Method and system for predicting limits of automatic adjustment range

Abstract

A method and system for predicting a limit of an auto-adjustment range to a subject is provided. The method comprises: a) determining a blood flow value representative of a blood flow of the subject in an autoregulation range of the subject during a time period; b) determining a blood pressure value representative of the blood pressure of the subject in the auto-regulation range of the subject during the time period; c) determining a mathematical function representative of the blood flow value and the blood pressure value within the auto-adjustment range; d) using the mathematical function to generate a representation of the blood flow value and the blood pressure value outside the auto-adjustment range; and e) predicting a limit for the auto-regulation range using the representation of the blood flow value and the blood pressure value outside the auto-regulation range.

Description

Background Art 1. Technical Field

[0001] The present disclosure relates generally to medical systems and methods, and in particular to medical systems and methods for predicting the limits of an autoregulatory range for a subject.

[0002] 2. Background Information

[0003] Autoregulation is a process in mammals that aims to maintain adequate and stable (e.g., "constant") blood flow to an organ (e.g., brain, heart, kidneys, etc.) over a range of perfusion pressures. Different organs exhibit varying degrees of autoregulatory behavior. Renal, cerebral, and coronary circulations generally show good autoregulation, while skeletal muscle and visceral circulations show moderate autoregulation. Skin circulation shows little or no autoregulatory capacity.

[0004] Cerebral autoregulation can be defined as the maintenance of constant cerebral blood flow (CBF) despite changes in cerebral perfusion pressure (CPP), where CPP is equal to mean arterial pressure (MAP) minus intracranial pressure (ICP). For ease of discussion in this article, CPP and MAP will be discussed in terms of MAP unless otherwise stated. Cerebral autoregulation plays an important protective role against the risk of cerebral hypoxia / ischemia at low MAP and the risk of cerebral edema at high MAP. In fact, in normotensive people, cerebral blood flow remains relatively but not absolutely constant within a certain range of perfusion pressures, which is often referred to as the autoregulatory range (sometimes referred to as the autoregulatory plateau). The lower endpoint of the autoregulatory range is often referred to as the lower limit of autoregulation (LLA), and the upper endpoint of the autoregulatory range is often referred to as the upper limit of autoregulation (ULA). Figure 1 is a schematic diagram of CBF and CPP, illustrating an autoregulatory curve with an autoregulatory range defined by LLA and ULA. As described above, in areas outside the LLA, subjects may be at risk of cerebral hypoxia / ischemia at low CPP / MAP, and in areas outside the ULA, subjects may be at risk of cerebral edema at higher CPP / MAP. Once the limits of autoregulation (i.e., LLA, ULA) are reached, cerebral blood flow will passively increase (above ULA) or decrease (below LLA), accompanied by an increase or decrease in perfusion pressure. It should be noted that LLA and ULA are not "hard points", but points at which the flow / pressure relationship maintained by autoregulation begins to change.

[0005] As will be discussed herein, the LLA and ULA can vary significantly from subject to subject, vary according to factors such as the subject's overall health, dietary factors, arterial carbon dioxide partial pressure level (hypercapnia, hypocapnia, normocapnia), and can vary over time for a particular subject, to name a few. Historically, a subject's LLA or ULA can be determined by monitoring the subject's CBF and CPP and evaluating the collected data for changes (e.g., inflection points in the curves) to determine the extent of the autoregulatory range. However, data collection methods like this require data to be collected in areas outside the LLA and / or outside the ULA; that is, areas where the subject may be suffering from ischemia or edema.

[0006] What is needed is a system and method for monitoring autoregulation that permits prediction of LLA before MAP reaches LLA and / or permits prediction of ULA before MAP reaches ULA. Summary of the invention

[0007] According to one aspect of the present disclosure, there is provided a method for predicting the limits of an autoregulatory range of a subject, the method comprising: a) determining a blood flow value representing the blood flow of the subject within the autoregulatory range of the subject during a time period; b) determining a blood pressure value representing the blood pressure of the subject within the autoregulatory range of the subject during the time period; c) determining a mathematical function representing the blood flow value and the blood pressure value within the autoregulatory range; d) using the mathematical function to generate a representation of the blood flow value and the blood pressure value outside the autoregulatory range; and e) using the representation of the blood flow value and the blood pressure value outside the autoregulatory range to predict the limits of the autoregulatory range.

[0008] In any of the aspects of the embodiments described above and herein, the predicted limit is at least one of an automatically regulated lower limit or an automatically regulated upper limit.

[0009] In any of the aspects of the embodiments described above and herein, the step of determining a blood flow value representing the blood flow of the subject in the autoregulatory range of the subject during the time period may be performed non-invasively and may be performed using a near infrared spectroscopy (NIRS) oximeter.

[0010] In any of the aspects of the embodiments described above and herein, the step of determining a blood flow value representative of the subject's blood flow in the autoregulatory range of the subject during the time period may include determining the subject's cerebral blood flow.

[0011] In any of the aspects of the embodiments described above and herein, the step of determining a blood pressure value representing the blood pressure of the subject in the autoregulatory range of the subject during the time period may include determining a mean arterial blood pressure (MAP) value of the subject or a parameter derived from the MAP value.

[0012] In any of the aspects of the embodiments described above and herein, the step of determining the mathematical function may include fitting a cubic polynomial equation to the blood flow values ​​and the blood pressure values ​​within the subject's autoregulatory range, and using the mathematical function to generate a representation of the blood flow values ​​and the blood pressure values ​​outside the autoregulatory range may include extrapolating the cubic polynomial equation to represent the blood flow values ​​and the blood pressure values ​​outside the autoregulatory range.

[0013] In any of the aspects of the embodiments described above and herein, the step of determining the mathematical function may include fitting one or more sigmoid functions to the blood flow values ​​and the blood pressure values ​​within the autoregulatory range of the subject.

[0014] In any of the aspects of the embodiments described above and herein, the step of determining a blood flow value representing the subject's blood flow within the subject's autoregulatory range may be performed continuously during the time period, and the step of determining a blood pressure value representing the subject's blood pressure flow within the subject's autoregulatory range may be performed continuously during the time period, and the step of predicting the limit of the autoregulatory range using the representation of the blood flow value and the blood pressure value outside the autoregulatory range may be performed in real time.

[0015] According to another aspect of the present disclosure, there is provided a system configured to determine the limits of an autoregulatory range for a subject, the system comprising a blood flow measurement device, a blood pressure sensing device, and a system controller. The blood flow measurement device is configured to determine a blood flow value representing the blood flow of the subject in the autoregulatory range of the subject during a time period and is configured to generate a first signal representing the blood flow value. The blood pressure sensing device is configured to determine a blood pressure value representing the blood pressure of the subject in the autoregulatory range of the subject during the time period and is configured to generate a second signal representing the blood flow value. The system controller communicates with the blood flow measurement device and the blood pressure sensing device. The system controller includes at least one processor and a memory device configured to store instructions, which when executed cause the controller to: a) control the blood flow measurement device to determine the blood flow value representing the blood flow of the subject within the autoregulatory range of the subject during the time period and generate the first signal representing the blood flow value; b) control the blood pressure sensing device to determine the blood pressure value representing the blood pressure of the subject within the autoregulatory range of the subject during the time period and generate the second signal representing the blood pressure value; c) use the first signal and the second signal to determine a mathematical function representing the blood flow value and the blood pressure value within the autoregulatory range; d) use the mathematical function to generate a representation of the blood flow value and the blood pressure value outside the autoregulatory range; and e) use the representation of the blood flow value and the blood pressure value outside the autoregulatory range to predict a limit to the autoregulatory range.

[0016] In any of the aspects of the embodiments described above and herein, the blood flow measurement device may be a NIRS oximeter configured to non-invasively determine blood flow values.

[0017] In any of the aspects of the embodiments described above and herein, the blood pressure sensing device may be configured to determine a mean arterial blood pressure (MAP) value of the subject or a parameter derived from the MAP value.

[0018] In any of the aspects of the embodiments described above and herein, the mathematical function may be a cubic polynomial equation fit to the blood flow values ​​and the blood pressure values ​​within the subject's autoregulatory range, and the instruction, when executed, may cause the controller to extrapolate the cubic polynomial equation to represent the blood flow values ​​and the blood pressure values ​​outside the autoregulatory range.

[0019] In any of the aspects of the embodiments described above and herein, the mathematical function may be a S-shaped function fitted to the blood flow values ​​and the blood pressure values ​​within the subject's autoregulatory range, and the instruction, when executed, may cause the controller to extrapolate the S-shaped function to represent the blood flow values ​​and the blood pressure values ​​outside the autoregulatory range.

[0020] In any of the aspects of the embodiments described above and herein, the instructions, when executed, may cause the controller to: a) control the blood flow measurement device to continuously determine the blood flow value representing the blood flow of the subject in the automatic regulation range during the time period; b) control the blood pressure measurement device to continuously determine the blood pressure value representing the blood flow of the subject in the automatic regulation range during the time period; and c) use the representation of the blood flow value and the blood pressure value outside the automatic regulation range to predict the limit of the automatic regulation range in real time.

[0021] According to another aspect of the present disclosure, a non-transitory computer-readable medium storing executable instructions is provided. The executable instructions are configured to, when executed, cause at least one processor to: a) control a blood flow measurement device to determine a blood flow value representing the blood flow of the subject in the subject's autoregulatory range during a time period and generate a first signal representing the blood flow value; b) control a blood pressure sensing device to determine a blood pressure value representing the blood pressure of the subject in the subject's autoregulatory range during the time period and generate a second signal representing the blood pressure value; c) use the first signal and the second signal to determine a mathematical function representing the blood flow value and the blood pressure value within the autoregulatory range; d) use the mathematical function to generate a representation of the blood flow value and the blood pressure value outside the autoregulatory range; and e) use the representation of the blood flow value and the blood pressure value outside the autoregulatory range to predict the limit of the autoregulatory range.

[0022] Unless otherwise clearly indicated, the aforementioned features and elements can be combined in various combinations without exclusivity. According to the following description and accompanying drawings, these features and elements and their operation will become more apparent. However, it should be understood that the following description and accompanying drawings are intended to be exemplary in nature and non-restrictive. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a schematic diagram of CBF and CPP illustrating the autoregulation curve, LLA, ULA, and autoregulation range / plateau.

[0024] Figure 2 is a schematic representation of an embodiment of the disclosed system.

[0025] Figure 3is a schematic representation of an embodiment of the disclosed system.

[0026] Figure 4 is a schematic representation of an exemplary frequency domain method.

[0027] Figure 5 is a schematic diagram illustrating AR Index versus MAP for data points collected in the autoregulation range.

[0028] Figure 6 It is a schematic illustration of the cubic polynomial equation of the function f(x) with respect to the X-axis.

[0029] Figure 7 A sigmoid function is illustrated.

[0030] Figure 8 is a diagram illustrating an example of the elbow point method.

[0031] Fig.9A is a graph of CBF versus MAP illustrating data points collected in an animal study, a cubic polynomial curve fit to the data points in and outside the autoregulatory range, and the LLA determined from the data points.

[0032] Fig. 9B It is an example Fig.9A A plot of CBF versus MAP of the data points shown shows only those data points collected in the autoregulatory range to which a cubic polynomial curve was fit.

[0033] Fig. 9C It is an example Fig.9A A plot of CBF versus MAP of the data points shown shows only those data points collected in the autoregulatory range to which a cubic polynomial curve is fit, which is now extrapolated outside the autoregulatory range.

[0034] Fig. 10A is a graph of CBF versus MAP illustrating data points collected in an animal study, a cubic polynomial curve fit to the data points in and outside the autoregulatory range, and the LLA determined from the data points.

[0035] Fig. 10B It is an example Fig. 10A A plot of CBF versus MAP of the data points shown shows only those data points collected in the autoregulatory range to which a cubic polynomial curve was fit.

[0036] Fig. 10C It is an example Fig. 10AA plot of CBF versus MAP of the data points shown shows only those data points collected in the autoregulatory range to which a cubic polynomial curve is fit, which is now extrapolated outside the autoregulatory range.

[0037] Fig.11 is a schematic diagram of NIRS versus MAP illustrating an autoregulatory curve based on a sigmoid curve fitted to the data points. DETAILED DESCRIPTION

[0038] The present disclosure relates to a system 20 and method for monitoring autoregulatory function, which permits prediction of a lower limit of autoregulation (LLA) for a subject's autoregulatory range (i.e., an autoregulatory plateau) and / or prediction of an upper limit of autoregulation (ULA) for a subject's autoregulatory range, which prediction does not require collection of data in areas outside the subject's autoregulatory range (i.e., outside the LLA and / or ULA), and to a non-transitory computer-readable medium containing instructions for implementing the disclosed method.

[0039] As indicated above, the goal of the autoregulatory process in mammals is to maintain adequate and stable (e.g., "constant") blood flow to an organ (e.g., brain, heart, kidney, etc.) over a range of perfusion pressures. For ease of description herein, the present disclosure is described in terms of brain autoregulation. The present disclosure is not limited to use with brain autoregulation and can be used to determine autoregulatory function for other organs.

[0040] The determination of the autoregulatory state of the subject utilizes the measurement of blood flow and the measurement of blood pressure. In terms of cerebral autoregulation, the autoregulatory state is determined by the measurement of cerebral blood flow (CBF) or its substitute and the measurement of cerebral perfusion pressure (CPP) or mean arterial pressure (MAP). The term "substitute" used with CBF herein refers to a physiological parameter corresponding to CBF, or a physiological parameter similar to CBF, or a physiological parameter from which CBF can be determined, etc. For the convenience of description herein, unless otherwise indicated, the term "CBF" as used herein is intended to mean cerebral blood flow and / or a substitute for cerebral blood flow. CBF can be measured in a variety of different ways. The CBF measuring device 22 that invasively measures CBF can use technology such as a flow laser Doppler flowmeter. The CBF measuring device 22 that directly measures CBF or indirectly measures CBF (e.g., via a substitute) can use non-invasive technology such as near infrared spectroscopy (NIRS), transcranial Doppler ultrasound imaging, phase contrast MRI, and arterial spin labeling MRI (ASL-MRI). The present disclosure is not limited to any particular method for measuring CBF or any configuration of a CBF measurement device 22. Non-limiting examples provided herein include a CBF measurement device 22 in the form of a NIRS tissue oximeter operable to produce blood parameter measurements as surrogates for CBF (e.g., NIRS indices such as StO2, rTHb, O2Hb, and differential changes in HHb, etc.).

[0041] MAP can be measured in a variety of different ways. Non-limiting examples of blood pressure sensing devices ("BP sensing device 24") that can be used to measure MAP of a subject include an arterial catheter line, a continuous non-invasive blood pressure device, a pulse oximetry sensor, and the like. However, the present disclosure is not limited to the use of any particular type of BP sensing device. The BP sensing device 24 can be configured to continuously generate MAP measurements. The term "continuously" (with respect to the BP sensing device 24) as used herein means that the BP sensing device 24 senses and collects subject data on a periodic basis during a monitoring period that is frequent enough to be considered clinically continuous. For example, some BP sensing devices 24 sample data every ten seconds or less, and can be configured to sample data more frequently (e.g., every two seconds or less).

[0042] A non-limiting example of the disclosed system 20 is Figure 2 and Figure 3 It is schematically shown in FIG. Figure 2 The system 20 embodiment schematically shown in FIG. 2 is configured to include system components (eg, CBF measurement device 22, BP sensing device 24, system controller 26, etc.) integrated into a single system device. Figure 3The system 20 embodiment schematically shown in FIG. 1 is configured to include a system controller 26 and other system components (e.g., CBF measurement device 22, BP sensing device 24, system controller 26, etc.) that are independently configured and communicate with (e.g., receive signal data from and / or send signal data to) the system controller 26. In other words, in the system 20 embodiment, such as Figure 3 In the illustrated embodiment, the system 20 may be configured to communicate with a BP sensing device 24 that can operate independently of the system 20, a CBF measurement device that can operate independently of the system 20, and the like. In other embodiments, the system 20 may include some combination of these components in an integrated and independent form. In those embodiments in which one or more of the foregoing components are independently configured, such independent components may communicate with the system controller 26 in any manner; for example, hardwired, wireless, etc.

[0043] The system controller 26 may include any type of computing device, computing circuit, or any type of process or processing circuit capable of executing a series of instructions stored in a memory. The system controller 26 may include multiple processors and / or multi-core CPUs, and may include any type of processor, such as a microprocessor, a digital signal processor, a coprocessor, a microcontroller, a microcomputer, a central processing unit, a field programmable gate array, a programmable logic device, a state machine, a logic circuit, an analog circuit, a digital circuit, etc., and any combination thereof. For example, in those embodiments of the system 20 described above that include multiple components integrated with the system 20 (e.g., BP sensing device 24, CBF measurement device 22, etc.), the system controller 26 may include multiple processors; for example, independent processors dedicated to each respective component, any and all of which may communicate with the central processor of the system 20 that coordinates the functions of the system 20. The instructions stored in the memory may represent one or more algorithms for controlling the system 20, and the stored instructions are not limited to any particular form (e.g., program files, system data, buffers, drivers, utilities, system programs, etc.), as long as they are executable by the system controller 26. The instructions are configured to perform the methods and functions described herein.

[0044] System controller 26 may be configured (e.g., via circuitry) to process various received signals (received from integrated or separate components) and may be configured to generate certain signals thereto; for example, signals configured to control one or more components within system 20. Alternatively, system 20 may be configured such that signals from the respective components are sent to one or more intermediate processing devices, and the intermediate processing devices, in turn, may provide processed signals or data to system controller 26. System controller 26 may be configured to execute stored instructions (e.g., algorithmic instructions) that cause system 20 to perform the steps or functions described herein, generate data related to the autoregulatory function of a subject (e.g., measurements, etc.), communicate, etc.

[0045] The memory may be a machine-readable storage medium configured to store instructions that, when executed by one or more processors, cause the one or more processors to perform certain functions or cause the performance of certain functions. The memory may be a single memory device or a plurality of memory devices. The memory devices may be non-transitory devices and may include storage area networks, network attached storage devices, and disk drives, read-only memories, random access memories, volatile memories, non-volatile memories, static memories, dynamic memories, flash memories, cache memories, and / or any device that stores digital information. Based on a review of this disclosure, those skilled in the art will appreciate that the implementation of the system controller 26 may be implemented via the use of hardware, software, firmware, or any combination thereof.

[0046] In some embodiments, system 20 may include one or more output devices 28 and one or more input devices 30. Non-limiting examples of input device 30 include a keyboard, touchpad, or other device into which a user may enter data, commands, or signal information, or a port configured to communicate with an external input device via a hardwired or wireless connection, etc. Non-limiting examples of output device 28 include any type of display, printer, or other device configured to display or transmit information or data generated by system 20. System 20 may be configured to connect to input device 30 or output device 28 via a hardwired connection or a wireless connection.

[0047] The implementation of the techniques, blocks, steps, and means described herein can be accomplished in various ways. For example, these techniques, blocks, steps, and means can be implemented in hardware, software, or a combination thereof. For hardware implementations, a processing device configured to implement the described functions and steps (e.g., by executing stored instructions) can be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, or other electronic units designed to perform the functions described herein and / or any combination thereof.

[0048] Embodiments of the present disclosure may be described herein as processes depicted in flow charts, flow diagrams, block diagrams, and the like. Although any of these logical structures may describe the operations as a sequential process, many of these operations may be performed in parallel or simultaneously or in a rearranged order. A process may correspond to a method, function, procedure, subroutine, subprogram, and the like.

[0049] As described above, determination of the subject's autoregulatory state utilizes a measurement of blood flow (e.g., a measurement of CBF) and a measurement of blood pressure (e.g., MAP or CPP), and the present disclosure permits determination of the LLA and / or ULA for the subject's autoregulatory range without requiring collection of data in regions outside the subject's autoregulatory range (e.g., outside the LLA and / or outside the ULA). The present disclosure is not limited to the use of any particular type of CBF measurement device 22 or BP sensing device 24 to determine data points within the subject's autoregulatory range.

[0050] In a first non-limiting example, the disclosed system utilizes a NIRS tissue oximeter as the CBF measurement device 22. U.S. Patent Publication No. 2020 / 038616 and / or PCT Patent Application No. PCT / US2022 / 027282 (hereinafter collectively referred to as the "NIRS AR system") describe a NIRS tissue oximetry system that includes a NIRS tissue oximeter configured to provide CBF data (e.g., indirectly via physiological parameter data corresponding to CBF, such as NIRS index values ​​(StO2), AR index values, etc.) and autoregulation status data. Two of these patent applications are jointly assigned to the present applicant, and both patent applications are hereby incorporated herein by reference in their respective entireties.

[0051] The NIRS tissue oximeter includes one or more sensors that communicate with a controller portion. Each sensor includes one or more light sources (e.g., light emitting diodes or "LEDs") and one or more light detectors (e.g., photodiodes, etc.). The light sources are configured to emit light at different wavelengths, for example, wavelengths of light in the red or near infrared range; 400 to 1000 nm. In some sensor embodiments, the sensor may be configured to include a light source, a near detector, and a far detector. The near detector is disposed closer to the light source than the far detector. Non-limiting examples of such sensors are disclosed in U.S. Patent No. 8,965,472. The NIRS tissue oximeter may utilize one or more algorithms to determine a NIRS index value that can be used to determine autoregulation data, or the aforementioned algorithms may be used to determine CBF data that can be used to determine autoregulation data. U.S. Patent Nos. 10,117,610; 9,913,601; 9,848,808; 9,456,773; 9,364,175; 9,923,943; 8,965,472; 8,788,004; 8,396,526; 8,078,250; 7,072,701; and 6,456,862 all describe non-limiting examples of NIRS tissue oximeters and corresponding algorithms that may be used with the present disclosure, and are all incorporated herein by reference in their respective entireties.

[0052] In some embodiments, a NIRS tissue oximeter can be used to continuously sense tissue to generate NIRS index data that can be used directly or indirectly (e.g., processed to determine CBF data) for automatic adjustment determination. As used herein, the term "continuously" (with respect to a NIRS tissue oximeter) means that the NIRS tissue oximeter senses and collects subject data on a periodic basis during a monitoring period that is frequent enough to be considered clinically continuous. For example, some NIRS tissue oximeters sample data every ten seconds or less, and can be configured to sample data more frequently (e.g., every two seconds or less).

[0053] The NIRS AR system utilizes real-time data collection of tissue oxygen saturation data, which is used to generate NIRS index data / CBF data and MAP data to generate autoregulatory function data. The NIRS AR system can be configured to generate autoregulatory data using an algorithm based on a frequency domain approach or using an algorithm based on a correlation / regression technique or some combination thereof to generate coherence (COHZ) analysis. Figure 4An exemplary frequency domain method is schematically depicted, which involves acquiring simultaneous blood pressure and NIRS index values ​​within a predetermined sampling window (e.g., a time period). In this exemplary frequency domain method, the blood pressure and NIRS index values ​​are each transformed from the time domain (e.g., via Fourier transform) to the frequency domain (shown as corresponding plots of blood pressure versus frequency and NIRS index versus frequency), and the transformed data are further analyzed to determine the degree of coherence within a single frequency band (i.e., a single frequency band). In some embodiments, the same process may be utilized to determine the degree of coherence in multiple frequency bands for each physiological parameter (i.e., NIRS index and BP), and a collective coherence value or a value representing it may be determined. In either case, the coherence value may be expressed as an autoregulatory representative value, such as an AR index value or a pressure passive index (PPI) value. The degree of coherence (e.g., COHZ value, AR index, or PPI) may be expressed according to an arbitrarily assigned scale of zero to one hundred (e.g., 0 to 100), where the degree of coherence increases from zero to one hundred. A coherence value of one hundred indicates a pressure passive condition as described above. Conversely, a coherence value approaching zero indicates a decreasing relationship between the NIRS index and the blood pressure parameter. Coherence values ​​(e.g., COHZ, AR index, or PPI) determined over a period of time may be binned by blood pressure increments (e.g., every 5 mmHg) or by incremental blood pressure ranges (e.g., 0 mmHg to 20 mmHg, 20 mmHg to 25 mmHg, 25 mmHg to 30 mmHg, etc.).

[0054] In some embodiments of the present disclosure utilizing a NIRS AR system, a coherence determination process may be performed for a plurality of different NIRS indices (e.g., StO2, rTHb, differential changes in O2Hb and HHb, HbD, etc.) In the event that one NIRS index is more sensitive to autoregulatory function than another, performing an autoregulatory function determination process as described herein (e.g., within a single frequency band, or within multiple frequency bands) may provide additional sensitivity and / or more rapid identification of changes in the subject's autoregulatory function.

[0055] The degree of coherence determined as a function of MAP (eg, COHZ value, AR index, or PPI) can be used to generate an autoregulatory curve. Figure 5A graph of AR index and MAP data points within the autoregulation range is illustrated. These data points (in the autoregulation range) represent determined data points based on physiological parameter data sensed from the subject. As will be clear from the following description, the present disclosure avoids the need to collect autoregulation data (e.g., CBF, AR index, NIRS index, MAP, etc.) under conditions below the LLA that may cause the subject to experience ischemia, and / or the need to collect autoregulation data under conditions above the ULA that may cause the subject to experience edema. Those skilled in the art will recognize the clinical benefits associated with avoiding a subject from suffering from a condition that may cause or cause ischemia and / or edema.

[0056] For clarity, the examples provided above illustrate how a NIRS tissue oximeter can be used with a BP sensing device 24 to determine such data points within a subject's autoregulatory range. The non-invasive NIRS tissue oximetry and methods described in U.S. Patent Publication No. 2020 / 038616 and / or PCT Patent Application No. PCT / US2022 / 027282 can provide significant advantages in the determination of autoregulatory data. However, the present disclosure is not limited to the foregoing non-invasive NIRS tissue oximetry and methods. In other embodiments, other devices configured to measure blood flow and blood pressure in different ways (the examples described above) can be used to determine data points within a subject's autoregulatory range.

[0057] The disclosed process for predicting LLA values ​​and / or ULA values ​​includes determining a mathematical function (e.g., a curve fit) that has the best fit to the CBF and MAP data points within the subject's autoregulatory range (the data points are based on sensed data), regardless of the method used to generate such data points. The curve fitting process permits not only characterization of the CBF and MAP data points within the autoregulatory range, but also permits the curve to be extrapolated beyond the data points within the autoregulatory range (based on sensed data); i.e., in a direction toward and through the LLA and / or in a direction toward and through the ULA. After determining an appropriate mathematical function that has the best fit to the CBF and MAP data points within the subject's autoregulatory range, the mathematical function can be used to extrapolate beyond the autoregulatory range, and the LLA and / or ULA can be predicted based on the mathematical function.

[0058] A variety of different techniques are known for fitting mathematical functions to data points, and the present disclosure is not limited to the use of any particular method to fit a mathematical function to the determined CBF and MAP data points collected within the subject's autoregulatory range. A first example of a method that can be used to fit a mathematical function to the determined CBF and MAP data points involves fitting the data points to a cubic polynomial equation as follows:

[0059] f(x)=ax3 +bx 2 +cx+d [Equation 1]

[0060] Where a, b, c, and d are coefficients that can be determined based on the determined CBF and MAP data points. Figure 6 A cubic polynomial equation for the function f(x) with respect to the X-axis is illustrated graphically. For autoranging data fitting purposes, the function f(x) may be constrained to positive values.

[0061] Another non-limiting example of a mathematical function that may be fit to CBF and MAP data points collected within a subject's autoregulatory range is a sigmoid function. Equation 2 below provides an example of a sigmoid function that may be used.

[0062]

[0063] A sigmoid function is a mathematical function having a characteristic "S" shaped curve (sometimes referred to as an "S-shaped curve"). Figure 7 An example of a sigmoid function that can be used with the present disclosure is graphically depicted in FIG. Figure 7 As can be seen in , the S-shaped curve has a unique flat region at two different values ​​plus a curved region that is a transition zone between the two flat regions. Fig.11 Also illustrated is a sigmoid curve fitted to the NIRS and MAP data points.

[0064] As described above, the cubic polynomial equation and the sigmoid function are non-limiting examples of mathematical functions that may be used to produce a best fit to the CBF and MAP data points within the subject's autoregulatory range, and the present disclosure is not limited thereto. In some embodiments, additional mathematical techniques such as nonlinear regression may be used in combination to facilitate the process of fitting a mathematical function to the CBF and MAP data points collected within the subject's autoregulatory range.

[0065] Once a mathematical function (e.g., a cubic polynomial equation, a sigmoid function, etc.) is fit to the CBF and MAP data points within the subject's autoregulatory range, and the function is extrapolated outside the autoregulatory range (i.e., in a direction toward and through the LLA and / or in a direction toward and through the ULA), a prediction of the LLA and / or ULA can be made based on the corresponding extrapolated curve portion. In some embodiments, a determination of the LLA and / or ULA can be made based on an inflection point within the extrapolated curve. The inflection point can be identified in a variety of different ways. For example, an inflection point can be identified using a slope value or based on a comparison of a slope value relative to a predetermined threshold. As another example, an inflection point can be identified based on a rate of change value; for example, a second derivative of the curve. Again, the identification of the inflection point can be based on the rate of change value itself (e.g., a maximum rate of change) or based on a comparison of the rate of change value relative to a predetermined threshold. As another example, an inflection point may be predicted as an "elbow point" on the extrapolated curve; for example, for any curve f(x), an "elbow point" method may be used to find the point "P" on the curve that has a maximum perpendicular distance "d" from the line connecting the first and last points on the curve (see, e.g., Figure 8 ; Satopaa et al., "Finding a needle in a haystack: Detecting knee points in system behavior"; 2011 31st International Symposium on Distributed Computing Systems, IEEE 2011). As another example, the inflection point can be identified using slope and autoregulation data (e.g., CBF value, NIRS index value, AR index value, etc.) within a predetermined threshold based on empirical data.

[0066] 9A to 9C and FIG. 10A to FIG. 10C A specific example illustrating the effectiveness of the present disclosure is provided in As described above, the autoregulatory data may be expressed as a function of blood flow (eg, CBF) and MAP, or as a function of a surrogate of CBF and MAP. Fig.11 Illustrated is a graph of NIRS (eg, StO2) versus MAP that can be developed using NIRS oximetry techniques as described herein. Fig.9A CBF and MAP data points collected during an autoregulation study conducted on piglets under appropriate supervision are illustrated. The autoregulation study collected CBF and MAP data points both within and outside the autoregulatory range; for example, in areas outside of the LLA and ULA. Fig.9A , including the vertical standard deviation range for each CBF and MAP data point. Fig.9AA cubic polynomial curve fitted to the collected CBF and MAP data points is also illustrated. The LLA is determined (e.g., using the techniques described above) at approximately 25 mmHg based on the CBF and MAP data points (i.e., sensed data), including data points set within the autoregulatory range and data points significantly outside the autoregulatory range and the LLA. Fig. 9B The collected CBF and MAP data points between 26 mmHg and 42 mmHg (i.e., only in the autoregulation range) and a cubic polynomial curve fitted to the collected CBF and MAP data points between 26 mmHg and 42 mmHg are illustrated. Visual inspection of the collected CBF and MAP data points between 26 mmHg and 42 mmHg revealed no clear LLA. Fig. 9C Illustrated are collected CBF and MAP data points between 26 mmHg and 42 mmHg, a cubic polynomial curve fitted to the collected CBF and MAP data points between 26 mmHg and 42 mmHg, now including an extrapolated curve portion below 26 mmHg and an extrapolated curve portion above 42 mmHg. The extrapolated curve portion is extrapolated from the curve fit to the curve fit to the collected CBF and MAP data points between 26 mmHg and 42 mmHg, rather than based on sensed data points outside of 26 mmHg and 42 mmHg. The extrapolated curve portion below 26 mmHg is used to predict the LLA; for example, using the techniques described above. The LLA predicted from the extrapolated curve portion below 26 mmHg (e.g., at approximately 25 mmHg) is consistent with the LLA determined using the collected CBF and MAP data points in the autoregulation range and in the region outside of the LLA. It should be understood that if the CBF data in the study were replaced with a NIRS index developed within the study (eg, StO2), the same results would have been obtained.

[0067] Fig. 10A CBF and MAP data points collected during another autoregulatory study conducted on piglets under appropriate guidance are illustrated. The autoregulatory study collected CBF and MAP data points both within and outside the autoregulatory range; for example, in areas outside the LLA and ULA. The CBF and MAP data points (i.e., sensed data) were collected in Fig. 10A , including the vertical standard deviation range for each CBF and MAP data point. Fig. 10A A cubic polynomial curve fitted to the collected CBF and MAP data points is also illustrated. The LLA is determined (e.g., using the techniques described above) between 25 mmHg and 30 mmHg based on the CBF and MAP data points (i.e., sensed data), including data points set in the autoregulatory range and data points significantly outside the autoregulatory range and the LLA. Fig. 10B The collected CBF and MAP data points between 32 mmHg and 45 mmHg (i.e., only in the autoregulation range) and a cubic polynomial curve fitted to the collected CBF and MAP data points between 32 mmHg and 45 mmHg are illustrated. Visual inspection of the collected CBF and MAP data points between 32 mmHg and 45 mmHg revealed no clear LLA. Fig. 10C Illustrated are collected CBF and MAP data points (i.e., sensed data) between 32 mmHg and 45 mmHg, a cubic polynomial curve fitted to the collected CBF and MAP data points between 32 mmHg and 45 mmHg, now including extrapolated curve portions below 32 mmHg and above 45 mmHg. The extrapolated curve portions are extrapolated from the curve fit to the curve fit to the collected CBF and MAP data points between 32 mmHg and 45 mmHg, rather than based on sensed data points outside of 32 mmHg and 45 mmHg. The extrapolated curve portion below 32 mmHg is used to predict the LLA; for example, using the techniques described above. The LLA predicted from the extrapolated curve portion below 32 mmHg (e.g., at approximately 30 mmHg) is consistent with the LLA determined from the collected CBF and MAP data points in the autoregulation range and in the region outside of the LLA. Likewise, it should be understood that if the CBF data in the study were replaced with a NIRS index developed within the study (eg, StO2), the same results would have been obtained.

[0068] 9A to 9C and FIG. 10A to FIG. 10C The example shown is a good illustration of how to predict the LLA based on CBF and MAP data points collected within the subject's autoregulatory range, without using (or needing) CBF and MAP data points collected outside the subject's autoregulatory range (i.e., outside the LLA). The aforementioned methods are equally applicable to determining / predicting the ULA.

[0069] from 9A to 9C and FIG. 10A to FIG. 10CAs can be seen from the examples shown, the present disclosure permits prediction of autoregulatory LLA and / or ULA that avoids the need to collect data in areas outside the autoregulatory range of a subject where the subject may be suffering from ischemia or edema. Predicting LLA and / or ULA before a subject experiences a perfusion pressure outside the full autoregulatory range has important clinical value. In addition, as described above, the process for determining CBF and MAP can be performed on a continuous or near-continuous basis. Therefore, the disclosed method for predicting LLA and / or ULA can also predict the subject's LLA and / or ULA on a continuous or near-continuous basis. Therefore, the present disclosure can provide clinicians with essentially real-time LLA and / or ULA prediction information that may be clinically important when treating a subject.

[0070] As indicated above, the functions described herein may be implemented, for example, in hardware, software tangibly embodied in a computer-readable medium, firmware, or any combination thereof. In some embodiments, at least a portion of the functions described herein may be implemented in one or more computer programs. Each such computer program may be implemented in a computer program product tangibly embodied in a non-transitory signal in a machine-readable storage device for execution by a computer processor. The method steps of the present disclosure may be performed by a computer processor executing a program tangibly embodied on a computer-readable medium to perform the functions of the present disclosure by operating on inputs and generating outputs. Each computer program within the scope of the appended claims may be implemented in any programming language, such as an assembly language, a machine language, a high-level procedural programming language, or an object-oriented programming language. For example, the programming language may be a compiled or interpreted programming language.

[0071] Although the principles of the present disclosure have been described above in conjunction with specific devices and methods, it should be clearly understood that this description is only carried out by way of example, rather than as a limitation on the scope of the present disclosure. Specific details are given in the above description to provide a thorough understanding of the embodiments. However, it should be understood that the embodiments can be practiced without these specific details.

[0072] It is noted that the embodiments may be described as a process depicted as a flow chart, a flowchart, a block diagram, etc. Although any of these structures may describe the operations as a sequential process, many of these operations may be performed in parallel or concurrently. In addition, the order of these operations may be rearranged. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.

[0073] The singular forms "a", "an", and "the" refer to one or more than one, unless the context clearly indicates otherwise. For example, the term "comprising a sample" includes a single or multiple samples and is considered equivalent to the phrase "comprising at least one sample". The term "or" refers to a single element or a combination of two or more elements of the stated alternative elements, unless the context clearly indicates otherwise. As used herein, "comprising" means "including". Thus, "comprising A or B" means "comprising A, or B, or A and B", without excluding additional elements.

[0074] It is noted that various connections between elements are set forth in this specification and the drawings (the contents of which are incorporated by reference into this disclosure). It is noted that these connections are general and, unless otherwise specified, may be direct or indirect, and this specification is not intended to be limiting in this regard. Any reference to attaching, fixing, connecting, etc. may include permanent, removable, temporary, partial, complete, and / or any other possible attachment options.

[0075] No element, component, or method step in this disclosure is intended to be dedicated to the public, regardless of whether the element, component, or method step is explicitly stated in the claims. No claim element herein shall be interpreted under the provisions of 35 U.S.C. 112(f) unless the element is explicitly stated using the phrase "means for..." As used herein, the term "comprises" or any other variation thereof is intended to cover non-exclusive inclusions, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements, but may also include other elements that are not explicitly listed or inherent to the process, method, article, or apparatus.

[0076] Although various inventive aspects, concepts and features of the present disclosure may be described and illustrated herein as being embodied in combination in exemplary embodiments, these various aspects, concepts and features may be used in many alternative embodiments individually or in various combinations and sub-combinations thereof. Unless expressly excluded herein, all such combinations and sub-combinations are intended to be within the scope of the present application. Further, although various alternative embodiments of various aspects, concepts and features of the present disclosure may be described herein, such as alternative structures, configurations, methods, devices and components, etc., such descriptions are not intended to be a complete or exhaustive list of available alternative embodiments, whether currently known or later developed. A person skilled in the art may easily make one or more of the inventive aspects, concepts and features suitable for use in additional embodiments and within the scope of the present application, even if such embodiments are not explicitly disclosed herein. For example, in the above exemplary embodiments within the specific embodiments section of this specification, elements may be described as separate units and are shown as being independent of each other for ease of description. In alternative embodiments, such elements may be configured as combined elements. It should be further noted that various methods or process steps of embodiments of the present disclosure are described herein. The description may present methods and / or process steps as a specific sequence. However, to the extent that the method or process does not rely on the specific order of the steps listed herein before, the method or process should not be limited to the specific sequence of the steps described. As will be appreciated by those of ordinary skill in the art, other step sequences are also possible.

[0077] In addition, although some features, concepts or aspects of the present disclosure may be described herein as preferred arrangements or methods, such descriptions are not intended to indicate that such features are required or essential unless explicitly stated. Further, exemplary or representative values ​​and ranges may be included to aid in understanding the present application, however, such values ​​and ranges are not to be interpreted as limiting and are intended to be critical values ​​or ranges only when so explicitly stated.

[0078] The treatment techniques, methods, and procedures described or suggested herein or in references incorporated herein may be performed on living animals or in non-living simulations, such as on cadavers, cadaver hearts, anthropomorphic phantoms, or mimics (e.g., having simulated body parts or tissues).

[0079] Any of the various systems, devices, equipment, etc. in the present disclosure may be sterilized (e.g., using heat, radiation, ethylene oxide, hydrogen peroxide) to ensure that they are safe for use by patients, and the methods herein may include sterilizing the associated systems, devices, equipment, etc.; e.g., using heat, radiation, ethylene oxide, hydrogen peroxide.

Claims

1. A method for determining the limits of an autoregulatory range for a subject, the method comprising: determining a blood flow value representing blood flow of the subject within an autoregulatory range of the subject during a time period; determining a blood pressure value representing the subject's blood pressure within the autoregulatory range of the subject during the time period; determining a mathematical function representing the blood flow value and the blood pressure value within the automatic adjustment range; using the mathematical function to generate representations of the blood flow values ​​and the blood pressure values ​​outside of the autoregulation range; as well as The representation of the blood flow values ​​and the blood pressure values ​​outside the autoregulatory range is used to predict limits to the autoregulatory range.

2. The method of claim 1, wherein the predicted limit is an automatically adjusted lower limit. The method of claim 1 , wherein the predicted limit is an automatically adjusted upper limit.

4. The method of claim 1, wherein the step of determining a blood flow value representative of the subject's blood flow in the autoregulatory range of the subject during the time period is performed non-invasively.

5. The method of claim 4, wherein the step of determining a blood flow value representative of the subject's blood flow in the autoregulatory range of the subject during the time period is performed using a NIRS oximeter.

6. The method of claim 1, wherein the step of determining a blood flow value representative of the subject's blood flow in the autoregulatory range of the subject during the time period comprises determining the subject's cerebral blood flow.

7. The method of claim 1 , wherein the step of determining a blood pressure value representative of the subject's blood pressure in the autoregulatory range of the subject during the time period comprises determining a mean arterial blood pressure (MAP) value of the subject or a parameter derived from the MAP value.

8. The method of claim 1, wherein the step of determining the mathematical function comprises fitting a cubic polynomial equation to the blood flow values ​​and the blood pressure values ​​within the subject's autoregulatory range.

9. The method of claim 8, wherein the step of using the mathematical function to generate a representation of the blood flow values ​​and the blood pressure values ​​outside the autoregulation range comprises extrapolating the cubic polynomial equation to represent the blood flow values ​​and the blood pressure values ​​outside the autoregulation range.

10. The method of claim 1, wherein the step of determining the mathematical function comprises fitting one or more sigmoid functions to the blood flow values ​​and the blood pressure values ​​within the subject's autoregulatory range.

11. The method of claim 10, wherein the step of using the mathematical function to generate a representation of the blood flow values ​​and the blood pressure values ​​outside the autoregulation range comprises extrapolating the one or more sigmoid functions to represent the blood flow values ​​and the blood pressure values ​​outside the autoregulation range.

12. The method of claim 1, wherein the step of determining a blood flow value representing the subject's blood flow within the autoregulatory range of the subject is performed continuously during the time period; and wherein the step of determining a blood pressure value representing the blood pressure flow of the subject within the autoregulatory range of the subject is performed continuously during the time period; and The step of predicting the limit of the autoregulatory range using the representation of the blood flow values ​​and the blood pressure values ​​outside the autoregulatory range is performed in real time.

13. A system configured to determine the limits of an autoregulatory range for a subject, the system comprising: a blood flow measurement device configured to determine a blood flow value representative of a blood flow of a subject within an autoregulatory range of the subject during a time period and configured to generate a first signal representative of the blood flow value; a blood pressure sensing device configured to determine a blood pressure value representative of the blood pressure of the subject in the autoregulatory range of the subject during the time period and configured to generate a second signal representative of the blood flow value; and a system controller in communication with the blood flow measurement device and the blood pressure sensing device, the system controller comprising at least one processor and a memory device configured to store instructions that, when executed, cause the system controller to: controlling the blood flow measurement device to determine the blood flow value representative of the subject's blood flow in the autoregulatory range of the subject during the time period and to generate the first signal representative of the blood flow value; controlling the blood pressure sensing device to determine the blood pressure value representing the blood pressure of the subject within the autoregulatory range of the subject during the time period and to generate the second signal representing the blood pressure value; using the first signal and the second signal to determine a mathematical function representing the blood flow value and the blood pressure value within the automatic regulation range; using the mathematical function to generate representations of the blood flow values ​​and the blood pressure values ​​outside of the autoregulation range; as well as The representation of the blood flow values ​​and the blood pressure values ​​outside the autoregulatory range is used to predict limits to the autoregulatory range.

14. The system of claim 13, wherein the predicted limit is at least one of an automatically regulated lower limit or an automatically regulated upper limit.

15. The system of claim 13, wherein the blood flow measurement device is a NIRS oximeter configured to non-invasively determine blood flow values.

16. The system of claim 13, wherein the blood pressure sensing device is configured to determine a mean arterial blood pressure (MAP) value of the subject or a parameter derived from the MAP value.

17. A system according to claim 13, wherein the mathematical function is a cubic polynomial equation fit to the blood flow values ​​and the blood pressure values ​​within the subject's autoregulatory range, and the instructions when executed cause the system controller to extrapolate the cubic polynomial equation to represent the blood flow values ​​and the blood pressure values ​​outside the autoregulatory range.

18. The system of claim 13, wherein the mathematical function is a sigmoid function fit to the blood flow values ​​and the blood pressure values ​​within the subject's autoregulatory range, and the instructions, when executed, cause the system controller to extrapolate the sigmoid function to represent the blood flow values ​​and the blood pressure values ​​outside the autoregulatory range.

19. The system of claim 13, wherein the instructions, when executed, cause the system controller to: controlling the blood flow measurement device to continuously determine the blood flow value representing the blood flow of the subject in the autoregulatory range during the time period; as well as controlling the blood pressure sensing device to continuously determine the blood pressure values ​​representing the blood pressure of the subject in the autoregulatory range during the time period; as well as The representation of the blood flow values ​​and the blood pressure values ​​outside the autoregulation range is used to predict the limits of the autoregulation range in real time.

20. The system of claim 19, wherein the predicted limit is at least one of an automatically regulated lower limit or an automatically regulated upper limit.

21. A non-transitory computer readable medium storing executable instructions that, when executed, cause at least one processor to: controlling a blood flow measurement device to determine a blood flow value representative of the subject's blood flow within the subject's autoregulatory range during a time period and to generate a first signal representative of the blood flow value; controlling a blood pressure sensing device to determine a blood pressure value representative of the subject's blood pressure within the autoregulatory range of the subject during the time period and to generate a second signal representative of the blood pressure value; using the first signal and the second signal to determine a mathematical function representing the blood flow value and the blood pressure value within the automatic regulation range; using the mathematical function to generate representations of the blood flow values ​​and the blood pressure values ​​outside of the autoregulation range; as well as The representation of the blood flow values ​​and the blood pressure values ​​outside the autoregulatory range is used to predict limits to the autoregulatory range.

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