Configuration of medical systems that include implantable medical devices to detect infections characterized by relative bradycardia

Abstract

A medical system is configured to determine an infectious disease state of a patient. The medical system includes a medical device including sensing circuitry configured to detect a cardiac signal and a temperature sensor. The medical system further includes processing circuitry configured to determine a relative bradycardia metric based on heart rates and body temperature measurements of the patient. The medical system is configured to determine an infectious disease state for the patient based on a set of patient metrics, where the set of patient metrics includes the relative bradycardia metric. The medical system is configured to generate an output indicating the infectious disease state to a user.

Description

CONFIGURATION OF MEDICAL SYSTEMS THAT INCLUDE IMPLANTABLE MEDICAL DEVICES TO DETECT INFECTIONS CHARACTERIZED BY RELATIVE BRADYCARDIAFIELD

[0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 498,718, filed April 27, 2023, the entire content of which is incorporated herein by reference.FIELD

[0002] This disclosure generally relates to systems including medical devices and, more particularly, to monitoring of patient health using such systems.BACKGROUND

[0003] A variety of devices are configured to monitor physiological signals of a patient. Such devices include implantable or wearable medical devices, as well as a variety of wearable health or fitness tracking devices. The physiological signals sensed by such devices include, as examples, electrocardiogram (ECG) signals, electroencephalogram (EEG) signals, respiration signals, perfusion signals, activity and / or posture signals, pressure signals, blood oxygen saturation signals, temperature, body composition, and blood glucose or other blood constituent signals. By collecting these signals, such devices may facilitate monitoring and evaluating patient health outside of a clinic setting.SUMMARY

[0004] In general, the disclosure is directed to techniques for patient health monitoring and risk management that may be implemented by medical systems that include at least one implantable medical device, such as an insertable cardiac monitor. According to the techniques of this disclosure, the implantable medical device may be configured to sense heart rate and temperature of a patient. Processing circuitry of the implantable medical device, or of a computing device or system configured to communicate with the implantable medical device, may identify a relative bradycardia metric based on the sensed heart rate and temperature, and may determine an infectious disease state of the patient based on the relative bradycardia metric (and, optionally, other metrics). In thismanner, a system including an implantable medical device may be advantageously configured to autonomously surveil for infectious disease states of a patient.

[0005] Conventional techniques for detecting infectious disease and relative bradycardia include application of point-of-care diagnostic equipment and tests by clinicians. Such techniques leave a patient unobserved between clinic visits, which may lead to disease advancement prior to identification. Additionally, collection of patient data with external devices requires compliance on the part of the patient and / or competence on the part of the person applying the external device to the patient.

[0006] Advantageously, the techniques of this disclosure may be implemented in a medical system including an implantable medical device (IMD) that can continuously (e.g., on a periodic or triggered basis without human intervention) sense heart rate and temperature while subcutaneously implanted in a patient over months or years and perform numerous operations per second on patient heart rate and temperature data to enable the systems herein to detect relative bradycardia and infectious disease, e.g., based on heart rates and temperatures measured at first and second times during the continuous monitoring. Using techniques of this disclosure with an IMD may be advantageous when a physician cannot be continuously present with the patient over weeks or months to evaluate the patient and / or where performing the operations on the patient parameter data described herein on weeks or months of data could not practically be performed in the mind of a physician.

[0007] Furthermore, the techniques of this disclosure may help a physician better understand how a patient’s physiology may have changed while trying to recover from an infection. This may be particularly useful when a patient has had multiple infections (e.g., of COVID-19). For example, rather than just having infection test results and self-reported symptom data, a physician can quantify changes in physiology. For example, more exaggerated relative bradycardia, along with very low heart rate variability, high respiration rate, coughing, low activity, etc., may indicate severe infection. In the shorter term, monitoring these metrics may help assess efficacy of various therapies.

[0008] Thus, an IMD in accordance with techniques of this disclosure may provide continuous, real-time monitoring of a patient's vital signs, physiological parameters, and other health metrics, allowing for earlier detection of infection, enabling prompt medical intervention and prevention of complications (as well as spread of the infection among thegeneral population). By collecting and analyzing individual patient data, the IMD may help tailor treatments and therapies to a patient’s specific needs, improving the effectiveness of care and reducing potential side effects. Additionally, the IMD may empower patients to manage their own health more effectively by enabling them to make better- informed decisions about their care (e.g., recommending the patients test themselves for infection). As a result, the techniques of this disclosure may lead to fewer hospital visits, shorter hospital stays, and a reduced need for emergency care, which can help lower overall healthcare costs for both patients and healthcare providers.

[0009] In some examples, a medical system comprises: a medical device comprising: sensing circuitry configured to sense a cardiac signal of a heart of a patient via a plurality of electrodes; a temperature sensor configured to measure body temperature of the patient; and processing circuitry configured to: determine a relative bradycardia metric based on a first heart rate determined based on a first set of heartbeats detected in the cardiac signal during a first period, a second heart rate determined based on a second set of heartbeats detected in the cardiac signal during a second period, a first body temperature measurement sensed by the temperature sensor during the first period, and a second body temperature measurement sensed by the temperature sensor during the second period; determine an infectious disease state for the patient based on a set of patient metrics, wherein the set of patient metrics comprises the relative bradycardia metric; and generate an output indicating the infectious disease state to a user.

[0010] In some examples, a medical system comprises: an insertable cardiac monitor comprising: a housing configured for subcutaneous implantation in a patient, the housing having a length between 40 millimeters (mm) and 60 mm between a first end and a second end, a width less than the length, and a depth less than the width; sensing circuitry configured to sense a cardiac signal of a heart of a patient via a plurality of electrodes comprising: a first electrode at or proximate to the first end; and a second electrode at or proximate to the second end a temperature sensor configured to measure body temperature of the patient; a memory within the housing; and first processing circuitry within the housing, the first processing circuitry configured to: determine a first heart rate based on a first set of heartbeats detected in the cardiac signal during a first period; determine a second heart rate based on a second set of heartbeats detected in the cardiac signal during a second period; determine a first body temperature measurement sensed by thetemperature sensor during the determine first period; determine a second body temperature measurement sensed by the temperature sensor during the second period; and one or more computing devices in communication with the insertable cardiac monitor, the one or more computing devices comprising second processing circuitry configured to: determine a relative bradycardia metric based on the first heart rate, the second heart rate, the first body temperature measurement, and the second body temperature measurement; determine an infectious disease state for the patient based on a set of patient metrics, wherein the set of patient metrics comprises the relative bradycardia metric; and generate an output indicating the infectious disease state to a user.

[0011] In some examples, a method comprises: determining, by processing circuitry, a relative bradycardia metric of a patient based on a first heart rate of the patient determined based on a first set of heartbeats detected in a cardiac signal during a first period, a second heart rate of the patient determined based on a second set of heartbeats detected in the cardiac signal during a second period, a first body temperature measurement of the patient sensed by a temperature sensor during the first period, and a second body temperature measurement of the patient sensed by a temperature sensor during the second period; determining, by the processing circuitry, an infectious disease state for the patient based on a set of patient metrics, wherein the set of patient metrics comprises the relative bradycardia metric; and generating an output indicating the infectious disease state to a user.

[0012] The summary is intended to provide an overview of the subject matter described in this disclosure. It is not intended to provide an exclusive or exhaustive explanation of the systems, device, and methods described in detail within the accompanying drawings and description below. Further details of one or more examples of this disclosure are set forth in the accompanying drawings and in the description below. Other features, objects, and advantages will be apparent from the description and drawings, and from the claims.BRIEF DESCRIPTION OF DRAWINGS

[0013] FIG. 1 is a block diagram illustrating an example system configured to detect relative bradycardia experienced by a patient, and to respond to such detections, in accordance with one or more techniques of this disclosure.

[0014] FIG. 2A is a conceptual drawing illustrating an insertable cardiac monitor.

[0015] FIG. 2B is a conceptual drawing illustrating another insertable cardiac monitor.

[0016] FIG. 2C is a conceptual drawing illustrating another implantable medical device in conjunction with a heart.

[0017] 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.

[0018] FIGS. 4 is a block diagram illustrating an example configuration of a computing device that operates in accordance with one or more techniques of the present disclosure.

[0019] FIG. 5 is a block diagram illustrating a logical perspective of an example service operated by the example system of FIG. 1, in accordance with one or more examples of the present disclosure.

[0020] FIG. 6 is a flow diagram illustrating an example operation in accordance with one or more techniques of the present disclosure.

[0021] FIG. 7 is a flow diagram illustrating an example operation in accordance with one or more techniques of the present disclosure.

[0022] FIG. 8 is a flow diagram illustrating an example operation in accordance with one or more techniques described herein.DETAIEED DESCRIPTION

[0023] In general, disease-specific tests may provide a diagnosis of an infectious disease prior to initiating treatment. For example, a polymerase chain reaction (PCR) test may be used to diagnose a coronavirus infection, such as COVID- 19. A patient may have to wait a considerable amount of time (e.g., days, weeks, etc.) to receive the results of the disease-specific test. While waiting for the results, the patient may need to quarantine, which can be highly inconvenient, or risk infecting other people, potentially leading to a public health crisis.

[0024] Various infectious diseases may include relative bradycardia as a symptom. Examples of such diseases may include COVID- 19, typhoid fever, Legionnaire’s disease, and Chlamydia pneumoniae. In accordance with techniques of this disclosure, a medical system may be configured to detect relative bradycardia. The detection of relative bradycardia may be used in combination with an assessment of other patient parameters or symptoms to expediently diagnose (e.g., within seconds, minutes, etc.) infectious diseases that include relative bradycardia as a symptom. In addition, the medical system may be configured to monitor relative bradycardia in a patient. Monitoring relative bradycardia in a patient diagnosed with an infectious disease may provide insight into the progression of the disease and the recovery status of the patient.

[0025] FIG. 1 illustrates the environment of an example medical system 2 in conjunction with a patient 4, in accordance with one or more techniques of this disclosure. In some examples, an IMD 10 may include the Reveal LINQ™ or LINQ II™ insertable cardiac monitor (ICM). The example techniques may be used with IMD 10, which may be in wireless communication with external device 12 and / or other devices not shown in FIG. 1.

[0026] In some examples, IMD 10 may be 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 a heart 14 of patient 4, e.g., at least partially within the cardiac silhouette. IMD 10 may be positioned at other locations, such as patient 4’s cranium region. IMD 10 may include one or more electrodes (not shown in FIG. 1) and may be configured to sense a cardiac EGM via the plurality of electrodes. IMD 10 may include additional sensors, such as one or more temperature sensors configured to measure body temperature of patient 4, one or more motion sensors configured to generate a motion signal of patient 4 (e.g., based on patient activity), etc. Using the one or more sensors, IMD 10 may sense one or more patient parameters (e.g., heart activity, temperature, impedance, motion, etc.), where each value of a patient parameter represents a measurement at a respective interval of time. A plurality of values may represent a sequence of parameter values (e.g., measured at a recurring time interval).

[0027] External device 12 may be a computing device with a display viewable by the user and an interface for receiving user input to external device 12. In some examples, external device 12 may be a notebook computer, tablet computer, workstation, one or moreservers, cellular phone, personal digital assistant, or another computing device that may run an application that enables the computing device to interact with IMD 10. External device 12 may be configured to wirelessly communicate with IMD 10 and (optionally) another computing device (not illustrated in FIG. 1). External device 12, for example, may communicate via near-field communication technologies (e.g., inductive coupling, NFC or other communication technologies operable at ranges less than 10-20 cm) and far- field communication technologies (e.g., radiofrequency (RF) telemetry according to the 802.11 or Bluetooth® specification sets, or other communication technologies operable at ranges greater than near-field communication technologies).

[0028] External device 12 may be used to configure operational parameters and / or device settings for IMD 10. External device 12 may be used to retrieve data from IMD 10. The retrieved data may include values of physiological parameters measured by IMD 10, indications of cardiac episodes (e.g., episodes of an arrhythmia) or other maladies detected by IMD 10, and physiological signals recorded by IMD 10. External device 12 may retrieve cardiac EGM segments recorded by IMD 10 due to IMD 10 determining that an episode of AT, AF, other atrial tachyarrhythmia, or another malady occurred during the segment, for example, as part of an adjudication operation. The cardiac EGM segments may comprise cardiac EGM data from a predetermined time period associated with the episode.

[0029] Although described in the context of examples in which IMD 10 includes an ICM, example systems including one or more implantable, wearable, or external devices of any type may be configured to implement the techniques of this disclosure. That is, in some examples, a medical device other than an ICM (e.g., another type of IMD, a wearable device, etc.) may perform some or all of the techniques described herein.

[0030] Medical system 2 may include a computing system 20. Processing circuitry 22 of computing system 20 may operate a health monitoring service (HMS) 26 stored in a memory 28 of computing system 20. computing system 20 may be configured to be network-accessible for transmitting and receiving various data to and from patient medical devices (e.g., IMD 10), their local devices (e.g., external device 12), computing devices of other users, and / or the like.

[0031] In general, patient 4 may wait until patient 4 exhibits symptoms before visiting a clinician. However, when symptoms appear, the health condition may have progressedto a more advanced stage, which can make treatment more difficult. For example, some conditions may not present with obvious symptoms until the conditions have reached an advanced stage, which can make them more difficult to diagnose and treat effectively. By waiting until specific symptoms appear, a patient may be delaying the diagnosis and treatment of a serious medical condition. This problem can be compounded if patient 4 has to wait a considerable amount of time (e.g., days, weeks, etc.) to receive the results of a disease-specific test.

[0032] In accordance with techniques of this disclosure, system 2 may be configured to detect relative bradycardia, which may lead to a more expedient diagnosis of various diseases (e.g., infectious diseases), potentially improving the quality of life of patient 4 and safeguarding the public health. For example, accurate and timely diagnosis may be critical for determining an appropriate treatment plan for patient 4, which may prevent the progression of a health condition and improve the efficacy of treatment. Furthermore, early diagnosis can help to prevent the spread of infectious diseases to others. By quickly identifying and treating infectious diseases, system 2 can help to prevent the spread of the disease to others and minimize the impact of outbreaks. Furthermore, system 2 may be configured to monitor relative bradycardia in patient 4, which may provide insight into the progression of a disease and the recovery status of the patient. For example, the collection of relative bradycardia data may improve understanding of the cardiac effects of a coronavirus infection as well as the characteristics of patients more likely to develop serious infection or poor health outcomes. The techniques may also help with determining when it is appropriate for patients to be hospitalized for certain infections.

[0033] As used herein, relative bradycardia refers to a phenomenon that occurs when the heart rate of patient 4 is lower than expected for a given body temperature. Relative bradycardia may also refer to a phenomenon that occurs when the change in heart rate of patient 4 is lower than expected for a change in the body temperature of patient 4. In general, an increase in body temperature is normally accompanied by an increase in heart rate (e.g., at an expected 18 heartbeats per minute for every 1 degree Celsius (°C) increase in body temperature). Thus, relative bradycardia may occur when the heart rate does not increase as expected with an increase in body temperature (e.g., the heart rate does not increase by about 18 heartbeats per minute per 1 °C increase).

[0034] Processing circuitry of system 2 ( e.g., of IMD 10, processing circuitry 22 of computing system 20, etc.) may be configured to determine a relative bradycardia metric based on heart rates of patient 4 sensed by IMD 10 and temperature of patient 4 sensed by a temperature sensor of IMD 10. IMD 10 may be configured to determine the heart rates of patient 4 by sensing a cardiac signal (e.g., a cardiac electrogram) of heart 14 of patient 4 via electrodes. The cardiac signal may indicate activity (e.g., a functioning status) of heart 14 of patient 4 at various times, such as in the morning or at night, when patient 4 is active or dormant, etc. IMD 10 may be configured to detect heartbeats at these various times. For example, IMD 10 may detect a first set of heartbeats during a first period and a second set of heartbeats during a second period. As used herein, a set refers to one or more elements. IMD 10 may be configured to determine a first heart rate based on the first set of heartbeats and a second heart rate based on the second set of heartbeats. In some examples, IMD 10 may sense heart rate based on different signal sensed in patient, such as accelerometer signals, acoustic signals, pressure signals, and photoplethysmography signals.

[0035] IMD 10 may be configured to sense a first body temperature measurement of patient 4 during the first period, and a second body temperature measurement of patient 4 during the second period. In this way, IMD 10 may obtain heart rate data and temperature data for each of the first period and the second period. Processing circuitry of IMD 10 (or any other processing circuitry of system 2) may be configured to determine a relative bradycardia metric based on the first heart rate, the second heart rate, the first body temperature measurement, and the second body temperature measurement. In some examples, IMD 10 may determine the relative bradycardia metric based at least on a slope equating to the change in heart rate versus change in temperature (“slope”). The slope may be computed using the following equation: h2- h y = - t2- ti where hi corresponds to the first heart rate, h2corresponds to the second heart rate, ti corresponds to the first body temperature measurement, t2corresponds to the second body temperature measurement, and y corresponds to the slope.

[0036] As described above, relative bradycardia may occur when the heart rate does not increase as expected with an increase in body temperature (e.g., the heart rate does not increase by about 18 heartbeats per minute per 1 °C increase). Accordingly, the relative bradycardia metric may indicate a likelihood of relative bradycardia corresponding to the deviation of the computed slope from the expected slope. For example, the relative bradycardia metric may indicate a low likelihood of relative bradycardia if the computed slope is 18 heartbeats per minute per 1 °C because the deviation of the computed slope from the expected slope is 0 heartbeats per minute per 1 °C. However, the relative bradycardia metric may indicate a high likelihood of relative bradycardia if the computed slope is 0 heartbeats per minute per 1 °C because the deviation of the computed slope from the expected slope is 18 heartbeats per minute per 1 °C.

[0037] In some examples, the relative bradycardia metric may also be based on a direction of the deviation of the computed slope from the expected slope (e.g., because bradycardia is an abnormally slow heart action). For example, the relative bradycardia metric may indicate a high likelihood of relative bradycardia if the computed slope is 8 heartbeats per minute per 1 °C because the deviation of the computed slope from the expected slope is 10 fewer heartbeats per minute per 1 °C. On the other hand, the relative bradycardia metric may indicate a low likelihood of relative bradycardia if the computed slope is 28 heartbeats per minute per 1 °C because the deviation of the computed slope from the expected slope is 10 more heartbeats per minute per 1 °C.

[0038] In another example, IMD 10 may determine the relative bradycardia metric based on whether a heart rate of patient 4 is below a threshold value (e.g., 90 beats per minute (BPM)) when a temperature of patient 4 is above a threshold value (e.g., 38.3 degrees Celsius (°C)). In such examples, the relative bradycardia metric may indicate a higher likelihood of relative bradycardia the lower the heart rate of patient 4 and / or the higher the temperature of patient 4 is. Conversely, the relative bradycardia metric may indicate a lower likelihood of relative bradycardia the higher the heart rate of patient 4 and / or the lower the temperature of patient 4 is.

[0039] IMD 10 may determine an infectious disease state (e.g., an incident of a coronavirus infection, such as COVID-19) for patient 4 based on a set of patient metrics that includes the relative bradycardia metric. IMD 10 may be more likely to determine an infectious disease state for patient 4 if the relative bradycardia metric indicates a highlikelihood of relative bradycardia. Conversely, IMD 10 may be less likely to determine an infectious disease state for patient 4 if the relative bradycardia metric indicates a low likelihood of relative bradycardia. In any case, IMD 10 may generate an output indicating (e.g., by presenting) the infectious disease state to a user (e.g., patient 4, a caretaker, a clinician, etc.).

[0040] In some examples, system 2 may communicate with computing devices to receive infection data. For example, patients may use the computing devices to report testing results and infections via an application. System 2 may receive the patient data from the applications to track the spread and effects of infection. In some examples, system 2 may use the patient data from the applications to improve the accuracy of detection and / or to notify patients to be tested for infection (e.g., via the application).

[0041] Although primarily described as determining an infectious disease state based on the set of patient metrics, IMD 10 may determine other information, such as the severity of the disease, the rate of recovery from the disease, etc. Furthermore, it should be understood that the techniques of this disclosure may be performed by processing circuitry of one or more devices of system 2, such as processing circuitry of one or more of IMD 10, external device 12, processing circuitry 22, and / or processing circuitry of a device not shown in FIG. 1.

[0042] FIG. 2A is a conceptual drawing illustrating an insertable cardiac monitor 10A, which may be an example configured of IMD 10 of FIG. 1 as an ICM. In the example shown in FIG. 2A, IMD 10A may be embodied as a monitoring device having housing 42, proximal electrode 46A and distal electrode 46B. Housing 42 may further comprise first major surface 44, second major surface 48, proximal end 50, and distal end 52. Housing 42 encloses electronic circuitry located inside the IMD 10A and protects the circuitry contained therein from body fluids. Electrical feedthroughs provide electrical connection of electrodes 46A and 46B.

[0043] In the example shown in FIG. 2A, IMD 10A is defined by a length L, 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. In one example, 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.For example, 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. For example, the spacing between proximal electrode 46A and distal electrode 46B may range from 30 millimeters (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 25 mm to 60 mm. In addition, 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 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. In addition, the width W of major surface 14 may range from 3 mm to 10 mm and may be any single or range of widths between 3 mm and 10 mm. The thickness of depth D of IMD 10A may range from 2 mm to 9 mm. In other examples, the depth D of IMD 10A may range from 2 mm to 5 mm and may be any single or range of depths from 2 mm to 9 mm. In addition, IMD 10A according to an example of the present disclosure is 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.

[0044] In the example shown in FIG. 2A, once inserted within the patient, the first major surface 44 faces outward, toward the skin of the patient while the second major surface 48 is located opposite the first major surface 44. In addition, in the example shown in FIG. 2A, proximal end 50 and distal end 52 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 Publication No. 2014 / 0276928, incorporated herein by reference in its entirety.

[0045] Proximal electrode 46A is at or proximate to proximal end 50, and distal electrode 46B is at or proximate to distal end 52. Proximal electrode 46A and distal electrode 46B are used to sense electrocardiogram (ECG) signals thoracically outside the ribcage, which may be sub-muscularly or subcutaneously. ECG signals may be stored in a memory of IMD 10A, and data may be transmitted via integrated antenna 60A to another device, which may be another implantable device or an external device, such as external device 12. In some example, electrodes 46A and 46B may additionally or alternatively be used for sensing any bio-potential signal of interest, which may be, for example, anelectrogram (EGM), electroencephalogram (EEG), electromyogram (EMG), or a nerve signal, or for measuring impedance, from any implanted location.

[0046] In the example shown in FIG. 2A, proximal electrode 46A is at or in close proximity to the proximal end 50 and distal electrode 46B is at or in close proximity to distal end 52. In this example, distal electrode 46B is not limited to a flattened, outward facing surface, but may extend from first major surface 44 around rounded edges 54 and / or end surface 56 and onto the second major surface 48 so that the electrode 46B has a three-dimensional curved configuration. In some examples, electrode 46B is an uninsulated portion of a metallic, e.g., titanium, part of housing 42.

[0047] In the example shown in FIG. 2A, proximal electrode 46A is located on first major surface 44 and is substantially flat, and outward facing. However, in other examples proximal electrode 46A may utilize the three-dimensional curved configuration of distal electrode 46B, providing a three-dimensional proximal electrode (not shown in this example). Similarly, in other examples distal electrode 46B may utilize a substantially flat, outward facing electrode located on first major surface 44 similar to that shown with respect to proximal electrode 46A.

[0048] The various electrode configurations allow for configurations in which proximal electrode 46A and distal electrode 46B are located on both first major surface 44 and second major surface 48. In other configurations, such as that shown in FIG. 2A, only one of proximal electrode 46A and distal electrode 46B is located on both major surfaces 44 and 48, and in still other configurations both proximal electrode 46A and distal electrode 46B are located on one of the first major surface 44 or the second major surface 48 (e.g., proximal electrode 46A located on first major surface 44 while distal electrode 46B is located on second major surface 48). In another example, IMD 10A may include electrodes on both major surface 44 and 48 at or near the proximal and distal ends of the device, such that a total of four electrodes are included on IMD 10A. Electrodes 16A and 16B 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.

[0049] In the example shown in FIG. 2A, proximal end 50 includes a header assembly 58 that includes one or more of proximal electrode 46 A, integrated antenna 60A, antimigration projections 62, and / or suture hole 64. Integrated antenna 60A is located on thesame major surface (i.e., first major surface 44) as proximal electrode 46A and is also included as part of header assembly 58. Integrated antenna 60A allows IMD 10A to transmit and / or receive data. In other examples, integrated antenna 60A may be formed on the opposite major surface as proximal electrode 46A, or may be incorporated within the housing 42 of IMD 10A.

[0050] In the example shown in FIG. 2A, anti-migration projections 62 are located adjacent to integrated antenna 60A and protrude away from first major surface 44 to prevent longitudinal movement of the device. In the example shown in FIG. 2A, antimigration projections 62 include a plurality (e.g., nine) small bumps or protrusions extending away from first major surface 44. As discussed above, in other examples antimigration projections 62 may be located on the opposite major surface as proximal electrode 46A and / or integrated antenna 60A. In addition, in the example shown in FIG. 2A, header assembly 58 includes suture hole 64, which provides another means of securing IMD 10A to the patient to prevent movement following insertion. In the example shown, suture hole 64 is located adjacent to proximal electrode 46A. In one example, header assembly 58 is a molded header assembly made from a polymeric or plastic material, which may be integrated or separable from the main portion of IMD 10A.

[0051] In examples where IMD 10A migrates (e.g., flips), the temperature sensing of IMD 10A may be affected. In such examples, IMD 10A may be configured to recalibrate temperature sensors of IMD 10A to ensure that temperature measurements (which are related to the relative bradycardia metric in accordance with techniques of this disclosure) are accurate.

[0052] FIG. 2B is a conceptual 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 10A of FIG. 2A, with differences between them discussed herein.

[0053] IMD 10B may include a leadless, subcutaneously implantable monitoring device, e.g. an ICM. IMD 10B includes housing having a base 70 and an insulative cover 72. Proximal electrode 46C and distal electrode 46D may be formed or placed on an outer surface of cover 72. Various circuitries and components of IMD 10B, e.g., described below with respect to FIG. 3, may be formed or placed on an inner surface of cover 72, or within base 70. In some examples, a battery or other power source of IMD 10B may beincluded within base 70. In the illustrated example, antenna 60B is formed or placed on the outer surface of cover 72, but may be formed or placed on the inner surface in some examples. In some examples, insulative cover 72 may be positioned over an open base 70 such that base 70 and cover 72 enclose the circuitries and other components and protect them from fluids such as body fluids.

[0054] Circuitries and components may be formed on the inner side of insulative cover 72, such as by using flip-chip technology. Insulative cover 72 may be flipped onto a base 70. When flipped and placed onto base 70, the components of IMD 10B formed on the inner side of insulative cover 72 may be positioned in a gap 74 defined by base 70. Electrodes 46C and 46D and antenna 60B may be electrically connected to circuitry formed on the inner side of insulative cover 72 through one or more vias (not shown) formed through insulative cover 72. Insulative cover 72 may be formed of sapphire (i.e., corundum), glass, parylene, and / or any other suitable insulating material. Base 70 may be formed from titanium or any other suitable material (e.g., a biocompatible material). Electrodes 46C and 46D may be formed from any of stainless steel, titanium, platinum, iridium, or alloys thereof. In addition, electrodes 46C and 46D 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.

[0055] In the example shown in FIG. 2B, the housing of IMD 10B defines a length L, 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. 2A. For example, the spacing between proximal electrode 46C and distal electrode 46D may range from 30 millimeters (mm) to 50 mm, from 35 mm to 45 mm, or be approximately 40 mm. In addition, IMD 10B may have a length E that ranges from 30 mm to about 70 mm. In other examples, the length L may range from 40 mm to 60 mm, 45 mm to 55 mm, or be approximately 45 mm. In addition, the width W may range from 3 mm to 10 mm, such as approximately 8 mm. The thickness of depth D of IMD 10B may range from 2 mm to 9 mm, from 3 mm to 5 mm, or be 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.

[0056] In the example shown in FIG. 2B, once inserted subcutaneously within the patient, outer surface of cover 72 faces outward, toward the skin of the patient. Inaddition, as shown in FIG. 2B, proximal end 76 and distal end 78 are rounded to reduce discomfort and irritation to surrounding tissue once inserted under the skin of the patient. In addition, edges of IMD 10B may be rounded.

[0057] FIG. 2C is a conceptual diagram illustrating another IMD 10C. IMD 10C may include one or more leads, such as leads 80 and 82. Leads 80, 82 may be implanted within a right ventricle and right atrium of heart 14, respectively. IMD 10C may be useful for physiological sensing and / or providing pacing, cardioversion, or other therapies to heart 14. Detection of patient metrics and differentiation of an infectious disease status according to this disclosure may be performed in leaded systems in the manner described herein with respect leadless systems. In some examples, diagnostic data may be implemented in systems utilizing subcutaneous leads, subcutaneous IMDs, or external medical devices. Although FIGS. 1-2 provide an example implantation site in accordance with techniques of this disclosure, IMD 10A-C and associated electrodes may be implemented in other locations of the patient’s body, and may include leads or may be leadless.

[0058] FIG. 3 is a block diagram illustrating an example configuration of IMD 10 in accordance with one or more techniques described herein. In the illustrated example, IMD 10 includes electrodes 46 (e.g., corresponding to any of electrodes 46A-46D), processing circuitry 100, storage device 102, sensing circuitry 104, one or more patient parameter sensors 106, one or more sound sensors 108, and communication circuitry 108, which may be connected to an antenna (e.g., antenna 60A of FIG. 2A, antenna 60B of FIG. 2B, etc.). Although the illustrated example includes two electrodes 46, IMDs including or coupled to more than two electrodes 46 may implement the techniques of this disclosure in some examples.

[0059] Processing circuitry 100 may include fixed function circuitry and / or programmable processing circuitry. Processing circuitry 100 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 analog logic circuitry. In some examples, processing circuitry 100 may include multiple components, such as any combination of one or more microprocessors, one or more controllers, one or more DSPs, one or more ASICs, or one or more FPGAs, as well asother discrete or integrated logic circuitry. The functions attributed to processing circuitry 100 herein may be embodied as software, firmware, hardware or any combination thereof.

[0060] Sensing circuitry 104 may be coupled to electrodes 46, e.g., to sense electrical signals of heart 14 of patient 4, e.g., an ECG, as controlled by processing circuitry 100. In some examples, sensing circuitry 104 may include one or more filters and amplifiers for filtering and amplifying signals received from electrodes 46 and sensors 106. Sensing circuitry 104 may include analog-to-digital conversion circuitry for converting the signals to digital samples for analysis by processing circuitry 100 and / or storage in storage device 102.

[0061] The ECG sensed via electrodes 46 may represent one or more physiological electrical signals corresponding to the heart of patient 4. For example, the ECG may indicate ventricular depolarizations (QRS complexes including R- waves), atrial depolarizations (P-waves), ventricular repolarizations (T-waves), among other events. Information relating to the aforementioned events, such as time separating one or more of the events or the morphology of such events, may be applied for a number of purposes, such as to determine a heart rate of patient 4.

[0062] Sensors 106 may include one or more temperature sensing devices configured to detect temperature or changes in temperature. In some examples, sensors 106 may include a thermocouple, a thermistor, a junction based thermal sensor, a thermopile, a fiber optic detector, an acoustic temperature sensor, a quartz or other resonant temperature sensor, a thermos-mechanical temperature sensor, a thin film resistive element, etc.

[0063] Sensors 106 may include one or more motion sensors configured to collect motion data. In some examples, the motion sensors may include an accelerometer configured to generate an accelerometer signal that reflects a measurement of a motion and / or posture of patient 4. In some examples, the accelerometer may collect a three-axis accelerometer signal indicative of a patient’s movements within a three-dimensional Cartesian space. Sensors 106 may also be configured to measure one or more of acoustic signals, pressure signals, and photoplethysmography signals.

[0064] Communication circuitry 108 may include any suitable hardware, firmware, software or any combination thereof for communicating with another device, such as external device 12, another external device, another IMD, or another sensor. Under the control of processing circuitry 100, communication circuitry 108 may receive downlinktelemetry from, as well as send uplink telemetry to external device 12 or another device with the aid of an internal or external antenna, e.g., antenna 60. Antenna 60 and communication circuitry 108 may be configured to transmit and / or receive signals via inductive coupling, electromagnetic coupling, Near Field Communication (NFC), Radio Frequency (RF) communication, Bluetooth, WiFi, or other proprietary or non-proprietary wireless communication schemes.

[0065] In some examples, storage device 102 includes computer-readable instructions that, when executed by processing circuitry 100, cause IMD 10 and processing circuitry 100 to perform various functions attributed to IMD 10 and processing circuitry 100 herein. Storage device 102 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), ferroelectric RAM (FRAM), dynamic random-access memory (DRAM), flash memory, or any other digital media. Storage device 102 may store, as examples, programmed values for one or more operational parameters of IMD 10. Storage device 102 may also store data collected by IMD 10 for transmission to another device using communication circuitry 108 and / or further analysis by processing circuitry 100.

[0066] IMD 10 is an example of a medical device configured to determine an infectious disease state for patient 4 based on a set of patient metrics that at least includes a relative bradycardia metric. The set of patient metrics may further include a heart rate variability (HRV) metric. Processing circuitry 100 may be configured to determine HRV based on the cardiac signal sensed via electrodes 46. For example, processing circuitry 100 may determine a metric of variability of a set of heart rates and / or R-R intervals determined by the processing circuitry.

[0067] The set of patient metrics may include one or more of a coughing metric, posture metric, or movement metric. The coughing metric may be based on coughing data (e.g., how frequently patient 4 is coughing, the intensity of the coughs, etc.) that derive from a motion signal generated by sensors 106. The posture metric may be based on posture data (e.g., whether patient 4 is standing, reclining, etc.) that derive from a motion signal generated by sensors 106. The movement metric may be based on movement data (e.g., whether patient 4 is active, stationary, etc.) that derive from a motion signal generated by sensors 106.

[0068] The set of patient metrics may include an impedance metric. The impedance metric may be based on an impedance signal. In some examples, sensing circuitry 104 may be configured to sense the impedance signal via electrodes 46. Additionally or alternatively, the set of patient metrics may include a respiration metric. The respiration metric may be based on a respiration rate, a ratio of inhalation duration to exhalation duration, etc. A higher ratio of inhalation duration to exhalation duration may indicate that the patient’s body is in a more sympathetic state, which is typical during infection. In some examples, sensing circuitry 104 may be configured to sense a respiration signal via electrodes 46, and processing circuitry 100 may be configured to determine respiration rate based on the respiration signal.

[0069] In any case, IMD 10 may be configured to determine an infectious disease state for patient 4 based on the set of patient metrics. For example, IMD 10 may be configured to determine an infection disease state for patient 4 based on a relative bradycardia metric and an impedance metric. In another example, IMD 10 may be configured to determine an infection disease state for patient 4 based on a relative bradycardia metric, a heart rate variability metric, a coughing metric, a posture metric, a movement metric, an impedance metric, and a respiration metric. Other examples are possible and contemplated by this disclosure.

[0070] In some examples, processing circuitry 100 may be configured to determine a respective trend for each patient metric of the set of patient metrics based on values of the patient metric over time. Examples of a trend may include or represent averages, such as a short-term average, an intermediate-term average, a long-term average, etc., each of which may be based on any number of values. In general, a short-term average may be based on fewer values than an intermediate-term average and a long-term average; an intermediateterm average may be based on more values than a short-term average but fewer values than a long-term average; a long-term average may be based on more values than a shortterm average and an intermediate-term average.

[0071] A trend may include or represent an average that is moving or rolling to create a series of averages of different subsets of the full data set. For example, processing circuitry 100 may determine a trend for the relative bradycardia metric based on a set of the most recent values for the relative bradycardia metric. Processing circuitry 100 may output for display the respective trend for each patient metric of the set of patient metrics.In this way, the techniques of this disclosure may provide important insights into the severity of an infection, a patient's immune response, and recovery from both prior infections and vaccinations. For example, the trends may help a physician visualize how a patient’s physiology may have changed while trying to recover from an infection. This may be particularly useful when a patient has had multiple infections (e.g., of COVID-19). Rather than just having infection test results and self-reported symptom data, a physician can quantify and understand changes in physiology. For example, more exaggerated relative bradycardia, along with very low heart rate variability, high respiration rate, coughing, low activity, etc., could indicate severe infection. In the shorter term, monitoring these metrics could help assess efficacy of various therapies. In addition, seeing these metrics worsen over the course of two or more infections could signal decline in immune health and / or overall health. These trend metrics could provide very useful data in monitoring and treating “long COVID syndrome.” The trend data may also help assess response to vaccination.

[0072] As shown in FIG. 3, storage device 102 may store applications 120 and data 130 used by IMD 10 to perform the techniques described in this disclosure related to determining an infectious disease state of patient 4. For example, applications 120 may include a health monitor application 112. A patient state module 126 of health monitor application 112 may determine (by processing circuitry 100) an infectious disease state of patient 4 based on a set of patient metrics that includes a relative bradycardia metric 132 (“relative bradycardia 132”). Patient state module 126 may determine relative bradycardia 132 for patient 4 based on heart rate and temperature data collected by sensors 106. In some examples, relative bradycardia 132 may be stored as data 130 in storage device 102 in association with a time at which the heart rate and temperature data were measured. Relative bradycardia module 124 may process (e.g., apply a table or function from storage device 102) relative bradycardia 132 to determine a likelihood of relative bradycardia.

[0073] In general, P-R interval prolongation or absence of shortening with increasing heart rates may be useful in identifying COVID- 19 and / or other infectious diseases. In some examples, P-R interval changes may be associated with more severe diseases (e.g., a higher risk of death, the need for endotracheal intubation, atrioventricular (AV) block (e.g., first degree AV block), etc.). In accordance with techniques of this disclosure, patient state module 126 may determine the infectious disease state of patient 4 based on aP-R interval metric 134 (“P-R interval 134”). In other words, the set of patient metrics may additionally or alternatively include P-R interval 134.

[0074] For example, IMD 10 may be configured to determine a first heart rate based on a first set of heartbeats and a second heart rate based on a second set of heartbeats.Patient state module 126 may determine P-R interval 134 based on the first heart rate, the second heart rate, a first representative P-R interval for the first set of heartbeats, and a second representative P-R interval for the second set of heartbeats. The first representative P-R interval may be any P-R interval for the first set of heartbeats and / or a derived P-R interval, such as an average P-R interval. Similarly, the second representative P-R interval may be any P-R interval for the second set of heartbeats and / or a derived P-R interval. In some examples, patient state module 126 may determine P-R interval 134 using the following equation:where RRi corresponds to a first R-R interval (e.g., hyperbolically related to the first heart rate), RR2corresponds to a second R-R interval (e.g., hyperbolically related to the second heart rate), PR] corresponds to the first representative P-R interval, PR2corresponds to the second representative P-R interval, and I corresponds to P-R interval 134.

[0075] Patients with implantable pacemakers may not benefit from a relative bradycardia detection feature as described herein due to possible difficulties in distinguishing relative bradycardia associated with specific infections from chronic bradycardia. In fact, in this case, it may be desirable to prevent relative bradycardia by increasing pacing rate in response to increases in body temperature. That said, other patient metrics, such as P-R interval 134, the impedance metric, the coughing metric, etc., may still be used to determine an infectious disease state of patients who may not benefit from relative bradycardia detection.

[0076] Patient state module 126 may determine the infectious disease state by determining whether the set of patient metrics satisfies one or more criteria. For example, patient state module 126 may determine that the criteria is satisfied based on whether relative bradycardia 132 is greater than, less than, or equal to the threshold value. Patientstate module 126 may similarly determine that the criteria is satisfied based on whether other patient metrics (e.g., P-R interval 134, the impedance metric, the coughing metric, etc.) are greater than, less than, or equal to corresponding threshold values.

[0077] In some examples, patient state module 126 may associate different thresholds with different infectious disease state levels, such as low, moderate, high, or other level gradations / designations. In some examples, infectious disease state levels may be numerical values on a scale (e.g., from 1-10). In some examples, infectious disease state levels may indicate a probability of patient 4 experiencing an infectious disease state. In some examples, patient state module 126 may be configured to apply the set of patient metrics (e.g., a time series of relative bradycardia 132, a time series of P-R interval 134, etc.) as inputs to one or more machine learning models 136, which may output one or more values indicative of a probability or other infectious disease state level. Inputs to machine learning models 136 may also include application data collected by computing devices used by patients, where the application data are related to infection.

[0078] FIG. 4 is a block diagram illustrating an example configuration of an external device 12. In some examples, external device 12 takes the form of a smartphone, a laptop, a tablet computer, a personal digital assistant (PDA), a smartwatch or other wearable computing device, or a smart speaker, smart home hub, or other internet of things (loT) device.

[0079] As shown in the example of FIG. 4, external device 12 may be logically divided into user space 142, kernel space 144, and hardware 146. Hardware 146 may include one or more hardware components that provide an operating environment for components executing in user space 142 and kernel space 144. User space 142 and kernel space 144 may represent different sections or segmentations of memory, where kernel space 144 provides higher privileges to processes and threads than user space 142. For instance, kernel space 144 may include operating system 188, which operates with higher privileges than components executing in user space 142.

[0080] Hardware 146 may include processing circuitry 190, memory 192, one or more input devices 194, one or more output devices 196, one or more sensors 198, and communication circuitry 199. Although shown in FIG. 4 as a stand-alone device for purposes of example, external device 12 may be any component or system that includes processing circuitry or other suitable computing environment for executing softwareinstructions and, for example, need not necessarily include one or more elements shown in FIG. 4.

[0081] Processing circuitry 190 may be configured to implement functionality and / or process instructions for execution within external device 12. For example, processing circuitry 190 may be configured to receive and process instructions stored in memory 192 that provide functionality of components included in kernel space 144 and user space 142 to perform one or more operations in accordance with techniques of this disclosure.Examples of processing circuitry 190 may include, any one or more microprocessors, controllers, GPUs, TPUs, DSPs, ASICs, FPGAs, or equivalent discrete or integrated logic circuitry.

[0082] Memory 192 may be configured to store information within external device 12, for processing during operation of external device 12. Memory 192, in some examples, is described as a computer-readable storage medium. In some examples, memory 192 includes a temporary memory or a volatile memory. 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. Memory 192, in some examples, also includes one or more memories configured for long-term storage of information, e.g. including non-volatile 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 (EEPROM) memories. In some examples, memory 192 includes cloud-associated storage.

[0083] One or more input devices 194 of external device 12 may receive input, e.g., from patient 4 or another user. Examples of input are tactile, audio, kinetic, and optical input. Input devices 194 may include, as examples, a mouse, keyboard, voice responsive system, camera, buttons, control pad, microphone, presence-sensitive or touch- sensitive component (e.g., screen), or any other device for detecting input from a user or a machine.

[0084] One or more output devices 196 of external device 12 may generate output, e.g., to patient 4 or another user. Examples of output are tactile, haptic, audio, and visual output. Output devices 194 of external device 12 may include a presence-sensitive screen, sound card, video graphics adapter card, speaker, cathode ray tube (CRT) monitor, liquidcrystal display (LCD), light emitting diodes (LEDs), or any type of device for generating tactile, audio, and / or visual output.

[0085] One or more sensors 198 of external device 12 may sense physiological parameters or signals of patient 4. Sensor(s) 198 may include electrodes, accelerometers (e.g., 3-axis accelerometers), an optical sensor, impedance sensors, temperature sensors, pressure sensors, heart sound sensors (e.g., microphones), and other sensors, and sensing circuitry (e.g., including an ADC), similar to those described above with respect to IMD 10 and FIG. 3.

[0086] Communication circuitry 199 of external device 12 may communicate with other devices by transmitting and receiving data. Communication circuitry 199 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. For example, communication circuitry 199 may include a radio transceiver configured for communication according to standards or protocols, such as 3G, 4G, 5G, WiFi (e.g., 802.11 or 802.15 ZigBee), Bluetooth®, or Bluetooth® Low Energy (BLE).

[0087] Health monitoring application 150 may execute in user space 142 of external device 12. Health monitoring application 150 may be logically divided into presentation layer 152, application layer 154, and data layer 156. Presentation layer 152 may include a user interface (UI) component 160, which generates and renders user interfaces of health monitoring application 150.

[0088] Application layer 154 may include a patient state module 170, location service 174, and clock 176. Patient state module 170 may determine an infectious disease state based on a set of patient metrics 180 (“patient metrics 180”) received from IMD 10 via communication circuitry 199. Location service 174 may determine the location of external device 12 and, thereby, the presumed location of patient 4. Location service 178 may use global position system (GPS) data, multilateration, and / or any other known techniques for locating computing devices. Clock 176 may generate data indicating the time associated with the locations of patient 4. Furthermore, based on time-stamp data from IMD 10, processing circuitry 190 may associate patient metrics 180 received from IMD 10 with locations and / or times.

[0089] FIG. 5 is a block diagram illustrating a logical perspective of health monitoring service (HMS) 26 operated by computing system 20 (shown in FIG. 1). An exampleimplementation typically involves various hardware / software components operating on processing circuitry of computing system 20 (e.g., processing circuitry 22) and generally configured to be network-accessible for transmitting and receiving various data to and from patient medical devices (e.g., IMD 10), their local devices (e.g., external device 12), computing devices of other users, and / or the like.

[0090] FIG. 5 provides an operating perspective of HMS 26 when hosted as a cloudbased platform. In the example of FIG. 5, components of HMS 26 are arranged according to multiple logical layers that implement the techniques of this disclosure. Each layer may be implemented by one or more modules comprised of hardware, software, or a combination of hardware and software.

[0091] Computing devices, such as external device 12 of FIG. 1, may operate as clients that communicate with HMS 26 via interface layer 202. The computing devices typically execute client software applications, such as desktop applications, mobile applications, and web applications, e.g., health monitoring application 150. Interface layer 202 represents a set of application programming interfaces (API) or protocol interfaces presented and supported by HMS 26 for the client software applications. Interface layer 202 may be implemented with one or more web servers.

[0092] HMS 26 may include an application layer 204 that represents a collection of services 210 for implementing the functionality ascribed to HMS 26 herein. Application layer 204 may receive information from client applications, e.g., infectious disease states determined by IMD 10 and, in some cases, associated locations of patient 4 and times determined by computing device(s) 12, and store the data in a patient data repository 240 (“patient data 240”). In some examples, an infectious disease analysis module 230 of HMS 26 may analyze patient data 240 to track disease transmission and the rise of different variants in specific geographies and populations. In other words, infectious disease analysis module 230 may analyze the infectious disease state and location data for a plurality of patients to track spread of the infectious disease on a large scale, which may be advantageous during a pandemic.

[0093] Application layer 204 may process the information according to one or more of the services 210 to respond to the information. Application layer 204 may be implemented as one or more discrete software services 210 executing on one or more application servers, e.g., physical or virtual machines. That is, the application servers provide runtimeenvironments for execution of services 210. In some examples, the functionality of interface layer 202 as described above and the functionality of application layer 204 may be implemented at the same server. Services 210 may communicate via a logical service bus 212. Service bus 212 generally represents a logical interconnection or set of interfaces that allows different services 210 to send messages to other services, such as by a publish / subscription communication model.

[0094] In some examples, application layer 204 may send notifications to a computing device (e.g., a patient’s device, a caretaker’s device, a clinician’s device, etc.) such as external device 12 (e.g., to provide reminders or updates related to patient health). The notifications may pertain to an infectious disease state of patient 4, upcoming appointments, test results, medication reminders, etc. Application layer 204 may send the notification via one or more of a variety of methods, such as text messaging, email, a dedicated healthcare application, etc. Application layer 204 may receive communications from a computing device (e.g., a patient’s device, a caretaker’s device, a clinician’s device, etc.).

[0095] Data layer 206 of HMS 26 provides persistence for information in HMS 26 using one or more data repositories 220. A data repository 220, generally, may be any data structure or software that stores and / or manages data. Examples of data repositories 220 include but are not limited to relational databases, multi-dimensional databases, maps, and hash tables, to name only a few examples. In some examples, data repositories 220 may include training data 248 for training one or more machine learning models 244 of HMS 26.

[0096] FIG. 6 is a flow diagram illustrating an example operation of system 2 in accordance with one or more techniques of the present disclosure. Although primarily described with respect to IMD 10 of FIG. 3, it should be understood that the techniques of FIG. 6 may be performed by any one or combination of the devices or systems, e.g., processing circuitry of such devices, described herein.

[0097] Processing circuitry 100 of IMD 10 may determine relative bradycardia 132. For example, IMD 10 may determine a first heart rate and a first body temperature measurement of patient 4 during a first period (300). Processing circuitry 100 may determine the heart rate of patient 4 based on a cardiac signal of heart 14 of patient 4sensed via electrodes 46. Sensing circuitry 104 may measure the body temperature of patient via sensors 106.

[0098] IMD 10 may determine a second heart rate and a second temperature of patient 4 during a second period (302). In this way, IMD 10 may obtain heart rate data and temperature data for each of the first period and the second period. In some examples, the second period may be after the first period. In some examples, the first period and the second period may be periods when patient 4 is sedentary (e.g., indicated by a motion signal generated by sensors 106). That is, IMD 10 may not determine relative bradycardia 132 when patient 4 is performing a physical activity (which may increase the heart rate and / or temperature of patient 4).

[0099] IMD 10 may determine relative bradycardia 132 based on the first heart rate, the second heart rate, the first body temperature measurement, and the second body temperature measurement (304). In some examples, IMD 10 may determine relative bradycardia 132 based at least on a slope equating to the change in heart rate versus change in temperature (“slope”). The slope may be computed using the following equation: h2- h y = - t2- ti where hi corresponds to the first heart rate, h2corresponds to the second heart rate, ti corresponds to the first body temperature measurement, t2corresponds to the second body temperature measurement, and y corresponds to the slope.

[0100] In some examples, processing circuitry 100 may calculate relative bradycardia 132 to indicate a likelihood of relative bradycardia. In such examples, relative bradycardia 132 may correspond to the deviation of the computed slope from the expected slope. For example, relative bradycardia 132 may indicate a low likelihood of relative bradycardia if the computed slope is 18 heartbeats per minute per 1 °C because the deviation of the computed slope from the expected slope is 0 heartbeats per minute per 1 °C. In another example, relative bradycardia 132 may indicate a high likelihood of relative bradycardia if the computed slope is 0 heartbeats per minute per 1 °C because the deviation of the computed slope from the expected slope is 18 heartbeats per minute per 1 °C.

[0101] FIG. 7 is a flow diagram illustrating an example operation of system 2 in accordance with one or more techniques of the present disclosure. Although primarily described with respect to IMD 10 of FIG. 3, it should be understood that the techniques of FIG. 7 may be performed by any one or combination of the devices or systems, e.g., processing circuitry of such devices, described herein.

[0102] Processing circuitry 100 of IMD 10 may determine P-R interval 134. For example, IMD 10 determine a first heart rate and a first representative P-R interval of patient 4 during a first period (400). IMD 10 may similarly determine a second heart rate and a second representative P-R interval of patient 4 during a second period (402).Processing circuitry 100 may then determine P-R interval 134 based on the first heart rate, the second heart rate, the first representative P-R interval, and the second representative P- R interval (404). In some examples, processing circuitry 100 may determine P-R interval 134 using the following equation:where RRi corresponds to a first R-R interval (e.g., hyperbolically related to the first heart rate), RR2 corresponds to a second R-R interval (e.g., hyperbolically related to the second heart rate), PR] corresponds to the first representative P-R interval, PR2 corresponds to the second representative P-R interval, and I corresponds to P-R interval 134.

[0103] FIG. 8 is a flow diagram illustrating an example operation of system 2 in accordance with one or more techniques of the present disclosure. Although primarily described with respect to IMD 10 of FIG. 3, it should be understood that the techniques of FIG. 8 may be performed by any one or combination of the devices or systems, e.g., processing circuitry of such devices, described herein.

[0104] IMD 10 may determine a set of patient metrics (500). The set of patient metrics may include relative bradycardia 132 and / or P-R interval 134. In some examples, the set of patient metrics may further include one or more of a heart rate variability metric, a coughing metric, a posture metric, a movement metric, an impedance metric, or a respiration metric.

[0105] Processing circuitry 100 may determine the infectious disease state of patient 4 by determining whether the set of patient metrics satisfies one or more criteria (502). For example, processing circuitry 100 may determine that the criteria is satisfied based on whether any of the patient metrics is greater than, less than, or equal to a corresponding threshold value. In another example, processing circuitry 100 may provide the set of patient metrics to machine learning models 136, and machine learning models 136, when executed by processing circuitry 100, may output the infectious disease state of patient 4.

[0106] Processing circuitry 100 may output (e.g., for display) the infectious disease state of patient 4 (504). In some examples, processing circuitry 100 may output an infectious disease state level, such as a low, moderate, high, or other level gradations / designations. In some examples, infectious disease state levels may be numerical values on a scale (e.g., from 1-10). In some examples, infectious disease state levels may indicate a probability of patient 4 experiencing an infectious disease state.

[0107] This disclosure includes various examples, such as the following examples.

[0108] Example 1 : A medical system includes sensing circuitry configured to sense a cardiac signal of a heart of a patient via a plurality of electrodes; a temperature sensor configured to measure body temperature of the patient; and processing circuitry configured to: determine a relative bradycardia metric based on a first heart rate determined based on a first set of heartbeats detected in the cardiac signal during a first period, a second heart rate determined based on a second set of heartbeats detected in the cardiac signal during a second period, a first body temperature measurement sensed by the temperature sensor during the first period, and a second body temperature measurement sensed by the temperature sensor during the second period; determine an infectious disease state for the patient based on a set of patient metrics, wherein the set of patient metrics includes the relative bradycardia metric; and generate an output indicating the infectious disease state to a user.

[0109] Example 2 : The medical system of example 1, wherein the processing circuitry is further configured to determine heart rate variability data based on the cardiac signal, and wherein the set of patient metrics further includes a heart rate variability metric based on the heart rate variability data.

[0110] Example 3: The medical system of any of examples 1 and 2, wherein the medical device further includes a motion sensor configured to generate a motion signal,wherein the set of patient metrics further includes one or more of a coughing metric, posture metric, or movement metric, and wherein the one or more of the coughing metric, posture metric, or movement metric is based on the motion signal.

[0111] Example 4 : The medical system of example 3, wherein the motion sensor includes an accelerometer.

[0112] Example 5 : The medical system of any of examples 1 to 4, wherein the sensing circuity is further configured to sense an impedance signal via the plurality of electrodes, and wherein the set of patient metrics further includes an impedance metric based on the impedance signal.

[0113] Example 6: The medical system of any of examples 1 to 5, wherein the sensing circuitry is further configured to sense a respiration signal via the plurality of electrodes, and wherein the set of patient metrics further includes a respiration metric based on the respiration signal.

[0114] Example 7 : The medical system of any of examples 1 to 6, wherein the processing circuitry is further configured to: determine a respective trend for each patient metric of the set of patient metrics based on values of the patient metric over time; and output for display the respective trend for each patient metric of the set of patient metrics.

[0115] Example 8: The medical system of any of examples 1 to 7, wherein the processing circuitry is further configured to: determine a metric of P-R interval based on the first heart rate, the second heart rate, a first representative P-R interval for the first set of heartbeats, and a second representative P-R interval for the second set of heartbeats, wherein the set of patient metrics further includes the metric of P-R interval.

[0116] Example 9: The medical system of any of examples 1 to 8, wherein the infectious disease includes COVID-19.

[0117] Example 10: The medical system of any of examples 1 to 9, wherein the medical device is an implantable medical device configured for subcutaneous implantation.

[0118] Example 11: The medical system of any of examples 1 to 10, wherein the medical device includes the processing circuitry.

[0119] Example 12: The medical system of any of examples 1 to 11, wherein the cardiac signal is a cardiac electrogram.

[0120] Example 13: A medical system includes an insertable cardiac monitor that includes a housing configured for subcutaneous implantation in a patient, the housing having a length between 40 millimeters (mm) and 60 mm between a first end and a second end, a width less than the length, and a depth less than the width; sensing circuitry configured to sense a cardiac signal of a heart of a patient via a plurality of electrodes includes a first electrode at or proximate to the first end; and a second electrode at or proximate to the second end a temperature sensor configured to measure body temperature of the patient; a memory within the housing; and first processing circuitry within the housing, the first processing circuitry configured to: determine a first heart rate based on a first set of heartbeats detected in the cardiac signal during a first period; determine a second heart rate based on a second set of heartbeats detected in the cardiac signal during a second period; determine a first body temperature measurement sensed by the temperature sensor during the determine first period; determine a second body temperature measurement sensed by the temperature sensor during the second period; and one or more computing devices in communication with the insertable cardiac monitor, the one or more computing devices includes determine a relative bradycardia metric based on the first heart rate, the second heart rate, the first body temperature measurement, and the second body temperature measurement; determine an infectious disease state for the patient based on a set of patient metrics, wherein the set of patient metrics includes the relative bradycardia metric; and generate an output indicating the infectious disease state to a user.

[0121] Example 14: The medical system of example 13, wherein the first processing circuitry is further configured to determine heart rate variability data based on the cardiac signal, and wherein the set of patient metrics further includes a heart rate variability metric based on the heart rate variability data.

[0122] Example 15: The medical system of any of examples 13 and 14, wherein the insertable cardiac monitor further includes a motion sensor configured to generate a motion signal, wherein the set of patient metrics further includes one or more of a coughing metric, posture metric, or movement metric, and wherein the one or more of the coughing metric, posture metric, or movement metric is based on the motion signal.

[0123] Example 16: The medical system of example 15, wherein the motion sensor includes an accelerometer.

[0124] Example 17: The medical system of any of examples 13 to 16, wherein the sensing circuity is further configured to sense an impedance signal via the plurality of electrodes, and wherein the set of patient metrics further includes an impedance metric based on the impedance signal.

[0125] Example 18: The medical system of any of examples 13 to 17, wherein the sensing circuitry is further configured to sense a respiration signal via the plurality of electrodes, and wherein the set of patient metrics further includes a respiration metric based on the respiration signal.

[0126] Example 19: The medical system of any of examples 13 to 18, wherein the second processing circuitry is further configured to: determine a respective trend for each patient metric of the set of patient metrics based on values of the patient metric over time; and output for display the respective trend for each patient metric of the set of patient metrics.

[0127] Example 20: The medical system of any of examples 13 to 19, wherein the processing circuitry is further configured to: determine a metric of P-R interval based on the first heart rate, the second heart rate, a first representative P-R interval for the first set of heartbeats, and a second representative P-R interval for the second set of heartbeats, wherein the set of patient metrics further includes the metric of P-R interval.

[0128] Example 21: The medical system of any of examples 13 to 20, wherein the infectious disease includes COVID-19.

[0129] Example 22: The medical system of any of examples 13 to 21, wherein the medical device is an implantable medical device configured for subcutaneous implantation.

[0130] Example 23: The medical system of any of examples 13 to 22, wherein the cardiac signal is a cardiac electrogram.

[0131] Example 24: A method for operating processing circuitry of a medical system includes determining, by processing circuitry, a relative bradycardia metric of a patient based on a first heart rate of the patient determined based on a first set of heartbeats detected in a cardiac signal during a first period, a second heart rate of the patient determined based on a second set of heartbeats detected in the cardiac signal during a second period, a first body temperature measurement of the patient sensed by a temperature sensor during the first period, and a second body temperature measurement ofthe patient sensed by a temperature sensor during the second period; determining, by the processing circuitry, an infectious disease state for the patient based on a set of patient metrics, wherein the set of patient metrics includes the relative bradycardia metric; and generating an output indicating the infectious disease state to a user.

[0132] Example 25: The method of example 24, wherein a medical device includes: the processing circuitry: sensing circuitry configured to sense the cardiac signal of a heart of the patient via a plurality of electrodes; and the temperature sensor configured to measure body temperature of the patient.

[0133] Example 26: The method of example 25, wherein the medical device includes an insertable cardiac monitor.

[0134] Example 27: The method of any of examples 24 to 26, wherein the processing circuitry includes: first processing circuitry within a housing of the medical device; and second processing circuitry within one or more computing devices in communication with the medical device.

[0135] In one or more examples, the described techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or code on a computer-readable medium and executed by a hardware -based processing unit. Computer-readable media may include non-transitory computer-readable media, which corresponds to a tangible medium such as data storage media (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer).

[0136] Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor” or “processing circuitry” as used herein may refer to any of the foregoing structure or any other physical structure suitable for implementation of the described techniques. Also, the techniques could be fully implemented in one or more circuits or logic elements.

[0137] Various examples have been described. These and other examples are within the scope of the following claims.

Claims

CLAIMSWhat is claimed is:

1. A medical system comprising: sensing circuitry configured to sense a cardiac signal of a heart of a patient via a plurality of electrodes; a temperature sensor configured to measure body temperature of the patient; and processing circuitry configured to: determine a relative bradycardia metric based on a first heart rate determined based on a first set of heartbeats detected in the cardiac signal during a first period, a second heart rate determined based on a second set of heartbeats detected in the cardiac signal during a second period, a first body temperature measurement sensed by the temperature sensor during the first period, and a second body temperature measurement sensed by the temperature sensor during the second period; determine an infectious disease state for the patient based on a set of patient metrics, wherein the set of patient metrics comprises the relative bradycardia metric; and generate an output indicating the infectious disease state to a user.

2. The medical system of claim 1, wherein the processing circuitry is further configured to determine heart rate variability data based on the cardiac signal, and wherein the set of patient metrics further comprises a heart rate variability metric based on the heart rate variability data.

3. The medical system of claim 1 or 2, further comprising a motion sensor configured to generate a motion signal, wherein the set of patient metrics further comprises one or more of a coughing metric, posture metric, or movement metric, and wherein the one or more of the coughing metric, posture metric, or movement metric is based on the motion signal.

4. The medical system of claim 3, wherein the motion sensor comprises an accelerometer.

5. The medical system of any of claims 1 to 4, wherein the sensing circuity is further configured to sense an impedance signal via the plurality of electrodes, and wherein the set of patient metrics further comprises an impedance metric based on the impedance signal.

6. The medical system of any of claims 1 to 5, wherein the sensing circuitry is further configured to sense a respiration signal via the plurality of electrodes, and wherein the set of patient metrics further comprises a respiration metric based on the respiration signal.

7. The medical system of any of claims 1 to 6, wherein the processing circuitry is further configured to: determine a respective trend for each patient metric of the set of patient metrics based on values of the patient metric over time; and output for display the respective trend for each patient metric of the set of patient metrics.

8. The medical system of any of claims 1 to 7, wherein the processing circuitry is further configured to: determine a metric of P-R interval based on the first heart rate, the second heart rate, a first representative P-R interval for the first set of heartbeats, and a second representative P-R interval for the second set of heartbeats, wherein the set of patient metrics further comprises the metric of P-R interval.

9. The medical system of any of claims 1 to 8, wherein the infectious disease comprises COVID- 19.

10. The medical system of any of claims 1 to 9, further comprising an implantable medical device configured for subcutaneous implantation, wherein the implantable medical device comprises the sensing circuitry and the temperature sensor.

11. The medical system of claim 10, wherein the implantable medical device comprises the processing circuitry.

12. The medical system of any of claims 1 to 11, wherein the cardiac signal is a cardiac electrogram.

13. A medical system comprising: an insertable cardiac monitor comprising: a housing configured for subcutaneous implantation in a patient, the housing having a length between 40 millimeters (mm) and 60 mm between a first end and a second end, a width less than the length, and a depth less than the width; sensing circuitry configured to sense a cardiac signal of a heart of a patient via a plurality of electrodes comprising: a first electrode at or proximate to the first end; and a second electrode at or proximate to the second end a temperature sensor configured to measure body temperature of the patient; a memory within the housing; and first processing circuitry within the housing, the first processing circuitry configured to: determine a first heart rate based on a first set of heartbeats detected in the cardiac signal during a first period; determine a second heart rate based on a second set of heartbeats detected in the cardiac signal during a second period; determine a first body temperature measurement sensed by the temperature sensor during the determine first period; determine a second body temperature measurement sensed by the temperature sensor during the second period; and one or more computing devices in communication with the insertable cardiac monitor, the one or more computing devices comprising second processing circuitry configured to:determine a relative bradycardia metric based on the first heart rate, the second heart rate, the first body temperature measurement, and the second body temperature measurement; determine an infectious disease state for the patient based on a set of patient metrics, wherein the set of patient metrics comprises the relative bradycardia metric; and generate an output indicating the infectious disease state to a user.

14. The medical system of claim 13, wherein the first processing circuitry is further configured to determine heart rate variability data based on the cardiac signal, and wherein the set of patient metrics further comprises a heart rate variability metric based on the heart rate variability data.

15. The medical system of claim 13 or 14, wherein the insertable cardiac monitor further comprises a motion sensor configured to generate a motion signal, wherein the set of patient metrics further comprises one or more of a coughing metric, posture metric, or movement metric, and wherein the one or more of the coughing metric, posture metric, or movement metric is based on the motion signal.

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