Identifying suitable candidates for denervation therapy

Abstract

An example computing device, the example computing device comprising: a memory; and one or more processors coupled to the memory, the one or more processors configured to: determine a baseline circadian pattern of blood pressure of the patient over a first time period; determining a subsequent circadian pattern of blood pressure of the patient over a second period of time, the second period of time following the first period of time; determining one or more differences between the subsequent circadian pattern and the baseline circadian pattern; determining whether the patient is a candidate for denervation therapy based on one or more differences between the subsequent circadian pattern and the baseline circadian pattern; and in response to determining whether the patient is a candidate for denervation therapy, outputting an indication of whether the patient is a candidate for denervation therapy.

Description

Technical Field

[0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 374,966, filed on September 8, 2022, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present disclosure generally relates to assessing responsiveness to denervation therapy. Background Art

[0004] Overstimulated or overactive nerves may cause adverse effects on the organs or tissues served by the corresponding nerves. For example, in some patients, heart disease, circulatory disease, or kidney disease may be associated with significant cardiorenal sympathetic nerve overactivity. Stimulation of the renal sympathetic nerves may result in increased renin release, sodium (Na + ) or one or more of increased renal reabsorption or decreased renal blood flow. In response to high renal nerve stimulation, the kidneys may be damaged by direct nephrotoxicity released from sympathetic neurotransmitters in the kidneys, such as norepinephrine. In addition, increased renin release may ultimately increase systemic vasoconstriction, thereby aggravating hypertension.

[0005] Percutaneous renal denervation is a procedure that can be used to treat hypertension. During the renal denervation procedure, the clinician delivers stimulation or energy, such as radiofrequency, ultrasound, cooling or other energy, to the treatment site to reduce the activity of the nerves around the blood vessels. The stimulation or energy delivered to the treatment site can provide various therapeutic effects by changing the sympathetic nerve activity. Regardless of the presence of concomitant antihypertensive drug therapy, percutaneous denervation of afferent and efferent renal nerves can reduce blood pressure (BP) in some patients with uncontrolled hypertension. However, not all patients experience a drop in BP immediately after a renal denervation (RDN) procedure, as some patients are "non-responders". Summary of the invention

[0006] Denervation therapy may provide therapeutic benefits to certain patients. For example, renal denervation may alleviate symptoms associated with renal sympathetic nerve overactivity. However, not all patients respond well to denervation therapy, such as patients with reduced basal sympathetic nervous system activity. Various aspects of the technology described in the present disclosure may configure a computing device to identify potential non-responders before performing an invasive RDN procedure to deliver a denervation therapy that would be ineffective in improving the patient's health. Therefore, various aspects of the technology may reduce the performance of potentially ineffective RDN procedures, which in turn improves health care efficiency while also reducing patient discomfort that undergoes an ineffective (and invasive) RDN procedure to deliver denervation therapy.

[0007] The present disclosure describes a non-invasive, accurate and reliable predictor to determine which potential patients will respond well to denervation therapy or respond poorly to denervation therapy before performing an RDN protocol to deliver denervation therapy. The predictor can help identify potential non-responders before performing an invasive denervation protocol that may end up being ineffective, which can improve healthcare efficiency and patient care by reducing the performance of ineffective protocols, while also reducing patient discomfort from experiencing ineffective protocols.

[0008] In one example, the present disclosure relates to a computing device comprising: a memory; and one or more processors coupled to the memory, the one or more processors configured to: determine a baseline circadian pattern of a patient's blood pressure during a first time period; determine a subsequent circadian pattern of the patient's blood pressure during a second time period, the second time period being after the first time period; determine one or more differences between the subsequent circadian pattern and the baseline circadian pattern; determine whether the patient is a candidate for denervation therapy based on the one or more differences between the subsequent circadian pattern and the baseline circadian pattern; and in response to determining whether the patient is a candidate for denervation therapy, output an indication of whether the patient is a candidate for denervation therapy.

[0009] In another example, the present disclosure relates to a system comprising: a wearable device configured to sense and collect data indicative of a patient's blood pressure; and a computing device communicatively coupled to the wearable device, wherein the computing device comprises: a memory; and one or more processors coupled to the memory, the one or more processors configured to: determine a baseline circadian pattern of the patient's blood pressure during a first time period; determine a subsequent circadian pattern of the patient's blood pressure during a second time period, the second time period being after the first time period; determine one or more differences between the subsequent circadian pattern and the baseline circadian pattern; determine whether the patient is a candidate for denervation therapy based on the one or more differences between the subsequent circadian pattern and the baseline circadian pattern; and in response to determining whether the patient is a candidate for denervation therapy, output an indication of whether the patient is a candidate for denervation therapy.

[0010] In another example, the present disclosure relates to a method comprising: determining a baseline circadian pattern of a patient's blood pressure during a first time period; determining a subsequent circadian pattern of the patient's blood pressure during a second time period, the second time period being after the first time period; determining one or more differences between the subsequent circadian pattern and the baseline circadian pattern; determining whether the patient is a candidate for denervation therapy based on the one or more differences between the subsequent circadian pattern and the baseline circadian pattern; and in response to determining whether the patient is a candidate for denervation therapy, outputting an indication of whether the patient is a candidate for denervation therapy.

[0011] The present invention also discloses a computing device comprising: a memory; and one or more processors coupled to the memory, the one or more processors being configured to: determine a baseline circadian rhythm pattern of a patient's blood pressure during a first time period; determine a subsequent circadian rhythm pattern of the patient's blood pressure during a second time period, the second time period being after the first time period; determine one or more differences between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern; determine whether the patient is a candidate for denervation therapy based on the one or more differences between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern; and in response to determining whether the patient is a candidate for denervation therapy, output an indication of whether the patient is a candidate for denervation therapy.

[0012] Further details of one or more examples of the disclosure are set forth in the following drawings and the description. Other features, objects, and advantages will be apparent from the description and drawings, and from the claims.

[0013] The above summary is not intended to describe each illustrated example or every implementation of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a conceptual diagram illustrating an exemplary system for identifying candidates for denervation therapy according to some examples of the present disclosure.

[0015] Figure 2 is a block diagram illustrating an exemplary configuration of a data collection device according to some examples of the present disclosure.

[0016] FIG. 3A to FIG. 3C is a conceptual diagram of determining and comparing circadian patterns of blood pressure data according to some examples of the present disclosure.

[0017] Figure 3D is a conceptual diagram of a sensed pulsation waveform according to some examples of the present disclosure.

[0018] Figure 4 is a conceptual diagram illustrating an example neural network configured to predict the efficacy of renal denervation therapy for patients diagnosed with hypertension.

[0019] Figure 5 is a flow chart illustrating an exemplary technique for operating a system to identify suitable patient candidates for denervation therapy. DETAILED DESCRIPTION

[0020] Denervation therapy, such as renal denervation (RDN) therapy, can be used to render a nerve inert, inactive, or otherwise completely or partially reduced in function, such as by ablation or injury of the nerve. Following denervation, the transmission of nerve signals along the target nerve may be reduced or even prevented. Denervation of overactive nerves may provide therapeutic benefits to the patient. For example, renal denervation may alleviate symptoms associated with overactivity of the renal sympathetic nerves, such as hypertension. Denervation therapy may include delivering electrical energy and / or thermal energy to the target nerve, and / or delivering a chemical agent to the target nerve. In the case of renal denervation therapy, the denervation energy or chemical agent may be delivered, for example, via a therapy delivery device (e.g., a catheter) disposed in a blood vessel (e.g., a renal artery) close to the renal nerves.

[0021] The renal sympathetic nervous system has been identified as a potential major contributor to the complex pathophysiology of hypertension or elevated systemic blood pressure (BP). Therefore, renal denervation, as a treatment for hypertension, can reduce renal sympathetic nerve overactivity and cause a reduction in systemic BP. In some patients, renal denervation can reduce systolic BP by a range of approximately 5 mmHg (mmHg) to 30 mmHg. However, renal denervation may not be able to reduce systolic BP in other patients. RDN therapy can also be used as a treatment for other diseases associated with altered sympathetic nervous system activity such as arrhythmias and heart failure.

[0022] Efforts to identify candidates who may respond better to renal denervation therapy have focused primarily on two areas. First, it is suggested to identify patients with potentially higher baseline sympathetic nerve activity, as indicated by variables such as increased heart rate, increased plasma renin levels, increased muscle sympathetic nerve activity, increased renal norepinephrine overflow, etc. Examples of increased sympathetic nerve activity include muscle sympathetic nerve activity, renal norepinephrine overflow, response to stimulation (cold, squeeze ball, mental stress) (BP / heart rate), heart rate variability, and biomarkers. However, in some examples, these parameters are difficult to obtain, have low reproducibility, and / or would be difficult to track diligently over long periods of time.

[0023] Second, it is also suggested that higher baseline aortic or arterial stiffness due to calcification or other vascular disease, which would prevent peripheral arterial vasodilation, could be identified. Various approaches to quantify aortic / arterial stiffness in patients who are otherwise suitable for renal denervation have been attempted, including imaging-based methods to estimate aortic distensibility, invasive determination of pulse wave velocity, non-invasive determination of pulse wave velocity using a two-point (Δd / Δt) technique or single-beat estimation based on morphometric models, arterial wave reflections (such as augmentation index), or estimation of high arterial pulse pressure identified as high systolic BP with low or normal diastolic BP (i.e., "isolated systolic hypertension").

[0024] However, none of these approaches are accurate and reliable predictors of renal denervation responders. Therefore, in some cases renal denervation is performed on patients who do not respond well to renal denervation, resulting in wasted healthcare resources, patient inconvenience and discomfort, potential side effects, etc.

[0025] In some examples, the circadian pattern of sympathetic nerve activity can be observed by tracking hemodynamic parameters such as BP and / or heart rate. Changes in the circadian pattern of a candidate for a given hemodynamic index such as BP can indicate changes in basal sympathetic nervous system activity. In some examples, a combination of circadian pattern changes from baseline readings and pulse waveform analysis can be used during a specific circadian pattern cycle to indicate the occurrence of basal sympathetic nerve activity.

[0026] The present disclosure describes various aspects of the technology related to determining whether a potential patient for renal denervation (e.g., to reduce hypertension) may be a responder or a non-responder. Although the above examples discuss renal denervation responsiveness to reduce hypertension, the technology described herein can be similarly used to determine renal denervation responsiveness for treating other diseases associated with changes in sympathetic nervous system activity, such as arrhythmias and heart failure.

[0027] For example, a computing device or system may generate a score indicative of renal denervation responsiveness in reducing hypertension in a patient, wherein the score is determined based on changes in the circadian pattern of a hemodynamic index, such as BP or heart rate. Changes in the circadian pattern of a candidate for a given hemodynamic index may indicate changes in basal sympathetic nervous system activity. Changes in the candidate's circadian pattern over a period of time may be compared to a threshold to determine whether the patient may be responsive or unresponsive to renal denervation. One or more parameters of the waveform morphology may indicate systemic vascular changes that occur during the circadian pattern, such as the time difference between the systolic peak and the recurrent notch or secondary peak. Pulse waveform analysis may determine changes in arterial compliance over a period of time, which may then be compared to a threshold to determine whether the patient may be responsive or unresponsive to renal denervation.

[0028] According to various techniques of the present disclosure, non-invasively identifying whether a patient may be responsive to denervation therapy prior to performing a denervation protocol to determine if they will be a candidate for denervation therapy can help prevent clinicians from performing invasive denervation protocols on patients who will not respond to the protocol. This can help reduce unnecessary healthcare costs by identifying which patients will respond to denervation protocols while also reducing invasive procedures and unnecessary discomfort that will be ineffective for non-responsive patients.

[0029] Figure 1is a conceptual diagram illustrating an exemplary system 10 for identifying patient candidates for denervation therapy and, in some examples, delivering denervation therapy. Figure 1 As shown, system 10 includes computing device 12. Computing device 12 can be a computing device used in a home, mobile clinic or hospital environment. Computing device 14 may include, for example, a clinician programmer, a desktop computer, a laptop computer, a workstation, a server, a mainframe, a cloud computing system, a smart phone, a combination thereof, etc. Computing device 12 may be configured to receive input from a user such as a clinician via a user interface device 14 ("UI 14"), output information to the user, or both. In some examples, UI 14 may include a display (e.g., a liquid crystal display (LCD) or a light emitting diode (LED) display), such as a touch-sensitive display; one or more buttons; one or more keys (e.g., a keyboard); a mouse; one or more dials; one or more switches; a speaker; one or more lights; a combination thereof; etc.

[0030] The computing device 12 may be communicatively coupled to the physiological sensor device 16. In some examples, the physiological sensor device 16 may be a wearable medical device, as well as a variety of wearable health or fitness tracking devices. Some examples of physiological signals that may be sensed by such devices may include blood pressure, electrocardiogram (ECG) signals, respiration signals, perfusion signals, activity and / or posture signals, pressure signals, blood oxygen saturation signals, body composition, fluid impedance signals, and blood glucose or other blood component signals. In some examples, the physiological sensor device 16 may include electrodes configured to contact the patient's skin, such as a patch, watch, ring, necklace, hearing aid, clothing, car seat, or bed sheet. In some examples, the physiological sensor device 16 may include electrodes and other sensors to sense the physiological signals of the patient 18, and may collect and store physiological data and detect attacks based on such signals. In some examples, the physiological sensor device 16 may be incorporated into the patient's 18 clothing, such as clothing, shoes, glasses, watches or wristbands, hats, etc. In some examples, the physiological sensor device 16 may be a smart phone, smart watch, or other smart clothing. In some examples, physiological sensor device 16 is an accessory or peripheral to a smartphone computing device.

[0031] The physiological sensor device 16 may be configured to collect and / or transmit sensed physiological signals and / or data based on the sensed physiological signals to the computing device 12. For example, the physiological sensor device 16 may detect a blood pressure value of the patient 18 and collect and / or communicate the detected blood pressure value to the computing device 12.

[0032] With each contraction of the left ventricle of the heart, the left ventricle ejects blood to generate a pressure pulse that travels through the patient's arteries. The pulse is detectable at different locations of the patient, including the patient's wrist. The sensor 34 may be located in a physiological sensing device 16 (such as an implantable medical device or a wearable device). In some examples, the wearable device may be configured to be placed around the patient's wrist, and a pulse sensor in the wearable device may generate pulse information representing a BP pulse. Although the BP value generated from the wearable device may have a range of error, the amount of error may be consistent.

[0033] Renal denervation is performed in certain circumstances on patients who do not respond well to renal denervation, resulting in wasted healthcare resources, patient inconvenience and discomfort, potential unnecessary side effects, and the like.

[0034] According to the techniques of the present disclosure, computing device 12 may determine whether a patient may be responsive or unresponsive to renal denervation based on circadian patterns of hemodynamic parameters such as BP and changes in circadian patterns of hemodynamic parameters such as BP over time.

[0035] The computing device 12 may receive a hemodynamic parameter of the patient, such as BP, from the physiological sensor device 16. In some examples, the hemodynamic parameter may be BP. The physiological sensing device 16 may be a wearable device. The computing device 12 may determine a baseline circadian rhythm pattern of the received hemodynamic parameters of the patient in the first time period based on the hemodynamic parameters of the patient received in the first time period. The computing device 12 may determine a subsequent circadian rhythm pattern of the hemodynamic parameters of the patient in the second time period based on the hemodynamic parameters of the patient received in the second time period, the second time period being after the first time period.

[0036] The computing device 12 may determine one or more differences between a baseline circadian pattern of a hemodynamic parameter and a subsequent circadian pattern of a hemodynamic parameter or a pulse waveform change. The computing device 12 may also determine whether the patient is a candidate for denervation therapy based on the one or more differences between the subsequent circadian pattern and the baseline circadian pattern, and output an indication of whether the patient is a candidate for denervation therapy in response to determining whether the patient is a candidate for denervation therapy.

[0037] In some examples, computing device 12 may determine whether the difference between the subsequent circadian pattern and the baseline circadian pattern is greater than a circadian pattern difference threshold. In response to determining that the difference between the subsequent circadian pattern and the baseline circadian pattern is greater than the circadian pattern difference threshold, computing device 12 may output an indication that the patient is a candidate for denervation therapy. In response to determining that the difference between the subsequent circadian pattern and the baseline circadian pattern is less than or equal to the circadian pattern difference threshold, computing device 12 outputs an indication that the patient is not a candidate for denervation therapy.

[0038] Based on the above techniques, computing device 12 can provide a non-invasive, accurate and reliable predictor to determine which potential patients will respond well to denervation therapy or respond poorly to denervation therapy before performing an RDN procedure to deliver denervation therapy. The predictor can help identify potential non-responders before performing an invasive denervation procedure that may end up being ineffective, which can improve healthcare efficiency and patient care by reducing the performance of ineffective procedures, while also reducing patient discomfort that occurs during ineffective procedures.

[0039] Figure 2 is an example Figure 1 A block diagram of an example configuration of a physiological sensing device 16 and a computing device 12. Figure 2 As shown, the physiological sensor device 16 may include processing circuitry 30 , memory 32 , one or more sensors 34 , sensing circuitry 36 coupled to the one or more sensors 36 , and communication circuitry 38 .

[0040] One or more sensors 34 of the physiological sensor device 16 may sense physiological parameters or signals of the patient 18. The sensors 34 may include electrodes, accelerometers (e.g., 3-axis accelerometers), optical sensors, impedance sensors, temperature sensors, pressure sensors, heart sound sensors (e.g., microphones or accelerometers), and other sensors and sensing circuits.

[0041] The sensor 34 may detect pulses and may include any suitable type of sensor configured to generate a signal representative of a pulse, such as a spectrophotometric sensor and / or a pneumatic pulse sensor, which may include one or more characteristics of the pulse, such as pulse rate, pulse amplitude, pulse rate variability, pulse waveform morphology, or pulse echo. The pulse rate is the rate or frequency at which successive pulses of blood travel through an artery. The pulse amplitude is the intensity or amplitude of the pulse, which may be expressed as, for example, absolute pressure, pressure relative to atmospheric pressure or baseline pressure, or a percentage increase over baseline amplitude. The pulse rate variability is a representation of how the pulse rate changes over time. The pulse waveform morphology may indicate the shape or characteristics of the pulse waveform, such as the width of the pulse peak, the slope of the front and / or back of the pulse wave, the sharpness of the peak, how many peaks are detected within the pulse wave, one or more notches or interruptions in the pulse wave, or any other characteristics indicating the shape of the pulse wave. The pulse echo may be a feature in the pulse wave caused by one or more reflected pressure waves within the artery, which in some examples may be detected as one or more inflection points in the pulse wave or multiple peaks, humps, or bumps within a single pulse wave. The sensor 34 may transmit the generated pulse information to a pulse monitoring device for conditioning and / or analysis of the pulse information, or the sensor 34 may transmit the pulse information directly to the computing device 12 for patient assessment.

[0042] like Figure 2 As shown, the computing device 12 may include a processing circuit 46, a communication circuit 48, a memory 40, and a UI 14. The memory 40 may include any volatile or non-volatile media, such as a random access memory (RAM), a read-only memory (ROM), a non-volatile RAM (NVRAM), an electrically erasable programmable ROM (EEPROM), a flash memory, etc. The memory 40 may store computer-readable instructions that, when executed by the processing circuit 46, cause the computing device 12 to perform the various functions described herein. The processing circuit 46 may include any combination of one or more processors, including one or more microprocessors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other equivalent integrated or discrete logic circuits. Therefore, the processing circuit 46 may include any suitable structure, whether hardware, software, firmware, or any combination thereof, to perform the functions attributed to the processing circuit 46 and the physiological sensor device 16 herein. The computing device 12 may be configured to receive data from the physiological sensor device 16 (e.g., via the communication circuit 368).

[0043] Sensor 34 may detect pulses and may include any suitable type of sensor, such as a spectrophotometric sensor and / or a pneumatic pulse sensor, configured to generate a signal representative of a pulse, which may include one or more characteristics of the pulse, such as pulse rate, pulse amplitude, pulse rate variability, pulse waveform morphology, or pulse echo. In some examples, sensor 34 may transmit the generated pulse information to processing circuitry 30 for conditioning and / or analyzing the pulse information, or physiological sensor device 16 may transmit the pulse information directly to computing device 12, such as via communication circuitry 38.

[0044] In some examples, computing device 12 may receive a BP value and / or a pulse generated by sensor 34 in physiological sensing device 16. The BP value received by computing device 12 may include the BP value itself and / or a physiological parameter indicating the BP value. The received pulse waveform may be the pulse waveform itself and / or a physiological parameter indicating the pulse waveform.

[0045] In some examples, physiological sensing device 16 may be a wearable device, such as a smart watch. In some examples, computing device 12 may be located (not shown) in physiological sensing device 16. In some examples, computing device 12 may be separate from physiological sensing device 16. In some examples, computing device 12 may generate a BP circadian rhythm pattern based on BP values ​​received from physiological sensing device 16. In some examples, a circadian rhythm pattern of BP may be generated during a first time period and may be designated as a baseline circadian rhythm pattern of BP. A subsequent circadian rhythm pattern of BP may be generated in a second time period, the second time period being after the first time period. This use of BP information generated from a pulse wave detected from a patient to determine whether a patient may respond or not to renal denervation is an unconventional use of a pulse detection sensor and / or a BP detection sensor.

[0046] In some examples, the BP circadian pattern may be a circadian pattern of BP itself and / or a circadian pattern of a physiological parameter indicative of BP values.

[0047] The examples herein relate to human patients. However, in other examples, the techniques and systems described herein can also be used to screen non-human mammal kidneys for renal denervation therapy.

[0048] According to the techniques described herein, a computing device may determine a baseline circadian pattern of BP for a patient during a first time period based on a BP signal of the patient during a first time period, determine a subsequent circadian pattern of BP for the patient during a second time period based on a BP signal of the patient during a second time period, and determine a difference between the baseline circadian pattern of BP and the subsequent circadian pattern of BP, the second time period being after the first time period. In response to the determined difference being less than or equal to a threshold, the computing device may determine that basal sympathetic nervous system activity and / or a change in basal sympathetic nervous system activity is less than or equal to the threshold, and in response to determining that the basal sympathetic nervous system activity and / or the change in basal sympathetic nervous system activity is less than or equal to the threshold, determine that the patient is not a candidate for an RDN procedure.

[0049] The physiological sensor device 16 can detect blood pressure values ​​and can generate a signal proportional to the instantaneous blood pressure change. In some examples where the detected blood pressure may include errors, or if the physiological sensor device 16 is not properly calibrated, the physiological sensor device 16 can be used to track the beat-to-beat changes in systolic blood pressure amplitude, including within a 24-hour circadian cycle.

[0050] like Figure 3A As shown in the example of , the pattern of blood pressure can change predictably during the 24-hour circadian cycle, with a decrease during sleep at night, a sudden increase before waking or in the morning, a peak in the late morning to midday, a relative decrease in the evening, and finally a secondary fluctuation at night. Figure 3A , the x-axis 50 is the time of day and the y-axis 52 is the average systolic blood pressure value at that particular time of day.

[0051] like Figure 3B As shown in the example of , computing device 12 may obtain blood pressure data and / or pulse waveforms from day 1 to day N from physiological sensor device 16. In some examples, computing device 12 may receive multiple blood pressure measurements per day, such as every 5 minutes, every 10 minutes, every 30 minutes, every hour, and / or another time period. In some examples, computing device 12 may not receive consistent blood pressure measurements, but may receive multiple blood pressure measurements within a 24-hour period, such as when physiological sensor device 16 is capable of measuring blood pressure measurements and sending the blood pressure measurements to the computing device. In some examples, such as Figure 3B As shown, the blood pressure measurement received by computing device 12 may be a systolic blood pressure measurement. In some examples, the blood pressure measurement received by computing device 12 may be a diastolic blood pressure measurement. In some examples, the blood pressure measurement received by computing device 12 may be a mean pressure measurement.

[0052] Computing device 12 may process the received circadian rhythm blood pressure data and / or pulse waveform to determine and generate an overall average for a time period from day 1 to day N. In some examples, the time period may be a week, two weeks, a month, two months, and / or other time period. The days used in the time period may be adjacent days (e.g., such as Monday to Friday) or may be non-adjacent days (e.g., such as every Monday). In some examples, computing device 12 may generate an overall average based on the received blood pressure data and / or pulse waveform to filter out higher frequency transient changes, thereby revealing the underlying lower frequency circadian rhythm pattern.

[0053] In some examples, computing device 12 may determine and generate an initial population average of the circadian pattern of the blood pressure data and / or pulse waveform during the first time period as the baseline circadian pattern. In some examples, computing device 12 may generate the baseline circadian pattern in response to receiving an instruction to generate the baseline circadian pattern from a user, such as a patient and / or a clinician.

[0054] After generating the baseline circadian pattern, computing device 12 may generate a subsequent circadian pattern in a second time period. The second time period occurs after the first time period, during which computing device 12 generated the baseline circadian pattern. In some examples, computing device 12 may determine and generate a subsequent circadian pattern similar to the baseline circadian pattern by receiving blood pressure data and / or pulse waveforms in the second time period and processing the circadian blood pressure data and / or pulse waveforms received in the second time period to determine and generate an overall average of the circadian pattern of the blood pressure data and / or pulse waveforms in the second time period. In some examples, computing device 12 may identify the overall average of the circadian pattern of the second time period as the subsequent circadian pattern.

[0055] In some examples, the second time period may be three days, five days, a week, two weeks, a month, two months, and / or other time periods. The days used in the time period may be adjacent days (e.g., such as Monday to Friday) or may be non-adjacent days (e.g., such as every Monday). In some examples, the second time period may be updated to the most recent days in the time period. For example, if the second time period is 7 days, when a new day passes and the computing device 12 obtains a new blood pressure measurement and / or a physiological parameter indicating a new blood pressure measurement from the physiological sensor device 16, the blood pressure measurement from the earliest day in the second time period is replaced by the blood pressure measurement obtained in the most recent day, and the value of the subsequent circadian rhythm pattern is updated. Therefore, in some examples, the computing device 12 may update the subsequent circadian rhythm pattern value every day, while the baseline circadian rhythm pattern remains constant. In some examples, the computing device may weight the blood pressure measurements of each day equally during the second time period. In some examples, the computing device 12 may weight the blood pressure measurements of each day during the second time period, so that the most recent days are more favored.

[0056] Figure 3C An example is shown of computing device 12 performing a comparison calculation 104 of baseline circadian pattern 100 and subsequent circadian pattern 102 to determine a difference 106 between the baseline circadian pattern and the subsequent circadian pattern. In some examples, the difference between the baseline circadian pattern and the subsequent circadian pattern may be referred to as a difference index 106. The difference between the baseline circadian pattern and the subsequent circadian pattern may indicate a change in underlying sympathetic nervous system activity, such as indicating an increase or decrease in underlying sympathetic nervous system activity.

[0057] Figure 3D An example of a pulsatile waveform sensed by the physiological sensor device 16 and its components is shown. One or more parameters of the waveform morphology may indicate arterial compliance, such as the time difference between the systolic peak and the recurrent notch or secondary peak. In addition, the area under the waveform may indicate cardiac output. In some examples, cardiac output may be affected by stroke volume and / or heart rate. The computing device 12 may perform pulse waveform analysis to determine one or more changes in the pulse waveform over a period of time, such as changes in arterial compliance or cardiac output, and compare these changes to corresponding thresholds to determine whether the patient may respond or not respond to renal denervation.

[0058] In some examples, computing device 12 may determine whether a difference (such as difference index 106 ) between subsequent circadian pattern 102 and baseline circadian pattern 100 is greater than a circadian pattern difference threshold.

[0059] In response to determining that difference 106 between subsequent circadian pattern 102 and baseline circadian pattern 100 is greater than a circadian pattern difference threshold, computing device 12 may output an indication that the patient is a candidate for denervation therapy. In some examples, in response to determining that the difference between subsequent circadian pattern 102 and baseline circadian pattern 100 is greater than the circadian pattern difference threshold, computing device 12 may determine that basal sympathetic nervous system activity is greater than a basal sympathetic nervous system activity threshold. In some examples, in response to determining that basal sympathetic nervous system activity is greater than the basal sympathetic nervous system activity threshold, computing device 12 may output an indication that the patient is a candidate for denervation therapy.

[0060] In response to determining that the difference 106 between subsequent circadian pattern 102 and baseline circadian pattern 100 is greater than the circadian pattern difference threshold, computing device 12 may output an indication that the patient is a candidate for denervation therapy. In some examples, in response to determining that the difference between subsequent circadian pattern 102 and baseline circadian pattern 100 is greater than the circadian pattern difference threshold, computing device 12 may determine that the change in basal sympathetic nervous system activity is greater than the change in basal sympathetic nervous system activity threshold. In response to determining that the change in basal sympathetic nervous system activity is greater than the change in basal sympathetic nervous system activity threshold, computing device 12 may output an indication that the patient is a candidate for denervation therapy.

[0061] In response to determining that difference 106 between subsequent circadian pattern 102 and baseline circadian pattern 100 is less than or equal to the circadian pattern difference threshold, computing device 12 may output an indication that the patient is not a candidate for denervation therapy.

[0062] In some examples, in response to determining that the difference between the subsequent circadian pattern and the baseline circadian pattern is less than or equal to the circadian pattern difference threshold, computing device 12 may determine that the basal sympathetic nervous system activity is less than or equal to the basal sympathetic nervous system activity threshold. In some examples, in response to determining that the basal sympathetic nervous system activity is less than or equal to the basal sympathetic nervous system activity threshold, computing device 12 may output an indication that the patient is not a candidate for denervation therapy.

[0063] In response to determining that difference 106 between subsequent circadian pattern 102 and baseline circadian pattern 100 is less than or equal to the circadian pattern difference threshold, computing device 12 may determine that the change in basal sympathetic nervous system activity is less than or equal to the change in basal sympathetic nervous system activity threshold. In response to determining that the change in basal sympathetic nervous system activity is less than or equal to the change in basal sympathetic nervous system activity threshold, computing device 12 may output an indication that the patient is not a candidate for denervation therapy.

[0064] In some examples, non-invasively identifying whether a patient may be responsive to denervation therapy prior to performing a denervation procedure to determine if they will be a candidate for denervation therapy can help prevent clinicians from performing invasive denervation procedures on patients who will not respond to the procedure. This can help reduce unnecessary healthcare costs by identifying which patients will respond to denervation procedures while also reducing invasive procedures that will be ineffective on non-responsive patients.

[0065] The computing device 12 may be configured to execute an AI engine that operates according to one or more models, such as a machine learning model. The machine learning model may include any number of different types of machine learning models, such as neural networks, deep neural networks, dense neural networks, etc. Although described with respect to machine learning models, the techniques described in the present disclosure may also be applicable to other types of AI models, including rule-based models, finite state machines, etc.

[0066] Machine learning generally enables a computing device to analyze input data and identify actions to be performed in response to the input data. Each machine learning model can be trained using training data that reflects possible input data. The training data can be labeled or unlabeled (meaning that the correct action to be taken based on a sample of the training data is explicitly stated or not, respectively).

[0067] The training of the machine learning model may be directed (in that a designer, such as a computer programmer, may direct the training to direct the machine learning model to identify the correct action given the input data) or undirected (in that the designer does not direct the machine learning model to identify the correct action given the input data). In some instances, the machine learning model is trained by a combination of labeled and unlabeled training data, a combination of directed and undirected training, or possibly a combination thereof. Examples of machine learning include nearest neighbors, naive Bayes, decision trees, linear regression, support vector machines, neural networks, k-means clustering, Q-learning, temporal differences, deep adversarial networks, evolutionary algorithms, or other supervised, unsupervised, semi-supervised, or reinforcement learning algorithms for training one or more models.

[0068] The computing device 12 may utilize machine learning such as a deep learning algorithm or model (e.g., a neural network or a deep belief network) to generate a score indicating whether the patient 18 may respond or not respond to renal denervation to determine whether the patient 18 may respond or not respond to renal denervation. The generated score may be output as an indication of whether the patient is a candidate for denervation therapy. The computing device 12 may train a deep learning model to represent the relationship between the circadian pattern of BP and changes in other patient metrics of the patient and the patient's responsiveness to renal denervation therapy. For example, the computing device 12 may train a deep learning model using the circadian pattern of BP, changes in patient metrics, and renal denervation responsiveness from other patients. In some examples, the computing device 12 may train the deep learning model by adjusting the weights of the hidden layers of the neural network model to balance the contribution of each input (e.g., the circadian pattern of BP, the pulse waveform, and / or the changing characteristics of each patient indicator value) according to how the patient responds to renal denervation therapy.

[0069] Once the deep learning model is trained, the computing device 12 may obtain data, such as a circadian pattern of the patient's BP and a plurality of values ​​representing corresponding patient metrics of the patient, and apply the data to the trained deep learning model. Example patient metrics may include the patient's age, the patient's gender, the patient's ethnic background, the patient's weight, the patient's height, the patient's diet, the patient's activity level, or the patient's stress level. The circadian pattern of BP may include a baseline circadian pattern of BP and one or more subsequent circadian patterns of BP. The output of the deep learning model may include a score indicating whether the patient will respond to renal denervation therapy. For example, the score may be the probability that the patient will achieve a target reduction in hypertension in response to receiving renal denervation therapy. In some examples, the score may indicate the magnitude of the systemic blood pressure reduction that the patient can achieve after renal denervation therapy. The computing device 12 may display the score to a clinician to help determine whether the patient should or should not receive renal denervation therapy.

[0070] In some examples, computing device 12 may send the received BP measurement results (such as a baseline circadian pattern of BP and one or more subsequent circadian patterns of BP) to a separate computing system to perform machine learning (such as a deep learning algorithm or model) on the received BP measurement results (such as a baseline circadian pattern of BP and one or more subsequent circadian patterns of BP) using the separate computing system.

[0071] Figure 4is a conceptual diagram illustrating an example neural network 80 that is configured to predict the efficacy of renal denervation therapy for a patient diagnosed with hypertension. Neural network 80 is an example of a deep learning model or deep learning algorithm that is trained to generate a score indicative of the efficacy of renal denervation therapy. As discussed above, other types of machine learning and deep learning models or algorithms may be utilized in other examples. Computing device 12 may train, store, and / or utilize neural network 80, but in other examples, other devices may apply inputs associated with a particular patient to neural network 80. Neural network 80 is an example of a neural network 58 ( FIG. 3 ) that may be stored by computing device 12.

[0072] like Figure 4 As shown in the example of , the neural network 80 includes three layers. The three layers include an input layer 82, a hidden layer 84, and an output layer 86. The output layer 88 includes the output of the transfer function from the output layer 86. The input layer 82 represents each of the input values ​​A to I provided to the neural network 80. The input value can be a value of a patient metric, such as the patient's age, the patient's gender, the patient's ethnic background, the patient's weight, the patient's height, the patient's diet, the patient's activity level, or the patient's stress level. For each patient metric, the input value can be numeric or categorical, as appropriate. In some examples, the values ​​of all of these patient metrics can be incorporated into the neural network 80.

[0073] Among other things, some of the input values ​​of input layer 82 may include one or more characteristics of hemodynamic information (such as blood pressure information) from physiological sensing device 16 and / or computing device 12. For example, the characteristics may include a circadian pattern of BP, such as a baseline circadian pattern or a subsequent circadian pattern, based on the BP values ​​received from physiological sensing device 16. These characteristics may be input into input layer 82. In some examples, characteristics such as pulse waveform morphology may be converted into a numerical value or some other input representing a waveform type identified from a pulse wave of blood in a patient.

[0074] Each of the input values ​​of each node in the input layer 82 is provided to each node in the hidden layer 84. Figure 4 In the example of the hidden layer 84, four nodes are included, but fewer or greater numbers of nodes may be used in other examples. Each input from the input layer 82 is multiplied by a weight and then added at each node of the hidden layer 84. During the training of the neural network 80, the weight of each input is adjusted to establish a relationship between the circadian pattern of BP based on BP values ​​and the efficacy of renal denervation. In some examples, two or more hidden layers may be incorporated into the neural network 80, wherein each layer includes the same or different number of nodes.

[0075] The results of each node within the hidden layer 84 are applied to a transfer function of the output layer 86. The transfer function can be linear or nonlinear, depending on the number of layers within the neural network 80. Example nonlinear transfer functions can be a sigmoid function or a rectifier function. The output 88 of the transfer function can be a score generated by the computing device 12 in response to applying the circadian rhythm pattern of BP based on the patient's BP value to the neural network 80. A deep learning model such as the neural network 80 can enable a computing system such as the computing device 12 to screen patients for renal denervation therapy using multiple values ​​representative of a particular patient's condition.

[0076] Figure 5 is a flow chart illustrating an exemplary technique for operating system 10. Figure 5 As indicated, the computing device 12 may obtain a first blood pressure value of the patient during a first time period and / or a physiological parameter indicative of the first blood pressure value (500). The computing device 12 may determine a baseline circadian pattern of blood pressure based on the first blood pressure value (502). The computing device 12 may obtain a second blood pressure value of the patient during a second time period and / or a physiological parameter indicative of the second blood pressure value, the second time period being after the first time period (504). The computing device 12 may determine a subsequent circadian pattern of blood pressure based on the second blood pressure value (506). The computing device 12 may determine whether a difference between the subsequent circadian pattern and the baseline circadian pattern indicates that the patient is a candidate for denervation therapy (508). In response to the computing device 12 determining that the difference between the subsequent circadian pattern and the baseline circadian pattern indicates that the patient is not a candidate for denervation therapy, the computing device 12 may output an indication that the patient is not a candidate for denervation therapy (510). In response to computing device 12 determining that the difference between the subsequent circadian pattern and the baseline circadian pattern indicates that the patient is a candidate for denervation therapy, computing device 12 may output an indication that the patient is a candidate for denervation therapy ( 512 ).

[0077] The techniques described in the present disclosure may be implemented at least in part in hardware, software, firmware, or any combination thereof. For example, various aspects of the techniques may be implemented in one or more processors or processing circuits, including one or more microprocessors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or any other equivalent integrated or discrete logic circuits, and any combination of such components. The term "processor" or "processing circuit" may generally refer to any of the aforementioned logic circuits, either alone or in combination with other logic circuits, or any other equivalent circuit. A control unit including hardware may also perform one or more of the techniques of the present disclosure.

[0078] Such hardware, software, and firmware may be implemented in the same device or in separate devices to support the various operations and functions described in this disclosure. In addition, any described units, circuits, or components may be implemented together, or independently as discrete but interoperable logic devices. Describing different features as circuits or units is intended to highlight different functional aspects, and does not necessarily imply that such circuits or units must be implemented by separate hardware components or software components. On the contrary, the functions associated with one or more circuits or units may be performed by separate hardware components or software components, or integrated in common or separate hardware components or software components.

[0079] The techniques described in the present disclosure may also be embodied or encoded in a computer-readable medium (such as a computer-readable storage medium) containing instructions, which may be described as a non-transitory medium. The instructions embedded or encoded in the computer-readable storage medium may cause a programmable processor or other processor to perform the method, for example, when executing these instructions. The computer-readable storage medium may include a random access memory (RAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a hard disk, a CD-ROM, a floppy disk, a cassette, a magnetic medium, an optical medium, or other computer-readable medium.

[0080] Various aspects of the technology may implement the following embodiments.

[0081] Embodiment 1: A medical device, comprising: a memory; and one or more processors coupled to the memory, the one or more processors being configured to: determine a baseline circadian rhythm pattern of a patient's blood pressure during a first time period; determine a subsequent circadian rhythm pattern of the patient's blood pressure during a second time period, the second time period being after the first time period; determine one or more differences between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern; determine whether the patient is a candidate for denervation therapy based on the one or more differences between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern; and in response to determining whether the patient is a candidate for the denervation therapy, output an indication of whether the patient is a candidate for the denervation therapy.

[0082] Embodiment 2: A computing device according to embodiment 1, wherein the one or more processors are further configured to: obtain from a sensing device a first blood pressure value of the patient during the first time period or one or more of the physiological parameters indicating the first blood pressure value; and obtain from the sensing device a second blood pressure value of the patient during the second time period or one or more of the physiological parameters indicating the second blood pressure value.

[0083] Embodiment 3: A computing device according to any one of Embodiments 1 to 2, wherein the one or more processors are further configured to: determine whether the difference between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern is greater than a circadian rhythm pattern difference threshold; in response to determining that the difference between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern is greater than the circadian rhythm pattern difference threshold, output an indication that the patient is a candidate for denervation therapy; and in response to determining that the difference between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern is less than or equal to the circadian rhythm pattern difference threshold, output an indication that the patient is not a candidate for denervation therapy.

[0084] Embodiment 4: A computing device according to any one of embodiments 1 to 3, wherein the one or more processors are further configured to: in response to determining that the difference between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern is greater than the circadian rhythm pattern difference threshold, determine that one or more of the basal sympathetic nervous system activities is greater than the basal sympathetic nervous system activity threshold or the change in basal sympathetic nervous system activity is greater than the change in the basal sympathetic nervous system activity threshold; in response to determining that the difference between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern is less than or equal to the circadian rhythm pattern difference threshold, determine that one or more of the basal sympathetic nervous system activities is less than or equal to the basal sympathetic nervous system activity. activity threshold or the change in the basal sympathetic nervous system activity is less than or equal to the change in the basal sympathetic nervous system activity threshold; in response to determining that one or more of the basal sympathetic nervous system activities is greater than the basal sympathetic nervous system activity threshold or the change in the basal sympathetic nervous system activity is greater than the change in the basal sympathetic nervous system activity threshold, outputting an indication that the patient is a candidate for denervation therapy; and in response to determining that one or more of the basal sympathetic nervous system activities is less than or equal to the basal sympathetic nervous system activity threshold or the change in the basal sympathetic nervous system activity is less than or equal to the change in the basal sympathetic nervous system activity threshold, outputting an indication that the patient is not a candidate for denervation therapy.

[0085] Embodiment 5: The computing device according to any one of embodiments 1 to 4, wherein the first time period is one week, two weeks or one month.

[0086] Embodiment 6: The computing device according to any one of embodiments 1 to 5, wherein the second time period is three days, five days, one week or two weeks.

[0087] Embodiment 7: A computing device according to any one of embodiments 1 to 6, wherein the characteristics of the baseline circadian rhythm pattern include at least one of the following: the slope of the morning fluctuation, the slope of the evening fluctuation, the amplitude of the nighttime blood pressure drop, the amplitude of the evening blood pressure fluctuation, the daytime variability, the nighttime variability or the 24-hour variability, and the characteristics of the subsequent circadian rhythm pattern include at least one of the following: the slope of the morning fluctuation, the slope of the evening fluctuation, the amplitude of the nighttime blood pressure drop, the amplitude of the evening blood pressure fluctuation, the daytime variability, the nighttime variability or the 24-hour variability.

[0088] Embodiment 8: A computing device according to embodiment 7, wherein the one or more processors are further configured to compare one or more of the characteristics of the baseline circadian rhythm pattern with corresponding one or more of the characteristics of the subsequent circadian rhythm pattern to determine the one or more differences between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern.

[0089] Embodiment 9: A computing device according to any one of Embodiments 1 to 8, wherein the first blood pressure value or a physiological parameter indicating the first blood pressure value and the second blood pressure value or a physiological parameter indicating the second blood pressure value are obtained from a wearable device.

[0090] Embodiment 10: The computing device of any one of embodiments 1 to 9, wherein the first blood pressure value and the second blood pressure value are systolic blood pressure values.

[0091] Embodiment 11: A system comprising: a wearable device configured to sense and collect data indicative of a patient's blood pressure; and a computing device communicatively coupled to the wearable device, wherein the computing device comprises: a memory; and one or more processors coupled to the memory, the one or more processors configured to: determine a baseline circadian rhythm pattern of the patient's blood pressure during a first time period; determine a subsequent circadian rhythm pattern of the patient's blood pressure during a second time period, the second time period being after the first time period; determine one or more differences between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern; determine whether the patient is a candidate for denervation therapy based on the one or more differences between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern; and in response to determining whether the patient is a candidate for denervation therapy, output an indication of whether the patient is a candidate for denervation therapy.

[0092] Embodiment 12: A system according to Embodiment 11, wherein the one or more processors are further configured to: obtain from a sensing device the first blood pressure value of the patient during the first time period or one or more of the physiological parameters indicating the first blood pressure value; and obtain from the sensing device the second blood pressure value of the patient during the second time period or one or more of the physiological parameters indicating the second blood pressure value.

[0093] Embodiment 13: A system according to any one of Embodiments 11 to 12, wherein the one or more processors are further configured to: determine whether the difference between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern is greater than a circadian rhythm pattern difference threshold; in response to determining that the difference between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern is greater than the circadian rhythm pattern difference threshold, output an indication that the patient is a candidate for denervation therapy; and in response to determining that the difference between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern is less than or equal to the circadian rhythm pattern difference threshold, output an indication that the patient is not a candidate for denervation therapy.

[0094] Embodiment 14: A system according to any one of embodiments 11 to 13, wherein the one or more processors are further configured to: in response to determining that the difference between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern is greater than the circadian rhythm pattern difference threshold, determine that one or more of the basal sympathetic nervous system activities is greater than the basal sympathetic nervous system activity threshold or the change in basal sympathetic nervous system activity is greater than the change in basal sympathetic nervous system activity threshold; in response to determining that the difference between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern is less than or equal to the circadian rhythm pattern difference threshold, determine that one or more of the basal sympathetic nervous system activities is less than or equal to the basal sympathetic nervous system activity. activity threshold or the change in the basal sympathetic nervous system activity is less than or equal to the change in the basal sympathetic nervous system activity threshold; in response to determining that one or more of the basal sympathetic nervous system activities is greater than the basal sympathetic nervous system activity threshold or the change in the basal sympathetic nervous system activity is greater than the change in the basal sympathetic nervous system activity threshold, outputting an indication that the patient is a candidate for denervation therapy; and in response to determining that one or more of the basal sympathetic nervous system activities is less than or equal to the basal sympathetic nervous system activity threshold or the change in the basal sympathetic nervous system activity is less than or equal to the change in the basal sympathetic nervous system activity threshold, outputting an indication that the patient is not a candidate for denervation therapy.

[0095] Embodiment 15: The system according to any one of embodiments 11 to 13, wherein the first time period is one week, two weeks or one month.

[0096] Embodiment 16: The system of any one of Embodiments 11 to 15, wherein the second time period is three days, five days, one week, or two weeks.

[0097] Embodiment 17: A system according to any one of embodiments 11 to 16, wherein the characteristics of the baseline circadian rhythm pattern include at least one of the following: the slope of the morning fluctuation, the slope of the evening fluctuation, the amplitude of the night-time blood pressure drop, the amplitude of the evening blood pressure fluctuation, the daytime variability, the night-time variability or the 24-hour variability, and the characteristics of the subsequent circadian rhythm pattern include at least one of the following: the slope of the morning fluctuation, the slope of the evening fluctuation, the amplitude of the night-time blood pressure drop, the amplitude of the evening blood pressure fluctuation, the daytime variability, the night-time variability or the 24-hour variability.

[0098] Embodiment 18: A system according to embodiment 17, wherein the one or more processors are further configured to compare one or more of the characteristics of the baseline circadian rhythm pattern with corresponding one or more of the characteristics of the subsequent circadian rhythm pattern to determine the one or more differences between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern.

[0099] Embodiment 19: A system according to any one of Embodiments 11 to 18, wherein the first blood pressure value or a physiological parameter indicating the first blood pressure value and the second blood pressure value or a physiological parameter indicating the second blood pressure value are obtained from a wearable device.

[0100] Embodiment 20: The system of any one of Embodiments 11 to 19, wherein the first blood pressure value and the second blood pressure value are systolic blood pressure values.

[0101] Example 21: A method comprising: determining a baseline circadian rhythm pattern of a patient's blood pressure during a first time period; determining a subsequent circadian rhythm pattern of the patient's blood pressure during a second time period, the second time period being after the first time period; determining one or more differences between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern; determining whether the patient is a candidate for denervation therapy based on the one or more differences between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern; and in response to determining whether the patient is a candidate for denervation therapy, outputting an indication of whether the patient is a candidate for denervation therapy.

[0102] Embodiment 22: A method according to Embodiment 21, wherein the method further comprises: obtaining from a sensing device the first blood pressure value of the patient during the first time period or one or more of the physiological parameters indicating the first blood pressure value; and obtaining from the sensing device the second blood pressure value of the patient during the second time period or one or more of the physiological parameters indicating the second blood pressure value.

[0103] Example 23: A method according to any one of Examples 21 to 22, wherein the method further includes: determining whether the difference between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern is greater than a circadian rhythm pattern difference threshold; in response to determining that the difference between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern is greater than the circadian rhythm pattern difference threshold, outputting an indication that the patient is a candidate for denervation therapy; and in response to determining that the difference between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern is less than or equal to the circadian rhythm pattern difference threshold, outputting an indication that the patient is not a candidate for denervation therapy.

[0104] Embodiment 24: A method according to embodiments 21 to 23, wherein the method further comprises: in response to determining that the difference between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern is greater than the circadian rhythm pattern difference threshold, determining that one or more of the basal sympathetic nervous system activities is greater than the basal sympathetic nervous system activity threshold or the change in basal sympathetic nervous system activity is greater than the change in the basal sympathetic nervous system activity threshold; in response to determining that the difference between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern is less than or equal to the circadian rhythm pattern difference threshold, determining that one or more of the basal sympathetic nervous system activities is less than or equal to the basal sympathetic nervous system activity threshold or the change in basal sympathetic nervous system activity is greater than the change in basal sympathetic nervous system activity threshold The change in the basal sympathetic nervous system activity is less than or equal to the change in the basal sympathetic nervous system activity threshold; in response to determining that one or more of the basal sympathetic nervous system activities is greater than the basal sympathetic nervous system activity threshold or the change in the basal sympathetic nervous system activity is greater than the change in the basal sympathetic nervous system activity threshold, outputting an indication that the patient is a candidate for denervation therapy; and in response to determining that one or more of the basal sympathetic nervous system activities is less than or equal to the basal sympathetic nervous system activity threshold or the change in the basal sympathetic nervous system activity is less than or equal to the change in the basal sympathetic nervous system activity threshold, outputting an indication that the patient is not a candidate for denervation therapy.

[0105] Embodiment 25: The method according to any one of embodiments 21 to 24, wherein the first time period is one week, two weeks or one month.

[0106] Embodiment 26: The method of any one of Embodiments 21 to 25, wherein the second period of time is three days, five days, one week or two weeks.

[0107] Embodiment 27: The method according to any one of embodiments 21 to 26, wherein the characteristics of the baseline circadian rhythm pattern include at least one of the following: the slope of the morning fluctuation, the slope of the evening fluctuation, the amplitude of the nighttime blood pressure drop, the amplitude of the evening blood pressure fluctuation, the daytime variability, the nighttime variability or the 24-hour variability, and the characteristics of the subsequent circadian rhythm pattern include at least one of the following: the slope of the morning fluctuation, the slope of the evening fluctuation, the amplitude of the nighttime blood pressure drop, the amplitude of the evening blood pressure fluctuation, the daytime variability, the nighttime variability or the 24-hour variability.

[0108] Embodiment 28: The method according to embodiment 27, wherein the method further comprises comparing one or more of the characteristics of the baseline circadian rhythm pattern with corresponding one or more of the characteristics of the subsequent circadian rhythm pattern to determine one or more differences between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern.

[0109] Embodiment 29: A method according to any one of Embodiments 21 to 28, wherein the first blood pressure value or a physiological parameter indicating the first blood pressure value and the second blood pressure value or a physiological parameter indicating the second blood pressure value are obtained from a wearable device.

[0110] Embodiment 30: The method according to any one of embodiments 21 to 29, wherein the first blood pressure value and the second blood pressure value are systolic blood pressure values.

[0111] Various embodiments have been described. These and other examples are within the scope of the following claims.

[0112] Further disclosed herein are the subject of the following clauses:

[0113] 1. A computing device, comprising:

[0114] Memory; and

[0115] one or more processors coupled to the memory, the one or more processors configured to:

[0116] determining a baseline circadian pattern of the patient's blood pressure during a first time period;

[0117] determining a subsequent circadian rhythm pattern of the patient's blood pressure during a second time period, the second time period being subsequent to the first time period;

[0118] determining one or more differences between the subsequent circadian pattern and the baseline circadian pattern;

[0119] determining whether the patient is a candidate for denervation therapy based on the one or more differences between the subsequent circadian pattern and the baseline circadian pattern; and

[0120] In response to determining whether the patient is a candidate for the denervation therapy, an indication of whether the patient is a candidate for the denervation therapy is output.

[0121] 2. The computing device of clause 1, wherein the one or more processors are further configured to:

[0122] obtaining, from a sensing device, one or more of a first blood pressure value of the patient during the first time period or a physiological parameter indicating the first blood pressure value; and

[0123] A second blood pressure value of the patient during the second time period or one or more of a physiological parameter indicating the second blood pressure value is obtained from the sensing device.

[0124] 3. A computing device according to any of clauses 1 to 2, wherein the one or more processors are further configured to:

[0125] determining whether the difference between the subsequent circadian pattern and the baseline circadian pattern is greater than a circadian pattern difference threshold;

[0126] responsive to determining that the difference between the subsequent circadian pattern and the baseline circadian pattern is greater than the circadian pattern difference threshold, outputting an indication that the patient is a candidate for denervation therapy; and

[0127] In response to determining that the difference between the subsequent circadian pattern and the baseline circadian pattern is less than or equal to the circadian pattern difference threshold, outputting an indication that the patient is not a candidate for denervation therapy.

[0128] 4. A computing device according to any of clauses 1 to 3, wherein the one or more processors are further configured to:

[0129] responsive to determining that the difference between the subsequent circadian pattern and the baseline circadian pattern is greater than the circadian pattern difference threshold, determining that one or more of basal sympathetic nervous system activities is greater than a basal sympathetic nervous system activity threshold or a change in basal sympathetic nervous system activity is greater than a change in basal sympathetic nervous system activity threshold;

[0130] responsive to determining that the difference between the subsequent circadian pattern and the baseline circadian pattern is less than or equal to the circadian pattern difference threshold, determining that one or more of the basal sympathetic nervous system activities is less than or equal to the basal sympathetic nervous system activity threshold or that a change in the basal sympathetic nervous system activity is less than or equal to a change in the basal sympathetic nervous system activity threshold;

[0131] In response to determining that one or more of the basal sympathetic nervous system activity is greater than the basal sympathetic nervous system activity threshold or the change in the basal sympathetic nervous system activity is greater than the change in the basal sympathetic nervous system activity threshold, outputting an indication that the patient is a candidate for denervation therapy; and

[0132] In response to determining that one or more of the basal sympathetic nervous system activities is less than or equal to the basal sympathetic nervous system activity threshold or the change in the basal sympathetic nervous system activity is less than or equal to the change in the basal sympathetic nervous system activity threshold,

[0133] An indication is output that the patient is not a candidate for denervation therapy.

[0134] 5. A computing device according to any one of clauses 1 to 4, wherein the first time period is one week, two weeks or one month.

[0135] 6. A computing device according to any of clauses 1 to 5, wherein the second time period is three days, five days, one week or two weeks.

[0136] 7. A computing device according to any one of clauses 1 to 6, wherein the characteristics of the baseline circadian rhythm pattern include at least one of the following: the slope of the morning fluctuation, the slope of the evening fluctuation, the magnitude of the nighttime blood pressure drop, the magnitude of the evening blood pressure fluctuation, the daytime variability, the nighttime variability or the 24-hour variability, and

[0137] The characteristics of the subsequent circadian pattern include at least one of the following:

[0138] The slope of the morning fluctuation, the slope of the evening fluctuation, the amplitude of the nocturnal drop in blood pressure, the amplitude of the evening blood pressure fluctuation, the diurnal variability, the nocturnal variability, or the 24-hour variability.

[0139] 8. A computing device according to claim 7, wherein the one or more processors are further configured to compare one or more of the characteristics of the baseline circadian rhythm pattern with corresponding one or more of the characteristics of the subsequent circadian rhythm pattern to determine the one or more differences between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern.

[0140] 9. A computing device according to any one of clauses 1 to 8, wherein the first blood pressure value or a physiological parameter indicative of the first blood pressure value and the second blood pressure value or a physiological parameter indicative of the second blood pressure value are obtained from a wearable device.

[0141] 10. The computing device of any one of clauses 1 to 9, wherein the first blood pressure value and the second blood pressure value are systolic blood pressure values.

[0142] 11. A system, comprising:

[0143] a wearable device configured to sense and collect data indicative of a patient's blood pressure; and

[0144] The computing device of any one of claims 1 to 10, wherein the computing device is communicatively coupled to the wearable device.

[0145] 12. A system, comprising:

[0146] a wearable device configured to sense and collect data indicative of a patient's blood pressure; and

[0147] a computing device communicatively coupled to the wearable device, wherein the computing device comprises:

[0148] Memory; and

[0149] one or more processors coupled to the memory, the one or more processors configured to:

[0150] determining a baseline circadian pattern of the patient's blood pressure during a first time period;

[0151] determining a subsequent circadian rhythm pattern of the patient's blood pressure during a second time period, the second time period being subsequent to the first time period;

[0152] determining one or more differences between the subsequent circadian pattern and the baseline circadian pattern;

[0153] determining whether the patient is a candidate for denervation therapy based on the one or more differences between the subsequent circadian pattern and the baseline circadian pattern; and

[0154] In response to determining whether the patient is a candidate for denervation therapy, an indication of whether the patient is a candidate for the denervation therapy is output.

[0155] 13. The system of clause 12, wherein the one or more processors are further configured to:

[0156] obtaining, from a sensing device, one or more of the first blood pressure value of the patient during the first time period or a physiological parameter indicating the first blood pressure value; and

[0157] One or more of the second blood pressure value of the patient during the second time period or a physiological parameter indicating the second blood pressure value is obtained from the sensing device.

[0158] 14. A system according to any of clauses 12 to 13, wherein the one or more processors are further configured to:

[0159] determining whether the difference between the subsequent circadian pattern and the baseline circadian pattern is greater than a circadian pattern difference threshold;

[0160] responsive to determining that the difference between the subsequent circadian pattern and the baseline circadian pattern is greater than the circadian pattern difference threshold, outputting an indication that the patient is a candidate for denervation therapy; and

[0161] In response to determining that the difference between the subsequent circadian pattern and the baseline circadian pattern is less than or equal to the circadian pattern difference threshold, outputting an indication that the patient is not a candidate for denervation therapy.

[0162] 15. A system according to any of clauses 12 to 14, wherein the one or more processors are further configured to:

[0163] responsive to determining that the difference between the subsequent circadian pattern and the baseline circadian pattern is greater than the circadian pattern difference threshold, determining that one or more of basal sympathetic nervous system activities is greater than a basal sympathetic nervous system activity threshold or a change in basal sympathetic nervous system activity is greater than a change in basal sympathetic nervous system activity threshold;

[0164] responsive to determining that the difference between the subsequent circadian pattern and the baseline circadian pattern is less than or equal to the circadian pattern difference threshold, determining that one or more of the basal sympathetic nervous system activities is less than or equal to the basal sympathetic nervous system activity threshold or that a change in the basal sympathetic nervous system activity is less than or equal to a change in the basal sympathetic nervous system activity threshold;

[0165] In response to determining that one or more of the basal sympathetic nervous system activity is greater than the basal sympathetic nervous system activity threshold or the change in the basal sympathetic nervous system activity is greater than the change in the basal sympathetic nervous system activity threshold, outputting an indication that the patient is a candidate for denervation therapy; and

[0166] In response to determining that one or more of the basal sympathetic nervous system activities is less than or equal to the basal sympathetic nervous system activity threshold or the change in the basal sympathetic nervous system activity is less than or equal to the change in the basal sympathetic nervous system activity threshold,

[0167] An indication is output that the patient is not a candidate for denervation therapy.

[0168] 16. The system of any one of clauses 12 to 14, wherein the first time period is one week, two weeks or one month.

[0169] 17. A system according to any one of clauses 12 to 16, wherein the second time period is three days, five days, one week or two weeks.

[0170] 18. A system according to any one of clauses 12 to 17, wherein the characteristics of the baseline circadian rhythm pattern include at least one of the following: the slope of the morning fluctuation, the slope of the evening fluctuation, the magnitude of the nighttime blood pressure drop, the magnitude of the evening blood pressure fluctuation, the daytime variability, the nighttime variability or the 24-hour variability, and

[0171] The characteristics of the subsequent circadian pattern include at least one of the following:

[0172] The slope of the morning fluctuation, the slope of the evening fluctuation, the amplitude of the nocturnal drop in blood pressure, the amplitude of the evening blood pressure fluctuation, the diurnal variability, the nocturnal variability, or the 24-hour variability.

[0173] 19. A system according to claim 18, wherein the one or more processors are further configured to compare one or more of the characteristics of the baseline circadian pattern with a corresponding one or more of the characteristics of the subsequent circadian pattern to determine one or more differences between the subsequent circadian pattern and the baseline circadian pattern.

[0174] 20. The system according to any of clauses 12 to 19, wherein the first blood pressure value or a physiological parameter indicative of the first blood pressure value and the second blood pressure value or a physiological parameter indicative of the second blood pressure value are obtained from a wearable device.

[0175] 21. A system according to any one of clauses 12 to 20, wherein the first blood pressure value and the second blood pressure value are systolic blood pressure values. 22. A method comprising:

[0176] determining a baseline circadian pattern of the patient's blood pressure during a first time period;

[0177] determining a subsequent circadian rhythm pattern of the patient's blood pressure during a second time period, the second time period being subsequent to the first time period;

[0178] determining one or more differences between the subsequent circadian pattern and the baseline circadian pattern;

[0179] determining whether the patient is a candidate for denervation therapy based on the one or more differences between the subsequent circadian pattern and the baseline circadian pattern;

[0180] as well as

[0181] In response to determining whether the patient is a candidate for denervation therapy, an indication of whether the patient is a candidate for denervation therapy is output.

[0182] 23. The method according to clause 22, wherein the method further comprises:

[0183] obtaining, from a sensing device, one or more of the first blood pressure value of the patient during the first time period or a physiological parameter indicating the first blood pressure value; and

[0184] One or more of the second blood pressure value or a physiological parameter indicating the second blood pressure value of the patient within a second time period is obtained from the sensing device.

[0185] 24. A method according to any one of clauses 22 or 23, wherein the method further comprises:

[0186] determining whether the difference between the subsequent circadian pattern and the baseline circadian pattern is greater than a circadian pattern difference threshold;

[0187] responsive to determining that the difference between the subsequent circadian pattern and the baseline circadian pattern is greater than the circadian pattern difference threshold, outputting an indication that the patient is a candidate for denervation therapy; and

[0188] In response to determining that the difference between the subsequent circadian pattern and the baseline circadian pattern is less than or equal to the circadian pattern difference threshold, outputting an indication that the patient is not a candidate for denervation therapy.

[0189] 25. The method according to clauses 22 to 24, wherein the method further comprises:

[0190] responsive to determining that the difference between the subsequent circadian pattern and the baseline circadian pattern is greater than the circadian pattern difference threshold, determining that one or more of basal sympathetic nervous system activities is greater than a basal sympathetic nervous system activity threshold or a change in basal sympathetic nervous system activity is greater than a change in basal sympathetic nervous system activity threshold;

[0191] responsive to determining that the difference between the subsequent circadian pattern and the baseline circadian pattern is less than or equal to the circadian pattern difference threshold, determining that one or more of the basal sympathetic nervous system activities is less than or equal to the basal sympathetic nervous system activity threshold or that a change in the basal sympathetic nervous system activity is less than or equal to a change in the basal sympathetic nervous system activity threshold;

[0192] In response to determining that one or more of the basal sympathetic nervous system activity is greater than the basal sympathetic nervous system activity threshold or the change in the basal sympathetic nervous system activity is greater than the change in the basal sympathetic nervous system activity threshold, outputting an indication that the patient is a candidate for denervation therapy; and

[0193] In response to determining that one or more of the basal sympathetic nervous system activities is less than or equal to the basal sympathetic nervous system activity threshold or the change in the basal sympathetic nervous system activity is less than or equal to the change in the basal sympathetic nervous system activity threshold,

[0194] An indication is output that the patient is not a candidate for denervation therapy.

[0195] 26. A method according to any one of clauses 22 to 25, wherein the first period of time is one week, two weeks or one month.

[0196] 27. A method according to any one of clauses 22 to 26, wherein the second time period is three days, five days, one week or two weeks.

[0197] 28. A method according to any one of clauses 22 to 27, wherein the characteristics of the baseline circadian rhythm pattern include at least one of the following: the slope of the morning fluctuation, the slope of the evening fluctuation, the magnitude of the nighttime blood pressure drop, the magnitude of the evening blood pressure fluctuation, the daytime variability, the nighttime variability or the 24-hour variability, and

[0198] The characteristics of the subsequent circadian pattern include at least one of the following:

[0199] The slope of the morning fluctuation, the slope of the evening fluctuation, the amplitude of the nocturnal drop in blood pressure, the amplitude of the evening blood pressure fluctuation, the diurnal variability, the nocturnal variability, or the 24-hour variability.

[0200] 29. A method according to clause 28, wherein the method further comprises comparing one or more of the characteristics of the baseline circadian rhythm pattern with corresponding one or more of the characteristics of the subsequent circadian rhythm pattern to determine the one or more differences between the subsequent circadian rhythm pattern and the baseline circadian rhythm pattern.

[0201] 30. The method according to any of clauses 22 to 29, wherein the first blood pressure value or a physiological parameter indicative of the first blood pressure value and the second blood pressure value or a physiological parameter indicative of the second blood pressure value are obtained from a wearable device.

[0202] 31. The method of any one of clauses 22 to 30, wherein the first blood pressure value and the second blood pressure value are systolic blood pressure values.

Claims

1. A computing device, comprising: Memory; and one or more processors coupled to the memory, the one or more processors configured to: determining a baseline circadian pattern of the patient's blood pressure during a first time period; determining a subsequent circadian rhythm pattern of the patient's blood pressure during a second time period, the second time period being subsequent to the first time period; determining one or more differences between the subsequent circadian pattern and the baseline circadian pattern; determining whether the patient is a candidate for denervation therapy based on the one or more differences between the subsequent circadian pattern and the baseline circadian pattern; and In response to determining whether the patient is a candidate for the denervation therapy, an indication of whether the patient is a candidate for the denervation therapy is output.

2. The computing device of claim 1 , wherein the one or more processors are further configured to: obtaining, from a sensing device, one or more of a first blood pressure value of the patient during the first time period or a physiological parameter indicating the first blood pressure value; and A second blood pressure value of the patient during the second time period or one or more of a physiological parameter indicating the second blood pressure value is obtained from the sensing device.

3. The computing device according to any one of claims 1 to 2, wherein the one or more processors are further configured to: determining whether the difference between the subsequent circadian pattern and the baseline circadian pattern is greater than a circadian pattern difference threshold; responsive to determining that the difference between the subsequent circadian pattern and the baseline circadian pattern is greater than the circadian pattern difference threshold, outputting an indication that the patient is a candidate for denervation therapy; and In response to determining that the difference between the subsequent circadian pattern and the baseline circadian pattern is less than or equal to the circadian pattern difference threshold, outputting an indication that the patient is not a candidate for denervation therapy.

4. The computing device of any one of claims 1 to 3, wherein the one or more processors are further configured to: responsive to determining that the difference between the subsequent circadian pattern and the baseline circadian pattern is greater than the circadian pattern difference threshold, determining that one or more of basal sympathetic nervous system activities is greater than a basal sympathetic nervous system activity threshold or a change in basal sympathetic nervous system activity is greater than a change in basal sympathetic nervous system activity threshold; responsive to determining that the difference between the subsequent circadian pattern and the baseline circadian pattern is less than or equal to the circadian pattern difference threshold, determining that one or more of the basal sympathetic nervous system activities is less than or equal to the basal sympathetic nervous system activity threshold or that a change in the basal sympathetic nervous system activity is less than or equal to a change in the basal sympathetic nervous system activity threshold; In response to determining that one or more of the basal sympathetic nervous system activity is greater than the basal sympathetic nervous system activity threshold or the change in the basal sympathetic nervous system activity is greater than the change in the basal sympathetic nervous system activity threshold, outputting an indication that the patient is a candidate for denervation therapy; and In response to determining that one or more of the basal sympathetic nervous system activity is less than or equal to the basal sympathetic nervous system activity threshold or the change in the basal sympathetic nervous system activity is less than or equal to the change in the basal sympathetic nervous system activity threshold, outputting an indication that the patient is not a candidate for denervation therapy.

5. The computing device of any one of claims 1 to 4, wherein the first time period is one week, two weeks, or one month.

6. The computing device of any one of claims 1 to 5, wherein the second time period is three days, five days, one week, or two weeks.

7. The computing device of any one of claims 1 to 6, wherein the characteristics of the baseline circadian rhythm pattern include at least one of the following: slope of morning fluctuations, slope of evening fluctuations, magnitude of nighttime blood pressure drop, magnitude of evening blood pressure fluctuations, daytime variability, nighttime variability, or 24-hour variability, and The characteristics of the subsequent circadian rhythm pattern include at least one of the following: the slope of the morning fluctuation, the slope of the evening fluctuation, the amplitude of the nighttime blood pressure drop, the amplitude of the nighttime blood pressure fluctuation, the daytime variability, the nighttime variability or the 24-hour variability.

8. A computing device according to claim 7, wherein the one or more processors are further configured to compare one or more of the characteristics of the baseline circadian pattern with corresponding one or more of the characteristics of the subsequent circadian pattern to determine the one or more differences between the subsequent circadian pattern and the baseline circadian pattern.

9. The computing device according to any one of claims 1 to 8, wherein the first blood pressure value or a physiological parameter indicating the first blood pressure value and the second blood pressure value or a physiological parameter indicating the second blood pressure value are obtained from a wearable device.

10. The calculation device according to any one of claims 1 to 9, wherein the first blood pressure value and the second blood pressure value are systolic blood pressure values.

11. A system, comprising: a wearable device configured to sense and collect data indicative of a patient's blood pressure; and The computing device of any one of claims 1 to 10, wherein the computing device is communicatively coupled to the wearable device.

12. A method comprising: determining a baseline circadian pattern of the patient's blood pressure during a first time period; determining a subsequent circadian rhythm pattern of the patient's blood pressure during a second time period, the second time period being subsequent to the first time period; determining one or more differences between the subsequent circadian pattern and the baseline circadian pattern; determining whether the patient is a candidate for denervation therapy based on the one or more differences between the subsequent circadian pattern and the baseline circadian pattern; and In response to determining whether the patient is a candidate for denervation therapy, an indication of whether the patient is a candidate for the denervation therapy is output.

13. The method according to claim 12, wherein the method further comprises: Obtaining from a sensing device one or more of the first blood pressure value of the patient during the first time period or a physiological parameter indicating the first blood pressure value; as well as obtaining, from the sensing device, one or more of the second blood pressure value of the patient within a second time period or a physiological parameter indicating the second blood pressure value; 14. The method according to any one of claims 12 or 13, wherein the method further comprises: determining whether the difference between the subsequent circadian pattern and the baseline circadian pattern is greater than a circadian pattern difference threshold; responsive to determining that the difference between the subsequent circadian pattern and the baseline circadian pattern is greater than the circadian pattern difference threshold, outputting an indication that the patient is a candidate for denervation therapy; as well as In response to determining that the difference between the subsequent circadian pattern and the baseline circadian pattern is less than or equal to the circadian pattern difference threshold, outputting an indication that the patient is not a candidate for denervation therapy.

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