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WO2025224686A1 - Systèmes de détermination d'éligibilité de patient pour des soins auto-administrés avec des anticoagulants oraux - Google Patents

Systèmes de détermination d'éligibilité de patient pour des soins auto-administrés avec des anticoagulants oraux

Info

Publication number
WO2025224686A1
WO2025224686A1 PCT/IB2025/054318 IB2025054318W WO2025224686A1 WO 2025224686 A1 WO2025224686 A1 WO 2025224686A1 IB 2025054318 W IB2025054318 W IB 2025054318W WO 2025224686 A1 WO2025224686 A1 WO 2025224686A1
Authority
WO
WIPO (PCT)
Prior art keywords
patient
episodes
potential
value
processing circuitry
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/IB2025/054318
Other languages
English (en)
Inventor
Leonardo RAPALLINI
Niranjan Chakravarthy
Yong K. Cho
Robert W. LIBBEY
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Medtronic Inc
Original Assignee
Medtronic Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Medtronic Inc filed Critical Medtronic Inc
Publication of WO2025224686A1 publication Critical patent/WO2025224686A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A61B5/361—Detecting fibrillation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4836—Diagnosis combined with treatment in closed-loop systems or methods
    • A61B5/4839—Diagnosis combined with treatment in closed-loop systems or methods combined with drug delivery
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6846—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive
    • A61B5/6847—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive mounted on an invasive device
    • A61B5/686—Permanently implanted devices, e.g. pacemakers, other stimulators, biochips
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

  • This disclosure generally relates to medical devices and, more particularly, to medical devices that monitor cardiac signals.
  • IMD implantable medical devices
  • cardiac signal such as an electrocardiogram (ECG)
  • ECG electrocardiogram
  • such devices are configured to monitor patient conditions based on one or more physiological signals.
  • Such IMDs may process and/or transmit device data to investigate patient health.
  • the data may be transmitted to a cloud computing system, which may process the data for analysis and/or presentation to the user.
  • a patient may have a medical device system including one or more implantable medical devices (IMDs) that continuously monitor one or more physiological signals, e.g., an electrocardiogram (ECG) signal.
  • IMDs implantable medical devices
  • ECG electrocardiogram
  • the system may monitor the physiological signal to determine whether the signal contains AF episodes.
  • the system may flag potential AF episodes over a first period of time.
  • the system and/or a clinician or other qualified person may further analyze the potential AF episodes and/or the flagged ECG signal data to determine whether the potential AF episodes and/or the flagged ECG signal data corresponding to the first period of time are true AF episodes. Based on the determination, the system determines a first diagnostic state of the patient based on an eligibility value for the patient based on a number of true AF episodes of a plurality of potential AF episodes. If the eligibility value meets a threshold, the system determines the patient is eligible to receive instructions from the system to adjust OAC prescriptions, e.g. stopping an OAC prescription, starting an OAC prescription, maintaining an OAC prescription, or adjusting a dosage of OACs.
  • OAC prescriptions e.g. stopping an OAC prescription, starting an OAC prescription, maintaining an OAC prescription, or adjusting a dosage of OACs.
  • the system determines whether to adjust an AF detection configuration of the system and identifies a second plurality of potential AF episodes over a second period of time. The system and/or clinician determines whether the potential AF episodes corresponding to the second period of time are true AF episodes. If the eligibility value meets a threshold, the system determines the patient is eligible to receive instructions from the system to adjust OAC prescriptions. If the eligibility value falls below the threshold, the system, the system determines the patient should have clinician-driven OAC prescription adjustments.
  • the system may implement one or more machine learning models to identify potential AF episodes, determine whether the potential AF episodes are true AF episodes, and/or to determine whether the patient is eligible for self-driven OAC administration.
  • a system includes an implantable medical device (IMD) comprising: sensing circuitry configured to sense a physiological signal of a patient; a first memory configured to store data indicative of values of the physiological signal; and transmitter circuitry configured to transmit the data to an external device; and a second memory configured to store a profile for the patient; processing circuitry configured to: identify a first plurality of potential atrial fibrillation (AF) episodes of the patient based on the physiological signal over a first period of time; determine whether the potential AF episode is a true AF episode for each of the first plurality of potential AF episodes; determine a first eligibility value for the patient based on a number of true AF episodes of the first plurality of potential AF episodes; update a diagnostic state value in the profile for the patient to a first value in response to the first eligibility value passing a first threshold value, wherein the first value indicates that the patient is ineligible for self-driven oral anticoagulant (OAC) administration; identify a second plurality of potential AF episodes over
  • a method includes sensing, by sensing circuitry of an implantable medical device (IMD) of a system, a physiological signal of a patient; storing, by memory of the IMD, data indicative of values of the physiological signal; transmitting, by transmitter circuitry of the IMD, data to an external device; identifying, by processing circuitry of the system, a first plurality of potential atrial fibrillation (AF) episodes of the patient based on the physiological signal over a first period of time; determining, by the processing circuitry, whether the potential AF episode is a true AF episode for each of the first plurality of potential AF episodes; determining, by the processing circuitry, a first eligibility value for the patient based on a number of true AF episodes of the first plurality of potential AF episodes; updating, by the processing circuitry, a diagnostic state value in a profile for the patient stored in a memory of the system to a first value in response to the first eligibility value passing a first threshold value, wherein the first value indicates that the patient
  • a non-transitory computer-readable medium stores instructions that when executed cause processing circuitry of a system to: identify a first plurality of potential atrial fibrillation (AF) episodes of a patient based on a physiological signal sensed by sensing circuitry of an implantable medical device (IMD) of a system over a first period of time; determine whether the potential AF episode is a true AF episode for each of the first plurality of potential AF episodes; determine a first eligibility value for the patient based on a number of true AF episodes of the first plurality of potential AF episodes; update a diagnostic state value in a profile for the patient stored in a memory of the system to a first value in response to the first eligibility value passing a first threshold value, wherein the first value indicates that the patient is ineligible for selfdriven oral anticoagulant (OAC) administration; identify a second plurality of potential AF episodes over a second period of time; for each of the second plurality of potential AF episodes, determine whether the potential AF episode
  • OFAC oral anticoagul
  • FIG. 1 illustrates the environment of an example medical device system in conjunction with a patient, in accordance with one or more techniques of this disclosure.
  • FIGS. 2 A and 2B are conceptual diagrams illustrating example implantable medical devices that operate in accordance with one or more techniques of this disclosure.
  • FIG. 3 is a block diagram illustrating an example configuration of a medical device that operates in accordance with one or more techniques of the present disclosure.
  • FIG. 4 is a block diagram illustrating an example configuration of the computing system of FIG. 1, in accordance with one or more techniques of this disclosure.
  • FIG. 5 is a flow diagram illustrating an example operation for determining whether a patient is eligible for self-driven oral anticoagulant (OAC) administration.
  • FIG. 6 is a flow diagram illustrating an example operation for determining whether a patient is eligible for self-driven OAC administration based on whether an eligibility value based on potential AF episodes and a number of true AF episodes passes a threshold value.
  • OAC oral anticoagulant
  • FIG. 7 is a flow diagram illustrating an example operation for determining to provide an indication to the patient indicative of an update in an OAC prescription.
  • FIG. 8 is a conceptual diagram illustrating an example machine learning model configured to determine whether a patient is eligible for self-driven OAC administration, in accordance with one or more techniques of this disclosure.
  • FIG. 9 is a conceptual diagram illustrating an example training process for a machine learning model in accordance with examples of the current disclosure.
  • a variety of types of implantable and external devices are configured to monitor health based on sensed physiological signals.
  • External devices that may be used to non-invasively sense and monitor physiological signals include wearable devices with electrodes configured to contact the skin of the patient, such as patches, watches, rings, necklaces, hearing aids, a wearable cardiac monitor or automated external defibrillator (AED), clothing, car seats, or bed linens.
  • Such external devices may facilitate relatively longer-term monitoring of patient health during normal daily activities.
  • Implantable medical devices also sense and monitor physiological signals and detect health events such as episodes of arrhythmia, cardiac arrest, myocardial infarction, stroke, and seizure.
  • Example IMDs include pacemakers and implantable cardioverter-defibrillators, which may be coupled to intravascular or extravascular leads, as well as pacemakers with housings configured for implantation within the heart, which may be leadless, such as the Mi eraTM leadless pacing device of Medtronic, Inc. Pacemakers provide cardiac pacing pulses to patients based on monitored physiological signals.
  • Atrial fibrillation is associated with clot formation, which can lead to strokes and other serious health events.
  • clinicians may prescribe oral anticoagulants (OAC) to prevent clot formation.
  • OACs prevent blood clot formation but are also associated with risks, such as excessive bleeding and other health concerns.
  • Patients with AF should, in most cases, take OACs to prevent clot formation. Due to the risks associated with taking OACs, patients who do not have AF or who have relatively infrequent AF episodes and who are therefore at a relatively low risk of developing blood clots should, in many cases, not be taking OACs, e.g., to prevent excessive bleeding and other health issues.
  • a patient’s AF can change, e.g., become more or less frequent, so an OAC prescription may need to be updated, e.g., initiated, stopped, or otherwise modified.
  • techniques for determining whether to update a patient’s OAC prescription include sensing physiological signals, e.g., ECG signals, for use by clinicians to determine whether a patient should stop or start OACs based on the patient’s AF status.
  • physiological signals e.g., ECG signals
  • the techniques of this disclosure may provide one or more technical advantages.
  • the techniques of this disclosure may include adjusting an AF detection configuration based on previous AF detection accuracy.
  • the techniques of this disclosure facilitate patient-specific AF detection, which may improve accuracy of AF detection for patients, thereby facilitating accurate identification of patient therapy, e.g., OAC prescription, needs.
  • the techniques may improve patient outcomes.
  • the techniques of this disclosure may additionally include identifying patients who are eligible for self-driven OAC administration, i.e., the techniques may include identifying patients who can start or stop OAC medication with no clinic visits and/or clinician review, using a medical device system. By identifying patients who are eligible for self-driven OAC administration, the techniques of this disclosure may provide one or more advantages.
  • the techniques of this disclosure may prevent unnecessary clinician review.
  • the techniques of this disclosure may reduce clinician burden.
  • the techniques may prevent unnecessary clinic visits, which may reduce patient time in clinic, allowing patients to perform activities of daily living more easily.
  • the techniques of this disclosure may result in faster identification of changes in AF and therefore facilitate changes in OAC prescriptions, which may improve patient outcomes. For example, patients who are ineligible for self-driven OAC administration and require clinician review. By reducing clinician review by identifying the patients who are eligible for self-driven OAC administration, the techniques of this disclosure may facilitate clinician review of patient data for ineligible patients, thereby improving outcomes for ineligible patients. Additionally, patients eligible for self-driven OAC administration may require little to no clinician review, which may facilitate relatively faster OAC prescription change identification and notification to the patient.
  • FIG. 1 illustrates the environment of an example medical device system 2 in conjunction with a patient 4, in accordance with one or more techniques of this disclosure.
  • the example techniques may be used with an implantable medical device (IMD) 10, which may be in wireless communication with an external device 12.
  • IMD 10 is an IMD implanted outside of a thoracic cavity of patient 4 (e.g., subcutaneously in the pectoral location illustrated in FIG. 1).
  • IMD 10 may be positioned near the sternum near or just below the level of the heart of patient 4, e.g., at least partially within the cardiac silhouette.
  • IMD 10 includes a plurality of electrodes (not shown in FIG. 1) and is configured to sense an electrocardiogram (ECG) via the plurality of electrodes.
  • ECG electrocardiogram
  • IMD 10 takes the form of the Reveal LINQTM or LINQ IITM insertable cardiac monitor (ICM).
  • ICM Reveal LINQTM or LINQ IITM insertable cardiac monitor
  • External device 12 is configured for wireless communication with IMD 10.
  • External device 12 may be configured to communicate with computing system 8 via network 16.
  • external device 12 may provide a user interface and allow a user to interact with IMD 10.
  • Computing system 8 may comprise external devices configured to allow a user to interact with IMD 10, or data collected from IMD 10, via network 16.
  • External device 12 may be used to retrieve data from IMD 10 and may transmit the data to computing system 8 via network 16.
  • the retrieved data may include flagged ECG signals collected by IMD 10 and other physiological signals recorded by IMD 10.
  • computing system 8 includes one or more handheld external devices, computer workstations, servers or other networked external devices.
  • computing system 8 may include one or more devices, including processing circuitry and storage devices, which may be distributed across multiple locations and/or multiple servers.
  • Computing system 8 may comprise a cloud computing system.
  • Computing system 8 and network 16 may be implemented by the Medtronic CareLinkTM Network or other patient monitoring system, in some examples.
  • Computing system 8 may implement a monitoring system (not shown) that may analyze data, e.g., flagged data, received from medical devices, including IMD 10.
  • Computing system 8 may implement one or more machine learning models for analysis of the flagged data to identify potential AF episodes.
  • the machine learning models may include neural networks, deep learning models, convolutional neural networks, or other types of predictive analytics systems.
  • Network 16 may include one or more external devices (not shown), such as one or more non-edge switches, routers, hubs, gateways, security devices such as firewalls, intrusion detection, and/or intrusion prevention devices, servers, computer terminals, laptops, printers, databases, wireless mobile devices such as cellular phones or personal digital assistants, wireless access points, bridges, cable modems, application accelerators, or other network devices.
  • Network 16 may include one or more networks administered by service providers and may thus form part of a large-scale public network infrastructure, e.g., the Internet.
  • Network 16 may provide external devices, such as computing system 8, and IMD 10, access to the Internet, and may provide a communication framework that allows the external devices to communicate with one another.
  • network 16 may be a private network that provides a communication framework that allows computing system 8, IMD 10, and/or external device 12 to communicate with one another but isolates one or more of computing system 8, IMD 10, or external device 12 from devices external to network 16 for security purposes.
  • the communications between computing system 8, IMD 10, and external device 12 are encrypted.
  • Computing system 8 is an example of a computing system configured to receive ECG data stored by a medical device, e.g., IMD 10, of a patient, e.g., patient 4.
  • Computing system 8 may be managed by a manufacturer of IMD 10 to, for example, provide cloud storage and analysis of collected data, maintenance and software services, or other networked functionality for their devices and users thereof.
  • External device 12 may transmit data, including flagged ECG signals and/or other physiological signals retrieved from IMD 10, to computing system 8 via network 16.
  • the ECG signals and/or other physiological signals may include, for example, flagged ECG signals recorded by IMD 10 and/or external device(s) 12 that may include AF episodes.
  • Computing system 8 may also retrieve data regarding patient 4 from one or more sources of electronic health records (EHR) via network 16.
  • EHR may include data regarding historical transmission data, previous health events and treatments, disease states, comorbidities, demographics, height, weight, and body mass index (BMI), as examples, of patients including patient 4.
  • Computing system 8 may use data from EHR to configure algorithms implemented by IMD 10, external device 12, and/or computing system 8 to determine whether patient 4 is eligible for self-driven OAC administration and/or monitor a cardiac condition of patient 4.
  • computing system 8 provides data from EHR to external device 12 and/or IMD 10 for storage therein and use as part of their algorithms for detecting AF episodes and/or determining self-driven OAC administration eligibility.
  • this disclosure describes example techniques of determining a self-driven OAC administration eligibility of the patient based on a subset of ECG signals from the plurality of ECG signals that IMD 10 stored.
  • the example techniques may be performed using any combination of the components illustrated in FIG. 1. In examples where fewer components than those illustrated in FIG. 1 are used to determine the self-driven OAC administration eligibility, the remaining components may not be necessary.
  • Computing system 8 may be configured to determine whether patient 4 is eligible for self-driven OAC administration based the eligibility value between potential AF episodes and true AF episodes. For instance, computing system 8 may execute a trained machine learning model to identify potential AF episodes, and the input to the trained machine learning model may be the flagged ECG signal data recorded by IMD 10. The clinician may then determine which of the potential AF episodes identified by computing system 8 are true AF episodes and provide user input to computing system 8. The clinician may additionally determine whether the flagged ECG signal data includes AF episodes that were not identified by computing system 8.
  • computing system 8 determines whether the patient is eligible for OAC administration.
  • an eligibility value based on a relationship between at least the potential AF episodes and the true AF episodes, e.g., positive predictive value (PPV) and/or sensitivity
  • computing system 8 determines whether the patient is eligible for OAC administration.
  • PSV positive predictive value
  • IMD 10 may be configured to determine the self-driven OAC administration eligibility.
  • the example techniques described in this disclosure may be performed by one or any combination of devices and systems illustrated in FIG. 1.
  • the example techniques are described with respect to processing circuitry performing the techniques.
  • the processing circuitry may be located within one of the illustrated components (e.g., computing system 8) or may be distributed across the components.
  • the processing circuitry may be considered as having different portions, where a first portion is located within medical device, a second portion is located within computing system 8, etc.
  • a first portion of the processing circuitry within IMD 10 may flag ECG signals and control transmission of the flagged ECG signals
  • a second portion of the processing circuitry within computing system 8 may identify potential AF episodes based on the flagged ECG signals, and, based on an eligibility value based on the potential AF episodes and clinician-identified true AF episodes, determine whether the patient is eligible for self-driven OAC administration.
  • the clinician may determine whether computing system 8 correctly identified the potential AF episodes and may provide input to system 2 indicative of which potential AF episodes are true AF episodes, as well as any AF episodes in the flagged ECG signal data that were not identified processing circuitry of computing system 8.
  • FIG. 2A is a perspective drawing illustrating an IMD 10A, which may be an example configuration of IMD 10 of FIG. 1 as an ICM.
  • IMD 10A may be embodied as a monitoring device having housing 212, proximal electrode 216A and distal electrode 216B.
  • Housing 212 may further comprise first major surface 214, second major surface 218, proximal end 220, and distal end 222.
  • Housing 212 encloses electronic circuitry located inside the IMD 10A and protects the circuitry contained therein from body fluids.
  • Housing 212 may be hermetically sealed and configured for subcutaneous implantation. Electrical feedthroughs provide electrical connection of electrodes 216A and 216B.
  • IMD 10A is defined by a length /., a width W and thickness or depth D and is in the form of an elongated rectangular prism wherein the length L is much larger than the width W, which in turn is larger than the depth D.
  • the geometry of the IMD 10A - in particular a width W greater than the depth D - is selected to allow IMD 10A to be inserted under the skin of the patient using a minimally invasive procedure and to remain in the desired orientation during insertion.
  • the device shown in FIG. 2A includes radial asymmetries (notably, the rectangular shape) along the longitudinal axis that maintains the device in the proper orientation following insertion.
  • the spacing between proximal electrode 216A and distal electrode 216B may range from 5 millimeters (mm) to 55 mm, 30 mm to 55 mm, 35 mm to 55 mm, and from 40 mm to 55 mm and may be any range or individual spacing from 5 mm to 60 mm.
  • IMD 10A may have a length L that ranges from 30 mm to about 70 mm. In other examples, the length L may range from 5 mm to 60 mm, 40 mm to 60 mm, 45 mm to 60 mm and may be any length or range of lengths between about 30 mm and about 70 mm.
  • the width W of major surface 214 may range from 3 mm to 15, mm, from 3 mm to 10 mm, or from 5 mm to 15 mm, and may be any single or range of widths between 3 mm and 15 mm.
  • the thickness of depth D of IMD 10A may range from 2 mm to 15 mm, from 2 mm to 9 mm, from 2 mm to 5 mm, from 5 mm to 15 mm, and may be any single or range of depths between 2 mm and 15 mm.
  • IMD 10A according to an example of the present disclosure has a geometry and size designed for ease of implant and patient comfort. Examples of IMD 10A described in this disclosure may have a volume of three cubic centimeters (cm) or less, 1.5 cubic cm or less or any volume between three and 1.5 cubic centimeters.
  • the first major surface 214 faces outward, toward the skin of the patient while the second major surface 218 is located opposite the first major surface 214.
  • proximal end 220 and distal end 222 are rounded to reduce discomfort and irritation to surrounding tissue once inserted under the skin of the patient.
  • IMD 10A including instrument and method for inserting IMD 10A is described, for example, in U.S. Patent No. 11,311,312, incorporated herein by reference in its entirety.
  • Proximal electrode 216A is at or proximate to proximal end 220, and distal electrode 216B is at or proximate to distal end 222.
  • Proximal electrode 216A and distal electrode 216B are used to sense ECG signals thoracically outside the ribcage, which may be implanted sub-muscularly or subcutaneously.
  • ECG signals may be stored in a memory of IMD 10 A, and data may be transmitted via integrated antenna 230 A to another device, which may be another implantable device or an external device, such as external device 12, and/or to computing system 8.
  • electrodes 216A and 216B may additionally or alternatively be used for sensing any bio-potential signal of interest, which may be, for example, an electroencephalogram (EEG), electromyogram (EMG), or a nerve signal, or for measuring impedance, from any implanted location.
  • EEG electroencephalogram
  • EMG electromyogram
  • nerve signal or for measuring impedance, from any implanted location.
  • proximal electrode 216A is at or in close proximity to the proximal end 220 and distal electrode 216B is at or in close proximity to distal end 222.
  • distal electrode 216B is not limited to a flattened, outward facing surface, but may extend from first major surface 214 around rounded edges 224 and/or end surface 226 and onto the second major surface 218 so that the electrode 216B has a three-dimensional curved configuration.
  • electrode 216B is an uninsulated portion of a metallic, e.g., titanium, part of housing 212.
  • proximal electrode 216A is located on first major surface 214 and is substantially flat, and outward facing.
  • proximal electrode 216A may utilize the three-dimensional curved configuration of distal electrode 216B, providing a three-dimensional proximal electrode (not shown in this example).
  • distal electrode 216B may utilize a substantially flat, outward facing electrode located on first major surface 214 similar to that shown with respect to proximal electrode 216A.
  • proximal electrode 216A and distal electrode 216B are located on both first major surface 214 and second major surface 218.
  • proximal electrode 216A and distal electrode 216B are located on both major surfaces 214 and 218, and in still other configurations both proximal electrode 216A and distal electrode 216B are located on one of the first major surface 214 or the second major surface 218 (e.g., proximal electrode 216A located on first major surface 214 while distal electrode 216B is located on second major surface 218).
  • IMD 10A may include electrodes on both major surface 214 and 218 at or near the proximal and distal ends of the device, such that a total of four electrodes are included on IMD 10A.
  • Electrodes 216A and 216B may be formed of a plurality of different types of biocompatible conductive material, e.g., stainless steel, titanium, platinum, iridium, or alloys thereof, and may utilize one or more coatings such as titanium nitride or fractal titanium nitride.
  • proximal end 220 includes a header assembly 228 that includes one or more of proximal electrode 216A, integrated antenna 230A, anti-migration projections 232, and/or suture hole 234.
  • Integrated antenna 230A is located on the same major surface (i.e., first major surface 214) as proximal electrode 216A and is also included as part of header assembly 228.
  • Integrated antenna 230A allows IMD 10A to transmit and/or receive data.
  • integrated antenna 230 A may be formed on the opposite major surface as proximal electrode 216A or may be incorporated within the housing 212 of IMD 10A. In the example shown in FIG.
  • antimigration projections 232 are located adjacent to integrated antenna 230A and protrude away from first major surface 214 to prevent longitudinal movement of the device.
  • anti-migration projections 232 include a plurality (e.g., nine) small bumps or protrusions extending away from first major surface 214.
  • anti-migration projections 232 may be located on the opposite major surface as proximal electrode 216A and/or integrated antenna 230A.
  • header assembly 228 includes suture hole 234, which provides another means of securing IMD 10A to the patient to prevent movement following insertion.
  • header assembly 228 is a molded header assembly made from a polymeric or plastic material, which may be integrated or separable from the main portion of IMD 10 A.
  • FIG. 2B is a perspective drawing illustrating another IMD 10B, which may be another example configuration of IMD 10 from FIG. 1 as an ICM.
  • IMD 10B of FIG. 2B may be configured substantially similarly to IMD lOA of FIG. 2A, with differences between them discussed herein.
  • IMD 10B may include a leadless, subcutaneously-implantable monitoring device, e.g., an ICM.
  • IMD 10B includes housing having a base 240 and an insulative cover 242.
  • Proximal electrode 216C and distal electrode 216D may be formed or placed on an outer surface of cover 242.
  • Various circuitries and components of IMD 10B may be formed or placed on an inner surface of cover 242, or within base 240.
  • a battery or other power source of IMD 10B may be included within base 240.
  • antenna 230B is formed or placed on the outer surface of cover 242 but may be formed or placed on the inner surface in some examples.
  • insulative cover 242 may be positioned over an open base 240 such that base 240 and cover 242 enclose the circuitries and other components and protect them from fluids such as body fluids.
  • the housing including base 270 and insulative cover 272 may be hermetically sealed and configured for subcutaneous implantation.
  • Circuitries and components may be formed on the inner side of insulative cover 242, such as by using flip-chip technology.
  • Insulative cover 242 may be flipped onto a base 240. When flipped and placed onto base 240, the components of IMD 10B formed on the inner side of insulative cover 242 may be positioned in a gap 244 defined by base 240.
  • Electrodes 216C and 216D and antenna 230B may be electrically connected to circuitry formed on the inner side of insulative cover 242 through one or more vias (not shown) formed through insulative cover 242.
  • Insulative cover 242 may be formed of sapphire (i.e., corundum), glass, parylene, and/or any other suitable insulating material.
  • Base 240 may be formed from titanium or any other suitable material (e.g., a biocompatible material). Electrodes 216C and 216D may be formed from any of stainless steel, titanium, platinum, iridium, or alloys thereof. In addition, electrodes 216C and 216D may be coated with a material such as titanium nitride or fractal titanium nitride, although other suitable materials and coatings for such electrodes may be used.
  • a material such as titanium nitride or fractal titanium nitride, although other suitable materials and coatings for such electrodes may be used.
  • the housing of IMD 10B defines a length I., a width W and thickness or depth D and is in the form of an elongated rectangular prism wherein the length L is much larger than the width W, which in turn is larger than the depth D, similar to IMD 10A of FIG. 2 A.
  • the spacing between proximal electrode 216C and distal electrode 216D may range from 5 mm to 50 mm, from 30 mm to 50 mm, from 35 mm to 45 mm, and may be any single spacing or range of spacings from 5 mm to 50 mm, such as approximately 40 mm.
  • IMD 10B may have a length L that ranges from 5 mm to about 70 mm.
  • the length L may range from 30 mm to 70 mm, 40 mm to 60 mm, 45 mm to 55 mm, and may be any single length or range of lengths from 5 mm to 50 mm, such as approximately 45 mm.
  • the width may range from 3 mm to 15 mm, 5 mm to 15 mm, 5 mm to 10 mm, and may be any single width or range of widths from 3 mm to 15 mm, such as approximately 8 mm.
  • the thickness or depth D of IMD 10B may range from 2 mm to 15 mm, from 5 mm to 15 mm, or from 3 mm to 5 mm, and may be any single depth or range of depths between 2 mm and 15 mm, such as approximately 4 mm.
  • IMD 10B may have a volume of three cubic centimeters (cm) or less, or 1.5 cubic cm or less, such as approximately 1.4 cubic cm.
  • outer surface of cover 242 faces outward, toward the skin of the patient.
  • proximal end 246 and distal end 248 are rounded to reduce discomfort and irritation to surrounding tissue once inserted under the skin of the patient.
  • edges of IMD 10B may be rounded.
  • FIG. 3 is a block diagram illustrating an example configuration of IMD 10 from FIG. 1 that operates in accordance with one or more techniques of the present disclosure.
  • IMD 10 includes processing circuitry 350, memory 352, sensing circuitry 354 coupled to electrodes 356A and 356B (hereinafter, “electrodes 356”) and one or more sensor(s) 358, and communication circuitry 360.
  • Processing circuitry 350 may include fixed function circuitry and/or programmable processing circuitry.
  • Processing circuitry 350 may include any one or more of a microprocessor, a controller, a graphics processing unit (GPU), a tensor processing unit (TPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or equivalent discrete or analog logic circuitry.
  • processing circuitry 350 may include multiple components, such as any combination of one or more microprocessors, one or more controllers, one or more GPUs, one or more TPUs, one or more DSPs, one or more ASICs, or one or more FPGAs, as well as other discrete or integrated logic circuitry.
  • memory 352 includes computer-readable instructions that, when executed by processing circuitry 350, cause IMD 10 and processing circuitry 350 to perform various functions attributed herein to IMD 10 and processing circuitry 350.
  • Memory 352 may include any volatile, non-volatile, magnetic, optical, or electrical media, such as a random-access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically-erasable programmable ROM (EEPROM), flash memory, or any other digital media.
  • RAM random-access memory
  • ROM read-only memory
  • NVRAM non-volatile RAM
  • EEPROM electrically-erasable programmable ROM
  • flash memory or any other digital media.
  • Sensing circuitry 354 may monitor signals from electrodes 356 in order to, for example, monitor electrical activity of a heart of patient 4 and produce ECG data for patient 4.
  • processing circuitry 350 may identify features of the sensed ECG to flag ECG signal data of patient 4.
  • Processing circuitry 350 may store the digitized flagged ECG and/or features of the ECG in memory 352 as physiological data 388.
  • IMD 10 includes one or more sensors 358, such as one or more accelerometers, gyroscopes, microphones, optical sensors, temperature sensors, pressure sensors, and/or chemical sensors.
  • sensing circuitry 354 may include one or more filters and amplifiers for filtering and amplifying signals received from one or more of electrodes 356 and/or sensors 358.
  • sensing circuitry 354 and/or processing circuitry 350 may include a rectifier, filter and/or amplifier, a sense amplifier, comparator, and/or analog-to-digital converter.
  • Processing circuitry 350 may determine physiological data 388, e.g., values of physiological parameters of patient 4, based on signals from sensors 358, which may be stored in memory 352.
  • Patient parameters determined from signals from sensors 358 may include oxygen saturation, glucose level, stress hormone level, heart sounds, body motion, body posture, or blood pressure.
  • Memory 352 may store applications 370 executable by processing circuitry 350, and data 380.
  • Applications 370 may include an event surveillance application 372.
  • Processing circuitry 350 may execute event surveillance application 372 to detect episodes and/or collect data to identify potential AF episodes of patient 4 based on a combination of one or more of the types of data described herein, which may be stored as physiological data 388.
  • Event surveillance application 372 may detect a potential episode of AF based on an ECG and/or other patient parameter data indicating the electrical or mechanical activity of the heart of patient 4.
  • physiological data 388 can include flagged ECG signal data, which may be flagged as being indicative of AF.
  • physiological data 388 may additionally include physiological data sensed by other devices, e.g., external device(s) 12, and received via communication circuitry 360.
  • event surveillance application 372 may be configured with an analysis engine 374.
  • Analysis engine 374 may apply rules 386 to physiological data 388.
  • Rules 386 may include one or more models, algorithms, decision trees, and/or thresholds. In some cases, rules 386 may be developed based on machine learning, e.g., may include one or more machine learning models.
  • Analysis engine 374 may compare physiological data 388 to one or more threshold(s) 384, e.g., to detect the potential AF episodes.
  • processing circuitry 350 of IMD 10 may determine whether the patient is eligible for self-driven OAC administration based on determining an eligibility value based on potential AF episodes and the number of true AF episodes confirmed by a subsequent analysis, e.g., a subsequent analysis by the clinician, of the potential AF episodes and/or AF episodes included in the flagged ECG signal data but not identified by processing circuitry 402.
  • the clinician provides input to, e.g., computing system 8 or external device 12, indicative of which potential AF episodes are true AF episodes. Based on the clinician input, processing circuitry 350 determines the eligibility value. Communication circuitry of computing system 8 or external device 12 may transmit the clinician input to IMD 10.
  • processing circuitry 350 may initially flag ECG signal data at a relatively high sensitivity and low complexity and store the flagged ECG signal data as physiological data 388.
  • processing circuitry 350 may control communication circuitry 360 to transmit physiological data 388, e.g., to external device 12 and/or computing system 8, for cloud-based or edge-based processing to identify potential AF episodes based on the flagged ECG signal data and/or to determine whether patient 4 is eligible for self-driven OAC administration.
  • Communication circuitry 360 may include any suitable hardware, firmware, software, or any combination thereof for wirelessly communicating with another device, such as external device(s) 12. Processing circuitry 350 may determine to control communication circuitry 360 to send a transmission to determine self-driven OAC administration eligibility on a regular schedule, e.g., monthly or yearly, or on an as-needed basis or a patient-requested basis. Processing circuitry 350 may additionally determine to control communication circuitry 360 to send a transmission to determine prescription updates on a regular schedule, e.g., weekly or monthly.
  • IMD 10 may transmit physiological data 388 to any device connected to network 16 of system 2 additionally, or alternatively, to computing system 8 and/or external device 12.
  • FIG. 4 is a block diagram illustrating an example configuration of computing system 8 of FIG. 1, in accordance with one or more techniques of this disclosure.
  • computing system 8 includes processing circuitry 402 for executing applications 424.
  • Computing system 8 may be any component or system that includes processing circuitry or other suitable computing environment for executing software instructions and, for example, need not necessarily include one or more elements shown in FIG. 4 (e.g., input devices 404, communication circuitry 406, user interface devices 410, or output devices 412, and in some examples components such as storage device(s) 408 or processing circuitry 402 may not be co-located or in the same chassis as other components).
  • computing system 8 may be a cloud computing system distributed across a plurality of devices.
  • computing system 8 includes processing circuitry 402, one or more input devices 404, communication circuitry 406, one or more storage device(s) 408, user interface (UI) device(s) 410, and one or more output devices 412.
  • Computing system 8 in some examples, further includes one or more application(s) 424 and operating system 416 that are executable by computing system 8.
  • Each of components 402, 404, 406, 408, 410, and 412 are coupled (physically, communicatively, and/or operatively) for inter-component communications.
  • communication channels 414 may include a system bus, a network connection, an interprocess communication data structure, or any other method for communicating data.
  • components 402, 404, 406, 408, 410, and 412 may be coupled by one or more communication channels 414.
  • Processing circuitry 402 in one example, is configured to implement functionality and/or process instructions for execution within computing system 8.
  • processing circuitry 402 may be capable of processing instructions stored in storage device(s) 408.
  • Examples of processing circuitry 402 may include any one or more of a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or equivalent discrete or integrated logic circuitry.
  • One or more storage device(s) 408 may be configured to store information within computing system 8 during operation, e.g., as patient profile 418.
  • Storage device(s) 408, in some examples, is described as a computer-readable storage medium.
  • storage device(s) 408 is a temporary memory, meaning that a primary purpose of storage device(s) 408 is not long-term storage.
  • Storage device(s) 408, in some examples, is described as a volatile memory, meaning that storage device(s) 408 does not maintain stored contents when the computer is turned off. Examples of volatile memories include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories known in the art.
  • RAM random access memories
  • DRAM dynamic random access memories
  • SRAM static random access memories
  • storage device(s) 408 is used to store program instructions for execution by processing circuitry 402.
  • Storage device(s) 408, in one example, is used by software or applications 424 running on computing system 8 to temporarily store information during program execution.
  • Storage device(s) 408 may be configured to store larger amounts of information than volatile memory.
  • Storage device(s) 408 may further be configured for long-term storage of information.
  • storage device(s) 408 include nonvolatile storage elements. Examples of such non-volatile storage elements include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable memories (EEPROM).
  • Computing system 8 also includes communication circuitry 406 to communicate with other devices and systems, such as IMD 10 and external device 12 of FIG. 1, as well as other networked client external devices of various users.
  • Communication circuitry 406 may include a network interface card, such as an Ethernet card, an optical transceiver, a radio frequency transceiver, or any other type of device that can send and receive information.
  • network interfaces may include 3G and Wi-Fi radios.
  • Computing system 8 also includes one or more user interface devices 410.
  • User interface devices 410 are configured to receive input from a user through tactile, audio, or video feedback.
  • Examples of user interface devices(s) 410 include a presence-sensitive display, a mouse, a keyboard, a voice responsive system, video camera, microphone or any other type of device for detecting a command from a user.
  • a presence-sensitive display includes a touch- sensitive screen.
  • One or more output device(s) 412 may also be included in computing system 8.
  • Output device(s) 412 in some examples, is configured to provide output to a user using tactile, audio, or video stimuli.
  • Output device(s) 412 in one example, includes a presencesensitive display, a sound card, a video graphics adapter card, or any other type of device for converting a signal into an appropriate form understandable to humans or machines.
  • Additional examples of output device(s) 412 include a speaker, a liquid crystal display (LCD), or any other type of device that can generate intelligible output to a user.
  • LCD liquid crystal display
  • Computing system 8 may include operating system 416.
  • Operating system 416 controls the operation of components of computing system 8.
  • operating system 416 in one example, facilitates the communication of one or more applications 424 with processing circuitry 402, communication circuitry 406, storage device(s) 408, input devices 404, user interface devices 410, and output devices 412.
  • Applications 424 may also include program instructions and/or data that are executable by computing system 8. Applications 424 may implement machine learning model(s) 452. Applications 424 may include applications not shown which may be included to provide functionality described herein and are not depicted for the sake of simplicity.
  • computing system 8 receives physiological data, e.g., flagged ECG signal data, stored by medical devices, such as physiological data 388 of IMD 10, via communication circuitry 406, e.g., to determine the self-driven OAC eligibility of patient 4.
  • Processing circuitry 402 may store the physiological data 388, in storage device(s) 408.
  • the physiological data may have been collected by IMD 10 in response to IMD 10 detecting signals indicative of AF.
  • processing circuitry 402 determines an eligibility value and updates a diagnostic state value based on the eligibility value in patient profile data 418.
  • Machine learning model(s) 452 controls the determination of self-driven OAC eligibility, and generation of reports for user, e.g., patient 4 or clinician review. In some examples, machine learning model(s) 452 additionally control the determination of potential AF episodes of the flagged ECG signal data.
  • Applications 424 may utilize input devices 404, output devices 412, and/or communication circuitry 406 to receive user input and to output self-driven OAC eligibility information to one or more users, e.g., via one or more graphical user interfaces presented on output devices 412 or other client external devices.
  • processing circuitry 402 may control, e.g., input device(s) 404, to collect input from the clinician to determine which of the potential AF episodes are true AF episodes and to identify any AF episodes in the flagged ECG signal data not identified by processing circuitry 402. Based on the clinician input, processing circuitry 402 determines an eligibility value based the potential AF episodes and the number of true AF episodes to determine self-driven OAC eligibility, and in some examples, the AF episodes in the flagged ECG signal data not identified by processing circuitry 402.
  • processing circuitry 402 may, for example, update a configuration of applications 424 and/or machine learning model(s) 452, e.g., to improve accuracy for subsequent potential AF episode determinations.
  • processing circuitry 402 may determine changes in a frequency of AF of patient 4.
  • processing circuitry 402 may implement machine learning model(s) 452 to determine the changes. For example, processing circuitry 402 may determine a change in AF frequency over a period of time, e.g., weeks or months. In some examples, changes in AF frequency over a period of time may be indicative of a change in patient condition.
  • processing circuitry 402 may determine to notify patient 4 to update an OAC prescription. For example, if patient 4 does not have any AF episodes for a period of time, e.g., 6 months, processing circuitry 402 may notify patient 4 to stop taking OACs.
  • Machine learning models 452 may include, as examples, neural networks, such as deep neural networks, which may include convolutional neural networks, multi-layer perceptrons, transformers, recurrent neural networks, and/or echo state networks, as examples.
  • neural networks such as deep neural networks, which may include convolutional neural networks, multi-layer perceptrons, transformers, recurrent neural networks, and/or echo state networks, as examples.
  • FIG. 5 is a flow diagram illustrating an example operation for determining whether a patient is eligible for self-driven OAC administration.
  • computing system 8 determines whether the patient is eligible for self-driven OAC administration
  • the techniques of this disclosure may additionally, or alternatively, be implemented in any device or combination of devices of system 2, such as IMD 10 and/or external device 12.
  • Processing circuitry 402 of computing system 8 determines a first self-driven OAC eligibility metric based on values indicative of AF information corresponding to a physiological signal, e.g., an ECG signal, over a first period of time, e.g., 30 days or 60 days (502).
  • the values indicative of AF information comprise flagged ECG signal data stored by IMD 10 as physiological data 388.
  • Processing circuitry 402 compares the eligibility metric to an eligibility threshold to determine whether a patient, e.g., patient 4, is eligible for self-driven OAC administration (504). If the eligibility metric meets the threshold (“YES” of 504), processing circuitry 402 determines patient 4 is eligible for self-driven OAC administration (514).
  • processing circuitry 402 determines patient 4 is not eligible for self-driven OAC administration and determines whether to adjust an AF detection configuration of the system (506).
  • adjusting an AF detection configuration of the system comprises adjusting a sensitivity of, e.g., machine learning model(s) 452.
  • Processing circuitry 402 may determine not to adjust the AF detection configuration if the eligibility metric falling below the threshold may be due to insufficient input data. For example, if the patient experiences less than a threshold number of AF episodes, there may not be enough data corresponding to the first period of time for processing circuitry 402 to determine whether the patient is eligible for self-driven OAC administration.
  • processing circuitry 402 may determine to adjust the AF detection configuration.
  • Processing circuitry 402 determines a second self-driven OAC eligibility metric based on values indicative of AF information corresponding to an ECG signal over a second period of time (508).
  • the second set of time includes the first set of time and additional time subsequent to the first period of time, e.g., 30 additional days.
  • Processing circuitry 402 compares the second self-driven OAC eligibility metric to the eligibility threshold (510). If the second self-driven OAC eligibility metric meets the eligibility threshold (“YES” of 510), processing circuitry 402 determines patient 4 is eligible for self-driven OAC administration (514).
  • processing circuitry 402 determines the patient is eligible for clinician-driven OAC administration, i.e., processing circuitry 402 determines the patient is ineligible for self-driven OAC administration (512). In some examples, processing circuitry 402 determines whether the patient is eligible for self-driven OAC administration during an initialization period only once. In other examples, processing circuitry 402 determines whether the patient is eligible for self-driven OAC administration on a regular schedule, e.g., annually.
  • FIG. 6 is a flow diagram illustrating an example operation for determining whether a patient is eligible for self-driven OAC administration based on whether an eligibility value based on potential AF episodes and true AF episodes meets a threshold.
  • FIG. 6 may be a specific example of the example operation of FIG. 5.
  • computing system 8 determines whether the patient is eligible for self-driven OAC administration
  • the techniques of this disclosure may additionally, or alternatively, be implemented in any device or combination of devices of system 2, such as IMD 10 and/or external device 12.
  • Sensing circuitry e.g., sensing circuitry 354 of IMD 10, senses a physiological signal, e.g., an ECG signal via electrodes 356, of a patient, e.g., patient 4 (602).
  • processing circuitry e.g., processing circuitry 350 of IMD 10, flags ECG signal data and stores the flagged ECG signal data as physiological data 388 for review by, e.g., processing circuitry 402 of computing system 8.
  • IMD 10 may not flag ECG signal data and may continuously transmit all the physiological data sensed by sensing circuitry 354 to, e.g., external device 12 and/or computing system 8.
  • Processing circuitry 402 identifies a plurality of potential AF episodes based on the physiological data, e.g., physiological data 388, over a first period of time (604).
  • the first period of time includes retrospective patient data, e.g., historical patient data.
  • processing circuitry 402 utilizes application(s) 424, which may implement machine learning model(s) 452 and may provide physiological data 388 as input. For each of the first plurality of potential AF episodes, processing circuitry 402 determines whether the potential AF episode is a true AF episode (606).
  • computing system 8 receives, e.g., via input device(s) 404, user input, e.g., clinician input, indicative of whether the potential AF episode is a true episode and whether any AF episodes in the flagged ECG signal were not identified by processing circuitry 402.
  • Processing circuitry 402 determines a first eligibility value based on a number of true AF episodes of the first plurality of potential AF episodes, and, in some examples, the AF episodes in the flagged ECG signal that were not identified by processing circuitry 402 (608).
  • processing circuitry 402 determines patient 4 is eligible for self-driven OAC administration and updates a diagnostic state value in the profile, e.g., patient profile data 418, for patient 4 to a first value in response to the first eligibility value not passing the first threshold value (624).
  • passing a threshold may indicate a value is going from above to below the threshold or from below to above the threshold.
  • the first threshold value includes one or more of a sensitivity threshold or a PPV threshold.
  • the sensitivity threshold may be, e.g., 95%
  • the PPV threshold may be, e.g., 95%.
  • the clinician determines whether the potential AF episode is a true episode. The clinician may additionally determine whether any flagged ECG signal data that was determined not to be an AF episode by processing circuitry 402 was an AF episode, i.e., the clinician identifies false negatives. Each of the potential AF episodes may be a true positive episode or a false positive episode.
  • Sensitivity defines a ratio of true positive AF episodes (e.g., the true episodes confirmed by the clinician of the potential episodes identified by processing circuitry 402) to the total number of true AF episodes (e.g., the sum of the true positive AF episodes and the AF episodes identified by the clinician based on the flagged ECG signal data but not identified by processing circuitry 402).
  • PPV defines a ratio of true positive AF episodes to the sum of true positive AF episodes and false positive AF episodes.
  • Processing circuitry 402 may determine self-driven OAC administration eligibility based on both sensitivity and PPV. In some examples, if processing circuitry 402 determines the sensitivity value is greater than or equal to the sensitivity threshold and the PPV value is less than the PPV threshold, processing circuitry 402 may determine the patient is ineligible for self-driven OAC administration. A relatively high sensitivity and a relatively low PPV can be indicative of a relatively high number of false positive AF episodes, which can lead to unnecessary OAC administration.
  • processing circuitry 402 determines the sensitivity value is less than the sensitivity threshold and the PPV value is greater than or equal to the PPV threshold, processing circuitry may likewise determine the patient is ineligible for self-driven OAC administration.
  • a relatively low sensitivity and a relatively high PPV can be indicative of a relatively high number of false negative AF episodes, which can lead to patients with AF not taking the appropriate OAC prescription.
  • processing circuitry 402 determines the patient is ineligible for self-driven OAC administration and updates the diagnostic state value in the profile for patient 4 to the first value (612). In some examples, processing circuitry 402 determines whether to adjust a configuration of the medical device system, e.g., to adjust a sensitivity of machine learning model(s) 452 (614). Processing circuitry 402 may determine not to adjust the configuration of the medical device system if the eligibility metric passing the threshold may be due to insufficient input data. If there is sufficient input data but the eligibility metric still passes the threshold, processing circuitry 402 may determine to adjust the configuration.
  • processing circuitry 402 may increase a machine learning model detection threshold of machine learning model(s) 452 used to identify potential AF episodes based on physiological data 388. Additionally, or alternatively, if the PPV is low, processing circuitry may adjust one or more parameters of one or more of IMD 10, external device 12, or computing system 8 to increase a threshold AF duration to flag ECG signal data and/or identify potential AF episodes based on the flagged ECG signal data.
  • processing circuitry 402 may determine to adjust processing circuitry 350 to flag ECG signal data in which at least a six-minute AF episode is suspected.
  • processing circuitry 402 may decrease the machine learning model detection threshold of machine learning model(s) 452 used to identify potential AF episodes based on physiological data 388.
  • processing circuitry 402 may determine to add additional OAC initiation criteria. For instance, processing circuitry 350 may determine to flag ECG signal data when processing circuitry 350 detects relatively longer AF episodes, e.g., at least a 6-minute AF episode instead of, e.g., a 2-minute AF episode, a ventricular rate of the patient is more than 120 beats per minute, and other indicators of patient symptoms are flagged as true.
  • relatively longer AF episodes e.g., at least a 6-minute AF episode instead of, e.g., a 2-minute AF episode, a ventricular rate of the patient is more than 120 beats per minute, and other indicators of patient symptoms are flagged as true.
  • Processing circuitry 402 identifies a second plurality of potential AF episodes over a second period of time (616).
  • the second period of time includes the first period of time and additional time, e.g., 30 days, subsequent to the first period of time.
  • Processing circuitry 402 determines whether each of the potential AF episodes is a true episode, e.g., based on clinician input (618).
  • Processing circuitry 402 determines a second eligibility value for patient 4 based on a number of true AF episodes of the second plurality of potential AF episodes (620).
  • Processing circuitry 402 determines whether the second eligibility value passes a second threshold value (622). In some examples, the second threshold value is the same as the first threshold value.
  • processing circuitry 402 updates the diagnostic state value in the profile of patient 4 to a second value and determines patient 4 is eligible for self-driven OAC administration (624).
  • processing circuitry 402 determines patient 4 is eligible for clinician-driven OAC administration, i.e., processing circuitry 402 determines the patient is ineligible for self-driven OAC administration (626).
  • processing circuitry may repeat steps 614 through 622 more one or more additional times over, e.g., a third period of time.
  • processing circuitry 402 may repeat the example operation at a subsequent time, e.g., one year later, to, for example, verify patient 4’s eligibility or ineligibility for selfdriven OAC administration.
  • FIG. 7 is a flow diagram illustrating an example operation for determining to provide an indication to the patient indicative of an update in an OAC prescription.
  • processing circuitry 402 determines an AF metric based on a quantification of AF episodes (702).
  • communication circuitry 360 of IMD 10 may transmit physiological data 388, e.g., flagged ECG signal data, to computing system 8 for review.
  • Processing circuitry 402 may identify AF episodes based on physiological data 388.
  • Processing circuitry 402 quantifies the AF episodes to determine the AF metric.
  • processing circuitry 402 determines to provide an indication to patient 4 to update an OAC prescription (704). For example, if patient 4 does not experience any AF episodes or experiences a number of AF episodes below a threshold over a period of time, e.g., 6 months, processing circuitry 402 may determine to output to patient 4 to stop taking OACs.
  • the threshold is a threshold window with an upper and lower limit. In other examples, the threshold may be a single value.
  • processing circuitry 402 determines to output to patient 4 to start taking OACs again. In some examples, based on the AF metric, processing circuitry 402 may determine to otherwise adjust an OAC prescription dosage of patient 4. In some examples, processing circuitry 402 additionally determines whether to output to patient 4 to start taking OACs again based on additional factors, such as patient 4’s demographics and/or whether patient 4 has any comorbid conditions.
  • processing circuitry 402 monitor patient 4 for AF during monitoring. For example, processing circuitry 402 may determine the AF metric based on a quantification of AF episodes, as described above with respect to examples in which patient 4 is eligible for self-driven OAC administration (702). Based on the AF metric meeting an AF threshold, processing circuitry may determine to output a suggestion to the clinician to consider adjusting patient 4’s OAC prescription. The clinician may then review the AF episodes identified by computing system 8 to determine whether to confirm the suggestion.
  • machine learning models 452 may continue to update based on new collated OAC therapy information.
  • external device 12 may deploy edge-based processing of AF data and may collect data passively in the background.
  • Processing circuitry 402 may use the AF data to determine whether to update edge-based processing. If processing circuitry 402 determines to update the edge-based processing, processing circuitry 402 may push an update from the cloud to external device 12.
  • FIG. 8 is a conceptual diagram illustrating an example machine learning model 800 configured to determine whether a patient is eligible for self-driven OAC administration, in accordance with one or more techniques of this disclosure.
  • Machine learning model 800 is an example of a set of rules, e.g., a set of rules implemented by machine learning models 452 of computing system 8 in wireless communication with IMD 10.
  • Machine learning model 800 is an example of a deep learning model, or deep learning algorithm, trained to identify potential AF episodes and/or determine self-driven OAC administration eligibility of patient 4 based on physiological data 388.
  • One or more of IMD 10, external device 12, or a computing system 8 may train, store, and/or utilize machine learning model 800, but other devices may apply inputs associated with a particular patient to machine learning model 800 in other examples.
  • a convolutional neural network model of ResNet-18 may be used.
  • models that may be used for transfer learning include AlexNet, VGGNet, GoogleNet, ResNet50, DenseNet, transformer models such as bidirectional encoder representations from transformers (BERT) and generative pretrained transformers (GPT), etc.
  • machine learning techniques include Support Vector Machines, K-Nearest Neighbor algorithm, and Multilayer Perceptron.
  • machine learning model 800 may include three layers. These three layers include input layer 802, hidden layers 804, and output layer 806. Output layer 806 comprises the output from the transfer function 805 of output layer 806. Input layer 802 represents each of the input values XI through X4 provided to machine learning model 800. The number of inputs may be less than or greater than 4, including much greater than 4, e.g., hundreds or thousands. In some examples, the input values may any of the of values input into a machine learning model, as described above. In some examples, input values may include samples of an ECG signal. In addition, in some examples input values of machine learning model 800 may include additional data, such as data relating to one or more additional parameters of patient 4.
  • Each of the input values for each node in the input layer 802 is provided to each node of hidden layer 804.
  • hidden layers 804 include two layers, one layer having four nodes and the other layer having three nodes, but fewer or greater number of nodes may be used in other examples.
  • Each input from input layer 802 is multiplied by a weight and then summed at each node of hidden layers 804.
  • the weights for each input are adjusted to establish the relationship between the inputs, e.g., physiological data 388, to identify potential AF episodes and/or determine self-driven OAC administration eligibility of patient 4.
  • one hidden layer may be incorporated into machine learning model 800, or three or more hidden layers may be incorporated into machine learning model 800, where each layer includes the same or different number of nodes.
  • the result of each node within hidden layers 804 is applied to the transfer function of output layer 806.
  • the transfer function may be linear or non-linear, and in some examples may depend on the number of layers within machine learning model 800.
  • Example non-linear transfer functions may be a sigmoid function or a rectifier function.
  • the output 807 of the transfer function may be a classification that indicates whether the particular ECG segment or other input set represents an AF episode.
  • processing circuitry By applying the ECG signal data and/or other patient parameter data to a machine learning model, such as machine learning model 800, processing circuitry, such as processing circuitry 402 of computing system 8, is able to identify potential AF episodes and/or determine self-driven OAC administration eligibility of patient 4.
  • model 800 is but one example, and other examples may be utilized in implementing the techniques of this disclosure.
  • a machine learning model may include different types of layers, such as pooling layers.
  • a machine learning model may include features not illustrated in the example of FIG. 8, such as skip connections and weight sharing.
  • FIG. 9 is a conceptual diagram illustrating an example training process for a machine learning model 900 in accordance with examples of the current disclosure.
  • Machine learning model 900 may be substantially similar to or the same as machine learning model 800 (FIG. 8).
  • Machine learning model 900 may be implemented using any number of models for supervised and/or reinforcement learning, such as but not limited to, an artificial neural network, a decision tree, a naive Bayes network, a support vector machine, or a k-nearest neighbor model. In other examples, machine learning model 900 may be implemented using any number of models for unsupervised learning.
  • processing circuitry one or more of HMD 10, external device 12, and/or computing system 8 initially trains the machine learning model 900 based on training set data 902 including numerous instances of input data corresponding to a plurality of AF episodes, e.g., as labeled by an expert.
  • a prediction or classification by the machine learning model 900 may be compared 906 to the target output 904, e.g., as determined based on the label.
  • the processing circuitry implementing a leaming/training function 908 may send or apply a modification to weights of machine learning model 900 or otherwise modify/update the machine learning model 900.
  • one or more of IMD 10, external device 12, and/or computing system 8 may, for each training instance in the training set 902, modify machine learning model 900 to change a potential AF episode identification and/or selfdriven OAC administration eligibility generated by the machine learning model 900 in response to data applied to the machine learning model 900.
  • Example 1 A system comprising: an implantable medical device (IMD) comprising: sensing circuitry configured to sense a physiological signal of a patient; a first memory configured to store data indicative of values of the physiological signal; and transmitter circuitry configured to transmit the data to an external device; and a second memory configured to store a profile for the patient; processing circuitry configured to: identify a first plurality of potential atrial fibrillation (AF) episodes of the patient based on the physiological signal over a first period of time; determine whether the potential AF episode is a true AF episode for each of the first plurality of potential AF episodes; determine a first eligibility value for the patient based on a number of true AF episodes of the first plurality of potential AF episodes; update a diagnostic state value in the profile for the patient to a first value in response to the first eligibility value passing a first threshold value, wherein the first value indicates that the patient is ineligible for self-driven oral anticoagulant (OAC) administration; identify a second plurality of potential AF episodes over a
  • Example 2 The system of example 1, wherein the processing circuitry is further configured to: determine an AF metric based on a quantification of AF episodes during a monitoring session; and provide an indication to the patient to adjust an OAC prescription based on the AF metric meeting an AF threshold.
  • Example 3 The system of any one or more of examples 1-2, wherein to determine the first eligibility value, the processing circuitry is configured to determine one or more of a specificity or a positive predictive value (PPV).
  • PSV positive predictive value
  • Example 4 The system of any one or more of examples 1-3, wherein if the second eligibility value does not pass the second threshold, the processing circuitry is configured to: determine the patient is eligible for clinician-driven OAC administration. [0102] Example 5. The system of any one or more of examples 1-4, wherein the processing circuitry is configured to apply the data to one or more machine learning models.
  • Example 6 The system of any one or more of example 1-5, wherein the second period of time comprises the first period of time and a period of time subsequent to the first period of time.
  • Example 7 The system of any one or more of examples 1-6, wherein the processing circuitry is configured to: determine whether to adjust an AF detection configuration of the system responsive to the first value indicating that the patient is ineligible for self-driven oral anticoagulant (OAC) administration.
  • OAC oral anticoagulant
  • Example 8 The system of example 7, wherein to adjust the AF detection configuration of the system, the processing circuitry is configured to: increase a first potential AF episode identification threshold responsive to a PPV passing the first threshold; or decrease the first potential AF episode identification threshold responsive to a sensitivity passing the first threshold.
  • Example 9 The system of example 8, wherein the processing circuitry is further configured to: adjust the AF detection configuration of the system to implement one or more additional AF episode identification thresholds different from the first potential AF episode identification threshold responsive to the PPV and sensitivity meeting the first threshold and not meeting a self-driven OAC accuracy threshold.
  • Example 10 The system of any one or more of examples 1-9, wherein the IMD comprises an insertable cardiac monitor comprising a housing configured for subcutaneous implantation, the housing having a length, a width, and a depth, wherein the length is greater than the width, and the width is greater than the depth, and the length is within a range from 40 millimeters (mm) to 60 mm, and wherein the insertable cardiac monitor comprises a plurality of electrodes on the housing and is configured to sense the ECG via the plurality of electrodes.
  • the insertable cardiac monitor comprises a plurality of electrodes on the housing and is configured to sense the ECG via the plurality of electrodes.
  • Example 11 The system of any one or more of examples 1-10, wherein the processing circuitry is configured to determine whether the potential AF episode is a true AF episode for each of the first plurality of potential AF episodes and the second plurality of potential AF episodes based on user input.
  • Example 12 A method comprising: sensing, by sensing circuitry of an implantable medical device (IMD) of a system, a physiological signal of a patient; storing, by memory of the IMD, data indicative of values of the physiological signal; transmitting, by transmitter circuitry of the IMD, data to an external device; identifying, by processing circuitry of the system, a first plurality of potential atrial fibrillation (AF) episodes of the patient based on the physiological signal over a first period of time; determining, by the processing circuitry, whether the potential AF episode is a true AF episode for each of the first plurality of potential AF episodes; determining, by the processing circuitry, a first eligibility value for the patient based on a number of true AF episodes of the first plurality of potential AF episodes; updating, by the processing circuitry, a diagnostic state value in a profile for the patient stored in a memory of the system to a first value in response to the first eligibility value passing a first threshold value, wherein the first value indicates that the patient is
  • Example 13 The method of example 12, further comprising: determining, by the processing circuitry, an AF metric based on a quantification of AF episodes during a monitoring session; and providing, by the processing circuitry, an indication to the patient to adjust an OAC prescription based on the AF metric meeting an AF threshold.
  • Example 14 The method of any one or more of examples 12-13, wherein the first eligibility value is based on one or more of a specificity or a positive predictive value (PPV).
  • PSV positive predictive value
  • Example 15 The method of any one or more of examples 12-14, further comprising: determining, by the processing circuitry, the patient is eligible for clinician- driven OAC administration in response to the second eligibility value not passing the second threshold.
  • Example 16 The method of any one or more of examples 12-15, wherein the processing circuitry is configured to apply the data to one or more machine learning models.
  • Example 17 The method of any one or more of example 12-16, wherein the second period of time comprises the first period of time and a period of time subsequent to the first period of time.
  • Example 18 The method of any one or more of examples 12-17, further comprising: determining, by the processing circuitry, whether to adjust an AF detection configuration responsive to the first value indicating that the patient is ineligible for selfdriven oral anticoagulant (OAC) administration.
  • OAC oral anticoagulant
  • Example 19 The method of any one or more of examples 12-18, wherein adjusting the AF detection configuration of the system comprises: increasing, by the processing circuitry, a first AF episode identification threshold responsive to a PPV passing the first threshold; or decreasing, by the processing circuitry, the first potential AF episode identification threshold responsive to a sensitivity passing the first threshold.
  • Example 20 The method of example 19, further comprising: adjusting, by the processing circuitry, the AF detection configuration of the system to implement one or more additional AF episode identification thresholds different from the first potential AF episode identification threshold responsive to the PPV and sensitivity passing the first threshold and not passing a self-driven OAC accuracy threshold.
  • Example 21 The method of any one or more of examples 12-20, wherein the IMD comprises an insertable cardiac monitor comprising a housing configured for subcutaneous implantation, the housing having a length, a width, and a depth, wherein the length is greater than the width, and the width is greater than the depth, and the length is within a range from 40 millimeters (mm) to 60 mm, and wherein the insertable cardiac monitor comprises a plurality of electrodes on the housing and is configured to sense the ECG via the plurality of electrodes.
  • the insertable cardiac monitor comprises a plurality of electrodes on the housing and is configured to sense the ECG via the plurality of electrodes.
  • Example 22 The method of any one or more of examples 12-21, wherein determining whether the potential AF episode is a true AF episode for each of the first plurality of potential AF episodes and determining whether the potential AF episode is a true AF episode for each of the second plurality of potential AF episodes comprises receiving user input indicative of whether the potential AF episode is a true AF episode. [0120]
  • Example 23 The method of any one or more of examples 12-21, wherein determining whether the potential AF episode is a true AF episode for each of the first plurality of potential AF episodes and determining whether the potential AF episode is a true AF episode for each of the second plurality of potential AF episodes comprises receiving user input indicative of whether the potential AF episode is a true AF episode.
  • a non-transitory computer-readable medium storing instructions that when executed cause processing circuitry of a system to: identify a first plurality of potential atrial fibrillation (AF) episodes of a patient based on a physiological signal sensed by sensing circuitry of an implantable medical device (IMD) of a system over a first period of time; determine whether the potential AF episode is a true AF episode for each of the first plurality of potential AF episodes; determine a first eligibility value for the patient based on a number of true AF episodes of the first plurality of potential AF episodes; update a diagnostic state value in a profile for the patient stored in a memory of the system to a first value in response to the first eligibility value passing a first threshold value, wherein the first value indicates that the patient is ineligible for selfdriven oral anticoagulant (OAC) administration; identify a second plurality of potential AF episodes over a second period of time; for each of the second plurality of potential AF episodes, determine whether the potential AF episode is a true AF episode

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Abstract

L'invention concerne un qui système surveille des patients pour identifier des épisodes de fibrillation auriculaire (AF) et pour déterminer une habilitation du patient à une administration d'anticoagulant par voie orale (OAC) autonome sur la base d'une précision de détection d'épisode d'AF. Par exemple, le système surveille un signal physiologique pour déterminer si le signal contient des épisodes d'AF. Le système signale des épisodes d'AF potentiels sur une première période. Le système et/ou un praticien analyse les épisodes d'AF potentiels et/ou les données de signal ECG signalées pour déterminer une première valeur d'habilitation. Si la première valeur d'habilitation atteint un seuil, le système détermine que le patient est habilité pour une administration d'OAC autonome. Dans le cas contraire, le système peut ajuster une configuration de détection d'AF et identifie une seconde pluralité d'épisodes d'AF potentiels sur une seconde période et détermine une seconde valeur d'habilitation. Si la seconde valeur d'habilitation atteint un seuil, le système détermine que le patient est habilité pour une administration d'OAC autonome.
PCT/IB2025/054318 2024-04-26 2025-04-25 Systèmes de détermination d'éligibilité de patient pour des soins auto-administrés avec des anticoagulants oraux Pending WO2025224686A1 (fr)

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Citations (2)

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US11311312B2 (en) 2013-03-15 2022-04-26 Medtronic, Inc. Subcutaneous delivery tool
WO2024072940A1 (fr) * 2022-09-30 2024-04-04 Northwestern University Procédé de surveillance et de traitement d'un trouble

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US11311312B2 (en) 2013-03-15 2022-04-26 Medtronic, Inc. Subcutaneous delivery tool
WO2024072940A1 (fr) * 2022-09-30 2024-04-04 Northwestern University Procédé de surveillance et de traitement d'un trouble

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Title
MARTIN DAVID T. ET AL: "Randomized trial of atrial arrhythmia monitoring to guide anticoagulation in patients with implanted defibrillator and cardiac resynchronization devices", EUROPEAN HEART JOURNAL, vol. 36, no. 26, 23 April 2015 (2015-04-23), GB, pages 1660 - 1668, XP093291250, ISSN: 0195-668X, Retrieved from the Internet <URL:https://academic.oup.com/eurheartj/article/36/26/1660/2293338> [retrieved on 20250701], DOI: 10.1093/eurheartj/ehv115 *
MCINTYRE WILLIAM F. ET AL: "Direct Oral Anticoagulants for Stroke Prevention in Patients With Device-Detected Atrial Fibrillation: A Study-Level Meta-Analysis of the NOAH-AFNET 6 and ARTESiA Trials", CIRCULATION, vol. 149, no. 13, 26 March 2024 (2024-03-26) - 12 March 2023 (2023-03-12), US, pages 981 - 988, XP093290820, ISSN: 0009-7322, DOI: 10.1161/CIRCULATIONAHA.123.067512 *

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