Implantable medical device using temperature sensor to determine patient's infection status

Abstract

This disclosure describes techniques for detecting infection in a patient based on temperature values ​​obtained from an implantable temperature sensor. An example implantable temperature sensor may be contained within a housing of an implantable medical device (IMD). In some examples, the temperature sensor may measure multiple temperature values ​​over time. Processing circuitry of the IMD or an external device may smooth the temperature values ​​and apply an infection detection model to the smoothed temperature signal to determine the patient's infection status.

Description

Technical Field

[0001] The present disclosure relates particularly to implantable medical devices, and more particularly to systems, devices, and methods for monitoring infection, such as infection in the vicinity of a medical device implanted in a patient. Background Art

[0002] Infections associated with implanted medical devices are a serious health and economic problem. Today, infections associated with implanted medical devices are uncommon due to the care and precautions taken during surgical implantation of the device. However, when an infection associated with an implantable medical device (IMD) does occur, removal of the device is often the only appropriate course of action.

[0003] For IMDs with battery-powered components, such as implantable cardiac pacemakers, cardioverter / defibrillators with pacing capabilities, other electrical stimulators (including spinal cord, deep brain, nerve, and muscle stimulators), infusion devices, cardiac and other physiological monitors, cochlear implants, etc., the battery-powered components are typically enclosed in a housing that is implanted at the surgical preparation site, called a "pouch." Associated devices such as elongated medical electrical leads or drug delivery catheters extend from the pouch to other subcutaneous sites or deeper into the body to organs or other implant sites.

[0004] Surgical preparation and implantation are performed in a sterile field, and IMD components are packaged in sterile containers or sterilized before being introduced into the sterile field. However, despite these precautions, there is always a risk of introducing microorganisms into the bag. Therefore, surgeons typically apply a disinfectant or antiseptic to the skin at the surgical site before surgery, directly to the incision before closure, and prescribe oral antibiotics for the patient to take during recovery.

[0005] Despite these precautions, infection can still occur. Furthermore, once the pocket becomes infected, the infection can migrate along the lead or catheter to the site where the lead or catheter was implanted. For example, removing a chronically implanted lead or catheter in response to such an infection can be very difficult. Therefore, aggressive systemic drug therapy is used to treat such infections. However, early detection of infections associated with implantable medical devices can allow for earlier intervention, thereby reducing device explantation. Summary of the Invention

[0006] The present disclosure describes techniques for determining an infection status of a patient (e.g., a bacterial infection, a device pocket infection, etc.) based on temperature measurements taken from at least one temperature sensing device placed within the patient's body. The techniques can be implemented by an implantable medical device (IMD) that includes at least one temperature sensing device. The techniques of the present disclosure can be implemented by any number of computing devices (e.g., an IMD or a remote computing device, such as a network device) using temperature measurements obtained by the temperature sensing device. In some instances, the processing circuitry of the IMD or remote computing device can determine whether an infectious agent, such as harmful bacteria, has infiltrated the device pocket where the IMD is implanted within the patient's body. Specifically, the device can utilize specific signal processing techniques and algorithms that allow the processing circuitry to accurately determine infection by conditioning the signal and applying various detection models, such as a sliding window detection model or a multiple low-pass filter integration model. Such detection models can allow the processing circuitry to accurately distinguish, for example, an increase in temperature signal caused by a device pocket infection, rather than an increase in temperature caused by daily temperature fluctuations.

[0007] In one example, the sliding window detection model includes a rate-of-change detection model that uses at least two temperature data points of the smoothed temperature signal to determine a slope value in a sliding window. In some examples, the sliding window detection model can include a maximum-minimum detection model that uses multiple sliding windows of temperature data points. In any case, the temperature data points are obtained from a temperature sensing device of the IMD. In another example, the processing circuitry can compare the smoothed temperature signals obtained using filters having different cutoff frequencies. In any case, monitoring for infection using a sensor, such as a temperature sensing device of the IMD, can provide information indicative of an infection.

[0008] In this way, the processing circuitry can detect localized temperature changes at the IMD that may not be reflected in the core body temperature monitor. That is, the temperature of the IMD can consistently have a temperature approximately 1-2 degrees lower than the core temperature, as the IMD temperature lags (e.g., follows) relative changes in core temperature. However, in the event of an infection involving the device bag, the IMD temperature may lead the core body temperature and, in some cases, may increase without any significant change in core body temperature. As such, the processing circuitry may need to employ algorithms that can distinguish temperature changes caused by different, less significant events than changes in bag temperature caused by a device bag infection. In accordance with various techniques of the present disclosure, the processing circuitry can accurately determine various infection types that are distinct from other infection types using the algorithms summarized above and described below.

[0009] In one example, the present disclosure provides a system for determining an infection status of a patient. The system includes an implantable medical device (IMD) including at least one temperature sensing device. The system further includes a processing circuit system configured to determine a plurality of temperature values ​​over time at least by the temperature sensing device; smooth the plurality of temperature values ​​determined over time to generate a smoothed temperature signal representing changes in the plurality of temperature values ​​over time; apply an infection detection model to the smoothed temperature signal to determine an infection indicator value, the infection detection model including one or more of a sliding window detection model or a multiple low-pass filter ensemble model; compare the infection indicator value with a threshold; and determine the infection status of the patient based at least in part on the infection indicator value satisfying the threshold.

[0010] In another example, the present disclosure provides a method for determining an infection status of a patient, the method comprising determining a plurality of temperature values ​​over time by a temperature sensing device of an implantable medical device (IMD); smoothing the plurality of temperature values ​​determined over time to produce a smoothed temperature signal representing changes in the plurality of temperature values ​​over time; applying an infection detection model to the smoothed temperature signal to determine an infection indicator value, the infection detection model comprising one or more of a sliding window detection model or a multiple low-pass filter ensemble model; comparing the infection indicator value to a threshold; and determining the infection status of the patient based at least in part on the infection indicator value satisfying the threshold.

[0011] In another example, the present disclosure provides a computer-readable storage medium having instructions stored thereon, which, when executed, cause one or more processors to measure a plurality of temperature values ​​over time, at least through a temperature sensing device of an implantable medical device (IMD); smooth the plurality of temperature values ​​measured over time to produce a smoothed temperature signal representing changes in the plurality of temperature values ​​over time; apply an infection detection model to the smoothed temperature signal to determine an infection indicator value, the infection detection model comprising one or more of a sliding window detection model or a multiple low-pass filter ensemble model; compare the infection indicator value with a threshold; and determine an infection status of a patient based at least in part on the infection indicator value satisfying the threshold.

[0012] The present disclosure also provides apparatus for performing any of the techniques described herein, as well as non-transitory computer-readable media including instructions that cause a programmable processor to perform any of the techniques described herein.

[0013] This summary is intended to provide an overview of the subject matter described in this disclosure. It is not intended to be an exclusive or exhaustive explanation of the systems, devices, and methods described in detail in the following figures and the specification. Further details of one or more embodiments of the present disclosure are set forth in the following figures and the specification. Additional features, objects, and advantages will become apparent from the description and drawings, as well as from the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The environment of an example healthcare system is presented in conjunction with patients.

[0015] Figure 2 It is a display Figure 1 A functional block diagram of an example configuration of an implantable medical device (IMD).

[0016] Figure 3 is a more detailed display Figure 1 and / or 2 are conceptual side views of example IMDs of medical systems.

[0017] Figure 4 It is a display Figure 1 A functional block diagram of an example configuration of an external device.

[0018] Figure 5 is a block diagram illustrating an example system including an access point, a network, an external computing device (such as a server), and one or more other computing devices that can be coupled to Figure 1-4 IMD and external devices.

[0019] Figure 6 is a flow diagram illustrating example operations for determining the infection status of a patient in accordance with one or more techniques disclosed herein.

[0020] Figure 7 is a conceptual diagram illustrating raw temperature data collected from a temperature sensing device coupled to an IMD, collected according to one or more techniques disclosed herein.

[0021] Figure 8 is a conceptual diagram illustrating smoothed temperature data smoothed according to one or more techniques disclosed herein.

[0022] Figure 9 is a flow diagram illustrating example operations of a sliding window detection for determining the infection status of a patient in accordance with one or more techniques disclosed herein.

[0023] Figure 10 is a flow diagram illustrating another example operation of a sliding window test for determining the infection status of a patient in accordance with one or more techniques disclosed herein.

[0024] Figure 11 is a conceptual diagram illustrating example operations of sliding window detection in accordance with one or more techniques disclosed herein.

[0025] Figure 12 is a flow chart illustrating example operations of multiple low-pass filter integrated detection for determining the infection status of a patient in accordance with one or more techniques disclosed herein.

[0026] Figure 13 is a conceptual diagram illustrating example operations of a multiple low-pass filter integration model in accordance with one or more techniques disclosed herein.

[0027] Figure 14 is a block diagram illustrating an example system including multiple temperature sensing devices in accordance with one or more techniques disclosed herein.

[0028] Figure 15 is a flowchart illustrating an example method according to one or more techniques disclosed herein, which may be performed by Figure 1 One or both of the IMD and the external device shown in FIG. 1 are implemented to use temperature alone to determine the infection status of the patient.

[0029] Figure 16 is a flowchart illustrating an example method according to one or more techniques disclosed herein, which may be performed by Figure 1 One or both of the IMD and the external device shown in FIG. 1 are implemented to provide an alert to a patient regarding the patient's infection status.

[0030] Like reference numerals refer to like elements throughout the specification and drawings. DETAILED DESCRIPTION

[0031] Implantable medical devices (IMDs) can sense and monitor signals and use these signals to determine various conditions in a patient and / or provide therapy to the patient. Example IMDs include monitors such as the Reveal LINQ available from Medtronic plc. TM Insertable cardiac monitor. This type of IMD can facilitate relatively long-term monitoring of a patient during normal daily activities and can periodically transmit the collected data to a Medtronic device such as one developed by Medtronic plc of Minneapolis, Minnesota. A network service such as a Web site or some other network that connects the patient 4 with the clinician.

[0032] Figure 1The following illustrates an example medical system 2 incorporating a patient 4 according to one or more techniques of the present disclosure. Patient 4 is typically, but not necessarily, a human. For example, patient 4 may be an animal requiring continuous cardiac monitoring. System 2 includes an IMD 10. IMD 10 may include one or more electrodes (not shown) on a housing of IMD 10 or may be coupled to one or more leads carrying one or more electrodes. System 2 may also include an external device 12.

[0033] Example techniques may be used with an IMD 10 that may communicate with an external device 12 and Figure 1 In some examples, IMD 10 may be implanted within patient 4. For example, IMD 10 may be implanted outside the chest of patient 4 (e.g., Figure 1 ). In some examples, IMD 10 may be positioned near the sternum near or just below the level of patient 4's heart, e.g., at least partially within the outline of the heart.

[0034] In some examples, IMD 10 can sense electrocardiogram (EGM) signals via a plurality of electrodes and / or operate as a therapy delivery device. For example, IMD 10 can operate as a therapy delivery device (e.g., an implantable pacemaker, cardioverter, and / or defibrillator) that delivers electrical signals to the heart of patient 4, a drug delivery device that delivers a therapeutic substance to patient 4 via one or more catheters, or a combination therapy device that delivers both electrical signals and a therapeutic substance.

[0035] In some examples, system 2 can include any suitable number of leads coupled to IMD 10, and each of the leads can extend to any location within or near the heart or into the chest of patient 4. For example, other example therapy systems can include three transvenous leads and an additional lead positioned within or near the left atrium of the heart. As other examples, a therapy system can include a single lead extending from IMD 10 into the right atrium or right ventricle, or two leads extending into respective ones of the right ventricle and right atrium.

[0036] In some examples, external device 12 can monitor temperature values ​​received from IMD 10 according to the techniques described herein. For example, IMD 10 can obtain temperature data via one or more temperature sensing devices housed within IMD 10 or otherwise affixed to the IMD, such as affixed to the housing of IMD 10 or with temperature probes / leads extending into and / or out of IMD 10. For example, the temperature sensing devices can be attached to an exterior wall of IMD 10, with temperature sensing leads inserted into IMD 10 and extending outward from the temperature sensing devices. In any case, IMD 10 can perform data transmission of temperature data to one or more external devices 12. IMD 10 can perform data transmission before or after processing the temperature data. For example, IMD 10 can transmit raw temperature data to external device 12, or in some cases, can transmit post-processed temperature data, such as smoothed temperature data that has been conditioned by a particular signal processing technique (e.g., a moving average or other low-pass filter, high-pass filter, band-pass filter, band-stop filter, etc.).

[0037] In some instances, the IMD 10 employs Reveal LINQ TM in the form of an insertable cardiac monitor (ICM), or similar to, for example, the LINQ TM Such IMDs can facilitate relatively long-term monitoring of patients during normal daily activities and can periodically transmit collected data to a network service such as Medtronic's network.

[0038] External device 12 may be a computing device having a display viewable by a user and an interface (i.e., a user input mechanism) for providing input to external device 12. The user may be a physician technician, a surgeon, an electrophysiologist, a clinician, or patient 4. In some examples, external device 12 may be a notebook computer, a tablet computer, a computer workstation, one or more servers, a cellular phone, a personal digital assistant, a handheld computing device, a networked computing device, or another computing device that can run an application that enables the computing device to interact with IMD 10. For example, external device 12 may be a clinician, physician, or user programmer configured to wirelessly communicate with IMD 10 and perform data transfer between external device 12 and IMD 10. External device 12 is configured to communicate with IMD 10 and, optionally, with another computing device ( Figure 1For example, the external device 12 may communicate via near field communication (NFC) technology (e.g., inductive coupling, NFC, or other communication technology that can operate within a range of less than 10-20 cm) and far field communication technology (e.g., radio frequency (RF) telemetry according to 802.11 or In some examples, external device 12 may include a programming head that can be placed proximate to the body of patient 4 near the site of implantation of IMD 10 to improve the quality or security of communication between IMD 10 and external device 12. In some examples, external device 12 may be coupled to external electrodes or to implanted electrodes via percutaneous leads.

[0039] The user interface of the external device 12 can receive input from the user. The user interface can include, for example, a keypad and a display, and the display can be, for example, a cathode ray tube (CRT) display, a liquid crystal display (LCD), or a light emitting diode (LED) display. The keypad can take the form of an alphanumeric keypad or a reduced set of keys associated with specific functions. The external device 12 can additionally or alternatively include a peripheral pointing device such as a mouse, through which the user can interact with the user interface. In some instances, the display of the external device 12 can include a touch screen display, and the user can interact with the external device 12 through the display. It should be noted that the user can also interact with the external device 12 remotely through a networked computing device.

[0040] In some instances, a user may use external device 12 to program or otherwise interact with IMD 10. External device 12 may be used to program various aspects of the sensing or data analysis performed by IMD 10 and / or the therapy provided by IMD 10. Additionally, external device 12 may be used to retrieve data from IMD 10. The retrieved data may include temperature values ​​measured by IMD 10, infection-indicating data, and / or other physiological signals recorded by IMD 10. For example, external device 12 may retrieve information related to the detection of temperature changes detected by IMD 10, such as a rate of change exceeding a predefined threshold. External device 12 may also retrieve cardiac EGM segments recorded by IMD 10, for example, because IMD 10 determines that an episode of an arrhythmia or other condition occurred during the segment, or in response to a request to record a segment from patient 4 or other users. In other instances, a user may also use external device 12 to retrieve information from IMD 10 about other sensed physiological parameters of patient 4, such as activity or posture. As described below with respect to Figure 5As discussed in greater detail, one or more remote computing devices may interact with IMD 10 over a network in a manner similar to external device 12 , eg, to program IMD 10 and / or retrieve data from IMD 10 .

[0041] Processing circuitry of medical system 2, e.g., processing circuitry of IMD 10, external device 12, and / or one or more other computing devices, may be configured to perform example techniques of the present disclosure for measuring temperature to determine indicators of infection. In some examples, processing circuitry of medical system 2 analyzes temperature values ​​sensed by IMD 10 to determine whether changes in temperature meet predefined infection-indicating thresholds.

[0042] Although described in the context of an example in which IMD 10 comprises an insertable or implantable IMD, example systems including one or more external devices of any type configured to sense temperature may be configured to implement the techniques of this disclosure. In some examples, IMD 10 or external device 12 may use one or more of the internal temperature and the external temperature relative to IMD 10 to determine an infection status of patient 4, such as a device pocket infection. For example, external device 12 may receive a signal indicative of an external temperature, such as a core body temperature signal from IMD 10, and may receive a signal indicative of an internal temperature of IMD 10, such as a device pocket temperature, and may use both temperature measurements to determine the infection status and identify the cause of the infection (e.g., a device pocket infection, a bacterial infection at a body site, such as a bladder infection, etc.).

[0043] When the infection indicator value indicates the start of an infection event, system 2 provides an alert to patient 4 and / or other users. The process for determining when to issue an alert to patient 4 involves comparing the infection indicator value to one or more threshold values ​​and is described in more detail below. The alert can be an audible alert generated by IMD 10 and / or external device 12, a visual alert generated by external device 12, such as a text prompt or a flashing button or screen, or a tactile alert generated by IMD 10 and / or external device 12, such as a vibration or vibration pattern. In addition, alerts can be provided to other devices, for example, via a network. Several different levels of alerts can be used based on the risk level detected by the techniques described herein.

[0044] In instances where IMD 10 also operates as a pacemaker, cardioverter, and / or defibrillator or otherwise monitors cardiac electrical activity, IMD 10 can sense electrical signals accompanying depolarization and repolarization of the heart of patient 4 via electrodes coupled to at least one lead. In some instances, IMD 10 can provide pacing pulses to the heart of patient 4 based on the electrical signals sensed within the heart of patient 4. IMD 10 can also provide defibrillation therapy and / or cardioversion therapy via electrodes positioned on at least one lead and housing electrodes. IMD 10 can detect cardiac arrhythmias, such as ventricular fibrillation, in the heart of patient 4 and deliver defibrillation therapy in the form of electrical pulses to the heart of patient 4.

[0045] Although described primarily in the context of an instance in which the IMD 10 is an insertable cardiac monitor, the techniques described herein may be implemented by a medical device system comprising any one or more implantable or external medical devices, such as any one or more monitors, pacemakers, cardioverters, defibrillators, cardiac assist devices such as a left ventricular assist device, neurostimulators, or drug delivery devices.

[0046] Figure 2 is a demonstration of one or more techniques described herein Figure 1 1 is a functional block diagram of an example configuration of IMD 10. In the example shown, IMD 10 includes electrodes 16A-16N (collectively, “electrodes 16”), antenna 26, processing circuitry 50, sensing circuitry 52, communication circuitry 54, storage device 56, switching circuitry 58, sensor 62, and power supply 91.

[0047] Processing circuitry 50 may include fixed-function circuitry and / or programmable processing circuitry. Processing circuitry 50 may include any one or more of the following: 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 50 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 as other discrete or integrated logic circuitry. The functionality attributed herein to processing circuitry 50 may be embodied in software, firmware, hardware, or any combination thereof.

[0048] As an example, the sensing circuitry 52 may also monitor signals from a sensor 62, which may include one or more temperature sensors, accelerometers, pressure sensors, and / or optical sensors. The sensor 62 may include one or more temperature sensing devices. Any suitable sensor 62 may be used to detect temperature or temperature changes. In some examples, the sensor 62 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 thermomechanical temperature sensor, a thin film resistor element, or the like.

[0049] In some examples, sensing circuitry 52 may include one or more filters and amplifiers for filtering and amplifying signals received from sensor 62 and / or electrode 16. For example, sensing circuitry 52 may include one or more low-pass filters having various predefined cutoff frequencies to be applied to temperature values ​​obtained from sensor 62, such as one or more temperature sensors. In some examples, sensing circuitry 52 may include circuitry configured to digitally filter measured temperature values ​​using one or more cutoff frequencies or otherwise using one or more different filtering processes to achieve varying degrees of smoothing over a series of temperature values. For example, sensing circuitry 52 may include processing circuitry, such as processing circuitry 50, configured to smooth temperature values ​​measured over time to produce a smoothed temperature signal. In some examples, processing circuitry of sensing circuitry 52 may perform smoothing of temperature values ​​measured by sensor 62, allowing processing circuitry 50 to perform various other techniques of the present disclosure based on the smoothed temperature signal. In some examples, processing circuitry 50 may include sensing circuitry 52 , where processing circuitry 50 is configured to smooth temperature values ​​measured over time to produce a smoothed temperature signal (eg, by performing digital and / or analog filtering).

[0050] In some examples, sensing circuitry 52 can be selectively coupled to electrodes 16 (e.g., for selecting electrodes 16 and polarity) via switching circuitry 58 to sense impedance and / or cardiac signals. Sensing circuitry 52 can sense signals from electrodes 16, for example, to generate a cardiac EGM or subcutaneous electrocardiogram (ECG) to facilitate monitoring of the electrical activity of the heart.

[0051] Processing circuitry 50 may cause sensing circuitry 52 to periodically measure physiological or other parameter values, such as temperature, of IMD 10. For temperature measurements, processing circuitry 50 may control sensing circuitry 52 to obtain temperature measurements via one or more sensors 62.

[0052] Because IMD 10 or external device 12 may be configured to include sensing circuitry 52, the sensing circuitry may be implemented in one or more processors, such as processing circuitry 50 of IMD 10 or processing circuitry 80 of external device 12. Similar to processing circuitry 50, 80, 98 and other circuitry described herein, sensing circuitry 52 may be embodied as one or more hardware modules, software modules, firmware modules, or any combination thereof.

[0053] In some examples, processing circuitry 50 may receive a temperature value of patient 4 from one or more other devices via communication circuitry 54. In some examples, the one or more other devices may include a sensor device, such as an activity sensor, a heart rate sensor, a wearable device worn by patient 4, a temperature sensor, etc. That is, in some examples, the one or more other devices may be external to IMD 10.

[0054] In some examples, processing circuitry 50 may receive temperature measurements from one or more of sensors 62 via sensing circuitry 52. ​​In another example, processing circuitry 50 may also receive temperature measurements from one or more other devices via communication circuitry 54.

[0055] In some examples, processing circuitry 50 may control the timing of temperature measurements based on a trigger. For example, processing circuitry 50 may control sensing circuitry 52 and sensor 62 based on a measurement trigger received via communication circuitry 54. In another example, processing circuitry 50 may transmit a request for data to one or more other devices via communication circuitry 54. For example, processing circuitry 50 may transmit a request to an external device, requesting the external device to provide physiological parameter data (e.g., heart rate, activity level, temperature value, etc.) to processing circuitry 50 via communication circuitry 54.

[0056] In some examples, triggers may include the activity level of patient 4, the heart rate of patient 4, the ambient temperature of the environment surrounding patient 4, and the like. For example, processing circuitry 50 may control the timing of temperature measurements based on when the ambient temperature of patient 4, received from another device via communication circuitry 54, indicates that patient 4 may be in a temperature-controlled area (e.g., indoors). In another example, processing circuitry 50 may control the timing of temperature measurements based on when the ambient temperature or the surface temperature of patient 4 is within a predefined temperature range. In some examples, processing circuitry 50 may receive a trigger from another device via communication circuitry 54. In some cases, the trigger may be based on information processing circuitry 50 receives from another device via communication circuitry 54.

[0057] In a non-limiting example, processing circuitry 50 may receive a signal indicating when patient 4 is experiencing low activity levels. In response to receiving the signal indicating activity levels, processing circuitry 50 may control sensing circuitry 52 to measure one or more temperature values ​​using one of sensors 62. In another example, processing circuitry 50 may receive a signal indicating when patient 4's heart rate falls below or rises above a heart rate threshold, or a signal indicating when patient 4's body temperature becomes too low or too high compared to certain temperature thresholds, etc. In such examples, processing circuitry 50 may determine a heart rate value for patient 4 based on signals from sensing circuitry 52 regarding cardiac activity sensed by electrodes (e.g., electrodes 16). In any case, processing circuitry 50 may determine whether the received signal contains triggering information for sensing circuitry 52 to perform a physiological parameter measurement (e.g., a temperature measurement) using one of sensors 62.

[0058] In some instances, processing circuitry 50 may determine whether a combination of one or more signals received from one or more transmitting devices contains trigger information. Processing circuitry 50 may determine that one or more signals individually contain trigger information. In some instances, processing circuitry 50 may determine that one or more signals in combination contain trigger information. In response to determining the presence of trigger information, processing circuitry 50 may cause sensing circuitry 52 to measure one or more temperature values ​​using one of sensors 62. In some instances, processing circuitry 50 may additionally use timing information. For example, processing circuitry 50 may start a timer based on the trigger information. In some instances, processing circuitry 50 may cause sensing circuitry 52 to measure temperature values ​​according to timing constraints after a trigger event (e.g., only performing measurements at night), regardless of when the trigger event occurs during the day.

[0059] In some examples, processing circuitry 50 may control the measurement of temperature values ​​periodically, such as hourly, daily, weekly, etc. In one example, sensing circuitry 52 may measure temperature values ​​during a specific portion of a day. As an example, sensing circuitry 52 may measure temperature values ​​every twenty minutes for a predetermined number of hours, such as between noon and 5 p.m. Processing circuitry 50 may determine a final measured temperature value by calculating an average of the measurements. In this case, the daily value may be an average of the temperature values ​​measured by sensing circuitry 52 during the day (e.g., over a 24-hour period, where measurements are selectively taken at specific times and / or in response to certain triggers, etc.).

[0060] In some examples, sensing circuitry 52 can be configured to sample temperature measurements at a particular sampling rate. In such examples, sensing circuitry 52 can be configured to perform downsampling of received temperature measurements. For example, sensing circuitry 52 can perform downsampling to reduce the throughput rate of processing circuitry 50. This can be particularly advantageous when sensing circuitry 52 has a high sampling rate when active.

[0061] As used herein, the term "temperature value" is used broadly to refer to any collected, measured, and / or calculated value. In some examples, the temperature value is derived from a temperature signal received from one or more of the sensors 62. For example, the temperature value may include an average value (e.g., mean, mode, standard deviation) of the temperature signals received from one or more of the sensors 62.

[0062] exist Figure 2 In the example shown in FIG, the processing circuit system 50 is capable of performing reference Figure 6-16 To avoid confusion, processing circuitry 50 is described as performing various temperature processing techniques prohibited by IMD 10, but it should be understood that these techniques may also be performed by other processing circuitry (e.g., processing circuitry 80 of external device 12, etc.).

[0063] In various instances, processing circuit system 50 may implement one, all, or any combination of a variety of infection indication techniques discussed in more detail below. When implementing the infection indication technique, IMD 10 may generate an alarm when it determines that an increase in the infection indicator value indicates that patient 4 may have an infection, such as a device pocket infection. For example, IMD 10 may provide an audible or tactile alarm in the form of a beep or vibration pattern. Alternatively, IMD 10 may send an alarm signal to external device 12, causing external device 12 to provide an alarm to patient 4. External device 12 may provide an audible, visual, or tactile alarm to patient 4. Once the alarm is issued to patient 4, patient 4 may then seek medical attention, for example, by visiting a hospital or clinic. The alarms may be categorized into different levels of severity, as indicated by the infection indicator value.

[0064] Sensing circuitry 52 may also provide one or more temperature values ​​to processing circuitry 50 for analysis, e.g., analysis to identify a possible infection according to the techniques of this disclosure. In some examples, processing circuitry 50 may store the temperature values ​​to storage device 56. Processing circuitry 50 of IMD 10 and / or processing circuitry of another device that retrieves data from IMD 10 may analyze the temperature values ​​to identify an infection condition according to the techniques of this disclosure.

[0065] Communication circuitry 54 may include any suitable hardware, firmware, software, or any combination thereof, for communicating with another device, such as external device 12, another networked computing device, or another IMD or sensor. Under the control of processing circuitry 50, communication circuitry 54 may receive downlink telemetry from and send uplink telemetry to external device 12 or another device via an internal or external antenna, such as antenna 26. Additionally, processing circuitry 50 may communicate with external devices (e.g., external device 12) and other devices, such as Medtronic. The antenna 26 and the communication circuit system 54 can be configured to communicate with the networked computing devices via inductive coupling, electromagnetic coupling, near field communication, RF communication, WI-FI™ or other proprietary or non-proprietary wireless communication schemes to transmit and / or receive signals. For example, processing circuitry 50 may provide data to be transmitted uplink to external device 12 via communication circuitry 54 and control signals using an address / data bus. In some examples, communication circuitry 54 may provide received data to processing circuitry 50 via a multiplexer.

[0066] In other examples, processing circuitry 50 may transmit temperature data to external device 12 via communication circuitry 54. For example, IMD 10 may transmit temperature measurements collected by external device 12, which may then be analyzed by external device 12. In such examples, external device 12 performs the processing techniques described herein. Alternatively, IMD 10 may perform the processing techniques and transmit the processed temperature data and / or an indication of whether an infection is detected to external device 12 for reporting purposes, such as for providing an alert to patient 4 or another user.

[0067] In some examples, storage device 56 includes computer-readable instructions that, when executed by processing circuitry 50, cause IMD 10 and processing circuitry 50 to perform the various functions attributed to IMD 10 and processing circuitry 50 herein. Storage device 56 may include any volatile, non-volatile, magnetic, optical, or electronic medium. For example, storage device 56 may include random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically erasable programmable ROM (EEPROM), erasable programmable ROM (EPROM), flash memory, or any other digital medium. As an example, storage device 56 may store programmed values ​​for one or more operating parameters of IMD 10 and / or data collected by IMD 10 for transmission to another device using communication circuitry 54. The data stored by storage device 56 and transmitted by communication circuitry 54 to one or more other devices may include a temperature value.

[0068] The various components of IMD 10 are coupled to power source 91, which may include a rechargeable or non-rechargeable battery. For example, a non-rechargeable battery may be able to hold a charge for several years, while a rechargeable battery may be inductively charged daily, weekly, or annually from an external device (e.g., external device 12).

[0069] Figure 3 It is a display Figure 1 A conceptual side view of an example configuration of an IMD 10. Figure 3 In the example shown in FIG, IMD 10 can include a leadless device having housing 15 and insulating cover 76. Electrodes 16 can be formed or placed on an outer surface of cover 76. Figure 2 The described circuitry 50-58 and / or sensor 62 may be formed or positioned on an inner surface of cover 76 or otherwise formed or positioned within housing 15. Sensor 62 may include one or more temperature sensing devices secured to housing 15 or insulating cover 76 of IMD 10, in place of or in addition to temperature sensors within housing 15. In a non-limiting example, IMD 10 may include more than two temperature sensing devices on the interior side of IMD 10, and additionally, more than two temperature sensing devices may be included on the exterior side of IMD 10. In some examples, temperature data obtained from multiple temperature sensing devices may be averaged or otherwise combined to obtain a representative temperature signal, which may also be used as described with reference to FIG. Figure 6 Smoothed as discussed.

[0070] In the illustrated example, antenna 26 is formed or positioned on the inner surface of cover 76, but in some examples, it may be formed or positioned on the outer surface. In some examples, one or more of sensors 62 may be formed or positioned on the outer surface of cover 76. In some examples, insulating cover 76 may be positioned over housing 15 such that housing 15 and insulating cover 76 surround antenna 26, sensor 62, and / or circuitry 50-62 and protect the antenna, sensors, and circuitry from fluids.

[0071] One or more of antenna 26, sensor 62, and / or circuitry 50-58 can be formed on the inside of insulating cover 76, such as using flip-chip technology. Insulating cover 76 can be flipped over onto housing 15. When flipped over and placed onto housing 15, the components of IMD 10 formed on the inside of insulating cover 76 can be positioned within gap 78 defined by housing 15. Electrode 16 can be electrically connected to switching circuitry 58 via one or more vias (not shown) formed through insulating cover 76. Insulating cover 76 can be formed from sapphire (i.e., corundum), glass, parylene, and / or any other suitable insulating material. Housing 15 can be formed from titanium or any other suitable material (e.g., a biocompatible material). Electrode 16 can be formed from any of stainless steel, titanium, platinum, iridium, or alloys thereof. In addition, electrode 16 can be coated with a material such as titanium nitride or fractal titanium nitride, although other suitable materials and coatings for such electrodes can be used.

[0072] In a non-limiting example, one or more temperature sensing devices can be formed or placed on an outer surface of the housing 15 or the insulating cover 76, and additional sensors 62, such as one or more additional temperature sensing devices, can be formed within the housing 15, such as on a printed circuit board (PCB) disposed within the housing 15. In some examples, the temperature sensing device can be formed or placed on an outer surface of the housing 15 or the insulating cover 76 using a connection interface. In some cases, the connection interface can include a wired connection interface. For example, the temperature sensing device can be placed on the outer surface of the housing 15 or the insulating cover 76 using a press-fit connector, solder paste, conductive mounting pins, input-output cables or other wire connectors, threaded connectors, wire pads, press-in pins, etc., or various combinations thereof. In examples involving a wireless connection interface, the temperature sensing device can include communication and processing circuitry to transmit the temperature value to one or more other devices, such as to the communication circuitry 54, the communication circuitry 82, or otherwise via the network 92. In one illustrative example, sensor 62 on the outer surface of cover 76 may be connected to circuitry within housing 15 via one or more through-holes (not shown) formed through insulating cover 76 .

[0073] Figure 4 is a block diagram showing an example configuration of components of the external device 12. Figure 4 In the example of , external device 12 includes processing circuitry 80 , communication circuitry 82 , storage 84 , and user interface 86 .

[0074] Processing circuitry 80 may include one or more processors configured to implement functions and / or process instructions for execution within external device 12. For example, processing circuitry 80 may be capable of processing instructions stored in storage device 84. Processing circuitry 80 may include, for example, a microprocessor, a DSP, an ASIC, an FPGA, or equivalent discrete or integrated logic circuitry, or a combination of any of the foregoing devices or circuitry. Thus, processing circuitry 80 may include any suitable structure, whether hardware, software, firmware, or any combination thereof, to perform the functions of processing circuitry 80 described herein.

[0075] Communication circuitry 82 may include any suitable hardware, firmware, software, or any combination thereof, for communicating with another device, such as IMD 10. Under the control of processing circuitry 80, communication circuitry 82 may receive downlink telemetry from IMD 10 or another device and send uplink telemetry thereto. Communication circuitry 82 may be configured to communicate via inductive coupling, electromagnetic coupling, near field communication (NFC), RF communication, WI-FI TM Communication circuitry 82 may also be configured to communicate with devices other than IMD 10 via any of various forms of wired and / or wireless communications and / or network protocols.

[0076] Storage device 84 can be configured to store information within external device 12 during operation. Storage device 84 can include computer-readable storage media or a computer-readable storage device. In some examples, storage device 84 includes one or more of short-term memory and long-term memory. Storage device 84 can include, for example, RAM, DRAM, SRAM, a magnetic disk, an optical disk, flash memory, or various forms of EPROM or EEPROM. In some examples, storage device 84 is used to store data indicating instructions executed by processing circuitry 80. Storage device 84 can be used by software or applications running on external device 12 to temporarily store information during program execution. Storage device 84 can also store historical temperature data, current temperature data, and the like.

[0077] The data exchanged between external device 12 and IMD 10 may include operating parameters (e.g., resolution parameters regarding the resolution of the temperature measurements, such as a sampling rate). External device 12 may transmit data including computer-readable instructions that, when implemented by IMD 10, may control IMD 10 to change one or more operating parameters and / or export collected data. For example, processing circuitry 80 may transmit instructions to IMD 10 requesting IMD 10 to export collected data (e.g., temperature data) to external device 12. In turn, external device 12 may receive the collected data from IMD 10 and store the collected data in storage device 84. Processing circuitry 80 may implement any of the techniques described herein to analyze the temperature values ​​received from IMD 10, e.g., to determine an indication of infection. Using the temperature analysis techniques disclosed herein, processing circuitry 80 may determine an infection status of patient 4 and / or generate an alarm based on the infection status.

[0078] A user, such as a clinician or patient 4, can interact with external device 12 through user interface 86. User interface 86 includes a display (not shown), such as a liquid crystal display (LCD) or light emitting diode (LED) display or other type of screen, which processing circuit system 80 can use to present information related to IMD 10, such as the cardiac EGM, an indication of the detection of temperature changes, and a quantification of the temperature changes. In addition, user interface 86 can include an input mechanism for receiving input from the user. The input mechanism can include, for example, any one or more of a button, a keypad (e.g., an alphanumeric keypad), a peripheral pointing device, a touch screen, or another input mechanism that allows a user to navigate through a user interface presented by processing circuit system 80 of external device 12 and provide input. In other examples, user interface 86 also includes an audio circuit system for providing auditory notifications, instructions, or other sounds to the user, receiving voice commands from the user, or both.

[0079] The power supply 108 delivers operating power to the components of the external device 12. The power supply 108 may include a battery and a power generation circuit for generating the operating power. In some embodiments, the battery may be rechargeable to allow for extended operation. Recharging may be accomplished by electrically coupling the power supply 108 to a holder or plug connected to an alternating current (AC) outlet. Additionally or alternatively, recharging may be accomplished through proximal inductive interaction between an external charger and an inductive charging coil within the external device 12. In other embodiments, conventional batteries (e.g., nickel-cadmium or lithium-ion batteries) may be used. Alternatively, the external device 12 may be directly coupled to an AC outlet to power the external device 12. The power supply 108 may include circuitry for monitoring the remaining power within the battery. In this manner, the user interface 86 may provide a current battery level indicator or a low battery level indicator when the battery needs to be replaced or recharged. In some cases, the power supply 108 may be able to estimate the remaining operating time using the current battery.

[0080] Figure 5 is a block diagram illustrating an example system including an access point 90, a network 92, an external computing device (e.g., a server 94), and one or more other computing devices 100A-100N (collectively, “computing devices 100”) that may be coupled to IMD 10 and external device 12 via network 92, in accordance with one or more techniques described herein. In this example, IMD 10 may communicate with external device 12 via a first wireless connection using communication circuitry 54 and with access point 90 via a second wireless connection. Figure 5 In the example of FIG, access point 90, external device 12, server 94, and computing device 100 are interconnected and can communicate with each other via network 92. Network 92 can include a local area network, a wide area network, or a global network such as the Internet. In some aspects, the same Medtronic The general network technology and functions provided by the network are similar to the general network technology and functions implemented Figure 5 system.

[0081] Access point 90 can include a device connected to network 92 via any of a variety of connections, such as a telephone dial-up, a digital subscriber line (DSL), or a cable modem connection. In other examples, access point 90 can be coupled to network 92 via different forms of connection, including a wired connection or a wireless connection. In some examples, access point 90 can be a user device that can be co-located with the patient, such as a tablet computer or a smartphone. IMD 10 can be configured to transmit data, such as a temperature value, an infection indicator value, and / or an electrocardiogram (EGM), to access point 90. Access point 90 can then transmit the retrieved data to server 94 via network 92.

[0082] In some cases, server 94 may be configured to provide a secure storage site for data that has been collected from IMD 10 and / or external device 12. In some cases, server 94 may compile the data in a web page or other document for viewing by a trained professional, such as a clinician, via computing device 100. Figure 5 One or more aspects of the illustrated system may be similar to those provided by Medtronic It is implemented using the general network technologies and functions provided by the network.

[0083] In some examples, server 94 may monitor IMD temperature, for example, based on measured temperature information received from IMD 10 and / or external device 12 via network 92, to identify the infection status of patient 4 using any of the techniques described herein. Server 94 may provide alerts regarding the infection status of patient 4 to patient 4 via access point 90 via network 92 or to one or more clinicians via computing device 100. In examples where IMD 10 and / or external device 12 monitor temperature as described above, server 94 may receive alerts from IMD 10 or external device 12 via network 92 and provide alerts to one or more clinicians via computing device 100. In some examples, server 94 may generate a webpage to provide alerts and information about the infection status of patient 4 and may include memory for storing alerts and diagnostic or physiological parameter information for multiple patients.

[0084] In some examples, one or more of computing devices 100 may be a tablet computer or other smart device located with a clinician, which the clinician can program to receive alerts and / or query IMD 10. For example, a clinician can access data collected by IMD 10 through computing device 100, such as when patient 4 is between clinician visits, to check the status of a medical condition. In some examples, the clinician can enter instructions for medical intervention for patient 4 into an application executed by computing device 100, such as based on the status of the patient's condition as determined by IMD 10, external device 12, server 94, or any combination thereof, or based on other patient data known to the clinician. Device 100 can then transmit the medical intervention instructions to another of computing devices 100 located with patient 4 or a caregiver of patient 4. For example, such instructions for medical intervention may include instructions for changing medication dosage, timing, or selection, for scheduling a visit with a clinician, or for seeking medical attention. In another example, computing device 100 may generate an alert to patient 4 based on the status of patient 4's medical condition, which may enable patient 4 to proactively seek medical attention before receiving medical intervention instructions. In this way, patient 4 may be empowered to take action as needed to address his or her medical condition, which may help improve clinical outcomes for patient 4.

[0085] In by Figure 5 In the example shown, server 94 includes, for example, a storage device 96 and processing circuitry 98 for storing data retrieved from IMD 10. Figure 5 Not shown, computing device 100 may similarly include storage devices and processing circuitry. Processing circuitry 98 may include one or more processors configured to implement functions and / or processing instructions for execution within server 94. For example, processing circuitry 98 may be capable of processing instructions stored in storage device 96 (e.g., stored in memory). Processing circuitry 98 may include, for example, a microprocessor, a DSP, an ASIC, an FPGA, or equivalent discrete or integrated logic circuitry, or a combination of any of the foregoing devices or circuitry. Thus, processing circuitry 98 may include any suitable structure, whether hardware, software, firmware, or any combination thereof, to perform the functions of processing circuitry 98 described herein. Processing circuitry 98 of server 94 and / or processing circuitry of computing device 100 may implement any of the techniques described herein to analyze temperature values ​​received from IMD 10, for example, to determine an infection status of patient 4 (e.g., device pocket infection).

[0086] Storage device 96 may include a computer-readable storage medium or a computer-readable storage device. In some examples, storage device 96 includes one or more of short-term memory and long-term memory. Storage device 96 may include, for example, RAM, DRAM, SRAM, a magnetic disk, an optical disk, flash memory, or various forms of EPROM or EEPROM. In some examples, storage device 96 is used to store data indicative of instructions to be executed by processing circuitry 98.

[0087] Figure 6 is a flow chart illustrating example operations for identifying an infection status of a patient in accordance with one or more techniques disclosed herein. Although described as being performed by IMD 10, Figure 6 The example methods of the present disclosure may be performed by any one or more of IMD 10, external device 12, or server 94, e.g., by processing circuitry or sensing circuitry of any one or more of these devices. For example, IMD 10 may transmit a temperature value or other temperature data to external device 12 and / or server 94, where external device 12 and / or server 94 perform various other techniques of the present disclosure.

[0088] In some examples, processing circuitry, such as processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, or sensing circuitry 52, may determine a temperature value over time based on the temperature signal (602). For example, processing circuitry 50 may obtain raw temperature data from one or more of sensors 62. For example, sensors 62 may include one or more temperature sensing devices positioned within or near IMD 10. Sensors 62 may detect the temperature in and / or around IMD 10. In another example, processing circuitry 80 of external device 12 or processing circuitry 98 of server 94 may receive the temperature value from IMD 10.

[0089] In some cases, the temperature sensing device of sensor 62 may sense a temperature value that includes an increase or decrease in the temperature of IMD 10 . Figure 7 An illustration of a graph showing temperature increases and decreases obtained from a temperature sensing device of IMD 10 is provided. Figure 7 Temperature data that can be obtained over time from one of the sensors 62 configured as a temperature sensing device is shown. One skilled in the art will appreciate that temperature values ​​can be acquired to include any temperature unit, including Celsius (C°), Fahrenheit (F°), etc., and can be converted between units thereof depending on the particular embodiment. Figure 7The x-axis shows "days" as an example x-axis unit, but the technology of the present disclosure is not limited in this regard, and temperature values ​​may be collected at any rate or periodicity. In some instances, the sensing circuit system 52 may obtain temperature values ​​from the sensor 62 every second, every minute, every hour, every day, etc., or may obtain temperature values ​​from the sensor 62 in a non-periodic manner. For example, the sensing circuit system 52 may obtain temperature values ​​over time in response to a triggering event (such as in response to an input temperature request signal from the external device 12), or may perform random temperature measurements at random times during a set time period (e.g., randomly every day). In some instances, the sensing circuit system 52 may determine the temperature value based on a sampling rate of one of the temperature sensing devices of the sensor 62. In some instances, the temperature values ​​obtained over time may be obtained daily, such as Figure 7 In some cases, the temperature values ​​obtained over time can be obtained on any periodic or non-periodic basis, or a combination thereof.

[0090] Processing circuitry 50 may determine the temperature value of IMD 10 over time as a series of discrete temperature values. In some examples, processing circuitry 50 may determine the temperature value at a sampling rate during each of a plurality of sampling time periods during a predefined time period. For example, processing circuitry 50 may determine the temperature value at a sampling rate of twice per hour over a 24-hour period. In another example, processing circuitry 50 may determine the temperature value at a sampling rate of once per hour during specific times of the day, such as between 11:00 PM and 6:00 AM or between 7:00 AM and 5:00 PM. In some examples, processing circuitry 50 may determine the temperature value at a sampling rate of once per minute.

[0091] In some examples, processing circuitry 50 may determine the sampling rate parameter based on input received from a user specifying such parameters. For example, processing circuitry 50 may receive the sampling rate parameter provided by the user via user interface 86 from external device 12. In any case, processing circuitry 50 may use various filtering techniques (e.g., a moving average filter, a digital filter, a low-pass differentiator filter, etc.) to smooth the temperature values ​​measured over time. For example, processing circuitry 50 may determine the average of discrete temperature values ​​measured at a particular sampling rate to determine the average of the temperature values ​​over time. In some examples, the average may comprise a series of averages, such as a moving average of the temperature values, low-pass filtered data, or other smoothing methods discussed herein.

[0092] It should be noted that those skilled in the art will understand that the illustrative diagrams referenced herein, such as Figure 7 、 8, 11 and 13 may be embodied in other forms, such as data tables. For example, a processing circuit system, such as processing circuit system 50 of IMD 10, processing circuit system 80 of external device 12, or processing circuit system 98 of server 94, may store temperature values ​​and / or smoothed temperature signals in a database (e.g., of storage device 56, storage device 84, or storage device 96). The database may include any data structure (and / or combination of multiple data structures) for storing and / or organizing data, including but not limited to relational databases (e.g., Oracle databases, MySQL databases, etc.), non-relational databases (e.g., NoSQL databases, etc.), in-memory databases, spreadsheets, such as comma-separated value (CSV) files, extensible markup language (XML) files, TeXT (TXT) files, flat files, spreadsheet files, and / or any other widely used or proprietary data storage formats. For example, the smoothed temperature signals may be arranged as separate structured XML fragments. That is, although Figure 7 、 8 , 11, and 13 are shown as graphical representations of temperature values ​​over time, but the technology of this disclosure is not limited in this regard, and processing circuitry, such as processing circuitry 50, may process or store temperature values ​​in other forms.

[0093] In some examples, processing circuitry, e.g., processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, or sensing circuitry 52, may smooth temperature values ​​measured over time to produce a smoothed temperature signal (604). Figure 8 An example illustration is provided showing a graph of a smoothed signal 802 based on a temperature value 702 obtained from one of the sensors 62 . Figure 8 Smoothed signal 802 represents changes over time relative to temperature value 702. The smoothed signal may be smoothed to reduce the amount of noise in temperature value 702 caused by various factors, including environmental factors, diurnal variations, and the like. For example, a particular detection model may be more sensitive to temperature changes during portions of the day when body temperature is naturally elevated but infection may not actually be present. Additionally, diurnal variations may be attributable to the amount of physical activity of patient 4, particular clothing worn by patient 4 that elevates patient 4's body temperature, and the like. In any case, processing circuitry 50 may perform more accurately to detect device pocket infection when smoothing the signal using various smoothing methods, including applying a low-pass filter or determining a moving average (e.g., a low-pass finite impulse response (FIR) filter, and the like).

[0094] In one example, processing circuitry, such as processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, or sensing circuitry 52, may apply a low-pass filter to the plurality of temperature values ​​to determine a smoothed signal. For example, processing circuitry 50 may use a digital filter, or in some cases, an analog filter, to smooth the temperature values. In one example, processing circuitry 50 may apply a digital filter that increases the signal-to-noise ratio (SNR) to produce a smoothed temperature signal by filtering out high-frequency noise or other high-frequency variations from the temperature values ​​measured over time. In another example, processing circuitry 50 may use a low-pass differentiator filter to smooth the temperature values, the low-pass differentiator filter performing smoothing based on predefined coefficients and / or a smoothing differentiator filter function to remove high-frequency variations in the temperature values ​​measured over time.

[0095] In some instances, the processing circuit system 50 can apply a low-pass filter that passes low-frequency temperature variations while blocking high-frequency temperature variations. The low-pass filter can have a predefined cutoff frequency that attenuates temperature variations that exceed the cutoff frequency. In this way, the processing circuit system 50 can apply a filter to filter, smooth, or otherwise account for normal daily variations in temperature values. In some instances, a low-pass filter such as a moving average filter or other smoothing filter can be applied to remove normal variations in temperature that occur on a daily basis. For example, in any given day, the temperature value may increase during parts of the day when the ambient temperature is elevated or when people are active, and decrease during parts of the day when the ambient temperature is lower.

[0096] In an illustrative example, the processing circuit system 50 can record temperature data at a specific rate. For example, the processing circuit system 50 can record temperature data at 1 sample / minute. In addition, the processing circuit system 50 can sample the temperature data at a specific sampling rate, such as at a sampling rate of X times per day. For example, the processing circuit system 50 can sample the temperature data at 1 sample / 360 minutes (for example, 4 times per day). In any case, the processing circuit system 50 can set the cutoff frequency of the low-pass filter to a specific nominal value. For example, the processing circuit system 50 can set the cutoff frequency to 1 / 10,000 minute or approximately 7 days. In some cases, the cutoff frequency value can be predefined by the user. Further, the low-pass filter can be a Butterworth filter. In a non-limiting example, the low-pass filter can be a sixth-order Butterworth low-pass filter.

[0097] By applying a smoothing filter, processing circuitry, such as processing circuitry 50, can attribute less weight to normal variations in daily temperature values ​​when determining an infection indicator value, and instead determine the infection indicator value from a less noisy signal. This is because high-frequency variations tend to be consistent from day to day, but the actual amplitude of low-frequency temperature values ​​may still vary over time, for example, in response to the presence of an infectious agent in the body. Thus, the low-pass filter can remove high-frequency variations from the overall temperature value while allowing low-frequency variations to still appear in the smoothed signal, such as Figure 7 and 8 The differences between are shown.

[0098] In some examples, processing circuitry, such as processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may determine a moving average of a plurality of temperature values ​​over time to determine a smoothed signal. For example, processing circuitry 50 may calculate a moving average of temperature values ​​702 to generate smoothed signal 802. In some examples, processing circuitry 50 may employ a moving average filter to generate a smoothed temperature signal, such as smoothed signal 802. The moving average may be based on a resolution parameter such that the moving average is determined based on a resolution of daily, bi-day, hourly, etc. In other words, processing circuitry 50 may calculate the moving average every hour, every day, etc.

[0099] In some examples, processing circuitry 50 may determine a moving average of the temperature value each day based on the average of the temperature values ​​for the week, month, or other arbitrary time period preceding the current moving average determination. In a non-limiting example, processing circuitry 50 may determine a moving average of the temperature value for day 10 by determining the average of the temperature values ​​for the previous 10 days (days 1-10). Alternatively, processing circuitry 50 may determine a moving average of the temperature value for day 11 by determining the average value for the same time period (days 2-11). In this manner, processing circuitry 50 may determine a moving average based on a first-in, first-out (FIFO) buffer (e.g., an X-day FIFO buffer) that stores a finite amount of temperature data over time. For example, the FIFO buffer may be a 10-day FIFO buffer that stores temperature values, average temperature values, a moving average of temperature values, etc. for 10 days at a time. In some examples, the moving average may be based on multiple moving averages determined over time. For example, the moving average for day 11 may be the sum of A2+A3+…+A11 divided by 11, where A represents the average value for the time period indicated by the subscript. For example, A1 may be the average temperature for day 1. A2 may be the average temperature values ​​for day 1 and day 2, or in some cases, A2 may be determined based on A1 and the average temperature for day 2. In another example, processing circuitry 50 may use an exponential moving average (EMA) or other weighted moving average (WMA) to determine the moving average.

[0100] In another example where the solution involves determining a moving average daily, processing circuitry 50 may determine the moving average for day 30 as the average of the temperature values ​​measured over the past 30 days, and may determine the moving average for day 31 as the average or moving average of the temperature values ​​measured over the past 31 days. In either case, a moving average filter may be used to generate a continuously updated average temperature. In this manner, the moving average filter may define a current directional trend for a set of temperature values.

[0101] In some examples, processing circuitry, e.g., processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may apply an infection detection model to the smoothed temperature signal to determine an infection indicator value, the infection detection model comprising one or more of a sliding window detection model or a multiple low-pass filter integration model (606). For example, processing circuitry 50 may apply a sliding window detection model to the smoothed temperature signal to determine an infection indicator value. In some examples, the sliding window detection model may comprise utilizing one or more sliding windows with respect to the smoothed temperature signal to determine an infection indicator value. Figure 9-11 Further described are example detection models that utilize a sliding window, such as one sliding window or multiple sliding windows.

[0102] In some examples, processing circuitry 50 may not apply an infection detection model until a predetermined amount of time has passed after implantation of IMD 10, or may not apply certain infection detection models during such an initial time period after implantation (e.g., a recovery period). For example, processing circuitry 50 may not apply a multiple low-pass filter detection model during the initial recovery period and may instead apply a different detection model during this time period. In another example, processing circuitry 50 may apply one or more infection detection models to determine early-stage device pocket infection, such as during the recovery period after implantation of IMD 10.

[0103] In some instances, the processing circuitry 50 may not apply one or more of the filters described herein to determine various smoothed temperature signals until a predefined amount of time has passed since implantation. In a non-limiting and illustrative example, the processing circuitry 50 may wait until 14-30 days have passed since device implantation. In any case, the predefined amount of time may be selected to coincide with a detected immune response (e.g., a temperature spike after implantation) because the immune response may cause abnormal bag temperature until healing occurs or is complete. At an initialization point, the filtered or smoothed temperature signal may be initialized. For example, the smoothed signal may be initialized to average the temperature values ​​over the last X days. In one example, after a predefined amount of time has passed since implantation or after the immune response temperature has risen, the smoothed signal may be determined based on an average of the temperature values ​​over the last 4 days.

[0104] In some examples, processing circuitry 50 may utilize patient 4's immune response when applying an infection detection model. For example, processing circuitry 50 may determine patient 4's immune response during the initial recovery period after IMD 10 implantation. Thus, processing circuitry 50 may adjust parameters of one or more infection detection models within the infection detection model based on patient 4's specific immune system. In some examples, processing circuitry 50 may determine that an initial response to implantation of IMD 10 has occurred based on an initial temperature change after implantation. This initial temperature change may occur due to patient 4's immune response in the days following IMD 10 implantation. Processing circuitry 50 may determine a measure of patient 4's immune response based on the magnitude of the initial temperature increase. Processing circuitry 50 may utilize the immune response measure to adjust parameters of any one of the detection models to improve the accuracy of the detection model. For example, processing circuitry 50 may automatically adjust threshold parameters of the detection model based on the patient 4-specific immune response measure.

[0105] In some examples, processing circuitry, such as processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may determine an infection indicator value based on the infection detection model used (608). For example, processing circuitry 50 of IMD 10 may determine the infection indicator value by applying one or more sliding window detection models. In another example, processing circuitry 50 of IMD 10 may determine the infection indicator value by applying a multiple low-pass filter detection model.

[0106] In some examples, processing circuitry, e.g., processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may compare 610 the infection indicator value to a predefined threshold. For example, processing circuitry 50 of IMD 10 may employ a comparator to compare the infection indicator value to the threshold. In some examples, the threshold may vary depending on the detection model used. In some examples, the threshold may be a static threshold. In some examples, the threshold may include multiple interleaved thresholds to act as a hysteresis threshold.

[0107] In some examples, processing circuitry, such as processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may determine the patient's infection status (612) based, at least in part, on the infection indicator value satisfying a threshold. For example, processing circuitry 50 may determine that the infection indicator value exceeds or is equal to a threshold, and therefore satisfies the threshold. In some examples, the threshold may be set to a value based on empirical data. In an illustrative example, processing circuitry 50 may determine that the infection indicator value is 8.1 using a multipass filter integration model. In some examples, the infection indicator value represents an integrated sum of temperature value differences. Thus, the infection indicator value (e.g., 8.1 above) may be expressed in units of nominal temperature units (e.g., degrees). In another example, the infection indicator value may have units that depend on the output of one of sensors 62 (e.g., mV, mA, etc.). For example, one of sensors 62 may be a temperature sensor that outputs values ​​in millivolts (mV). The processing circuitry 50 can determine the integrated sum of the difference values ​​as an infection indicator value, which can then be expressed as a function of the mV output of the corresponding sensor 62. In some instances, the infection indicator value and threshold value can be unitless values ​​that increase, decrease, or are configured as described herein, and the specific values ​​identified herein are merely examples, which can vary based on the configuration of the infection indication technology for a particular patient, patient category, particular temperature sensor or IMD, or category of temperature sensor or IMD. Based on empirical data studies, the threshold value for such a model can be set to be satisfied by a difference value equal to or exceeding 8.0. Similarly, threshold values ​​can be set for all or some of the infection detection models to cover a range of threshold values, such that the threshold value comprises a range of threshold values. A user or program can set one or more threshold values ​​based on the type of detection model. In some cases, the threshold value can be based on empirical studies. The threshold value can be different for different detection models. For example, the threshold value for a multiple low-pass filter detection model can be different from the threshold value defined for a maximum-minimum detection model. In a non-limiting example and for illustration purposes only, the threshold for a multiple low-pass filter detection model may be greater than 100 (e.g., 200), while the threshold for another detection model may be between 2 and 3 (e.g., 2.5). The difference in thresholds may vary because the type of detection model used may produce a different range of output values.

[0108] Depending on the general placement of one or more temperature sensing devices, the infection status may indicate an infection in the device pocket of IMD 10. For example, at least one temperature sensing device that measures temperature values ​​(e.g., temperature value 702) over time may be located within or near a medical device, such as IMD 10, implanted in a device pocket of patient 4. Thus, the infection status may indicate the presence of an infection in the device pocket of IMD 10. In some instances, IMD 10 may also detect an infection elsewhere in patient 4's body based on the infection indicator value. In some instances, the temperature of IMD 10 may increase when the core body temperature of patient 4 increases, in which case the temperature increase may not be due to a device pocket infection. However, in the case of a device pocket infection, the increase in patient 4's core body temperature may lag behind the increase in IMD 10's temperature. In such instances, processing circuitry, such as processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may determine that the infection indicator value includes an indication of a device pocket infection at the implant site of IMD 10.

[0109] In some examples, processing circuitry 50 may receive core body temperature values ​​from another temperature sensing device, such as an external thermometer, and may compare the smoothed or raw temperature data with the core body temperature data to determine whether an increase in one temperature lags or leads the other. In some cases, an increase in core body temperature due to factors other than a device pocket infection may cause an increase in IMD 10 temperature. In other cases, processing circuitry 50 may increase a confidence interval for a device pocket infection based on such a comparison. For example, when an increase in IMD 10 temperature causes an infection-indicating value to exceed a threshold, but patient 4's core body temperature is not increasing at a specific rate, processing circuitry 50 may increase the confidence interval. In this manner, the confidence interval may indicate a high degree of confidence that an increase in IMD 10 temperature may be an early indicator of a device pocket infection.

[0110] In some examples, a system for determining a patient's infection status can include multiple temperature sensing devices that provide temperature values, wherein at least one temperature sensor is located within or affixed to IMD 10. One or more other temperature sensing devices can be located within or affixed to IMD 10, and in some cases, some temperature sensing devices can also be located in or on other parts of patient 4's body, such as with another IMD or external device that communicates via network 92. The type of temperature sensing device can be different from or the same as the type of temperature sensing device (e.g., sensor 62) of IMD 10. In some examples, the other temperature sensing devices can be configured to measure core body temperature. In such examples, processing circuitry 50 can determine multiple temperature values ​​over time based at least in part on temperature measurements from each of the temperature sensors. For example, processing circuitry 50 can determine multiple temperature values ​​over time based at least in part on temperature measurements from each of the temperature sensors included with IMD 10.

[0111] In some cases, processing circuitry 50 may determine a high confidence interval for a device pocket infection in IMD 10 where one of sensors 62 measures a temperature that results in an infection-indicating value that meets a threshold, but another IMD 10 or a second sensor of an external medical device measures a temperature value within a normal range or within a range that does not indicate a separate body bacterial infection (e.g., ear, lung, skin, throat, bladder, or kidney infection, or other non-device pocket infection). In some instances, infection-indicating values ​​obtained from a temperature sensor device from IMD 10 may be used to indicate a separate body infection (e.g., ear, lung, skin, throat, bladder, kidney, etc.) because, for example, an increase in core temperature may precede or otherwise be antecedent to an increase in the temperature of IMD 10. In this manner, multiple temperature sensors may be used to determine whether a device pocket infection or another type of infection has occurred. In some cases, multiple temperature sensors may be included with a single IMD 10. Similarly, temperature values ​​from each of the multiple temperature sensors may be compared to determine whether the temperature increase stems from a device pocket infection or whether the source of the temperature increase is located elsewhere, remote from IMD 10. For example, the temperature value from a temperature sensor on the outside of IMD 10 may increase before the temperature value from within IMD 10, indicating that the temperature increase may not be due to a device pocket infection. In some examples, processing circuitry 50 may also determine core body temperature and IMD 10 temperature values ​​via one or more temperature sensing devices of IMD 10.

[0112] Although described as being performed by processing circuitry 50, these techniques may be performed by any one or more of IMD 10, external device 12, or server 94, e.g., by processing or sensing circuitry of any one or more of these devices. For example, IMD 10 may transmit raw temperature data to external device 12, where external device 12 may determine a first infection indicator value. In some instances, external device 12 may include multiple computing devices (e.g., a remote cloud server) that collectively determine the infection status of patient 4. Additionally, it should be understood that components of system 2 (e.g., processing circuitry 50, processing circuitry 80, etc.) may perform the reference processing in parallel or in conjunction with one another. Figure 6-16 Some or all of the techniques described above. Figure 6 The technique can also be performed periodically.

[0113] Figure 9 is a flow chart illustrating an example method for applying an example sliding window detection model to determine the infection status of patient 4 in accordance with one or more techniques of the present disclosure. Although described as being performed by IMD 10, Figure 9 The example methods may be performed by any one or more of the IMD 10, the external device 12, or the server 94, e.g., by processing circuitry or sensing circuitry of any one or more of these devices. For example, the processing circuitry 50 may apply a detection model to determine the infection status of the patient 4. The processing circuitry 50 may periodically apply the detection model to determine the infection status at various intervals. In some instances, the processing circuitry 50 may determine the infection status in response to a user request for an infection status update. In some instances, the external device 12 may determine the infection status of the patient 4. For brevity, reference is made to the IMD 10 (e.g., reference is made to the Figure 3 Certain techniques are described herein with reference to components of IMD 10 described herein. However, those skilled in the art will appreciate that, in some examples, external device 12 and components of external device 12 may utilize input from IMD 10 to determine infection status. In some examples, server 94 (e.g., a cloud server) may receive data from external device 12 or directly from IMD 10 and perform certain techniques of this disclosure.

[0114] In some examples, processing circuitry, e.g., processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may apply one or more of a max-min detection model or a rate-of-change detection model as a sliding window detection model. For example, processing circuitry 50 may apply a max-min detection model as a Figure 6In another example, the processing circuit system 50 may apply a rate of change detection model as Figure 6 Sliding window detection model.

[0115] In the case of a max-min detection model, processing circuitry 50 may determine a plurality of sliding windows (906). For example, Figure 11 An example smoothed temperature signal 1002 is shown, similar to smoothed temperature signal 802, with a plurality of sliding windows 1006A and 1006B. Sliding windows 1006A and 1006B include respective subsets of temperature data points of the smoothed temperature signal. In a non-limiting example, sliding window 1006A includes temperature data points of the smoothed temperature signal from day 20 to day 30, while sliding window 1006B includes temperature data points from day 40 to day 50.

[0116] In some examples, processing circuitry, e.g., processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may determine that the sliding windows include the same number of days or other time units (e.g., hours). In some examples, the sliding windows may include different numbers of days or other time units. For example, sliding window 1006A may cover a span of 10 days (or 10 hours), while sliding window 1006B may cover a span of 8 days. In another example, sliding window 1006A may cover a span of 10 hours, while sliding window 1006B may cover a span of 8 hours. The sliding parameters may be determined based on the center point of each window and adding and subtracting a specific number of days to obtain the window (e.g., 5 days, 6 days, etc.).

[0117] In the illustrative example, processing circuitry, e.g., processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may determine sliding window 1006A by starting at a first point and adding 5 days and subtracting 5 days from the first point to obtain sliding window 1006A. Similarly, a second point offset by 5 days in both directions may be used to obtain a second sliding window 1006B. As described below, the first point and the second point may coincide with at least two temperature data points for a rate-of-change detection model. That is, the first point may be a first temperature data point for the rate-of-change detection model and the second point may be a second temperature data point, the first temperature data point and the second temperature data point defining sliding window 1004 for the rate-of-change detection model. In some examples, the temperature data points for the rate-of-change detection model sliding window may be determined without regard to windows 1006A and / or 1006B. Additionally, the first sliding window 1006A includes temperature data points corresponding to an earlier time period relative to the second sliding window 1006B, which includes temperature data points corresponding to a later time period.

[0118] In some examples, processing circuitry, e.g., processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may determine a minimum temperature value of smoothed signal 1002 relative to first sliding window 1006A (908). Additionally, processing circuitry, e.g., processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may determine a maximum temperature value of smoothed signal 1002 relative to second sliding window 1006B (910).

[0119] In some examples, processing circuitry, e.g., processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may determine a difference between the maximum temperature value and the minimum temperature value (912). For example, processing circuitry 50 may subtract the minimum temperature value from the maximum temperature value to determine the difference. In some examples, processing circuitry 50 may Figure 9 For example, the processing circuit system may slide the plurality of sliding windows, such as by returning to the Figure 9 The indicated number of sliding windows is determined.

[0120] Additionally, processing circuitry, such as processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may compare the difference to a predefined threshold (e.g., a reference threshold). Figure 612 ). When the difference satisfies a predefined threshold, processing circuitry, e.g., processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may determine an infection status. For example, when the difference of the maximum-minimum detection model exceeds a predefined threshold, processing circuitry 50 may determine that a device pocket infection has occurred with respect to IMD 10. If processing circuitry 50 determines that the difference does not satisfy the predefined threshold, the sliding window may be shifted and processing circuitry 50 may return to determine an updated plurality of sliding windows, as was performed for shifting the previous sliding window (e.g., shifting by one hour, shifting by one day). For example, processing circuitry 50 may shift the sliding window according to a resolution parameter that defines how often processing circuitry 50 determines the presence of an infection using one or more infection detection models.

[0121] Figure 10 is a flow chart illustrating an example method for applying an example rate of change detection model to determine the infection status of patient 4 in accordance with one or more techniques of the present disclosure. Although described as being performed by IMD 10, Figure 10 The example method may be performed by any one or more of the IMD 10, the external device 12, or the server 94, e.g., by processing circuitry or sensing circuitry of any one or more of these devices. For example, the processing circuitry 50 may apply a rate of change detection model to determine the infection state of the patient 4. The processing circuitry 50 may periodically apply the rate of change detection model to determine the infection state at various intervals. In some instances, the processing circuitry 50 may determine the infection state in response to a user request for an infection state update. In some instances, the external device 12 may determine the infection state of the patient 4. For brevity, reference is made to the IMD 10 (e.g., reference is made to the Figure 3 Certain techniques are described herein with reference to components of IMD 10 described herein. However, those skilled in the art will appreciate that, in some examples, external device 12 (e.g., components of external device 12) may utilize input from IMD 10 to determine infection status. In some examples, server 94 (e.g., a cloud server) may receive data from external device 12 or directly from IMD 10 and perform certain techniques of this disclosure.

[0122] In some examples, processing circuitry, such as processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may apply a rate-of-change detection model as Figure 6For example, the processing circuit system 50 may determine a sliding window (914). In the illustrative example, the sliding window may include temperature data points of the smoothed temperature signal 1002. For example, the sliding window may include a sliding window 1004 shown as a sliding box for illustrative purposes. The sliding window 1004 may be used to review data retroactively. In some examples, when the temperature data is collected in real time, the sliding window 1004 (and the reference Figure 9 10. In some embodiments, the sliding window 1004 may be a fixed time window with both edges of the sliding window 1004 sliding at the same rate. In some embodiments, the parameters of the sliding window 1004 may be set by a user. For example, processing circuitry 50 may receive user input specifying the parameters of sliding window 1004 or sliding window 1006, defining whether the sliding window is fixed and, if so, how the outer edges of the sliding window are to be defined or spaced apart over time.

[0123] In some examples, processing circuitry, such as processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may determine temperature data points based on the sliding window (918). For example, processing circuitry 50 may determine at least two temperature data points for sliding window 1004. In such examples, processing circuitry 50 may determine a first temperature data point and a second temperature data point for sliding window 1004. For example, processing circuitry 50 may determine the first temperature data point as corresponding to the leftmost data point of sliding window 1004 and the second temperature data point as corresponding to the rightmost data point of sliding window 1004, where the rightmost temperature data point may correspond to the most recent temperature value measured by IMD 10.

[0124] In some examples, processing circuitry, e.g., processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may determine a slope value between at least two temperature data points (920). In this illustrative example, Figure 11 A sliding window 1004 is shown that is defined by at least two temperature data points. In such an example, processing circuitry 50 may determine a slope value between a first temperature data point and a second temperature data point of sliding window 1004 (e.g., where sliding window 1004 intersects signal 1002). Figure 11The temperature data points in are shown as being spaced approximately 20 days apart, but the technology of the present disclosure is not so limited, and the processing circuit system 50 can determine the sliding window to space the temperature data points farther apart or closer in various instances. In a non-limiting example, the processing circuit system 50 can determine that at least two temperature data points of the sliding window are spaced 1-3 days apart. In some instances, sliding windows 1006A and 1006B can follow a similar pattern to have a center point spaced 1-3 days apart. In other instances, the sliding window can include more or less time spans. For example, sliding window 1004 or sliding window 1006 can include more than 3 days of temperature data or less than 1 day of data. In one example, sliding window 1006A can include 6 hours of temperature data collected immediately before the 21st day and 6 hours of temperature data collected continuously thereafter, with a total of 12 hours of temperature data defining sliding window 1006A.

[0125] In some examples, processing circuitry, such as processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may compare the slope value to a predefined threshold value (e.g., a reference value). Figure 6 When the slope value satisfies a predefined threshold, processing circuitry, such as processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, can determine an infection status. For example, when the slope value of the rate-of-change detection model exceeds a predefined threshold, processing circuitry 50 can determine that a device pocket infection has occurred with respect to IMD 10. If the slope value of the rate-of-change detection model does not exceed the predefined threshold, processing circuitry 50 can return to determining an updated sliding window and determine the slope value between updated temperature data points defining the updated sliding window. For example, processing circuitry 50 can shift sliding window 1004 forward in time by predefined intervals. In some instances, the sliding window can be applied retroactively to data points collected over time. In some instances, when temperature data is collected, the sliding window can be applied to the current temperature data such that the sliding window slides at the same rate as the sampling rate of the temperature data, or is otherwise synchronized with the generation of the smoothed temperature signal.

[0126] As previously mentioned Figure 6 As shown, Figure 10The above-described techniques can also be performed periodically. For example, the temperature value can be determined based on a resolution parameter setting of IMD 10 (e.g., a resolution parameter for transmitting a signal at a frequency at which sensor 64 should detect for temperature measurement). In other examples, the infection indicator value can be calculated independently of the resolution parameter. For example, IMD 10 can identify infection status at various time intervals daily (e.g., once in the morning, once in the afternoon, once in the evening, once after a meal, etc.). IMD 10 can identify infection status daily, weekly, biweekly, monthly, etc.

[0127] In some examples, IMD 10 may also identify an infection status in response to a user command (e.g., from a physician, from a user interface) or in response to the satisfaction of another condition (e.g., based on activity level or other physiological parameter). For example, IMD 10 may identify an infection status on a per-measurement basis, such as on a per-temperature basis. Those skilled in the art will appreciate that there may be various time periods when IMD 10 or external device 12 may transmit an infection status, a temperature value, and / or a smoothed temperature value, and / or otherwise identify an infection status for subsequent analysis.

[0128] In addition, in some cases, multiple sliding window detection models can be used in series to measure the infection status of patient 4. For example, in some cases, the rate of change detection model can be used in combination with the maximum-minimum detection model. In one example, the processing circuit system 50 can combine the difference measured using the maximum-minimum detection model with the slope value measured using the rate of change detection model. For example, the processing circuit system 50 can add together the values ​​measured according to the corresponding detection model. In addition, the processing circuit system 50 can multiply each value from the corresponding detection model by a predefined weighting parameter to scale the corresponding value. In some examples, the processing circuit system 50 can perform scaling before combining these values. In any case, the processing circuit system 50 can measure the infection status of patient 4 by determining whether the combined value exceeds a predefined threshold value. The predefined threshold value can be the same or different from the predefined threshold value used alone for any detection model (for example, multiple low-pass filter integrated model, rate of change detection model, etc.).

[0129] Figure 12 is a flow chart illustrating an example method for applying a detection model to determine the infection status of patient 4 in accordance with one or more techniques of the present disclosure. Although described as being performed by IMD 10, Figure 12The example method may be performed by any one or more of IMD 10, external device 12, or server 94, e.g., by processing circuitry or sensing circuitry of any one or more of these devices. For example, processing circuitry 50 may apply a detection model to determine the infection status of patient 4. Processing circuitry 50 may periodically apply the detection model to determine the infection status at various intervals. In some instances, processing circuitry 50 may determine the infection status in response to a user request for an infection status update. In some instances, external device 12 may determine the infection status of patient 4. For brevity, reference is made to IMD 10 (e.g., reference to FIG. 1 ). Figure 3 Certain techniques are described herein with reference to components of IMD 10 described herein. However, those skilled in the art will appreciate that, in some examples, external device 12 and components of external device 12 may utilize input from IMD 10 to determine infection status. In some examples, server 94 (e.g., a cloud server) may receive data from external device 12 or directly from IMD 10 and perform certain techniques of this disclosure.

[0130] In some examples, processing circuitry, such as processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may apply a multiple low-pass filter integration model. Figure 12 is an example of a multiple low-pass filter ensemble model used as an infection detection model.

[0131] In some examples, the processing circuit system 50 may apply a second low-pass filter having a second cutoff frequency to the temperature values ​​measured over time to generate a second smoothed signal (1104). In such examples, the first smoothed signal may include a signal obtained by applying the first low-pass filter to multiple temperature values. The first low-pass filter may be the same as the first low-pass filter described above with reference to other infection detection models. In other examples, the first low-pass filter used to generate the first smoothed signal in the multiple low-pass filter integration model may not be the same low-pass filter. For example, the low-pass filters may have different cutoff frequencies. In any case, the first low-pass filter may have a first cutoff frequency that is different from the second cutoff frequency. For example, the first low-pass filter may have a first cutoff frequency that is less than the second cutoff frequency. In such examples, the processing circuit system 50 may then have applied two filters to generate two different smoothed signals. The two smoothed signals may then be compared with each other as described below.

[0132] In one illustrative example, the low-pass filter used to obtain the first smoothed temperature signal and the second smoothed temperature signal may include a type of low-pass filter, namely a moving average filter. For example, processing circuitry 50 may use a moving average filter with a first setting (e.g., window size) to obtain the first smoothed signal. The first setting may include a window size of X samples. In a non-limiting and illustrative example, processing circuitry 50 may set the window size to a nominal 10 samples for the sliding window. In such an example, when processing circuitry 50 uses a sampling rate of 4 samples / day, 10 samples may correspond to 2.5 days. Similarly, processing circuitry 50 may use a moving average filter with a second setting to obtain the second smoothed signal. The second setting may correspond to a setting that utilizes an increased amount of filtering compared to the first setting. For example, processing circuitry 50 may use a second setting that obtains a nominal moving average with more filtering than the first setting. For example, processing circuitry 50 may set the second setting to a window size of Y samples, where Y is greater than X. In a non-limiting example, Y may be 20 samples, which in the above example may correspond to 5 days, 4 samples per day. In any case, processing circuitry 50 may utilize one or more filters with various filter settings (e.g., window size, cutoff frequency, etc.) to obtain the first smoothed signal and the second smoothed signal.

[0133] In some examples, processing circuitry, eg, processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may compare the first smoothed temperature signal to the second smoothed signal (1106). Figure 13 An example graph is shown of first smoothed signal 1202 and second smoothed signal 1204. As such, processing circuitry may determine whether second smoothed signal 1204 is greater than first smoothed signal 1202 (1108).

[0134] In some examples, processing circuitry, e.g., processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may increase the infection indicator value (1110) when second smoothed signal 1204 is greater than first smoothed signal 1202. Figure 13 An example infection indicator value 1206 is shown that increases when second smoothed signal 1204 is greater than first smoothed signal 1204. In some examples, the amount by which infection indicator value 1206 increases can be equal to or proportional to the difference between the values ​​corresponding to first smoothed signal 1202 and second smoothed signal 1204. In this manner, the integrated difference between the values ​​corresponding to first smoothed signal 1202 and second smoothed signal 1204 is represented as infection indicator value 1206.

[0135] In some cases, infection indicator value 1206 may increase or decrease proportionally to the difference between the values ​​corresponding to first smoothed signal 1202 and second smoothed signal 1204, rather than necessarily being equal to the difference between the values ​​corresponding to first smoothed signal 1202 and second smoothed signal 1204. For example, infection indicator value 1206 may increase by X times the difference and decrease by Y times the difference, where X and Y may or may not have the same scaling multiplier or fractional value. For example, X may be equal to 0.5, 1, 2, etc., while Y may be equal to the same or different values. In this way, the integrated difference between the values ​​corresponding to first smoothed signal 1202 and second smoothed signal 1204 may be scaled to determine the intervals of increase and decrease of infection indicator value 1206. The proportional difference value (e.g., X or Y) can be predefined by a user (e.g., a clinician) and implemented by a processing circuit system, e.g., processing circuit system 50 of IMD 10, processing circuit system 80 of external device 12, or processing circuit system 98 of server 94, when determining how much to increase or decrease infection indicator value 1206.

[0136] In some examples, the calculation of the difference between corresponding values ​​of first smoothed signal 1202 and second smoothed signal 1204 can be constrained to occur within a predefined, recent time window. The predefined time window can be an integer number of 24-hour days. Processing circuitry 50 can use 24-hour days as the predefined window because diurnal variations in temperature tend to naturally produce differences between first smoothed signal 1202 and second smoothed signal 1204 within a 24-hour period. That is, past temperature measurements may become outdated, and therefore, the integral calculation of the difference value can allow past temperature measurements to be subtracted from the final integrated difference value unless the infection indicator value has been reset in the meantime.

[0137] Additionally, processing circuitry, e.g., processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may reset infection indicator value 1206 to the baseline value when second smoothed signal 1204 is not greater than first smoothed signal 1202. For example, when second smoothed signal 1204 is equal to or less than first smoothed signal 1202, processing circuitry 50 may reset infection indicator value 1206 to the baseline value (1112). Note that although Figure 13 1208, but in some cases, the infection indicator value 1206 may be reset to the baseline value 1208 (e.g., zero) and may not decrease further than the baseline value 1208. In other words, the infection indicator value 1206 may flatten the line at zero as long as the second smoothed signal 1204 is less than or equal to the first smoothed signal 1202.

[0138] In an illustrative example, processing circuitry 50 may decrease infection indication value 1206 to be as low as baseline value 1208. For example, if baseline value 1208 is set to zero, processing circuitry 50 may continue to decrease infection indication value 1206 when appropriate conditions are met until infection indication value 1206 reaches baseline value 1208 of zero (e.g., a stop limit). Then, when appropriate conditions for increasing infection indication value 1206 are met, processing circuitry 50 may increase infection indication value 1206 above baseline value 1208. In other examples, processing circuitry 50 may decrease infection indication value 1206 when second smoothed signal 1202 is less than first smoothed signal 1202 without a stop limit. For example, as Figure 13 As shown in FIG, in some cases, processing circuitry 50 may decrease infection indicator value 1206 to below baseline value 1208, rather than stopping the decrease at baseline value 1208 as discussed above. In the illustrative example, Figure 13 Baseline value 1208 is shown equal to zero.

[0139] Once the infection value has increased (1110) or reset to the baseline value 1208 (1112), processing circuitry 50 may return to applying the second low-pass filter to the one or more updated temperature values ​​measured over time to produce an updated second smoothed signal. Additionally, processing circuitry 50 may apply the first low-pass filter to the updated temperature values ​​measured over time to produce an updated first smoothed signal. Processing circuitry 50 may use the updated smoothed signal to determine an updated infection value according to various techniques of the present disclosure. Figure 13 As shown in , the infection indicator value may be an integrated value determined as a sum of the integrals or differences between the smoothed signals. Thus, the processing circuitry 50 may determine the integrated value by determining the difference between the temperature values ​​of the smoothed signals using an integral determination.

[0140] In some examples, processing circuitry, e.g., processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may compare the increased infection indicator value to a predefined threshold and may determine the infection status of patient 4 based at least in part on the infection indicator value satisfying the predefined threshold. For example, Figure 13 Predefined threshold 1210 is shown as being equal to approximately 8.0. In such an example, if infection indication value 1206 exceeds predefined threshold 1210, processing circuitry, e.g., processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may determine that a device pocket infection has occurred.

[0141] Figure 14 14 is a block diagram illustrating an IMD 1400 having multiple sensors 1402A-N (such as sensors 62 of IMD 10). IMD 1400 is an example configuration of IMD 10. Housing 1406 is an example of housing 15. Temperature sensors 1402A-N are examples of sensors 62 positioned within and outside of IMD 1400. Although shown as blocks, the sensors may comprise wires, resistive devices, or other elements configured to measure temperature. For example, sensor 1402A may be a temperature sensor configured as leads or other elements extending outward from IMD 1400 to measure the temperature around the perimeter of IMD 1400. At least two of sensors 1402 are temperature (T) sensors. In some cases, other non-temperature sensing devices may also be included, such as additional sensors 62 within and around IMD 1400. In some cases, one of T sensors 1402 may be configured to perform additional sensing, such as motion sensing. In any case, T-sensors 1402 can be positioned on the interior or exterior of IMD 1400, as shown. For example, T-sensors 1402A can be placed around IMD 1400 so as to be secured to the top, bottom, or side of IMD 1400. Processing circuitry, such as processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, can use data from multiple T-sensors 1402 to determine a rate of heat loss from patient 4 under specific environmental factors. For example, the rate of heat loss can be based on ambient temperature conditions, insulating properties of clothing, and the thermodynamics of activities that cause patient heat loss at a quantifiable rate.

[0142] In some examples, IMD 1400 may interact with or include multiple temperature sensing devices 1402A-N. In some examples, processing circuitry, such as processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may receive and compare temperature values ​​from at least two of sensors 1402A-N to derive one or more temperature gradients between multiple locations within patient 4. For example, processing circuitry 50 may correlate the temperature value data to infer changes in device bag temperature or core body temperature. Additional processing circuitry may determine which temperature change preceded the other. For example, if an increase in core body temperature follows an increase in device bag temperature, processing circuitry 50 may infer that the source of the temperature increase is within the device bag. Therefore, such a temperature gradient may indicate a device bag infection that is present or has the potential to cause an increase in core body temperature, such as a febrile event in patient 4.

[0143] In one illustrative example, the use of multiple temperature sensing devices can provide spatial resolution for temperature gradients. For example, the processing circuit system 50 may be able to determine an improved diagnosis for fever sensitivity relative to environmental factors and infection temperature sensitivity. In some instances, the IMD 10 can reside in a subcutaneous tissue pocket, and the lead extends to the therapy site. Device infection (e.g., bag infection) typically occurs first in the subcutaneous device pocket. In some cases, the infection can spread from the IMD 10 to the therapy site. Using multiple temperature sensing devices on leads extending away from the housing 1406, from the therapy site, or from other locations can allow the processing circuit system 50 to detect such infections at an early stage. In one example, a temperature sensor can be added to a cardiac lead that provides a temperature value to a processing circuit system, such as the processing circuit system 50 of the IMD 10, the processing circuit system 80 of the external device 12, or the processing circuit system 98 of the server 94.

[0144] Any suitable sensor 62 or temperature sensor device can be used to detect temperature or temperature changes. In some examples, the sensor 62 can 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 thermomechanical temperature sensor, a thin film resistor element, etc. In some examples, calibration of the temperature sensor is performed at the time of manufacture, wherein each sensor is calibrated and / or trimmed for absolute temperature measurement.

[0145] Figure 15 is a flowchart illustrating an example method according to one or more techniques of the present disclosure that may be performed by one or both of IMD 10 and / or one or more external devices (such as at least one of external devices 12) to determine the infection status of patient 4 using a single temperature value (e.g., temperature values ​​obtained from multiple temperature sensors, single temperature values ​​obtained from temperature sensors measuring temperature at various locations in and / or around IMD 10, etc.).

[0146] Although described as being performed by IMD 10, Figure 15 The example method of can be performed by any one or more of IMD 10, external device 12, or server 94, e.g., by processing circuitry or sensing circuitry of any one or more of these devices. For example, processing circuitry 50 can receive a plurality of temperature values ​​from a plurality of temperature sensors, such as plurality of sensors 62 or 1402. In some examples, processing circuitry 50 can transmit the temperature values ​​to another device, such as external device 12, via communication circuitry 54. In such examples, external device 12 can receive the temperature values ​​from IMD 10 and determine the infection status of patient 4 based on the plurality of temperature values. For simplicity, reference will be made to IMD 10 (e.g., reference to FIG. 1 ). Figure 3 Certain techniques are described herein with reference to components of IMD 10 described herein. However, those skilled in the art will appreciate that, in some examples, server 94 (e.g., components of server 94) may utilize input from IMD 10 and / or external device 12 to determine infection status. For example, server 94 may receive temperature data, IMD orientation data, etc. from external device 12 and / or IMD 10 and perform certain techniques of this disclosure.

[0147] In some examples, external device 12 may be a separate device, such as a wearable device, an external / portable device, etc., configured to measure the core body temperature or other temperature-related measurements of patient 4. For example, external device 12 may include a temperature sensing device. As such, processing circuitry 80 of external device 12 may transmit the temperature value to another device, such as IMD 10 or server 94, via communication circuitry 82. In any case, processing circuitry, such as processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may use such temperature values ​​in addition to the temperature values ​​obtained from the temperature sensor of IMD 10 to determine the infection status of patient 4, such as by correlating the temperature data and identifying the source / origin of the infection event (e.g., in a bag of IMD 10).

[0148] In one example method, processing circuitry, such as processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may determine temperature values ​​from at least two sensors (1502). For example, processing circuitry 50 may determine one or more temperature values ​​(e.g., a first temperature value) based on a first sensor (e.g., a T1 temperature value). Processing circuitry 50 may also determine one or more temperature values ​​(e.g., a first temperature value) based on a second sensor (e.g., a T2 temperature value). In some examples, the first sensor may include a body-side sensor (e.g., Tb) and the second sensor may include a skin-side sensor (Ts). In other examples, processing circuitry 50 may determine one or more temperature values ​​based on a plurality of temperature sensors located in and around IMD 10, or, in some cases, one or more temperature values ​​received from another device, such as one of external devices 12.

[0149] In some instances, for example, when Figure 14In the illustrated configuration of IMD 1400, one side of IMD 10 can include a relatively flat surface 1408A, and the other side of IMD 10 can include a curved surface 1408B that is less flat than flat surface 1408A. In some examples, curved surface 1408B can extend around a sidewall of IMD 10 and to flat surface 1408A. For example, and for purposes of visual illustration, reference is made to Figure 14 In the example IMD 1400, a sidewall of the IMD 10 can include, for example, a side having a sensor 1402N, such that, in some examples, a curved surface 1408B and a flat surface 1408A abut at the ends of each surface.

[0150] IMD 10 can be positioned with flat surface 1408A oriented against or adjacent to the muscles of patient 4 and curved surface 1408B can face the skin of the chest (e.g., the skin side of IMD 10). In such an example, the body-side sensor can be positioned on the flat side, such as on the inside of IMD 10 on the side of flat surface 1408A, and the skin-side sensor can be positioned on the curved side of IMD 10. The orientation of IMD 10 can be controlled so as to be consistent between other patients. That is, the orientation can provide a normal, known, or otherwise trackable location of each sensor. In any case, the orientation can be selected to coincide with the predominant direction of the temperature gradient.

[0151] By implanting IMD 10 such that the body-side sensor is positioned inside IMD 10 on the flat side of IMD 10 and the skin-side sensor is positioned on the curved side of IMD 10, processing circuitry 50 may utilize the temperature values ​​received from the at least two separately positioned sensors to identify a temperature gradient at IMD 10 and use the identified temperature gradient information to deduce whether an increase in the infection-indicator value is due to an infection at IMD 10 or other areas of patient 4. Additionally, processing circuitry 50 may use data from other sensors (e.g., temperature sensors) to provide additional resolution and / or confirmation of the temperature values ​​and location of the infection source.

[0152] In some examples, processing circuitry 50 may determine the orientation of IMD 10 and certain sensors of IMD 10 based on accelerometer data from IMD 10 or patient 4. In the example of accelerometer data from patient 4, patient 4 may have a wearable device that tracks the orientation of patient 4, allowing processing circuitry 50 to infer or deduce the orientation of IMD 10 and sensors therein or extending therefrom (e.g., from one or more leads). Processing circuitry 50 may use such data to determine the direction of the increasing temperature gradient and determine whether the increase in the infection indicator value is due to an infection in another area of ​​IMD 10 or patient 4.

[0153] In one example, processing circuitry, such as processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, or processing circuitry 98 of server 94, may determine whether T1 is greater than T2 (1504). For example, processing circuitry 50 may determine that one or more temperature values ​​(e.g., average temperature) from the body-side sensor are less than or equal to one or more temperature values ​​from the skin-side sensor (no branch of 1506). In such a case, processing circuitry 50 may determine whether T1 meets a predefined threshold (1506). If so (yes branch of 1508), processing circuitry 50 may indicate a possible event of infection originating at IMD 10 (e.g., device pocket infection). That is, processing circuitry 50 may attribute the increase in the infection indicator value to a possible device pocket infection. In some examples, IMD 10 may do so by increasing the confidence interval indicating the likelihood that the infection originated at IMD 10, or otherwise attributing it to another cause, such as a separate fever event in patient 4 (e.g., influenza). In any case, based on the event indication and / or high confidence interval, the processing circuitry 50 may, as described with reference to Figure 16 In some cases, processing circuitry 50 may include a confidence interval value and infection status, such as by correcting the infection indicator value based on whether processing circuitry 50 determines a high confidence interval or a low confidence interval that an increase or decrease in the infection indicator value is attributable to or not attributable to an infection at IMD 10.

[0154] When T1 does not meet the predefined threshold at 1506, processing circuitry 50 may optionally indicate a normal state for patient 4 (NO branch from 1506 to 1510). Similarly, when T1 is not greater than T2, processing circuitry 50 may optionally indicate a normal state for patient 4 (NO branch from 1512 to 1510). In any case, IMD 10 may continue to monitor patient 4 with or without indicating such a state.

[0155] Processing circuitry 50 may determine whether T1 is significantly greater than T2, such as greater than T2 by more than a predefined threshold amount (1512). When T1 is greater than T2 by more than the predefined threshold amount, processing circuitry 50 may indicate a possible febrile event for patient 4 (yes branch to 1514). This may occur when patient 4 is infected with a virus that causes an increase in core body temperature, thereby causing an increase in temperature at IMD 10. In such instances, T1 may increase before T2. For example, one or more temperature values ​​from a first sensor (e.g., a body-side sensor) may indicate an elevated body temperature before that of a second sensor (e.g., a skin-side sensor), which may indicate a possible febrile event for patient 4. If T1 is greater than T2 by only a small amount relative to the predefined threshold amount (no branch from 1512 to 1510), processing circuitry 50 may optionally indicate a normal state (1510), as it is expected that temperature values ​​obtained from certain areas of patient 4's body (e.g., the core area) will generally be slightly higher than other areas (e.g., the skin surface area, the muscle layer, etc.). It should be understood that T1 may represent a temperature value (eg, a combined or average temperature value) from one or more similarly located sensors, and T2 may represent a temperature value from one or more other sensors.

[0156] Now turn Figure 16 , external device 12 may receive the infection status of patient 4 from IMD 10 (1602). In some examples, external device 12 may determine the cardiac condition status and receive other data, such as raw temperature values, from processing circuitry 50. Although described as being generally performed by IMD 10, Figure 16 The example methods of may be performed by any one or more of IMD 10, external device 12, or server 94, eg, by processing circuitry of any one or more of these devices.

[0157] External device 12 may determine medical intervention instructions based on the infection status of patient 4 (1604). For example, if the infection indicator value is greater than a predefined threshold, external device 12 may determine medical intervention instructions based on the determination of infection. In some examples, external device 12 may determine different instructions for different risk levels or categories. For example, external device 12 may determine a first set of instructions for an infection indicator value greater than a first threshold and a second set of instructions for an infection indicator value greater than a second threshold.

[0158] In some instances, external device 12 may not determine any indication that the infection-indicating value may track with an elevated core body temperature value. That is, in some cases, an elevated core body temperature value may result in an elevated temperature of IMD 10. In such cases, the infection-indicating value may track with an elevated core body temperature value, which may not indicate a device pocket infection. In the event that the alarm is configured to indicate the occurrence of a device pocket infection, processing circuitry, e.g., processing circuitry 50 of IMD 10, processing circuitry 80 of external device 12, processing circuitry 98 of external server 94, etc., may determine that the elevated temperature of IMD 10 is a result of elevated core body temperature.

[0159] In some examples, the external device 12 can provide an alert, such as a text-based or graphic notification, a visual notification, etc. In some examples, the external device 12 can provide an audible or tactile warning to the patient 4, alerting them to the determined risk level. In other examples, the external device 12 can provide a visible light indication, such as a red light for a high average device bag temperature or a yellow light for a medium average device bag temperature.

[0160] In some examples, external device 12 may transmit the medical intervention instructions to the user interface (1606). In some examples, external device 12 may transmit the instructions to a caregiver's device, such as a pager. In examples where processing circuitry 50 generates instructions based on the infection status, IMD 10 may transmit the medical intervention instructions to the user interface. The instructions may not include an infection indicator value, a temperature value, a smoothed temperature value, etc. In some cases, a physician or caregiver may not need to know the actual temperature value and may only want to receive the infection status determined based on the temperature value.

[0161] Various examples have been described. However, those skilled in the art will appreciate that various modifications may be made to the described examples without departing from the scope of the claims. For example, one or more secondary infection indicators may be used to determine whether an indication based on a first infection indicator is accurate, as described in commonly assigned U.S. Application No. 11 / 737,173, filed April 19, 2007, by Gerber et al., entitled “INFECTION MONITORING.”

[0162] The techniques described in this disclosure may be implemented, at least in part, in the form of hardware, software, firmware, or any combination thereof. For example, various aspects of these techniques may be implemented in one or more processors, DSPs, ASICs, FPGAs, or any other equivalent integrated or discrete logic QRS circuit systems, as well as any combination of such components, which are embodied in external devices (such as physician or patient programmers, simulators, or other devices). The terms "processor" and "processing circuit system" may generally refer to any of the aforementioned logic circuit systems, alone or in combination with other logic circuit systems, or any other equivalent circuit system, alone or in combination with other digital or analog circuit systems.

[0163] For various aspects implemented in software, at least some of the functionality attributed to the systems and devices described in this disclosure may be embodied as instructions on a computer-readable storage medium, such as RAM, ROM, NVRAM, DRAM, SRAM, flash memory, magnetic disks, optical disks, flash memory, or various forms of EPROM or EEPROM. The instructions may be executed to support one or more aspects of the functionality described in this disclosure.

[0164] In addition, in some aspects, the functionality described herein can be provided within dedicated hardware and / or software modules. Depicting different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be implemented by separate hardware or software components. On the contrary, the functions associated with one or more modules or units can be performed by separate hardware or software components, or integrated within common or separate hardware or software components. Likewise, the technology can be implemented entirely in one or more circuits or logic elements. The technology disclosed herein can be implemented in a variety of devices or equipment, including an IMD, an external programmer, a combination of an IMD and an external programmer, an integrated circuit (IC) or a collection of ICs, and / or a discrete circuit system resident in the IMD and / or the external programmer.

[0165] Furthermore, although primarily described with reference to examples in which an infection status is provided in response to detecting a temperature change in the device bag to indicate an infection in the device bag, other examples may additionally or alternatively automatically modify therapy in response to detecting an infection status of the patient. As examples, the therapy may be a substance delivered by an implantable pump, delivery of antibiotics, etc. These and other examples are within the scope of the appended claims.

Claims

1. A system for determining the infection status of a patient, the system comprising: an implantable medical device (IMD), the IMD comprising at least one temperature sensing device; as well as processing circuitry configured to: measuring a plurality of temperature values ​​over time by the temperature sensing device; smoothing the plurality of temperature values ​​measured over time to generate a smoothed temperature signal representing changes in the plurality of temperature values ​​over time; applying an infection detection model to the smoothed temperature signal to determine an infection indicator value, the infection detection model comprising one or more of a sliding window detection model or a multiple low-pass filter ensemble model; comparing the infection indicator value to a threshold value; and An infection status of the patient is determined based at least in part on the infection indicator value satisfying the threshold.

2. The system of claim 1 , wherein to smooth the plurality of temperature values, the processing circuitry is further configured to: A low pass filter is applied to the plurality of temperature values ​​to determine the smoothed temperature signal.

3. The system of claim 1 , wherein to smooth the plurality of temperature values, the processing circuitry is further configured to: A moving average of the plurality of temperature values ​​over time is determined to determine the smoothed temperature signal.

4. The system of claim 1 , wherein the sliding window detection model comprises a max-min detection model, wherein the processing circuitry is further configured to: determining a plurality of sliding windows, the plurality of sliding windows including temperature data points of the smoothed temperature signal; determining a difference between a maximum value of the temperature data point in a first sliding window of the plurality of sliding windows and a minimum value of the temperature data point in a second sliding window of the plurality of sliding windows, the difference comprising the infection indicator value; and The difference is compared to the threshold value to determine the infection status of the patient.

5. The system of claim 4, wherein the first sliding window and the second sliding window include the same number of days, and wherein the second sliding window includes temperature data points corresponding to an earlier time period relative to the first sliding window, and the first sliding window includes temperature data points corresponding to a later time period.

6. The system of claim 1 , wherein the sliding window detection model comprises a rate-of-change detection model, wherein the processing circuitry is further configured to: determining a slope value between at least two temperature data points of the smoothed temperature signal, the slope value comprising the infection indicator value; and The slope value is compared to the threshold value to determine the infection status of the patient.

7. The system of claim 1 , wherein to smooth the plurality of temperature values ​​measured over time to generate the smoothed temperature signal, the processing circuitry is configured to: applying a first low-pass filter to the plurality of temperature values ​​to produce a first smoothed temperature signal, the first low-pass filter comprising a first cutoff frequency; and Wherein, in order to apply the multiple low-pass filter integrated model, the processing circuit system is further configured to: applying a second low-pass filter to smooth the plurality of temperature values ​​measured over time to produce a second smoothed temperature signal, wherein the second low-pass filter comprises a second cutoff frequency that is higher than the first cutoff frequency; comparing the first smoothed temperature signal to the second smoothed temperature signal over time; and When the second smoothed temperature signal includes a value greater than a corresponding value of the first smoothed temperature signal, the infection indication value is increased.

8. The system of claim 7, wherein the processing circuitry is further configured to: When the second smoothed temperature signal includes a value less than or equal to a corresponding value of the first smoothed temperature signal, the infection indicator value is reset to a baseline value.

9. The system of claim 1, wherein the infection status indicates an infection in a device pocket of the IMD.

10. The system of claim 1 , wherein the IMD comprises a second temperature sensor, wherein the processing circuitry is further configured to: The plurality of temperature values ​​are determined over time based at least in part on temperature measurements from at least two temperature sensors included with the IMD.

11. A non-transitory computer-readable storage medium having stored thereon instructions that, when executed, cause one or more processors to at least: measuring a plurality of temperature values ​​over time by a temperature sensing device of an implantable medical device (IMD); smoothing the plurality of temperature values ​​measured over time to generate a smoothed temperature signal representing changes in the plurality of temperature values ​​over time; applying an infection detection model to the smoothed temperature signal to determine an infection indicator value, the infection detection model comprising one or more of a sliding window detection model or a multiple low-pass filter ensemble model; comparing the infection indicator value to a threshold value; and An infection status of the patient is determined based at least in part on the infection indicator value satisfying the threshold.

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