Method and system for predicting the limits of a subject's autoregulatory range

Abstract

A method and system for predicting the limits of a subject's autoregulatory range is provided, comprising the steps of: a) determining blood flow values representative of the subject's blood flow within the subject's autoregulatory range over a period of time; b) determining blood pressure values representative of the subject's blood pressure within the subject's autoregulatory range over a period of time; c) determining a mathematical function representative of blood flow versus blood pressure values within the autoregulatory range; d) using the mathematical function to generate a representation of blood flow versus blood pressure values outside the autoregulatory range; and e) predicting the limits of the autoregulatory range using the representation of blood flow versus blood pressure values outside the autoregulatory range.

Description

[Technical Field]

[0001] FIELD OF THE DISCLOSURE The present disclosure relates generally to medical systems and methods, and more particularly to medical systems and methods for predicting the limits of a subject's autoregulatory range. [Background technology]

[0002] Autoregulation is a process in mammals aimed at maintaining adequate and stable (e.g., "constant") blood flow to organs (e.g., brain, heart, kidneys) against various perfusion pressures. Different organs exhibit different degrees of autoregulatory behavior. The renal, cerebral, and coronary circulations typically exhibit excellent autoregulation, whereas the skeletal muscle and splanchnic circulations exhibit moderate autoregulation. The cutaneous circulation exhibits little or no autoregulatory capacity.

[0003] Cerebral autoregulation can be defined as maintaining constant cerebral blood flow (CBF) despite changes in cerebral perfusion pressure (CPP), where CPP is the mean arterial pressure (MAP) minus the intracranial pressure (ICP). For ease of discussion, CPP and MAP will be discussed in terms of MAP unless otherwise specified. Cerebral autoregulation plays an important protective role against the risk of cerebral hypoxia / ischemia at low MAP and cerebral edema at high MAP. Indeed, in normotensive humans, cerebral blood flow remains relatively, but not absolutely, constant across a range of perfusion pressures; this range is commonly referred to as the autoregulatory range (sometimes also called the autoregulatory plateau). The lower end of the autoregulatory range is usually referred to as the lower limit of autoregulation (LLA), and the upper end of the autoregulatory range is usually referred to as the upper limit of autoregulation (ULA). Figure 1 is a schematic graph of CBF versus CPP showing the autoregulatory curve with the autoregulatory range defined by the LLA and ULA. As mentioned 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 high CPP / MAP. When the limits of autoregulation (i.e., the LLA and ULA) are reached, cerebral blood flow passively increases (above the ULA) or decreases (below the LLA) with increases or decreases in perfusion pressure. It is important to note that the LLA and ULA are not "hard points" but rather points at which the flow / pressure relationship maintained by autoregulation begins to change.

[0004] As discussed here, the LLA and ULA can vary significantly from subject to subject, are influenced by factors such as the subject's overall health, dietary factors, and arterial blood carbon dioxide partial pressure levels (hypercapnia, hypocapnia, eucapnia), and may change over time in a given subject. Historically, a subject's LLA or ULA may be determined by monitoring the subject's CBF and CPP and assessing changes in the collected data (e.g., inflection points of the curves) to determine the extent of their autoregulatory range. However, such data collection approaches require collecting data in areas outside the LLA and / or outside the ULA, i.e., areas where the subject may be exposed to ischemia or edema. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] US Patent Application Publication No. 2020 / 038616 [Patent Document 2] International Application No. PCT / US2022 / 027282 [Patent Document 3] U.S. Patent No. 8,965,472 [Patent Document 4] U.S. Patent No. 10,117,610 [Patent Document 5] U.S. Patent No. 9,913,601 [Patent Document 6] U.S. Patent No. 9,848,808 [Patent Document 7] U.S. Patent No. 9,456,773 [Patent Document 8] U.S. Patent No. 9,364,175 [Patent Document 9] U.S. Patent No. 9,923,943 [Patent Document 10] U.S. Patent No. 8,788,004 [Patent Document 11] U.S. Patent No. 8,396,526 [Patent Document 12] U.S. Patent No. 8,078,250 [Patent Document 13] U.S. Patent No. 7,072,701 [Patent Document 14] U.S. Patent No. 6,456,862 [Non-patent literature]

[0006] [Non-Patent Document 1] Satopaa et al., "Finding a needle in a haystack: Detecting knee points in system behavior," in 2011 31st International Conference on Distributed Computing Systems Workshops, IEEE 2011 Summary of the Invention [Problem to be solved by the invention]

[0007] What is needed is a system and method for monitoring autoregulation that allows for predicting LLA before MAP reaches LLA and / or that allows for predicting ULA before MAP reaches ULA. [Means for solving the problem]

[0008] According to one aspect of the present disclosure, a method for predicting limits of a subject's autoregulatory range is provided, the method including: a) determining blood flow values representative of the subject's blood flow in the subject's autoregulatory range over a period of time; b) determining blood pressure values representative of the subject's blood pressure in the subject's autoregulatory range over a period of time; c) determining a mathematical function representing blood flow values versus blood pressure values within the autoregulatory range; d) generating a representation of blood flow values versus blood pressure values outside the autoregulatory range using the mathematical function; and e) predicting the limits of the autoregulatory range using the representation of blood flow values versus blood pressure values outside the autoregulatory range.

[0009] In any aspect of the embodiments described above and herein, the predicted limit is at least one of a lower limit of autoregulation or an upper limit of autoregulation.

[0010] In any aspect of the embodiments described above and herein, the step of determining a blood flow value representative of the subject's blood flow in the subject's autoregulatory range over a period of time may be performed non-invasively and may be performed using a near-infrared spectroscopy (NIRS) oximeter.

[0011] In any aspect of the embodiments described above and herein, the step of determining a blood flow value representative of the subject's blood flow in the subject's autoregulatory range over a period of time may include determining the subject's cerebral blood flow.

[0012] In any aspect of the embodiments described above and herein, determining a blood pressure value representative of the subject's blood pressure in the subject's autoregulatory range over a period of time may include determining the subject's mean arterial blood pressure (MAP) value or a parameter derived from the MAP value.

[0013] In any aspect of the embodiments described above and herein, determining the mathematical function may include fitting a third-order polynomial equation to blood flow versus blood pressure values within the subject's autoregulatory range, and generating a representation of blood flow versus blood pressure values outside the autoregulatory range using the mathematical function may include extrapolating the third-order polynomial equation to represent blood flow versus blood pressure values outside the autoregulatory range.

[0014] In any aspect of the embodiments described above and herein, the step of determining the mathematical function may include fitting one or more sigmoid functions to blood flow versus blood pressure values within the subject's autoregulatory range.

[0015] In any aspect of the embodiments described above and herein, the step of determining blood flow values representative of the subject's blood flow in the subject's autoregulatory range may be performed continuously over a period of time, the step of determining blood pressure values representative of the subject's blood flow in the subject's autoregulatory range may be performed continuously over a period of time, and the step of predicting the limits of the autoregulatory range using the representation of blood flow values versus blood pressure values outside the autoregulatory range may be performed in real time.

[0016] According to another aspect of the present disclosure, there is provided a system configured to determine limits of a subject's autoregulatory range, the system including a blood flow measurement device, a blood pressure sensor device, and a system controller. The blood flow measurement device is configured to determine a blood flow value representative of the subject's blood flow within the subject's autoregulatory range over a period of time and generate a first signal representative of the blood flow value. The blood pressure sensor device is configured to determine a blood pressure value representative of the subject's blood pressure within the subject's autoregulatory range over a period of time and generate a second signal representative of the blood pressure value. The system controller is in communication with the blood flow measurement device and the blood pressure sensor 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) determine a blood flow value representative of the subject's blood flow within the subject's autoregulatory range over a period of time and control the blood flow measurement device to generate a first signal representative of the blood flow value; b) determine a blood pressure value representative of the subject's blood pressure within the subject's autoregulatory range over a period of time and control the blood pressure sensor device to generate a second signal representative of the blood pressure value; c) determine a mathematical function using the first and second signals to represent blood flow versus blood pressure values within the autoregulatory range; d) generate a representation of blood flow versus blood pressure values outside the autoregulatory range using the mathematical function; and e) predict the limits of the autoregulatory range using the representation of blood flow versus blood pressure values outside the autoregulatory range.

[0017] In any aspect 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.

[0018] In any aspect of the embodiments described above and herein, the blood pressure sensor device may be configured to determine the subject's mean arterial blood pressure (MAP) value or a parameter derived from the MAP value.

[0019] In any aspect of the embodiments described above and herein, the mathematical function may be a third-order polynomial equation that fits to blood flow versus blood pressure values within the subject's autoregulatory range, and when the instructions are executed, the controller may extrapolate the third-order polynomial equation to represent blood flow versus blood pressure values outside the autoregulatory range.

[0020] In any aspect of the embodiments described above and herein, the mathematical function may be a sigmoid function that fits blood flow versus blood pressure values within the subject's autoregulatory range, and when the instructions are executed, the controller may extrapolate the sigmoid function to represent blood flow versus blood pressure values outside the autoregulatory range.

[0021] In any aspect of the embodiments described above and herein, when the instructions are executed, the controller may a) control the blood flow measurement device to continuously determine blood flow values representative of the subject's blood flow in the autoregulation range over a period of time, b) control the blood pressure sensor device to continuously determine blood pressure values representative of the subject's blood pressure in the autoregulation range over a period of time, and c) predict in real time the limits of the autoregulation range using the representation of blood flow values versus blood pressure values outside the autoregulation range.

[0022] According to another aspect of the present disclosure, a non-transitory computer-readable medium is provided that stores executable instructions that, when executed, configure at least one processor to: a) determine blood flow values representative of a subject's blood flow within the subject's autoregulatory range over a period of time and control a blood flow measurement device to generate a first signal representative of the blood flow values, b) determine blood pressure values representative of the subject's blood pressure within the subject's autoregulatory range over a period of time and control a blood pressure sensor device to generate a second signal representative of the blood pressure values, c) determine a mathematical function using the first and second signals to represent blood flow versus blood pressure values within the autoregulatory range, d) generate a representation of blood flow versus blood pressure values outside the autoregulatory range using the mathematical function, and e) predict limits of the autoregulatory range using the representation of blood flow versus blood pressure values outside the autoregulatory range.

[0023] The foregoing features and elements may be combined in various non-exclusive combinations unless expressly indicated otherwise. These features and elements, and their operation, will become more apparent in light of the following description and accompanying drawings. It should be understood, however, that the following description and drawings are illustrative in nature and not limiting. [Brief explanation of the drawings]

[0024] [Figure 1] 1 is a graphical graph of CBF versus CPP showing the autoregulatory curve, LLA, ULA, and autoregulatory range / plateau. [Figure 2] 1 is a diagrammatic representation of an embodiment of the disclosed system. [Figure 3] 1 is a diagrammatic representation of an embodiment of the disclosed system. [Figure 4] 1 is a diagrammatic representation of an exemplary frequency domain method. [Figure 5] 1 is a graphical graph of AR index versus MAP showing data points collected over the autoregulatory range. [Figure 6] A graphical illustration of a cubic polynomial equation of a function f(x) on the X-axis. [Figure 7] Shows the sigmoid function. [Figure 8] 1 is a graph showing an example of the elbow point technique. [Figure 9A] Graph of CBF vs. MAP showing data points collected in an animal study, a third-order polynomial curve fitted to the data points within and beyond the autoregulatory range, and LLA determined from the data points. [Figure 9B] FIG. 9B is a graph of CBF versus MAP showing the data points shown in FIG. 9A, where only data points collected within the autoregulatory range are shown, a third-order polynomial curve fitted to the data points. [Figure 9C] 9B is a graph of CBF versus MAP showing the data points shown in Figure 9A, showing only the data points collected within the autoregulatory range, a third-order polynomial curve fitted to the data points, now extrapolated beyond the autoregulatory range. [Figure 10A] Graph of CBF vs. MAP showing data points collected in an animal study, a third-order polynomial curve fitted to the data points within and beyond the autoregulatory range, and LLA determined from the data points. [Figure 10B] FIG. 10B is a graph of CBF versus MAP showing the data points shown in FIG. 10A, where only the data points collected within the autoregulatory range are shown, a third-order polynomial curve fitted to the data points. [Figure 10C] 10B is a graph of CBF versus MAP showing the data points shown in FIG. 10A , showing only the data points collected within the autoregulatory range, a third-order polynomial curve fitted to the data points, now extrapolated beyond the autoregulatory range. [Figure 11] 1 is a graphical graph of NIRS versus MAP showing the autoregulation curve based on a sigmoidal curve fitted to the data points. DETAILED DESCRIPTION OF THE INVENTION

[0025] The present disclosure relates to a system 20 and method for monitoring autoregulatory function that allows prediction of the lower autoregulatory limit (LLA) of a subject's autoregulatory range (i.e., autoregulatory plateau) and / or the upper autoregulatory limit (ULA) of a subject's autoregulatory range without the need to collect data outside the subject's autoregulatory range (i.e., outside the LLA and / or ULA), as well as a non-transitory computer-readable medium containing instructions for performing the disclosed method.

[0026] As noted above, autoregulatory processes in mammals aim to maintain adequate and stable (e.g., "constant") blood flow to organs (e.g., brain, heart, kidneys) over a range of perfusion pressures. To facilitate explanation of the present disclosure, the present disclosure will be described with respect to cerebral autoregulation. The present disclosure is not limited to cerebral autoregulation and may also be used to determine the autoregulatory function of other organs.

[0027] Determining a subject's autoregulatory status utilizes measurements of blood flow and blood pressure. With respect to cerebral autoregulation, determining autoregulatory status utilizes measurements of cerebral blood flow (CBF) or surrogates thereof, and measurements of cerebral perfusion pressure (CPP) or mean arterial pressure (MAP). The term "surrogate" as used herein in conjunction with CBF refers to a physiological parameter corresponding to CBF, or something similar to CBF, or something from which CBF may be determined. For ease of explanation, the term "CBF" as used herein is intended to refer to cerebral blood flow and / or surrogates for cerebral blood flow, unless otherwise indicated. CBF can be measured in a variety of ways. CBF measurement devices 22 that invasively measure CBF may use technologies such as flow laser Doppler flowmetry. CBF measurement devices 22 that measure CBF directly or indirectly (e.g., via surrogates) may use non-invasive technologies such as near-infrared spectroscopy (NIRS), transcranial Doppler ultrasound imaging, phase-contrast MRI, and arterial spin labeling MRI (ASL-MRI). This disclosure is not limited to any particular methodology for measuring CBF or configuration of CBF measurement device 22. Non-limiting examples provided herein include CBF measurement device 22 in the form of a NIRS tissue oximeter capable of producing blood parameter measurements that are surrogates for CBF (e.g., NIRS indices such as StO2, rTHb, differential changes in O2Hb and HHb).

[0028] MAP may be measured in a variety of ways. Non-limiting examples of blood pressure sensor devices ("BP sensor devices 24") that may be used to measure a subject's MAP include an arterial catheter line, a continuous noninvasive blood pressure device, a pulse oximetry sensor, etc. However, the present disclosure is not limited to the use of any particular type of BP sensor device. The BP sensor device 24 may be configured to generate a MAP measurement continuously. As used herein, the term "continuously" (with respect to the BP sensor device 24) means that the BP sensor device 24 senses and collects subject data periodically during the monitoring period, the periodicity being sufficiently frequent to be considered clinically continuous. For example, some BP sensor devices 24 sample data every 10 seconds or less, and may also be configured to sample data more frequently (e.g., every 2 seconds or less).

[0029] Non-limiting examples of the disclosed system 20 are illustrated in FIGS. 2 and 3. The embodiment of system 20 illustrated in FIG. 2 is configured to include system components (e.g., CBF measurement device 22, BP sensor device 24, system controller 26, etc.) integrated into a single system device. The embodiment of system 20 illustrated in FIG. 3 is configured to include system controller 26 and other system components (e.g., CBF measurement device 22, BP sensor device 24, system controller 26, etc.) that are independently configured and communicate with (e.g., receive signal data from and / or transmit signal data to) system controller 26. In other words, in the embodiment of system 20 as illustrated in FIG. 3, system 20 may be configured to communicate with BP sensor device 24, which can function independently of system 20, or with a CBF measurement device, which can function independently of system 20. In other embodiments, system 20 may include some combination of these components, either integrated or independent. In embodiments in which one or more of the aforementioned components are independently configured, the independent components may communicate with system controller 26 in any manner, for example, wired, wireless, etc.

[0030] System controller 26 may include any type of computing device, computing circuit, or processing circuit capable of executing a sequence of instructions stored in memory. System controller 26 may include multiple processors and / or multi-core CPUs, and may include any type of processor, such as a microprocessor, digital signal processor, coprocessor, microcontroller, microcomputer, central processing unit, field programmable gate array, programmable logic device, state machine, logic circuit, analog circuit, digital circuit, or any combination thereof. For example, in the embodiment of system 20 described above, if system 20 includes multiple integrated components (e.g., BP sensor device 24, CBF measurement device 22, etc.), system controller 26 may include multiple processors, e.g., independent processors dedicated to each component, and any and all of these processors may communicate with a central processor of system 20 that coordinates the functioning of system 20. The instructions stored in memory may represent one or more algorithms for controlling system 20, and the stored instructions are not limited to a particular format (e.g., program files, system data, buffers, drivers, utilities, system programs, etc.), as long as they are executable by system controller 26. The instructions are configured to perform the methods and functions described herein.

[0031] System controller 26 may be configured to process various received signals (received from integrated or separate components) and generate specific signals for the same, e.g., signals configured to control one or more components in system 20. Alternatively, system 20 may be configured such that signals from each component are transmitted to one or more intermediate processing devices, which 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 and generate and communicate data (e.g., measurements) related to the subject's autoregulatory functions.

[0032] A 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 or execute particular functions. A memory may be a single memory device or multiple memory devices. A memory device may be a non-transitory device and may include a storage area network, network-attached storage, a disk drive, read-only memory, random-access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and / or any device that stores digital information. One skilled in the art will understand, based on a review of this disclosure, that implementation of system controller 26 can be achieved using hardware, software, firmware, or any combination thereof.

[0033] 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 devices 30 include a keyboard, touchpad, or other device through which a user can input data, commands, or signal information, or a port configured to communicate with an external input device via a hardwired or wireless connection. Non-limiting examples of output devices 28 include any type of display, printer, or other device configured to display or communicate information or data generated by system 20. System 20 may be configured to connect to input devices 30 or output devices 28 via a hardwired or wireless connection.

[0034] The techniques, blocks, steps, and means described herein can be implemented in various ways. For example, these techniques, blocks, steps, and means can be implemented in hardware, software, or a combination thereof. In the case of a hardware implementation, a processing device configured to perform the described functions and steps (e.g., by executing stored instructions) may 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.

[0035] Embodiments of the present disclosure may be described herein as a process that is depicted as a flowchart, flow diagram, block diagram, etc. While any of these logical structures may describe operations as a sequential process, many operations may be performed in parallel, simultaneously, or out of order. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.

[0036] As described above, determining a subject's autoregulatory status utilizes measurements of blood flow (e.g., CBF) and blood pressure (e.g., MAP or CPP), and the present disclosure enables determination of the subject's LLA and / or ULA of the autoregulatory range without requiring data collection outside the subject's autoregulatory range (e.g., outside the LLA and / or outside the ULA). The present disclosure is not limited to using a particular type of CBF measuring device 22 or BP sensor device 24 to determine data points within the subject's autoregulatory range.

[0037] In a first non-limiting example, the disclosed system utilizes a NIRS tissue oximeter as the CBF measuring device 22. U.S. Patent Application Publication No. 2007 / 0129999 and / or U.S. Patent Application Publication No. 2007 / 0129999 (collectively, "NIRS AR systems") describe NIRS tissue oximetry systems that include a NIRS tissue oximeter configured to provide CBF data (e.g., indirectly via physiological parameter data corresponding to CBF, such as an NIRS index value (StO2), an AR index value, etc.) and autoregulatory status data. These patent applications are all commonly assigned to the present applicant and are incorporated herein by reference in their entireties.

[0038] A NIRS tissue oximeter includes one or more sensors in communication with a controller portion. Each sensor includes one or more light sources (e.g., light-emitting diodes, or "LEDs") and one or more photodetectors (e.g., photodiodes, etc.). The light sources are configured to emit light at different wavelengths, e.g., wavelengths of light in the red or near-infrared range, 400 nm-1000 nm. In some sensor embodiments, the sensor may be configured to include a light source, a near-field detector, and a far-field detector. The near-field detector is positioned closer to the light source than the far-field detector. Non-limiting examples of such sensors are disclosed in U.S. Patent No. 6,229,999. A NIRS tissue oximeter can utilize one or more algorithms to determine NIRS index values, which can be used to determine autoregulation data, or the aforementioned algorithms can be used to determine CBF data, which can be used to determine autoregulation data. U.S. Patent Nos. 6,229,999; 6,229,999; and 6,330,434 all describe non-limiting examples of NIRS tissue oximeters and their respective algorithms that may be used in the present disclosure, and are all incorporated herein by reference in their entirety.

[0039] In some embodiments, a NIRS tissue oximeter can be used to continuously sense tissue and generate NIRS index data that may be used directly or indirectly to determine autoregulation (e.g., processed to determine CBF data). As used herein, the term "continuously" (with respect to a NIRS tissue oximeter) means that the NIRS tissue oximeter senses and collects subject data periodically during the monitoring period, the periodicity being frequent enough to be considered clinically continuous. For example, some NIRS tissue oximeters sample data every 10 seconds or less, and can be configured to sample data more frequently (e.g., every 2 seconds or less).

[0040] NIRS AR systems utilize real-time data collection of tissue oxygen saturation data, which are used to generate NIRS index data / CBF data, MAP data, and autoregulatory function data. NIRS AR systems can be configured to generate coherence (COHZ) analysis using algorithms based on frequency-domain techniques, algorithms based on correlation / regression techniques, or a combination thereof to generate autoregulatory data. Figure 4 illustrates an exemplary frequency-domain technique, which involves acquiring synchronized blood pressure and NIRS index values over a predetermined sampling window (e.g., a fixed period of time). In this exemplary frequency-domain method, blood pressure and NIRS index values are each transformed (e.g., via a Fourier transform) from the time domain to the frequency domain (shown as plots of blood pressure vs. frequency and NIRS index vs. frequency, respectively), and the transformed data is further analyzed to determine the degree of coherence within a single band of frequency (i.e., a single frequency band). In some embodiments, the same process can be used to determine the degree of coherence across multiple frequency bands for each physiological parameter (i.e., NIRS index and blood pressure) to determine a collective coherence value or a value representative thereof. In either case, the coherence value may be expressed as a value representing autoregulation, 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 on an arbitrarily assigned zero-to-one hundred scale (e.g., 0-100), with the degree of coherence increasing from zero to one hundred. A coherence value of one hundred represents a pressure-passive state, as described above. Conversely, as the coherence value approaches zero, it indicates an increasingly weak 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., 5 mmHg increments) or incremental blood pressure ranges (e.g., 0 mmHg-20 mmHg, 20 mmHg-25 mmHg, 25 mmHg-30 mmHg, etc.).

[0041] In some embodiments of the present disclosure utilizing a NIRS AR system, the coherence determination process may be performed for multiple different NIRS indices (e.g., StO2, rTHb, differential changes in O2Hb and HHb, HbD, etc.). If some NIRS indices are more sensitive to autoregulatory function than others, performing the autoregulatory function determination process described herein (e.g., within a single frequency band or within multiple frequency bands) may allow for additional sensitivity or more rapid identification of changes in a subject's autoregulatory function.

[0042] The degree of coherence (e.g., COHZ value, AR index, or PPI) determined as a function of MAP may be used to generate an autoregulation curve. Figure 5 shows a graph of AR index and MAP data points within the autoregulation range. These data points represent data points determined based on physiological parameter data sensed from the subject within the autoregulation range. As will be apparent from the following discussion, the present disclosure eliminates the need to collect autoregulation data (e.g., CBF, AR index, NIRS index, MAP, etc.) under conditions below the LLA, where the subject may experience ischemia, and / or the need to collect autoregulation data under conditions above the ULA, where the subject may experience edema. Those skilled in the art will recognize the clinical benefits associated with avoiding subjects from conditions that may cause ischemia or edema.

[0043] For clarity, the above example illustrates how a NIRS tissue oximeter can be used in conjunction with a BP sensor device 24 to determine such data points within a subject's autoregulatory range. Noninvasive NIRS tissue oximetry and the methodology described in U.S. Patent No. 6,275,999 and / or U.S. Patent No. 6,275,999 can offer distinct advantages in determining autoregulatory data. However, this disclosure is not limited to the aforementioned noninvasive NIRS tissue oximetry and methodology. In other embodiments, other devices (such as the above examples) configured to measure blood flow and blood pressure in different ways can be used to determine data points within a subject's autoregulatory range.

[0044] The disclosed process for predicting LLA and / or ULA values involves determining a mathematical function (e.g., curve fitting) that best fits the CBF vs. MAP data points within a subject's autoregulatory range (these data points are based on sensed data), regardless of the method used to generate such data points. The curve fitting process not only characterizes the CBF vs. MAP data points within the autoregulatory range, but also allows for the curve to be extrapolated beyond the data points (based on sensed data) within the autoregulatory range, i.e., toward and beyond the LLA or toward and beyond the ULA. After a suitable mathematical function that best fits the CBF vs. MAP data points within a subject's autoregulatory range is determined, 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.

[0045] Various techniques for fitting a mathematical function to data points are known, and the present disclosure is not limited to the use of a particular methodology for fitting a mathematical function to determined CBF vs. MAP data points collected within a subject's autoregulatory range. A first example methodology for fitting a mathematical function to determined CBF vs. MAP data points includes fitting the data points to a third-order polynomial equation such as:

number

[0046] Another non-limiting example of a mathematical function that may be fitted to CBF vs. MAP data points collected within a subject's autoregulatory range is a sigmoid function. Equation 2 below shows an example of a sigmoid function that may be used.

number

[0047] As noted above, a third-order polynomial equation and a sigmoid function are non-limiting examples of mathematical functions that may be used to generate an optimal fit to the CBF and MAP data points within a subject's autoregulatory range, and the present disclosure is not limited thereto. In some embodiments, additional mathematical techniques, such as non-linear regression, can be incorporated to facilitate the process of fitting a mathematical function to the CBF and MAP data points collected within a subject's autoregulatory range.

[0048] If a mathematical function (e.g., a third-order polynomial equation, a sigmoid function, etc.) is fitted to the CBF vs. MAP data points within the subject's autoregulatory range and the function is extrapolated beyond the autoregulatory range (i.e., toward and beyond the LLA or toward and beyond the ULA), a prediction of the LLA and / or ULA can be made based on the respective extrapolated curve portions. In some embodiments, the determination of the LLA and / or ULA can be made based on inflection points in the extrapolated curve. Inflection points may be identified in various ways. For example, inflection points may be identified using slope values or based on comparing the slope values to a predetermined threshold. As another example, inflection points may be identified based on rate-of-change values, e.g., the second derivative of the curve. Again, the identification of inflection points may be based on the rate-of-change value itself (e.g., the maximum rate of change) or based on comparing the rate-of-change value to a predetermined threshold. As another example, inflection points may be predicted as "elbow points" on the extrapolated curve; for example, for any curve f(x), the "elbow point" approach can be used to find the point "P" with the greatest perpendicular distance "d" to the line connecting the first and last points on the curve (see, e.g., FIG. 8, Non-Patent Document 1). As another example, inflection points may be identified using slope and autoregulation data (e.g., CBF values, NIRS index values, AR index values, etc.) that are within a predetermined threshold set based on empirical data.

[0049] Specific examples demonstrating the effectiveness of the present disclosure are shown in Figures 9A-9C and 10A-10C. As previously discussed, autoregulation data may be expressed as a function of blood flow (e.g., CBF) and MAP, or as a function of surrogates of CBF and MAP. Figure 11 shows a graph of NIRS (e.g., StO2) vs. MAP that may be developed using the NIRS oximetry techniques described herein. Figure 9A shows CBF vs. MAP data points collected in an autoregulation study on piglets conducted under appropriate guidelines. In the autoregulation study, CBF vs. MAP data points were collected within and beyond the autoregulatory range, e.g., in regions outside the LLA and ULA. The CBF vs. MAP data points are shown in Figure 9A, including the vertical standard deviation range for each CBF vs. MAP data point. Figure 9A also shows a third-order polynomial curve fitted to the collected CBF vs. MAP data points. The LLA was determined (e.g., using the techniques described above) at approximately 25 mmHg based on CBF vs. MAP data points (i.e., sensed data), including data points located within the autoregulatory range and data points clearly outside the autoregulatory range and the LLA. Figure 9B shows a third-order polynomial curve fit for collected CBF vs. MAP data points between 26 mmHg and 42 mmHg (i.e., only within the autoregulatory range) and collected CBF vs. MAP data points between 26 mmHg and 42 mmHg. Visual inspection of the collected CBF vs. MAP data points between 26 mmHg and 42 mmHg reveals no clear LLA. Figure 9C shows a third-order polynomial curve fit for collected CBF vs. MAP data points between 26 mmHg and 42 mmHg, including extrapolated curve portions below 26 mmHg and above 42 mmHg. The extrapolated curve portion was extrapolated from a curve fit to the collected CBF vs. MAP data points between 26 mmHg and 42 mmHg and was not based on sensed data points outside of 26 mmHg and 42 mmHg. The extrapolated curve portion below 26 mmHg is used to predict LLA using the techniques described above.The predicted LLA from the extrapolated curve portion below 26 mmHg (e.g., at approximately 25 mmHg) is consistent with the LLA determined using CBF vs. MAP data points collected within the autoregulatory range and in regions outside the LLA. It is understood that the same results were reached even when the CBF data from the study were replaced with a NIRS index developed within the study (e.g., StO2).

[0050] Figure 10A shows CBF vs. MAP data points collected in another autoregulation study on piglets conducted under appropriate guidelines. In the autoregulation study, CBF vs. MAP data points were collected within and beyond the autoregulatory range, e.g., in regions outside the LLA and ULA. The CBF vs. MAP data points (i.e., sensed data) are shown in Figure 10A, including the vertical standard deviation range for each CBF vs. MAP data point. Figure 10A also shows a third-order polynomial curve fitted to the collected CBF vs. MAP data points. The LLA was determined (e.g., using the techniques described above) between 25 mmHg and 30 mmHg based on the CBF vs. MAP data points (i.e., sensed data), and included data points located within the autoregulatory range as well as data points clearly outside the autoregulatory range and the LLA. Figure 10B shows a third-order polynomial curve fit to the collected CBF vs. MAP data points between 32 mmHg and 45 mmHg (i.e., only within the autoregulatory range) and the collected CBF vs. MAP data points between 32 mmHg and 45 mmHg. Visual inspection of the collected CBF vs. MAP data points between 32 mmHg and 45 mmHg reveals no obvious LLA. Figure 10C shows a third-order polynomial curve fit to the collected CBF vs. MAP data points between 32 mmHg and 45 mmHg (i.e., sensed data) and the collected CBF vs. MAP data points between 32 mmHg and 45 mmHg, including extrapolated curve portions below 32 mmHg and above 45 mmHg. The extrapolated curve portions are extrapolated from the curve fit to the collected CBF vs. MAP data points between 32 mmHg and 45 mmHg and are not based on sensed data points outside of 32 mmHg and 45 mmHg. The portion of the extrapolated curve below 32 mmHg is used to predict the LLA using the techniques described above. The predicted LLA (e.g., at approximately 30 mmHg) from the portion of the extrapolated curve below 32 mmHg is consistent with the LLA determined from CBF vs. MAP data points collected in regions within the autoregulatory range and outside the LLA.Again, it is understood that the same results were reached when CBF data from the study were replaced with a NIRS index developed within the study (e.g., StO2).

[0051] The examples shown in Figures 9A-9C and 10A-10C clearly illustrate how LLA can be predicted based on CBF vs. MAP data points collected within a subject's autoregulatory range without using (or requiring) CBF vs. MAP data points collected outside the subject's autoregulatory range (i.e., outside the LLA). The methodology described above applies equally to determining / predicting ULA.

[0052] As can be seen from the examples shown in Figures 9A-9C and 10A-10C, the present disclosure enables prediction of autoregulatory LLA and / or ULA, eliminating the need to collect data in areas outside the autoregulatory range where a subject may be exposed to ischemia or edema. Predicting LLA and / or ULA before a subject experiences perfusion pressures outside the full autoregulatory range has significant clinical value. Furthermore, as described above, the process of determining CBF and MAP can be performed continuously or nearly continuously. The presently disclosed methodology for predicting LLA and / or ULA can therefore also provide continuous or nearly continuous prediction of a subject's LLA and / or ULA. Thus, the present disclosure may provide clinicians with substantially real-time LLA and / or ULA prediction information that is clinically important in treating a subject.

[0053] As noted above, the functionality 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 functionality 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, performing operations on inputs and generating outputs to perform the functions of the present disclosure. Each computer program within the scope of the present claims may be implemented in any programming language, such as assembly language, machine language, a high-level procedural programming language, or an object-oriented programming language. The programming language may be, for example, a compiled or interpreted programming language.

[0054] While the principles of the disclosure have been described above with reference to specific devices and methods, it should be clearly understood that this description is illustrative only and is not intended to be a limitation on the scope of the disclosure. Specific details are set forth in the above description to provide a thorough understanding of the embodiments. However, it will be understood that embodiments may be practiced without these specific details.

[0055] The embodiments may be described as a process that is depicted as a flowchart, flow diagram, block diagram, etc. While any of these structures may describe operations as a sequential process, many operations may be performed in parallel or concurrently. Additionally, the order of operations may be rearranged. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.

[0056] The singular forms "a," "an," and "the" refer to one or more than one, unless the context clearly dictates otherwise. For example, the term "comprising a specimen" includes one or more specimens and is considered equivalent to the phrase "comprising at least one specimen." The term "or" refers to a single element of the stated alternative elements or a combination of two or more elements, unless the context clearly dictates otherwise. As used herein, "comprises" means "includes." Thus, "comprising A or B" means "including A, B, or A and B," without excluding additional elements.

[0057] It is noted that various connections between elements in this description and in the drawings are shown, the contents of which are incorporated by reference into this disclosure. These connections are general and, unless otherwise specified, may be direct or indirect, and this specification is not intended to be limiting in this respect. References to attached, fixed, connected, or the like may include permanent, detachable, temporary, partial, complete, and / or other possible attachment options.

[0058] 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 expressly recited in a claim. No claim element described herein is to be construed under the provisions of 35 U.S.C. 112(f) unless expressly recited using the phrase "means for." As used herein, "comprises," "comprising," or any other variation thereof, is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus comprising a list of elements does not include only those elements, but may include other elements not expressly listed or inherent in such process, method, article, or apparatus.

[0059] While various inventive aspects, concepts, and features of the present disclosure may be described and illustrated as being embodied in combination in exemplary embodiments, these various aspects, concepts, and features may be used individually or in various combinations and subcombinations in many alternative embodiments. Unless expressly excluded herein, all such combinations and subcombinations are intended to be within the scope of the present application. Furthermore, while various alternative embodiments (e.g., alternative structures, configurations, methods, devices, components, etc.) of various aspects, concepts, and features of the disclosure may be described herein, these descriptions are not intended to be a complete or exhaustive list of all available alternative embodiments, whether currently known or later developed. Those skilled in the art may readily employ one or more of the inventive aspects, concepts, and features in additional embodiments or applications within the scope of the present application, even in embodiments not explicitly disclosed herein. For example, in the example embodiments described above, within the "Detailed Description" section of this specification, elements may be described as individual units and shown separately from each other for ease of description. In alternative embodiments, such elements may be configured as combined elements. It is also noted that various method or process steps related to embodiments of the present disclosure are described herein. The description may present method and / or process steps in a particular order. However, unless the method or process depends on the particular order of steps set forth herein, the method or process should not be limited to the particular order of steps described. As one of ordinary skill in the art would understand, other orders of steps may be possible.

[0060] Furthermore, even if some features, concepts, or aspects of the disclosure are described herein as preferred arrangements or methods, such description does not imply that the feature is necessary or essential unless expressly stated. Furthermore, to aid in understanding the present application, example or representative values and ranges may be included, but such values and ranges should not be construed in a limiting sense, and only when explicitly stated are significant values or ranges intended.

[0061] The treatment techniques, methods, and procedures described or suggested herein or incorporated by reference may be performed in live animals or cadavers, non-biological simulations such as cadaver hearts, anthropomorphic ghosts, or simulators (e.g., where body parts or tissues are simulated).

[0062] Various systems, devices, apparatus, etc. included in this disclosure may be sterilized (e.g., heat, radiation, ethylene oxide, hydrogen peroxide) to ensure safety for use on patients, and the methods described herein may include sterilization (e.g., heat, radiation, ethylene oxide, hydrogen peroxide) of the associated systems, devices, apparatus, etc.

Claims

1. 1. A method for determining the limits of a subject's autoregulatory range, comprising: determining a blood flow value representative of the subject's blood flow in the subject's autoregulatory range over a period of time; determining a blood pressure value representative of the subject's blood pressure within the subject's autoregulatory range during the period of time; determining a mathematical function representing the blood flow value versus the blood pressure value within the autoregulatory range; generating a representation of the blood flow value versus the blood pressure value outside the autoregulatory range using the mathematical function; and predicting a limit of the autoregulatory range using the representation of the blood flow value versus the blood pressure value outside the autoregulatory range.

2. The method of claim 1 , wherein the predicted limit is a lower limit of autoregulation.

3. The method of claim 1 , wherein the predicted limit is an upper limit of autoregulation.

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

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

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

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

8. 2. The method of claim 1, wherein the step of determining the mathematical function comprises fitting a third-order polynomial equation to the blood flow values versus the blood pressure values within the subject's autoregulatory range.

9. 9. The method of claim 8, wherein the step of generating a representation of the blood flow versus blood pressure values outside the autoregulatory range using the mathematical function comprises extrapolating the third-order polynomial equation to represent the blood flow versus blood pressure values outside the autoregulatory range.

10. 2. 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 versus the blood pressure values within the subject's autoregulatory range.

11. 11. The method of claim 10, wherein the step of generating a representation of the blood flow versus blood pressure values outside the autoregulatory range using the mathematical function comprises extrapolating the one or more sigmoidal functions to represent the blood flow versus blood pressure values outside the autoregulatory range.

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

13. 1. A system configured to determine limits of a subject's autoregulation range, comprising: a blood flow measurement device configured to determine a blood flow value representative of the subject's blood flow within the subject's autoregulatory range over a period of time and to generate a first signal representative of the blood flow value; a blood pressure sensor device configured to determine a blood pressure value representative of the subject's blood pressure within the subject's autoregulatory range during the period of time and to generate a second signal representative of the blood pressure value; a system controller in communication with the blood flow measurement device and the blood pressure sensor device, the system controller including at least one processor and a memory device configured to store instructions, the stored instructions, when executed, causing the system controller to: determining a blood flow value representative of the subject's blood flow in the subject's autoregulatory range during the period of time and controlling the blood flow measurement device to generate the first signal representative of the blood flow value; determining a blood pressure value representative of the subject's blood pressure within the subject's autoregulatory range during the period of time and controlling the blood pressure sensor device to generate the second signal representative of the blood pressure value; determining a mathematical function representing the blood flow value versus the blood pressure value within the autoregulatory range using the first signal and the second signal; using the mathematical function to generate a representation of the blood flow value versus the blood pressure value outside the autoregulatory range; The system predicts limits of the autoregulatory range using the representation of the blood flow versus blood pressure values outside the autoregulatory range.

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

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

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

17. 14. The system of claim 13, wherein the mathematical function is a third-order polynomial equation that fits the blood flow versus blood pressure values within the subject's autoregulatory range, and wherein, when the instructions are executed, the system controller extrapolates the third-order polynomial equation to represent the blood flow versus blood pressure values outside the autoregulatory range.

18. 14. The system of claim 13, wherein the mathematical function is a sigmoid function that fits the blood flow versus blood pressure values within the subject's autoregulatory range, and wherein, when the instructions are executed, the system controller extrapolates the sigmoid function to represent the blood flow versus blood pressure values outside the autoregulatory range.

19. When the instructions are executed, the system controller: controlling the blood flow measurement device to continuously determine the blood flow value representative of the subject's blood flow within the autoregulatory range during the period of time; controlling the blood pressure sensor device to continuously determine the blood pressure value representative of the subject's blood pressure within the autoregulatory range during the period of time; The system of claim 13 , wherein the representation of the blood flow versus blood pressure values outside the autoregulatory range is used to predict the limits of the autoregulatory range in real time.

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

21. A non-transitory computer-readable medium storing executable instructions that, when executed, cause at least one processor to: determining a blood flow value representative of the subject's blood flow within the subject's autoregulatory range for a period of time and controlling the blood flow measurement device to generate a first signal representative of the blood flow value; determining a blood pressure value representative of the subject's blood pressure within the subject's autoregulatory range during the period of time and controlling the blood pressure sensor device to generate a second signal representative of the blood pressure value; determining a mathematical function representing the blood flow value versus the blood pressure value within the autoregulatory range using the first signal and the second signal; using the mathematical function to generate a representation of the blood flow value versus the blood pressure value outside the autoregulatory range; A non-transitory computer-readable medium for predicting limits of the autoregulatory range using the representation of the blood flow value versus the blood pressure value outside the autoregulatory range.

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