CN110428901B - Cerebral apoplexy attack risk prediction system and application - Google Patents
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Abstract
The invention provides a stroke morbidity risk prediction system and application. In a first aspect, the present invention provides the use of a composition of reagent materials and/or instrumentation for taking the following risk factor information of an individual for the manufacture of a predictive system for assessing the risk of stroke onset in an individual: age, gender, smoking, blood pressure level and treatment, diabetes, residence, and fasting blood lipid measurements. The invention also provides a prediction system for evaluating the individual stroke morbidity risk, which is particularly suitable for Chinese adults, can accurately evaluate the individual stroke morbidity risk for 10 years and the lifetime, and can identify high-risk individuals.
Description
Technical Field
The invention relates to a stroke onset risk prediction system and application, in particular to application of a composition of a reagent material and/or an instrument device for taking individual risk factor information in preparing a prediction system for evaluating the stroke onset risk of an individual, and also relates to a prediction system for evaluating the stroke onset risk of the individual, which is particularly suitable for adults in China.
Background
Stroke is the current leading cause of death in china. According to the global disease burden research, the death caused by the stroke in China in 2013 is up to 192 ten thousand. The stroke risk prediction model can be used for identifying high-risk individuals in the crowd and provides an important basis for taking appropriate preventive intervention measures aiming at the high-risk individuals. Therefore, a stroke onset risk prediction model which is accurate in assessment, convenient and easy to use is developed, and the method has important significance for primary prevention of stroke in China. Although risk prediction models for atherosclerotic cardiovascular diseases, such as the PCE model in the united states, the SCORE model in europe, and the China atherosclerotic cardiovascular disease prediction (China-PAR) model, have been developed at home and abroad, there is no 10-year risk and lifetime risk prediction model for stroke development in chinese adults.
The internationally known model for predicting the risk of stroke is a stroke risk assessment model (FSRP) developed by Fuminghan research in the United states. Since then, prediction models, such as an ARIC stroke risk calculator, a QStroke model and the like, which can be used for evaluating 10-year stroke incidence risks have been published internationally one after the other. However, due to the differences between stroke risk factors and disease spectra among the Chinese and western populations, the model developed by the data of the foreign population is not suitable for being directly applied to the Chinese population. Therefore, there is a need to develop a 10-year risk prediction model suitable for the onset of stroke in adults in China. On the other hand, the 10-year risk of stroke onset is mainly influenced by age, and for young and middle-aged people < 50 years of age, the 10-year risk of stroke onset is generally low, but some people may still have a high lifetime risk (i.e., the cumulative risk of stroke onset during the period from the current age of the individual to the age of 85 years). Unlike the 10-year risk prediction of stroke, lifetime risk appears to increase significantly in middle-aged and young people as the level of cardiovascular risk factors increases. Therefore, developing a lifetime risk prediction model of stroke onset would be an important addition to a 10-year risk prediction model. At present, researches on age, sex, blood pressure level and the influence of diabetes on the life-long risk of stroke are evaluated at home and abroad, but the research results cannot provide specific risk prediction values of each person, namely, the research results cannot be used for individualized life-long risk evaluation of stroke attack.
Disclosure of Invention
One purpose of the invention is to establish a system and a method for predicting 10-year risk and lifetime risk of stroke of Chinese adults.
The inventor establishes a model suitable for predicting 10-year stroke incidence risks and lifetime risks of Chinese adults by using individual data of China-PAR queue research objects, can accurately evaluate the 10-year stroke incidence risks and lifetime incidence risks of individuals, can identify high-risk individuals, and provides a basis for further individual prevention of stroke incidence and individual health guidance and intervention.
In particular, in one aspect, the invention provides the use of a composition of reagent materials and/or instrumentation devices that take the following risk factor information of an individual for the manufacture of a predictive system for assessing the risk of stroke onset in an individual:
age, gender, waist circumference, whether to smoke, blood pressure level and treatment, whether to have diabetes, family history of stroke, residence, and fasting state blood lipid measurements.
The individual risk factor information may be taken using any available technique in the art, for example, a method of measurement, or a questionnaire or questionnaire format, etc. In the present invention, the composition of the reagent material and/or the instrument for collecting the individual risk factor information includes a combination of various reagent materials and/or instruments for detection and/or questionnaires for investigation used in collecting the individual risk factor information, and may include a virtual material and/or an instrument (for example, information related to acquisition by manual inquiry).
In another aspect, the present invention also provides a prediction system for evaluating the risk of stroke in an individual, which includes a data acquisition unit and a data analysis unit;
the data taking unit is used for taking individual risk factor condition data; specifically, the individual risk factors include age, gender, waist circumference, whether to smoke, blood pressure level and treatment, whether to suffer from diabetes, family history of stroke, residence, and fasting state blood lipid measurements;
the data analysis unit is used for analyzing and processing the data collected by the data collection unit to obtain a stroke onset risk score.
According to the embodiment of the invention, in the system for predicting the stroke onset risk and the related application, the individual is a Chinese adult.
According to a specific embodiment of the present invention, the system for predicting stroke onset risk and the related application of the present invention, the stroke onset risk is 10-year risk and/or lifetime risk.
According to a specific embodiment of the present invention, in the system for predicting the onset risk of stroke and the related application, the subject is male or female. Risk factors for male individuals include: age, post-treatment systolic pressure, untreated systolic pressure, total cholesterol, high density lipoprotein cholesterol, whether to smoke, whether to have diabetes, home in south or north, urban and rural, family history of stroke. Risk factors for female individuals include: age, post-treatment systolic pressure, untreated systolic pressure, total cholesterol, high density lipoprotein cholesterol, whether smoking, whether diabetic, southern or northern residence, urban or rural residence, waist circumference.
In the invention, the judgment of the south or the north of the residence is divided according to the traditional Chinese habit, and the Yangtze river is generally taken as the boundary.
According to the embodiment of the invention, in the system for predicting stroke onset risk and the related application thereof, the stroke onset risk score obtained by the data analysis unit is in accordance with the value obtained according to the following model formula 1 and/or formula 2:
1-S10 exp(IndX B-MeanX B)formula 1;
in formula 1, S10Baseline 10-year survival; MeanX' B, the product of the specific value of each variable and its parameter in the study populationThe average of the sums; IndX' B is the sum of the products of the specific values of the individual variables and the corresponding parameters (see Table 2);
in formula 2, F (a, t; Z) is the cumulative stroke risk of a subject with a covariate of Z in the period from age a to age t; beta Z0Researching the average value of the sum of the specific numerical values of all the variables of the population and the product of the parameters of the variables; beta Z is the sum of products of specific values of each variable of the object with the covariate Z and the corresponding parameter (see Table 3); f (a; Z)0) And F (t; z0) Mean level of covariates in the population Z0Age a and age t correspond to cumulative stroke incidence.
In some embodiments of the invention, the baseline 10-year survival rate S10Male is 0.9807, female is 0.9891.
In some embodiments of the invention, mean MeanX' B of the sum of the product of the specific value of each variable and its parameter in the study population is 159.84 for males and 98.79 for females.
In some embodiments of the invention, the average value β Z of the sum of the product of a particular value of a variable and its parameter in the study population is0The number of males is 17.51 and the number of females is 19.67.
According to the specific embodiment of the invention, in the system for predicting stroke onset risk and the related application, the score according to the numerical value obtained according to the formula 1 is the stroke onset risk score of 10 years, and the score according to the numerical value obtained according to the formula 2 is the stroke lifetime onset risk score.
According to the embodiment of the invention, in the system for predicting the stroke onset risk and the related application, the level of the stroke onset risk score reflects the level of the stroke onset risk of an individual.
In some embodiments of the invention, individuals with a 10-year risk score of stroke greater than or equal to 7.0% or a lifetime risk score of greater than or equal to 25.0% in adults in china are at "high risk" for stroke.
The system for predicting stroke onset risk of the invention may further comprise an individualized health guidance unit for giving individualized health guidance according to the risk score of the data analysis unit.
The system for predicting stroke onset risk of the present invention may be a virtual device as long as the functions of the data collection unit and the data analysis unit can be realized. The data collecting unit can comprise various detection reagent materials and/or detection instrument equipment and the like; the data analysis unit can be any operation instrument, module or virtual device which can analyze and process the information of the data acquisition unit to obtain the risk score, and can also be used for formulating corresponding data charts and the like by using various possible scoring results and corresponding risk grades and/or corresponding health guidance and the like in advance.
In some embodiments of the present invention, the present invention further provides an electronic device for assessing an individual's risk of stroke onset, comprising a first memory, a first processor, and a computer program stored on the first memory and executable on the first processor, wherein the first processor implements a scoring process comprising the following steps when executing the program:
receiving individual sex information and judging male and female;
if the male is judged, the following risk factor information of the individual is obtained: age, post-treatment systolic pressure, untreated systolic pressure, total cholesterol, high density lipoprotein cholesterol, whether to smoke, whether to have diabetes, home in the south or north, home in the city or country, family history of stroke;
calculating an individual 10-year risk and/or lifetime risk score based on the acquired individual risk factor information;
wherein, the process of calculating the individual 10-year risk score comprises the following steps:
calculating the sum IndX' B of the products of the specific numerical values of the following variables and the corresponding parameters: ln (age), age; ln (post-treatment systolic pressure), mmHg; ln (untreated systolic blood pressure), mmHg; ln (total cholesterol), mg/dL; ln (high density lipoprotein cholesterol), mg/dL; smoking (yes, no 0); diabetes (1 ═ yes, 0 ═ no); southern or northern of a residential area (1 is northern, 0 is southern); a residential city or country (1 ═ city, 0 ═ country); family history of stroke (yes 1, no 0); ln (age) × smoking; ln (age) × Ln (post-treatment systolic blood pressure); ln (age) × Ln (untreated systolic blood pressure); ln (age) × family history of stroke (1 yes, 0 no);
obtaining a specific numerical value of a formula 1 as an individual 10-year risk score based on the calculated IndX' B and in combination with the following model formula 1;
1-S10 exp(IndX B-MeanX B)formula 1;
in formula 1, S10Baseline 10-year survival; MeanX' B, the average value of the sum of the product of each variable specific value and the parameter of the study population; IndX' B is the sum of the products of the specific values of the individual variables and the corresponding parameters (see Table 2);
wherein, the process of calculating the lifetime risk score of the individual comprises the following steps:
calculating the sum beta Z of the products of the specific values of the variables of the object with the covariate Z and the corresponding parameters (see Table 3): ln (post-treatment systolic pressure), mmHg; ln (untreated systolic blood pressure), mmHg; ln (total cholesterol), mg/dL; ln (high density lipoprotein cholesterol), mg/dL; smoking (yes, no 0); diabetes (1 ═ yes, 0 ═ no); southern or northern of a residential area (1 is northern, 0 is southern); a residential city or country (1 ═ city, 0 ═ country); family history of stroke (yes 1, no 0);
based on the calculated beta Z and in combination with the following model formula 2, obtaining a specific numerical value of the formula 2 as an individual lifetime risk score;
in formula 2, F (a, t; Z) is the cumulative stroke risk of a subject with a covariate of Z in the period from age a to age t; beta Z0Researching the average value of the sum of the specific numerical values of all the variables of the population and the product of the parameters of the variables; beta Z is the sum of products of specific values of each variable of the object with the covariate Z and the corresponding parameter (see Table 3); f (a; Z)0) And F (t; z0) Mean level of covariates in the population Z0Cumulative onset of stroke corresponding to age a and age tAnd (4) rate.
In another aspect, the present invention further provides an electronic device for assessing an individual's risk of developing a stroke, including a first memory, a first processor, and a computer program stored in the first memory and executable on the first processor, wherein the first processor implements a scoring process including the following steps when executing the program:
receiving individual sex information and judging male and female;
if the individual is judged to be a woman, the following risk factor information of the individual is obtained: age, post-treatment systolic pressure, untreated systolic pressure, total cholesterol, high density lipoprotein cholesterol, whether smoking, whether diabetic, southern or northern residence, urban or rural residence, waist circumference;
calculating an individual 10-year risk and/or lifetime risk score based on the acquired individual risk factor information;
wherein, the process of calculating the individual 10-year risk score comprises the following steps:
calculating the sum IndX' B of the products of the specific numerical values of the following variables and the corresponding parameters: ln (age), age; ln (post-treatment systolic pressure), mmHg; ln (untreated systolic blood pressure), mmHg; ln (total cholesterol), mg/dL; ln (high density lipoprotein cholesterol), mg/dL; ln (waist circumference), cm; smoking (yes, no 0); diabetes (1 ═ yes, 0 ═ no); southern or northern of a residential area (1 is northern, 0 is southern); a residential city or country (1 ═ city, 0 ═ country); ln (age) × Ln (post-treatment systolic blood pressure); ln (age) × Ln (untreated systolic blood pressure); ln (age) × Ln (high density lipoprotein cholesterol);
obtaining a specific numerical value of a formula 1 as an individual 10-year risk score based on the calculated IndX' B and in combination with the following model formula 1;
1-S10 exp(IndX B-MeanX B)formula 1;
in formula 1, S10Baseline 10-year survival; MeanX' B, the average value of the sum of the product of each variable specific value and the parameter of the study population; IndX' B is the sum of the products of the specific values of the individual variables and the corresponding parameters (see Table 2);
wherein, the process of calculating the lifetime risk score of the individual comprises the following steps:
calculating the sum beta Z of the products of the specific values of the variables of the object with the covariate Z and the corresponding parameters (see Table 3): ln (post-treatment systolic pressure), mmHg; ln (untreated systolic blood pressure), mmHg; ln (total cholesterol), mg/dL; ln (high density lipoprotein cholesterol), mg/dL; ln (waist circumference), cm; smoking (yes, no 0); diabetes (1 ═ yes, 0 ═ no); southern or northern of a residential area (1 is northern, 0 is southern); a residential city or country (1 ═ city, 0 ═ country);
based on the calculated beta Z and in combination with the following model formula 2, obtaining a specific numerical value of the formula 2 as an individual lifetime risk score;
in formula 2, F (a, t; Z) is the cumulative stroke risk of a subject with a covariate of Z in the period from age a to age t; beta Z0Researching the average value of the sum of the specific numerical values of all the variables of the population and the product of the parameters of the variables; beta Z is the sum of products of specific values of each variable of the object with the covariate Z and the corresponding parameter (see Table 3); f (a; Z)0) And F (t; z0) Mean level of covariates in the population Z0Age a and age t correspond to cumulative stroke incidence.
According to a specific embodiment of the present invention, the electronic device for assessing an individual's risk of stroke onset of the present invention, wherein the first processor when executing the program further implements a process of giving personalized health guidance according to the individual's risk of stroke onset for 10 years and/or lifetime risk score.
The system for predicting stroke onset risk and the related application have good prediction effect through other external queue verification, and are suitable for predicting 10-year risk and lifetime risk of stroke onset of adults in China.
The system for predicting the stroke risk and the related application make up the defects of the Chinese past stroke risk prediction model. For example, unlike the conventional risk prediction model, the model innovatively incorporates waist circumference variables as a prediction index of female stroke onset risk, and suggests that more importance should be placed on prevention of central obesity in primary prevention of female stroke in China. In male predictive variables, the family history of stroke was first included as a predictor of risk of onset. In addition, the male and female models incorporate variables which can reflect the characteristics of stroke onset and risk factors in China: the residence is south or north, and the residence is urban and rural. These two variables may further enhance the predictive capabilities of the model.
The model has the following clinical significance: the model can quickly evaluate the 10-year risk and the lifetime risk of the individual cerebral apoplexy, and is applied to the primary prevention of the cerebral apoplexy. The first-level prevention guidelines for cardiovascular and cerebrovascular diseases at home and abroad point out that accurate 10-year risk assessment is an important basic stone for first-level prevention. In particular, a young person has a low 10-year risk of stroke onset even if the levels of multiple risk factors are elevated, but may be at high risk for life-long. Therefore, in the screening and prevention work of the high-risk group of stroke in China, 10-year risk and lifetime risk assessment of stroke attack need to be simultaneously considered. The model for predicting the 10-year and lifetime risks of stroke is researched and developed by using observation data obtained by long-term follow-up visits of Chinese people, and is suitable for identifying potential high-risk individuals with stroke in Chinese adults.
In conclusion, the model for predicting the 10-year risk and the lifetime risk of the stroke of the Chinese adult, developed by the invention, provides a basis for accurately identifying the high-risk individuals of the stroke of the Chinese adult, and adopting individual prevention and control as early as possible, and also provides an effective evaluation tool for the prevention and control of the cardiovascular and cerebrovascular diseases of the community.
Drawings
FIG. 1 is a calibration curve of 10-year risk models (A, male; B, female) and lifetime risk models (C, male; D, female) of stroke onset in ChinaMUCA (1992-1994) cohort. The abscissa is the predicted value of stroke risk and the ordinate is the observed value of stroke risk (risks are all expressed in percentage). Lifetime risk model validation to predict stroke onset risk representation for 15 years of accumulation.
Detailed Description
In order that the invention may be more clearly understood, it will now be further described with reference to the following examples. The examples are for illustration only and do not limit the invention in any way. The experimental methods in the examples, in which specific conditions are not noted, are conventional methods and conventional conditions well known in the art, or conditions as recommended by the manufacturer.
Examples
In the embodiment, individual data of China-PAR queue research objects are utilized to establish models suitable for predicting 10-year-old stroke risks and lifetime risks of Chinese adults, the models can accurately evaluate 10-year-old stroke risks and lifetime risks of one object, high-risk individuals can be identified, and a basis is provided for further individualized prevention and intervention.
People group
And developing and verifying a 10-year and lifetime risk prediction model of cerebral apoplexy by using individual data information of the China-PAR queue without cardiovascular diseases. In the China-PAR cohort, China cardiovascular epidemic multicenter cooperation research (China MUCA) (1998) and Asian cardiovascular disease international cooperation research (InterASIA) are combined together as a derivative cohort, and 21320 people are counted in total, and are used for constructing a 10-year risk and lifetime risk prediction model. In addition, the risk prediction model was validated using ChinaMUCA (1992-.
Baseline health data collection and determination of stroke onset
Using questionnaires, physical examinations and blood biochemical tests, the following baseline variable information was collected: age, gender, waist circumference, whether to smoke, blood pressure levels and treatment, whether to have diabetes, family history of stroke, southern or northern residence, urban and rural residence, and fasting blood lipid and blood glucose measurements. Diabetes is defined as having fasting blood glucose of greater than or equal to 126mg/dL or being treated with insulin or a hypoglycemic agent.
Stroke episodes are defined as: the neurological dysfunction is caused by rupture or blockage of cerebral vessels and has an acute onset and duration exceeding 24 hours. Including ischemic stroke, hemorrhagic stroke, and undetermined stroke, without transient ischemic attack. Death from diseases other than stroke is defined as a competitive event.
Baseline characterization of derived cohort population
See table 1 for baseline characteristics of the derivative cohort population.
TABLE 1 Baseline characteristics of derived cohort population
Establishment and application of 10-year-old stroke risk prediction model
A Cox proportional risk regression model is adopted to construct a 10-year risk prediction model of stroke in males and females respectively. The continuity variables are first logarithmically transformed before the model is built. Second, major cardiovascular risk factors including age, post-treatment or untreated systolic blood pressure, whether smoking is present, whether diabetes is present, and total cholesterol are directly entered into the model. After the model is added, variables with the integrated discriminant improvement index (IDI) of more than or equal to 6 percent are also included in the model. Finally, a 10-year risk prediction model of stroke onset is constructed for male and female, wherein the 10-year risk prediction model of male incorporates 14 variables (age, post-treatment systolic pressure, untreated systolic pressure, total cholesterol, high-density lipoprotein cholesterol, whether to smoke, whether to suffer from diabetes, southern or northern residence, urban and rural residence, family history of stroke, age x whether to smoke, age x post-treatment systolic pressure, age x untreated systolic pressure, age x family history of stroke); the 10-year risk prediction model for women included 13 variables, and compared to the model for men, two new variables (waist circumference, age × high density lipoprotein cholesterol) were included, while family history of stroke, age × smoking, age × family history of stroke were not included, and the other variables were the same as the model for men.
The 10-year risk prediction inclusion variables and their parameters for stroke onset in men and women are shown in table 2.
TABLE 2 variables and corresponding parameters required for a 10-year risk prediction model of stroke onset
Note: ln, natural logarithm conversion; N/A, the variable not included in the model; MeanX' B, the average of the sum of the product of the specific value of each variable and the parameter thereof in the population; s10Baseline 10-year survival.
If an adult knows the specific values of the variables such as age, treated or untreated systolic blood pressure level and the like of the adult, and multiplies the parameters corresponding to different variables in the table 2, the IndX 'B (namely the sum of the products of the specific values of the variables and the corresponding parameters of the adult) can be calculated, and the IndX' B is substituted into the following formula 1 to calculate the 10-year risk of the stroke:
1-S10 exp(IndX B-MeanX B)equation 1
Wherein S is10Baseline 10-year survival, 0.9807 for men and 0.9891 for women; MeanX' B is the average of the sum of the product of the specific value of each variable and the parameter of the study population, 159.84 for male and 98.79 for female (see Table 2); IndX' B is the sum of the products of the specific values of the variables of an individual and the corresponding parameters (see Table 2).
Establishment and application of stroke lifetime risk prediction model
And constructing a lifetime risk prediction model of stroke attack for male and female. Variables included in a 10-year risk prediction model of stroke onset are directly used for modeling stroke lifetime risk, but age is not used as a prediction variable but is used as a basic time function of the model. After the stroke cumulative morbidity function after correcting the competitive risk is determined, a sub-distribution risk algorithm for correcting the competitive risk is adopted to calculate the stroke cumulative morbidity risk of the individual from the current age to 85 years old, namely the lifetime risk of the stroke.
The inclusion variables and their parameters for life-long stroke risk prediction for men and women are shown in table 3.
TABLE 3 variables and corresponding parameters required by the model for predicting the lifetime risk of stroke
| Variables of | Male sex | Female with a view to preventing the formation of wrinkles | |
| Ln (post-treatment systolic pressure), mmHg | 3.88 | 2.75 | |
| Ln (untreated systolic blood pressure), mmHg | 3.83 | 2.69 | |
| Ln (Total Cholesterol), mg/dL | 0.30 | 0.11 | |
| Ln (high density lipoprotein cholesterol), mg/dL | -0.63 | -0.31 | |
| Ln (waist circumference, cm) | N/A | 1.71 | |
| Whether or not to smoke (1 is Yes, 0 is No) | 0.27 | 0.51 | |
| Whether or not to suffer from diabetes (1-yes, 0-no) | 0.21 | 0.41 | |
| Southern or northern residential area (1 ═ northern, 0 ═ southern) | 0.39 | 0.52 | |
| Residence city and countryside (1 ═ city, 0 ═ country) | -0.35 | -0.25 | |
| Family history of cerebral apoplexy (1 is yes, 0 is no) | 0.40 | N/A | |
| βZ0 | 17.51 | 19.67 |
Note: ln, natural logarithm conversion; N/A, the variable not included in the model; beta Z0And researching the average value of the sum of the specific numerical values of all the variables of the population and the product of the parameters.
The lifetime risk of stroke onset is calculated by the following formula:
where F (a, t; Z) is the cumulative risk of stroke onset for subjects with covariate Z over the period from age a (i.e., baseline age) to age t (i.e., cutoff age for lifetime risk calculation, 85 years old for this model). Beta Z0The mean value of the sum of the product of the specific value of each variable and its parameter in the study population was 17.51 for male and 19.67 for female. Betaz is the sum of the products of the specific values of the variables of the subject whose covariate is Z and their coefficients (see table 3). F (a; Z)0) And F (t; z0) Mean level of covariates in the population Z0Age a and age t correspond to cumulative stroke incidence.
Risk stratification based on 10-year risk and lifetime risk prediction model of stroke onset
The 10-year risk estimation value of stroke attack and the 90% quantile of the lifetime risk estimation value (the 10-year risk estimation value is 7.0%, and the lifetime risk estimation value is 25.0%) in the research population are used as critical values for judging the high attack risk of stroke, namely, individuals with the 10-year risk of stroke attack of more than or equal to 7.0% or the lifetime risk of more than or equal to 25.0% of stroke attack of Chinese adults are defined, and the individuals are at high risk of stroke.
Model accuracy verification
The accuracy of the 10-year risk of stroke and the prediction effect of a lifetime risk model is verified by using China MUCA (1992-1994) of another independent population as a verification queue. ChinaMUCA (1992-.
TABLE 4 ChinaMUCA (1992-1994) cohort study Baseline characteristics
The 10-year risk and lifetime risk model of stroke onset was applied to ChinaMUCA (1992-2And judging the prediction effect of the model. C statistic close to or greater than 0.8, degree of calibration χ2A prediction effect of the model is considered good, close to or less than 20.
Therefore, the 10-year risk model of stroke onset was verified by substituting the values of the individual variables of nearly 1.4 million persons in the ChinaMUCA (1992-2The results were 20.6(P ═ 0.014) and 13.7(P ═ 0.132), respectively, and the results were excellent.
The results of the verification of the lifetime risk model of stroke onset, namely substituting the individual variable values of nearly 1.4 million persons in the ChinaMUCA (1992-2Are respectively 9.9(P ═ P ═0.358), and 20.5(P ═ 0.015), the predicted effect was good.
The above 10-year risk and lifetime risk of stroke onset predicted effects in the ChinaMUCA (1992) -1994) cohort are summarized in Table 5.
TABLE 5 predictive efficacy of a model for predicting 10-year risk of stroke onset and lifetime risk in ChinaMUCA (1992-
95% CI, 95% confidence interval
Since it is impossible to observe stroke onset when the population lives all the way to age 85, the lifetime risk prediction model was validated to predict stroke onset risk for 15 years of cumulative life.
In addition, a "calibration curve" is drawn, and the predicted values and the actual observed values of the 10-year risk and lifetime risk models of stroke onset are closely distributed around the 45-degree diagonal of fig. 1, which shows that the predicted values and the observed values have better consistency (see fig. 1).
Examples are 1: a45 year old male, currently untreated with hypotensive drugs and having a systolic blood pressure of 140mmHg, a total cholesterol of 240mg/dL, a high density lipoprotein cholesterol of 40mg/dL, smoking, not suffering from diabetes, residing in northern cities in China, having a family history of stroke, was calculated as "IndX' B":
Ln(45)×35.58+Ln(140)×29.49+Ln(240)×0.29–Ln(40)×0.64+1×4.72+0×0.30+1×0.32–1×0.38+1×7.56–Ln(45)×1×1.10–Ln(45)×Ln(140)×6.40–Ln(45)×1×1.84＝161.09
substituting equation 1, the risk of stroke in the future 10 years for this male is:
1-0.9807exp(161.09-159.84)＝6.6％
the risk of stroke in the male in the next 10 years can be assessed as "low risk".
For example, 2: a45 year old female, currently untreated with hypotensive drugs and having a systolic blood pressure of 140mmHg, a total cholesterol of 240mg/dL, a high density lipoprotein cholesterol of 40mg/dL, a waist circumference of 90cm, no smoking, suffering from diabetes, living in northern cities in China, has an "IndX' B" of:
Ln(45)×19.97+Ln(140)×25.06+Ln(240)×0.16-Ln(40)×11.35+Ln(90)×1.60+0×0.51+1×0.50+1×0.50–1×0.23-Ln(45)×Ln(140)×5.59+Ln(45)×Ln(40)×2.75＝100.27
substituting formula 1, the risk of stroke in the next 10 years for this woman is:
1-0.9891exp(100.27-98.79)＝4.7％
the risk of stroke in this woman in the next 10 years can be assessed as "low risk".
For example, 3: a45 year old male, currently untreated with hypotensive drugs and having a systolic blood pressure of 140mmHg, a total cholesterol of 240mg/dL, a high density lipoprotein cholesterol of 40mg/dL, smoking, not suffering from diabetes, residing in northern cities in China, having a family history of stroke, has a "β Z" calculated as:
Ln(140)×3.83+Ln(240)×0.30–Ln(40)×0.63+1×0.27+0×0.21+1×0.39–1×0.35+1×0.40＝18.91
f (45; Z) in males aged 45 and 850) And F (85; z0) 5.607X 10 respectively-3And 1.081X 10-1。
By substituting equation 2, the risk of stroke in the male for life-long (up to 85 years) is:
the male may be assessed as "at risk" for stroke lifelong onset (up to 85 years of age).
For example, 4: a45 year old female, currently not receiving antihypertensive medication, has a systolic blood pressure of 140mmHg, total cholesterol of 240mg/dL, high density lipoprotein cholesterol of 40mg/dL, waist circumference of 90cm, does not smoke, suffers from diabetes, lives in northern cities in China, and has a beta Z of:
Ln(140)×2.69+Ln(240)×0.11–Ln(40)×0.31+Ln(90)×1.71+0×0.51+1×0.41+1×0.52–1×0.25＝21.15
f (45; Z) for women aged 45 and 85 years0) And F (85; z0) 1.684X 10 respectively-3And 7.720 × 10-2。
Substituting equation 2, the risk of stroke in the female (up to 85 years) for life-long is:
the woman may be assessed as "at high risk" for life-long stroke onset (up to 85 years of age).
Claims (12)
1. A method of assessing the risk of stroke onset in an individual, comprising:
evaluating the stroke onset risk of an individual comprises data acquisition and data analysis;
the data acquisition is to adopt individual risk factor information; wherein the subject is male or female; risk factors for male individuals include: age, post-treatment systolic pressure, untreated systolic pressure, total cholesterol, high density lipoprotein cholesterol, whether to smoke, whether to have diabetes, family history of residence in the south or north of the residence, urban and rural areas, cerebral apoplexy; risk factors for female individuals include: age, post-treatment systolic pressure, untreated systolic pressure, total cholesterol, high density lipoprotein cholesterol, whether to smoke, whether to have diabetes, in the south or north of residence, urban and rural, waist circumference;
the data analysis is to analyze and process the collected information to obtain a stroke onset risk score; the obtained stroke onset risk score is in accordance with a numerical value obtained according to the following model formula 1 and/or formula 2:
the process of calculating an individual 10-year risk score includes:
the process of calculating a lifetime risk score for an individual includes:
in the formula 1, the first and second groups of the compound,S 10baseline 10-year survival;MeanX'Bresearching the average value of the sum of the specific numerical values of all the variables of the population and the product of the parameters of the variables;IndX'Bthe sum of products of specific numerical values of each variable of the individual and corresponding parameters; wherein, the variables are: ln (age), age; ln (post-treatment systolic pressure), mmHg; ln (untreated systolic blood pressure), mmHg; ln (total cholesterol), mg/dL; ln (high density lipoprotein cholesterol), mg/dL; smoking (1 = yes, 0= no); diabetes (1 = yes, 0= no); southern or northern of the residence (1 = northern, 0= southern); residential city or country (1 = city, 0= country); family history of stroke (1 = yes, 0= no); ln (age) × smoking; ln (age) × Ln (post-treatment systolic blood pressure); ln (age) × Ln (untreated systolic blood pressure); ln (age) × family history of stroke (1 = yes, 0= no); ln (waist circumference), cm; ln (age) × Ln (high density lipoprotein cholesterol);
in the formula 2, the first and second groups of the compound,F(a，t; Z) Is a covariate ofZIs at age of the subjectaTo agetCumulative risk of stroke onset over the course of time;βZ 0researching the average value of the sum of the specific numerical values of all the variables of the population and the product of the parameters of the variables;βZas a covariate ofZThe sum of the product of each variable specific value of the object and the corresponding parameter;F(a; Z 0) And F(t; Z 0) The covariates are at the average level of the populationZ 0Time, ageaAnd agetThe cumulative incidence of stroke respectively; wherein, the variables are: ln (post-treatment systolic pressure)) mmHg; ln (untreated systolic blood pressure), mmHg; ln (total cholesterol), mg/dL; ln (high density lipoprotein cholesterol), mg/dL; smoking (1 = yes, 0= no); diabetes (1 = yes, 0= no); southern or northern of the residence (1 = northern, 0= southern); residential city or country (1 = city, 0= country); family history of stroke (1 = yes, 0= no); ln (waist circumference), cm.
2. The method of claim 1, wherein,
the variables and corresponding parameters in equation 1 are as follows:
Ln, natural logarithm conversion; N/A, the variable not included in the model;
the variables and corresponding parameters in equation 2 are as follows:
Ln, natural logarithm conversion; N/A, the variable is not included in the model.
3. A prediction system for assessing the risk of stroke onset in an individual, comprising a data extraction unit and a data analysis unit;
the data collecting unit is used for collecting individual risk factor information; wherein the subject is male or female; risk factors for male individuals include: age, post-treatment systolic pressure, untreated systolic pressure, total cholesterol, high density lipoprotein cholesterol, whether to smoke, whether to have diabetes, family history of residence in the south or north of the residence, urban and rural areas, cerebral apoplexy; risk factors for female individuals include: age, post-treatment systolic pressure, untreated systolic pressure, total cholesterol, high density lipoprotein cholesterol, whether to smoke, whether to have diabetes, in the south or north of residence, urban and rural, waist circumference;
the data analysis unit is used for analyzing and processing the information collected by the data collection unit to obtain a stroke risk score;
the stroke onset risk score obtained by the data analysis unit accords with a numerical value obtained according to the following model formula 1 and/or formula 2:
the process of calculating an individual 10-year risk score includes:
the process of calculating a lifetime risk score for an individual includes:
in the formula 1, the first and second groups of the compound,S 10baseline 10-year survival;MeanX'Bresearching the average value of the sum of the specific numerical values of all the variables of the population and the product of the parameters of the variables;IndX'Bthe sum of products of specific numerical values of each variable of the individual and corresponding parameters; wherein, the variables are: ln (age), age; ln (post-treatment systolic pressure), mmHg; ln (untreated systolic blood pressure), mmHg; ln (total cholesterol), mg/dL; ln (high density lipoprotein cholesterol), mg/dL; smoking (1 = yes, 0= no); diabetes (1 = yes, 0= no); southern or northern of the residence (1 = northern, 0= southern); residential city or country (1 = city, 0= country); family history of stroke (1 = yes, 0= no); ln (age) × smoking; ln (age) × Ln (post-treatment systolic blood pressure); ln (age) × Ln (untreated systolic blood pressure); ln (age) × family history of stroke (1 = yes, 0= no); ln (waist circumference), cm; ln (age) × Ln (high density lipoprotein cholesterol);
in the formula 2, the first and second groups of the compound,F(a，t; Z) Is a covariate ofZIs at age of the subjectaTo agetCumulative risk of stroke onset over the course of time;βZ 0researching the average value of the sum of the specific numerical values of all the variables of the population and the product of the parameters of the variables;βZas a covariate ofZThe sum of the product of each variable specific value of the object and the corresponding parameter;F(a; Z 0) And F(t; Z 0) The covariates are at the average level of the populationZ 0Time, ageaAnd agetThe cumulative incidence of stroke respectively; wherein, the variables are: ln (post-treatment systolic pressure), mmHg; ln (untreated systolic blood pressure), mmHg; ln (total cholesterol), mg/dL; ln (high density lipoprotein cholesterol), mg/dL; smoking (1 = yes, 0= no); diabetes (1 = yes, 0= no); southern or northern of the residence (1 = northern, 0= southern); residential city or country (1 = city, 0= country); family history of stroke (1 = yes, 0= no); ln (waist circumference), cm.
4. The prediction system of claim 3,
the variables and corresponding parameters in equation 1 are as follows:
Ln, natural logarithm conversion; N/A, the variable not included in the model;
the variables and corresponding parameters in equation 2 are as follows:
Ln, natural logarithm conversion; N/A, the variable is not included in the model.
5. The prediction system of claim 3, wherein the level of the stroke onset risk score reflects the level of stroke onset risk of the individual.
6. The prediction system according to any one of claims 3 to 5, wherein individuals with a 10-year risk score of stroke of 7.0% or more or a lifetime risk score of 25.0% or more in adults in China are at "high risk" for stroke.
7. An electronic device for assessing an individual's risk of stroke onset comprising a first memory, a first processor and a computer program stored on the first memory and executable on the first processor, the first processor when executing the program implementing a scoring process comprising the steps of:
receiving individual sex information and judging male and female;
if the male is judged, the following risk factor information of the individual is obtained: age, post-treatment systolic pressure, untreated systolic pressure, total cholesterol, high density lipoprotein cholesterol, whether to smoke, whether to have diabetes, home in the south or north, home in the city or country, family history of stroke;
calculating an individual 10-year risk and/or lifetime risk score based on the acquired individual risk factor information;
wherein, the process of calculating the individual 10-year risk score comprises the following steps:
calculating the sum of products of specific numerical values of the following variables and corresponding parametersIndX'B: ln (age), age; ln (post-treatment systolic pressure), mmHg; ln (untreated systolic blood pressure), mmHg; ln (total cholesterol), mg/dL; ln (high density lipoprotein cholesterol), mg/dL; smoking (1 = yes, 0= no); diabetes (1 = yes, 0= no); southern or northern of the residence (1 = northern, 0= southern); residential city or country (1 = city, 0= country); family history of stroke (1 = yes, 0= no)(ii) a Ln (age) × smoking; ln (age) × Ln (post-treatment systolic blood pressure); ln (age) × Ln (untreated systolic blood pressure); ln (age) × family history of stroke (1 = yes, 0= no);
based on calculated resultsIndX'BAnd combining the following model formula 1 to obtain a specific numerical value of the formula 1 as an individual 10-year risk score;
in the formula 1, the first and second groups of the compound,S 10baseline 10-year survival;MeanX'Bresearching the average value of the sum of the specific numerical values of all the variables of the population and the product of the parameters of the variables;IndX'Bthe sum of products of specific numerical values of each variable of the individual and corresponding parameters;
wherein, the process of calculating the lifetime risk score of the individual comprises the following steps:
calculating covariate asZThe sum of the product of each specific value of each variable of the object and the corresponding parameterβZThe variables are: ln (post-treatment systolic pressure), mmHg; ln (untreated systolic blood pressure), mmHg; ln (total cholesterol), mg/dL; ln (high density lipoprotein cholesterol), mg/dL; smoking (1 = yes, 0= no); diabetes (1 = yes, 0= no); southern or northern of the residence (1 = northern, 0= southern); residential city or country (1 = city, 0= country); family history of stroke (1 = yes, 0= no);
based on calculated resultsβZAnd combining the following model formula 2 to obtain a specific numerical value of the formula 2 as an individual lifetime risk score;
in the formula 2, the first and second groups of the compound,F(a，t; Z) Is a covariate ofZIs at age of the subjectaTo agetCumulative risk of stroke onset over the course of time;βZ 0researching the average value of the sum of the specific numerical values of all the variables of the population and the product of the parameters of the variables;βZas a covariate ofZThe sum of the product of each variable specific value of the object and the corresponding parameter;F(a; Z 0) And F(t; Z 0) The covariates are at the average level of the populationZ 0Time, ageaAnd agetThe corresponding cumulative incidence of stroke.
8. The electronic device for assessing an individual's risk of stroke onset as recited in claim 7, wherein:
the variables and corresponding parameters in equation 1 are as follows:
Ln, natural logarithm conversion;
the variables and corresponding parameters in equation 2 are as follows:
Ln, natural log transform.
9. The electronic device for assessing the risk of an individual having a stroke onset as claimed in claim 7 or 8, wherein said first processor when executing said program further implements a process for providing individualized health guidance based on the individual's 10-year risk of stroke onset and/or lifetime risk score.
10. An electronic device for assessing an individual's risk of stroke onset comprising a first memory, a first processor and a computer program stored on the first memory and executable on the first processor, the first processor when executing the program implementing a scoring process comprising the steps of:
receiving individual sex information and judging male and female;
if the individual is judged to be a woman, the following risk factor information of the individual is obtained: age, post-treatment systolic pressure, untreated systolic pressure, total cholesterol, high density lipoprotein cholesterol, whether smoking, whether diabetic, southern or northern residence, urban or rural residence, waist circumference;
calculating an individual 10-year risk and/or lifetime risk score based on the acquired individual risk factor information;
wherein, the process of calculating the individual 10-year risk score comprises the following steps:
calculating the sum of products of specific numerical values of the following variables and corresponding parametersIndX'B: ln (age), age; ln (post-treatment systolic pressure), mmHg; ln (untreated systolic blood pressure), mmHg; ln (total cholesterol), mg/dL; ln (high density lipoprotein cholesterol), mg/dL; ln (waist circumference), cm; smoking (1 = yes, 0= no); diabetes (1 = yes, 0= no); southern or northern of the residence (1 = northern, 0= southern); residential city or country (1 = city, 0= country); ln (age) × Ln (post-treatment systolic blood pressure); ln (age) × Ln (untreated systolic blood pressure); ln (age) × Ln (high density lipoprotein cholesterol);
based on calculated resultsIndX'BAnd combining the following model formula 1 to obtain a specific numerical value of the formula 1 as an individual 10-year risk score;
in the formula 1, the first and second groups of the compound,S 10baseline 10-year survival;MeanX'Bresearching the average value of the sum of the specific numerical values of all the variables of the population and the product of the parameters of the variables;IndX'Bthe sum of products of specific numerical values of each variable of the individual and corresponding parameters;
wherein, the process of calculating the lifetime risk score of the individual comprises the following steps:
calculating covariate asZThe sum of the product of each specific value of each variable of the object and the corresponding parameterβZThe variables are: ln (post-treatment systolic pressure), mmHg; ln (untreated systolic blood pressure), mmHg; ln (total cholesterol), mg/dL; ln (high density lipoprotein cholesterol), mg/dL; ln (waist circumference), cm; smoking (1 = yes, 0= no); diabetes (1 = yes, 0= no); southern or northern of the residence (1 = northern, 0= southern); residential city or country (1 = city, 0= country);
based on calculated resultsToβZAnd combining the following model formula 2 to obtain a specific numerical value of the formula 2 as an individual lifetime risk score;
in the formula 2, the first and second groups of the compound,F(a，t; Z) Is a covariate ofZIs at age of the subjectaTo agetCumulative risk of stroke onset over the course of time;βZ 0researching the average value of the sum of the specific numerical values of all the variables of the population and the product of the parameters of the variables;βZas a covariate ofZThe sum of the product of each variable specific value of the object and the corresponding parameter;F(a; Z 0) And F(t; Z 0) The covariates are at the average level of the populationZ 0Time, ageaAnd agetThe corresponding cumulative incidence of stroke.
11. The electronic device for assessing an individual's risk of stroke onset as recited in claim 10, wherein:
the variables and corresponding parameters in equation 1 are as follows:
Ln, natural logarithm conversion;
the variables and corresponding parameters in equation 2 are as follows:
Ln, natural log transform.
12. The electronic device for assessing the risk of an individual having a stroke onset as claimed in claim 10 or 11, wherein said first processor when executing said program further implements a process for providing individualized health guidance based on the individual's 10-year risk of stroke onset and/or lifetime risk score.
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