# US 12,213,789 B2 · Identifying patients suited for renal denervation therapy

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- Number: US 12,213,789 B2
- Document ID: US12213789B2
- Status: Granted
- Kind: B2
- Application number: 18/308,382
- Filed: 2023-04-27
- Published: 2023-08-24
- Granted: 2025-02-04
- Earliest claimed: 2018-04-27
- Assignees: Medtronic Ireland Manufacturing Unlimited Company
- Applicants: Medtronic Ireland Manufacturing Unlimited Company
- Inventors: Abhijeet Dubhashi
- Web page: https://patentgrep.com/patent/US12213789B2

## CPC

- A61B 5/201: {Assessing renal or kidney functions}
- A61B 5/4041: {Evaluating nerves condition}
- A61B 5/02108: {from analysis of pulse wave characteristics}
- A61B 5/4848: {Monitoring or testing the effects of treatment, e.g. of medication}
- A61B 5/6824: {Arm or wrist}
- A61B 5/7267: {involving training the classification device}
- G06N 3/0499: Feedforward networks
- G06N 3/08: Learning methods
- G06N 3/09: Supervised learning
- A61B 5/02007: {Evaluating blood vessel condition, e.g. elasticity, compliance}
- A61B 5/02438: {with portable devices, e.g. worn by the patient}
- A61N 1/36: for stimulation
- G06N 3/044: Recurrent networks, e.g. Hopfield networks
- G06N 3/047: Probabilistic or stochastic networks
- G06N 3/048: Activation functions
- G16H 10/60: for patient-specific data, e.g. for electronic patient records
- G16H 50/30: for calculating health indices; for individual health risk assessment
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- A61N: ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY (measurement of bioelectric currents A61B; surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body A61B18/00; anaesthetic apparatus in general A61M; incandescent lamps H01K; infrared radiators for heating H05B)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

Example devices, systems, and techniques predict renal denervation efficacy for reducing hypertension in a patient based on pulse information. For example, a system may include processing circuitry configured to obtain pulse information representative of pulses from both wrists of a patient, obtain a plurality of values representative of respective patient metrics for the patient, and apply the pulse information and the plurality of values to a deep learning model trained to represent a relationship of the pulse information and the patient metrics to an efficacy of renal denervation in reducing hypertension. In some examples, responsive to applying the pulse information and the plurality of values to the deep learning model, the processing circuitry obtains, from the deep learning model, a score indicative of renal denervation efficacy in reducing hypertension for the patient, and generates a graphical user interface comprising a graphical representation of the score for the patient.

## Claims

### Claim 1

A system comprising:
a memory configured to store a deep learning model, the deep learning model trained to represent a relationship of pulse information and patient metrics to renal denervation efficacy; and
processing circuitry configured to:
obtain pulse information representative of pulses of a patient;
obtain a plurality of values representative of respective patient metrics for the patient;
apply the pulse information and the plurality of values to the deep learning model;
responsive to applying the pulse information and the plurality of values to the deep learning model, obtain, from the deep learning model, a score indicative of renal denervation efficacy for the patient; and
output an indication of the score indicative of renal denervation efficacy for the patient.

### Claim 2 (depends on 1)

The system of claim 1, wherein the processing circuitry is configured to:
compare the score to a threshold; and
output an indication the patient would likely benefit from renal denervation therapy in response to determining the score is greater than or equal to the threshold.

### Claim 3 (depends on 1)

The system of claim 1, wherein the score comprises a percentage of hypertension in the patient that is associated with kidney function.

### Claim 4 (depends on 1)

The system of claim 1, wherein the score comprises a probability that the patient would achieve a target reduction in hypertension in response to receiving renal denervation therapy.

### Claim 5 (depends on 1)

The system of claim 1, wherein the patient metrics for the patient comprise one or more of: an age of the patient, a gender of the patient, an ethnic background of the patient, a weight of the patient, a height of the patient, a diet of the patient, an activity level of the patient, or a stress level of the patient.

### Claim 6 (depends on 1)

The system of claim 1, wherein the pulse information comprises at least one of: a pulse rate, a pulse amplitude, a pulse wave velocity, a pulse rate variability, a pulse waveform morphology, or a pulse echo.

### Claim 7 (depends on 1)

The system of claim 1, wherein the pulse information comprises first pulse information and second pulse information, and wherein the system further comprises:
a first sensor configured to generate the first pulse information representative of pulses from a first location of the patient and transmit the first pulse information to the processing circuitry; and
a second sensor configured to generate the second pulse information representative of pulses from a second location of the patient and transmit the second pulse information to the processing circuitry.

### Claim 8 (depends on 7)

The system of claim 7, wherein the first sensor and the second sensor each comprise a respective pneumatic pulse sensor.

### Claim 9 (depends on 7)

The system of claim 7, wherein the first sensor and the second sensor each comprise a respective spectrophotometric sensor.

### Claim 10 (depends on 1)

The system of claim 1, further comprising a display configured to present a graphical user interface comprising a graphical representation of the score for the patient.

### Claim 11

A method comprising:
obtaining, by processing circuitry, pulse information representative of pulses of a patient;
obtaining, by the processing circuitry, a plurality of values representative of respective patient metrics for the patient;
applying, by the processing circuitry, the pulse information and the plurality of values to a deep learning model, the deep learning model trained to represent a relationship of the pulse information and the patient metrics to renal denervation efficacy;
responsive to applying the pulse information and the plurality of values to the deep learning model, obtaining, by the processing circuitry and from the deep learning model, a score indicative of renal denervation efficacy for the patient; and
outputting, by the processing circuitry, an indication of the score indicative of renal denervation efficacy for the patient.

### Claim 12 (depends on 11)

The method of claim 11, further comprising:
comparing the score to a threshold; and
outputting an indication the patient would likely benefit from renal denervation therapy in response to determining the score is greater than or equal to the threshold.

### Claim 13 (depends on 11)

The method of claim 11, wherein the score comprises a percentage of hypertension in the patient that is associated with kidney function.

### Claim 14 (depends on 11)

The method of claim 11, wherein the score comprises a probability that the patient would achieve a target reduction in hypertension in response to receiving renal denervation therapy.

### Claim 15 (depends on 11)

The method of claim 11, wherein the patient metrics for the patient comprise one or more of an age of the patient, a gender of the patient, an ethnic background of the patient, a weight of the patient, a height of the patient, a diet of the patient, an activity level of the patient, or a stress level of the patient.

### Claim 16 (depends on 11)

The method of claim 11, wherein the pulse information comprises at least one of: a pulse rate, a pulse amplitude, a pulse wave velocity, a pulse rate variability, a pulse waveform morphology, or a pulse echo.

### Claim 17 (depends on 11)

The method of claim 11, wherein the pulse information comprises first pulse information and second pulse information, and wherein the method further comprises:
generating, by a first sensor, the first pulse information representative of pulses from a first location of the patient and transmit the first pulse information to the processing circuitry; and
generating, by a second sensor, the second pulse information representative of pulses from a second location of the patient and transmit the second pulse information to the processing circuitry.

### Claim 18 (depends on 17)

The method of claim 17, wherein:
generating the first pulse information comprises generating the first pulse information by a first pneumatic pulse sensor; and
generating the second pulse information comprises generating the second pulse information by a second pneumatic pulse sensor.

### Claim 19 (depends on 11)

The method of claim 11, further comprising displaying, via a display, a graphical user interface comprising a graphical representation of the score for the patient.

### Claim 20

A non-transitory computer-readable medium, comprising instructions that, when executed, cause at least one processor to:
obtain pulse information representative of pulses of a patient;
obtain a plurality of values representative of respective patient metrics for the patient;
apply the pulse information and the plurality of values to a deep learning model, the deep learning model trained to represent a relationship of the pulse information and patient metrics to renal denervation efficacy;
responsive to applying the pulse information and the plurality of values to the deep learning model, obtain, from the deep learning model, a score indicative of renal denervation efficacy for the patient; and
output an indication of the score indicative of renal denervation efficacy for the patient.