73 results about "Cardiovascular event" patented technology

The invention discloses a myocardial perfusion image classification method and system based on a single resting state, and belongs to intelligent analysis and auxiliary diagnosis of medical images. The method comprises the following steps: acquiring SPECT three-dimensional voxel data in the single resting state, and performing image reconstruction by adopting an OSEM algorithm; segmenting the myocardial region by using a pre-trained U-Net convolutional neural network, and mapping a segmentation result to a two-dimensional polar coordinate graph conforming to the AHA17 segment model; extracting a multi-dimensional feature vector; a single-phase inference model MSR-Net based on biphase labeling is constructed, in the training stage, segment classification labels of resting-load biphase images are used, in combination with segment consistency indexes, label correction is carried out, and in the inference stage, only resting state features are input, and then a pixel-level perfusion defect distribution diagram and ischemia scores of 17 myocardial segments can be output. Quantitative and segmental evaluation of myocardial ischemia can be completed by using single resting state imaging, exercise or drug load examination is avoided, and cardiovascular adverse events and complication risks are reduced.

The invention relates to the field of biosensors, and discloses a cardiovascular data monitoring method for the cardiovascular medicine department. According to the method, electrocardio, respiration and blood oxygen signals are synchronously collected through a non-invasive terminal, sleep apnea events are analyzed and recognized in a combined mode, the electrocardio signals are deeply analyzed to detect cardiovascular abnormalities, and time sequence characterization of the function state of the autonomic nervous system is generated based on heart rate variability; and further constructing a time sequence causal reasoning model, quantifying the triggering effect of the respiratory event on the cardiovascular event, generating a comprehensive night cardiovascular risk layering index, and triggering graded early warning. According to the invention, non-sensitive, long-time-history and high-precision night cardiovascular risk dynamic assessment and causal mechanism analysis are realized.

The invention relates to the technical field of disease monitoring, in particular to a multi-device collaborative disease risk prediction system. The system comprises a non-contact type signal receiving and transmitting device, a gait collecting device, an intelligent bracelet and a central processing unit, the non-contact type signal receiving and transmitting device captures macroscopic movement data of a user through a millimeter wave signal transmitting and receiving module, and the macroscopic movement data comprises periodic characteristics of gaits, movement tracks of four limbs and posture changes; the gait acquisition device acquires plantar pressure distribution data of a user through a pressure sensor array; the smart bracelet collects motion data of a user through an accelerometer and a gyroscope; and the central processing unit carries out space-time alignment on data of the non-contact signal transceiving device, the gait acquisition device and the intelligent bracelet through a timestamp synchronization algorithm, so that the disease risk is predicted according to the analysis data. The system supports all-weather and multi-scene monitoring, is suitable for disease risk assessment, and can early warn cardiovascular events and other health problems.

Described herein are methods of for treating and / or preventing Major adverse cardiovascular events (MACE). by administering to a subject in need thereof, a combination of a sodium-glucose transport protein 2 (SGLT2) inhibitor and a compound of Formula I or a stereoisomer, tautomer, pharmaceutically acceptable salt, or hydrate thereof, wherein the variables of Formula I are as defined herein.

A computer-implemented method for predicting cardiovascular disease risk, the method including: receiving a first patient history data comprising imaging data and / or non-imaging data for a patient at a first time point; selecting prediction report parameters defining a type of cardiovascular event and a risk prediction time scale; processing the first patient history data using a trained machine learning model configured to predict disease onset, progression, and / or regression over time, wherein the trained machine learning model is trained using patient subsets created based on patient history characteristics and outcomes; generating a risk prediction for the selected type of cardiovascular event over the selected risk prediction time scale based on the processed first patient history data; generating a risk prediction report based on the risk prediction; and outputting the risk prediction report.

According to the subclinical non-obstructive coronary atherosclerosis metaphase plaque progress risk prediction method based on interpretable machine learning provided by the invention, multi-dimensional images and clinical features are integrated, and individualized risk prediction of subclinical non-obstructive coronary atherosclerosis is realized. Quantitative imaging biomarkers are managed for a long time, a key tool is provided for early screening of high-risk individuals, clinical doctors can be helped to judge whether comprehensive strategies such as enhanced lipid lowering, anti-inflammation and lifestyle intervention need to be adopted in time or not, plaque stabilization or fading is maximized, and the clinical effect is improved. And further, the future cardiovascular event risk is reduced. According to the method, a Shapley additive SAHP interpretation technology is introduced, the relative contribution of each prediction feature is quantified, and the interpretability of a machine learning model is improved. According to the method, a webpage tool for calculating the plaque progress probability on line is deployed, and the contribution degree of each feature to the plaque progress prediction result of a specific patient can be displayed more visually.

The invention relates to an intelligent monitoring and risk early warning system for cardiovascular acute and critical diseases, which is characterized in that electrocardiosignals are acquired in real time through a wearable twelve-lead ECG monitor (ADS1298 chip, 512Hz sampling), and the electrocardiosignals are encrypted and transmitted to a cloud end through an MQTT protocol in combination with edge end wavelet denoising and QRS wave detection. And the cloud end adopts a multi-modal fusion engine, integrates a random forest-XGBoost hybrid model, performs modeling based on a postoperative patient physiological index time sequence, predicts the risk through Kalman filtering and triggers secondary early warning. Clinical tests in a third-A hospital prove that the end-to-end response time is 2.8 seconds, the D2B time is shortened to 70 minutes, and the occurrence rate of main adverse cardiovascular events in 30 days after operation is reduced by 47%. According to the invention, the limitation of traditional single-mode monitoring is broken through, a whole-process closed loop of collection-analysis-intervention is constructed, and an intelligent solution is provided for prevention and control of cardiovascular acute and critical diseases.

The application belongs to the technical field of molecular biology, and particularly relates to metabolic markers for evaluating the prognosis of patients with hypertrophic cardiomyopathy and application thereof. The application determines that one or more of 3-hydroxyrilotene, asparagine-lysine, cyclohexylamine, arachidonic acid, aconitic acid, propionyl carnitine, ceramide, tryptophan, 3-hydroxysebacic acid, traumatic acid, phosphatidylcholine, alanine-proline, lysophosphatidylcholine, taurine and S-garlicin cysteine are related to the prognosis of hypertrophic cardiomyopathy, can evaluate the prognosis of patients with hypertrophic cardiomyopathy after undergoing muscle resection of interventricular septum, and shows accurate judgment on all-cause death or rehospitalization, cardiovascular events and postoperative new-onset arrhythmia. Finally, all the prognostic biomarkers are reproduced in an independent cohort, which is helpful for postoperative risk evaluation of patients with hypertrophic cardiomyopathy, has high specificity and high sensitivity.

According to the remazolam target control infusion analysis method and system, a three-chamber model is adopted to construct an individualized pharmacokinetic model, and plasma concentration response of unit dose of remazolam is characterized through the individualized pharmacokinetic model; constructing an infusion pump control model, wherein the infusion pump control model characterizes the plasma drug concentration through a time function; defining a state variable for the infusion pump control model, and calculating the plasma drug concentration state variable at the moment t + delta t according to the plasma drug concentration state variable at the current moment t and the drug infusion amount within the interval from the current moment t to the moment t + delta t; and the infusion pump control model determines the medicine infusion rate required for the plasma medicine concentration to reach the target concentration CT according to the plasma medicine concentration state variable at the current moment t and the plasma medicine concentration state variable at the moment t + delta t. Individual medication can be achieved, cardiovascular adverse events of a patient are reduced, and a comfortable and safe infusion scheme is provided for the patient.

This invention discloses a dynamic prediction system for long-term adverse cardiovascular event risk after stent implantation, belonging to the field of personal health risk assessment technology. It includes: a cardiovascular assessment module for determining the target cardiovascular adverse event presentation coefficient based on the target patient's first chest pain rating and real-time heart rate after stent implantation; a cardiac function assessment module for determining the emphasis of cardiac function analysis at each stage based on the target patient's physical examination data after stent implantation; a probability determination module for determining the probability of the first adverse event based on the target cardiovascular adverse event presentation coefficient and the emphasis of each cardiac function analysis; and an indicator determination module for comparing the target patient's first adverse event probability with the second adverse event probability of each monitored patient to obtain the target cardiovascular risk warning indicator for the target patient. This invention can improve the accuracy of long-term risk prediction for adverse cardiovascular events.

A computer-implemented method of prediction of a cardiovascular, CV, event of a patient, based on a retinal image from the patient, is provided. The method comprises extracting a feature data set from the retinal image using a neural network, the neural network being pretrained to output feature data sets based on retinal images; inputting the extracted feature data set to a machine learning model, ML model, the ML model being pretrained to output a CV event indicator, the pretraining being based on the following data: training feature data sets extracted from retinal images; and training CV event indicators, one training CV event indicator per each one of the training feature data sets; and obtaining a CV event indicator of the patient from the ML model. A computer-implemented method of training a deep learning model based on retinal images and CV event indicators wherein the deep learning model comprises a neural network, NN, and a machine learning, ML, model, is provided. A data processing system, a training data processing system and a computer program product are also provided.

The invention discloses a health assessment method and system based on continuous blood pressure monitoring of an intelligent ring. The method comprises the following steps: continuously and noninvasively acquiring sensing data of a target user at a plurality of time points through the intelligent ring; calculating a user blood pressure parameter corresponding to the sensing data at each time point based on a preset blood pressure calculation model; according to the user blood pressure parameters corresponding to the at least two time points, health scores of the target user in multiple dimensions are calculated; and determining a cardiovascular health score corresponding to the target user according to the health scores of the multiple dimensions and a preset scoring rule. Therefore, all-weather non-inductive blood pressure monitoring and cardiovascular risk quantitative evaluation can be achieved, the cardiovascular abnormality early-stage discovery capability is improved, and the cardiovascular event risk caused by intermittent measurement or symptom delay awareness is reduced.

The application discloses a glycolipid metabolism index prediction model for predicting cardiovascular events and a construction method thereof. The model aims to solve the problems that existing cardiovascular risk assessment tools are not accurate in describing glycolipid metabolism state and are single in prediction dimension. The construction method comprises the following steps: obtaining demographic information and clinical data of a subject; measuring the content of high-density lipoprotein subcomponent HDL2b; taking body mass index, lipoprotein a, triglyceride-glucose index and HDL2b as core evaluation parameters, taking whether a cardiovascular event occurs within five years as a target variable, and adopting a multivariate logistic regression to construct a prediction model. The model can realize rapid and accurate stratification of the risk of cardiovascular event occurrence of an individual by quantifying index scores and adding up. The application integrates multi-dimensional key indicators of glycolipid metabolism, has the advantages of high prediction performance, clear risk stratification and strong repeatability, and is suitable for early risk screening and evaluation of cardiovascular diseases.

A computer-implemented method for predicting cardiovascular disease risk, the method including: receiving a first patient history data comprising imaging data and / or non-imaging data for a patient at a first time point; selecting prediction report parameters defining a type of cardiovascular event and a risk prediction time scale; processing the first patient history data using a trained machine learning model configured to predict disease onset, progression, and / or regression over time, wherein the trained machine learning model is trained using patient subsets created based on patient history characteristics and outcomes; generating a risk prediction for the selected type of cardiovascular event over the selected risk prediction time scale based on the processed first patient history data; generating a risk prediction report based on the risk prediction; and outputting the risk prediction report.

In various embodiments, the present disclosure provides methods of reducing the risk of cardiovascular events in a subject on statin therapy by administering to the subject a pharmaceutical composition comprising from about 1 g to about 4 g of eicosapentaenoic acid ethyl ester or a derivative thereof.

In various embodiments, the present disclosure provides methods reducing the risk of cardiovascular events in a subject on statin therapy by administering to the subject a pharmaceutical composition comprising about 1 g to about 4 g of eicosapentaenoic acid ethyl ester or a derivative thereof.

Method of lowering low-density lipoprotein cholesterol or preventing a cardiac event in a subject who has atherosclerotic cardiovascular disease or who is atherosclerotic cardiovascular disease risk equivalent, involving administering to the subject a prophylactically effective amount of an RNAi agent. Also, a method of preventing development of atherosclerotic cardiovascular disease in a subject involving administering to the subject a prophylactically effective amount of an RNAi agent. Further, a method of treating a subject who has atherosclerotic cardiovascular disease or who is atherosclerotic cardiovascular disease risk equivalent involving administering to the subject a therapeutically effective amount of an RNAi agent.

The invention discloses a cardiovascular acute event early warning method based on reinforcement learning, and aims to solve the problems that an early warning scheme is difficult to consider individualized trigger opportunity and evaluation frequency and is difficult to balance false alarm and missed alarm. The method comprises the following steps of: outputting risks, trends and uncertainty of current and future time windows by using multiple heads of an uncertainty perception deep network, constructing a state in combination with historical alarm records, individualized risk thresholds and evaluation time intervals, and inputting the state into a security constraint reinforcement learning network; in the same forward reasoning, whether early warning, threshold value adjustment amount and next evaluation time interval are determined in a combined manner, and a constraint weight updating strategy is adaptively adjusted based on early warning income, false alarm and missed alarm indexes and prediction uncertainty after a real label is obtained. The technical effects of reducing the false alarm rate, optimizing the early warning advance and the evaluation frequency and improving the early warning accuracy and reliability of the cardiovascular acute event on the premise that the missed alarm risk is controllable are achieved.

The invention discloses a visual multi-time-point hypertension risk scoring system. The system comprises a patient baseline data parameter and daily health information acquisition module, a project parameter acquisition module, a scoring module, a survival rate calculation module, a graphic display module and a project parameter correction module. The patient baseline data parameter and daily health information acquisition module is used for acquiring general health information, clinical text data, laboratory examination, sleep examination, blood pressure and heart rate of each patient; the project parameter acquisition module is used for acquiring parameters of a to-be-scored project; the scoring module is used for calculating a total risk score of the hypertensive patient; the survival rate calculation module is used for calculating the occurrence risk probability value of the specific adverse cardiovascular event according to the total risk score; the graphic display module is used for displaying the occurrence risk probability of the adverse cardiovascular event and the proportion weight of related risk factors; and the item parameter correction module is used for enabling the user to manually correct the parameter value of the scoring item.

The invention relates to the field of cardiovascular disease monitoring and early warning, and provides a cardiovascular early warning method and system based on millimeter wave radar and physiological signal decoupling, and the method comprises the steps: obtaining a regional synchronization signal set through time-space domain secondary mapping according to an original radar data stream, mapping the regional synchronization signal set into a modal component set, inputting the modal component set into a deep residual network to generate a physiological feature vector, obtaining a body fluid retention index based on the regional synchronization signal set; generating a linkage feature matrix according to the physiological feature vector and the body fluid retention index, and generating an interaction enhancement coefficient through danger mode recognition; according to the physiological feature vector and the linkage feature matrix, generating a grading early warning decision instruction by combining an interaction enhancement coefficient through dual-path risk assessment; and generating a baseline offset vector based on the comprehensive risk evaluation value, and outputting an adaptive decision parameter set in combination with a clinical feedback signal. According to the method, multi-region physiological signal decoupling and cross-disease pathology association logic are fused, and a multi-mode intelligent early warning and parameter adaptive closed-loop calibration mechanism of cardiovascular events is constructed.

This invention provides the use of RNA interferants that inhibit PCSK9 synthesis in prophylactic or therapeutic methods. [Solution] A method for reducing low-density lipoprotein cholesterol or preventing cardiac events in a subject having atherosclerotic cardiovascular disease or being at equivalent risk to atherosclerotic cardiovascular disease, comprising the step of administering a prophylactic effective amount of RNAi to the subject. Also, a method for preventing the onset of atherosclerotic cardiovascular disease in a subject, comprising the step of administering a prophylactic effective amount of RNAi to the subject. Furthermore, a method for treating a subject having atherosclerotic cardiovascular disease or being at equivalent risk to atherosclerotic cardiovascular disease, comprising the step of administering a therapeutic effective amount of RNAi to the subject.

To provide an injection device for subcutaneous administration, for preventing cardiovascular events in a human subject.SOLUTION: The present invention provides an injection device for subcutaneous administration, comprising a fixed dose of 275 mg to 325 mg of an interfering ribonucleic acid (RNAi) agent. The device comprises a double-stranded ribonucleic acid comprising a sense strand and an antisense strand forming a double-stranded region, where the antisense strand comprises the nucleotide sequence of 5'-asCfsaAfAfAfgCfaAfaAfcAfgGfuCfuagsasa-3' (SEQ ID NO: 3), and the sense strand comprises the nucleotide sequence of 5'-csusagacCfuGfudTuugcuuuugu-3' (SEQ ID NO: 4), where a, g, c and u are 2'-O-methyl (2'-OMe) A, G, C, or U; Af, Gf, Cf or Uf are 2'-fluoro A, G, C or U; dT is 2'-deoxythymidine; and s is a phosphorothioate linkage.SELECTED DRAWING: None

The invention discloses a cardiovascular complication risk prediction and dialysis method based on multi-dimensional data, which comprises a dialysate blending device, the dialysate blending device adds a potassium chloride solution into dialysate according to the blood potassium level of a patient to regulate and control the potassium ion concentration of the dialysate, and conveys the potassium ion concentration to a dialysis machine; the prediction and dialysis method comprises the following steps: firstly, collecting multi-dimensional data of a patient, and constructing a cardiovascular complication risk prediction model by utilizing big data analysis and machine learning technologies and combining the multi-dimensional data; predicting the possibility of cardiovascular events caused by blood potassium imbalance of the patient in a future period of time in advance; according to a prediction result, adjusting a dialysate potassium ion concentration strategy of the patient before dialysis; in the later treatment stage, the blood potassium level of the current patient is monitored, regulated and controlled, and individualized treatment is conducted according to the current blood potassium concentration. The potassium ion concentration strategy of the dialysate can be adjusted in advance, preventive treatment is implemented, and the treatment gate is moved forward.

The present invention relates to methods for reducing the risk of major adverse cardiovascular events (MACE) in a subject, including administering an RNAi agent, RNAi agents and pharmaceutical compositions for use in such methods, and uses of RNAi agents for the manufacture of medicaments for such methods. Provided herein are particularly effective methods for preventing cardiovascular death, non-fatal myocardial infarction (MI), non-fatal ischemic stroke, or emergency coronary revascularization in subjects who have not experienced a major atherosclerotic cardiovascular disease (ASCVD) event, particularly those at high risk for a first MACE.

The present disclosure is directed to methods of reducing the risk of a cardiovascular event with conjugated antisense compounds targeting apo(a). Specifically, a method of reducing the risk of a cardiovascular event in a patient who has established cardiovascular disease with conjugated antisense compound ISIS 681257 or a salt thereof.

The invention relates to the technical field of chronic disease management, in particular to an intelligent chronic disease management system based on LC9 multi-dimensional quantitative evaluation and adaptive feedback optimization, and the system comprises the steps: obtaining nine-dimensional original data containing health behaviors, biological indexes and psychological states through a data collection module; the health assessment engine generates a health state index through fusion by using a dynamic weight algorithm; the personalized intervention strategy device generates and decomposes an intervention scheme according to the result; and the adaptive feedback optimizer dynamically adjusts the evaluation weight according to the user execution compliance to form closed-loop optimization. Meanwhile, the system is integrated with an acute event early warning module, and intelligent recognition and early warning of acute cardiovascular events are achieved by fusing clinical symptoms described by natural languages and real-time physiological data. According to the invention, the defects of single evaluation, stiff strategy and insufficient early warning of the existing system are overcome, and real personalized and self-adaptive chronic disease intelligent management is realized.

The embodiment of the application discloses a blood pressure monitoring method and related device, which are applied to a smart wearable device; the method comprises the following steps: counting physiological signals of a user in a sampling period; intercepting a motion signal in a first time window as a target motion signal; determining a motion state of the user according to the target motion signal; determining at least one second time window in a plurality of first time windows, taking a PPG signal in the second time window as a target PPG signal, and the motion state of the user corresponding to the second time window satisfying a preset state; and determining a blood pressure rhythm pattern of the user according to the motion state of the user and the target PPG signal. In this way, the physiological signals of the user are continuously monitored, the motion state, the sleep state and the blood pressure rhythm pattern of the user are analyzed, more comprehensive and accurate blood pressure monitoring is provided, the blood pressure rhythm pattern can be more accurately determined, and the risk of a cardiovascular event is reduced.

Methods of reducing the risk of cardiovascular events in left ventricular hypertrophy patients without hypertension are disclosed. Preferred methods comprise administering a therapeutically effective amount of dual SGLT1 / 2 inhibitor sofogliflozin.