Patent Application
20240008749
The present disclosure relates generally to hemodynamic monitoring, and in particular to detecting and predicting nociception in a patient using monitored hemodynamic data.
Nociception is the process in which nerve endings called nociceptors detect noxious stimuli and send a signal to the central nervous system which is interpreted as pain. Nociception can cause automatic responses without reaching consciousness or before reaching consciousness, thus an unconscious patient in surgery can experience pain. To prevent a patient from awaking out of surgery in pain, medical workers administer analgesics to the patient before and/or during surgery. However, knowing the amount of analgesic to administer can be difficult as pain thresholds and tolerances vary from patient to patient and the patient is unable to verbally communicate or signal feedback while unconscious. Administering too little analgesic to the patient during surgery can result in the patient awaking in pain after the surgery. Administering too much analgesic to the patient during surgery can result in the patient experiencing nausea, drowsiness, impaired thinking skills, and impaired function.
In view of the negative consequences of administering too little analgesic to the patient and the negative consequences of administering too much analgesic to the patient, a solution is needed that will allow medical workers the ability to detect or predict nociception of an unconscious patient during surgery. Accurately detecting or predicting nociception of a patient during surgery can help medical workers know the appropriate amount of analgesic to administer to the patient so that the patient does not awake from surgery with significant pain, and without providing too much analgesic to the patient.
In one example, a hemodynamic monitor is disclosed for detecting nociception of a patient. The hemodynamic monitor includes a non-invasive blood pressure sensor with an inflatable blood pressure bladder, a pressure controller pneumatically connected to the inflatable blood pressure bladder, and an optical transmitter and an optical receiver that are electrically connected to the pressure controller. The hemodynamic monitor also includes an integrated hardware unit with a system processor, a system memory, and a display with a user interface. The system memory includes nociception detection instructions that, when executed by the system processor, are configured to: adjust, by the pressure controller, a pressure within the inflatable blood pressure bladder to maintain a constant volume of an artery of a patient for a period of time based on a feedback signal generated by the optical transmitter and the optical receiver; generate an arterial pressure waveform data of the patient based on the adjusted pressure within the inflatable blood pressure bladder over the period of time; extract a plurality of signal measures from the arterial pressure waveform data of the patient; extract detection input features from the plurality of signal measures that are indicative of a current nociception event of the patient; determine a nociception score of the patient based on the detection input features; generate a first sensory alarm signal configured to generate a first sensory alert that indicates that the patient is experiencing the current nociception event when the nociception score satisfies a predetermined detection criterion; transmit the first sensory alarm signal to the user interface; and output the first sensory alert through the user interface.
In another example, a hemodynamic monitor is disclosed for detecting nociception of a patient. The hemodynamic monitor includes an arterial blood pressure sensor with a housing, a fluid input port connected via tubing to a fluid source, and a catheter-side fluid port connected to a catheter for insertion within an arterial system of the patient. The arterial blood pressure sensor further includes a pressure transducer in communication with the fluid source through the fluid port and an I/O cable in electrical communication with the pressure transducer. The hemodynamic monitor further includes an integrated hardware unit with a system processor, a system memory, a display with a user interface, and an analog-to-digital (ADC) converter. The system memory includes instructions that, when executed by the system processor, are configured to: receive an electrical signal from the pressure transducer over a period of time, the electrical signal based on a pressure of the arterial system of the patient transmitted through the fluid source; convert the electrical signal to a digital signal; generate an arterial pressure waveform data of the patient based on the digital signal; extract a plurality of signal measures from the arterial pressure waveform data of the patient; extract detection input features from the plurality of signal measures that are indicative of a nociception event of the patient; determine a nociception score of the patient based on the detection input features; generate a sensory alarm signal configured to generate a sensory alert that indicates that the patient is experiencing a current nociception event when the nociception score satisfies a predetermined detection criterion; transmit the sensory alarm signal to the user interface; and output the sensory alert through the user interface.
In another example, a method is disclosed for monitoring of arterial pressure of a patient for current or predicted future nociception of the patient. The method includes receiving, by a hemodynamic monitor, a continuous signal from a blood pressure sensor connected to the patient over a period of time. The hemodynamic monitor generates continuously over the period of time an arterial pressure waveform of the patient based on the signal. The hemodynamic monitor extracts a plurality of signal measures from the arterial pressure waveform of the patient. Input features are extracted by the hemodynamic monitor from the plurality of signal measures that are indicative of a current nociception event of the patient and predictive of a future nociception event of the patient. The hemodynamic monitor determines, based on the input features, a nociception score representing a probability of the current nociception event of the patient and/or a probability of the future nociception event of the patient. The hemodynamic monitor invokes a sensory alarm to produce a sensory signal in response to the nociception score satisfying a predetermined level criterion. An analgesic is administered to the patient when the nociception score satisfies the predetermined level criterion.
As described herein, a hemodynamic monitoring system implements a predictive model that produces risk scores representing a probability of a current nociception event for a patient, a probability of a future nociception event for the patient, and a probability that the patient is experiencing a stable episode. The predictive model of the hemodynamic monitoring system can also optionally produce a risk score representing a probability that the patient is experiencing the effects of a previously administered hemodynamic drug (hereinafter referred to as a “hemodynamic drug administration event”) and is not experiencing a current nociception event. The predictive model of the hemodynamic monitoring system can also optionally produce a risk score representing a probability that the patient is experiencing the onset of a future hemodynamic drug administration event and is not experiencing the onset of a future nociception event.
The predictive model of the hemodynamic monitoring system uses machine learning to extract sets of input features from the arterial pressure of the patient. The sets of input features are used by the hemodynamic monitoring system to produce the above-described risk scores for the patient during operation in, e.g., an operating room (OR), an intensive care unit (ICU), or other patient care environment. Depending on the level of the risk scores, the hemodynamic monitoring system can raise a signal or an alarm to medical workers to alert the medical workers that the patient is experiencing a nociception event or will soon be experiencing a nociception event. After receiving the signal, the medical workers can administer an analgesic to the patient to mitigate or prevent the nociception event. If the risk scores determine that the patient is experiencing a hemodynamic drug event, or will soon be experiencing a hemodynamic drug event, the hemodynamic monitoring system can send a signal to the medical workers so that the medical workers do not confuse the hemodynamic drug event of the patient with a nociception event.
The machine learning of the predictive model of the hemodynamic monitoring system is trained using a clinical data set containing arterial pressure waveforms labeled with clinical annotations of administration of analgesics, vasopressors, inotropes, fluids, and other medication that alter cardiovascular hemodynamics. The hemodynamic monitoring system is described in detail below with reference to
As further described below, hemodynamic monitor 10 includes one or more processors and computer-readable memory that stores nociception detection and prediction software code which is executable to produce a score representing a probability of a present (i.e., current) nociception event for a patient and/or a score representing a probability of a future nociception event for the patient. Hemodynamic monitor 10 can receive sensed hemodynamic data representative of an arterial pressure waveform of the patient, such as via one or more hemodynamic sensors connected to hemodynamic monitor 10 via I/O connectors 14. Hemodynamic monitor 10 executes the nociception prediction software code to obtain, using the received hemodynamic data, multiple nociception profiling parameters (e.g., input features), which can include one or more vital sign parameters characterizing vital sign data of the patient, as well as differential and combinatorial parameters derived from the one or more vital sign parameters, as is further described below.
As illustrated in
As illustrated in
In operation, a column of fluid (e.g., saline solution) is introduced from a fluid source (e.g., a saline bag) through hemodynamic sensor 16 via fluid input port 20 to catheter-side fluid port 22 toward the catheter inserted into the patient. Arterial pressure is communicated through the fluid column to pressure sensors located within housing 16 which sense the pressure of the fluid column. Hemodynamic sensor 16 translates the sensed pressure of the fluid column to an electrical signal via the pressure transducers and outputs the corresponding electrical signal to hemodynamic monitor 10 (
In operation, the pressure controller continually adjusts pressure within the finger cuff to maintain a constant volume of the arteries in the finger (i.e., the unloaded volume of the arteries) as measured via the optical transmitter and optical receiver of inflatable finger cuff 28. The pressure applied by the pressure controller to continuously maintain the unloaded volume is representative of the blood pressure in the finger, and is communicated by the pressure controller to hemodynamic monitor 10 shown in
As illustrated in
Hemodynamic monitor 10, as described above with respect to
As illustrated in
Hemodynamic sensor 34 can be attached to patient 36 to sense hemodynamic data representative of the arterial pressure waveform of patient 36. Hemodynamic sensor 34 is operatively connected to hemodynamic monitor 10 (e.g., electrically and/or communicatively connected via wired or wireless connection, or both) to provide the sensed hemodynamic data to hemodynamic monitor 10. In some examples, hemodynamic sensor 34 provides the hemodynamic data representative of the arterial pressure waveform of patient 36 to hemodynamic monitor 10 as an analog signal, which is converted by ADC 44 to digital hemodynamic data representative of the arterial pressure waveform. In other examples, hemodynamic sensor 34 can provide the sensed hemodynamic data to hemodynamic monitor 10 in digital form, in which case hemodynamic monitor 10 may not include or utilize ADC 44. In yet other examples, hemodynamic sensor 34 can provide the hemodynamic data representative of the arterial pressure waveform of patient 36 to hemodynamic monitor 10 as an analog signal, which is analyzed in its analog form by hemodynamic monitor 10.
Hemodynamic sensor 34 can be a non-invasive or minimally invasive sensor attached to patient 36. For instance, hemodynamic sensor 34 can take the form of minimally invasive hemodynamic sensor 16 (
In certain examples, hemodynamic sensor 34 can be configured to sense an arterial pressure of patient 36 in a minimally invasive manner. For instance, hemodynamic sensor 34 can be attached to patient 36 via a radial arterial catheter inserted into an arm of patient 36. In other examples, hemodynamic sensor 34 can be attached to patient 36 via a femoral arterial catheter inserted into a leg of patient 36. Such minimally invasive techniques can similarly enable hemodynamic sensor 34 to provide substantially continuous beat-to-beat monitoring of the arterial pressure of patient 36 over an extended period of time, such as minutes or hours.
System processor 40 is a hardware processor configured to execute nociception software code 48, which implements first module 50, second module 51, and third module 52 to produce a nociception score representing a probability of a current nociception event or a probability of a future nociception event for patient 36. Examples of system processor 40 can include any one or more of a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other equivalent discrete or integrated logic circuitry.
System memory 42 can be configured to store information within hemodynamic monitor 10 during operation. System memory 42, in some examples, is described as computer-readable storage media. In some examples, a computer-readable storage medium can include a non-transitory medium. The term “non-transitory” can indicate that the storage medium is not embodied in a carrier wave or a propagated signal. In certain examples, a non-transitory storage medium can store data that can, over time, change (e.g., in RAM or cache). System memory 42 can include volatile and non-volatile computer-readable memories. Examples of volatile memories can include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories. Examples of non-volatile memories can include, e.g., magnetic hard discs, optical discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories.
Display 12 can be a liquid crystal display (LCD), a light-emitting diode (LED) display, an organic light-emitting diode (OLED) display, or other display device suitable for providing information to users in graphical form. User interface 54 can include graphical and/or physical control elements that enable user input to interact with hemodynamic monitor 10 and/or other components of hemodynamic monitoring system 32. In some examples, user interface 54 can take the form of a graphical user interface (GUI) that presents graphical control elements presented at, e.g., a touch-sensitive and/or presence sensitive display screen of display 12. In such examples, user input can be received in the form of gesture input, such as touch gestures, scroll gestures, zoom gestures, or other gesture input. In certain examples, user interface 54 can take the form of and/or include physical control elements, such as a physical buttons, keys, knobs, or other physical control elements configured to receive user input to interact with components of hemodynamic monitoring system 32.
In operation, hemodynamic sensor 34 senses hemodynamic data representative of an arterial pressure waveform of patient 36. Hemodynamic sensor 34 provides the hemodynamic data (e.g., as analog sensor data), to hemodynamic monitor 10. ADC 44 converts the analog hemodynamic data to digital hemodynamic data representative of the arterial pressure waveform of the patient.
Nociception software code 48 can include nociception detection software code. System processor 40 executes the nociception detection software code of nociception software code 48 to determine, using the received hemodynamic data, a nociception detection score representing a probability of a current nociception event for patient 36. For instance, system processor 40 can execute first module 50 to perform waveform analysis of the hemodynamic data to determine a plurality of signal measures. System processor 40 executes second module 51 to extract nociception detection input features from the plurality of signal measures that detect the nociception event of patient 36. System processor 40 executes third module 52 to determine, based on the nociception detection input features, a nociception detection score representing a probability of the nociception event of patient 36. If the nociception detection score satisfies a predetermined detection criterion, system processor 40 invokes sensory alarm 58 of user interface 54 to send a first sensory signal to alert medical worker 38 that patient 36 is presently experiencing a current nociception event. Medical worker 38 can respond to the warning by administering an analgesic to patient 36, or administering any other form of treatment to patient 36, to mitigate the current nociception event.
Nociception software code 48 can also include nociception prediction software code. System processor 40 executes the nociception prediction software code of nociception software code 48 to determine, using the received hemodynamic data, a nociception prediction score representing a probability of a future nociception event for patient 36. For instance, system processor 40 can execute first module 50 to perform waveform analysis of the hemodynamic data to determine the plurality of signal measures. System processor 40 executes second module 51 to extract nociception prediction input features from the plurality of signal measures that predict the future nociception event of patient 36. System processor 40 executes third module 52 to determine, based on the nociception prediction input features, a nociception prediction score representing a probability of the future nociception event of patient 36. If the nociception prediction score satisfies a predetermined prediction criterion, system processor 40 invokes sensory alarm 58 of user interface 54 to send a second sensory signal to alert medical worker 38 that patient 36 will soon be experiencing a future nociception event. Medical worker 38 can respond to this warning by administering an analgesic to patient 36, or administering any other form of treatment to patient 36, to mitigate or prevent the onset of the predicted future nociception event.
In addition to detecting current nociception events and predicting future nociception events of patient 36, hemodynamic monitoring system 32 can discern when patient 36 is experiencing a current nociception event and when patient 36 is merely reacting to a hemodynamic drug previously administered to patient 36 by medical worker 38 (hereinafter referred to as a hemodynamic drug administration event). The hemodynamic drug administration event is defined as an event where patient 36 experiences an increase in heart rate and an increase in blood pressure due to the administration of a compound that alters cardiovascular hemodynamics (e.g., analgesics, vasopressors, inotropes, fluids, and/or other medication) and is not a nociception event of patient 36. Nociception software code 48 includes hemodynamic drug detection software code for detecting the presence of hemodynamic drug administration event of patient 36. System processor 40 executes the hemodynamic drug detection software code of nociception software code 48 to determine, using the received hemodynamic data, a hemodynamic drug detection score representing a probability that the hemodynamic drug administration event is responsible for increasing a heart rate and a blood pressure of patient 36. For instance, system processor 40 can execute first module 50 to perform waveform analysis of the hemodynamic data to determine the plurality of signal measures. System processor 40 executes second module 51 to extract hemodynamic drug detection input features from the plurality of signal measures that detect the current effects of the hemodynamic drug administration event of patient 36. System processor 40 executes third module 52 to determine, based on the hemodynamic drug detection input features, the hemodynamic drug detection score of patient 36. If the hemodynamic drug detection score satisfies a predetermined hemodynamic detection criterion, system processor 40 invokes sensory alarm 58 of user interface 54 to send a third sensory signal to alert medical worker 38 that patient 36 is experiencing a hemodynamic drug administration event and not a current nociception event. The hemodynamic drug detection score and the third sensory signal help to prevent medical worker 38 from confusing the hemodynamic drug administration event with a nociception event and prevent medical worker 38 from unnecessarily administering analgesics to patient 36.
Nociception software code 48 also includes hemodynamic drug prediction software code for detecting the onset of a future hemodynamic drug administration event of patient 36. System processor 40 executes the hemodynamic drug prediction software code of nociception software code 48 to determine, using the received hemodynamic data, a hemodynamic drug prediction score representing a probability that the hemodynamic drug administration event is responsible for increasing a heart rate and a blood pressure of patient 36. For instance, system processor 40 can execute first module 50 to perform waveform analysis of the hemodynamic data to determine the plurality of signal measures. System processor 40 executes second module 51 to extract hemodynamic drug prediction input features from the plurality of signal measures that detect the onset of the hemodynamic drug administration event of patient 36. System processor 40 executes third module 52 to determine, based on the hemodynamic drug prediction input features, the hemodynamic drug prediction score of patient 36. If the hemodynamic drug prediction score satisfies a predetermined hemodynamic prediction criterion, system processor 40 invokes sensory alarm 58 of user interface 54 to send a fourth sensory signal to alert medical worker 38 that patient 36 will soon be experiencing a hemodynamic drug administration event. The hemodynamic drug prediction score and the fourth sensory signal help to prevent medical worker 38 from confusing the future hemodynamic drug administration event with a future nociception event and prevent medical worker 38 from unnecessarily administering analgesics to patient 36.
System memory 42 of hemodynamic monitor 10 can also include stable detection software code for detecting a stable episode of patient 36. The stable episode is defined as a period during which patient 36 does not experience a nociception event or a hemodynamic drug administration event. The stable detection software code can be a subpart of nociception software code 48. System processor 40 executes stable detection software code to extract stable detection input features from the plurality of signal measures. Stable detection software code can extract the stable detection input features from the plurality of signal measures using second module 51. The stable detection input features detect the stable episode of patient 36. System processor 40 executes third module 52 to determine, based on the stable detection input features, a stable score of patient 36. System processor 40 outputs the stable score of patient 36 to user interface 54 of display 12.
System processor 40 can execute first module 50 to extract a single batch of the plurality of signal measures for a given unit of time, and that single batch of signal measures can be used by second module 51 to extract all of nociception detection input features, nociception prediction input features, hemodynamic drug detection input features, hemodynamic drug prediction input features, and stable detection input features for that unit of time. Second module 51 can extract all of nociception detection input features, nociception prediction input features, hemodynamic drug detection input features, hemodynamic drug prediction input features, and stable detection input features concurrently from the plurality of signal measures. System processor 40 can execute third module 52 to concurrently determine the nociception detection score, the nociception prediction score, the hemodynamic drug detection score, the hemodynamic drug prediction score, and the stable score. Nociception software code 48 of hemodynamic monitor 10 can utilize, in some examples, a classification-type machine learning model with binary positive versus negative labels. Processor 40 can, in certain examples, output the nociception detection score and the hemodynamic drug detection score together to display 12 to compare and contrast the two probabilities and help medical worker 38 better understand whether a nociception event or a hemodynamic drug administration event is causing the increase in blood pressure and heart rate in patient 36.
Alternatively, nociception software code 48 of hemodynamic monitor 10 can utilize a multi-class machine learning model with three labels: nociception event verses hemodynamic drug event verses stable episode. For example, processor 40 can output to display 12 the nociception detection score with both the stable score and the hemodynamic drug detection score, so that all three probabilities are compared together: the probability the patient is undergoing a current nociception event, the probability that the patient is experiencing a current hemodynamic drug administration event, and the probability that the patient is stable. As discussed below with reference to
As shown in
Prediction data segments 71 in
Hemodynamic drug prediction data segments 85 in
To machine train hemodynamic monitor 10 to identify the nociception detection input features described in
Signal measures are extracted from each of the systolic rise phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles of each of the arterial pressure waveforms of nociception data segments 66. The signal measures can correspond to hemodynamic effects from each of the systolic rise phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles. Those hemodynamic effects can include contractility, aortic compliance, stroke volume, vascular tone, afterload, and full cardiac cycle. The signal measures calculated or extracted by the waveform analysis of first step 88 of method 86 includes a mean, a maximum, a minimum, a duration, an area, a standard deviation, derivatives, and/or morphological measures from each of the systolic rise phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles. The signal measures can also include heart rate, respiratory rate, stroke volume, pulse pressure, pulse pressure variation, stroke volume variation, mean arterial pressure (MAP), systolic pressure (SYS), diastolic pressure (DIA), heart rate variability, cardiac output, peripheral resistance, vascular compliance, and/or left-ventricular contractility extracted from each of the individual cardiac cycles of each of the arterial pressure waveforms of nociception data segments 66.
After the signal measures are determined for nociception data segments 66, step 90 of method 86 is performed on the signal measures of nociception data segments 66. Step 90 of method 86 computes combinatorial measures between the signal measures of nociception data segments 66. Computing the combinatorial measures between the signal measures of nociception data segments 66 can include performing steps 92, 94, 96, and 98 shown in
Similar to how method 86 was applied to nociception data segments 66, method 86 is applied to prediction data segments 71, stable data segments 72, HDA data segments 78, and HDP data segments 85 in clinical data set 60 to determine the nociception prediction input features, the stable detection input features, the hemodynamic drug detection input features, and the hemodynamic drug prediction input features respectively.
While the invention has been described with reference to an exemplary embodiment(s), it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment(s) disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
This application claims priority to the PCT application having International Application No. PCT/US2022/020988, filed Mar. 18, 2022, and entitled “HEMODYNAMIC MONITOR WITH NOCICEPTION PREDICTION AND DETECTION.” The above-identified PCT application in turn claims priority to U.S. Provisional Patent Application Ser. No. 63/165,702, filed Mar. 24, 2021 and entitled “HEMODYNAMIC MONITOR WITH NOCICEPTION PREDICTION AND DETECTION.” The disclosures of the above patent applications are hereby incorporated by reference in their entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63165702 | Mar 2021 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/US2022/020988 | Mar 2022 | US |
| Child | 18473090 | US |
