Heart valve postoperative management method based on cardiac function dynamic monitoring and analysis

Abstract

The invention discloses a cardiac valve postoperative management method based on cardiac function dynamic monitoring and analysis. A multi-modal dynamic monitoring system is innovatively constructed, a comprehensive sensor array and implantable monitoring equipment are fused, and multi-dimensional data such as electrocardio, blood pressure, myocardial strain and intracardiac pressure are comprehensively collected. A personalized digital heart model is built by means of a deep learning algorithm, a real-time risk assessment and early warning system is formed by combining big data and machine learning, and heart function changes and risks are accurately predicted. According to monitoring and analysis results, a drug treatment scheme is automatically and intelligently adjusted, and a personalized rehabilitation plan covering exercise, diet and psychological guidance is customized. By means of the method, precision and individuation of cardiac valve postoperative management are achieved, data integrity and accuracy are remarkably improved, the complication occurrence rate is effectively reduced, patient compliance is enhanced, and patient rehabilitation is powerfully promoted.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and in particular to a method and related technologies for achieving precise management of patients after heart valve surgery by dynamically monitoring cardiac function through multimodality and performing intelligent analysis. Background Art

[0002] In the medical field, heart valve disease poses a serious threat to human health. Heart valve surgery, as an important treatment method, can significantly improve the patient's condition, but postoperative management is crucial. Traditional cardiac function monitoring methods have many limitations in the postoperative management of heart valves. Conventional ECG and blood pressure monitoring equipment can only provide limited basic data and cannot fully reflect the overall functional status of the heart. For example, a single ECG monitor cannot directly obtain the actual working efficiency of the myocardium and the operating status of the valve, and lacks sufficient sensitivity for the early detection of potential subtle changes in heart function.

[0003] The current postoperative management model mostly relies on regular outpatient follow-up examinations, which means that there is a time interval between data acquisition, making it difficult to capture the real-time fluctuations in cardiac function in patients' daily lives outside the hospital. If a patient develops abnormal cardiac function between two follow-up examinations, it may not be detected in time and effectively treated, delaying the disease. Moreover, traditional management methods are mainly based on the doctor's experience and limited data, and lack precise analysis and personalized management strategies for individual patients. The physical condition, surgical conditions and recovery ability of different patients vary greatly. A universal management model is difficult to meet the specific needs of patients, resulting in poor recovery effects for some patients and a high incidence of complications.

[0004] With the continuous advancement of medical technology, people have higher expectations for the recovery quality and quality of life of patients after heart valve surgery. There is an urgent need for a more comprehensive, accurate and personalized postoperative management method to improve patients' recovery effects, reduce the risk of complications and improve patients' long-term prognosis. Summary of the Invention

[0005] The present invention aims to construct a comprehensive, accurate and personalized postoperative management method for heart valve surgery based on dynamic monitoring and analysis of cardiac function. By integrating advanced monitoring technology, intelligent data processing methods and customized management strategies, it can comprehensively improve the rehabilitation effect of patients after heart valve surgery, reduce the incidence of complications, and improve the patients' long-term quality of life and prognosis.

[0006] Technical Solution

[0007] The multimodal dynamic monitoring system is constructed as follows:

[0008] Comprehensive sensor array construction: A highly sensitive, miniaturized photoelectric ECG sensor is used. It uses light of a specific wavelength to penetrate skin tissue, detecting changes in light absorption and reflection caused by cardiac electrical activity, converting these changes into electrical signals to accurately capture ECG data. Alternatively, a flexible electrode patch ECG sensor can be used. Its electrodes adhere tightly to the skin, enabling stable acquisition of weak current signals generated by cardiac electrical activity, providing a reliable basis for analyzing cardiac electrophysiological activity. A small blood pressure sensor based on the oscillometric principle is assembled. A cuff wrapped around the limb detects oscillatory waves generated by the vibration of the arterial wall during pressure changes. Advanced signal processing algorithms are used to accurately calculate systolic, diastolic, and mean arterial pressure values. Alternatively, a piezoresistive blood pressure sensor can be used. When pressure acts on a pressure-sensitive resistor, its resistance changes. This resistance change accurately measures blood pressure, providing critical data for assessing the heart's pumping function and peripheral vascular resistance. A photoelectric pulse wave sensor can be configured to transmit and receive light signals, monitoring changes in light absorption and reflection caused by changes in blood volume within the blood vessels, thereby determining the heart's beat rate. Alternatively, a mechanical vibration-sensing heart rate sensor can be used. This utilizes the characteristic of piezoelectric materials that generate electrical signals in response to mechanical vibrations caused by the heartbeat, continuously tracking heart rate and reflecting the heart's rhythm in real time. A modified myocardial strain sensor based on optical coherence tomography can be integrated. This sensor emits near-infrared light and measures the interference signal within myocardial tissue to accurately measure myocardial deformation during contraction and relaxation. For example, by calculating the rate of change in myocardial length during different phases of the cardiac cycle, it can intuitively reflect the myocardial strain state, providing a key indicator for assessing myocardial function and facilitating early detection of myocardial pathology or dysfunction. A cardiac micro-vibration sensor based on microelectromechanical systems (MEMS) technology can be installed. This sensor utilizes the changes in capacitance or resistance generated by micromechanical structures in response to cardiac microvibrations to capture subtle vibration signals generated by the heartbeat. By analyzing characteristics such as vibration frequency, amplitude, and pattern, it is possible to infer the integrity of the heart's internal structure and its hemodynamic state, providing a unique perspective for assessing cardiac function. These various sensors can be integrated into a package design to form a comprehensive sensor array. Using Bluetooth Low Energy or Wi-Fi Direct technology, data collected by each sensor is packaged, encrypted, and sent to the data collection terminal in real time at a set 5-second interval. The data collection terminal performs preliminary collation and verification of the received data to ensure its accuracy and completeness, providing a reliable data source for subsequent data analysis and processing.

[0009] Implantable monitoring device deployment: For patients identified as having complex conditions by a professional medical team based on their preoperative condition, surgical complexity, and postoperative recovery risk assessment, a biocompatible micro-monitoring chip is implanted through minimally invasive surgery in an operating room environment that adheres to strict aseptic standards. This chip is constructed from specialized polymer materials, such as parylene, proven to be non-toxic and non-irritating to human tissue through long-term clinical trials, or biocompatible metal alloys, such as titanium alloy, to ensure long-term stable operation in the body and prevent immune rejection. The chip incorporates a piezoelectric pressure sensor. When intracardiac pressure changes, the piezoelectric material generates an electrical charge proportional to the pressure. This charge change is precisely measured to determine intracardiac pressure. Alternatively, capacitive pressure sensing is employed to measure pressure by detecting changes in capacitance caused by pressure changes. The chip also incorporates an ultrasonic ranging transvalvular pressure differential measurement module, which transmits and receives ultrasonic waves to measure the pressure difference across the valve. Alternatively, an electromagnetic induction module uses the induced electromotive force generated by blood flow to calculate the transvalvular pressure differential. These measurement components can accurately obtain key pressure and pressure differential data inside the heart in real time, providing core data support for in-depth understanding of the heart's hemodynamic state. The chip encodes and modulates the collected data such as intracardiac pressure and transvalvular pressure differential through a specific 433MHz radio frequency, and transmits it to an external receiving device in the form of an encrypted signal. After the external receiving device demodulates and decodes the received signal, it integrates the data into the monitoring data set, which together with the data collected by the integrated sensor array constitutes a comprehensive and accurate cardiac function monitoring data set. Through this multimodal data acquisition method, the cardiac function status of patients after heart valve surgery can be monitored in all directions and in real time, providing a solid data foundation for subsequent intelligent analysis and personalized management.

[0010] The intelligent data analysis and processing:

[0011] Personalized digital heart model construction: Comprehensive preoperative high-resolution cardiac magnetic resonance imaging (MRI) data is collected from the patient. Advanced image segmentation algorithms, such as a deep learning-based fully convolutional neural network (FCNN), are used to precisely segment the cardiac anatomical structures, including the left and right ventricles, left and right atria, and valves such as the aortic, mitral, pulmonary, and tricuspid valves, capturing detailed parameters such as their shape, size, and wall thickness. Computed tomography (CT) data is also collected to supplement and validate the cardiac structure and enhance the accuracy of the model. Postoperatively, various cardiac function data, including electrocardiogram (ECG) signals, blood pressure, myocardial strain, intracardiac pressure, and transvalvular pressure gradients, collected in real time via a comprehensive sensor array and implantable monitoring devices, are preprocessed using denoising and filtering to remove noise and interference, ensuring data quality and reliability. The preprocessed preoperative imaging data and postoperative real-time monitoring data are then fed into a deep learning-based recurrent neural network (RNN) or long short-term memory (LSTM) model. Through extensive iterative training, the weights and biases of the connections between neurons within the model are continuously adjusted, enabling the model to accurately simulate the heart's normal pumping function at rest, the adaptive changes in cardiac output during exercise, and the compensatory response of the heart during stress, among other physiological conditions. For example, the model can simulate the increase in myocardial contractility, the upward trend in heart rate, and the changes in the duration and amplitude of valve opening and closing as exercise intensity gradually increases. During training, cross-validation and other methods are used to evaluate and optimize the model, improving its generalization ability and predictive accuracy, ensuring that the model accurately reflects the personalized cardiac function characteristics of each patient.

[0012] To establish a real-time risk assessment model, a large amount of historical monitoring data from patients undergoing heart valve surgery was collected, including long-term dynamic data such as electrocardiogram (ECG), blood pressure, heart rate, and cardiac function indicators; clinical diagnostic information such as surgery type, duration, and intraoperative complications; and treatment records such as medication type, dosage, and duration, as well as rehabilitation treatment plans and effectiveness. After cleaning, annotating, and structuring this data, a large-scale big data sample library was constructed. Data mining techniques were used to extract valuable information and features from the sample library, providing a rich data resource for subsequent risk assessment model training. Various machine learning algorithms were applied, such as logistic regression, which analyzed the linear relationship between data features and cardiac complications to identify key data features and risk factors closely associated with various cardiac complications (such as arrhythmias, heart failure, and valvular thrombosis); a decision tree algorithm, which constructed a tree structure to gradually classify and make decisions on the data, determining the complication risk level under different data feature combinations; and a random forest algorithm, which used ensemble learning of multiple decision trees to improve the stability and accuracy of the risk assessment model. These algorithms are trained and optimized using a big data sample library, and the algorithm parameters are adjusted so that the model can accurately calculate the patient's current risk probability of various complications based on real-time monitoring data. A comprehensive early warning mechanism is established based on the trained real-time risk assessment model. When the risk probability exceeds a pre-set threshold, such as the arrhythmia risk probability exceeds 0.2, or when the data shows a specific change pattern consistent with known risk signs, such as abnormal waveforms in multiple consecutive ECG cycles accompanied by large fluctuations in heart rate, the early warning mechanism is immediately triggered. Early warning information is sent to patients and medical staff in a timely manner through various means such as text messages and mobile application push notifications. The early warning information contains detailed information on the risk type, severity, and recommended intervention measures, so that patients and medical staff can take appropriate countermeasures in a timely manner to reduce the risk of complications.

[0013] The dynamic management policy adjustment:

[0014] Medication Regimen Adjustment: A comprehensive and detailed medication knowledge base is established, covering the pharmacological mechanisms of action of various medications used in heart valve surgery. For example, anticoagulants prevent thrombosis by inhibiting coagulation factor activity, and cardiotonic drugs improve cardiac function by enhancing myocardial contractility. Pharmacokinetic parameters include parameters related to drug absorption, distribution, metabolism, and excretion, such as drug half-life and time to peak blood concentration. Drug interactions are also identified, clarifying potential synergistic or antagonistic effects when different drugs are used together. For example, the combined use of certain anticoagulants and antibiotics may increase bleeding risk. The medication knowledge base is regularly updated to ensure its accuracy and timeliness, providing reliable knowledge support for medication regimen adjustments. The risk results output by the real-time risk assessment model, along with the latest monitoring data (such as heart rate, blood pressure, and cardiac function classification), and possible blood concentration monitoring data (if used), are input into a specially designed medication adjustment algorithm module. This module uses an optimization algorithm to calculate recommended adjustments to medication dosage and type based on the patient's current physiological status and the medication knowledge base. For example, when a patient's blood pressure remains consistently above target and cardiac function indicators indicate increased cardiac afterload, the algorithm module, based on the characteristics and interrelationships of antihypertensive and cardiac stress-reducing drugs in the drug knowledge base, recommends adjusting the antihypertensive dosage and adding vasodilators. The medication adjustment algorithm module is continuously optimized and updated to adapt to individual patient differences and complex disease progression. Adjustment recommendations undergo a multi-faceted review process. Clinical pharmacists conduct an initial review based on their expertise, including medication rationale, dosage accuracy, and drug-drug interaction risks. Cardiologists then conduct a final review and confirmation, taking into account individual patient factors such as age, weight, liver and kidney function, and other underlying medical conditions, to ensure the safety and effectiveness of the medication adjustment. During this review process, the electronic medical record system and clinical decision support system are fully utilized to assist physicians and pharmacists in their judgment and decision-making. Once approved, the adjustment recommendations are accurately transmitted to the pharmacy in a standardized electronic prescription format via the highly integrated electronic prescription system within the hospital information system. The pharmacy then prepares medications based on the new prescription, ensuring timely and accurate adjustments to the medication regimen.

[0015] The personalized rehabilitation plan is tailored to:

[0016] Exercise program development: Cardiopulmonary exercise testing involves patients performing progressively increasing workloads on specialized exercise equipment while monitoring cardiopulmonary function indicators such as oxygen uptake, carbon dioxide output, heart rate, and blood pressure to determine maximal oxygen uptake, a key indicator of cardiopulmonary endurance. A 6-minute walk test involves patients walking as briskly as possible in a defined straight corridor for 6 minutes, with walking distance measured to assess exercise capacity. A comprehensive assessment of the patient's exercise tolerance is conducted, taking into account factors such as age, gender, underlying medical condition, surgery type, and recovery time, providing a scientific basis for developing a personalized exercise program. Based on these assessment results, an exercise prescription formula is used, such as the heart rate reserve method, to calculate the target heart rate range: target heart rate = (maximum heart rate - resting heart rate) × exercise intensity percentage + resting heart rate, to determine the appropriate exercise intensity. The frequency of exercise is determined based on the patient's stage of recovery and physical recovery, such as three times per week in the early postoperative recovery phase, gradually increasing to five times per week during the maintenance phase. The duration of exercise is determined based on the patient's endurance and fatigue recovery, such as starting with 20 minutes per session and gradually increasing to 60 minutes per session. Exercise prescriptions also include selecting appropriate exercise methods for the patient, such as aerobic exercise (such as walking, jogging, and swimming), strength training (such as simple muscle exercises with lightweight dumbbells), and flexibility training (such as yoga and stretching exercises), to promote cardiac recovery, enhance physical function, and improve the patient's quality of life. Wearable devices such as smart bracelets and sports watches utilize their built-in accelerometers, gyroscopes, and heart rate sensors to monitor the patient's heart rate, movement trajectory, number of steps, and calories burned during exercise in real time. When these parameters exceed a preset safety range, such as heart rate exceeding 10% of the target heart rate upper limit or abnormal fluctuations in movement trajectory (which may indicate incorrect exercise posture or physical discomfort), the accompanying mobile app sends reminders in various forms, such as vibration, sound, and pop-up windows, to the patient, guiding them to adjust their exercise intensity or take a break. At the same time, abnormal data is fed back to the doctor's management platform, who then further evaluates and adjusts the exercise plan based on the feedback to ensure its safety and effectiveness.

[0017] Dietary recommendations: Collect the patient's height, weight, body fat percentage and other physical indicators, which can be accurately measured using a professional body composition analyzer. At the same time, collect the results of nutritional status tests such as blood routine and blood biochemistry, including hemoglobin, albumin, blood lipids, blood sugar and other indicators, to comprehensively assess the patient's nutritional status. Develop a personalized diet plan based on the patient's heart function recovery needs, food allergy history, religious beliefs, dietary preferences and other factors. According to the needs of heart function recovery, follow the low-salt (no more than 6g per day to reduce the burden on the heart and prevent water and sodium retention), low-fat (fat energy supply accounts for 20%-30% of total calories to avoid the adverse effects of hyperlipidemia on the cardiovascular system), and high-fiber (daily intake of 25-30g to promote intestinal motility and reduce the risk of cardiovascular disease) dietary principles, and use professional nutrition calculation software such as MyFitnessPal to develop detailed daily recipes. The diet clearly defines the types of foods to be consumed, such as lean meats, fish, and beans rich in high-quality protein, and vegetables, fruits, and whole grains rich in dietary fiber; the recommended intake amounts, such as the amount of staple food, protein intake, and number of servings of vegetables and fruits per meal; and recommended cooking methods, such as steaming, boiling, and stewing, while avoiding high-fat cooking methods like deep-frying and pan-frying. The diet plan is regularly adjusted based on the patient's recovery progress and changes in physical indicators to ensure adequate and appropriate nutritional support for the recovery of heart function.

[0018] Implementation of Psychological Counseling: A user-friendly, fully functional online psychological counseling platform has been established. The platform features high-definition video calling, ensuring clear and smooth face-to-face communication between counselors and patients. Voice chat facilitates patient communication with counselors when video is inconvenient. Text communication allows patients to record and provide feedback on their psychological state, while counselors can also provide written guidance and advice. The platform utilizes a secure and reliable technical architecture to ensure the security of data transmission and storage, protecting patient privacy. Psychological counselors on the platform have all undergone specialized training courses specifically tailored to the psychological needs of patients undergoing heart valve surgery. Coursework covers the pathogenesis, clinical manifestations, assessment methods, and intervention techniques for common postoperative psychological disorders, such as anxiety, depression, and post-traumatic stress disorder. Counselors must pass rigorous assessments and certifications, possess extensive clinical experience and professional expertise, and be able to accurately identify patients' psychological issues and provide effective psychological support. Counselors receive regular continuing education and case supervision to continuously enhance their professionalism and service quality. Patients can access the platform through a specially developed mobile app or desktop to schedule psychological counseling services. During the consultation process, counselors use cognitive behavioral therapy to help patients identify and change negative thought patterns and behavioral habits. Relaxation exercises, such as deep breathing exercises and progressive muscle relaxation, are used to relieve tension and anxiety. Supportive psychotherapy involves listening to patients and providing emotional support and encouragement to help them alleviate postoperative stress and improve their mental well-being. The platform also incorporates psychological assessment tools to regularly assess patients' mental well-being. The counseling program is then adjusted based on the results to ensure targeted and effective counseling. Patient feedback on the rehabilitation program is regularly collected, including information on exercise experience (fatigue, breathlessness, etc.), dietary adherence (following dietary recommendations, food tolerance), and changes in psychological well-being (anxiety, depression, etc.). Based on this feedback, data analysis methods are used to tailor and optimize the rehabilitation program. For example, if most patients report fatigue due to excessive exercise intensity, the intensity of exercise can be appropriately reduced. If patients struggle with certain dietary recommendations, the menu can be adjusted based on their feedback. If patients' mental well-being does not improve significantly, counseling methods can be adjusted or the frequency of consultations can be increased. Through continuous feedback and optimization, we ensure that the rehabilitation plan can better meet the needs of patients and improve their rehabilitation effects and quality of life.

[0019] The above technical solution can bring the following technical effects:

[0020] 1. Comprehensive and precise monitoring, early risk warning: The multimodal dynamic monitoring system uses a variety of sensors to comprehensively collect data related to cardiac function. From conventional electrocardiogram, blood pressure, and heart rate, to unique myocardial strain and cardiac micro-vibration, combined with the intracardiac pressure and transvalvular pressure difference data obtained by the implantable chip, comprehensive and real-time monitoring of the heart state is achieved. This allows subtle changes in cardiac function to be captured in a timely manner. Combined with the intelligent real-time risk assessment model, when the risk probability exceeds the preset threshold or specific risk signs appear, an early warning can be issued, allowing medical staff and patients to know the potential risks in advance, such as early detection of arrhythmias and heart failure, buying precious time for timely intervention and significantly reducing the possibility of complications.

[0021] 2. Personalized treatment and rehabilitation to improve treatment effects: By constructing a personalized digital heart model, based on the patient's preoperative imaging and postoperative real-time monitoring data, it can accurately simulate the working conditions of the heart under different conditions. Based on this, the drug treatment plan can use optimization algorithms to accurately adjust the dosage and type of drugs according to the patient's individual cardiac function indicators, blood drug concentrations and drug knowledge base, and is reviewed by clinical pharmacists and cardiologists to ensure safety and effectiveness. The rehabilitation plan also varies from person to person. The exercise plan is formulated based on the patient's exercise capacity assessment results, combined with factors such as age and type of surgery. Dietary recommendations are designed based on factors such as cardiac function recovery needs, food allergy history, and religious beliefs. Psychological counseling is carried out for patients with postoperative psychological problems. This personalized strategy greatly improves the targeted treatment and rehabilitation, promotes patients to better restore heart function and improve their quality of life.

[0022] 3. Dynamic management and continuous optimization to ensure long-term prognosis: The entire management method adopts a dynamic adjustment strategy, and continuously optimizes management measures based on real-time monitoring data and patient feedback. The drug treatment plan is adjusted in a timely manner as the patient's condition changes, and the exercise, diet, psychological counseling and other aspects of the rehabilitation plan are also optimized according to the patient's rehabilitation progress and feedback. For example, the exercise plan adjusts the intensity according to the exercise monitoring data, the diet plan adjusts the menu according to the changes in physical indicators and the patient's acceptance, and the psychological counseling method changes according to the results of the psychological assessment. This continuous optimization model ensures that patients can always receive the most appropriate management during the long-term rehabilitation process after surgery, ensuring a good long-term prognosis, reducing the risk of disease recurrence, and extending the patient's healthy life span. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 Flowchart for constructing personalized digital heart model of the present invention

[0024] Figure 2 Adjustment flow chart for the drug treatment regimen of the present invention

[0025] Figure 3Flowchart for the formulation and feedback optimization of personalized rehabilitation plans of the present invention DETAILED DESCRIPTION

[0026] The following is a combination of the embodiments of the present invention Figures 1 to 3 The technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0027] Example 1: Elderly patients after heart valve replacement surgery

[0028] Patient: A 68-year-old man underwent aortic valve replacement surgery due to severe aortic valve stenosis. He experienced dyspnea and chest pain with even the slightest bit of daily activity, severely impacting his quality of life. He was extremely frail after the surgery, with a significant decrease in his ability to carry out daily activities. He also had a long history of hypertension with poorly controlled blood pressure, as well as mild renal insufficiency, a slightly elevated creatinine level, and a decreased glomerular filtration rate.

[0029] Key technical application points:

[0030] Multimodal dynamic monitoring: Patients wear a carefully designed integrated sensor array. The ECG sensor uses a high-precision electrode patch that adheres tightly to the skin, accurately capturing every subtle change in the heart's electrical activity and continuously providing ECG data to help doctors determine whether there are any arrhythmias or other issues. The blood pressure sensor uses the oscillometric method to automatically measure blood pressure every 15 minutes, transmitting systolic, diastolic, and mean arterial pressure data in real time. The heart rate sensor uses photoelectric pulse wave technology to track heart rate in real time. Given the slow recovery of cardiac function and the susceptibility of myocardial problems in the elderly, the myocardial strain sensor uses optical coherence tomography to measure myocardial deformation during contraction and relaxation in real time, such as measuring the strain rate of specific myocardial sites every hour. The cardiac micro-vibration sensor, based on microelectromechanical system technology, captures the subtle vibrations generated by the heartbeat, analyzes characteristics such as frequency and amplitude, and records data every 30 minutes. At the same time, a biocompatible micro-monitoring chip is implanted in the patient. The chip measures the pressure in the heart cavity through the piezoelectric effect, obtains the pressure value once an hour, and uses the ultrasonic ranging principle to measure the pressure difference across the valve. The pressure difference data is recorded every 2 hours, providing a solid basis for accurately assessing the heart load and valve working status.

[0031] Intelligent Data Analysis: The medical team collects preoperative cardiac magnetic resonance imaging (MRI) and computed tomography (CT) imaging data from patients and uses advanced image segmentation algorithms to accurately extract cardiac anatomical information, including the shape and dimensions of the ventricles, atria, and valves. Combining various cardiac function data collected in real time by a comprehensive postoperative sensor array and implantable chip, a deep learning algorithm is used to construct a highly personalized digital heart model. This model can simulate the heart's workings in various states, including rest, daily activity, and sleep. For example, it can simulate changes in cardiac output and adjustments in myocardial contractility during slow walking. A real-time risk assessment model uses machine learning algorithms such as logistic regression and decision trees to comprehensively analyze various monitoring data and accurately assess the risk of complications such as arrhythmias and heart failure. For example, if abnormal waveforms are detected over multiple consecutive ECG cycles and heart rate fluctuations exceed a certain range, combined with elevated intracardiac pressure, the model will determine an increased risk of heart failure.

[0032] Dynamic management strategy: Regarding medication, clinical pharmacists and cardiologists thoroughly researched the drug knowledge base based on the patient's real-time blood pressure data, cardiac function classification, and renal function. For antihypertensive medications, if the patient's blood pressure remains above target and renal insufficiency is a concern, angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin II receptor blockers (ARBs), which have less renal effects, are preferred. Optimization algorithms are used to precisely adjust the drug dosage based on blood drug concentration monitoring results. For anticoagulants, the dosage is adjusted appropriately based on the type of heart valve replacement, intracardiac pressure, and coagulation function indicators to prevent thrombosis while minimizing bleeding risk. Within the rehabilitation program, the exercise regimen is based on the results of the cardiopulmonary exercise test and the 6-minute walk test. Given the patient's short 6-minute walk distance and poor cardiopulmonary endurance, a low-intensity, short-duration, and infrequent exercise plan of 15 minutes of slow walking daily, three times a week, was established. During exercise, a smart wristband monitors heart rate, movement trajectory, and other parameters in real time. If the heart rate exceeds the target range, the wristband vibrates to remind the patient to adjust the intensity. Dietary recommendations follow the principles of low salt (no more than 4g per day), low fat (fat energy supply accounts for 20% of total calories), and high-quality protein (choose foods rich in essential amino acids, such as lean meat, fish, and beans. However, considering renal insufficiency, protein intake should be appropriately controlled to 0.8-1.0g / (kg·day)). Through an online psychological counseling platform, professional counselors regularly conduct video calls with patients and use cognitive behavioral therapy to help patients identify and change anxious thought patterns caused by slow postoperative recovery. For example, they guide patients to correctly understand the gradual nature of physical recovery and alleviate anxiety.

[0033] Comprehensive monitoring enables doctors to promptly detect frequent fluctuations in patients' blood pressure and subtle abnormalities in myocardial strain. For example, during a routine monitoring session, the myocardial strain sensor detected that the strain rate of a certain part of the myocardium exceeded the normal range. Combined with the increase in intracardiac pressure, the real-time risk assessment model warned of the risk of heart failure three days in advance. The doctor immediately adjusted the drug treatment plan, increased the diuretic dose, and adjusted the exercise and diet plan to effectively control the patient's heart failure risk. Personalized medication, exercise, diet and psychological counseling fully meet the physical condition and psychological needs of elderly patients. After three months of management, the patient's heart function gradually recovered, and his heart function grade improved from NYHA class III to class II. His ability to perform daily activities was significantly enhanced, his anxiety was effectively relieved, his quality of life was significantly improved, and the incidence of complications was reduced by 30% compared to traditional management methods.

[0034] Example 2: Young patients with congenital heart valve disease after surgery

[0035] Patient Profile: A 25-year-old woman with congenital mitral valve anomaly, manifesting as mitral stenosis and regurgitation, had long-standing poor physical endurance, fatigued easily, and experienced palpitations and shortness of breath after exercise. She underwent successful mitral valve repair surgery. She is in good health and has no other serious underlying medical conditions. However, she has high expectations for recovery and is eager to resume normal life, return to work, and participate in daily social activities as soon as possible.

[0036] Key technical application points:

[0037] Multimodal dynamic monitoring: The integrated sensor array worn by the patient utilizes advanced photoelectric ECG sensors, enabling rapid and accurate acquisition of ECG data and real-time monitoring of cardiac electrophysiological activity. The blood pressure sensor utilizes a piezoresistive principle to measure blood pressure every 10 minutes, ensuring timely data. The heart rate sensor utilizes mechanical vibration sensing technology to continuously track heart rate. While young patients have strong cardiac compensation capabilities, they still require close monitoring of subtle changes during recovery. Therefore, a myocardial strain sensor measures myocardial strain every 45 minutes, analyzing myocardial deformation during the cardiac cycle to assess the state of cardiac recovery. A cardiac micro-vibration sensor collects cardiac micro-vibration signals every 20 minutes, analyzing vibration characteristics to promptly identify potential abnormalities in the heart's internal structure and hemodynamics. The implantable chip measures intracardiac pressure using a capacitive pressure sensor, acquiring pressure data every 1.5 hours. It also utilizes electromagnetic induction to measure the transvalvular pressure gradient, recording this data every 3 hours, providing critical data for evaluating valve repair effectiveness.

[0038] Intelligent data analysis: Based on the patient's detailed preoperative cardiac imaging data, including high-resolution MRI and CT images, a personalized digital heart model is accurately constructed using deep learning algorithms. This model can dynamically simulate changes in the heart during postoperative recovery, such as simulating the gradual improvement of cardiac hemodynamics after mitral valve repair, as well as the adaptive changes of the myocardium at different stages. The real-time risk assessment model uses machine learning algorithms such as random forests to accurately assess the risk of common arrhythmias in young patients. By analyzing a large amount of monitoring data, when a specific abnormal rhythm is detected in the ECG signal and the duration exceeds a certain threshold, the model will promptly issue an arrhythmia warning.

[0039] Dynamic management strategy: Medication dosages are precisely adjusted based on the patient's recovery and regularly monitored blood drug concentrations. For example, if a patient's cardiac function recovers well after surgery, the dose of cardiotonic medications is gradually reduced. At the same time, the dose of anticoagulants is adjusted appropriately based on coagulation function indicators to ensure therapeutic efficacy while minimizing drug side effects. Within the rehabilitation plan, the exercise regimen is tailored to the patient's exercise capacity assessment. A cardiopulmonary exercise test revealed significant potential for improvement in the patient's cardiopulmonary endurance, so a high-intensity, long-duration, and high-frequency exercise plan was established, consisting of three 30-minute jogging sessions per week. During exercise, a sports watch monitors heart rate, movement trajectory, and step count in real time. When the heart rate approaches the target upper limit, the watch issues a voice reminder, suggesting that the patient reduce the intensity of the exercise appropriately. Dietary recommendations emphasize balanced nutrition, ensuring the nutrients necessary for cardiac recovery while also meeting the body's growth and development needs. Daily intake of foods rich in vitamins, minerals, and high-quality protein, such as fresh vegetables, fruits, eggs, and milk, should be maintained within a reasonable energy ratio of carbohydrates, fat, and protein (50%-65%, 20%-30%, and 15%-20%). Psychological counseling is provided through an online platform, where professional counselors regularly communicate with patients to help alleviate the stress caused by concerns about the impact of surgery on their future lives. Relaxation training and other methods are used to help patients reduce anxiety and strengthen their confidence in recovery.

[0040] Precise monitoring helped doctors detect transient abnormalities in the patient's heart micro-vibration signal within one week after surgery. After further analysis and combined with myocardial strain data, it was determined to be a mild adaptive change in the heart during the recovery process. The rehabilitation plan was adjusted in time to avoid the deterioration of potential problems. The personalized exercise and nutrition plan fully meets the physical recovery and life needs of young patients. After two months of rehabilitation management, the patient's heart function has basically returned to normal, exercise endurance has been significantly improved, and he can easily carry out daily work and social activities. Dynamic management ensures the rehabilitation effect, and the patient is very satisfied with the rehabilitation results, highlighting the significant advantages of the present invention in meeting the personalized needs of patients of different ages.

[0041] Example 3: Patients after complex heart valve surgery (combined valve replacement)

[0042] Patient: A 50-year-old man underwent combined aortic and mitral valve replacement surgery due to severe disease in both the aortic and mitral valves. He suffered from long-term symptoms such as dyspnea, fatigue, and edema, which severely impacted his life and work. The surgery was complex and prolonged, with high recovery risks and a relatively weak body, requiring prolonged bed rest.

[0043] Key technical application points:

[0044] Multimodal dynamic monitoring: A comprehensive sensor array comprehensively covers all cardiac function monitoring needs. The ECG sensor utilizes a bipolar lead design to more accurately detect cardiac electrical activity and continuously record ECG data, providing a reliable basis for diagnosing arrhythmias. The blood pressure sensor utilizes a combination of advanced oscillometric and Korotkoff sound techniques to measure blood pressure every 12 minutes, ensuring accurate data. The heart rate sensor utilizes high-precision photoelectric pulse wave technology to track heart rate in real time. To address the complex structural and functional changes of the heart after combined valve replacement surgery, the myocardial strain sensor measures strain in different myocardial regions every 30 minutes. By comparing and analyzing strain differences among these regions, the patient assesses overall cardiac function recovery. The cardiac microvibration sensor collects cardiac microvibration signals every 15 minutes, deeply analyzing characteristics such as vibration frequency, amplitude, and phase to promptly detect potential abnormalities within the heart. The implantable chip utilizes advanced piezoelectric effect and ultrasonic ranging technology to measure intracardiac pressure and transvalvular pressure gradient every hour, providing critical data for assessing cardiac hemodynamic status.

[0045] Intelligent data analysis: The medical team uses the patient's detailed preoperative cardiac imaging data, including multi-angle MRI and CT images, and applies deep learning algorithms to build a highly accurate personalized digital heart model. This model can simulate the complex hemodynamic changes of the heart after combined valve replacement surgery, such as simulating the opening and closing of the new replacement valve under different physiological conditions, as well as the dynamic changes in pressure and volume of each chamber of the heart. The real-time risk assessment model uses a variety of machine learning algorithms, such as logistic regression and support vector machines, to integrate a large amount of monitoring data to comprehensively assess the risks of various complications. For example, when it is monitored that the pressure in the heart cavity continues to rise, the difference in transvalvular pressure of the valve often increases, and is accompanied by abnormal myocardial strain, the model will determine that there is a risk of valve dysfunction or heart failure.

[0046] Dynamic management strategy: Medication therapy utilizes advanced optimization algorithms to adjust medication dosage and type based on the patient's complex cardiac function parameters, drug interactions, and regularly monitored blood drug concentrations. Clinical pharmacists and cardiologists work closely together to develop a personalized medication plan, taking into account the patient's physical condition, such as age, weight, and liver and kidney function. For anticoagulants, the dosage is precisely adjusted based on the type of valve replacement, intracardiac blood flow, and coagulation parameters to ensure anticoagulant efficacy while minimizing bleeding risk. Within the rehabilitation plan, exercise regimens follow a gradual approach. Initially, patients should focus on bedside limb activities, such as simple limb flexion and extension exercises, performed 3-4 times daily for 10-15 minutes each time. As recovery progresses, patients can gradually transition to low-intensity aerobic exercise, such as deep breathing exercises performed while sitting at the bedside, performed 2-3 times daily for 15-20 minutes each time. The intensity and duration of activity can then be gradually increased. Dietary recommendations strictly adhere to a low-salt (no more than 5g per day) and low-fat (25% of total calories from fat), while ensuring adequate nutrition. Increase intake of foods rich in vitamins, minerals, and dietary fiber, such as fresh vegetables, fruits, and whole grains, to promote recovery. Psychological counseling focuses on addressing depression caused by complex surgeries and slow recovery. Through online counseling platforms, counselors regularly conduct video conferences with patients, utilizing cognitive behavioral therapy and supportive psychotherapy to help them develop positive beliefs about recovery and alleviate depression.

[0047] Multi-dimensional monitoring enables doctors to fully grasp the changes in patients' heart function. Two weeks after the operation, through comprehensive analysis of monitoring data, it was discovered in time that the patient's intracardiac pressure increased and the valve transvalvular pressure difference was abnormal. Combined with the myocardial strain data, it was determined that there may be a risk of valve thrombosis. The doctor immediately adjusted the dosage of anticoagulants and strengthened monitoring, successfully avoiding the occurrence of serious complications. Personalized models and risk assessments accurately judge complex conditions, and dynamic management strategies are precisely adjusted according to the patient's recovery stage. After 3 months of careful management, the patient's heart function gradually stabilized, and the heart function grade was improved from NYHA grade IV to grade III. His physical condition improved significantly, and he was able to perform simple daily activities. The complication rate was reduced by 40% compared with traditional management methods, highlighting the powerful advantages of the present invention in managing patients undergoing complex surgeries.

[0048] Example 4: Patients undergoing heart valve surgery in remote areas

[0049] Patient: A 42-year-old woman underwent heart valve surgery at a local county hospital and required long-term, professional rehabilitation. However, due to the remote mountainous area and extremely inconvenient transportation, she faced significant difficulties in traveling to a major hospital in a major city for follow-up examinations, and was unable to obtain timely professional medical guidance.

[0050] Key technical applications:

[0051] Multimodal dynamic monitoring: Patients are fitted with a wearable integrated sensor array designed for low power consumption and high stability, making it convenient for daily wear. The ECG sensor utilizes a flexible electrode patch that fits comfortably against the skin and continuously collects ECG data. This data is transmitted every 10 minutes via Bluetooth to a data collection terminal carried by the patient. The blood pressure sensor automatically measures blood pressure every 20 minutes using the oscillometric method and transmits the data to the data collection terminal. The heart rate sensor uses photoelectric pulse wave technology to track heart rate in real time, also transmitting data in real time. The myocardial strain sensor measures myocardial strain every hour. Data acquired using optical coherence tomography is encrypted and transmitted to the data collection terminal. The cardiac micro-vibration sensor collects cardiac micro-vibration signals every 30 minutes and transmits signal characteristics to the data collection terminal. If necessary, patients undergo a biocompatible micro-monitoring chip implanted at a local hospital. The chip transmits intracardiac pressure and transvalvular pressure gradient data using a specific radio frequency to an external receiver, which then transmits the data to the data collection terminal. The data collection terminal uploads all monitoring data to the data center of the tertiary hospitals in large cities via mobile networks or Wi-Fi.

[0052] Intelligent Data Analysis: Medical teams at tertiary hospitals in major cities use advanced deep learning algorithms to construct personalized digital heart models based on received monitoring data. This model combines limited preoperative cardiac imaging data with real-time postoperative monitoring data to simulate the heart's workings under different conditions. A real-time risk assessment model uses machine learning algorithms to remotely assess a patient's risk of complications. For example, by analyzing arrhythmia characteristics in electrocardiogram (ECG) data, blood pressure and heart rate trends, and intracardiac pressure data, it can determine whether a patient is at risk for arrhythmia, heart failure, or other conditions.

[0053] Dynamic management strategy: Physicians remotely adjust medication regimens based on data analysis results through a specially developed mobile application. Doctors access detailed patient monitoring data and risk assessment reports on the mobile application, and adjust medication dosage and type based on changes in cardiac function, such as blood pressure fluctuations and heart rate abnormalities. Furthermore, rehabilitation plans are developed for patients through the online platform, including exercise, diet, and psychological counseling. Exercise plans are tailored to the patient's physical condition and monitoring data. For example, simple indoor activities, such as 15-20 minutes of stepping in place 3-4 times a day, are recommended. Dietary recommendations are presented through the online platform with illustrations and text, guiding patients to adhere to a low-salt and low-fat diet and providing meal combinations suitable for local ingredients. Psychological counseling is conducted via online video calls, with counselors regularly communicating with patients to help alleviate anxiety caused by inconvenient medical care and enhance their confidence in recovery. Patients can provide feedback on their recovery progress through the mobile application, such as how they feel after exercise and how they are following their diet. Physicians then adjust their rehabilitation plans accordingly.

[0054] It effectively solves the problem of inconvenience in medical treatment for patients in remote areas and realizes remote and precise management. Multimodal monitoring and intelligent analysis are not restricted by geographical location, so that patients in remote areas can enjoy the same level of medical monitoring and analysis services as patients in big cities. Through dynamic management strategies, doctors adjust rehabilitation plans in a timely manner according to patient feedback. In the case of a patient's feedback that heart palpitations worsened after exercise, the doctor immediately checked the monitoring data, adjusted the exercise plan, reduced the intensity and time of exercise, and relieved the patient's symptoms. After half a year of remote management, the patient's heart function recovered well, the heart function grade remained stable, and the quality of life was significantly improved, highlighting the huge advantages of the present invention in telemedicine applications, and providing a practical solution for patients in remote areas with insufficient medical resources.

[0055] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for postoperative management of heart valve surgery based on dynamic monitoring and analysis of cardiac function, characterized in that: The following steps are involved: S1 multimodal dynamic monitoring system construction steps: s1.1 Construction of integrated sensor array: Select a highly sensitive miniaturized ECG sensor and adopt photoelectric sensing technology to emit light of a specific wavelength and receive changes in light signals caused by the heart's electrical activity, or use electrode patch technology to fit the electrode tightly to the skin to collect the weak current signals generated by the heart's electrical activity, so as to accurately obtain ECG-related data. A small blood pressure sensor based on the oscillometric principle can be installed to calculate systolic, diastolic, and mean arterial pressure by detecting the oscillation waves generated by the vibration of the arterial wall during changes in cuff pressure. Alternatively, a piezoresistive principle can be used to measure blood pressure by utilizing the change in resistance of a pressure-sensitive resistor under pressure. A photoelectric pulse wave sensor can be configured to detect changes in light absorption and reflection caused by changes in blood volume within the blood vessels to obtain heart rate. Alternatively, a mechanical vibration-sensing heart rate sensor can be used to continuously track heart rate by using piezoelectric materials to generate electrical signals in response to the mechanical vibrations caused by the heartbeat. A modified myocardial strain sensor based on optical coherence tomography can be integrated to precisely measure myocardial deformation during contraction and relaxation by emitting near-infrared light and measuring the interference signal of the light within myocardial tissue. For example, the rate of change in myocardial length during different stages of the cardiac cycle can be calculated to reflect myocardial strain. A cardiac micro-vibration sensor based on microelectromechanical systems (MEMS) technology is installed. This sensor utilizes the changes in capacitance or resistance generated by the micromechanical structure under the action of cardiac micro-vibrations to capture the subtle vibration signals generated by the heartbeat and analyze characteristics such as vibration frequency, amplitude, and vibration pattern. These characteristics can be used to infer the integrity of the heart's internal structure and its hemodynamic state. These various sensors are integrated into a package design to form a comprehensive sensor array. Using Bluetooth low energy technology or Wi-Fi direct technology, the data collected by each sensor is sent in real time to the data collection terminal in the form of encrypted data packets at a set interval of every 5 seconds, ensuring the stability and security of data transmission. S1.2 Deployment of Implantable Monitoring Devices: For patients identified as having complex conditions by a professional medical team based on their preoperative condition, surgical complexity, and postoperative recovery risk assessment, a biocompatible micro-monitoring chip is implanted through minimally invasive surgery in an operating room environment that adheres to strict aseptic standards. This chip is constructed from specialized polymer materials, such as parylene, proven to be non-toxic and non-irritating to human tissue through long-term clinical trials, or biocompatible metal alloys, such as titanium alloy. The chip integrates a pressure sensor based on the piezoelectric effect. When intracardiac pressure changes, the piezoelectric material generates a charge proportional to the pressure, thereby measuring intracardiac pressure. Alternatively, the chip utilizes capacitive pressure sensing, detecting changes in capacitance caused by pressure changes to obtain pressure data. The chip also incorporates an ultrasonic ranging transvalvular pressure differential measurement component, which measures the pressure difference across the valve by transmitting and receiving ultrasonic waves. Alternatively, an electromagnetic induction component utilizes the induced electromotive force generated by blood flow to calculate the transvalvular pressure differential. The chip encodes and modulates data collected via a specific 433MHz radio frequency, including intracardiac pressure and transvalvular pressure gradients, and transmits the data as an encrypted signal to an external receiver. The receiver then demodulates and decodes the received signal and integrates it into a monitoring dataset. Together with data collected by the integrated sensor array, this data forms a comprehensive and accurate cardiac function monitoring dataset. s2 intelligent data analysis and processing steps: s2.1 Construction of a personalized digital heart model: Comprehensively collect the patient's preoperative high-resolution cardiac magnetic resonance imaging (MRI) data. Utilizing advanced image segmentation algorithms, such as a deep learning-based fully convolutional neural network algorithm, the various anatomical structures of the heart are accurately segmented, including the left and right ventricles, left and right atria, and valves such as the aortic, mitral, pulmonary, and tricuspid valves, obtaining detailed parameters such as their shape, size, and wall thickness. Computed tomography (CT) data is also collected to supplement and verify the heart structure and improve the accuracy of model construction. Various cardiac function data, such as electrocardiogram (ECG) signals, blood pressure values, myocardial strain data, intracardiac pressure, and transvalvular pressure gradients, collected in real time postoperatively through integrated sensor arrays and implantable monitoring devices, are input into a deep learning-based recurrent neural network (RNN) or long short-term memory network (LSTM) model along with preoperative imaging data. Through extensive iterative training, the model continuously adjusts the weights and biases of the connections between neurons within the model, enabling it to accurately simulate the heart's normal pumping function at rest, the adaptive changes in cardiac output during exercise, and the compensatory response of the heart during stress, among other physiological conditions. For example, the model can simulate the strengthening of myocardial contractility, the upward trend in heart rate, and the changes in the duration and amplitude of valve opening and closing as exercise intensity gradually increases, providing a precise simulation basis for subsequent risk assessment and management strategy development. s2.2 Establishment of a real-time risk assessment model: extensively collect historical monitoring data of a large number of patients who have undergone heart valve surgery, including long-term dynamic data such as electrocardiogram, blood pressure, heart rate, and cardiac function indicators; clinical diagnostic information, such as the type of surgery, surgery time, and intraoperative complications; treatment records, such as the type of drugs used, dosage, medication time, rehabilitation treatment plan, and implementation effect. After cleaning, labeling, and structuring these data, a large-scale big data sample library is constructed. Using a variety of machine learning algorithms, such as logistic regression algorithms, by analyzing the linear relationship between data features and cardiac complications, key data features and risk factors that are closely related to various cardiac complications (such as arrhythmias, heart failure, valve thrombosis, etc.) are screened out; The decision tree algorithm, by constructing a tree structure, gradually classifies and makes decisions on the data, determining the risk level of complications under different combinations of data features; the random forest algorithm, based on ensemble learning of multiple decision trees, improves the stability and accuracy of the risk assessment model. Based on the above analysis results, a real-time risk assessment model is established. This model continuously receives the latest data from the monitoring equipment through a real-time interface and uses the trained algorithm model to calculate the patient's current risk probability of various complications. When the risk probability exceeds a pre-set threshold, such as the arrhythmia risk probability exceeding 0.2, or when the data shows a specific change pattern consistent with known risk signs, such as the appearance of abnormal waveforms in multiple consecutive ECG cycles accompanied by large fluctuations in heart rate, the early warning mechanism is immediately triggered. S3 dynamic management strategy adjustment steps: S3.1 Medication Adjustment: A comprehensive and detailed medication knowledge base should be established, covering the pharmacological mechanisms of action of various medications used in heart valve surgery. For example, anticoagulants prevent thrombosis by inhibiting coagulation factor activity, and cardiotonic drugs improve cardiac function by enhancing myocardial contractility. Pharmacokinetic parameters, including those related to drug absorption, distribution, metabolism, and excretion, such as half-life and time to peak plasma concentration, should be included. Drug interactions should also be identified, clarifying potential synergistic or antagonistic effects when different drugs are used together. For example, the combined use of certain anticoagulants and antibiotics may increase bleeding risk. The risk results output by the real-time risk assessment model, along with the latest monitoring data (such as heart rate, blood pressure, and cardiac function classification), and any available blood concentration monitoring data (if used), should be input into a specially designed medication adjustment algorithm module. This module uses an optimization algorithm to calculate recommended adjustments to medication dosage and type based on the patient's current physiological status and the medication knowledge base. For example, when a patient's blood pressure remains above the target value and cardiac function indicators show an increase in cardiac afterload, the algorithm module combines the characteristics and interrelationships of antihypertensive drugs and drugs that reduce cardiac load in the drug knowledge base to provide recommendations for adjusting the dosage of antihypertensive drugs and adding vasodilators. The adjustment recommendations undergo a multi-review mechanism. First, clinical pharmacists conduct a preliminary review from professional perspectives such as drug rationality, dosage accuracy, and drug interaction risks. Then, cardiologists conduct a final review and confirmation based on individual patient differences, such as age, weight, liver and kidney function, and other underlying diseases, to ensure the safety and effectiveness of drug adjustments. After approval, the adjustment recommendations are accurately transmitted to the pharmacy in a standardized electronic prescription format through the highly integrated electronic prescription system in the hospital information system. The pharmacy then dispenses medication for the patient based on the new prescription. s3.2 Personalized rehabilitation plan customization: S3.2.1 Exercise Program Development: Cardiopulmonary exercise testing is used to assess exercise capacity by having patients perform progressively increasing exercise loads on specific exercise equipment while simultaneously monitoring cardiopulmonary function indicators such as oxygen uptake, carbon dioxide output, heart rate, and blood pressure. This is done to determine maximal oxygen uptake, a key indicator of cardiopulmonary endurance. A 6-minute walk test is used to assess exercise capacity by having patients walk as briskly as possible in a defined straight corridor for 6 minutes. The walking distance is then measured. Based on these assessment results and the patient's age, gender, and basic physical condition, an appropriate exercise intensity is determined using an exercise prescription formula, such as the heart rate reserve method, which calculates the target heart rate range (target heart rate = (maximum heart rate - resting heart rate) × exercise intensity percentage + resting heart rate). The frequency of exercise is determined based on the patient's stage of recovery and physical recovery, such as three times per week in the early postoperative recovery phase, gradually transitioning to five times per week during the maintenance phase. The duration of exercise is determined based on the patient's endurance and fatigue recovery, such as starting with 20 minutes per session and gradually increasing to 60 minutes per session. Wearable devices such as smart bracelets and sports watches utilize their built-in accelerometers, gyroscopes, and heart rate sensors to monitor a patient's heart rate, movement trajectory, number of steps, calories burned, and other parameters during exercise in real time. When these parameters exceed the preset safety range, such as heart rate exceeding 10% of the target heart rate upper limit, or abnormal fluctuations in movement trajectory (which may indicate incorrect exercise posture or physical discomfort), the accompanying mobile app sends reminders in various forms, such as vibration, sound, and pop-up windows, to the patient, guiding them to adjust their exercise intensity or pause for rest. Abnormal data is also fed back to the doctor's management platform for further evaluation and adjustment of the exercise plan. s3.2.2 Dietary recommendations: Collect the patient's height, weight, body fat percentage and other physical indicators, which can be accurately measured using a professional body composition analyzer; at the same time, collect the results of nutritional status tests such as blood routine and blood biochemistry, including hemoglobin, albumin, blood lipids, blood sugar and other indicators, to comprehensively assess the patient's nutritional status. Based on the needs of cardiac function recovery, a low-salt (no more than 6g per day to reduce cardiac burden and prevent water and sodium retention), low-fat (fat energy supply accounts for 20%-30% of total calories to avoid the adverse effects of hyperlipidemia on the cardiovascular system), and high-fiber (25-30g per day to promote intestinal motility and reduce the risk of cardiovascular disease) diet is followed. A detailed daily menu is developed using professional nutrition calculation software such as MyFitnessPal, taking into account factors such as the patient's food allergy history, religious beliefs, and dietary preferences. The menu clearly specifies the types of foods, such as lean meat, fish, and beans rich in high-quality protein, and vegetables, fruits, and whole grains rich in dietary fiber. Intake, such as the amount of staple food, protein intake, and number of vegetables and fruits per meal; and cooking method recommendations, such as using healthy cooking methods such as steaming, boiling, and stewing, and avoiding high-fat cooking methods such as frying and pan-frying. s3.2.3 Implementation of psychological counseling: Build a user-friendly, fully functional online psychological counseling platform with high-definition video calling capabilities to ensure clear and smooth face-to-face communication between counselors and patients; and voice chat functions to facilitate communication between patients and counselors when video calls are inconvenient; The text communication function can be used by patients to record and provide feedback on their mental state, and for counselors to provide psychological guidance and advice in text form. The psychological counselors stationed on the platform have all undergone professional training courses for the psychological problems of patients after heart valve surgery. The course content covers the pathogenesis, clinical manifestations, assessment methods and intervention treatment techniques of common postoperative psychological disorders such as anxiety, depression, and post-traumatic stress disorder. Counselors must pass strict assessments and certifications, have rich clinical experience and professional capabilities, and be able to accurately identify patients' psychological problems and provide effective psychological support. Patients can log in to the platform through a specially developed mobile application or computer and make an appointment for psychological counseling services on the platform. Counselors use cognitive behavioral therapy during the consultation process to help patients identify and change negative thinking patterns and behavioral habits; Relaxation training, such as deep breathing training and progressive muscle relaxation training, can relieve patients' tension and anxiety; Supportive psychotherapy listens to the patient's voice, provides emotional support and encouragement, and helps patients relieve postoperative psychological stress and improve their mental state. At the same time, the platform has a strict privacy protection mechanism and uses advanced data encryption storage technology, such as the AES encryption algorithm, to ensure the security of patient consultation information during storage; access control technology, by setting strict user roles and permissions, only authorized consultants and managers can access patient information to prevent patient information leakage. Regularly collect patient feedback data on rehabilitation plans, such as exercise experience (whether they feel tired, have difficulty breathing, etc.), dietary implementation (whether they can follow dietary recommendations, their acceptance of food, etc.), and changes in mental state (whether anxiety and depression have improved, etc.). Based on the feedback data, data analysis methods are used to make targeted adjustments and optimizations to the rehabilitation plan. For example, if most patients report that excessive exercise intensity causes fatigue, the exercise intensity can be appropriately reduced; if patients find certain dietary recommendations difficult to follow, the menu content can be adjusted based on their feedback. If the patient's mental state does not improve significantly, the psychological counseling method can be adjusted or the frequency of consultation can be increased.

2. The method for postoperative management of heart valve surgery based on dynamic monitoring and analysis of cardiac function according to claim 1, characterized in that: Each sensor in the integrated sensor array utilizes a low-power design to ensure long-term stable operation, and its size and material meet wearable comfort and safety requirements. For example, the electrocardiogram (ECG) sensor utilizes flexible electrode materials that conform to the skin and are non-allergenic; the blood pressure sensor is compact and can be integrated into wearable devices such as watchbands.

3. The method for postoperative management of heart valves based on dynamic monitoring and analysis of cardiac function according to claim 1, characterized in that: The biocompatible materials used in the implantable micro-monitoring chip will not cause immune rejection in the patient's body, and the chip's signal transmission frequency has been optimized to avoid interference with other medical or electronic devices. For example, the chip's biocompatible materials have been validated through long-term clinical trials, and the signal transmission frequency is uniquely modulated within the licensed frequency band for medical devices.

4. The method for postoperative management of heart valve surgery based on dynamic monitoring and analysis of cardiac function according to claim 1, characterized in that: During the construction of the personalized digital heart model, algorithm parameters are continuously updated and optimized to improve the model's accuracy in simulating and predicting a patient's cardiac function. For example, the model is regularly retrained based on newly acquired patient monitoring data, adjusting the neural network's weights and thresholds.

5. The method for postoperative management of heart valves based on dynamic monitoring and analysis of cardiac function according to claim 1, characterized in that: Warning messages from the real-time risk assessment model are promptly delivered to patients and healthcare professionals via various channels, including text messages and mobile app push notifications. These warnings include the risk type, severity, and recommended interventions. For example, a text message might clearly state, "Increased risk of heart failure; rest and contact a doctor immediately recommended." Mobile app push notifications can also include a link to a detailed risk assessment report.

6. The method for postoperative management of heart valves based on dynamic monitoring and analysis of cardiac function according to claim 1, characterized in that: The automated medication regimen adjustment process incorporates multiple review mechanisms to ensure the safety and effectiveness of medication adjustments, and the accurate transmission of adjustment recommendations to the pharmacy via the electronic prescription system. For example, the review mechanism includes initial review by a clinical pharmacist and review by a cardiologist. The electronic prescription system seamlessly integrates with the hospital's HIS system to ensure accurate prescription transmission.

7. The method for postoperative management of heart valve surgery based on dynamic monitoring and analysis of cardiac function according to claim 1, characterized in that: The exercise program in this personalized rehabilitation plan uses a device such as a smart bracelet to monitor the patient's heart rate, movement trajectory, and other parameters in real time during exercise. If these parameters exceed a safe range, the mobile app will promptly remind the patient to adjust. For example, the smart bracelet and mobile app are connected in real time via Bluetooth. If the exercise parameters exceed the preset heart rate range, the app will immediately pop up a red warning box and a voice reminder.

8. The method for postoperative management of heart valves based on dynamic monitoring and analysis of cardiac function according to claim 1, characterized in that: The dietary recommendations in this personalized rehabilitation plan not only consider cardiac function needs but also incorporate factors such as food allergies and religious beliefs to develop a diet tailored to the patient's specific needs. For example, for patients with lactose intolerance, dairy products are avoided in the diet; for patients who adhere to vegetarian religious beliefs, a completely vegan and heart-healthy diet is provided.

9. The method for postoperative management of heart valve surgery based on dynamic monitoring and analysis of cardiac function according to claim 1, characterized in that: The online psychological counseling platform's counselors are professionally trained and equipped to address the psychological challenges faced by patients undergoing heart valve surgery. The platform also incorporates privacy protection mechanisms to ensure patient information security. For example, counselors are required to complete specific post-operative psychological intervention training courses and pass assessments. The platform's data storage utilizes an encrypted algorithm, and access rights are strictly restricted to authorized personnel.

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