Cardiovascular health risk intervention method and system based on dynamic assessment
Through the cardiovascular risk assessment model and behavioral characteristic classification model, risk prediction and classification are carried out for physical examination personnel, combined with dynamic risk assessment tools, individualized intervention strategies are output, which solves the problem that the existing technology central vascular disease risk assessment is not comprehensive and personalized enough, and improves the accuracy and personalization of risk prediction.
Patent Information
- Application Number
- CN202411680295.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-22
- Publication Date
- 2025-03-21
AI Technical Summary
The existing technology lacks professional guidelines for individual cardiovascular risk assessment and management, and insufficient dynamic assessment and intervention methods for cardiovascular health risks, resulting in insufficient comprehensive and personalized cardiovascular risk assessment.
The risk prediction results of physical examination personnel are obtained through the cardiovascular risk assessment model, divided into three types of people receiving treatment (motivation formation, resource demand, and behavior change), and dynamic risk assessment is carried out through risk assessment tools to output individualized intervention strategies.
The model's ability to discriminate large base populations is enhanced, the misclassification situation is reduced, and the CVD risk is predicted in real time through dynamic assessment, which improves the personalization and accuracy of predictions.
Smart Images
Figure CN119673448A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field, and in particular relates to a cardiovascular health risk intervention method and system based on dynamic assessment. Background Art
[0002] Cardiovascular disease (CVD) is the leading cause of death worldwide, with a large number of people dying from CVD every year. CVD risk assessment and management are important foundations for preventing CVD, but my country currently lacks professional guidelines for CVD risk assessment and management at the individual level. Therefore, dynamic assessment and intervention methods for cardiovascular health risks are particularly important.
[0003] Although some guidelines, such as the Chinese Guidelines for Primary Prevention of Cardiovascular Disease, have put forward recommendations for lifestyle intervention and risk factor prevention and control, these guidelines often lack specific operational guidance for individuals, resulting in limited effectiveness in actual applications;
[0004] The cardiovascular assessment model currently used is based on the China-PAR model, which mainly focuses on the risk prediction of atherosclerotic cardiovascular disease (ASCVD) events and ignores other related events such as lipid-lowering therapy. This leads to an incomplete assessment of cardiovascular disease risk. The performance of this model in different populations still needs to be recalibrated and externally validated, and short-term risk assessment alone may not fully take into account the importance of lifestyle improvement and cardiovascular disease prevention. Summary of the invention
[0005] In order to solve the above problems existing in the prior art, the present invention provides a cardiovascular health risk intervention method and system based on dynamic assessment. The purpose of the present invention can be achieved through the following technical solutions:
[0006] A cardiovascular health risk intervention method based on dynamic assessment, including:
[0007] Obtain risk prediction results of the examinees through a cardiovascular risk assessment model, and divide the examinees corresponding to high risk risk prediction results into three types of patients through a behavioral characteristic classification model, including motivation formation type, resource demand type, and behavior change type;
[0008] A dynamic risk assessment is performed on the patient population using a risk assessment tool, and an individualized intervention strategy for the patient population is output based on the result of the dynamic risk assessment.
[0009] Specifically, the cardiovascular risk prediction model uses an electronic medical record database as a validation data set, imports the medical record data of the physical examination personnel through a medical information system, associates the incidental diagnosis of CVD determined in the validation data set with the medical record data, and fits two independent cause-specific Cox models according to the gender of the physical examination personnel to obtain a risk equation based on cardiovascular disease, and the predictor variables are set as age interaction terms, Nelson-Aalen estimates of CVD baseline cumulative risk, CVD outcome indicators, baseline cumulative risk, and non-CVD death outcome indicators.
[0010] Specifically, the incidental CVD diagnoses include fatal or non-fatal myocardial infarction, ischemic heart disease, ischemic, hemorrhagic, unspecified stroke, and transient ischemic attack.
[0011] Specifically, the behavioral characteristic classification model is used to integrate behavioral attribute characteristics and perform conditional statistics based on the quantitative representation of the behavioral attribute characteristics, and divide the patient population into categories based on the statistical results. The behavioral attribute characteristics are obtained through the medical information system, and specifically include high, medium, and low three-dimensional quantitative representations of risk perception, perceived psychological stress, lifestyle health, cultural level, and communication enthusiasm.
[0012] Specifically, the risk assessment tool is an externally validated performance measure used to estimate actual individual risk for behavioral attribute characteristics of the patient population, and to associate the review behavior of the patient population with the risk of incidental CVD diagnosis through a cause-specific risk regression model.
[0013] Specifically, the dynamic risk assessment method is: identifying patient groups with similar development trajectories through longitudinal data in medical record data.
[0014] Specifically, the individualized intervention strategy is a behavioral guidance measure for the patient group with the same behavioral type identified after the dynamic risk assessment.
[0015] A cardiovascular health risk intervention system based on dynamic assessment, comprising: a risk prediction module, a behavior characteristic classification module, and a dynamic risk assessment module;
[0016] The risk prediction module is used to obtain the risk prediction results of the physical examination personnel through the cardiovascular risk assessment model;
[0017] The behavior feature classification module is used to divide the medical examinees corresponding to the high risk predicted results into three types of reception groups through the behavior feature classification model;
[0018] The dynamic risk assessment module is used to perform a dynamic risk assessment on the patient population through a risk assessment tool, and output an individualized intervention strategy for the patient population based on the result of the dynamic risk assessment.
[0019] The beneficial effects of the present invention are:
[0020] The performance metric of the prediction model is comprehensively evaluated through risk assessment tools, which enhances the model's ability to distinguish large populations, reduces the misclassification of the Cox model, and predicts CVD risk in real time through dynamic assessment, thereby improving the personalization and accuracy of the prediction. Behavior-based risk factors are added to the cardiovascular risk prediction model to maximize consideration of the characteristics and applicability of different populations, and a cardiovascular disease risk equation based on the cause-specific Cox model is established to more accurately predict the impact of different causes on CVD risk and provide personalized risk assessment. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.
[0022] Figure 1 The figure is a flow chart of a cardiovascular health risk intervention method based on dynamic assessment according to the present invention. DETAILED DESCRIPTION
[0023] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.
[0024] See also Figure 1 , a cardiovascular health risk intervention method based on dynamic assessment:
[0025] Obtain risk prediction results of the examinees through a cardiovascular risk assessment model, and divide the examinees corresponding to high risk risk prediction results into three types of patients through a behavioral characteristic classification model, including motivation formation type, resource demand type, and behavior change type;
[0026] A dynamic risk assessment is performed on the patient population using a risk assessment tool, and an individualized intervention strategy for the patient population is output based on the result of the dynamic risk assessment.
[0027] Specifically, the cardiovascular risk prediction model uses an electronic medical record database as a validation data set, imports the medical record data of the physical examination personnel through a medical information system, associates the incidental diagnosis of CVD determined in the validation data set with the medical record data, and fits two independent cause-specific Cox models according to the gender of the physical examination personnel to obtain a risk equation based on cardiovascular disease, and the predictor variables are set as age interaction terms, Nelson-Aalen estimates of CVD baseline cumulative risk, CVD outcome indicators, baseline cumulative risk, and non-CVD death outcome indicators.
[0028] In this embodiment, the age interaction term refers to the interaction between age and other variables (such as CVD risk factors) considered in the analysis. This interaction can help identify the difference in the impact of specific risk factors in people of different age groups. The Nelson-Aalen cumulative risk estimate is estimated by calculating the length of time each patient is observed. Specifically, data on death time, censoring time, and observation time are used to calculate the cumulative risk function. CVD outcome indicators generally include the incidence and mortality of CVD, and the estimation of the number of CVD deaths is achieved by calculating the number of deaths at a specific age and year. The baseline cumulative risk refers to the probability that an individual will experience a specific event (such as CVD death) in a certain period of time in the future in the absence of intervention measures. The baseline cumulative risk can be estimated by age-specific mortality rates; non-CVD death outcome indicators refer to mortality rates caused by other causes other than CVD, and the probability of non-CVD death can be estimated by subtracting the probability of CVD death from the total probability of death. Based on the cumulative subdistribution baseline risk and the regression coefficient estimate of the Fine and Gray model, the risk equation for cardiovascular disease can directly calculate the predicted risk at a specific time point:
[0029]
[0030] in, is the baseline cumulative hazard, I1(t|Z) is the cumulative association function between risk influencing factors and behavioral characteristic attributes, which is determined after a given covariate Z. It is used to describe the risk association between risk influencing factors and behavioral characteristic attributes under the corresponding risk factors as time t increases. The cumulative sum is 1 when time is infinite.
[0031] Specifically, the incidental CVD diagnoses include fatal or non-fatal myocardial infarction, ischemic heart disease, ischemic, hemorrhagic, unspecified stroke, and transient ischemic attack.
[0032] Specifically, the behavioral characteristic classification model is used to integrate behavioral attribute characteristics and perform conditional statistics based on the quantitative representation of the behavioral attribute characteristics, and divide the patient population into categories based on the statistical results. The behavioral attribute characteristics are obtained through the medical information system, and specifically include high, medium, and low three-dimensional quantitative representations of risk perception, perceived psychological stress, lifestyle health, cultural level, and communication enthusiasm.
[0033] Specifically, the risk assessment tool is an externally validated performance measure used to estimate actual individual risk for behavioral attribute characteristics of the patient population, and to associate the review behavior of the patient population with the risk of incidental CVD diagnosis through a cause-specific risk regression model.
[0034] Specifically, the dynamic risk assessment method is: identifying patient groups with similar development trajectories through longitudinal data in medical record data.
[0035] Specifically, the individualized intervention strategy is a behavioral guidance measure for the patient group with the same behavioral type identified after the dynamic risk assessment.
[0036] In this embodiment,
[0037] A cardiovascular health risk intervention system based on dynamic assessment, comprising: a risk prediction module, a behavior characteristic classification module, and a dynamic risk assessment module;
[0038] The risk prediction module is used to obtain the risk prediction results of the physical examination personnel through the cardiovascular risk assessment model;
[0039] The behavior feature classification module is used to divide the medical examinees corresponding to the high risk predicted results into three types of reception groups through the behavior feature classification model;
[0040] The dynamic risk assessment module is used to perform a dynamic risk assessment on the patient population through a risk assessment tool, and output an individualized intervention strategy for the patient population based on the result of the dynamic risk assessment.
[0041] The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device.
[0042] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.
[0043] The program code contained on the computer readable medium can be transmitted with any appropriate medium, including but not limited to wireless, electric wire, optical cable, RF, etc., or any suitable combination of the above. The computer program code for performing the operation of the present invention can be written in one or more programming languages or their combinations, and the programming language includes object-oriented programming languages-such as Java, Smalltalk, C++, and also includes conventional procedural programming languages-such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).
[0044] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.
Claims
1. A cardiovascular health risk intervention method based on dynamic assessment, characterized in that: include: Obtain risk prediction results of the examinees through a cardiovascular risk assessment model, and divide the examinees corresponding to high risk risk prediction results into three types of patients through a behavioral characteristic classification model, including motivation formation type, resource demand type, and behavior change type; A dynamic risk assessment is performed on the patient population using a risk assessment tool, and an individualized intervention strategy for the patient population is output based on the result of the dynamic risk assessment.
2. The method according to claim 1, characterized in that The cardiovascular risk prediction model uses an electronic medical record database as a validation data set, imports the medical record data of the physical examinee through a medical information system, associates the incidental diagnosis of CVD determined in the validation data set with the medical record data, and fits two independent cause-specific Cox models according to the gender of the physical examinee to obtain a risk equation based on cardiovascular disease, with the predictor variables set as age interaction terms, Nelson-Aalen estimates of baseline cumulative risk of CVD, CVD outcome indicators, baseline cumulative risk, and non-CVD death outcome indicators.
3. The method according to claim 2, characterized in that The incidental CVD diagnoses included fatal or nonfatal myocardial infarction, ischemic heart disease, ischemic, hemorrhagic, unspecified stroke, and transient ischemic attack.
4. The method according to claim 1, characterized in that: The behavioral characteristic classification model is used to integrate behavioral attribute characteristics and perform conditional statistics based on the quantitative representation of the behavioral attribute characteristics, and divide the patient population into categories based on the statistical results. The behavioral attribute characteristics are obtained through the medical information system, and specifically include high, medium, and low three-dimensional quantitative representations of risk perception, perceived psychological stress, lifestyle health, cultural level, and communication enthusiasm.
5. The method according to claim 4, characterized in that The risk assessment tool is an externally validated performance measure used to estimate actual individual risk for behavioral attribute characteristics of the patient population, and to associate the review behavior of the patient population with the risk of incidental CVD diagnosis through a cause-specific risk regression model.
6. The method according to claim 1, characterized in that The dynamic risk assessment method is to identify patient groups with similar development trajectories through longitudinal data in medical record data.
7. The method according to claim 1, characterized in that The individualized intervention strategy is a behavioral guidance measure for the patient group with the same behavioral type identified after the dynamic risk assessment.
8. A cardiovascular health risk intervention system based on dynamic assessment, used to implement the method as claimed in claims 1-7, characterized in that: include: Risk prediction module, behavior feature classification module, dynamic risk assessment module; The risk prediction module is used to obtain the risk prediction results of the physical examination personnel through the cardiovascular risk assessment model; The behavior feature classification module is used to divide the medical examinees corresponding to the high risk predicted results into three types of reception groups through the behavior feature classification model; The dynamic risk assessment module is used to perform a dynamic risk assessment on the patient population through a risk assessment tool, and output an individualized intervention strategy for the patient population based on the result of the dynamic risk assessment.