103 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 biological medicine, in particular to application of ADAMTS13 to occlusive cardiovascular and cerebrovascular diseases. On one hand, the invention provides the biomarker ADAMTS13 for establishing the collateral circulation of the patient suffering from the occlusive cardiovascular and cerebrovascular diseases, and the biomarker ADAMTS13 can be used as a target spot for diagnosing, preventing and treating the occlusive cardiovascular and cerebrovascular diseases and can effectively predict the establishment of the collateral circulation of the patient suffering from the occlusive cardiovascular and cerebrovascular diseases and prevent the development of adverse cardiovascular events; the method has important value on clinical cardiovascular events, and can improve the survival rate of patients with occlusive vascular diseases and improve long-term prognosis; on the other hand, the invention provides an ADAMTS13 recombinant protein, the amino acid sequence of which is as shown in SEQ ID No: 1, and the ADAMTS13 recombinant protein can improve the proliferation, migration, invasion and tube forming capabilities of endothelial cells, and has important clinical application value for developing drugs for preventing and / or treating occlusive cardiovascular and cerebrovascular diseases.

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.

The invention discloses a cardiovascular data monitoring method for the cardiovascular medicine department, and relates to the technical field of cardiovascular monitoring, and the method comprises the steps: collecting and preprocessing multi-source cardiovascular data of a patient, and building a cardiovascular data set in combination with a conditional generative adversarial network; constructing a cardiovascular network framework based on the established cardiovascular data set, and optimizing cardiovascular network parameters by using a meta-learning algorithm to capture feature data in the cardiovascular data set; analyzing a causal relationship between the feature data and the cardiovascular event by using a causal discovery algorithm, and constructing a causal relationship graph based on an analysis result; and a fuzzy membership function and an entropy weight algorithm are fused, nodes in the causal relationship graph are adjusted, and a hierarchical monitoring strategy is generated in combination with a multi-objective evolutionary algorithm. According to the method, the multi-source cardiovascular data of the patient is collected and preprocessed, and the conditional generative adversarial network is introduced to generate high-quality and diversified synthetic data, so that the data scale is effectively expanded, and the balance and representativeness of data distribution are improved.

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 present invention provides a pharmaceutically promising combination of allisartan medoxomil and / or its salt and pitavastatin and / or its salt. The pharmaceutical composition can inhibit myocardial cell apoptosis and reduce the incidence of cardiovascular events.

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 invention discloses a PCI postoperative MACE risk prediction marker for an acute ST segment elevation type myocardial infarction patient, and belongs to the field of biological medicine. The application comprises application of a reagent for detecting the lipid metabolism marker in preparation of a kit for predicting the risk of main adverse cardiovascular events after the PCI operation of a patient suffering from the acute ST segment elevation type myocardial infarction, and the reagent for detecting the lipid metabolism marker comprises glycodeoxycholic acid, [(2R)-3-[(6Z, 9Z, 12Z)-octadeca-6, 9, 10-trimethyl-1, 3, 5-trimethyl-1, 3, 5-trimethyl-1, 3, 5-trimethyl-1, 3, 5-trimethyl-1, 3, 5-trimethyl-1, 3, 5-trimethyl-1, 3, 5-trimethyl-1, the detection reagent is selected from one or more of a detection reagent for N-((2S, 3R)-1, 3-dihydroxy octadecane-4-alkene-2-yl) acetamide, a detection reagent for 2-(2, 6, 7, 12-trienoyl) oxy-2-[(Z)-octadecane-9-enoyl] oxypropyl] 2-(trimethyl ammonium) ethyl phosphate, 8-hydroxy-8-(3-octyl oxirane-2-yl) octanoic acid and N-((2S, 3R)-1, 3-dihydroxy octadecane-4-alkene-2-yl) acetamide. The four lipid metabolism markers (C44H80NO8P, C18H34O4, C20H39NO3 and C26H43NO5) screened by the invention are used for the prediction efficiency of MACE prediction, the sensitivity of the four lipid metabolism markers reaches 0.889, the specificity of the four lipid metabolism markers is 0.894, the AUC of the four lipid metabolism markers is 0.921, and the four lipid metabolism markers have high sensitivity and high specificity.

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.

To provide biomarkers, methods, devices, reagents, systems, and kits that enable prediction of cardiovascular events within a 5 year period, in view of the fact that an improved test would optimally require only a single blood, urine, or other sample and a single assay.SOLUTION: Provided herein are biomarkers, methods, devices, reagents, systems, and kits used to assess an individual for prediction of the risk of developing a primary or secondary cardiovascular (CV) event over a 4 year period.SELECTED DRAWING: Figure 1

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 disclosure is directed to systems and techniques for detecting change in patient health based upon patient data. In one example, a medical system comprising processing circuitry communicably coupled to a glucose sensor and configured to generate continuous glucose sensor measurements of a patient. The processing circuitry is further configured to: extract at least one feature from the continuous glucose sensor measurements over at least one time period, wherein the at least one feature comprises one or more of an amount of time within a pre-determined glucose level range, a number of hypoglycemia events, a number of hyperglycemia events, or one or more statistical metrics corresponding to the continuous glucose sensor measurements; apply a machine learning model to the at least one extracted feature to produce data indicative of a risk of a cardiovascular event; and generate output data based on the risk of the cardiovascular event.

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.

According to an aspect of the present invention, there is provided computer-implemented method for forecasting near-term sudden cardiovascular events, comprising: pretraining a large language model transformer architecture using a processor with an associated computer memory device to recognize text associated with sudden cardiovascular events cases reporting sudden cardiovascular events following a medical exam appointment; obtaining relevant data regarding an asymptomatic individual comprising one or more of a coronary artery calcium (CAC) scan, a coronary CT angiography (CCTA), chest CT, blood markers, and electrocardiogram; providing the relevant data of the asymptomatic individual to the trained computer-implemented artificial neural network; receiving a forecast of the chance of near-term sudden cardiovascular events in the asymptomatic individual; and recommending the next diagnostic or therapeutic step for the asymptomatic individual.

The present invention relates to the field of ultrasounds and imagining of the coronary blood flow of the heart. Patients with coronary microvascular dysfunction (CMD) have poor prognostic with significantly higher rates of cardiovascular events, including hospitalization for heart failure, sudden cardiac death, and myocardial infarction (MI). Despite the urgent clinical need, there are no non-ionizing and non-invasive techniques available in clinic to directly visualize the coronary microvasculature and assess the local coronary microvascular system. Flow imaging remains a difficult task to perform in the heart because of the fast movements of this organ. In order to overcome the limitations of actual imaging methods for the coronary blood flow, the inventors proposed an ultrasound ultrafast imaging method that automatically detect the time periods in which the myocardium velocity is low and estimate the coronary flow velocity and the tissue velocity from the same data acquisition.

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 invention relates to the technical field of metabolic markers, in particular to application of metabolic markers in preparation of products for detecting or diagnosing coronary heart disease adverse cardiovascular events and a construction method of a coronary heart disease adverse cardiovascular event diagnosis model. The coronary heart disease adverse cardiovascular event product is used for detecting or diagnosing coronary heart disease cardiovascular death events and / or coronary heart disease heart failure events. According to the application, the risks of death events and heart failure events of the coronary heart disease patients are predicted by using the specific differential metabolites, dense monitoring can be performed on the coronary heart disease patients, and effective customized treatment guidance is provided for clinicians.

The inventors have identified new peptide biomarkers related to methylglyoxal (MGO)-induced glycation of plasma albumin, as well as the most abundant plasma apolipoproteins. Plasma concentrations of MGH-APOB1003184-3194, MGH-ALB219-225, CEL-APOA229-39 were higher in patients with type 2 diabetes (T2D). After follow-up, cases of major adverse cardiovascular events (MACE), cases of severe coronary artery disease (CAD) events, cases of cardiovascular (CV) deaths and cases of all-cause deaths were reported in The SURDIAGENE study (n = 1425). Increased plasma concentrations of MGH-APOB1003184-3194,MGH-ALB219- 225, CEL-APOA229-39 were significantly associated with an increased risk of developing MACE, CAD, CV death, and / or CV death. Thus the present invention refers to a method of predicting the risk of having or developing a cardiovascular event in a patient.

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.

The present application discloses the use of a biomarker in the preparation of a kit for a prognostic risk warning method for patients with acute ST-segment elevation myocardial infarction. The biomarker includes HBP protein. By monitoring the change in the level of HBP protein, the present application can effectively evaluate whether a patient with acute ST-segment elevation myocardial infarction will have a poor prognosis within 30 days after PCI, so as to achieve early identification of high-risk patients and timely clinical intervention, improve the treatment response speed and effect, reduce the incidence of adverse cardiovascular events, and ultimately improve the prognosis of patients.

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.