Aortic valve stenosis severity quantification using heart sounds

Abstract

Systems and methods for monitoring and managing aortic valve stenosis are disclosed. An example medical device system includes a data receiver to receive heart sound information sensed by a mobile heart sound sensor, and a stenosis detector to detect changes in aortic valve surface area from a baseline non-stenosis state using heart sound components such as S1 and S2 sounds. Based on changes in aortic valve surface area, the stenosis detector may generate an aortic stenosis indicator indicating the presence and severity of aortic valve stenosis. Such indicators may be provided to the user, trigger symptom monitoring and estimation of functional deterioration of the patient, facilitate evaluation of patient candidate qualifications for aortic valve replacement, or triage of heart failure management strategies.

Description

[0001] Priority requirements

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 523,152, filed June 26, 2023, which is incorporated herein by reference in its entirety. Technical Field

[0003] This document generally relates to medical systems, and more specifically to systems, devices, and methods for detecting aortic stenosis in patients using heart sound information. Background Technology

[0004] Aortic stenosis, also known as aortic valve stenosis, is a major form of cardiovascular disease, accounting for more than 60% of valvular heart disease (VHD). Aortic stenosis typically occurs when the aortic valve narrows and blood cannot flow normally. Over time, aortic stenosis may cause the left ventricle of the heart to pump harder to push blood through the narrowed aortic valve, which can further lead to thickening, enlargement, and weakening of the left ventricle. If not treated and corrected promptly and properly, aortic stenosis can lead to heart failure.

[0005] Heart sounds are typically associated with the mechanical vibrations of the heart and the flow of blood through it. Heart sounds repeat with each cardiac cycle and are distinguished and categorized based on the activities associated with the vibrations. Historically, heart sounds were assessed by humans, and therefore only the audible portion of the vibrations was used. Devices can now assess the full spectrum of cardiac vibrations, including both audible and sub-audible components; therefore, the term "sound" generally refers to the full spectrum of vibrations. Typically, heart sounds sensed by a subject can include several components within the cardiac cycle, including the first heart sound (S1), second heart sound (S2), third heart sound (S3), or fourth heart sound (S4). S1 is associated with the vibrations produced by the heart during mitral valve tension. S2 is produced by the closure of the aortic and pulmonary valves and marks the beginning of diastole. S3 is produced by the early diastolic vibrations corresponding to the passive filling of the ventricles during diastole (when blood rushes into the ventricles). S4 is produced by the late diastolic vibrations corresponding to the active filling of the ventricles as the atria contract and push blood into the ventricles. In healthy subjects, S3 is usually faint, and S4 is almost inaudible. However, pathological S3 or S4 may be higher in pitch and louder.

[0006] Heart sounds have been used to assess cardiac systolic and diastolic function. Systole is the contraction of the heart, or a period of contraction, which forces blood out of the heart, such as into the ventricles, and into the aorta and pulmonary arteries. Diastole is the relaxation of the heart, or a period of relaxation, during which blood flows back into the heart, such as into the ventricles. In patients with heart failure, either systolic or diastolic function may be impaired. Summary of the Invention

[0007] Aortic stenosis can be classified as mild, moderate, severe, or critical. The rate of progression varies depending on the patient's overall health, symptoms, aortic valve leaflet anatomy (e.g., leaflet thickening and calcification) and activity, cardiac (especially left ventricular) function, stage of diagnosis, treatment received, and other factors. Although many patients with severe aortic stenosis experience a variety of symptoms, including fatigue, shortness of breath, chest pain, or irregular heartbeat (e.g., rapid fluttering), a large number of patients with severe aortic stenosis are asymptomatic at initial diagnosis. However, while aortic stenosis may remain asymptomatic for decades, survival can be severely impaired once symptoms develop. Early detection and classification of aortic stenosis, along with appropriate treatment, including balloon valvuloplasty or valve replacement surgery such as transcatheter aortic valve replacement (TAVR) or surgical aortic valve replacement (SAVR), can reduce mortality and improve prognosis.

[0008] Routine diagnosis of aortic stenosis typically requires regular transthoracic echocardiography to assess anatomical and hemodynamic changes in the aortic valve. Other cardiovascular imaging studies, such as computed tomography (CT), magnetic resonance imaging (MRI), and transesophageal echocardiography (TEE) and cardiac catheterization, can also be used to diagnose and assess the severity of aortic stenosis. Based on imaging studies, symptoms, and cardiac function, aortic stenosis can be classified into one of four stages: Stage I with stenosis risk, Stage II with progressive (or mild to moderate) stenosis, Stage III with asymptomatic severe stenosis, and Stage IV with symptomatic severe stenosis. Early stages of aortic stenosis are typically characterized by changes in aortic valve anatomy, including reduced aortic valve surface area, leaflet thickening, and calcification. Although echocardiography is considered the standard test for diagnosing aortic stenosis, it typically provides a "snapshot" of valve anatomy and function at the time of testing but does not track the progression of aortic stenosis over time. Regular or frequent echocardiography can help track the progression of aortic stenosis, but it is costly and may be inconvenient for some patients. This can affect patient adherence and delay treatment until the aortic stenosis worsens and symptoms appear. The inventors have recognized that the need for medical systems, devices, and technologies for mobile monitoring of patients with aortic stenosis, using effective but less expensive sensors to diagnose aortic stenosis, improve the early detection and quantification of the severity of aortic stenosis, and thus prevent or slow the progression of such patients to severe symptomatic aortic stenosis, remains unmet.

[0009] This document discusses systems, devices, and methods for monitoring valvular heart disease such as aortic stenosis. An exemplary medical device system includes: a data receiver for receiving heart sound information sensed by a mobile heart sound sensor, and a stenosis detector for detecting changes in aortic valve surface area from a baseline stenosis-free state using heart sound components such as S1 and S2 sounds. Based on the changes in aortic valve surface area, the stenosis detector can generate an aortic stenosis indicator that indicates the presence and severity of aortic stenosis. This indicator can be provided to a user to trigger symptom monitoring and assessment of functional deterioration in patients, facilitate evaluation of patient eligibility for aortic valve replacement surgery, or triage of heart failure management strategies.

[0010] Example 1 is a medical device system for managing valvular heart disease, comprising: a data receiver circuit configured to receive heart sound information sensed from a patient; and a controller circuit including a stenosis detector circuit configured to: detect heart sound components including S1 and S2 sounds from the received heart sound information; use the detected S1 and S2 sounds to determine a change in aortic valve surface area from a baseline stenosis-free state; and generate an aortic stenosis indicator based at least in part on the determined change in aortic valve surface area, wherein the controller circuit is configured to provide the aortic stenosis indicator to a user or a procedure that can be performed by the medical device system.

[0011] In Example 2, the subject matter of Example 1 may optionally include, wherein determining the change in aortic valve surface area includes detecting a reduction in aortic valve surface area from a baseline stenosis-free state, wherein the stenosis detector is configured to generate an aortic valve stenosis indicator indicating the presence of aortic valve stenosis in response to a reduction in aortic valve surface area exceeding a threshold.

[0012] In Example 3, the subject matter of any one or more of Examples 1-2 may optionally include, in order to determine the change in aortic valve surface area, a stenosis detector is configured to: generate an estimate of the left ventricular ejection time using the time interval between the detected S1 and S2 tones, and generate an estimate of the aortic-left ventricular pressure gradient using the intensity of the S2 tones; and determine the aortic valve surface area based on the estimate of the aortic-left ventricular pressure gradient and the estimate of the left ventricular ejection time.

[0013] In Example 4, the subject of Example 3 may optionally include a stenosis detector, which can be configured to determine that the aortic valve surface area is inversely proportional to the estimate of the left ventricular ejection time.

[0014] In Example 5, any one or more of the subjects in Examples 3-4 may optionally include a stenosis detector that can be configured to determine that the aortic valve surface area is inversely proportional to the square root of an estimate of the aortic-left ventricular pressure gradient.

[0015] In Example 6, any one or more of the subjects in Examples 1-5 may optionally include controller circuitry that can be configured to generate an alert to the user in response to an aortic stenosis indicator indicating the presence of aortic stenosis.

[0016] In Example 7, any one or more of the subjects in Examples 1-6 may optionally include controller circuitry that can be configured to, in response to an aortic stenosis indicator indicating the presence of aortic stenosis: trigger data receiver circuitry to receive symptom or physiological information of a patient indicating functional deterioration associated with the presence of aortic stenosis; and classify the severity level of stenosis based on the aortic stenosis indicator and the received symptom or physiological information.

[0017] In Example 8, the subject of Example 7 may optionally include a user interface coupled to the data receiver circuitry, which is configured to receive user input of symptoms in response to an indication of the presence of aortic stenosis.

[0018] In Example 9, the subject of any one or more of Examples 7-8 may optionally include triggered received physiological information, which may include the intensity of the S1 sound indicating cardiac contractility.

[0019] In Example 10, any one or more of the subjects in Examples 7-9 may optionally include one or more sensors coupled to a data receiver circuit and configured to sense physiological information in response to an indication of the presence of aortic stenosis, the one or more sensors including at least one of a body activity sensor, a respiratory sensor, a heart rate sensor, or an electrocardiogram sensor.

[0020] In Example 11, any one or more of the subjects in Examples 1-10 may optionally include controller circuitry that can be configured to generate indicators for patient eligibility for aortic valve replacement surgery based at least in part on aortic stenosis indicators.

[0021] In Example 12, the subject matter of Example 11 may optionally include controller circuitry that can be configured to receive symptom or physiological information indicating functional deterioration associated with aortic stenosis, and further generate indicators for patient eligibility for aortic valve replacement surgery based on the received symptom or physiological information.

[0022] In Example 13, any one or more of the subjects in Examples 1-12 may optionally include controller circuitry that includes heart failure detector circuitry configured to detect worsening heart failure (WHF) using physiological information received from data receiver circuitry.

[0023] In Example 14, the subject of Example 13 may optionally include controller circuitry that can be configured to generate a diagnosis of WHF secondary to aortic stenosis in response to an aortic stenosis indicator indicating the presence of aortic stenosis, and to provide the diagnosis to the user.

[0024] In Example 15, the subject matter of Example 14 optionally includes controller circuitry that can be configured to generate recommendations for titration therapy based on a diagnosis of WHF secondary to aortic stenosis.

[0025] Example 16 is a method for managing valvular heart disease using a medical device system, the method comprising: receiving heart sound information sensed from a patient; detecting heart sound components including S1 and S2 sounds from the received heart sound information; determining, via a stenosis detector circuit, a change in aortic valve surface area from a baseline non-stenotic state using the detected S1 and S2 sounds; generating an aortic valve stenosis indicator based at least in part on the determined change in aortic valve surface area; and providing the aortic valve stenosis indicator to a user or a process executable by the medical device system.

[0026] In Example 17, the subject matter of Example 16 may optionally include determining changes in aortic valve surface area, including detecting a reduction in aortic valve surface area from a baseline stenotic state, wherein an aortic valve stenosis indicator indicates the presence of aortic valve stenosis when the reduction in aortic valve surface area exceeds a threshold.

[0027] In Example 18, the subject matter of any one or more of Examples 16-17 may optionally include determining changes in the aortic valve surface area, which may include: generating an estimate of the left ventricular ejection time using the time interval between the detected S1 and S2 tones, and generating an estimate of the aortic-left ventricular pressure gradient using the intensity of the S2 tones; and determining the aortic valve surface area based on the estimate of the aortic-left ventricular pressure gradient and the estimate of the left ventricular ejection time.

[0028] In Example 19, any one or more of the topics in Examples 16-18 may optionally include generating an alert to the user in response to an aortic stenosis indicator indicating the presence of aortic stenosis.

[0029] In Example 20, the subject matter of any one or more of Examples 16-19 may optionally include, in response to an aortic stenosis indicator indicating the presence of aortic stenosis: receiving symptom or physiological information of a patient indicating functional deterioration associated with the presence of aortic stenosis; and classifying the severity level of stenosis based on the aortic stenosis indicator and the received symptom or physiological information.

[0030] In Example 21, the subject matter of Example 20 may optionally include receiving a patient’s symptoms or physiological information, which may include receiving user input of symptoms or sensing physiological information using one or more sensors, including at least one of a heart sound sensor, a body activity sensor, a breathing sensor, a heart rate sensor, or an electrocardiogram sensor.

[0031] In Example 22, any one or more of the topics in Examples 16-21 may optionally include indicators that are generated, at least in part, based on aortic stenosis indicators and provide the user with patient eligibility for aortic valve replacement surgery.

[0032] In Example 23, any one or more of the topics in Examples 16-22 may optionally include: detecting worsening heart failure (WHF) using physiological information; and generating a diagnosis of WHF secondary to aortic stenosis in response to an aortic stenosis indicator indicating the presence of aortic stenosis, and generating a recommendation for titration therapy based on the diagnosis of WHF secondary to aortic stenosis.

[0033] The phonation-based (HS) aortic stenosis detection described in this disclosure can improve the functionality of mobile medical devices or medical diagnostic systems or equipment that use phonations of the heart to monitor and manage patients with aortic stenosis. Compared to conventional echocardiography-based stenosis diagnosis, the HS-based aortic stenosis detection described herein allows for early detection and assessment of aortic stenosis in a mobile setting, reducing the time and burden associated with frequent echocardiographic examinations to track stenosis progression in a clinical setting, and saving costs. In various embodiments, HS-based aortic stenosis detection involves using phonation-based components to determine changes in aortic valve surface area. Aortic valve surface area is typically measured during cardiac catheterization, which poses risks to patients in addition to outpatient visits, potential hospitalizations, and additional costs. This document describes a non-invasive, catheter-free, HS-based method to effectively and efficiently estimate changes in aortic valve surface area in a mobile setting. Heart sounds can be collected by mobile sensors, such as sensors embedded in implantable medical devices. By using phonations of the heart in the diagnosis of aortic stenosis, lower-cost or less disruptive systems, devices, and methods can be used. It is particularly beneficial for patients in the early stages of aortic stenosis or those limited by various contraindications to accessibility, scheduling, affordability, or cardiac catheterization. The systems, devices, and techniques described in this article also improve patient eligibility for valve replacement surgery (e.g., TAVR or SAVR) and prevent or slow the progression to severe symptomatic aortic stenosis.

[0034] The efficient HS-based stenosis detection described in this disclosure can also improve power and resource utilization in medical devices. A technical problem exists in medical devices and systems where, in low-power monitoring modes, mobile medical devices (e.g., including IMDs) powered by one or more rechargeable or non-rechargeable batteries must make certain trade-offs between battery life (or, in the case of implantable medical devices with non-rechargeable batteries, device replacement cycles typically including surgical procedures) and sampling resolution, sampling period, processing, storage, and transmission of sensed physiological information, or features or mode selection within the medical device. Medical devices may include higher-power modes and lower-power modes. Physiological information, such as indications of potential adverse physiological events, can be used to switch from a low-power mode to a high-power mode. In some examples, a low-power mode may include a low-resource mode characterized by requiring less power, processing time, memory, or communication time or bandwidth (e.g., transmitting less data, etc.) than a corresponding high-power mode. A high-power mode may include a relatively high-resource mode characterized by requiring more power, processing time, memory, or communication time or bandwidth than a corresponding low-power mode. However, when physiological information detected in low-power mode indicates a possible event, valuable information is lost and cannot be recorded in high-power mode. Conversely, incorrect or inaccurate determinations triggering high-power mode unnecessarily overly limit the lifespan of some mobile medical devices. The HS-based aortic stenosis detection described herein allows for more accurate detection and determination of stenotic valves and physiological events secondary to stenosis (e.g., WHF), thereby avoiding unnecessary transitions from low-power to high-power modes and improving the use of medical device resources and power.

[0035] This overview provides some guidance for this application and is not intended to be exclusive or exhaustive of the subject matter. Further details regarding the subject matter can be found in the detailed description and the appended claims. Other aspects of this disclosure will become apparent to those skilled in the art upon reading and understanding the following detailed description and examining the accompanying drawings, each of which should not be construed as limiting. The scope of this disclosure is defined by the appended claims and their legal equivalents. Attached Figure Description

[0036] Various embodiments are shown by way of example in the accompanying drawings. Such embodiments are exemplary and are not intended to be exhaustive or exclusive embodiments of the subject matter.

[0037] Figure 1 An example of a patient management system and parts of the environment in which the system can operate are shown in general.

[0038] Figure 2This paper presents an example of an aortic stenosis detection system that uses heart sound information to detect the presence and severity of aortic stenosis.

[0039] Figure 3 The normal aortic valve and the stenotic aortic valve are shown in the open and closed states.

[0040] Figure 4A and Figure 4B Overall, examples of changes in heart sounds before and after TAVR surgery are shown in patients with aortic stenosis.

[0041] Figure 5 This is a flowchart illustrating an example method for managing valvular heart disease, such as aortic stenosis.

[0042] Figure 6 The block diagram of an example machine on which any one or more of the techniques (e.g., methods) discussed in this paper can be executed is shown in general. Detailed Implementation

[0043] This document discloses systems, devices, and methods for monitoring and managing patients with valvular heart disease, such as aortic stenosis. An exemplary medical device system includes a data receiver circuit for receiving heart sound information and a stenosis detector circuit for detecting and quantifying aortic stenosis using heart sounds sensed from the patient. The stenosis detector circuit can use heart sound components, such as S1 and S2 sounds, to determine changes in aortic valve surface area from a baseline stenosis-free state and generate an aortic stenosis indicator based on the determined changes in aortic valve surface area. A controller can provide the aortic stenosis indicator to a user or by procedures executable by the medical device system, such as triggering symptom monitoring, classifying aortic stenosis into different stages, facilitating patient eligibility assessment for valve replacement surgery, or triaging heart failure management strategies.

[0044] Figure 1 The example patient management system 100 and a portion of the environment in which it may operate are shown in general. The patient management system 100 can perform a range of activities, including remote patient monitoring and diagnosis of disease conditions such as aortic stenosis or other valvular heart diseases. These activities can be performed near the patient 101, such as in the patient's home or office, via a centralized server, such as in a hospital, clinic, or doctor's office, or via a remote workstation, such as a secure wireless mobile computing device.

[0045] The patient management system 100 may include one or more mobile medical devices, an external system 105, and a communication link 111, which provides communication between the one or more mobile medical devices and the external system 105. The one or more mobile medical devices may include an implantable medical device (IMD) 102, a wearable medical device (WMD) 103, or one or more other implantable, leadless, subcutaneous, external, wearable, or mobile medical devices configured to monitor, sense, or detect information from the patient 101, determine physiological information about the patient 101, or provide one or more therapies to treat various conditions of the patient 101, such as one or more cardiac or non-cardiac conditions (e.g., dehydration, sleep apnea, etc.).

[0046] In one example, IMD 102 may include one or more conventional cardiac rhythm management devices implanted in the patient's chest, having a lead system including one or more transvenous, subcutaneous, or non-invasive leads or catheters to position one or more electrodes or other sensors (e.g., heart sound sensors) within, on, or around the heart, or in one or more other locations in the chest, abdomen, or neck of the patient 101. In another example, IMD 102 may include, for example, a monitor implanted subcutaneously in the chest of the patient 101. IMD 102 includes a housing containing a circuitry system and, in some examples, one or more sensors, such as temperature sensors, etc.

[0047] IMD 102 may include assessment circuitry configured to detect or determine specific physiological information of patient 101, or to determine one or more conditions, or to provide information or alerts to users such as patient 101 (e.g., a patient), a clinician, or one or more other caregivers or processes. In one example, IMD 102 may be an implantable cardiac monitor (ICM) configured to collect cardiac information from the patient, optionally along with other physiological information. IMD 102 may alternatively or additionally be configured as a treatment device configured to treat one or more medical conditions of patient 101. Treatment may be delivered to patient 101 via a lead system and associated electrodes or using one or more other delivery mechanisms. Treatment may include delivering one or more medications to patient 101, such as using IMD 102 or one or more other mobile medical devices. In some examples, treatment may include cardiac resynchronization therapy to correct asynchrony and improve cardiac function in patients with heart failure. In other examples, IMD 102 may include a drug delivery system, such as a drug infusion pump, for delivering medication to a patient for the management of arrhythmias or complications arising from arrhythmias, hypertension, or one or more other physiological conditions. In other examples, IMD 102 may include one or more electrodes configured to stimulate the patient's nervous system or to provide stimulation to the muscles of the patient's airway.

[0048] WMD 103 may include one or more wearable or external medical sensors or devices (e.g., automatic external defibrillator (AED), Holter monitor, patch-based device, smartwatch, smart accessory, wrist-worn or finger-worn medical device, such as finger-based photoplethysmography sensor, etc.).

[0049] In one example, IMD 102 or WMD 103 may include or be coupled to an implantable or wearable sensor to sense heart sound signals and includes heart sound recognition circuitry to identify one or more heart sound components, such as S1, S2, S3, or S4, based on a spectral entropy time series derived from the sensed heart sound signals. IMD 102 or WMD 103 also includes heart sound (HS)-based event detector circuitry that can detect physiological events (e.g., arrhythmia onset or worsening heart failure (WHF) events) based at least on heart sound metrics of the detected one or more heart sound components. Examples of such heart sound metrics may include the amplitude or timing of the heart sound components within the cardiac cycle relative to a reference point. In some examples, at least a portion of the heart sound recognition circuitry and / or the HS-based event detector circuitry may be implemented in and executed by external system 105.

[0050] External system 105 may include dedicated hardware / software systems, such as a programmer, a remote server-based patient management system, or alternatively, a software-defined system primarily running on a standard personal computer. External system 105 may manage patient 101 via IMD 102 or one or more other mobile medical devices connected to external system 105 via communication link 111. In other examples, IMD 102 may be connected to WMD 103, or WMD 103 may be connected to external system 105 via communication link 111. This may include, for example, programming IMD 102 to perform one or more of the following: acquiring physiological data, performing at least one self-diagnostic test (such as for device operating status), analyzing physiological data, or optionally delivering or adjusting treatment to patient 101. Furthermore, external system 105 may send or receive information to or from IMD 102 or WMD 103 via communication link 111. Examples of information may include real-time or stored physiological data from patient 101, diagnostic data such as detection of patient hydration status, hospitalization, response to treatment delivered to patient 101, or device operating status of IMD 102 or WMD 103 (e.g., battery status, lead impedance, etc.). Communication link 111 may be an inductive telemetry link, a capacitive telemetry link, or a radio frequency (RF) telemetry link, or wireless telemetry based on standards such as “Strong” Bluetooth or IEEE 802.11 Wireless Fidelity “Wi-Fi” interface standards. Other configurations and combinations of patient data source interfaces are also possible.

[0051] External system 105 may include an external device 106 located near one or more mobile medical devices, and a remote device 108 located relatively far from the one or more mobile medical devices, communicating with external device 106 via communication network 107. Examples of external device 106 may include a medical device programmer. Remote device 108 may be configured to evaluate collected patient or patient information and provide alarm notifications and other possible functions. In one example, remote device 108 may include a centralized server acting as a central hub for storing and analyzing the collected data. The server may be configured as a single, multiple, or distributed computing and processing system. Remote device 108 may receive data from multiple patients. Data may be collected by one or more mobile medical devices and other data acquisition sensors or devices associated with patient 101. The server may include storage devices to store data in a patient database. The server may include alarm analyzer circuitry for evaluating the collected data to determine whether specific alarm conditions are met. Meeting alarm conditions may trigger the generation of alarm notifications, such as those provided by one or more human-perceptible user interfaces. In some examples, alarm conditions may alternatively or additionally be evaluated by one or more mobile medical devices, such as implantable medical devices. For example, alert notifications can include web page updates, telephone or pager calls, emails, SMS, text or "instant" messages, as well as messages to patients and simultaneous direct notifications to emergency services and clinicians. Other alert notifications are also possible. The server can include alert priority sorting circuitry configured to prioritize alert notifications. For example, alerts for detected physiological events can be prioritized using a similarity metric between physiological data associated with a detected physiological event and physiological data associated with historical alerts.

[0052] Remote device 108 may additionally include one or more locally configured clients or remote clients securely connected to the server via communication network 107. Examples of clients may include personal desktop computers, laptops, mobile devices, or other computing devices. System users, such as clinicians or other qualified medical professionals, can use the clients to securely access patient data stored in a database on the server and select and prioritize patients and alerts for healthcare delivery. In addition to generating alert notifications, remote device 108, including the server and interconnected clients, can also execute follow-up protocols by sending follow-up requests to one or more mobile medical devices, or by sending messages or other communications as compliance notifications to patient 101 (e.g., the patient), clinicians, or authorized third parties.

[0053] The communication network 107 can provide wired or wireless interconnection. In one example, the communication network 107 can be based on the Transmission Control Protocol / Internet Protocol (TCP / IP) network communication standard, although other types or combinations of network implementations are also possible. Similarly, other network topologies and arrangements are also possible.

[0054] One or more of the external device 106 or remote device 108 may output detected physiological events to a system user, such as a patient or clinician, or to a process including, for example, an instance of a computer program executable in a microprocessor. In one example, the process may include automatically generating recommendations for antiarrhythmic treatment, or recommendations for further diagnostic tests or treatments. In one example, the external device 106 or remote device 108 may include a corresponding display unit for displaying physiological or functional signals, or alarms, alerts, emergency calls, or other forms of warning to signal the detection of an arrhythmia. In some examples, the external system 105 may include an external data processor configured to analyze physiological or functional signals received by one or more mobile medical devices and confirm or reject the detection of an arrhythmia. Computationally intensive algorithms, such as machine learning algorithms, may be implemented in the external data processor to retrospectively process data to detect arrhythmias.

[0055] One or more portions of a mobile medical device or external system 105 may be implemented using hardware, software, firmware, or a combination thereof. One or more portions of a mobile medical device or external system 105 may be implemented using dedicated circuitry, which may be constructed or configured to perform one or more functions, or may be implemented using general-purpose circuitry, which may be programmed or otherwise configured to perform one or more functions. Such general-purpose circuitry may include a microprocessor or a portion thereof, a microcontroller or a portion thereof, or programmable logic circuitry, memory circuitry, network interfaces, and various components for interconnecting these components. For example, a “comparator” may, among other things, include an electronic circuit comparator that may be constructed to perform a specific function of comparing two signals, or the comparator may be implemented as part of a general-purpose circuitry that may be driven by code instructing a portion of the general-purpose circuitry to perform the comparison between the two signals. A “sensor” may include electronic circuitry configured to receive information and provide an electronic output representing such received information.

[0056] Treatment device 110 can be configured to send or receive information from one or more mobile medical devices or external systems 105 using communication link 111. In one example, one or more mobile medical devices, external devices 106, or remote devices 108 can be configured to control one or more parameters of treatment device 110. External system 105 can allow programming of one or more mobile medical devices and can receive information about one or more signals acquired by one or more mobile medical devices, such as information that can be received via communication link 111. External system 105 may include a local external implantable medical device programmer. External system 105 may include a remote patient management system that can, for example, monitor patient status from a remote location or adjust one or more therapies.

[0057] Figure 2 An example of an aortic stenosis detection system 200 is shown in general, configured to detect the presence and severity of aortic stenosis in a patient using heart sound information. The aortic stenosis detection system 200 may include one or more of a data receiver circuitry 210, a controller circuitry 220, a user interface 230, and treatment circuitry 240. At least a portion of the aortic stenosis detection system 200 may be implemented in an IMD 102, a WMD 103, or an external system 105 (such as one or more of an external device 106 or a remote device 108).

[0058] Data receiver circuitry 210 can receive physiological information from a patient. In one example, data receiver circuitry 210 may include sensing amplifier circuitry configured to sense physiological signals from the patient via a physiological sensor, such as an implantable, wearable, or otherwise mobile sensor or electrode associated with the patient. The sensor may be incorporated into or otherwise associated with a mobile device such as IMD 102 or WMD 103. In some examples, physiological signals sensed from the patient may be stored in a storage device, such as an electronic medical record (EMR) system. Data receiver circuitry 210 can receive physiological signals from the storage device, such as in response to user commands or triggering events. This is by way of example and not limitation, and as... Figure 2As shown, the data receiver circuit 210 may include one or more of the following: a heart sound sensing circuit 212, a heart sensing circuit 214, a heart rate circuit 216, and a patient symptom receiver 218. The heart sound sensing circuit 212 may receive heart sound information, such as heart sound signals sensed from a patient. In one example, the heart sound sensing circuit 212 may be coupled to a heart sound sensor to sense body motion / vibration signals that indicate heart sounds, which are correlated with or indicate heart sounds. The heart sound sensor may take the form of an accelerometer, an acoustic sensor, a microphone, a piezoelectric-based sensor, or other vibration or acoustic sensors. The accelerometer may be a single-axis, dual-axis, or triaxial accelerometer. Examples of accelerometers may include flexible piezoelectric crystal (e.g., quartz) accelerometers or capacitive accelerometers fabricated using microelectromechanical systems (MEMS) technology. The heart sound sensor may be included in the IMD 102 or WMD 103, or disposed on leads, such as part of a lead system associated with the IMD 102 or WMD 103. In one example, an accelerometer (or other sensor type) can sense endocardial acceleration (EA) signals from a part of the heart, such as on the endocardial or epicardial surface of one of the left ventricle, right ventricle, left atrium, or right atrium. The EA signal may contain components corresponding to various heart sound components, such as one or more of S1, S2, S3, or S4.

[0059] The cardiac sensing circuit 214 can sense cardiac electrical signals. Examples of cardiac electrical signals may include surface electrocardiography (ECG) sensed from electrodes placed on the body surface, subcutaneous ECG sensed from electrodes placed under the skin, and intracardiac electrogram (EGM) sensed from one or more implantable electrodes. Sensing electrodes may be included in or communicatively coupled to IMD 102 or WMD 103. The heart rate circuit 216 can detect the patient's heart rate, such as based on the signals sensed by the cardiac sensing circuit 214. Alternatively, heart rate (or pulse rate) may be detected from cardiac mechanical signals. As discussed further below, the control circuitry may use one or more of the cardiac electrical signals or heart rate information to construct representative heart segments from which a spectral entropy time series can be derived.

[0060] In some examples, the data receiver circuit 210 may receive other physiological or functional signals, including, for example, body activity signals, posture signals, chest or cardiac impedance signals, arterial pressure signals, pulmonary artery pressure signals, left atrial pressure signals, RV pressure signals, LV coronary artery pressure signals, coronary artery blood temperature signals, blood oxygen saturation signals, heart sound signals, physiological responses to activity, apnea-hypopnea index, one or more respiratory signals (such as respiratory rate signals or tidal volume signals), brain natriuretic peptide (BNP), complete blood count, sodium and potassium levels, glucose levels, and other biomarkers and biochemical markers.

[0061] The patient symptom receiver 218 can receive information about patient symptoms associated with aortic stenosis. Examples of such symptoms may include: fatigue (e.g., difficulty walking short distances, decreased activity level or duration, or reduced ability to perform normal physical activities); shortness of breath or difficulty breathing; chest pain; dizziness, fainting, or lightheadedness; heart murmurs, palpitations, or other irregular heartbeats; and so on. Some symptoms (e.g., fatigue, shortness of breath, or chest pain) may be prominent or worsened during or after physical activity. The presence and / or severity of patient symptoms may be received as user input, such as via user interface 230. Additionally or alternatively, one or more sensors may sense physiological information that is related to or indicates the presence and severity of certain symptoms. For example, a chest impedance sensor may sense respiratory signals that can be analyzed to detect respiratory rate and tidal volume that are related to or indicate the symptoms of shortness of breath. In another example, cardiac electrical signals and / or heart rate may be analyzed to detect irregular heartbeats or rhythms associated with heart murmurs or palpitations.

[0062] Controller circuitry 220 can identify and track heart sound components (e.g., S1 or S2 sounds) and detect aortic stenosis using at least the heart sound components. Controller circuitry 220 can be implemented as part of a microprocessor circuit, which can be a dedicated processor such as a digital signal processor, application-specific integrated circuit (ASIC), microprocessor, or other type of processor for processing information including bodily activity information. Alternatively, the microprocessor circuit can be a general-purpose processor that can receive and implement a set of instructions to perform the functions, methods, or techniques described herein.

[0063] Controller circuitry 220 may include a circuit group comprising one or more other circuits or sub-circuits, such as a heart sound (HS) recognition circuitry 222, an aortic stenosis detector 224, a valve replacement assessment unit 226, and a heart failure detector 228. These circuits may perform individually or in combination the functions, methods, or techniques described herein. In one example, the hardware of the circuit group may be invariably designed to perform a specific operation (e.g., hardwired). In one example, the hardware of the circuit group may include physically connected components (e.g., execution units, transistors, simple circuits, etc.) including physically modified computer-readable media (e.g., magnetic, electrical, movable placement of invariant aggregated particles, etc.) to encode instructions for a specific operation. When the physical components are connected, the fundamental electrical characteristics of the hardware composition are altered, e.g., from an insulator to a conductor, and vice versa. Instructions enable embedded hardware (e.g., execution units or loading mechanisms) to create members of the circuit group within the hardware via variable connections to perform a portion of a specific operation during operation. Thus, while the device is operating, computer-readable media are communicatively coupled to other components of the circuit group members. In one example, any one of the physical components can be used in more than one member of more than one circuit group. For example, under operation, an execution unit can be used in a first circuit of a first circuit group at one point in time, and reused by a second circuit in the first circuit group or a third circuit in the second circuit group at different times.

[0064] The HS recognition circuit 222 can preprocess the heart sound signal received from the HS sensing circuit 212. In one example, the HS recognition circuit 222 may include a filter or filter bank to remove or attenuate one or more of low-frequency signal baseline drift, high-frequency noise, or other unwanted frequency components. In one example, the heart sound segment may be bandpass filtered to a frequency range of approximately 5-90 Hz or approximately 9-90 Hz. In one example, the filter may include a second-order or higher-order differentiator configured to calculate the second-order or higher-order derivative of the heart sound signal.

[0065] The HS recognition circuit 222 can identify heart sound components from the preprocessed heart sound signal, optionally further using cardiac electrical signals and heart rate information received from the heart sensing circuit 214 and heart rate circuit 216, respectively. Examples of heart sound components include S1, S2, S3, and S4 components. S1 and S2 heart sounds typically have frequencies in the range of approximately 10–250 Hz. Due to wide interpersonal differences and minor variations in technical devices, the frequency of S2 is typically higher than that of S1. For example, most of the power of S1 falls within approximately 10–50 Hz, and most of S2 falls within approximately 20–70 Hz. When present, the early diastolic sound S3, generated by the rapid filling of the dilated ventricle, and the late diastolic sound S4, generated by the left atrium's response to non-compliant left ventricular contraction, typically have lower intensity and lower frequency. The HS recognition circuit 222 can use a time-domain, amplitude-based method to identify one or more of the heart sounds S1, S2, S3, or S4. This method includes detecting the maximum signal amplitude or its variation within a heart sound detection window, such as peak heart sound signal power or the root-mean-square (RMS) value of the heart sound signal. The timing of the maximum amplitude can be identified as the timing of the heart sound component. In some examples, the HS recognition circuit 222 can use frequency-domain methods (such as the power spectrum or spectral entropy time series of the heart sound signal) to identify one or more of the heart sounds S1, S2, S3, or S4.

[0066] The aortic stenosis detector 224 can detect the presence and severity of aortic stenosis at least in part based on heart sound components recognized by the HS recognition circuit 222. The aortic stenosis detector 224 may include an aortic valve surface area estimator 225, which can estimate the aortic valve surface area (S) using one or more heart sound components. AV ) or S AV Changes in aortic valve anatomy. Aortic stenosis can lead to changes in the aortic valve's anatomy, including a decrease in valve surface area, as well as thickening and calcification of the valve leaflets. Now refer to Figure 3 These figures illustrate a normal aortic valve 310 and a stenotic aortic valve 320 in their open and closed states. The normal aortic valve surface area in a healthy adult is approximately 3.0 to 4.0 cm². 2 In patients with symptomatic aortic stenosis, the valve surface area may be less than 0.8 cm². 2 As aortic stenosis progresses, there is a minimum pressure gradient until the orifice area (the open space defined by the valve leaflets) is less than half the normal value. The pressure gradient across the stenotic valve is directly related to the orifice area and transvalvular flow. Because the stenotic valve cannot fully open and interferes with the normal flow of blood out of the heart, this can lead to heart failure or other cardiac and systemic problems. Restricted blood flow limits the oxygen supply to the body's organs and tissues, causing a variety of symptoms, including chest pain, shortness of breath, and fainting.

[0067] In cardiac catheterization, the Göring equation (also known as the Göring formula) has been used to estimate the aortic valve surface area (S). AV This is used to assess the severity of aortic stenosis. One form of the Goring equation is given below:

[0068] (1)

[0069] In the above Göring equation (1), This represents the pressure gradient between the aorta and the left ventricle (LV), which can be measured at peak systole. The pressure gradient is calculated from this. This is also known as the peak instantaneous pressure gradient during systolic ejection. Q represents standardized cardiac output (CO, the amount of blood pumped by the heart per minute), which can be calculated as the ratio of CO to the systolic ejection period (SEP, the cumulative systolic time per minute). Since SEP can be determined as the product of left ventricular ejection time (LVET) and heart rate (HR), the Goring equation can be rewritten as follows:

[0070] (2)

[0071] To estimate S from the baseline aortic valve stenosis-free state AV or S AV The aortic valve surface area estimator 225 can use the time interval between S1 and S2 heart sounds (T) to estimate the changes in aortic valve surface area. S1-S2 ) to estimate LVET, and use the intensity of S2 heart sounds ( (Such as peak amplitude or peak signal power within the S2 detection window) to estimate the aortic-LV pressure gradient The time interval T between S1 and S2 S1-S2 Proportional to LVET. The intensity of the S2 heart sound. Peak instantaneous aortic-LV pressure gradient during systolic ejection Proportional. During ejection, the aortic pressure is slightly lower than the LV pressure. In the case of aortic stenosis, the narrowed aortic valve increases further resistance to blood flow during systole, resulting in a large LV-aortic pressure gradient. As the LV begins to relax at the end of ejection, the reversal of the aortic-LV pressure gradient causes the aortic valve to close with a slam. Unlike a normal heart, where the reversal occurs from a relatively small LV-aortic difference during systole, in a stenotic aortic valve, the relatively high LV-aortic pressure gradient during systole results in a relatively large aortic-LV gradient at aortic valve closure. The aortic valve surface area estimator 225 can estimate the S from the baseline aortic valve without stenosis using the following HS-based Goring formula. AV or S AV Changes:

[0072] (3)

[0073] The above Göring formula based on HS shows that S AV With HR, T S1-S2 It is inversely proportional to the square root of the S2 intensity. An increase in S2 intensity and / or a prolongation of the S1-S2 time interval will lead to an increase in the aortic valve surface area S. AV A decrease in aortic surface area indicates the presence or increased severity of aortic stenosis. In one example, to estimate changes in aortic surface area and detect aortic stenosis, the aortic valve surface area estimator 225 can calculate a product metric. The calculated product metric X is compared to a threshold (X0) representing the product metric calculated during a baseline aortic stenosis-free period. If the calculated product metric X exceeds a specific margin of the threshold X0, the aortic stenosis detector 224 can generate an aortic stenosis indicator indicating a significant reduction in aortic valve surface area and the presence of aortic stenosis. In some examples, the aortic stenosis detector 224 can also determine the degree or severity of aortic stenosis based on a relative measure (e.g., difference or ratio) between the calculated product metric X and the baseline product metric value X0. For example, a higher increase in X0 corresponds to a higher reduction in aortic valve surface area and more severe aortic stenosis, and vice versa.

[0074] A HS-based aortic stenosis indicator can be provided to the user (e.g., a clinician), such as by displaying it on user interface 230. In one example, the aortic valve surface area estimator 225 can be commanded to continuously or periodically estimate the aortic surface area and track its changes in order to promptly detect valve deterioration and thus provide early detection of aortic stenosis. In response to the aortic stenosis indicator indicating the presence of aortic stenosis, an alert can be provided to the patient for further cardiac performance evaluation with the physician (e.g., echocardiographic assessment of LV function). The alert can be a visual alert, an auditory alert, or a tactile alert (e.g., vibration), as well as other forms of alertness.

[0075] In some examples, when the aortic stenosis indicator indicates the presence of aortic stenosis, the controller circuit 220 can trigger the data receiver circuit 210 to receive patient symptoms at the time of detection of aortic stenosis. As described above, patient symptoms can be received as subjective input or as objective physiological information sensed by one or more physiological sensors associated with the patient, which is related to or indicates functional deterioration associated with or caused by aortic stenosis. Examples of sensors for detecting functional deterioration associated with aortic stenosis include physical activity sensors for detecting reduced activity and fatigue, respiratory sensors for detecting shortness of breath, heart rate sensors and / or electrocardiogram sensors for detecting heart murmurs or palpitations or other irregular heartbeats, and heart sound sensors for detecting increased cardiac contractility based on elevated S1 sound intensity, etc. In some examples, the alert to the patient can also be based on patient symptoms or sensor-indicated patient functional deterioration. The aortic stenosis detector 224 can classify the severity level of aortic stenosis based on the HS-based aortic stenosis indicator and the received symptom information.

[0076] The HS-based aortic stenosis indicator generated by the aortic stenosis detector 224 can be used to facilitate patient triage and titration. The valve replacement assessment unit 226 can generate indicators for patient eligibility for aortic valve replacement surgery (such as transcatheter aortic valve replacement (TAVR) or surgical aortic valve replacement (SAVR)) based at least in part on the aortic stenosis indicator. Valve replacement eligibility can be further based on patient symptoms or physiological information indicating functional deterioration associated with the presence of aortic stenosis. For example, if a change in aortic valve surface area exceeds a threshold indicating the presence of aortic stenosis, and if a patient's symptoms or functional deterioration meet specific criteria (e.g., a change in cardiac contractility estimated based on S1 intensity or changes in S1 intensity exceeding a threshold, or a paroxysmal increase in the Rapid Shallow Breathing Index (RSBI, the ratio of respiratory rate to tidal volume) measured by physiological sensors, indicating worsening tachypnea, irregular heartbeat, or syncope, etc.), an alert can be provided to the patient or physician to consider aortic valve correction or replacement surgery.

[0077] The heart failure detector 228 can detect worsening heart failure (WHF) using physiological information received from the data receiver circuitry, such as information collected from a set of sensors. In response to an aortic stenosis indicator indicating the presence of aortic stenosis, the controller circuitry 220 can generate a diagnosis of WHF secondary to aortic stenosis, also known as “aortic stenosis-triggered” WHF, and provide the diagnosis to the user. Differentiating between “non-aortic stenosis-triggered” and “aortic stenosis-triggered” WHF can aid in the triage of heart failure management strategies. For example, for patients with WHF without aortic stenosis or with unchanged stenosis severity, standard heart failure treatment (e.g., diuretic upregulation or adjustment of electrical stimulation) can be administered. If WHF is accompanied by detected worsening of aortic stenosis, aortic stenosis-triggered WHF is suspected, and interventions such as balloon valvuloplasty or aortic valve replacement can be recommended to reverse the valvular worsening.

[0078] User interface 230 may include input and output units. In one example, at least a portion of user interface 230 may be implemented in external system 105. The input unit may receive user input for programming data receiver circuitry 210 and controller circuitry 220, such as parameters for sensing heart sound signals, detecting heart sound components, or detecting or determining the severity of aortic stenosis based on estimated changes in aortic surface area. The input unit may include a keyboard, on-screen keyboard, mouse, trackball, touchpad, touchscreen, or other pointing or navigation device. The output unit may include a display for displaying sensed heart sound signals, representative heart sound segments, spectral entropy time series, heart sound metrics, information about detected physiological events, and any intermediate measurements or calculations. The output unit may also present treatment titration protocols and recommended treatments to the user, for example, via the display unit, including changes in parameters of treatment provided by the implanted device, recommendations for the implanted device or procedure (e.g., valve replacement surgery such as TAVR or SAVR), initiation or changes in drug therapy, or other treatment options for the patient. The output unit may include a printer for printing hard copies of the information that can be displayed on the display unit. Signals and information can be presented in tables, charts, diagrams, or any other type of text, tabular, or graphical format. The presentation of output information may include audio or other media formats. In one example, the output unit may generate alarms, alerts, emergency calls, or other forms of warnings to signal to system users about a detected medical event.

[0079] Treatment circuit 240 can be configured to deliver treatment to a patient, such as in response to the detected presence of aortic stenosis or based on the severity of the stenosis. This treatment can be preventative or therapeutic in nature, such as modifying, restoring, or improving the patient's cardiac function. In one example, treatment circuit 240 can deliver heart failure treatment in response to a heart failure detector 228 detecting a WHF. If the detected WHF is determined to be secondary to aortic stenosis (i.e., "aortic stenosis-triggered" WHF), treatment circuit 240 can provide treatment options to manage aortic stenosis, including pharmacological therapy to help control symptoms and reduce the chance of developing certain complications, or recommended surgical procedures such as balloon valvuloplasty or aortic valve replacement. If the detected WHF is determined to be "non-aortic stenosis-triggered" WHF, standard heart failure management protocols, such as pharmacological therapy or electrical stimulation, can be administered. Examples of treatment may include electrical stimulation delivered to the heart, nerve tissue, other target tissues, cardioversion, defibrillation, or pharmacological therapy that includes delivering medication to the patient. In some examples, the treatment circuit 240 can modify existing treatments, such as adjusting the stimulation parameters of electrical stimulation therapy (e.g., stimulation amplitude, frequency, pulse width, electrode configuration, stimulation time) or adjusting the drug dosage of drug therapy.

[0080] Figure 4A and Figure 4B Example changes in heart sounds before and after TAVR in patients with aortic stenosis are shown. In the example shown, various heart sound components, such as those generated using the HS recognition circuit 222, were used to assess the improvement in cardiac function after TAVR. The heart sound components include the S1 sound intensity, which indicates cardiac systolic function, the S2 sound intensity, which indicates aortic valve function, and the S3 sound intensity, which indicates cardiac filling pressure. Figure 4A As shown, compared with baseline heart sound data before TAVR (before operation time T), the S1 intensity 412 significantly increased from baseline S1 intensity 410 after TAVR (after operation time T), indicating improved systolic function. The S2 intensity 422 significantly increased from baseline S2 intensity 420 after TAVR, indicating improved aortic valve function. The S3 intensity 432 also significantly increased from baseline S3 intensity 430 after TAVR, indicating reduced filling pressure and improved diastolic function. Figure 4B A phonocardiogram is shown to illustrate beat-by-beat changes in heart sound intensity (e.g., amplitude) before and after TAVR surgery at time T, as shown on horizontal timeline 401. The phonocardiogram represents multiple heart sound segments stacked together along the y-axis, indexed by the number of heart sound segments (and thus corresponding to time). Each heart sound segment is extracted from multiple cardiac cycles and aligned relative to a reference point (the origin of the x-axis), such as the R wave of the corresponding cardiac cycle. Each heart sound segment (on the x-axis) has a color-coded or grayscale-coded signal amplitude to show how the amplitude changes over time. Figure 4B As shown, the S2 amplitude 452 after TAVR significantly increased from the baseline S2 amplitude 450 before TAVR, indicating that aortic valve function was improved after TAVR surgery.

[0081] Figure 5 This is a flowchart illustrating an example method 500 for managing valvular heart disease, such as aortic stenosis. Method 500 can be implemented and performed in a mobile medical device, such as an implantable or wearable medical device, or in a remote patient management system. In one example, method 500 can be implemented and performed by an IMD 102 or WMD 103, an external system 105, or an aortic stenosis detection system 200.

[0082] Method 500 begins at 510 to receive heart sound information sensed from the patient. The heart sound information can be sensed using sensors associated with or included in a mobile or wearable device. In some examples, endocardial acceleration signals sensed from inside the heart can be used to analyze heart sounds. Other physiological information may also be received, including cardiac electrical signals such as an electrocardiogram (ECG) or electrocardiogram (EGM), heart rate, signals indicating cardiac mechanical activity, including, for example, chest or cardiac impedance signals, arterial pressure signals, pulmonary artery pressure signals, left atrial pressure signals, RV pressure signals, LV pressure signals, heart sounds or endocardial acceleration signals, physiological responses to activity, apnea-hypopnea index, one or more respiratory signals such as respiratory rate signals or tidal volume signals, and so on.

[0083] At position 520, cardiac components including the S1 and S2 tones can be detected from the received heart sound information. Time-domain or frequency-domain methods can be used to detect the S1 and S2 tones, as described above. Figure 2 As stated above.

[0084] At 530, the characteristics of the detected S1 and S2 tones can be used to determine the change in aortic valve surface area from the baseline stenosis-free state. In one example, a modified Goring equation can be used to estimate the aortic valve surface area (S... AV The left ventricular ejection time (LVET) can be determined by the time interval between the S1 and S2 heart sounds (T). S1-S2 (Approximated by) and the aortic-LV pressure gradient It can be determined by the S2 tone intensity ( To approximate, such as the peak amplitude or peak signal power within the S2 detection window, as mentioned above. Figure 2 The aortic valve surface area S is described above. AV It can be identified as related to HR, T S1-S2 and Inversely proportional.

[0085] At 540, an aortic stenosis indicator can be generated based on changes in aortic valve surface area. An increase in S2 intensity and / or a prolonged S1-S2 time interval leads to an increase in aortic valve surface area S... AV A decrease in [value] indicates the presence or increased severity of aortic stenosis. In one example, the product measure [value]... The product can be compared to a threshold (X0) representing the product determined during a baseline aortic valve stenosis-free period, and the comparison can be used to estimate changes in aortic surface area. If the product measure X exceeds a specific margin of the threshold X0, a significant reduction in aortic valve surface area is detected, indicating the presence of aortic valve stenosis.

[0086] The aortic stenosis indicator generated at 540 can be provided to a user (e.g., a patient or a clinician managing the patient) or can be a procedure performed by a medical device system. In one example, the aortic surface area based on HS can be monitored continuously or periodically to capture significant changes in the aortic surface area in a timely manner, and thus generate early detection of aortic stenosis. At 550A, in response to the aortic stenosis indicator indicating the presence of aortic stenosis, an alert can be provided to the patient for further cardiac performance assessment with a physician (e.g., echocardiographic assessment of LV function). Additionally or alternatively, at 550B, the detection of the presence of aortic stenosis can trigger patient symptom monitoring, or sensing of patient functional deterioration associated with or caused by aortic stenosis via one or more sensors (e.g., a physical activity sensor for detecting reduced activity and fatigue, a respiratory sensor for detecting shortness of breath, a heart rate sensor and / or an electrocardiogram sensor for detecting heart murmurs or palpitations or other irregular heartbeats, a heart sound sensor for detecting increased cardiac contractility based on elevated S1 sound intensity, etc.). Alerts to patients can also be based on patient symptoms or measurements of functional deterioration.

[0087] Aortic stenosis indicators may be used alternatively or additionally for medical diagnosis and / or titration therapy. At 550C, patient eligibility for aortic valve replacement surgery (e.g., TAVR or SAVR) may be evaluated at least in part based on aortic stenosis indicators. Valve replacement eligibility may be further based on patient symptoms or physiological information indicating functional deterioration associated with the presence of aortic stenosis. For example, in the presence of aortic stenosis, an alert may be provided to the patient or physician to consider TAVR or SAVR surgery if the patient's symptoms or functional deterioration meet specific criteria (e.g., changes in cardiac contractility estimated based on S1 intensity exceeding a threshold, or paroxysmal increases in RSBI indicated by physiological sensor measurements, suggesting worsening tachypnea, irregular heartbeat, or syncope, etc.). Additionally or alternatively, at 550D, worsening heart failure (WHF) may be detected using patient physiological information sensed from multiple sensors. If aortic stenosis is present in a wheezing heart failure (WHF), manifested as a reduction in aortic valve surface area based on heart failure (HS), it can be diagnosed as WHF secondary to aortic stenosis (i.e., "aortic stenosis-triggered" WHF), and titration therapy should be recommended. For example, if aortic stenosis is detected as absent or unchanged in severity during a WHF, standard heart failure management strategies (e.g., diuretic upregulation or adjustment of electrical stimulation) can be administered. If aortic stenosis is present or its severity is increased, "aortic stenosis-triggered" WHF is suspected, and interventions such as aortic valve replacement or balloon valvuloplasty can be considered to reverse the valvular deterioration.

[0088] Figure 6 A block diagram of the example machine 600 is shown in general, on which any one or more techniques (e.g., methods) discussed herein can be executed. Parts of this description can be applied to the computational framework of various parts of the IMD 102, WMD 103, external system 105, or aortic stenosis detection system 200.

[0089] In alternative embodiments, machine 600 may operate as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, machine 600 may operate as a server machine, a client machine, or both in a server-client network environment. In one example, machine 600 may act as a peer machine in a peer-to-peer (P2P) (or other distributed) network environment. Machine 600 may be a personal computer (PC), tablet PC, set-top box (STB), personal digital assistant (PDA), mobile phone, web appliance, network router, switch, or bridge, or any machine capable of executing instructions (sequentially or otherwise) specifying the actions to be taken by that machine. Furthermore, although only a single machine is shown, the term "machine" should also be considered to include any collection of machines that individually or jointly execute a set (or more) of instructions to perform any one or more methods discussed herein, such as cloud computing, software as a service (SaaS), and other computer cluster configurations.

[0090] As described herein, examples may include logic or multiple components or mechanisms, or may be operated by logic or multiple components or mechanisms. A circuit group is a collection of circuits implemented in a tangible entity, including hardware (e.g., simple circuits, gates, logic, etc.). Circuit group members can become flexible over time and with changes in the underlying hardware. A circuit group includes members that can perform a specified operation individually or in combination during operation. In one example, the hardware of the circuit group may be invariably designed to perform a specific operation (e.g., hardwired). In one example, the hardware of the circuit group may include physically connected components (e.g., execution units, transistors, simple circuits, etc.) including physically modified computer-readable media (e.g., magnetic, electrical, movable placement of invariant aggregated particles, etc.) to encode instructions for a specific operation. When the physical components are connected, the underlying electrical characteristics of the hardware composition are changed, for example, from an insulator to a conductor, and vice versa. Instructions enable embedded hardware (e.g., execution units or loading mechanisms) to create members of the circuit group in the hardware via variable connections to perform a portion of a specific operation during operation. Thus, when the device is operating, computer-readable media are communicatively coupled to other components of the circuit group members. In one example, any physical component can be used in more than one member of more than one circuit group. For example, under operation, an execution unit can be used in a first circuit of a first circuit group at one point in time and reused by a second circuit in the first circuit group or a third circuit in the second circuit group at different times.

[0091] Machine (e.g., computer system) 600 may include a hardware processor 602 (e.g., a central processing unit (CPU), graphics processing unit (GPU), hardware processor core, or any combination thereof), main memory 604, and static memory 606, some or all of which may communicate with each other via interconnect (e.g., bus) 608. Machine 600 may also include a display unit 610 (e.g., raster display, vector display, holographic display, etc.), an alphanumeric input device 612 (e.g., keyboard), and a user interface (UI) navigation device 614 (e.g., mouse). In one example, display unit 610, input device 612, and UI navigation device 614 may be a touchscreen display. Machine 600 may also include a storage device (e.g., drive unit) 616, a signal generation device 618 (e.g., speaker), a network interface device 620, and one or more sensors 621, such as a global positioning system (GPS) sensor, compass, accelerometer, or other sensors. Machine 600 may include an output controller 628, such as a serial (e.g., universal serial bus, USB), parallel, or other wired or wireless (e.g., infrared, near field communication, NFC, etc.) connection, to communicate with or control one or more peripheral devices (e.g., printers, card readers, etc.).

[0092] Storage device 616 may include machine-readable medium 622 on which one or more sets of data structures or instructions 624 (e.g., software) embody or utilize any one or more of the techniques or functions described herein. During execution of instructions 624 by machine 600, instructions 624 may also reside wholly or at least partially within main memory 604, static memory 606, or hardware processor 602. In one example, one or any combination of hardware processor 602, main memory 604, static memory 606, or storage device 616 may constitute a machine-readable medium.

[0093] Although machine-readable medium 622 is shown as a single medium, the term "machine-readable medium" can include a single medium or multiple media (e.g., a centralized or distributed database and / or associated cache and server) configured to store one or more instructions 624.

[0094] The term "machine-readable medium" can include any medium capable of storing, encoding, or carrying instructions executable by machine 600 and causing machine 600 to perform any one or more of the technologies disclosed herein, or any medium capable of storing, encoding, or carrying data structures used by or associated with those instructions. Examples of non-limiting machine-readable media can include solid-state memory as well as optical and magnetic media. In one example, a large-scale machine-readable medium includes a machine-readable medium having multiple particles with invariant (e.g., rest) masses. Therefore, a large-scale machine-readable medium is not a transient propagating signal. Specific examples of large-scale machine-readable media can include: non-volatile memory, such as semiconductor storage devices (e.g., electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EPSOM)) and flash memory devices; disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROMs and DVD-ROMs.

[0095] Instructions 624 can also be sent or received on the communication network 626 using a transmission medium via network interface device 620, utilizing any of a variety of transmission protocols (e.g., Frame Relay, Internet Protocol (IP), Transmission Control Protocol (TCP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), etc.). Example communication networks may include local area networks (LANs), wide area networks (WANs), packet data networks (e.g., the Internet), mobile phone networks (e.g., cellular networks), plain old telephone (POTS) networks, and wireless data networks (e.g., the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard family known as WiFi®, the IEEE 802.16 standard family known as WiMax®), the IEEE 802.15.4 standard family, peer-to-peer (P2P) networks, etc. In one example, network interface device 620 may include one or more physical jacks (e.g., Ethernet, coaxial interface, or telephone jack) or one or more antennas for connection to communication network 626. In one example, network interface device 620 may include multiple antennas for wireless communication using at least one of single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) technologies. The term "transmission medium" should be considered to include any intangible medium capable of storing, encoding, or carrying instructions executed by machine 600, and includes digital or analog communication signals or other intangible media to facilitate communication of such software.

[0096] Various embodiments are illustrated in the accompanying drawings. One or more features from one or more of these embodiments may be combined to form other embodiments.

[0097] The methods described herein can be implemented, at least in part, by a machine or computer. Some examples may include a computer-readable or machine-readable medium encoded with instructions operable to configure an electronic device or system to perform the methods described in the examples above. Implementations of this method may include code, such as microcode, assembly language code, high-level language code, etc. Such code may include computer-readable instructions for performing various methods. The code may form part of a computer program product. Furthermore, the code may be tangibly stored on one or more volatile or non-volatile computer-readable media during execution or at other times.

[0098] The above detailed description is intended to be illustrative and not restrictive. Therefore, the scope of this disclosure should be determined by reference to the appended claims and the full scope of their equivalents.

Claims

1. A medical device system for managing valvular heart disease, comprising: A data receiver circuit is configured to receive heart sound information sensed from the patient; as well as The controller circuit includes a narrow detector circuit, the narrow detector circuit being configured to: Detect heart sound components, including S1 and S2 sounds, from the received heart sound information; The detected S1 and S2 tones were used to determine changes in aortic valve surface area from a baseline, stenotic state; and The aortic stenosis indicator is generated at least in part based on the determined changes in aortic valve surface area. The controller circuitry is configured to provide the aortic stenosis indicator to a user or a process that can be performed by the medical device system.

2. The medical device system according to claim 1, wherein, Determining the change in aortic valve surface area includes detecting a decrease in aortic valve surface area from the baseline, stenotic state. The stenosis detector is configured to generate an aortic stenosis indicator in response to a reduction in the aortic valve surface area exceeding a threshold.

3. The medical device system according to any one of claims 1-2, wherein, To determine the change in the aortic valve surface area, the stenosis detector is configured to: An estimate of the left ventricular ejection time is generated using the time interval between the detected S1 and S2 tones, and an estimate of the aortic-left ventricular pressure gradient is generated using the intensity of the S2 tones; and The aortic valve surface area is determined based on the estimation of the aortic-left ventricular pressure gradient and the estimation of the left ventricular ejection time.

4. The medical device system according to claim 3, wherein, The stenosis detector is configured to determine that the aortic valve surface area is inversely proportional to the estimate of the left ventricular ejection time.

5. The medical device system according to any one of claims 3-4, wherein, The stenosis detector is configured to determine that the aortic valve surface area is inversely proportional to the square root of the estimated aortic-left ventricular pressure gradient.

6. The medical device system according to any one of claims 1-5, wherein, The controller circuit is configured to generate an alert to the user in response to the aortic stenosis indicator indicating the presence of aortic stenosis.

7. The medical device system according to any one of claims 1-6, wherein, The controller circuit is configured to respond to the aortic stenosis indicator indicating the presence of aortic stenosis: The data receiver circuit is triggered to receive symptoms or physiological information of the patient indicating functional deterioration associated with the presence of the aortic stenosis; as well as The severity of stenosis is classified based on the aortic stenosis indicator and the received symptom or physiological information.

8. The medical device system of claim 7, comprising a user interface coupled to a data receiver circuit, the user interface being configured to receive user input of symptoms in response to an indication of the presence of the aortic stenosis.

9. The medical device system according to any one of claims 7-8, wherein, The received physiological information that is triggered includes the intensity of the S1 sound, which indicates cardiac contractility.

10. The medical device system according to any one of claims 7-9, comprising one or more sensors coupled to a data receiver circuit and configured to sense physiological information in response to an indication of the presence of the aortic stenosis, the one or more sensors comprising at least one of a body activity sensor, a respiratory sensor, a heart rate sensor, or an electrocardiogram sensor.

11. The medical device system according to any one of claims 1-10, wherein, The controller circuitry is configured to generate an indicator for patient eligibility for aortic valve replacement surgery, based at least in part on the aortic stenosis indicator.

12. The medical device system according to claim 11, wherein, The controller circuit is configured to receive symptoms or physiological information of the patient indicating functional deterioration associated with the aortic stenosis, and further generate indicators for patient eligibility for aortic valve replacement based on the received symptoms or physiological information.

13. The medical device system according to any one of claims 1-12, wherein, The controller circuit includes a heart failure detector circuit configured to detect worsening heart failure (WHF) using physiological information received from the data receiver circuit.

14. The medical device system according to claim 13, wherein, The controller circuit is configured to generate a diagnosis of WHF secondary to aortic stenosis in response to the aortic stenosis indicator indicating the presence of aortic stenosis, and to provide the diagnosis to the user.

15. The medical device system according to claim 14, wherein, The controller circuit is configured to generate titration treatment recommendations based on a diagnosis of WHF secondary to aortic stenosis.

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