Digital intelligent management method for CIED patient and related equipment

Abstract

The invention discloses a digital intelligent management method for CIED patients and related equipment. The method comprises the following steps: acquiring an implantation period of a cardiac pacemaker of a target patient, wherein the implantation period comprises an acute period, a stable period and an adjacent replacement period; based on the implantation period of the cardiac pacemaker where the target patient is currently located, selecting a monitoring management strategy associated with the current implantation period; and performing intelligent management on the target patient according to the selected monitoring management strategy. The problems that the self-management ability of patients is generally not high, CIED needs to be carried for lifetime, management is improper, the life quality is affected, manual management is mostly adopted at present, traditional follow-up visit depends on outpatient service, and continuous tracking is difficult can be solved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of smart medical care. More specifically, the present invention relates to a digital intelligent management method for CIED patients and related equipment. Background Art

[0002] A CIED (Cardiac Implantable Electronic Device) is an electronic therapeutic device implanted in the body and is an effective treatment for bradyarrhythmias and chronic heart failure. It primarily uses a pulse generator to emit battery-powered electrical pulses, which are transmitted through wire electrodes to stimulate the myocardium they contact, causing the heart to excite and contract, thereby treating cardiac dysfunction caused by certain arrhythmias.

[0003] Patients are discharged from the hospital 3-4 days after CIED implantation. After the operation, they face the transition stage to family or society, which includes wound self-care and observation, home heart rate monitoring, shoulder joint movement of the operated limb, environmental magnetic field safety and other care needs. Patients generally have low self-management ability, and CIEDs need to be carried for life. Improper management affects the quality of life. Currently, most of them are managed manually, and traditional follow-up relies on outpatient clinics, which makes continuous tracking difficult. Summary of the Invention

[0004] The Summary of the Invention introduces a series of simplified concepts that will be further described in the Detailed Description of the Invention. The Summary of the Invention is not intended to limit the key features and essential features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0005] To address the issues of generally low self-management capabilities among patients, the lifelong need for CIEDs, improper management, and the impact on quality of life, currently mostly managed manually, with traditional follow-up relying on outpatient clinics and difficulty in continuous tracking, the present invention proposes a digital intelligent management method for CIED patients, comprising:

[0006] Obtaining a pacemaker implantation cycle of a target patient, wherein the implantation cycle includes an acute phase, a stable phase, and a near-replacement phase;

[0007] Selecting a monitoring and management strategy associated with the current implantation cycle of the cardiac pacemaker of the target patient based on the current implantation cycle of the cardiac pacemaker;

[0008] The target patients are intelligently managed according to the selected monitoring and management strategy.

[0009] Optionally, selecting a monitoring and management strategy associated with the current implantation cycle of the cardiac pacemaker of the target patient based on the current implantation cycle of the cardiac pacemaker includes:

[0010] When the target patient is currently in an acute pacemaker implantation cycle, the target patient's physiological data, pacemaker telemetry data, and pocket environment monitoring data are obtained to perform pocket infection assessment based on an infection prediction model.

[0011] Optionally, selecting a monitoring and management strategy associated with the current implantation cycle of the cardiac pacemaker of the target patient based on the current implantation cycle of the cardiac pacemaker includes:

[0012] When the target patient is currently in an acute phase of the pacemaker implantation cycle, predicting the actual posture of the target patient's implanted limb based on sensor data in a wearable device worn on the implanted limb;

[0013] When the actual posture exceeds the ideal activity posture range, a vibration warning prompt is generated.

[0014] Optionally, selecting a monitoring and management strategy associated with the current implantation cycle of the cardiac pacemaker of the target patient based on the current implantation cycle of the cardiac pacemaker includes:

[0015] When the target patient is currently in an acute phase of the pacemaker implantation cycle, predicting the actual posture of the target patient's implanted limb based on sensor data in a wearable device worn on the implanted limb;

[0016] When the actual posture is within the lower limit of activity posture recognition for more than a preset time, an activity prompt is generated.

[0017] Optionally, selecting a monitoring and management strategy associated with the current implantation cycle of the cardiac pacemaker of the target patient based on the current implantation cycle of the cardiac pacemaker includes:

[0018] The step of predicting the target patient's exercise habits based on the patient's posture data and physiological data collected by the target patient's wearable device, when the target patient's current pacemaker implantation cycle is a stable period;

[0019] The theoretical period of the cardiac pacemaker's replacement period is updated based on the exercise habit and the current ideal pacing voltage.

[0020] Optionally, also include:

[0021] Generate multiple exercise plans based on the patient's posture data and physiological data collected by the target patient's wearable device;

[0022] The theoretical replacement period associated with each exercise plan is predicted based on the current ideal pacing voltage for presentation to the target patient.

[0023] Optionally, also include:

[0024] Obtain the ideal replacement period for target patients;

[0025] Generate a target exercise plan based on the ideal replacement period and the patient's posture data and physiological data collected by the wearable device of the target patient;

[0026] Perform exercise management on the target patient based on the target exercise plan.

[0027] In a second aspect, the present invention further provides a digital intelligent management device for CIED patients, comprising:

[0028] an acquisition unit, configured to acquire an implantation cycle of a cardiac pacemaker of a target patient, wherein the implantation cycle includes an acute phase, a stable phase, and a phase close to replacement;

[0029] a selection unit, configured to select a monitoring and management strategy associated with a current implantation cycle of the cardiac pacemaker of the target patient based on the current implantation cycle of the cardiac pacemaker;

[0030] The management unit is used to perform intelligent management on the target patient according to the selected monitoring and management strategy.

[0031] In a third aspect, an electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the steps of the digital intelligent management method for CIED patients as described in any one of the first aspects above when executing the computer program stored in the memory.

[0032] In a fourth aspect, the present invention further proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the digital intelligent management method for CIED patients according to any one of the above items in the first aspect.

[0033] In summary, the digital intelligent management method for CIED patients proposed in this application obtains the implantation cycle of the target patient's pacemaker, which includes the acute phase, the stable phase, and the near-replacement phase; based on the target patient's current implantation cycle of the pacemaker, selects the monitoring and management strategy associated with the current implantation cycle; and intelligently manages the target patient according to the selected monitoring and management strategy. Through cycle identification and real-time data access, the system can capture signs of functional abnormalities and complications earlier than clinical means, intervene in advance, and delay the development of failures or complications; match different monitoring strategies and contents to different stages, reduce the patient's operational burden, and improve the professional accuracy of management; gradually shift traditional regular outpatient resources to on-demand response, improve the efficiency of the medical system, and is especially suitable for remote areas or people with limited mobility; provide visual feedback, voice interaction, and behavioral task check-ins through digital platforms to increase patient participation and health management awareness; accurately track the operating status of CIEDs, reduce false alarms and delays, extend the service life of the equipment, and ensure the quality of life of patients.

[0034] The digital intelligent management method for CIED patients of the present invention, and other advantages, objectives and features of the present invention will be partially reflected in the following description, and will also be understood by those skilled in the art through research and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present description. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0036] Figure 1 A schematic flow chart of a digital intelligent management method for CIED patients provided in an embodiment of the present application;

[0037] Figure 2 A schematic diagram of the structure of a digital intelligent management device for CIED patients provided in an embodiment of the present application;

[0038] Figure 3 A schematic diagram of the structure of a digital intelligent management electronic device for CIED patients provided in an embodiment of the present application. DETAILED DESCRIPTION

[0039] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the embodiments described are only part of the embodiments of the present application, not all of the embodiments.

[0040] To address the widespread lack of patient self-management capabilities and the lifelong need for CIEDs, improper management can negatively impact quality of life. Currently, manual management is often used, and traditional follow-up relies on outpatient clinics, making continuous tracking difficult. A comprehensive intelligent management system for CIED patients is being developed. Early detection of complication risks is crucial for reducing major adverse events in the management of CIED patients. To this end, the system establishes a risk monitoring mechanism through multimodal data fusion, integrating pacemaker telemetry data (such as battery status and lead impedance), heart rate and activity information collected by wearable devices, and smart materials monitoring local pH and temperature trends in the pocket to dynamically capture precursor signals of high-risk events such as infection and mechanical failure. Incorporating historical evolution patterns, an infection prediction model is constructed, for example, by superimposing the pocket temperature gradient rate on the local acidity trend to estimate infection probability. Furthermore, an AI-based dynamic early complication recognition algorithm is introduced. For specific scenarios, such as pocket infection, it identifies inflammatory trends by integrating local temperature abnormalities, white blood cell count fluctuations, and abnormal blood glucose control. For pacemaker syndrome, a smart wristband collects heart sound signals and accelerometer data to analyze the magnitude of exercise tolerance decline and predict the risk of hemodynamic mismatch. Through this series of integrated perception and intelligent analysis, it is technically possible to complete screening of high-risk groups before patients have obvious clinical symptoms, issue intervention reminders in a timely manner, and significantly improve the level of complication prevention and management.

[0041] For example, in response to the problem of insufficient understanding and compliance of patients with postoperative care, the system has built a customized care push module for the APP. Based on the patient's implant cycle stage (acute phase, stable phase, replacement phase), changes in telemetry data (such as fluctuations in stimulation threshold), and physiological characteristics (such as age, history of complications), an exclusive care plan is generated, including wound management, surgical limb rehabilitation, pacemaker protection measures and other content, and pushed in batches according to patient characteristics. In order to enhance patients' interest in active learning, the APP incorporates video explanations, animation demonstrations and gamification learning modules, and sets up knowledge challenges and points reward mechanisms to enhance the patient's learning experience. At the same time, the AR augmented reality function is introduced to display the installation structure and working process of the pacemaker and wire in a three-dimensional visual way, helping patients to intuitively understand the operating mechanism of the device in the body and its importance to postoperative behavioral norms. In terms of overall technical effects, through multi-faceted interactions and intuitive understanding, the patient's mastery of nursing requirements and long-term compliance are significantly improved, thereby reducing the incidence of complications.

[0042] For example, during the postoperative recovery period, patients managing themselves at home are prone to undetected heart rhythm abnormalities and functional deterioration of the operated limb. To address this, the system utilizes an integrated remote monitoring and rehabilitation solution. Using devices such as smart bracelets and smart shoulder straps, the system collects real-time data on the patient's heart rate, respiratory rate, sleep status, and shoulder range of motion, and simultaneously uploads this data to a cloud platform. Based on continuous data analysis, the platform identifies heart rhythm abnormalities (such as atrial fibrillation) and ICD discharge events, and generates real-time abnormality alerts that are sent to both the patient and the physician. To address the risk of shoulder dysfunction, the system has developed VR rehabilitation training scenarios that combine electromyography sensors and a 6-degree-of-freedom robotic arm device to guide patients through progressive shoulder rehabilitation exercises to prevent the formation of frozen shoulder. The smart shoulder strap continuously monitors the range and frequency of shoulder movement to prevent lead traction caused by excessive movement. By digitizing paper CIED identification cards, patients can easily display their device information when traveling or seeking medical attention, improving emergency response efficiency. This integrated remote and rehabilitation management model not only dynamically protects patients' physiological safety, but also systematically promotes postoperative functional recovery and improves quality of life.

[0043] For example, infection is one of the most serious complications after CIED implantation. Based on this, the system has introduced a full-process infection prevention and control and re-implantation decision support module. The system continuously records patient follow-up data. If the set maximum follow-up interval (such as 6 months) is exceeded, a reminder will be automatically pushed to prompt the patient to make an outpatient appointment or start remote follow-up. Medication compliance is managed, and the patient enters or the APP automatically prompts the medication time (especially antibiotics and anticoagulants). If the medication is missed or deviates from the dosage, timely reminders and guidance adjustments will be provided. For patients who have already developed infection or have high-risk factors, the system automatically calculates the re-implantation risk score based on the patient's medical history (such as diabetes, abnormal renal function), previous infection type (such as Staphylococcus, Candida) and venous access status (ultrasound, CT confirmation results), and intelligently recommends a leadless pacemaker (such as Micra) plan or a contralateral implantation strategy to minimize the risk of reinfection and complex complications. Through this module, early prevention and control of postoperative infection can be achieved, and scientific decision support can be provided when re-implantation is needed, greatly improving the success rate of re-operation and the long-term quality of life of patients.

[0044] For example, in order to alleviate the burden of patients in remote areas or with limited mobility who frequently return to the hospital, the system has established a home-hospital collaborative management channel. Portable pacemaker threshold detectors are configured in community health service centers, supporting NFC or Bluetooth communication. Patients only need to complete the rapid reading and preliminary evaluation of parameters such as device voltage, stimulation threshold, and lead impedance at the local community health station. The test data is synchronized to the cloud platform in real time and interpreted by remote experts from tertiary hospitals. For abnormalities found during the test (such as critical low battery voltage and sudden changes in lead impedance), doctors can initiate remote consultations or dispatch patients to the hospital for follow-up visits through the system, greatly reducing the number of unnecessary visits to the hospital and shortening the response time for events that require urgent intervention. This collaborative mechanism effectively breaks down geographical barriers, improves the breadth and depth of patient management, and improves the level of benefits for patients in remote areas.

[0045] For example, to further extend CIED patient management to the grassroots level, a community grid-based care network is systematically constructed. Each grassroots area is staffed with trained CIED-dedicated nurses who use the NFC function of the issued PDA or smart handheld terminal to identify the patient's implant information and automatically obtain data such as device model, implantation time, last threshold, and follow-up records. Nurses complete health follow-up based on system prompts, including functional assessment of the operated limb, heart rate monitoring, wound observation, and immediate reporting of major abnormalities. Grassroots medical institutions connect in real time with the remote expert system of tertiary hospitals through a data sharing platform, initiate remote consultations when necessary, and formulate follow-up or referral plans. Through this model, the intelligent guidance of high-level medical resources is extended to the community, improving the rapid response capabilities of grassroots communities, promoting the formation of a closed loop of early screening, early intervention, and early rehabilitation, and significantly improving the overall level of CIED patient management at the social level.

[0046] Combined with the above-mentioned CIED patient full cycle intelligent management system, refer to Figure 1 , is a flow chart of a digital intelligent management method for CIED patients provided in an embodiment of the present application, which may specifically include: steps S110 to S130.

[0047] S110, obtaining an implantation cycle of a cardiac pacemaker of a target patient, where the implantation cycle includes an acute phase, a stable phase, and a near-replacement phase.

[0048] S120: Based on the target patient's current pacemaker implantation cycle, select a monitoring management strategy associated with the current implantation cycle.

[0049] S130: Perform intelligent management on the target patient according to the selected monitoring and management strategy.

[0050] Understandably, the duration of CIED implantation influences management priorities. The first 90 days after surgery (acute phase) prioritize lead stability and postoperative recovery. Three months after implantation and until battery criticality (stable phase) emphasizes cardiac rhythm changes and device stability. When the battery voltage drops near the threshold (replacement phase), early warning of device exhaustion and functional decline is crucial. Therefore, identifying the patient's current stage is fundamental to developing personalized follow-up management.

[0051] For example, the system first obtains the implantation time and the current date for calculation, and combines the battery voltage value, electrode impedance, pacing threshold and other parameters read by telemetry to determine whether the patient is in the acute phase (within 90 days after surgery), the stable phase (after 90 days and the battery voltage is normal) or the replacement period (voltage is critically low and functional parameters fluctuate) using algorithmic rules. In addition, the cycle status is marked in the patient file to provide a basis for subsequent matching strategies. Thus, through stage identification, the system can automatically distinguish management priorities, avoid a "one-size-fits-all" follow-up model, and achieve dynamic response. For example, wound monitoring is strengthened in the acute phase, while the stable phase focuses on the identification of cardiac rhythm events, thereby improving management efficiency and the pertinence of risk response.

[0052] Understandably, patients face different risks at different cycles, requiring different types and frequencies of monitoring data. By mapping cycles to management strategies, we can create phased task packages, including telemetry parameter analysis, home health data reporting, and behavioral indicator tracking, enabling strategic resource allocation.

[0053] Exemplarily, the system has a strategy library that associates the acute phase with wound healing monitoring, surgical limb range of motion assessment, and initial electrode stability; the stable phase is associated with telemetry parameters such as lead impedance, battery voltage, R-wave amplitude, and life behaviors (number of steps, sleep); and the replacement phase focuses on battery status, pouch temperature, and electrode function fluctuations. After the matching is completed, the system automatically pushes the corresponding task list according to the current cycle, including daily / weekly telemetry collection, exercise task check-in, uploading of wound pictures, temperature monitoring, etc., all integrated into the APP or remote platform. This mechanism ensures that the management content is highly consistent with actual needs and prevents information redundancy or omission of key risks. Taking a patient in a stable period as an example, if there is a sudden change in the impedance of his lead, the system will recognize that this event exceeds the stability threshold and immediately upgrade the management strategy, issue a reminder, and recommend remote review, greatly reducing the risk of complications caused by lead detachment or electrode dislocation.

[0054] It is understandable that by integrating multi-source data (CIED telemetry, wearable devices, patient self-reported data, etc.), the system can establish a dynamic health portrait; identify and respond to abnormal changes based on graph rules, statistical models or AI algorithms, forming a closed-loop management, and automatically executing the entire process from discovery to judgment to intervention to verification.

[0055] For example, the system continuously accesses pacemaker telemetry data (battery voltage, electrode impedance, cardiac event records, etc.), while also integrating data from wearable devices (heart rate, steps, body temperature), and supports patients to proactively clock in information such as shoulder range of motion and medication adherence. When the system identifies changes in key parameters (such as a voltage drop below 2.6V or frequent nocturnal arrhythmias), it triggers intervention processes: automatically issuing warnings, sending alerts to physicians, recommending examination items, or automatically scheduling outpatient follow-up appointments. Furthermore, the system can also push daily tasks based on behavioral data, such as suggesting stretching the operated limb, uploading wound recovery images, and performing magnetic field environment self-checks. Elderly patients can also receive feedback and prompts through voice assistants, enhancing interactivity. This intelligent management approach enables seamless, 24 / 7 monitoring and response, particularly addressing the long follow-up cycles and delayed response times of traditional outpatient clinics. For example, in the case of pocket infection, if an adhesive temperature sensor patch detects a persistent temperature increase accompanied by abnormal R-wave amplitude, the system can link these two abnormal data points to infer the possibility of infection and initiate early intervention recommendations, thereby avoiding hospitalization for serious infection. The entire process does not require active perception by the patient, achieving a closed-loop prevention and control from automatic monitoring to automatic identification to active reminders.

[0056] For example, after surgery, patients enter the home care phase, where they receive customized care content (such as surgical limb movement guidelines, magnetic field avoidance tips, and dietary recommendations) based on their implant cycle, individual characteristics, and risk level via an app. The introduction of videos, animations, and gamification mechanisms is particularly suitable for elderly and adolescent patients, improving information acceptance and implementation.

[0057] For example, the smart bracelet / shoulder strap collects heart rate, respiratory rate, sleep, shoulder joint range of motion and other indicators in real time, connects to the cloud platform, and forms a continuous physiological monitoring curve.

[0058] For example, if the time interval between follow-up visits is greater than the threshold, the system automatically prompts a follow-up visit; based on the drug management module, the time of taking antibiotics and anticoagulants is recorded and a reminder is sent; and linked with the infection prediction model, a risk trend chart is formed.

[0059] For example, an electronic identification card function is provided, which can display the device model, implantation time, threshold history, and hospital information with one click, replacing paper cards. Community health service stations are equipped with NFC portable detectors that can read pacemaker parameters (such as pacing voltage, electrode impedance, and event records). Family-side apps can receive follow-up reminders and medication prompts to assist in the management of elderly patients. Each grid area designates a responsible CIED nurse and a weekly follow-up plan. PDAs or smart handheld terminals are distributed to scan the electronic tags at the implant site to retrieve device information and patient history management data. Tertiary hospitals and grassroots institutions share platform data to assist in remote interpretation and emergency treatment decisions.

[0060] In summary, the digital intelligent management method for CIED patients provided in the embodiment of the present application obtains the implantation cycle of the target patient's pacemaker, which includes the acute phase, the stable phase, and the near-replacement phase; based on the implantation cycle of the target patient's current pacemaker, selects the monitoring and management strategy associated with the current implantation cycle; and performs intelligent management on the target patient according to the selected monitoring and management strategy. Through cycle identification and real-time data access, the system can capture signs of functional abnormalities and complications earlier than clinical means, intervene in advance, and delay the development of failures or complications; match different monitoring strategies and contents to different stages, reduce the operational burden on patients, and improve the professional accuracy of management; gradually shift traditional regular outpatient resources to on-demand response, improve the efficiency of the medical system, and is especially suitable for remote areas or people with limited mobility; provide visual feedback, voice interaction, and behavioral task punch-in through a digital platform to increase patient participation and health management awareness; accurately track the operating status of CIEDs, reduce false alarms and delays, extend the service life of the equipment, and ensure the quality of life of patients.

[0061] According to some embodiments, selecting a monitoring management strategy associated with the current implantation cycle of the cardiac pacemaker of the target patient based on the current implantation cycle of the cardiac pacemaker includes:

[0062] When the target patient is currently in an acute pacemaker implantation cycle, the target patient's physiological data, pacemaker telemetry data, and pocket environment monitoring data are obtained to perform pocket infection assessment based on an infection prediction model.

[0063] Understandably, the acute phase after CIED implantation (typically 0 to 90 days post-surgery) is a high-risk window for infection. In particular, bacterial invasion of the surgical area (pocket) can easily lead to local inflammation, exudation, and even systemic infection. During this phase, traditional methods that rely solely on visual observation and intermittent temperature recordings make it difficult to promptly identify early signs of infection. Therefore, it is necessary to integrate pacemaker telemetry data (such as abnormal electrode parameters), physiological data (such as persistent low-grade fever), and pocket environmental data (such as local temperature increase and pH shift) to construct an infection prediction model for comprehensive intelligent judgment.

[0064] Exemplarily, when the system identifies that the patient is in the acute phase, it automatically activates the high-frequency infection monitoring process, which includes three types of data collection and analysis: physiological data collection, in which the patient wears a body temperature sensor to continuously obtain axillary temperature or skin temperature, combined with heart rate, activity level, sleep and other data to identify signs of systemic inflammation (such as fever with increased heart rate); pacemaker telemetry data, in which the home telemetry terminal or remote implant device collects the current operating parameters of the CIED, including increased lead impedance (indicating inflammation of the lead-tissue interface), increased pacing threshold (indicating tissue edema or inflammation), abnormal R-wave voltage and other signals, which are used as indirect indications of potential infection; pocket environment data, in which an attached sensor patch is used to collect the subcutaneous temperature and local pH of the implant area (such as micro-sensing colloid changes to identify acidic metabolites) to identify changes in the local inflammatory microenvironment.

[0065] The system uses a built-in infection risk prediction model. When the predicted probability exceeds a preset threshold (e.g., 70%), the system automatically issues a risk warning, prompting the patient to take and upload photos of the surgical area and recommending a remote outpatient or video follow-up consultation. This strategy uses multiple channels to jointly identify infection risks. Compared with the traditional approach that relies solely on fever symptoms, the model can detect abnormalities in the early stages of parameter changes (e.g., impedance rise), allowing the capture of infection signs within 5 to 7 days after surgery. By integrating local indicators (e.g., pH, temperature) and systemic parameters (e.g., heart rate, pacemaker waveforms), the detection accuracy is improved, and false positives and missed negatives are reduced. Model-guided intervention can reduce the incidence of severe infection from 8% three months after surgery to below 2% (based on simulation analysis of certain literature). Patients do not need to actively monitor, as the system automatically collects and interprets data, and only reminds patients to take photos or follow-up visits when the risk is high, thus lowering the compliance threshold.

[0066] For example, a comprehensive risk score for infection is modeled by combining multiple sources of signals (telemetry, physiology, and local pocket) to identify potential pocket infection events early. A weighted risk score is constructed by multiplying each abnormal indicator by the corresponding risk weight to form a linear combination score: S = w1·ΔT + w2·ΔZ + w3·ΔpH + w4·ΔHR + w5·ΔR + b, where b is a bias term used to adjust the overall risk baseline, ΔT is the abnormal increase in temperature in the pocket area (unit: °C), ΔZ is the change in lead impedance (unit: ohm), ΔpH is the decrease in local pH, ΔHR is the relative increase in resting heart rate, ΔR is the proportion of the decrease in induced R wave amplitude, wi is the weight of each indicator (determined based on model training), S is the comprehensive infection risk score, and σ(x) is the Sigmoid mapping function used to convert the score into a probability. The score is input into the Sigmoid function to obtain the risk probability (between 0 and 1): The system sets the warning threshold θ. For example, when P infection >0.7, an alarm is triggered. The above model can be synchronized with the regular collection rhythm of telemetry data, for example, once a day; it can also be used to implement initial threshold judgment on devices with strong edge processing capabilities to reduce the burden on the platform; model parameters (such as weight w i ) can be obtained based on regression training of real clinical samples and supports cross-population migration optimization.

[0067] In some examples, selecting a monitoring management strategy associated with the current implantation cycle of the cardiac pacemaker based on the target patient's current implantation cycle includes:

[0068] When the target patient is currently in an acute phase of the pacemaker implantation cycle, predicting the actual posture of the target patient's implanted limb based on sensor data in a wearable device worn on the implanted limb;

[0069] When the actual posture exceeds the ideal activity posture range, a vibration warning prompt is generated.

[0070] Understandably, during the acute postoperative period after CIED implantation, vigorous swinging or elevation of the operated limb (usually the left or right thoracic upper limb) can easily cause microdisplacement of the lead, compression of the pacemaker pocket, or tearing of tissue in the surgical area. This is especially true during the first two to three weeks, when the lead is not yet firmly attached to the wall, a high-risk period. Traditionally, patient education has been used to instruct patients to "avoid raising the operated limb above the shoulder" or "avoid vigorous extension," but the effectiveness of these instructions depends on patient compliance. By attaching a wearable device (such as a smart armband) to the operated limb and combining it with its built-in IMU sensor (inertial measurement unit), the system can continuously predict the operated limb's posture, enabling dynamic posture perception and risk intervention.

[0071] For example, upon recognizing that the patient is in the acute phase, the system automatically activates the posture monitoring module for the operated limb. The wearable device worn on the patient's operated limb (the implanted side) incorporates a six-axis or nine-axis IMU sensor, comprising an accelerometer, gyroscope, and magnetometer, which collects three-axis acceleration and angular velocity data every second. The system then uses a posture calculation algorithm (such as a Madgwick or Kalman filter fusion algorithm) to convert the raw sensor data into Euler angles (pitch, roll, yaw) or quaternions in real time, thereby inferring the spatial posture of the operated limb. The system also presets an ideal range of active postures for the operated limb (e.g., upper arm-torso angle <60°, rotation angle <30°, and no sustained arm elevation for more than 15 seconds). This range is configured by the postoperative rehabilitation guide or the physician. Every 500ms, the system calculates whether the current posture exceeds this range. If it detects a deviation from the ideal posture (e.g., shoulder elevation exceeding 80° or prolonged abduction), the system immediately triggers the vibration motor on the wearable device to issue a vibration prompt, reminding the patient to retract the movement. In addition, the system records the frequency and duration of abnormal movements, allowing doctors to assess the compliance of the operated limb and conduct re-education. Real-time motion control can effectively reduce the electrode displacement rate within 2 weeks after surgery (according to clinical literature, improper movement of the operated limb after surgery is one of the main causes of electrode displacement); the "automatic detection + instant vibration" method replaces traditional passive education to enhance the effectiveness of patient behavioral intervention; abnormal posture trigger records can be used by doctors in subsequent remote outpatient clinics to judge the patient's postoperative rehabilitation and guide behavior; compared with voice prompts or text notifications, vibration prompts are more suitable for elderly patients or those with limited hearing or vision.

[0072] In some examples, selecting a monitoring management strategy associated with the current implantation cycle of the cardiac pacemaker based on the target patient's current implantation cycle includes:

[0073] When the target patient is currently in an acute phase of the pacemaker implantation cycle, predicting the actual posture of the target patient's implanted limb based on sensor data in a wearable device worn on the implanted limb;

[0074] When the actual posture is within the lower limit of activity posture recognition for more than a preset time, an activity prompt is generated.

[0075] It is understandable that in the acute phase after CIED implantation, patients usually experience reduced activity of the operated limb on the implanted side due to pain, concerns about pulling on the wires, etc., especially insufficient functional activities of shoulder abduction, forward extension and rotation, which may lead to joint stiffness, muscle adhesions, and even the formation of "frozen shoulder" (periarthritis of the shoulder). Traditionally, only "avoiding excessive activity" has been emphasized, while ignoring the guidance and monitoring of "moderate active activity". Therefore, this embodiment introduces a low-activity recognition mechanism, which uses sensors in wearable devices worn on the operated limb to identify the time when the operated limb is in a static or low-amplitude state. If the set threshold is exceeded (such as 30 consecutive minutes of immobility), the activity prompt mechanism is triggered to achieve dual protection of postoperative rehabilitation and complication prevention.

[0076] For example, after the system identifies that the patient is in the acute phase, it activates the limb activity detection submodule. The smart device worn by the limb has a built-in IMU (inertial measurement unit) sensor that continuously collects acceleration and angular velocity data. Through low-pass filtering or FFT frequency domain analysis, it determines in real time whether the limb is in a low-dynamic or static state. The system sets a lower limit for activity posture recognition, for example: the limb must have at least one angle change of ≥10° and a three-axis acceleration amplitude change of ≥0.05g every 15 minutes; if this activity threshold is not reached continuously, the "static duration counter" is accumulated. When the counter exceeds the preset time (such as 30 minutes), the system issues a "surgical limb light activity reminder" through the wearable device vibration module, APP pop-up window or voice assistant to encourage the patient to complete shoulder flexion or rotation movements. The activity movements can be displayed by the system in an animated form to facilitate imitation by elderly users; at the same time, the activity result feedback will be used to train the model to identify whether the real activity is performed or not, supporting doctors to remotely evaluate compliance. Early active activity reminders can effectively reduce the incidence of limited shoulder joint function within 3 to 6 weeks after surgery; regularly guiding patients to perform gentle active activities can help prevent soft tissue adhesion and muscle atrophy; the activity recognition threshold can be set by the doctor based on the patient's postoperative tolerance, and distinguish between elderly patients, weak muscle strength, and patients with previous shoulder diseases; the system can record the average daily number of activity reminders and response rates, providing data support for doctors to judge patients' execution ability and psychological state.

[0077] In some examples, selecting a monitoring management strategy associated with the current implantation cycle of the cardiac pacemaker based on the target patient's current implantation cycle includes:

[0078] The step of predicting the target patient's exercise habits based on the patient's posture data and physiological data collected by the target patient's wearable device, when the target patient's current pacemaker implantation cycle is a stable period;

[0079] The theoretical period of the cardiac pacemaker's replacement period is updated based on the exercise habit and the current ideal pacing voltage.

[0080] It is understandable that during the stable period, the pacemaker mainly enters the routine maintenance state, and its battery consumption is closely related to the auxiliary pacing frequency. Generally speaking, the patient's autonomous heartbeat increases during exercise or activities, and the pacemaker's auxiliary pacing needs decrease, resulting in a decrease in energy consumption per unit time; conversely, the auxiliary pacing frequency increases during sedentary or resting states, and the battery load increases. Therefore, if the patient's auxiliary pacing reduction rate can be modeled based on their individual exercise habits, the energy consumption curve under the current ideal pacing voltage can be adjusted, thereby more scientifically predicting the time point when the replacement period is approaching, avoiding premature replacement or delayed replacement.

[0081] For example, after the system identifies a patient as being in a stable period, it initiates the exercise habit modeling module. This module compiles daily exercise behavior patterns based on continuous posture data (acceleration and angular velocity acquired through IMU sensors) and physiological data (such as heart rate and HRV) collected by the patient's wearable device (such as a smart bracelet or wristband). The system analyzes the ratio between high-activity states (such as walking, housework, light physical labor) and resting states within a time window (such as a 24-hour sliding window) to create an individual activity profile. Simultaneously, the system reads the pacing ratio and current stimulation voltage from the pacemaker's telemetry data. By comparing the decrease in the pacing ratio between active and resting states (for example, a 30% decrease in active state), an auxiliary pacing reduction model is established. This model is applied to the battery life prediction logic to form a new theoretical replacement cycle estimation model. The system ultimately provides a personalized replacement period and dynamically updates this prediction (e.g., monthly evaluation), which can be used by physicians to determine whether to schedule a replacement in advance, conduct a functional assessment, or postpone replacement. Therefore, it no longer relies solely on voltage thresholds, but combines real-life behavior with auxiliary frequency to dynamically and continuously optimize predictions. If the patient has a high level of exercise and less assisted pacing, the originally planned replacement time can be appropriately postponed to reduce unnecessary surgery. Conversely, if the patient's behavioral analysis shows little activity and a high pacing rate, the system will predict that the battery will be exhausted in advance and issue an intervention reminder in advance. The dynamic model constructed by combining the three indicators of behavior, electrophysiology and power consumption represents the trend of future CIED management transitioning to a physiological closed loop combined with behavioral adaptation.

[0082] In some examples, this also includes:

[0083] Generate multiple exercise plans based on the patient's posture data and physiological data collected by the target patient's wearable device;

[0084] The theoretical replacement period associated with each exercise plan is predicted based on the current ideal pacing voltage for presentation to the target patient.

[0085] It is understandable that different daily exercise patterns can significantly affect the degree of activation of the heart's autonomous rhythm, thereby changing the auxiliary pacing frequency of the pacemaker and, in turn, its energy consumption pattern. For example, moderate exercise (such as walking, yoga) can increase the autonomous heart rate and reduce the proportion of auxiliary pacing; while completely sedentary or high-intensity exercise may produce different power consumption curves in some modes (such as ICD with ATP function). Therefore, by constructing a linkage relationship between exercise-physiological response-pacing load, it is possible to simulate the battery replacement period under multiple exercise programs, thereby assisting patients in making rational choices in rehabilitation behavior.

[0086] For example, after identifying that the patient is in the "stable period," the system combines the wearable device data (posture data + physiological data) from the past 1 to 2 weeks with existing behavioral habits and heart rate response models to generate multiple feasible exercise plans, such as: Plan A: low intensity, an average of 3,000 steps per day (such as short walks); Plan B: moderate intensity, 6,000 steps per day, including shoulder joint rehabilitation exercises; Plan C: high intensity, 8,000 steps per day + brisk walking three times a week. The system calls the aforementioned auxiliary pacing reduction model and, based on the current ideal pacing voltage, predicts the reduction in the proportion of auxiliary pacing and the battery energy saving effect under each exercise plan. By combining the manufacturer's nominal lifespan with individualized parameters (such as average daily pacing time and stimulation voltage), the theoretical replacement periods corresponding to different plans are calculated, as shown in the following examples: Exercise Plan A, 0% reduction in pacing assistance, 0% battery life extension, and an estimated replacement date of March 2029; Exercise Plan B, 15% reduction in pacing assistance, 5-month battery life extension, and an estimated replacement date of August 2029; Exercise Plan B, 28% reduction in pacing assistance, 11-month battery life extension, and an estimated replacement date of February 2030. This data is displayed graphically on the patient's end (e.g., bar chart, timeline, color coding), along with physician recommendations or rehabilitation instructions. For example, "Plan B is recommended, provided it does not affect postoperative rehabilitation. It can improve shoulder range of motion and extend battery life by approximately 5 months." Therefore, through the method of "visualizing battery impact", abstract energy consumption issues are linked to daily behaviors, stimulating patients' enthusiasm for participation; encouraging patients to choose "energy-saving rehabilitation plans" to achieve dual optimization of health and equipment life; the replacement period simulation brought by different exercise plans can support doctors to adjust treatment plans, while enhancing patient perception and trust; in the remote follow-up system, doctors can set target behavior intervals and intervention nodes according to the plan selected by the patient to achieve dynamic closed-loop management.

[0087] For example, the remaining life of the pacemaker is predicted from the current point in time, and the battery exhaustion time (i.e., the time point when the replacement period is approaching) is adjusted based on the behavior-energy consumption coupling mechanism. When the voltage decay mapping is approximately linear with the capacity change, the current remaining power is: Among them, V init is the initial voltage of the pacemaker (e.g., 3.0V), and C(t)∈(0,1). The actual energy consumption rate of the pacemaker depends not only on the basic parameters (pacing voltage, current, mode), but also on the behavioral intervention. The equivalent energy consumption rate after considering the behavioral energy saving factor is: eff =λ0·(1-η·A ratio ), where λ0 is the theoretical energy consumption under the current auxiliary pacing ratio; η·A ratio This reflects the average load reduction due to the increase in the autonomous heart rate. The remaining time (in months) can be estimated as: Then we can get the theoretical replacement time point: T replace =T now +T remain If the time is earlier than the planned replacement time, the system can recommend strengthening exercise behavior to prolong life; if the time is delayed too much, the replacement can be postponed or the risk can be reassessed. nom is the nominal life of the pacemaker (defined by the manufacturer, in months); V(t) is the current battery voltage (obtained by telemetry); V EOL is the battery exhaustion threshold voltage (End Of Life, usually 2.6V); P base is the current daily average assisted pacing ratio (%pacing); A ratio is the current daily average proportion of moderate and above intensity activity time; η is the behavioral impact coefficient (indicating the energy saving rate brought about by the enhancement of autonomic rhythms due to exercise); C0 is the initial battery capacity (normalized to 1); C(t) is the current remaining battery capacity, and λ is the battery energy consumption rate per unit time.

[0088] In some examples, this also includes:

[0089] Obtain the ideal replacement period for target patients;

[0090] Generate a target exercise plan based on the ideal replacement period and the patient's posture data and physiological data collected by the wearable device of the target patient;

[0091] Perform exercise management on the target patient based on the target exercise plan.

[0092] It is understandable that in CIED management, the device battery replacement time is controlled by the device voltage consumption pattern and is also affected by the patient's physiological state and activity behavior. Current mainstream systems mostly adopt a forward strategy of "predicting replacement time based on behavior", while this implementation method proposes "after obtaining the preset ideal replacement period target (T_target), reversely optimizing the movement behavior model" to generate a target behavior path that can maximize the delay of assisted pacing, control energy consumption, and extend battery life. This path will be analyzed for differences with the patient's current activity pattern, and through daily intervention management, the patient will be helped to gradually transition, thereby achieving coordinated regulation of the device operation cycle and health behavior.

[0093] For example, the system first obtains the ideal replacement period T_target set by the doctor or patient (such as January 2030). Next, the system retrieves the posture data (activity frequency, angle, number of steps, posture changes) and physiological data (heart rate, pacing assistance rate, HRV) continuously collected by the wearable device, evaluates the battery consumption curve under the patient's current behavioral habits, and calculates the behavioral optimization amplitude difference (ΔActivity) by comparing it with the ideal target period. Based on this difference, the system selects or constructs a target exercise plan from the behavioral model library. For example, the daily step count needs to gradually transition from 3,000 steps to 6,000 steps; the daily low-activity rest time is reduced from 10 hours to 7 hours; the daily light-moderate heart rate zone proportion is increased by 10%; shoulder rehabilitation exercises are performed at least 5 times a week, totaling more than 30 minutes. This target plan will be broken down into executable task units (such as "today's target step count + pacing response difference" and "15-minute shoulder abduction exercise"), and daily exercise management is carried out through the app task list, vibration reminders, illustrated training assistance modules, voice guidance, etc. Each time a task is completed, the system records and calculates the progress of the target deviation correction, updates the battery energy-saving simulation curve, and determines whether the ideal replacement period is gradually approaching. This transforms the abstract replacement time target into a specific and actionable exercise behavior, guiding patients to form positive feedback. The replacement period, exercise behavior, assisted pacing, and battery energy consumption are integrated into a logical closed loop, making dynamic management more predictable and controllable. Through systematic task push and real-time feedback, patients' understanding of abstract goals is compensated for, improving compliance. Doctors can view the patient's execution behavior and target deviation trends on a remote platform, dynamically adjusting the difficulty and structure of the exercise plan. Plan generation is reversed based on individual data to avoid excessive or insufficient exercise caused by general recommendations, thereby improving management accuracy.

[0094] See also Figure 2 An embodiment of the digital intelligent management device for CIED patients in the present application may include:

[0095] An acquiring unit 21 is configured to acquire an implantation cycle of a cardiac pacemaker of a target patient, wherein the implantation cycle includes an acute phase, a stable phase, and a near-replacement phase;

[0096] A selection unit 22 is configured to select a monitoring and management strategy associated with a current implantation cycle of the cardiac pacemaker of the target patient based on the current implantation cycle of the cardiac pacemaker of the target patient;

[0097] The management unit 23 is used to perform intelligent management on the target patient according to the selected monitoring management strategy.

[0098] In summary, the digital intelligent management device for CIED patients provided in the embodiment of the present application obtains the implantation cycle of the target patient's pacemaker, which includes the acute phase, the stable phase, and the near-replacement phase; based on the implantation cycle of the target patient's current pacemaker, selects the monitoring and management strategy associated with the current implantation cycle; and intelligently manages the target patient according to the selected monitoring and management strategy. Through cycle identification and real-time data access, the system can capture signs of functional abnormalities and complications earlier than clinical means, intervene in advance, and delay the development of failures or complications; match different monitoring strategies and contents to different stages, reduce the operational burden on patients, and improve the professional accuracy of management; gradually shift traditional regular outpatient resources to on-demand response, improve the efficiency of the medical system, and is especially suitable for remote areas or people with limited mobility; provide visual feedback, voice interaction, and behavioral task check-ins through a digital platform to increase patient participation and health management awareness; accurately track the operating status of CIEDs, reduce false alarms and delays, extend the service life of the equipment, and ensure the quality of life of patients.

[0099] like Figure 3 As shown, an embodiment of the present application further provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 320 and executable on the processor. When the processor 320 executes the computer program 311, the steps of any of the above-mentioned methods for digital intelligent management of CIED patients are implemented:

[0100] Obtaining a pacemaker implantation cycle of a target patient, wherein the implantation cycle includes an acute phase, a stable phase, and a near-replacement phase;

[0101] Selecting a monitoring and management strategy associated with the current implantation cycle of the cardiac pacemaker of the target patient based on the current implantation cycle of the cardiac pacemaker;

[0102] The target patients are intelligently managed according to the selected monitoring and management strategy.

[0103] Since the electronic device introduced in this embodiment is a device used to implement a digital intelligent management device for CIED patients in the embodiment of this application, based on the method introduced in the embodiment of this application, technical personnel in this field can understand the specific implementation method of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of this application will not be introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of this application falls within the scope of protection of this application.

[0104] In the specific implementation process, the computer program 311 can be implemented when executed by the processor Figure 1 Any implementation manner in the corresponding embodiment:

[0105] Obtaining a pacemaker implantation cycle of a target patient, wherein the implantation cycle includes an acute phase, a stable phase, and a near-replacement phase;

[0106] Selecting a monitoring and management strategy associated with the current implantation cycle of the cardiac pacemaker of the target patient based on the current implantation cycle of the cardiac pacemaker;

[0107] The target patients are intelligently managed according to the selected monitoring and management strategy.

[0108] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0109] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0110] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0111] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0113] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device is caused to execute the following Figure 1 The process of digital intelligent management of CIED patients in the corresponding embodiment.

[0114] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)).

[0115] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0116] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0117] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0118] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0119] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0120] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A digital intelligent management method for CIED patients, characterized in that: include: Obtaining a pacemaker implantation cycle of a target patient, wherein the implantation cycle includes an acute phase, a stable phase, and a near-replacement phase; Selecting a monitoring and management strategy associated with the current implantation cycle of the cardiac pacemaker of the target patient based on the current implantation cycle of the cardiac pacemaker; The target patients are intelligently managed according to the selected monitoring and management strategy.

2. The method according to claim 1, wherein The selecting, based on the target patient's current pacemaker implantation cycle, a monitoring and management strategy associated with the current implantation cycle includes: When the target patient is currently in an acute pacemaker implantation cycle, the target patient's physiological data, pacemaker telemetry data, and pocket environment monitoring data are obtained to perform pocket infection assessment based on an infection prediction model.

3. The method according to claim 1, wherein The selecting, based on the target patient's current pacemaker implantation cycle, a monitoring and management strategy associated with the current implantation cycle includes: When the target patient is currently in an acute phase of the pacemaker implantation cycle, predicting the actual posture of the target patient's implanted limb based on sensor data in a wearable device worn on the implanted limb; When the actual posture exceeds the ideal activity posture range, a vibration warning prompt is generated.

4. The method according to claim 1, wherein The selecting, based on the target patient's current pacemaker implantation cycle, a monitoring and management strategy associated with the current implantation cycle includes: When the target patient is currently in an acute phase of the pacemaker implantation cycle, predicting the actual posture of the target patient's implanted limb based on sensor data in a wearable device worn on the implanted limb; When the actual posture is within the lower limit of activity posture recognition for more than a preset time, an activity prompt is generated.

5. The method according to claim 1, wherein The selecting, based on the target patient's current pacemaker implantation cycle, a monitoring and management strategy associated with the current implantation cycle includes: The step of predicting the target patient's exercise habits based on the patient's posture data and physiological data collected by the target patient's wearable device, when the target patient's current pacemaker implantation cycle is a stable period; The theoretical period of the cardiac pacemaker's replacement period is updated based on the exercise habit and the current ideal pacing voltage.

6. The method according to claim 5, wherein Also includes: Generate multiple exercise plans based on the patient's posture data and physiological data collected by the target patient's wearable device; The theoretical replacement period associated with each exercise plan is predicted based on the current ideal pacing voltage for presentation to the target patient.

7. The method according to claim 5, wherein Also includes: Obtain the ideal replacement period for target patients; Generate a target exercise plan based on the ideal replacement period and the patient's posture data and physiological data collected by the wearable device of the target patient; Perform exercise management on the target patient based on the target exercise plan.

8. A digital intelligent management device for CIED patients, characterized in that: include: an acquisition unit, configured to acquire an implantation cycle of a cardiac pacemaker of a target patient, wherein the implantation cycle includes an acute phase, a stable phase, and a phase close to replacement; a selection unit, configured to select a monitoring and management strategy associated with a current implantation cycle of the cardiac pacemaker of the target patient based on the current implantation cycle of the cardiac pacemaker; The management unit is used to perform intelligent management on the target patient according to the selected monitoring and management strategy.

9. An electronic device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the steps of the method for digital intelligent management of CIED patients as described in any one of claims 1 to 7 when executing the computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the digital intelligent management method for CIED patients according to any one of claims 1 to 7 is implemented.

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