the method further comprises evaluating, by the processor, a lower limit of autoregulation (LLA), wherein the LLA is a value of the arterial pressure of the patient below which the correlation coefficient is always above the correlation threshold.

[0106] In embodiments of the above method, the method further comprises evaluating, by the processor, an upper limit of autoregulation (ULA), wherein the ULA is a value of the arterial pressure of the patient above which the correlation coefficient is always above the correlation threshold.

[0107] In embodiments of the above method, the method further comprises setting, by the processor, at least one alarm in the blood flow monitor that activates in response to the blood hemodynamic pressure sensor sensing an increase in the arterial pressure of the patient above the ULA or a decrease in the arterial pressure of the patient below the LLA.

[0108] In embodiments of the above method, the method further comprises evaluating, by the processor of the blood flow monitor, a real-time acute kidney injury risk score of the patient as a function of the autoregulation curve of the patient and a predetermined threshold; and outputting a real-time representation of the real-time acute kidney injury risk score of the patient over time to a display.

[0109] In embodiments of the above method, the predetermined threshold comprises at least one of the LLA and the ULA.

[0110] In embodiments of the above method, the method further comprises setting, by the processor, a hypotension threshold and / or a definition of a hypotension prediction algorithm of the blood flow monitor based on the patient's autoregulation curve.

[0111] In embodiments of the above method, the hypotension threshold and / or the definition of the hypotension prediction algorithm of the blood flow monitor comprises at least one of an LLA and / or a ULA.

[0112] In embodiments of the above method, the method further comprises: collecting, by the blood flow monitor, a time cumulative sum of a change in the arterial pressure signal and a change in the flow rate of the renal blood flow; assessing, by the processor of the blood flow monitor, a real-time acute kidney injury risk score of the patient from the time cumulative sum of the change in the arterial pressure signal and the change in the flow rate of the renal blood flow; and outputting, in real-time, to the display a representation of the real-time acute kidney injury risk score of the patient over time.

[0113] In embodiments of the above method, the method further comprises continuously measuring, using a first sensor attached to the patient, a signal of the renal blood flow of the patient, comprising: sampling a waveform of the signal of the 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 over each cardiac cycle of the patient; sampling an average of the signal of the renal blood flow over a time window; and / or sampling an average of the signal of the renal blood flow over a rolling time window.

[0114] In embodiments of the above method, the signal of the renal blood flow is a relative change in the renal blood flow, a flow rate of the renal blood flow, and / or a peak flow rate of the renal blood flow.

[0115] In embodiments of the above method, the first sensor comprises an ultrasound transducer probe affixed to the patient's abdomen at a fixed position, and the signal of the renal blood flow of the patient is a Doppler flow signal.

[0116] In embodiments of the above method, the method further comprises positioning the ultrasound transducer probe on the patient's abdomen and affixing the ultrasound transducer probe to the patient's abdomen using an adhesive patch to maintain contact between the ultrasound transducer probe and the patient without the need for an ultrasound operator; and scanning the patient's abdomen using the ultrasound transducer probe to locate the Doppler flow signal of the renal blood flow of the patient.

[0117] In embodiments of the above method, the method further comprises executing, by the processor, beamformer software code to track scan the Doppler flow signal of the renal blood flow of the patient with a two-dimensional phased array of transducer elements of the ultrasound transducer probe to continuously sense the Doppler flow signal of the renal blood flow of the patient without the need for an ultrasound operator during a procedure, medical procedure, or medical observation.

[0118] In embodiments of the above method, the method further comprises: executing, by the processor, beamformer software code to transmit a set of sequential beams from the array of transducer elements to track a center of the renal blood flow relative to the array of transducer elements; focusing, by the processor executing the beamformer software code, each beam of the set of sequential beams at a different location; and adjusting, by the processor executing the beamformer software code, the locations of the set of sequential beams to the center of the renal blood flow to maintain the Doppler flow signal of the patient's renal blood flow.

[0119] In embodiments of the above method, the second sensor comprises a hemodynamic pressure sensor attached to the patient through a radial artery catheter.

[0120] In embodiments of the above method, the second sensor comprises a hemodynamic pressure sensor attached to the patient through a femoral artery catheter.

[0121] In embodiments of the above method, the second sensor comprises a non-invasive hemodynamic pressure sensor.

[0122] In embodiments of the above method, the method further comprises setting, by the processor, a blood pressure alarm in the blood flow monitor based on the autoregulation curve of the patient.

[0123] The system comprises a first sensor configured to continuously measure a signal of a renal blood flow of a patient during a surgery, medical procedure, or medical observation. A second sensor is configured to continuously measure an arterial pressure signal of the patient during the surgery, medical procedure, or medical observation. A blood flow monitor is in communication with the first sensor and the second sensor. The blood flow monitor comprises a system memory storing monitoring software code and a processor. The processor is configured to execute the monitoring software code to assess a flow rate of the renal blood flow of the patient from the signal of the renal blood flow and monitor changes in the flow rate of the renal blood flow over time. The processor is further configured to execute the monitoring software code to monitor changes in the arterial pressure signal over time and assess a mathematical relationship between changes in the arterial pressure signal and changes in the flow rate of the renal blood flow.

[0124] The system of the preceding paragraph can optionally, additionally and / or alternatively include one or more features, configurations, and / or additional components of one or more of the following paragraphs.

[0125] In embodiments of the above system, the processor is configured to execute the monitoring software code to: determine an autoregulation curve of the renal blood flow of the patient based on the mathematical relationship between changes in the arterial pressure signal and changes in the flow rate of the renal blood flow.

[0126] In embodiments of the above system, the signal of renal blood flow comprises a Doppler flow signal, and the first sensor comprises an ultrasound transducer probe containing a two-dimensional array of transducer elements configured to continuously measure the Doppler flow signal of the patient's renal blood flow during the surgery, medical procedure, or medical observation.

[0127] In embodiments of the above system, the two-dimensional array of transducer elements of the ultrasound transducer probe comprises a phased array of transducer elements.

[0128] In embodiments of the above system, the system memory stores probe control software code and beamformer software code, and wherein the processor is configured to execute the beamformer software code to: track the Doppler flow signal of the patient's renal blood flow by transmitting a plurality of ultrasound beams from the phased array of transducer elements to track the Doppler flow signal of the patient's renal blood flow relative to the phased array of transducer elements.

[0129] In embodiments of the above system, the second sensor comprises a hemodynamic pressure sensor connected to a radial artery catheter.

[0130] In embodiments of the above system, the second sensor comprises a hemodynamic pressure sensor connected to a femoral artery catheter.

[0131] In embodiments of the above system, the second sensor comprises a non-invasive hemodynamic pressure sensor.

[0132] In embodiments of the above system, the system further comprises a display in communication with the processor for receiving and displaying a representation of the flow rate of the renal blood flow over time, a representation of the arterial pressure signal over time, and / or a representation of a mathematical relationship between changes in the arterial pressure signal and changes in the flow rate of the renal blood flow over time.

[0133] In embodiments of the above system, the processor is configured to execute the monitoring software code to: continuously monitor, by the processor, the autoregulation curve of the patient's renal blood flow over time during the surgery, medical procedure, or medical observation of the patient.

[0134] In embodiments of the above system, the processor is configured to execute the monitoring software code to: assess a correlation or lack of correlation between changes in the arterial pressure and changes in the flow rate of the renal blood flow; and determine the autoregulation curve of the patient's renal blood flow based on the correlation or lack of correlation between changes in the arterial pressure and changes in the flow rate of the renal blood flow.

[0135] In embodiments of the above system, the processor is configured to execute the monitoring software code to: assess a correlation or lack of correlation between changes in the arterial pressure and changes in the flow rate of the renal blood flow in the time domain.

[0136] In embodiments of the above system, the processor is configured to execute the monitoring software code to assess the correlation or lack of correlation between the changes in arterial pressure and the changes in flow rate of renal blood flow using the Pearson correlation coefficient computed over a rolling time window.

[0137] In embodiments of the above system, the processor is configured to execute the monitoring software code to assess the correlation or lack of correlation between the changes in arterial pressure and the changes in flow rate of renal blood flow in the frequency domain.

[0138] In embodiments of the above system, the processor is configured to execute the monitoring software code to assess the correlation or lack of correlation between the changes in arterial pressure and the changes in flow rate of renal blood flow using the coherence function computed over a pre-specified frequency range.

[0139] In embodiments of the above system, the processor is configured to execute the monitoring software code to assess the correlation or lack of correlation between the changes in arterial pressure and the changes in flow rate of renal blood flow 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 renal blood flow.

[0140] In embodiments of the above system, the processor is configured to execute the monitoring software code to assess a real-time acute kidney injury risk score of the patient from the patient’s own regulation curve and a pre-determined threshold; and output a real-time representation of the real-time acute kidney injury risk score of the patient over time to a display.

[0141] In embodiments of the above system, the processor is configured to execute the monitoring software code to: collect a time cumulative sum of the changes in arterial pressure signal correlated to the changes in flow rate of renal blood flow; and assess a real-time acute kidney injury risk score of the patient from the time cumulative sum of the changes in arterial pressure signal correlated to the changes in flow rate of renal blood flow; and output a real-time representation of the real-time acute kidney injury risk score of the patient over time to a display.

[0142] In embodiments of the above system, the processor is configured to execute the monitoring software code to set a blood pressure alarm in the blood flow monitor based on the patient’s own regulation curve.

[0143] In embodiments of the above system, the processor is configured to execute the monitoring software code to set a hypotension threshold and / or definition of a hypotension prediction algorithm of the blood flow monitor based on the patient’s own regulation curve.

[0144] A method for continuously monitoring a patient's kidneys during surgery, a medical procedure, or a medical observation includes continuously measuring a Doppler flow signal of a patient's renal blood flow using an ultrasound transducer probe. The ultrasound transducer probe is affixed to the patient's abdomen in a fixed 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 an arterial pressure signal of the patient. The hemodynamic pressure sensor is in communication with the blood flow monitor. The processor monitors changes in the arterial pressure signal over time and assesses a mathematical relationship between changes in the arterial pressure signal and changes in the renal blood flow. The processor determines an autoregulation curve of the patient's renal blood flow based on the mathematical relationship between changes in the arterial pressure signal and changes in the renal blood flow.

[0145] A method for continuously monitoring a patient's kidneys during surgery, a medical procedure, or a medical observation includes continuously measuring a Doppler flow signal of a patient's renal blood flow using an ultrasound transducer probe. The ultrasound transducer probe is affixed to the patient's abdomen in a fixed 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 an arterial pressure signal of the patient. The hemodynamic pressure sensor is in communication with the blood flow monitor. The processor monitors changes in the arterial pressure signal over time and assesses a mathematical relationship between changes in the arterial pressure signal and changes in the renal blood flow. The processor determines an autoregulation curve of the patient's renal blood flow based on the mathematical relationship between changes in the arterial pressure signal and changes in the renal blood flow.

[0146] The method of the preceding paragraph can optionally, additionally and / or alternatively include any one or more of the following features, configurations and / or additional components in the following paragraphs.

[0147] In embodiments of the above method, the processor is configured to execute the monitoring software code to determine the autoregulation curve of the patient's renal blood flow based on the mathematical relationship between changes in the arterial pressure signal and changes in the renal blood flow.

[0148] While the application has been described with reference to example embodiments(s), it will be understood by those skilled in the art that various changes can be made and equivalents can be substituted for elements thereof without departing from the scope of the application. In addition, many modifications can be made to adapt a particular situation or material to the teachings of the application without departing from the essential scope thereof. Therefore, the application is not limited to the particular embodiments disclosed, but includes all embodiments falling within the scope of the appended claims.

Claims

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

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

3. The method of claim 1 or 2, further comprising: outputting, to a display in communication with the processor, a representation of the flow rate of the renal blood flow 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 flow rate of the renal blood flow over time in real-time.

4. The method of any of the preceding claims, further comprising: continuously monitoring, by the processor, the mathematical relationship between changes in the arterial pressure signal and changes in the flow rate of the renal blood flow over time during the patient's surgical, medical procedure, or medical observation.

5. The method of any of the preceding claims, further comprising: evaluating, by the processor, a correlation coefficient representing a correlation or lack of correlation between changes in the arterial pressure and changes in the flow rate of the renal blood flow; wherein the processor determines the autoregulation curve of the patient's renal blood flow 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 time window. the processor evaluates the correlation coefficient in the frequency domain.

8. The method of claim 5, wherein, 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. 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. The method of any of claims 5 to 10, further comprising: ​ setting, by the processor, a correlation threshold that demarcates a boundary above which the correlation coefficient indicates a correlation between changes in the arterial pressure and changes in the flow rate of the renal blood flow and below which the correlation coefficient indicates an absence of correlation between changes in the arterial pressure and changes in the flow rate of the renal blood flow.

12. The method of claim 11, further comprising: evaluating, by the processor, a lower limit of autoregulation (LLA), wherein the LLA is an arterial pressure value of the patient below which the correlation coefficient is always above the correlation threshold.

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

14. The method of claim 13, further comprising: setting, by the processor, at least one alarm in the blood flow monitor that activates in response to the blood hemodynamic pressure sensor sensing an arterial pressure of the patient rising above the ULA or falling below the LLA.

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

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

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

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

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

20. The method of any one of the preceding claims, wherein continuously measuring a signal of the renal blood flow of the patient with the first sensor attached to the patient comprises: sampling a waveform of the signal of the 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 over each cardiac cycle of the patient; sampling an average of the signal of the renal blood flow over a time window; and / or sampling an average of the signal of the renal blood flow over a rolling time window.

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

22. The method of any one of the preceding claims, wherein the first sensor comprises an ultrasound transducer probe affixed to the patient’s abdomen at a fixed location, and the signal of the renal blood flow of the patient is a Doppler flow signal.

23. The method of claim 22, further comprising: positioning the ultrasound transducer probe on the patient’s abdomen and affixing the ultrasound transducer probe to the patient’s abdomen with an adhesive patch to maintain 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 locate the Doppler flow signal of the renal blood flow of the patient.

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

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

26. The method of any one of the preceding claims, wherein the second sensor comprises a hemodynamic pressure sensor attached to the patient through a radial artery catheter.

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

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

29. The method of any of the preceding claims, further comprising: setting, by the processor, a blood pressure alarm in the blood flow monitor based on the autoregulation curve of the patient.

30. A system comprising: a first sensor configured to continuously measure a signal of renal blood flow of a patient during a surgery, medical procedure, or medical observation; a second sensor configured to continuously measure an arterial pressure signal of the patient during the surgery, the medical procedure, or the medical observation; a blood flow monitor in communication with the first sensor and the second sensor, wherein the blood flow monitor comprises: a system memory storing monitoring software code; and a processor configured to execute the monitoring software code to: estimate a flow rate of the renal blood flow of the patient from the signal of the renal blood flow; monitor changes in the flow rate of the renal blood flow over time; monitor changes in the arterial pressure signal over time; and estimate a mathematical relationship between changes in the arterial pressure signal and changes in the flow rate of the renal blood flow.

31. The system of claim 30, wherein the processor is configured to execute the monitoring software code to: determine an autoregulation curve of the renal blood flow of the patient based on the mathematical relationship between changes in the arterial pressure signal and changes in the flow rate of the renal blood flow.

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 ultrasonic transducer probe comprising 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 surgery, the medical procedure, or the medical observation.

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

34. The system of claim 33, wherein the system memory stores probe control software code with beamformer software code, and wherein the processor is configured to execute the beamformer software code to: track the Doppler flow signal of the renal blood flow of the patient by transmitting a plurality of ultrasonic beams from the phased array of transducer elements to track the Doppler flow signal of the renal blood flow of the patient relative to the phased array of transducer elements.

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

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

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

38. The system of any of claims 30 to 37, further comprising: a display in communication with the processor for receiving and displaying a representation of the flow rate of the renal blood flow 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 flow rate of the renal blood flow over time.

39. The system of any one of claims 30 to 38, wherein the processor is configured to execute the monitoring software code to: continuously monitor, by the processor, the self-regulation curve of the renal blood flow of the patient over time during the surgery, medical procedure, or medical observation of the patient.

40. The system of any one of claims 30 to 39, wherein the processor is configured to execute the monitoring software code to: evaluate a correlation or an absence of correlation between changes in the arterial pressure and changes in the flow rate of the renal blood flow; and determine the self-regulation curve of the renal blood flow of the patient based on the correlation or the absence of correlation between changes in the arterial pressure and changes in the flow rate of the renal blood flow.

41. The system of any one of claims 30-40, wherein the processor is configured to execute the monitoring software code to: evaluate the correlation or the absence of correlation between changes in the arterial pressure and changes in the flow rate of the renal blood flow in the time domain.

42. The system of claim 41, wherein the processor is configured to execute the monitoring software code to: evaluate the correlation or the absence of correlation between changes in the arterial pressure and changes in the flow rate of the renal blood flow using a Pearson correlation coefficient computed over a rolling time window.

43. The system of any one of claims 30 to 40, wherein the processor is configured to execute the monitoring software code to: evaluate the correlation or the absence of correlation between changes in the arterial pressure and changes in the flow rate of the renal blood flow in the frequency domain.

44. The system of claim 43, wherein the processor is configured to execute the monitoring software code to: evaluate the correlation or the absence of correlation between changes in the arterial pressure and changes in the flow rate of the renal blood flow using a coherence function computed over a pre-given frequency range.

45. The system of claim 43, wherein the processor is configured to execute the monitoring software code to: evaluate the correlation or the absence of correlation between changes in the arterial pressure and changes in the flow rate of the renal blood flow 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.

46. The system of any one of claims 38 to 45, wherein the processor is configured to execute the monitoring software code to: evaluate a real-time acute kidney injury risk score of the patient as a function of a predetermined threshold and the self-regulation curve of the patient; and output, to the display, a representation of the real-time acute kidney injury risk score of the patient over time in real-time.

47. The system of any one of claims 30 to 46, wherein the processor is configured to execute the monitoring software code to: determine a real-time acute kidney injury risk score of the patient as a function of a predetermined threshold and the self-regulation curve of the patient; and output, to the display, a representation of the real-time acute kidney injury risk score of the patient over time in real-time.

48. The system of any one of claims 30 to 47, wherein the processor is configured to execute the monitoring software code to: determine a real-time acute kidney injury risk score of the patient as a function of a predetermined threshold and the self-regulation curve of the patient; and output, to the display, a representation of the real-time acute kidney injury risk score of the patient over time in real-time.

49. The system of any one of claims 30 to 48, wherein the processor is configured to execute the monitoring software code to: determine a real-time acute kidney injury risk score of the patient as a function of a predetermined threshold and the self-regulation curve of the patient; and output, to the display, a representation of the real-time acute kidney injury risk score of the patient over time in real-time.

50. The system of any one of claims 30 to 49, wherein the processor is configured to execute the monitoring software code to: determine a real-time acute kidney injury risk score of the patient as a function of a predetermined threshold and the self-regulation curve of the patient; and output, to the display, a representation of the real-time acute kidney injury risk score of the patient over time in real-time.

47. The system of any of claims 38-45, wherein the processor is configured to execute the monitoring software code to: collect a cumulative sum of times related to changes in the arterial pressure signal and changes in the flow rate of the renal blood flow; and evaluate a real-time acute kidney injury risk score for the patient from the cumulative sum of times related to changes in the arterial pressure signal and changes in the flow rate of the renal blood flow; and output a representation of the real-time acute kidney injury risk score for the patient over time to the display in real-time.

48. The system of any of claims 30-47, wherein processor is configured to execute the monitoring software code to: set a blood pressure alarm in the blood flow monitor based on the self-regulation curve for the patient.

49. The system of any of claims 30-48, wherein the processor is configured to execute the monitoring software code to: set a hypotension threshold and / or definition for a hypotension prediction algorithm of the blood flow monitor based on the self-regulation curve for the patient.

50. A method of continuously monitoring a kidney of a patient during a surgery, medical procedure, or medical observation, the method comprising: continuously measuring a Doppler flow signal of a renal blood flow of the patient using an ultrasound transducer probe affixed to an abdomen of the patient at a fixed location and in communication with a processor of a blood flow monitor; monitoring, by the processor, changes in the renal blood flow over time; continuously measuring an arterial pressure signal of the patient using a hemodynamic pressure sensor, wherein the hemodynamic pressure sensor is 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 changes in the arterial pressure signal and changes in the renal blood flow; and determining, by the processor, a self-regulation curve for the renal blood flow of the patient based on the mathematical relationship between changes in the arterial pressure signal and changes in the renal blood flow.

51. A system comprising: an ultrasound transducer probe comprising a two-dimensional array of transducer elements configured to continuously measure a Doppler flow signal of a renal blood flow of a patient during a surgery, medical procedure, or medical observation; an adhesive patch connected to the ultrasound transducer probe and configured to affix 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 surgery, the medical procedure, or the medical observation; a blood flow monitor in communication with the ultrasound transducer probe and the hemodynamic pressure sensor, wherein the blood flow monitor comprises: a system memory storing monitoring software code; and a processor configured to execute the monitoring software code to: determine changes in the renal blood flow of the patient from the Doppler flow signal of the renal blood flow; monitor changes in the renal blood flow over time; ​ monitoring changes in the arterial pressure signal over time; and evaluating a mathematical relationship between changes in the arterial pressure signal and changes in the renal blood flow.

52. The system of claim 51, wherein the processor is configured to execute the monitoring software code to: determine an autoregulation curve for the renal blood flow of the patient based on the mathematical relationship between changes in the arterial pressure signal and changes in the renal blood flow.

Images