184 results about "Cardiovascular event" patented technology

The invention relates to the technical field of cardiovascular medicine, in particular to an automatic alarm system for cardiovascular medicine, which comprises a biomarker analysis module, a physiological parameter analysis module, a cardiovascular risk assessment module, a cardiovascular early warning adjustment module and an alarm trigger notification module. According to the invention, by integrating real-time monitoring of blood biomarkers and cardiovascular physiological parameters, the early recognition capability of cardiovascular disease risks is improved, key biomarkers are dynamically measured and compared with historical marker data, and subtle physiological changes can be captured at the early stage of asymptomatic conditions, so that potential cardiovascular events are warned; through correlation analysis of electrocardiogram, blood pressure and pulse waveforms, the system can accurately draw a comprehensive image of cardiovascular functions, so that more targeted monitoring and intervention are implemented, timeliness and accuracy of medical response are ensured, doctors can formulate targeted monitoring schemes according to real-time data, and the monitoring efficiency is improved. Therefore, deterioration or occurrence of cardiovascular events of patients can be effectively prevented.

The disclosure provides methods of reducing the risk of a major adverse cardiovascular event (MACE) in a subject having previously experienced acute coronary syndrome (ACS) and for reducing risk of a MACE in a patient who is diagnosed with coronary artery disease and has blood high-sensitivity C- reactive (hs-CRP) of >2.0 mg / L. The methods include administering to the subject an anti-oxLDL antibody. The anti-oxLDL antibody may be administered to the subject alone or in combination with one or more additional therapeutic agents, such as one or more lipid lowering agents.

The invention discloses an application of a biomarker in preparation of a kit for a prognosis risk early warning method of an acute ST segment elevation type myocardial infarction patient. The biomarker comprises an HBP (Hepatitis Binding Protein). By monitoring the change of the HBP protein level, whether a patient suffering from the acute ST segment elevation type myocardial infarction has poor prognosis within 30 days after a PCI operation can be effectively evaluated, so that early recognition and timely clinical intervention on high-risk patients are realized, the treatment response speed and effect are improved, the occurrence rate of adverse cardiovascular events is reduced, and the treatment effect of the patient suffering from the acute ST segment elevation type myocardial infarction is improved. And the prognosis of the patient is finally improved.

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 provides methods and dosing regimens for safely and effectively treating androgen-dependent prostate cancer with a gonadotrophin releasing hormone (GnRH) antagonist without causing a testosterone spike and / or other side effect of GnRH agonist therapy such as a urinary tract infection, or an arthralgia-related or cardiovascular side effect. The present disclosure also provides for methods for treating prostate cancer in a patient with a history of at least one cardiovascular event, wherein administration of degarelix to the subject decreases the likelihood of developing or experiencing an additional cardiovascular event compared to treatment with a gonadotrophin releasing hormone (GnRH) agonist.

The invention relates to the technical field of cardiovascular disease detection, and discloses a blood ceramide and cardiovascular event correlation analysis system, a data acquisition module is used for acquiring a plurality of blood samples of different groups of patients, acquiring blood ceramide data by using a mass spectrometry detection method, and collecting clinical index data corresponding to the patients; the data processing module is used for cleaning the acquired blood ceramide data and clinical index data, removing abnormal values and missing values, and performing standardization processing on the data to obtain processed data; the feature extraction module is used for extracting target feature data associated with the cardiovascular event from the processed data; the correlation analysis module is used for inputting the target characteristic data into a blood ceramide and cardiovascular event correlation analysis model, predicting the risk of the cardiovascular event through the correlation analysis model, formulating a personalized prevention and treatment scheme and outputting a correlation analysis result; the present invention is helpful to reveal complex relationships between blood ceramides and cardiovascular events.

The invention discloses an application of nitro fatty acid in preparation of a product for predicting, preventing and improving adverse cardiovascular events of patients with acute coronary syndrome. The invention provides application of nitro fatty acid in preparation of products for preventing and improving adverse cardiovascular events of ACS patients. According to the application, for the ACS patient, when the concentration of the 12-nitro conjugated linoleic acid in the body is larger than 0.196 ng / ml and the concentration of the 9-nitro conjugated linoleic acid is larger than 0.296 ng / ml, it is judged that the risk that the ACS patient is re-hospitalized and dead due to non-fatal myocardial infarction / unstable angina pectoris / blood supply reconstruction is low. By detecting the content of nitro fatty acid in a biological sample and supplementing nitro fatty acid, the occurrence rate of adverse events can be greatly reduced. Meanwhile, the invention further provides a detection method of nitro fatty acid, and the accuracy of biological sample detection is improved.

To provide methods for treating an atherosclerotic cardiovascular disease with LPA-targeted RNAi constructs.SOLUTION: The present invention relates to methods for treating or preventing an atherosclerotic cardiovascular disease and other conditions associated with elevated levels of lipoprotein (a) (Lp(a)) using RNAi constructs targeting the LPA gene, which encodes apolipoprotein(a) being a component of Lp(a) particles. In particular, the present invention relates to methods for reducing serum Lp(a) levels and reducing a risk of cardiovascular events in patients with elevated levels of Lp(a) comprising administering an LPA-targeted RNAi construct according to specific dosage regimens. Further, pharmaceutical compositions containing the LPA-targeted RNAi constructs for use in the methods are also disclosed.SELECTED DRAWING: None

The invention discloses a cardiac patient personalized rehabilitation plan generation and optimization method, and relates to the technical field of medical treatment, and the method comprises the steps: S1, carrying out the real-time collection and analysis of the physiological parameters and behavior patterns of a patient based on multi-source data, S2, monitoring the psychological state of the patient through psychological scale evaluation and an emotion calculation model, s3, dynamically adjusting the personalized rehabilitation plan according to the psychological state to reduce sympathetic nerve hyperexcitation; S4, optimizing the rehabilitation plan based on patient feedback and clinical data; according to the personalized rehabilitation plan generation and optimization method for the heart disease patient, a comprehensive rehabilitation management scheme is provided for the heart disease patient, potential inducements of cardiovascular events are effectively reduced, the heart burden is relieved, and an important guarantee is provided for long-term rehabilitation of the patient; and the psychological state of the patient is dynamically monitored and actively intervened to regulate and control so as to solve the problem of hyperexcitation of sympathetic nerves caused by psychological stress.

According to the atherosclerotic plaque reversal strategy recommendation method based on AI evaluation, comprehensive analysis of multi-modal data is realized through an AI large model algorithm, and a personalized intervention scheme based on dynamic risk grading is provided for doctors, so that acute cardiovascular events are reduced, and long-term prognosis of patients is improved. A set of intelligent risk grading system is constructed by using a large model algorithm and multi-modal data, and drug and nutritional supplement recommendation is performed through a large model, so that atherosclerotic plaque reversal of a patient is realized. Clinical artery image data of a patient can be processed, and risk assessment and grading are performed through the trained AI model to generate a personalized intervention scheme. Compared with a system based on rules, the AI algorithm has higher flexibility and robustness through large-scale data training, can dynamically predict the risk level of the patient according to the multi-dimensional features of the patient and provide an accurate intervention scheme, and meets the requirements of different patients.

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.

The invention discloses a cardiovascular event occurrence probability prediction method and system based on ambulatory blood pressure monitoring, and relates to the technical field of artificial intelligence. According to the method, a feature data set is constructed according to ambulatory blood pressure sequence features and personnel information features; inputting the feature data set into a trained ambulatory blood pressure encoder, and outputting a hidden feature matrix; according to a preset moment, dividing the hidden feature matrix into a first half-day hidden feature and a second half-day hidden feature; based on the first half day hidden feature and the second half day hidden feature, obtaining a morning period hidden feature and a night period hidden feature; the morning period hidden features and the night period hidden features are input into a feature fusion and occurrence probability output module, cardiovascular event occurrence probabilities corresponding to the morning period and the night period are obtained, and the feature fusion and occurrence probability output module is constructed based on a cross attention mechanism. By implementing the technical scheme of the invention, the occurrence probability of the cardiovascular event corresponding to the early morning time period and the night time period can be accurately obtained.

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.

Computer implemented biometric methods and systems incorporate sensing biophysical phenomena, translating the phenomena into digital data and transmitting the data to a series of servers operating in an open feedback loop to generate a module. A biometric networking system can include a biometric monitoring cloud computing platform with AI / machine learning augmented models are generated to make user assessments, programs and confidence scores to the healthcare provider systems. The AI / machine learning models can be used by the biometric monitoring network to generate health-related AI processes that analyze relationships treatment techniques and outcomes. AI techniques can be used to calculate movement modeling and confidence scoring including support vector machines, neural networks, and decision trees. The biophysical phenomena may include biometric parameters based on data, such as medical history, exertion, sleep, temperature, cardiovascular events, respiratory events, and muscle and blood pH.

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 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

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.