System and method for monitoring heart failure conditions using dynamic predictive indicators

Abstract

The present invention, in some embodiments, relates to devices and methods for monitoring heart failure (HF) conditions, and more particularly, but not exclusively, to devices and methods for monitoring heart failure using dynamic predictive indicators.

Description

Related applications

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 458,467, filed April 11, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0002] In some embodiments, the present invention relates to apparatus and methods for monitoring heart failure (HF) conditions, and more specifically, but not exclusively, to apparatus and methods for monitoring HF using dynamic predictive indicators. Background Technology

[0003] Implantable devices for heart failure (HF) typically include several treatment alternatives, including, for example, cardiac resynchronization therapy (CRT) and cardiac contractility modulation (CCM). Such devices generally include sensing elements, devices for collecting patient clinical data during device operation, and devices for evaluating device operating parameters. Typical sensors may include electrocardiogram (ECG) sensors, bioimpedance sensors, activity sensors, position sensors, pressure sensors, and temperature sensors. In addition, patient medical data is collected, such as:

[0004] Patient history, such as hospitalization history and comorbidity data, and patient physical parameters, such as weight, height, waist circumference and gait parameters.

[0005] Clinical parameters, such as: blood pressure, blood markers, blood oxidation level, respiratory status, action potential conduction velocity between two or more cardiac leads, internal impedance of leads, impedance between two or more cardiac leads; cardiac demand parameters, such as: average heart rate (HR) or heart rate variability (HRV) over a set time period; cardiac abnormalities, including: atrial tachycardia, premature ventricular contraction (PVC) rate; cardiac condition, including: historical chest impedance values ​​and HF score.

[0006] Device operating parameters may include operating percentages, such as the number of heartbeats with CRT or CCM stimulation during an active treatment period, typically expressed as a CRT percentage or a CCM percentage. Summary of the Invention

[0007] The following is a non-exclusive list of some examples including embodiments of the present invention. The present invention also includes embodiments that include fewer than all the features in the examples, as well as embodiments that use features from multiple examples (even if not explicitly listed below).

[0008] Example 1. A system for monitoring user status, comprising:

[0009] a. An implantable device configured to communicate with external electronic devices;

[0010] b. External electronic devices, including:

[0011] i. The generated reference database;

[0012] ii. An evaluation module, including instructions for generating personalized predictive metrics; and

[0013] iii. A user database that includes user data.

[0014] Example 2. The system of claim 1, wherein the implantable device is an implantable cardioverter defibrillator (ICD).

[0015] Example 3. The system according to Embodiment 2, wherein the ICD is configured to perform one or more of the following: monitoring cardiac activity, providing cardiac resynchronization therapy (CRT), and providing cardiac contractility modulation (CCM).

[0016] Example 4. The system according to Embodiment 1, wherein the generated reference database is generated according to a method including the following:

[0017] a. Collect historical data from multiple users; the historical data includes a timeline in chronological order;

[0018] b. Index the collected data;

[0019] c. Analyze the index data;

[0020] d. Group the analyzed index data based on the similarity at the start of the timeline in chronological order;

[0021] e. Analyze each of the analysis groups to recover the similarities and differences in the results from the multiple users;

[0022] f. Identify the branching points in the chronologically ordered timeline;

[0023] g. Provide index values ​​at the bifurcation points of the timeline in chronological order in the analysis;

[0024] h. Store the index value in a database as a reference prediction indicator.

[0025] Example 5. The system of claim 1, wherein the personalized prediction index is generated according to the following method:

[0026] a. Collect multiple data points related to multiple different users;

[0027] b. Generate multiple reference prediction indicators based on the collected data;

[0028] c. Collect data related to the identified users;

[0029] d. Compare the collected data related to the identified users with the reference prediction indicators;

[0030] e. Generate personalized predictive indicators based on the comparison.

[0031] Example 6. The system of claim 1, wherein the external electronic device is a server.

[0032] Example 7. The system of claim 1, wherein the external electronic device is an electronic device that includes instructions to deliver data to the server.

[0033] Example 8. A system according to Embodiment 1, wherein the external electronic device includes dedicated software that includes instructions that allow the user to insert user data.

[0034] Example 9. The system of claim 8, wherein the dedicated software includes instructions to provide the user with a dedicated interrogator.

[0035] Example 10. The system according to Embodiment 1 further includes one or more external sources for providing data related to the user.

[0036] Example 11. The system of claim 1, wherein the personalized prediction metric is used to predict the expected change in the user's state.

[0037] Example 12. A method for generating personalized predictive metrics, comprising:

[0038] a. Collect multiple data points related to multiple different users;

[0039] b. Generate multiple reference prediction indicators based on the collected data;

[0040] c. Collect data related to the identified users;

[0041] d. Compare the collected data related to the identified users with the reference prediction indicators;

[0042] e. Generate personalized predictive indicators based on the comparison.

[0043] Example 13. The method according to embodiment 12, wherein the data related to the determined user includes one or more of the following: user medical history, clinical parameters, data from an implantable device, and data from external sensors.

[0044] Example 14. The method of claim 13, wherein the external sensor is one or more of an ECG sensor, a blood pressure sensor, a bioimpedance sensor, a respiration sensor, a motion sensor, and a sleep sensor.

[0045] Example 15. The method according to embodiment 12, wherein the collection of data related to the identified user includes one or more data collected from the user himself, one or more sensors connected to the user, and one or more external sources.

[0046] Example 16. The method according to embodiment 15, wherein collecting data from the user himself includes providing the user with a dedicated interrogator.

[0047] Example 17. The method of claim 12, wherein the personalized prediction metric is used to predict expected changes in the user's state.

[0048] Example 18. A method for monitoring user status, comprising:

[0049] a. Generate personalized forecast indicators;

[0050] b. Collect one or more status data of the user;

[0051] c. Update the personalized prediction index based on the collected status data.

[0052] Example 19. The method according to Embodiment 18, wherein generating personalized predictive metrics includes:

[0053] a. Collect multiple data points related to multiple different users;

[0054] b. Generate multiple reference prediction indicators based on the collected data;

[0055] c. Collect data related to the identified users;

[0056] d. Compare the collected data related to the identified users with the reference prediction indicators;

[0057] e. Generate personalized predictive indicators based on the comparison.

[0058] Example 20. The method according to embodiment 19, wherein the data related to the determined user includes one or more of the following: user medical history, clinical parameters, data from an implantable device, and data from external sensors.

[0059] Example 21. The method of claim 20, wherein the external sensor is one or more of an ECG sensor, a blood pressure sensor, a bioimpedance sensor, a respiration sensor, a motion sensor, and a sleep sensor.

[0060] Example 22. The method according to embodiment 20, wherein the collection of data related to the determined user includes one or more data collected from the user himself, one or more sensors connected to the user, and one or more external sources.

[0061] Example 23. The method according to embodiment 22, wherein collecting data from the user himself includes providing the user with a dedicated interrogator.

[0062] Example 24. The method of claim 18, wherein the personalized prediction metric is used to predict the expected change in the user's state.

[0063] Example 25. The method of claim 24 further includes analyzing the collected data after the collection and before the update.

[0064] Example 26. The method of claim 25, wherein the analysis includes comparing the collected data with one or more reference data of one or more reference predictive indicators.

[0065] Example 27. The method of claim 18 further includes using the updated personalized prediction metric to update at least one external entity.

[0066] Example 28. The method of claim 18 further includes sending an alarm when the change in the personalized prediction metric reaches a predetermined threshold.

[0067] Example 29. The method according to embodiment 18, wherein the collection of one or more status data of the user includes one or more data collected from the user himself, one or more sensors connected to the user, or one or more external sources.

[0068] Example 30. The method according to embodiment 29, wherein collecting data from the user himself includes providing the user with a dedicated interrogator.

[0069] Example 31. The method of claim 18, wherein updating the personalized prediction metric is used to predict the expected change in the user's state.

[0070] Example 32. A method for generating a database of reference predictive indicators, comprising:

[0071] a. Collect historical data from multiple users; the historical data includes a timeline in chronological order;

[0072] b. Index the collected data;

[0073] c. Analyze the index data;

[0074] d. Group the analyzed index data based on the similarity at the start of the timeline in chronological order;

[0075] e. Analyze each of the analysis groups to recover the similarities and differences in the results from the multiple users;

[0076] f. Identify the branching points in the chronologically ordered timeline;

[0077] g. Provide index values ​​at the bifurcation points of the timeline in chronological order in the analysis;

[0078] h. Store the index value in a database as a reference prediction indicator.

[0079] Example 33. The method according to embodiment 32, wherein the grouping at the beginning of the timeline in chronological order is based on one or more of the following user-related parameters: age, gender, medical condition, cardiac parameters, physiological parameters, and medical history.

[0080] Unless otherwise defined, all technical and / or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. While similar or equivalent methods and materials may be used in the practice or testing of embodiments of the invention, exemplary methods and / or materials will be described below. In case of conflict, the patent specification (including definitions) shall prevail. Furthermore, materials, methods, and examples are illustrative only and are not intended to be necessarily limiting.

[0081] Those skilled in the art will understand that some embodiments of the present invention can be embodied as systems, methods, or computer program products. Therefore, some embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments (including firmware, resident software, microcode, etc.), or embodiments combining software and hardware aspects, all of which may generally be referred to herein as “circuit,” “module,” or “system.” Furthermore, some embodiments of the present invention can take the form of computer program products embodied in one or more computer-readable media having computer-readable program code embodied thereon. Implementation of the methods and / or systems of some embodiments of the present invention can involve manually, automatically, or a combination of both performing and / or completing selected tasks. Furthermore, practical instruments and devices according to certain embodiments of the methods and / or systems of the present invention can implement several selected tasks through hardware, software, or firmware and / or combinations thereof (e.g., using an operating system).

[0082] For example, according to some embodiments of the invention, the hardware for performing a specific task can be implemented as a chip or circuit. As software, according to some embodiments of the invention, the selected task can be implemented as the execution of multiple software instructions by a computer using any suitable operating system. In exemplary embodiments of the invention, according to some exemplary embodiments of the methods and / or systems described herein, one or more tasks are performed by a data processor (such as a computing platform for executing multiple instructions). The data processor may optionally include volatile memory for storing instructions and / or data and / or non-volatile memory for storing instructions and / or data, such as a magnetic hard disk and / or removable media. Optionally, a network connection is also provided. A display and / or a user input device such as a keyboard or mouse are also optionally provided.

[0083] Some embodiments of the present invention may use any combination of one or more computer-readable media. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination thereof. More specific examples (not an exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer floppy disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable optical disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the context of this document, "computer-readable storage medium" may be any tangible medium that may include or store programs for use by or associated with an instruction execution system, apparatus, or device.

[0084] Computer-readable signal media can include propagated data signals, including computer-readable program code, for example, in baseband or as part of a carrier wave. Such propagated signals can take any of a variety of forms, including, but not limited to, electromagnetic, optical, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium, but not a computer-readable storage medium, that can communicate, propagate, or transmit programs for use by or in connection with an instruction execution system, apparatus, or device.

[0085] Program code embodied on a computer-readable medium and / or data used therefrom may be transmitted using any suitable medium, including but not limited to wireless, wired, fiber optic cable, RF, or any suitable combination thereof.

[0086] The computer program code for performing operations in some embodiments of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​(such as Java, Smalltalk, C++, etc.) and traditional procedural programming languages ​​(such as the "C" programming language or similar programming languages). The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN)), or can be connected to an external computer (e.g., using the Internet through an Internet service provider).

[0087] Some embodiments of the present invention will now be described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It will be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus to generate a machine that executes the instructions via the processor of the computer or other programmable data processing apparatus, thereby creating means for implementing the functions / behaviors specified in the flowchart illustrations and / or block diagrams.

[0088] These computer program instructions may also be stored in a computer-readable medium, which may instruct a computer, other programmable data processing apparatus or other device to operate in a particular manner, thereby causing the instructions stored in the computer-readable medium to produce a product, including instructions that implement the functions / behaviors specified in flowcharts and / or block diagrams.

[0089] Computer program instructions may also be loaded onto a computer, other programmable data processing apparatus or other device to cause the computer, other programmable apparatus or other device to perform a series of operational steps, thereby producing a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide a process for implementing the functions / behaviors specified in one or more boxes of a flowchart and / or block diagram.

[0090] Some of the methods described in this article are typically designed for computers only, and for human experts, performing these methods manually may be infeasible or impractical. If a human expert wanted to perform a similar task manually, they might use entirely different methods, such as leveraging expert knowledge and / or the pattern recognition capabilities of the human brain, which would be more efficient than manually following the steps described in this article. Attached Figure Description

[0091] The following description, by way of example only and with reference to the accompanying drawings, illustrates some embodiments of the invention. Referring now specifically to the drawings, it should be emphasized that the details shown are merely illustrative and intended to discuss embodiments of the invention in an illustrative manner. In this regard, the description, taken in conjunction with the accompanying drawings, will enable those skilled in the art to clearly understand how embodiments of the invention are practiced.

[0092] In the attached image:

[0093] Figure 1 These are schematic diagrams of exemplary systems in relation to exemplary implantable devices according to some embodiments of the present invention;

[0094] Figure 2 This is a schematic diagram illustrating the exemplary principle of system monitoring according to some embodiments of the present invention;

[0095] Figure 3 This is a schematic diagram of an exemplary system for monitoring patient status according to some embodiments of the present invention;

[0096] Figure 4 These are exemplary interrogators shown in personal devices according to some embodiments of the present invention;

[0097] Figure 5 This is a flowchart of an exemplary method for monitoring patients according to some embodiments of the present invention;

[0098] Figure 6 This is a flowchart of an exemplary method for generating a dedicated reference database according to some embodiments of the present invention; and

[0099] Figure 7 This is a schematic diagram of an exemplary process for monitoring a patient according to some embodiments of the present invention. Detailed Implementation

[0100] In some embodiments, the present invention relates to apparatus and methods for monitoring heart failure (HF) conditions, and more specifically, but not exclusively, to apparatus and methods for monitoring HF using dynamic predictive indicators.

[0101] Overview

[0102] One aspect of some embodiments of the present invention relates to monitoring patient status according to a predetermined set of parameters to allow estimation and / or possible prediction of future (possible or inevitable) changes in patient status. In some embodiments, changes in patient status can be improvements or deteriorations. In some embodiments, monitoring results may show no change in patient status. In some embodiments, individual predictive indicators are generated based on information specific to each patient. In some embodiments, each individual predictive indicator is compared with one or more similar predictive indicators stored in a dedicated database. In some embodiments, the individual predictive indicators are updated based on new information relevant to the particular patient, and then the new individual predictive indicators are compared with one or more similar predictive indicators stored in the dedicated database. In some embodiments, in addition to providing estimates and / or possible predictions of changes in patient status, the data generated from the comparisons is used to assess possible treatments for the patient.

[0103] One aspect of some embodiments of the present invention relates to a patient-interactive software application (software application) configured to allow self-updating of a patient's status to supplement data recovered from other sources, thereby generating personalized predictive indicators. In some embodiments, the other sources are one or more of databases and data recovered from an implanted device.

[0104] Before explaining at least one embodiment of the present invention in detail, it should be understood that the application of the present invention is not necessarily limited to the structural details, component arrangements, and / or methods described in the following specification and / or drawings and / or examples. The present invention can be implemented or practiced in various ways with other embodiments.

[0105] Now for reference Figure 1 , Figure 1 A schematic diagram illustrating the relationship between an exemplary system and an exemplary implantable device is shown according to some embodiments of the present invention. In some embodiments, the system associated with the exemplary implantable device includes the implantable device 100 itself, one or more external devices 110, and at least one external server 112. In some embodiments, as will be further explained below, the implantable device 100 includes dedicated hardware and software that allows bidirectional communication between the implantable device 100 and one or more external devices 110 and / or at least one external server 112. In some embodiments, one or more external devices 110 and at least one external server 112 communicate with each other.

[0106] In some embodiments, the implantable device 100 includes a pulse generator, which includes a housing 102 and one or more leads 104. Figure 1 Only one lead wire 104 is shown, which is optionally coupled to the housing 102 of the device 100 via one or more connectors (not shown). In some embodiments, the housing 102 is implanted outside the heart, for example in the subclavian region. Optionally, implantation is performed via minimally invasive surgery. In some embodiments, the housing 102 is implanted subcutaneously, near the left chest. In some embodiments, the housing 102 includes all the necessary electronics and power supply for proper functioning of the implantable device 100, for example, optionally using a pulse generator to generate signals, for example, including power supply circuitry, for example, including one or more storage capacitors. In some embodiments, the device 100 also includes a ventricular detector for detecting atypical ventricular activation, which may be a contraindication to signaling. In some embodiments (… Figure 1 (Not shown in the figure because only an exemplary conductor 104 is shown in the figure). Device 100 also includes an atrial detector for detecting atypical atrial activation, which can be used as input to decisions made by the device. In some embodiments, device 100 also includes one or more sensors configured to deliver one or more sensor input data, such as electrical sensors or other sensors such as flow, pressure, and / or acceleration sensors. In some embodiments, data from these sensors may optionally be further processed (e.g., by a controller and / or detector) and may optionally be used as input to the decision-making process in device 100, as will be explained further below. In some embodiments, device 100 also includes a controller configured to execute one or more logics to make decisions such as the timing and / or other parameters of a signal, and / or whether to apply a signal, as will be explained further below. In some embodiments, the controller controls the application of stimulation pulses according to a treatment plan. Optionally, the controller makes changes to the plan, for example, to compensate for real-time deviations from the treatment plan (e.g., skipped stimulation). In some embodiments, device 100 also includes a memory configured to store logic, past efficacy, treatment protocols, adverse events, and / or pulse parameters. In some embodiments, the controller and / or memory are programmed with one or more treatment plans (optionally set for a specific patient) and / or with one or more alternative treatment plans.

[0107] In some embodiments, the controller and / or memory are programmed with one or more instructions for monitoring the morphology of ECG signals for managing the implantable device 100, such as an implantable cardioverter defibrillator (ICD).

[0108] In some embodiments, device 100 further includes a logger configured to store activities of device 100 and / or the patient. In some embodiments, such logging and / or programming can use a communication module to send data from device 100 to, for example, a programmer (not shown) and / or a dedicated external device 110, such as a mobile phone, tablet, etc., and / or an external server 112; and / or receive data, such as programming, such as pulse parameters. In some embodiments, data is shared among all three: the external device, the server, and the implantable device.

[0109] In an exemplary option of implantable device 100, such as an implantable cardioverter defibrillator (ICD), the same lead 104 is used for all necessary activities, such as monitoring cardiac activity, providing cardiac resynchronization therapy (CRT), providing cardiac contractility modulation (CCM), and any combination thereof. In some embodiments, regarding the activation and operation of the ICD, activation is achieved via an ICD module including or connected to: an ICD controller, a defibrillation pulse generator (via one or more capacitors), a power supply (e.g., a battery) and power management circuitry, and an ICD sensor that senses the applied pulse to verify that the pulse is within a selected (e.g., programmed) amplitude and / or duration. In some embodiments, activation of cardiac contractility modulation is achieved via a cardiac contractility modulation module including or connected to: cardiac contractility modulation control and a cardiac contractility modulation generator. As previously described, in some embodiments, the ICD coil and one or more electrodes for pacing and / or cardiac contractility modulation are configured on the same lead. In some embodiments, a separate lead is used for the ICD coil, and one or more electrodes are used for pacing and / or cardiac contractility modulation.

[0110] In some embodiments, the lead 104 extends from the housing 102, and at least the distal end of the lead is implanted in the heart, such as, for example, in Figure 1 The diagram is schematically shown. In some embodiments, the lead includes an ICD defibrillation coil 106 positioned along the lead 104. In some embodiments, the end of the lead 104 is a tip electrode 108. In some embodiments, the tip electrode may be configured as a contact electrode, a screw-in electrode, a suture electrode, a free-floating electrode, and / or other types. In some embodiments, the tip electrode is threaded to be screwed into tissue. Alternatively, the tip electrode is positioned separately to contact the tissue.

[0111] In some embodiments, the cardiac contractility modulation signal is applied to the heart during its relative and / or absolute refractory period. In some embodiments, the signal is selected to increase the contractility of the heart's ventricles when the electric field of the signal stimulates such ventricular tissue (e.g., the left ventricle, right ventricle, and / or ventricular septum). In some embodiments of the invention, contractility modulation is provided by phosphorylation of phosphoproteins induced by the signal. In some embodiments of the invention, contractility modulation is caused by changes in protein transcription and / or mRNA creation induced by the signal, optionally in the form of reversal of fetal genetic programming. Notably, in some embodiments, the cardiac contractility modulation signal may have an excitatory effect on tissues other than those to which it is applied.

[0112] While not limited to a single pulse sequence, the term cardiac contractility modulation is used to describe any signal in a family of signals that includes a significant component applied during the absolute refractory period and has a clinically significant effect on cardiac contractility in an acute or chronic manner, and / or leads to the reversal of fetal genetic programs, and / or increases phosphorylation of phosphoproteins. In some embodiments, the signal may be excitatory to one part of the heart but not to others. For example, the signal may be excitatory in the atria but applied at a time when it is not excitatory in the ventricles (relative to ventricular excitation).

[0113] In some embodiments of the invention, the possible stimulation of the signal during the receiving period of the cardiac cycle is non-excitatory due to its time. Specifically, the signal is applied during the refractory period of the affected tissue, and (optionally) during the absolute refractory period.

[0114] In some embodiments, device electrodes (e.g., cardiac contractility modulation application electrodes) are used to measure the R-wave amplitude and / or RR time interval of the cardiac cycle.

[0115] In some embodiments, the implantable device 100 includes one or more sensors, such as an ECG sensor, a bioimpedance sensor, an activity sensor, a position sensor, and a pressure sensor.

[0116] Exemplary Stimuli

[0117] In some embodiments, the exemplary device provides one or more of the following stimuli: ICD, CCM, and CRT.

[0118] Exemplary Principles of Monitoring Systems

[0119] Now for reference Figure 2This figure illustrates a schematic diagram of an exemplary principle of system monitoring according to some embodiments of the present invention. In some embodiments, monitoring data 202 is provided to the system and / or collected by one or more components in the system (see...). Figure 1 More specifically, data is provided to the evaluation module 204 within the system. In some embodiments, the evaluation module 204 performs an evaluation using data found in the reference database 206 and stored data associated with a specific patient 208 (this will be explained further below). In some embodiments, the evaluation results are sent 210 to one or more entities outside the system (e.g., patients, doctors, insurance companies, etc.). In some embodiments, based on the evaluation results, an alert is sent to one or more entities when the evaluation results reach a predetermined threshold (see below for details).

[0120] The monitoring system will be described in more detail in the following paragraphs.

[0121] Example monitoring of patient status

[0122] Now for reference Figure 3 This figure illustrates a schematic diagram of an exemplary system for monitoring patient status according to some embodiments of the present invention. Identical elements are kept by the same reference numerals. In some embodiments, the system utilizes one or more information sources to monitor the individual status of a particular patient. In some embodiments, as described above, information (data) is collected by an implantable device 100 and, for example, sent to a server 112 and / or an external device 110 (or sent to an external device 110 and then from that external device 110 to the server 112). In some embodiments, another information source is data received from a doctor and / or nurse 302. In some embodiments, another information source is data from a pharmacy 304. In some embodiments, the server includes… Figure 2 The evaluation module 204 shown.

[0123] In some embodiments, another source of information is the patient himself (not shown), and in some embodiments, the patient is equipped with a dedicated external patient interactive software application (hereinafter referred to as the software application) configured to allow the patient to input information that will be used to monitor the patient's condition.

[0124] In some embodiments, the exemplary data collected by the system may be one or more of the following:

[0125] 1. Patient medical history: such as hospitalization and comorbidity data, patient physical parameters (such as weight, height, waist circumference, and gait parameters), which are stored as follows: Figure 2 The "stored user data 208" shown.

[0126] 2. Clinical parameters: such as blood pressure, blood markers, blood oxygen levels, respiratory status, action potential conduction velocity between two or more cardiac leads, lead internal impedance, impedance between two or more cardiac leads, cardiac demand parameters such as: mean HR or HRV over a set time period, cardiac abnormalities including: atrial tachycardia, PVC rate, including the following cardiac conditions: historical chest impedance values ​​and HF score (monitoring data 202 and / or stored user data 208, such as...). Figure 2 (As shown).

[0127] 3. Device operating parameters: such as operating percentage (e.g., heart rate during active treatment periods with CRT or CCM stimulation). In some embodiments, the operating percentage is typically correlated with the CRT percentage or CCM percentage ( Figure 2 This is related to the monitoring data 202 shown and / or the stored user data 208.

[0128] 4. External monitoring devices: For example, one or more sensors worn by the user within a dedicated wearable device, optionally internal sensors (including those in an implant), optionally external sensors. In some embodiments, the monitored parameters are provided to the system for continuous evaluation and generation of personalized predictive metrics. In some embodiments, exemplary sensors may be one or more of the following:

[0129] -ECG sensor for detecting heart rate / heart rate variability / arrhythmia events;

[0130] - Blood pressure sensor for detecting RA / RV / LA / LV;

[0131] - A bioimpedance sensor for detecting pulmonary congestion;

[0132] - A respiratory sensor used to detect respiratory rate and volume, and respiratory arrest;

[0133] - Motion sensors are used to detect patient activity;

[0134] - Sleep sensor used to detect sleep duration and sleep stage duration;

[0135] - Temperature sensor used to measure body temperature.

[0136] Exemplary external patient interaction software app

[0137] In some embodiments, the system includes dedicated software configured to allow a user (patient) to input personal information, such as health-related information (but not limited to this), to help the system monitor the user's status. In some embodiments, the personal information is automatically collected and stored by one or more external sources, such as via an implantable device (where a dedicated storage device is provided for the role), a doctor, a pharmacy, a mobile device, a smartwatch, a portable medical device, etc. In some embodiments, exemplary data collected manually and / or automatically includes one or more of the following: physiological data, including daily average activity levels (e.g., based on accelerometers), daily average posture (e.g., based on accelerometers), daily average heart rate, daily average heart rate variability, VT / VF episodes (in implantable devices incorporated into ICD therapy), patient demographics, and quality of life data from a patient health survey collected via a software app (see the table below, which includes example questioners for patients).

[0138] An exemplary interrogator for patients

[0139] In some embodiments, a specially designed interrogator is provided to the patient to collect relevant data to assess the patient’s current condition, and then the data is used to assess possible changes in the patient’s condition. Figure 4 An exemplary interrogator shown in a personal device (mobile device) is illustrated.

[0140] In some embodiments, exemplary interrogators are shown in the following table:

[0141] Exemplary problem Example answer options How was the overall impression? Visual scale scores 1 to 10 How many pillows did you need to sleep with last night? 0, 1, 2, 3, sitting upright and falling asleep Has your weight changed since yesterday? (Using a scale) Decrease > 2 lbs, Decrease 1-2 lbs, No change, Increase 1-2 lbs, Increase > 2 lbs Has there been any change in mobility? (Using scale) Significant decrease, slight decrease, no change, slight increase, significant increase What was your average daily activity level over the past few days? High (5 hours / day), Medium (3-5 hours / day), Low (1-2 hours / day), Very Low (<1 hour / day) Do you feel dizzy? No, yes but lighter than usual, yes is similar to usual, yes but heavier than usual. Do you feel tired? No, yes but lighter than usual, yes is similar to usual, yes but heavier than usual. Is climbing stairs difficult? No, yes but lighter than usual, yes is similar to usual, yes but heavier than usual. Do you feel your heart racing? No, yes but lighter than usual, yes is similar to usual, yes but heavier than usual. Do you feel an irregular heartbeat? No, yes but lighter than usual, yes is similar to usual, yes but heavier than usual. Are you experiencing shortness of breath? No, yes but lighter than usual, yes is similar to usual, yes but heavier than usual. Are there any swollen areas in your ankles and legs? No, yes but lighter than usual, yes is similar to usual, yes but heavier than usual. Do you experience chest pain during activities? No, yes but lighter than usual, yes is similar to usual, yes but heavier than usual. Do you experience chest pain at rest? No, yes but lighter than usual, yes is similar to usual, yes but heavier than usual. Have you missed any medications in the past 24 hours? whether

[0142] It should be understood that the interrogator may include more or fewer or different questions than those disclosed above, and these questions are provided to allow those skilled in the art to understand the invention.

[0143] Detailed description of exemplary monitoring of patient status

[0144] Now for reference Figure 5 The diagram illustrates a flowchart of an exemplary method for monitoring a patient according to some embodiments of the present invention.

[0145] In some embodiments, a reference database 502 is generated. In some embodiments, the reference database is generated using, for example, historical data from multiple patients. (Related information...) Figure 6 See below for a flowchart of an exemplary method for generating a reference database according to some embodiments of the present invention.

[0146] In some embodiments, a user profile is generated 504. In some embodiments, historical data related to a specific user / patient is generated. In some embodiments, the data is generated from inputs from one or more of the patient, physician, pharmacist, and any other dedicated authorized entity.

[0147] In some embodiments, the user profile is compared with one or more data in reference database 506.

[0148] In some embodiments, a personalized predictive metric 508 (see below) is generated and provided based on the comparison results.

[0149] In some embodiments, the system monitors the status of patient 510, as explained elsewhere herein.

[0150] In some embodiments, the system is configured to assess the patient’s condition using data received from monitoring based on the patient’s personalized predictive indicators.

[0151] In some embodiments, the assessment results are then sent to one or more external entities that use the information to make treatment decisions 514.

[0152] In some embodiments, when a change in a personalized prediction metric reaches a predetermined threshold, the system is configured to send an alert to one or more entities.

[0153] Now for reference Figure 6 A flowchart illustrating an exemplary method for generating a dedicated reference database according to some embodiments of the present invention is shown. In some embodiments, the system includes a reference database used during assessment of a patient's condition. In some embodiments, an exemplary method for generating the reference database is as follows:

[0154] In some embodiments, historical data is collected in database 602. In some embodiments, historical data includes a timeline of data in chronological order, which is used to analyze the data according to determined parameters. In some embodiments, historical data may be provided as “raw data”; and in some embodiments, the database is configured to organize the raw data and generate a timeline of historical data in chronological order.

[0155] In some embodiments, historical data 604 is indexed. In some embodiments, the indexing is performed in accordance with methods known in the art, and it includes, for example, providing internal tags to the data to allow for data identification and organization. In some embodiments, a potential advantage of indexing the data is that it facilitates comparative calculations during evaluation.

[0156] In some embodiments, the 606 index data is then analyzed according to predetermined parameters.

[0157] In some embodiments, exemplary predetermined analysis parameters are data with similar data at the start of the timeline, such as patients experiencing their first heart failure. In some embodiments, further subgroups of predetermined analysis parameters may be found, such as patients experiencing their first heart failure (HF) and having diabetes.

[0158] In some embodiments, index data with similar parameters at the beginning of the timeline are grouped together 608. In some embodiments, the grouping at the beginning of the timeline in chronological order is based on one or more of the following user-related parameters: age, gender, medical condition, cardiac parameters, physiological parameters, and medical history.

[0159] In some embodiments, each group is further analyzed to restore similarities and differences in patient outcomes, while marking divergences in timeline 610. For example, two different patients (Patient A and Patient B) both presented with HF at age 45 and had no diabetes, at a relative time zero (T0) on the timeline. At relative time T5 (5 years after T0), Patient A died, while Patient B did not. In some embodiments, the system is configured to analyze the data between T0 and T5 to find divergences.

[0160] In some embodiments, the system provides index values ​​at the bifurcation points of the timeline 612 of the analysis. In some embodiments, the index values ​​are provided to provide a numerical value of the patient's relative state at a specific time on the timeline, relative to a specific parameter (e.g., a health parameter).

[0161] In some embodiments, the results are then stored in a reference database for future use, and reference 614.

[0162] It should be understood that the reference database can also be generated in other ways and using other steps. The methods described above have been provided to allow those skilled in the art to understand the invention. The scope of the term "reference database" also includes reference databases generated by different methods but used by the system in a similar manner.

[0163] Example of generating user profiles

[0164] In some embodiments, the system is configured to collect all relevant data associated with a specific user and generate a user profile, which is initially used as a base data profile for monitoring patient status and later (due to updates to the user data) as a more up-to-date patient data profile for assessing the patient's status. In some embodiments, exemplary data collected are as described in other sections of this document.

[0165] Example of generating personalized predictive metrics

[0166] In some embodiments, the system includes instructions to generate personalized predictive metrics for each patient / user.

[0167] For ease of explanation below, personal data will be referred to simply as "data," but it should be understood that it refers to any type of data as described above. Furthermore, "predictive indicators" are calculated and / or recalculated by comparing changes in one or more parameters recovered and / or derived from the "data," as will be further explained below.

[0168] In some embodiments, once a user profile is generated, it is compared with profiles stored in a reference database. In some embodiments, at the start of monitoring, when a user profile is first entered into the system, the system uses the new user profile as the starting point for monitoring and performs comparisons based on that starting point. For example, when a user profile is generated, a starting point for a patient at T0 is defined. The system compares the starting point with similar reference profiles within the reference database. In some embodiments, the starting point may be matched with one or more reference profiles within the reference database. In some embodiments, the system uses the comparison results to provide the user / patient with a first personalized predictive indicator score. In some embodiments, at T0, since no new data has been entered into the system, the personalized predictive indicator score is more "flexible" because many outcomes may occur depending on the specific new data to be entered. For example, at T0, a patient without diabetes has just suffered from HF and no new data has been entered, so the first personalized predictive indicator score is, for example, "5". The first personalized predictive indicator score is matched with many reference profiles with different outcomes (as explained above regarding the generation of the reference database). Over time, for example at T2 (two years from T0), the system inserts new data to update the user profile, for example, the patient develops diabetes. This new data "eliminates" irrelevant results from the reference archive, leaving more relevant ones used to calculate (now recalculated) personalized predictive metric scores (now modified to, for example, "3") and assess a user's condition. In this example, having diabetes is a factor contributing to the patient's worsening condition, which, if left untreated, could cause irreversible damage. This information will be provided to external entities that will use it to make health decisions relevant to a specific patient.

[0169] In some embodiments, the system is configured to assign “weights” to newly input data to facilitate the assessment of patient status and the calculation of new personalized indexes, as illustrated and explained below.

[0170] In some embodiments, for patients receiving implantable device therapy (such as CCM or CRT), historical data has been collected, and a set of dedicated parameters has been set to generate personalized predictive indicators. In some embodiments, multiple parameters are monitored, but only some parameters affect the personalized predictive indicator. In some embodiments, as described above, exemplary conditions may be: likelihood of hospitalization, degree of HF compensation, and likelihood of developing cardiac events (e.g., arrhythmias). In some embodiments, the personalized predictive indicator takes each specific condition into account. In some embodiments, an alert is provided whenever the personalized predictive indicator indicates a high probability of disease progression.

[0171] Example scale of personalized predictive indicators

[0172] In some embodiments, any type of scale may be used to reflect the predictive indicators. For example, a numerical scale, such as numbers from 1 to 10, may be used, where 1 is a “bad” predictor (the patient’s condition may worsen or even lead to death if no action is taken, as shown in the reference archives in the reference database), and 10 is a “good” predictor (the patient’s condition will improve and / or remain unchanged, as shown in the reference archives in the reference database).

[0173] In some embodiments, descriptive scales are used. For example, when the patient is in good condition, the predictive indicator is "sporadic monitoring required," while when the patient is bedridden, the predictive indicator is "frequent monitoring and immediate action required." It should be understood that these are merely examples, and any language used is included within the scope of this invention.

[0174] Example

[0175] Exemplary predictive indicators include scales of 1 to 10 with a time resolution of weekly, designed to track cardiac function elements in patients with heart failure. In some embodiments, assessment results are provided to physicians to evaluate changes in functional elements, such as assessing the effectiveness of treatment (CCM, etc.), and to guide treatment to prevent index-predicted decompensation and hospitalization. In some embodiments, assessment results are provided to patients as a warning signal of disease deterioration so that they can make lifestyle modifications to avoid hospitalization, while also serving as a motivator for adhering to treatment (e.g., recharging implantable devices, not missing medications) and maintaining a healthy lifestyle (e.g., diet, exercise, reducing salt intake, etc.).

[0176] In some embodiments, the change of personalized predictive indicators over time is used as a risk stratification parameter applicable to predicting hospitalizations and deaths associated with HF.

[0177] Now for reference Figure 7 The diagram illustrates an exemplary process for monitoring a patient according to some embodiments of the present invention. Figure 7 A table is shown with exemplary data inserted into the system, such as average heart rate. The table also shows an assessment based on the provided response, in the example of average heart rate, where an increase in heart rate is associated with an increased risk for HF patients. The table also shows the weight given to that particular data, which in this case is “weak,” meaning that a change in that particular parameter has little impact on the patient’s overall condition. In some embodiments, multiple data points, assessments, and weights are delivered for evaluation, where user profiles and reference databases are combined to reconstruct the user’s current condition and personalized predictive indicators.

[0178] As used in this article, the term “approximately” for reference quantities or values ​​means “within ±20%”.

[0179] The terms “comprise,” “comprising,” “include,” “including,” “has,” “having,” and their synonyms all mean “including but not limited to.”

[0180] The term "composed of" means "including but not limited to".

[0181] The term "consistent primarily of" means that a composition, method, or structure may include additional ingredients, steps, and / or components, provided that these additional ingredients, steps, and / or components do not materially alter the basic and novel features of the claimed composition, method, or structure.

[0182] The singular forms “a,” “an,” and “the” used herein include plural references unless the context clearly specifies otherwise. For example, the term “compound” or “at least one compound” can include a variety of compounds, including mixtures thereof.

[0183] In this application, embodiments of the invention may be presented with reference to a scope format. It should be understood that the use of a scope format is merely for convenience and brevity and should not be construed as an inflexible limitation of the scope of the invention. Therefore, a description of a scope should be considered to explicitly disclose all possible sub-scopes and the individual numerical values ​​within that scope. For example, a description of a scope such as "from 1 to 6" should be considered to explicitly disclose sub-scopes such as "from 1 to 3", "from 1 to 4", "from 1 to 5", "from 2 to 4", "from 2 to 6", "from 3 to 6", etc.; and the individual numbers within that scope, such as 1, 2, 3, 4, 5, and 6. This principle applies regardless of how broad the scope may be.

[0184] Whenever a range of numbers is indicated in this document (e.g., “10-15”, “10 to 15”, or any pair of numbers connected by such and other similar range indications), it is intended to include any numbers (fractions or integers) within the indicated range limit, including the range limit itself, unless the context explicitly states otherwise. In this document, the phrases “range / ranging / ranges between” the first and second indicator numbers, and “range / ranging / ranges from” the first indicator number to, “up to”, “until”, or “through” (or other such range indication terms) the second indicator number, may be used interchangeably and are intended to include the first and second indicator numbers and all fractions and integers between them.

[0185] Unless otherwise stated, the figures used herein and any ranges of figures based thereon are approximations within the reasonable range of measurement precision and rounding errors as understood by those skilled in the art.

[0186] As used herein, the term "method" refers to the manner, means, techniques, and processes used to accomplish a given task, including but not limited to those known to practitioners in the fields of chemistry, pharmacology, biology, biochemistry, and medicine, or those readily developed from known manner, means, techniques, and processes.

[0187] As used herein, the term "treatment" includes eliminating, substantially inhibiting, slowing or reversing the progression of a disease, substantially improving the clinical or aesthetic symptoms of a disease, or substantially preventing the occurrence of the clinical or aesthetic symptoms of a disease.

[0188] It should be understood that, for clarity, certain features of the invention described in the context of a single embodiment may also be provided in combination in a single embodiment. Conversely, for brevity, various features of the invention described in the context of a single embodiment may also be provided individually or in any suitable sub-combination, or suitably provided in any other described embodiment of the invention. Certain features described in the context of various embodiments should not be considered as essential features of those embodiments unless the embodiment does not function without these elements.

[0189] Although the invention has been described in combination with specific embodiments thereof, it will be apparent to those skilled in the art that many alternatives, modifications, and variations will be readily apparent. Therefore, it is intended to cover all such alternatives, modifications, and variations falling within the spirit and broad scope of the appended claims.

[0190] All publications, patents, and patent applications referenced in this specification are incorporated herein by reference in their entirety, as if each individual publication, patent, or patent application were specific and separate, and where cited it is indicated that it is incorporated herein by reference. Furthermore, any reference or identification of any reference in this application should not be construed as an admission that such reference is used as prior art of the invention. The use of section headings should not be construed as necessarily limiting. Additionally, any priority documents of this application are incorporated herein by reference in their entirety.

Claims

1. A system for monitoring user status, comprising: a. An implantable device configured to communicate with external electronic devices; b. External electronic devices, including: i. The generated reference database; ii. An evaluation module, including instructions for generating personalized predictive metrics; and iii. A user database containing user data.

2. The system as claimed in claim 1, wherein, The implanted device is an implantable cardioverter defibrillator (ICD).

3. The system as described in claim 2, wherein, The ICD is configured to perform one or more of the following: monitor cardiac activity, provide cardiac resynchronization therapy (CRT), and provide cardiac contractility modulation (CCM).

4. The system as claimed in claim 1, wherein, The generated reference database is generated according to a method that includes the following: a. Collect historical data from multiple users; the historical data includes a timeline in chronological order; b. Index the collected data; c. Analyze the index data; d. Group the analyzed index data based on the similarity at the start of the timeline in chronological order; e. Analyze each of the analysis groups to recover the similarities and differences in the results from the multiple users; f. Identify the branching points in the chronologically ordered timeline; g. Provide index values ​​at the bifurcation points of the timeline in chronological order in the analysis; h. Store the index value in a database as a reference prediction indicator.

5. The system as claimed in claim 1, wherein, The personalized prediction metrics are generated using the following method: a. Collect multiple data points related to multiple different users; b. Generate multiple reference prediction indicators based on the collected data; c. Collect data related to the identified users; d. Compare the collected data related to the identified users with the reference prediction indicators; e. Generate personalized predictive indicators based on the comparison.

6. The system of claim 1, wherein, The external electronic device is a server.

7. The system as claimed in claim 1, wherein, The external electronic device is an electronic device that includes instructions for transmitting data to the server.

8. The system of claim 1, wherein, The external electronic device includes dedicated software that includes instructions that allow the user to insert user data.

9. The system of claim 8, wherein, The dedicated software includes instructions to provide the user with a dedicated interrogator.

10. The system of claim 1, further comprising: One or more external sources are used to provide data related to the user.

11. The system of claim 1, wherein, The personalized prediction metrics are used to predict the expected changes in the user's state.

12. A method for generating personalized predictive indicators, comprising: a. Collect multiple data points related to multiple different users; b. Generate multiple reference prediction indicators based on the collected data; c. Collect data related to the identified users; d. Compare the collected data related to the identified users with the reference prediction indicators; e. Generate personalized predictive indicators based on the comparison.

13. The method of claim 12, wherein, The data related to the identified user includes one or more of the following: user medical history, clinical parameters, data from the implantable device, and data from external sensors.

14. The method of claim 13, wherein, The external sensor is one or more of the following: ECG sensor, blood pressure sensor, bioimpedance sensor, respiration sensor, motion sensor, and sleep sensor.

15. The method of claim 12, wherein, The collected and identified user-related data includes data collected from the user themselves, one or more sensors connected to the user, and one or more external sources.

16. The method of claim 15, wherein, The collection of data from the user himself includes providing the user with a dedicated interrogator.

17. The method of claim 12, wherein, The personalized prediction metrics are used to predict expected changes in the user's status.

18. A method for monitoring user status, comprising: a. Generate personalized forecast indicators; b. Collect one or more status data of the user; c. Update the personalized prediction index based on the collected status data.

19. The method of claim 18, wherein, The generated personalized prediction metrics include: a. Collect multiple data points related to multiple different users; b. Generate multiple reference prediction indicators based on the collected data; c. Collect data related to the identified users; d. Compare the collected data related to the identified users with the reference prediction indicators; e. Generate personalized predictive indicators based on the comparison.

20. The method of claim 19, wherein, The data related to the identified user includes one or more of the following: user medical history, clinical parameters, data from the implantable device, and data from external sensors.

21. The method of claim 20, wherein, The external sensor is one or more of the following: ECG sensor, blood pressure sensor, bioimpedance sensor, respiration sensor, motion sensor, and sleep sensor.

22. The method of claim 20, wherein, The collected and identified user-related data includes data collected from the user themselves, one or more sensors connected to the user, and one or more external sources.

23. The method of claim 22, wherein, The collection of data from the user himself includes providing the user with a dedicated interrogator.

24. The method of claim 18, wherein, The personalized prediction metrics are used to predict the expected changes in the user's state.

25. The method of claim 24, further comprising: The collected data is analyzed after the collection and before the update.

26. The method of claim 25, wherein, The analysis includes comparing the collected data with one or more reference data for one or more reference predictive indicators.

27. The method of claim 18, further comprising: At least one external entity is updated using the updated personalized forecast metrics.

28. The method of claim 18, further comprising: An alert is sent when the change in the personalized prediction indicator reaches a predetermined threshold.

29. The method of claim 18, wherein, The collection of one or more status data of the user includes data collected from the user himself, one or more sensors connected to the user, or one or more external sources.

30. The method of claim 29, wherein, The collection of data from the user himself includes providing the user with a dedicated interrogator.

31. The method of claim 18, wherein, The updated personalized prediction metrics are used to predict the expected changes in the user's state.

32. A method for generating a reference predictive index database, comprising: a. Collect historical data from multiple users; the historical data includes a timeline in chronological order; b. Index the collected data; c. Analyze the index data; d. Group the analyzed index data based on the similarity at the start of the timeline in chronological order; e. Analyze each of the analysis groups to recover the similarities and differences in the results from the multiple users; f. Identify the branching points in the chronologically ordered timeline; g. Provide index values ​​at the bifurcation points of the timeline in chronological order in the analysis; h. Store the index value in a database as a reference prediction indicator.

33. The method of claim 32, wherein, The grouping at the beginning of the chronological timeline is based on one or more of the following user-related parameters: age, gender, medical condition, cardiac parameters, physiological parameters, and medical history.

Images