# US 2023/0056430 A1 · Hvad adverse event detection from cardiac compass data

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2023/0056430 A1
- Document ID: US20230056430A1
- Status: Published application
- Kind: A1
- Application number: 17/820,132
- Filed: 2022-08-16
- Published: 2023-02-23
- Earliest claimed: 2021-08-17
- Assignees: Medtronic, Inc.
- Applicants: Medtronic, Inc.
- Inventors: Abhijit Kadrolkar; Troy E. Jackson
- Web page: https://patentgrep.com/patent/US20230056430A1

## CPC

- A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor}
- A61B 5/02405: {Determining heart rate variability}
- A61B 5/686: {Permanently implanted devices, e.g. pacemakers, other stimulators, biochips (A61B5/6861 takes precedence)}
- A61B 5/7282: {Event detection, e.g. detecting unique waveforms indicative of a medical condition (cough events A61B5/0823; seizures A61B5/4094; sleep apnoea A61B5/4818)}
- A61M 60/122: Implantable pumps or pumping devices, i.e. the blood being pumped inside the patient's body
- A61M 60/178: drawing blood from a ventricle and returning the blood to the arterial system via a cannula external to the ventricle, e.g. left or right ventricular assist devices
- A61M 60/216: including a rotating member acting on the blood, e.g. impeller
- A61M 60/515: Regulation using real-time patient data
- G16H 40/63: for local operation
- G16H 40/67: for remote operation
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/30: for calculating health indices; for individual health risk assessment
- A61B 5/0245: by using sensing means generating electric signals, {i.e. ECG signals}
- A61B 5/0538: invasively, e.g. using a catheter
- A61M 2205/18: with alarm
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- A61M: DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY (introducing media into or onto the bodies of animals A61D7/00; means for inserting tampons A61F13/26; devices for administering food or medicines orally A61J; containers for collecting, storing or administering blood or medical fluids A61J1/05); DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY (surgery A61B; chemical aspects of surgical articles A61L); DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR {(Electrotherapy, e.g. producing anaesthesia by the use of alternating or intermittent currents A61N1/36021)}
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

An example system includes an implantable medical device configured to obtain measurement values of one or more patient metrics; and processing circuitry configured to: determine a baseline value for each of the respective one or more patient metrics based on measurement values of the one or more patient metrics over a first period of time; determine a short-term value for each of the one or more patient metrics based on measurement values of the one or more patient metrics over a second period of time, determine a difference between each of the short-term values and the respective baseline value for each of the one or more patient metrics; determine that a risk of an adverse event occurring in the patient is high in response to the determined difference meeting a respective adverse event risk threshold; and generate for output an adverse event high risk alert.

## Claims

### Claim 1

A system comprising:
an implantable medical device configured to obtain measurement values of one or more patient metrics of a patient; and
processing circuitry configured to:
determine a baseline value for each of the respective one or more patient metrics based on measurement values of the one or more patient metrics over a first period of time;
determine a short-term value for each of the one or more patient metrics based on measurement values of the one or more patient metrics over a second period of time, the second period of time being shorter in duration than the first period of time;
determine a difference between each of the short-term values and the respective baseline value for each of the one or more patient metrics;
determine that a risk of an adverse event occurring in the patient is high in response to the determined difference of at least one patient metric of the one or more patient metrics meeting a respective adverse event risk threshold or in response to an accumulated value of the determined differences for each of the one or more patient metrics meeting a respective accumulated adverse event risk threshold; and
generate for output an adverse event high risk alert in response to determining the patient has the high risk of the adverse event occurring.

### Claim 2 (depends on 1)

The system of claim 1, wherein the one or more patient metrics comprise at least one of (a) heart rate during a day time period; (b) heart rate during a night time period; (c) percent atrial pacing; (d) percent ventricular pacing; (e) heart rate variability; (f) activity; (g) daily intrathoracic impedance; or (h) ventricular tachycardia or ventricular fibrillation status.

### Claim 3 (depends on 2)

The system of claim 2, wherein the processing circuitry is further configured to apply a long-term filter to the measurement values of each of the one or more patient metrics over the first period of time to determine the baseline value for each of the respective one or more patient metrics.

### Claim 4 (depends on 3)

The system of claim 3, wherein the processing circuitry is further configured to apply a short-term filter to the measurement values of each of the one or more patient metrics over the second period of time to determine the short-term value for each of the respective one or more patient metrics.

### Claim 5 (depends on 4)

The system of claim 4, wherein the first period of time is 6 months, and the second period of time is 30 days.

### Claim 6 (depends on 4)

The method of claim 4, wherein the long-term filter is a moving average filter applied over the first period of time.

### Claim 7 (depends on 4)

The system of claim 4, wherein the short-term filter is a moving average filter applied over the second period of time.

### Claim 8 (depends on 1)

The system of claim 1, wherein the adverse event is a heart ventricular assist device (HVAD) induced ventricular arrythmia.

### Claim 9 (depends on 1)

The system of claim 1, wherein the generated adverse event high risk alert is to cause a medical intervention to be performed on the patient.

### Claim 10 (depends on 2)

The system of claim 2, wherein the adverse event is a thrombus and the processing circuitry is further configured to determine a risk of the thrombus occurring in the patient is high in response to the determined difference of at least one patient metric of patient metrics (a)-(g) meeting a respective thrombus risk threshold or in response to an accumulated value of the determined differences for each of the patient metrics (a)-(g) meeting a respective accumulated thrombus risk threshold.

### Claim 11 (depends on 2)

The system of claim 2, wherein the adverse event is a stroke and the processing circuitry is further configured to determine a risk of the stroke occurring in the patient is high in response to the determined difference of at least one patient metric of patient metrics (a)-(e) meeting a respective stroke risk threshold or in response to an accumulated value of the determined differences for each of the patient metrics (a)-(e) meeting a respective accumulated stroke risk threshold.

### Claim 12 (depends on 2)

The system of claim 2, wherein the adverse event is a heart ventricular assist device (HVAD) induced ventricular arrythmia and the processing circuitry is further configured to determine a risk of the HVAD induced ventricular arrythmia occurring in the patient is high in response to the determined difference of patient metric (h) meeting a respective HVAD induced ventricular arrythmia risk threshold.

### Claim 13

A method comprising:
obtaining one or more measurement values of one or more patient metrics of a patient;
determining a baseline value for each of the respective one or more patient metrics based on measurement values of the one or more patient metrics over a first period of time;
determining a short-term value for each of the one or more patient metrics based on measurement values of the one or more patient metrics over a second period of time, the second period of time being shorter than the first period of time;
determining a difference between each of the short-term values and the respective baseline value for each of the one or more patient metrics;
determining that a risk of an adverse event occurring in the patient is high in response to the determined difference of at least one patient metric of the one or more patient metrics meeting a respective adverse event risk threshold or in response to an accumulated value of the determined differences for each of the one or more metrics meeting a respective accumulated adverse event risk threshold; and
generating for output an adverse event high risk alert in response to determining the patient has the high risk of the adverse event occurring.

### Claim 14 (depends on 13)

The method of claim 13, wherein the one or more patient metrics comprise at least one of (a) heart rate during a day time period; (b) heart rate during a night time period; (c) percent atrial pacing; (d) percent ventricular pacing; (e) heart rate variability; (f) activity; (g) daily intrathoracic impedance; or (h) ventricular tachycardia or ventricular fibrillation status.

### Claim 15 (depends on 14)

The method of claim 14, further comprising applying a long-term filter to the measurement values of each of the one or more patient metrics over the first period of time to determine the baseline value for each of the respective one or more patient metrics.

### Claim 16 (depends on 15)

The method of claim 15, further comprising applying a short-term filter to the measurement values of each of the one or more patient metrics over the second period of time to determine the short-term value for each of the respective one or more patient metrics.

### Claim 17 (depends on 16)

The method of claim 16, wherein the long-term filter is a moving average filter applied over the first period of time and the short-term filter is a moving average filter applied over the second period of time.

### Claim 18 (depends on 14)

The method of claim 14, wherein the adverse event is a thrombus and the method further comprising determining a risk of the thrombus occurring in the patient is high in response to the determined difference of at least one patient metric of patient metrics (a)-(g) meeting a respective thrombus risk threshold or in response to an accumulated value of the determined differences for each of the patient metrics (a)-(g) meeting a respective accumulated thrombus risk threshold.

### Claim 19 (depends on 14)

The method of claim 14, wherein the adverse event is a stroke and the method further comprising determining a risk of the stroke occurring in the patient is high in response to the determined difference of at least one patient metric of patient metrics (a)-(e) meeting a respective a stroke risk threshold or in response to an accumulated value of the determined differences for each of the patient metrics (a)-(e) meeting a respective accumulated stroke risk threshold.

### Claim 20 (depends on 14)

The method of claim 14, wherein the adverse event is a heart ventricular assist device (HVAD) induced ventricular arrythmia and the method further comprising determining a risk of the HVAD induced ventricular arrythmia occurring in the patient is high in response to the determined difference of patient metric (h) meeting a respective HVAD induced ventricular arrythmia risk threshold.