A method and system for predicting the risk of developing essential hypertension

Abstract

The application relates to the technical field of heart disease diagnosis, and discloses an intelligent hypertension disease prediction method, in particular to a primary hypertension disease risk prediction method and system. A prediction model is used to fuse various evaluation data, indexes of primary hypertension are fused, and the corresponding points of the indexes are calculated in the form of nomograms, and finally, a risk value is obtained through linear calculation. Based on the risk value, the prognosis risk is judged and guided. The technical scheme provided in the application embodiment solves the defect that the primary hypertension disease risk cannot be directly evaluated and predicted in the prior art.

Description

Technical Field

[0001] This application relates to the field of cardiac disease diagnostic technology, specifically an intelligent method for predicting the incidence of hypertension, and more specifically, a method and system for predicting the risk of developing primary hypertension. Background Technology

[0002] Hypertension is one of the most serious health problems worldwide, with its prevalence consistently increasing year by year. Simultaneously, due to its low treatment and control rates, and the frequent occurrence of multiple complications, its mortality rate is also rising. Statistics show that in 2019, hypertension accounted for approximately one-fifth of all deaths globally (about 11 million). Essential hypertension (EH) is regulated by the renin-angiotensin-aldosterone system (RAAS), the central nervous system, the sympathetic nervous system, and immune inflammation. Among these, inflammation is considered one of the important pathophysiological mechanisms of hypertension. When leukocytes are recruited and activated, vascular endothelium is damaged, vascular function is disrupted, and blood pressure rises. At the same time, long-term local inflammatory infiltration leads to the release of large amounts of cytokines, matrix metalloproteinases, and reactive oxygen species by immune cells, mediating damage to hypertensive target organs.

[0003] Furthermore, the current diagnosis of essential hypertension lacks a priori accuracy; it can only be determined through direct indicators, which leads to a lack of diagnostic information and inaccurate prediction results. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this application provides a predictive model that integrates multiple evaluation indicators. The model integrates the incidence indicators of primary hypertension and calculates the corresponding scores of the indicators using a nomogram. The final risk value is obtained through linear calculation, and treatment guidance is provided based on the risk value.

[0005] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0006] In a first aspect, a method for predicting the risk of developing primary hypertension is provided. The method includes: acquiring platelet data, fibrinogen data, white blood cell data, age data, weight data, height data, and prothrombin time data of a patient to be diagnosed; constructing a coagulation inflammation index based on the platelet data, fibrinogen data, and white blood cell data, and constructing a body mass index based on the weight data and height data; inputting the fibrinogen data, prothrombin time data, body mass index, coagulation inflammation index, and age data into a prediction model constructed from clinical data to calculate a risk value; the prediction model is a nomogram model.

[0007] Furthermore, the coagulation inflammation index is constructed based on the following formula: SCI = PLT * Fib / WBC, where PLT is platelet data, Fib is fibrinogen data, and WBC is white blood cell data.

[0008] Furthermore, the step of inputting the fibrinogen data, the prothrombin time data, the body mass index, the coagulation inflammation index, and the age data into the predictive model constructed from clinical data to calculate the risk value includes: inputting the fibrinogen data, prothrombin time data, the body mass index, the coagulation inflammation index, and the age data into the predictive model to obtain corresponding sub-scores, obtaining a total score based on multiple sub-scores, and obtaining the corresponding risk value based on the total score.

[0009] Furthermore, the step of obtaining the total score based on multiple sub-scores includes: comparing the fibrinogen data of the patient to be diagnosed with a first score distribution interval to obtain a first score, and performing a linear comparison based on the first score through a preset score distribution to obtain a first score value; comparing the prothrombin time data with a second score distribution interval to obtain a second score, and performing a linear comparison based on the second score through a preset score distribution to obtain a second score value; comparing the body mass index with a third score distribution interval to obtain a third score, and performing a linear comparison based on the third score through a preset score distribution to obtain a third score value; comparing the coagulation inflammation index with a fourth score distribution interval to obtain a fourth score, and performing a linear comparison based on the fourth score through a preset score distribution to obtain a fourth score value; comparing the age data with a fifth score distribution interval to obtain a fifth score, and performing a linear comparison based on the fifth score through a preset score distribution to obtain a fifth score value; and summing the first score value, the second score value, the third score value, the fourth score value, and the fifth score value to obtain the total score.

[0010] Furthermore, the first score distribution interval includes three sub-intervals and two corresponding first scores. Comparing the fibrinogen data of the patient to be diagnosed with the first score distribution interval to obtain the first score includes: determining the distribution of the fibrinogen data within the three sub-intervals and determining the corresponding first score based on its distribution; the second score distribution interval includes two sub-intervals and two corresponding second scores. Comparing the prothrombin time data with the second score distribution interval to obtain the second score includes: determining the distribution of the prothrombin time data within the two sub-intervals and determining the corresponding second score based on its distribution; the third score distribution interval includes three sub-intervals and three corresponding third scores. Comparing the body mass index with the first score distribution interval... The third score is obtained by comparing the three sub-intervals of the body mass index, including: determining the distribution of the body mass index in the three sub-intervals and determining the corresponding third score based on the distribution; the fourth score distribution interval includes two sub-intervals and two corresponding fourth scores, and the fourth score is obtained by comparing the coagulation inflammation index with the fourth score distribution interval, including: determining the distribution of the coagulation inflammation index in the two sub-intervals and determining the corresponding fourth score based on the distribution; the fifth score distribution interval includes three sub-intervals and three corresponding fifth scores, and the fifth score is obtained by comparing the age data with the fifth score distribution interval, including: determining the distribution of the age data in the three sub-intervals and determining the corresponding fifth score based on the distribution.

[0011] Secondly, a risk prediction system for primary hypertension is provided. The system includes: a data acquisition module for acquiring platelet data, fibrinogen data, white blood cell data, age data, weight data, height data, and prothrombin time data of a patient to be diagnosed; an index calculation module for constructing a coagulation inflammation index based on the platelet data, fibrinogen data, and white blood cell data, and constructing a body mass index based on the weight data and height data; and a risk prediction module for inputting the fibrinogen data, prothrombin time data, body mass index, coagulation inflammation index, and age data into a prediction model constructed from clinical data to calculate a risk value; the prediction model is a nomogram model.

[0012] Furthermore, the risk prediction module includes a sub-score calculation unit, a total score calculation unit, and a risk value calculation unit; the sub-score calculation unit is used to determine multiple sub-scores corresponding to multiple sub-data, the total score calculation unit sums the multiple sub-scores to obtain a total score, and the risk value calculation unit obtains the final risk value based on the total score.

[0013] Furthermore, the sub-score calculation unit includes a first score calculation unit, a second score calculation unit, a third score calculation unit, a fourth score calculation unit, and a fifth score calculation unit.

[0014] Furthermore, the first score calculation unit is used to compare the fibrinogen data of the patient to be diagnosed with a first score distribution interval to obtain a first score, and to perform a linear comparison based on the first score through a preset score distribution to obtain a second score value; the second score calculation unit is used to compare the prothrombin time data with a second score distribution interval to obtain a second score, and to perform a linear comparison based on the second score through a preset score distribution to obtain a second score value; the third score calculation unit is used to compare the body mass index with a third score distribution interval to obtain a third score, and to perform a linear comparison based on the third score through a preset score distribution to obtain a third score value; the fourth score calculation unit is used to compare the coagulation inflammation index with a fourth score distribution interval to obtain a fourth score, and to perform a linear comparison based on the fourth score through a preset score distribution to obtain a fourth score value; the fifth score calculation unit is used to compare the age data with a fifth score distribution interval to obtain a fifth score, and to perform a linear comparison based on the fifth score through a preset score distribution to obtain a fifth score value.

[0015] Furthermore, the total score calculation unit sums the scores corresponding to the first score calculation unit, the second score calculation unit, the third score calculation unit, the fourth score calculation unit, and the fifth score calculation unit to obtain the total score.

[0016] Thirdly, a terminal device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in any of the preceding claims.

[0017] Fourthly, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program that, when executed by a processor, implements the method as described in any of the preceding claims.

[0018] The technical solution provided in this application embodiment integrates multiple evaluation data into a predictive model. It fuses primary hypertension indicators and calculates the corresponding scores using a nomogram. A final risk value is obtained through linear calculation, and prognostic risk assessment and guidance are based on this risk value. The technical solution provided in this application embodiment overcomes the deficiency in existing technologies that cannot directly assess and predict the risk of developing primary hypertension. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] The methods, systems, and / or procedures shown in the accompanying drawings will be further described with reference to exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments, wherein example figures represent similar mechanisms in the various views of the drawings.

[0021] Figure 1 This is a schematic diagram of the process for predicting the risk of developing primary hypertension provided in the embodiments of this application.

[0022] Figure 2 This is a schematic diagram of the line graph model provided in the embodiments of this application.

[0023] Figure 3 This is a schematic diagram of the structure of the primary hypertension risk prediction system provided in the embodiments of this application.

[0024] Figure 4 This is a schematic diagram of the terminal device structure provided in the embodiments of this application. Detailed Implementation

[0025] To better understand the above technical solutions, the technical solutions of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of this application and the specific features in the embodiments are detailed descriptions of the technical solutions of this application, rather than limitations on the technical solutions of this application. In the absence of conflict, the embodiments of this application and the technical features in the embodiments can be combined with each other.

[0026] In the detailed description below, numerous specific details are illustrated with examples to provide a comprehensive understanding of the relevant guidance. However, it will be apparent to those skilled in the art that this application can be practiced without these details. In other instances, well-known methods, procedures, systems, components, and / or circuits have been described at a relatively high level without detail to avoid unnecessarily obscuring aspects of this application.

[0027] This application uses flowcharts to illustrate the execution process performed by a system according to embodiments of this application. It should be clearly understood that the execution processes in the flowcharts may not be executed sequentially. Instead, these execution processes may be executed in reverse order or simultaneously. Additionally, at least one other execution process may be added to the flowchart. One or more execution processes may be deleted from the flowchart.

[0028] Before providing a further detailed description of the embodiments of the present invention, the nouns and terms involved in the embodiments of the present invention will be explained, and the nouns and terms involved in the embodiments of the present invention shall be interpreted as follows.

[0029] (1) In response to, used to indicate the conditions or states on which the operation is performed depends. When the conditions or states on which the operation is performed are met, one or more operations may be performed in real time or with a set delay. Unless otherwise specified, there is no restriction on the order in which the multiple operations are performed.

[0030] (2) Based on, used to indicate the conditions or states on which the operation is performed depends. When the conditions or states on which it depends are met, one or more operations can be performed in real time or with a set delay. Unless otherwise specified, there is no restriction on the order of execution of the multiple operations.

[0031] This application embodiment uses a novel verification index, through the combined effect of multiple indicators, to achieve prediction of the risk of developing primary hypertension.

[0032] In this embodiment of the application, the nomogram model has the particularity of visualization, which can intuitively display the results of the Logistic regression model and perform individualized predictions.

[0033] See Figure 1 In this application embodiment, the method for predicting the risk of developing primary hypertension includes the following steps:

[0034] Step S110. Obtain platelet data, fibrinogen data, white blood cell data, age data, weight data, height data, and prothrombin time data of the patient to be diagnosed.

[0035] In this embodiment, the acquisition of the above data can be directly determined through the electronic medical record of the patient to be diagnosed. The method for acquiring data based on the electronic medical record can be any existing electronic medical record processing method, and will not be elaborated further in this embodiment. The acquisition time of the above data refers to the data collected most recently since the prediction was initiated.

[0036] Step S120. Construct a coagulation inflammation index based on the platelet data, the fibrinogen data, and the white blood cell data, and construct a body mass index based on the weight data and the height data.

[0037] In this embodiment of the application, in order to determine the potential risk of the disease, it is necessary to find pathogenic indicators that are highly correlated with essential hypertension from a large amount of clinical data. Among them, the coagulation inflammation index is constructed based on the following formula: SCI = PLT * Fib / WBC, where PLT is platelet data, Fib is fibrinogen data, and WBC is white blood cell data.

[0038] The evaluation and analysis of highly correlated pathogenic indicators involved randomly selecting inpatients hospitalized in the Department of Internal Medicine III of the First Affiliated Hospital of Xinjiang Medical University from June 2021 to June 2023. Ultimately, 1211 patients with acute hypertension (EH) were included as the case group, and 1272 non-EH patients were included as the control group. EH was identified from the discharge diagnosis obtained from the patients' electronic medical records, and any new or existing EH diagnosis was included in the EH patient group. The inclusion criteria for the EH group were based on the "Guidelines for the Prevention and Treatment of Hypertension in China (2018 Revised Edition)": three consecutive blood pressure measurements taken on different days with systolic pressure (SBP) ≥140 mmHg and / or diastolic pressure (DBP) ≥90 mmHg, or prior use of antihypertensive medication. The control group participants also underwent relevant laboratory tests to confirm that they were not diagnosed with EH. Exclusion criteria: secondary hypertension, severe hematologic, cardiac, renal and hepatobiliary diseases, thyroid diseases, malignant tumors, etc.; acute and chronic infectious diseases such as hepatitis B and tuberculosis; recent use of drugs that affect coagulation function, or taking antifibrinolytic and anticoagulant drugs.

[0039] General information of the study subjects was collected through medical history taking, including age, smoking, alcohol consumption, and history of hypertension. Height and weight were measured, and body mass index (BMI) was calculated. Laboratory tests were performed on subjects after fasting for at least 8 hours, with fasting venous blood drawn in the morning. These tests included white blood cell (WBC), neutrophil count (NE), red blood cell (RBC), hemoglobin (HGB), platelet (PLT), prothrombin time (PT), activated partial thromboplastin time (APTT), and fibrinogen (FIB). All measurements were performed at the Department of Laboratory Medicine, First Affiliated Hospital of Xinjiang Medical University, using a fully automated biochemical analyzer (Oliympuse 1000 / 2700 model, Japan). Calculate the systemic coagulation inflammation index (SCI): (PLT×FIB) / WBC (PLT and WBC are both in units of 109 g / L, and FIB is in units of g / L).

[0040] Data were analyzed using SPSS 26.0 and R 4.3.2 software. First, SPSS 26.0 was used to test the normality and homogeneity of variance of the continuous data. Normally distributed data were expressed as mean ± standard deviation (x ± s), and t-tests were used for comparisons between two groups. Non-normally distributed data were expressed as median M (P25, P75), and U-tests were used for comparisons between two groups. Count data were expressed as frequency (percentage), and Pearson chi-square tests were used for comparisons between groups. The "car+rms+qROC+dplyr" package in R 4.3.2 was used to construct a nomogram of the predictive model, plot the receiver operating characteristic (ROC) curve, calculate the area under the curve (AUC) to assess the model's predictive ability, and plot a calibration curve to evaluate the model's discrimination and calibration. Spearman correlation analysis was performed on each indicator, and Origin 2022 was used to create correlation cluster plots for data visualization. A p-value < 0.05 was considered statistically significant.

[0041] Referring to Table 1 (baseline data of non-EH group and EH group), there were statistically significant differences between the EH group and the non-EH group in terms of age, ethnicity, BMI, HGB, PT, FIB, and (PLT×FIB) / WBC (P<0.05). The baseline FIB and SCI index of the EH group were higher than those of the non-EH group.

[0042] Table 1. Baseline data for non-EH and EH groups (n=2483)

[0043]

[0044]

[0045] Normally distributed measurement data are expressed as mean ± standard deviation (x ± s), while non-normally distributed measurement data are expressed as median M (P25, P75). BMI is body mass index; RBC is red blood cell count; HGB is hemoglobin; NE is neutrophil count; PLT is platelet count; PT is prothrombin time; APTT is activated partial thromboplastin time; FIB is fibrinogen; (PLT × FIB) / WBC is the systemic coagulation inflammation index. a represents the t-value; b represents the Z-value; c represents the χ² value.

[0046] The aforementioned controlled experiments revealed a negative correlation between fibrin permeability and age in healthy subjects. This may be related to low levels of pro-inflammatory states and impaired antioxidant capacity, which alters fibrin clot structure. Subsequent studies showed this association was even stronger in patients with coronary heart disease and diabetes. In this study, subgroup analyses of age and BMI showed that in the age subgroup (45-65 years and >65 years), the SCI index was positively correlated with the risk of EH (extracorporeal hemorrhage) with increasing age. After adjusting for BMI, PT, HGB, and FIB, the SCI index remained associated with hypertension when age >65. This indicates that age, as an uncontrollable factor, plays a significant role. In the BMI subgroup, after adjusting for age, the correlation between the SCI index and the risk of EH was significantly increased when BMI >28 kg / m² compared to BMI 24-28 kg / m². Therefore, we can infer that in individuals aged 45-65 and over 65 years who are also overweight or obese, SCI has significant clinical value and can, to some extent, assess the risk of EH at an early stage. However, this inference still requires further research to confirm. Furthermore, a prospective study found that the younger the age of hypertension onset, the stronger the correlation with the development of cardiovascular disease.

[0047] Referring to Table 2, logistic regression analysis was performed on the risk factors for hypertension, with the presence or absence of hypertension (values: yes = 1, no = 0) as the dependent variable. Univariate analysis was used to determine the statistically significant and clinically potentially significant results: age (values: <45 years = 1, 45-65 years = 2, >65 years = 3), ethnicity (values: Han = 1, minority = 2), BMI (values: <24 kg / m² = 1, 24-28 kg / m² = 2, >28 kg / m² = 3), HGB (values: <132 g / L = 1). With age, BMI, PT (≤10.6s=1, >10.6s=2), FIB (<2.94g / L=1, 2.94-3.85g / L=2, >3.85g / L=3), and SCI (≤100.33=1, >100.33=2) as independent variables, a multivariate logistic regression analysis was performed. The results showed that age, BMI, PT, FIB, and SCI were risk factors for hypertension (P<0.05).

[0048]

[0049]

[0050] Table 2. Logistic regression analysis of risk factors for EH (n=2483)

[0051] The above verification analysis shows that both inflammation and coagulation pathways are involved in the development of essential hypertension (EH). Leukocytes, platelets, and fibrinogen are important components of this process. Elevated leukocyte concentration can regulate platelet activity, directly damage vascular endothelium, activate coagulation pathways, and produce prothrombin tissue factor, which contributes to the development of prothrombin status. Fibrinogen, an acute-phase reactant and coagulation factor, can cause platelet aggregation under pathological conditions, affecting blood viscosity and erythrocyte aggregation, and leading to endothelial cell damage. Multiple studies have shown that, compared with controls, hypertensive patients have significantly higher fibrinogen levels, which are positively correlated with the time of hypertension onset. Furthermore, elevated systolic and diastolic blood pressure in hypertensive patients are associated with elevated PT (prothrombin time) and APTT (activated partial thromboplastin time). During angiotensin II stimulation, activated platelets (PLT) and P-selectin produced by endothelial cells also play important roles in the development of hypertension-related cardiac inflammation and fibrosis. In this embodiment, univariate analysis revealed statistically significant differences in age, ethnicity, BMI (body mass index), HGB (hemoglobin), PT (prothrombin time), FIB (fibrinogen), and SCI (coagulation inflammation index) between the EH group and the non-EH group. The FIB and SCI indices in the EH group were higher than those in the non-EH group, which is largely consistent with the description in the aforementioned study. Multivariate logistic regression analysis showed that age, BMI, PT, FIB, and SCI index were risk factors for hypertension.

[0052] Prolonged inflammatory stimulation causes vascular endothelial damage, exposing it to the circulating blood, activating the coagulation cascade and thrombosis system, leading to the formation of local thrombi or systemic microthrombi. A significant advantage of the SCI index is that it calculates the SCI index by adding fibrinolytic enzymes (FIBs) to platelets and leukocytes, simultaneously assessing the fibrinolytic pathway in addition to coagulation and inflammation. This index has previously been used to predict complications and short-term mortality in diseases such as acute coronary syndrome and acute type A aortic dissection. The embodiments of this application construct a risk prediction model for EH (extracorporeal hemorrhage) based on the SCI index combined with traditional risk factors (age, BMI, PT, FIB). The area under the curve of the model is 0.812 (95% CI (0.795, 0.828)), indicating good risk prediction performance of this model. Internal validation and evaluation confirm that the prediction model has good calibration and outperforms the risk prediction performance of the single index SCI.

[0053] Therefore, the core indicator data used in the prediction model in this application embodiment are platelet data, fibrinogen data, white blood cell data, and body mass index.

[0054] Step S130. Input the fibrinogen data, the prothrombin time data, the body mass index, the coagulation inflammation index, and the age data into the predictive model constructed from clinical data to calculate the risk value.

[0055] In this process, the data obtained in step S120 is input into the prediction model. The prediction model scores each data point and sums the scores to obtain the total score. Based on the total score, the probability of the occurrence of primary hypertension is obtained.

[0056] Specifically, the risk value calculation for the prediction model mainly involves inputting the fibrinogen data, the prothrombin time data, the body mass index, the coagulation inflammation index, and the age data into the prediction model to obtain corresponding sub-scores, obtaining a total score based on multiple sub-scores, and obtaining the corresponding risk value based on the total score.

[0057] Specifically, for obtaining the total score based on multiple sub-scores, including:

[0058] The fibrinogen data of the patient to be diagnosed is compared with the first score distribution interval to obtain the first score. Based on the first score, a linear comparison is performed through a preset score distribution to obtain the first score value.

[0059] The prothrombin time data is compared with the second score distribution interval to obtain the second score. Based on the second score, a linear comparison is performed through a preset score distribution to obtain the second score value.

[0060] The third score is obtained by comparing the body mass index with the third score distribution interval, and the third score value is obtained by linearly comparing the third score with the preset score distribution.

[0061] The fourth score is obtained by comparing the coagulation inflammation index with the fourth score distribution interval, and the fourth score value is obtained by linear comparison with the fourth score distribution through a preset score distribution.

[0062] The age data is compared with the fifth score distribution interval to obtain the fifth score, and a fifth score value is obtained by linearly comparing the fifth score with a preset score distribution.

[0063] The total score is obtained by summing the first score, the second score, the third score, the fourth score, and the fifth score.

[0064] For reference on the nodal chart model Figure 2Specifically, the first score distribution interval includes three sub-intervals and two corresponding first scores. The first score is obtained by comparing the fibrinogen data of the patient to be diagnosed with the first score distribution interval. This includes determining the distribution of the fibrinogen data within the three sub-intervals and determining the corresponding first score based on this distribution. Specifically, the three sub-intervals of fibrinogen data are <2.94 g / L, 2.94-3.8 g / L, and >3.8 g / L. The two scores corresponding to these three intervals are: <2.94 g / L corresponds to one score, and 2.94-3.8 g / L and >3.8 g / L correspond to another score. These two scores then correspond to two first scores, and the corresponding first score can be determined by these first scores. For example, when FIB is 3.0 g / L, i.e., it falls within the second sub-interval and has a score of 2, the first score corresponding to score 2 is 12. Therefore, the first score for the patient to be diagnosed, i.e., the score for the fibrinogen data, is 12.

[0065] The second score distribution interval includes two sub-intervals and two corresponding second scores. The second score is obtained by comparing the prothrombin time data with the second score distribution interval, including: determining the distribution of the prothrombin time data within the two sub-intervals, and determining the corresponding second score based on the distribution. Similar to fibrinogen data, the judgment logic for prothrombin time data is the same. In this embodiment, the two sub-intervals for prothrombin time data are ≤10.6s and >10.6s, where the two scores corresponding to these two sub-intervals are 1 and 2. The second score corresponding to score 1 is 0, and the second score corresponding to score 2 is 1. For example, for a patient's prothrombin time data of 10s, the corresponding second score is 9.

[0066] The third score distribution interval includes three sub-intervals and three corresponding third scores. The third score is obtained by comparing the body mass index (BMI) with the third score distribution interval, including: determining the distribution of the BMI within the three sub-intervals, and determining the corresponding third score based on the distribution. In this embodiment, the three sub-intervals for BMI are <24 kg / m², 24-28 kg / m², and >28 kg / m², with corresponding scores of 1, 2, and 3, respectively. The score values ​​corresponding to the three scores are 0, 36, and 100, respectively. For example, if the BMI of the patient to be diagnosed is 28 kg / m², then the third score value is 100.

[0067] The fourth score distribution interval includes two sub-intervals and two corresponding fourth scores. The fourth score is obtained by comparing the coagulation inflammation index with the fourth score distribution interval, including: determining the distribution of the coagulation inflammation index within the two sub-intervals, and determining the corresponding fourth score based on the distribution. In this embodiment, the two sub-intervals of the coagulation inflammation index are ≤100.33 and >100.33, with scores of 1 and 2 corresponding to these sub-intervals, respectively. The fourth score corresponding to score 1 is 0, and the fourth score corresponding to score 2 is 8.

[0068] The fifth score distribution interval includes three sub-intervals and three corresponding fifth scores. The fifth score is obtained by comparing the age data with the fifth score distribution interval, including: determining the distribution of the age data within the three sub-intervals, and determining the corresponding fifth score based on the distribution. In this embodiment, the three sub-intervals corresponding to the age data are <45 years old, 45-65 years old, and >65 years old, with scores of 1, 2, and 3 respectively for each sub-interval. The fifth score for score 1 is 0, the fifth score for score 2 is 15, and the fifth score for score 3 is 33.

[0069] The final score is obtained by summing the first, second, third, fourth, and fifth scores mentioned above. For example, if the patient to be diagnosed is 55 years old, the corresponding score is 15 points; if the BMI is 28 kg / m2, the corresponding score is 100 points; if the PT is 10s, the corresponding score is 9 points; if the FIB is 3.0 g / L, the corresponding score is 12 points; and if the SCI is 120, the corresponding score is 8 points. The corresponding total score is 15 + 100 + 9 + 12 + 8 = 144 points. The risk prediction value of the nomogram is approximately 91%, indicating that the subject has a high risk of EH.

[0070] See Figure 3 A system 300 for predicting the risk of developing primary hypertension is provided, the system comprising:

[0071] The data acquisition device 310 is used to acquire platelet data, fibrinogen data, white blood cell data, age data, weight data, height data, and prothrombin time data of the patient to be diagnosed.

[0072] The index calculation device 320 is used to construct a coagulation inflammation index based on the platelet data, the fibrinogen data and the white blood cell data, and to construct a body mass index based on the weight data and the height data;

[0073] The risk prediction device 330 is used to input the fibrinogen data, the prothrombin time data, the body mass index, the coagulation inflammation index and the age data into a prediction model constructed from clinical data to calculate the risk value.

[0074] The risk prediction device includes a sub-score calculation unit, a total score calculation unit, and a risk value calculation unit.

[0075] Specifically, the sub-score calculation unit includes a first score calculation unit, a second score calculation unit, a third score calculation unit, a fourth score calculation unit, and a fifth score calculation unit.

[0076] In this embodiment of the application, the first score calculation unit is used to compare the fibrinogen data of the patient to be diagnosed with the first score distribution interval to obtain a first score, and to obtain a first score value by performing a linear comparison based on the first score distribution through a preset score distribution.

[0077] The second score calculation unit is used to compare the prothrombin time data with the second score distribution interval to obtain the second score, and to obtain the second score value by performing a linear comparison based on the second score distribution through a preset score distribution.

[0078] The third score calculation unit is used to compare the body mass index with the third score distribution interval to obtain the third score, and to obtain the third score value by linear comparison with the third score distribution through a preset score distribution.

[0079] The fourth score calculation unit is used to compare the coagulation inflammation index with the fourth score distribution interval to obtain the fourth score, and to obtain the fourth score value by linear comparison with the fourth score distribution through a preset score distribution.

[0080] The fifth score calculation unit is used to compare the age data with the fifth score distribution interval to obtain the fifth score, and to obtain the fifth score value by performing a linear comparison based on the fifth score distribution through a preset score distribution.

[0081] In this embodiment of the application, the prediction model is a noctilinear graph model.

[0082] See Figure 4The above methods can also be integrated into the provided terminal device 400. Since the device may vary significantly due to differences in configuration or performance, it may include one or more processors 401 and memories 402. The memories 402 may store one or more application programs or data. The memories 402 can be temporary or persistent storage. The application programs stored in the memories 402 may include one or more modules (not shown in the figure), each module may include a series of computer-executable instructions from the terminal device. Furthermore, the processor 401 may be configured to communicate with the memories 402, and the terminal device may execute the series of computer-executable instructions stored in the memories 402. The terminal device may also include one or more power supplies 403, one or more wired or wireless network interfaces 404, one or more input / output interfaces 405, one or more keyboards 406, etc.

[0083] In one specific embodiment, the terminal device includes a memory and one or more programs, wherein one or more programs are stored in the memory, and one or more programs may include one or more modules, and each module may include a series of computer-executable instructions in a device for predicting the risk of poor prognosis in patients with acute myocardial infarction, and is configured to be executed by one or more processors. The one or more programs include computer-executable instructions for performing the following:

[0084] Obtain platelet data, fibrinogen data, white blood cell data, age data, weight data, height data, and prothrombin time data from patients to be diagnosed;

[0085] A coagulation inflammation index is constructed based on the platelet data, fibrinogen data, and white blood cell data; a body mass index is constructed based on the weight data and height data.

[0086] The fibrinogen data, prothrombin time data, body mass index, coagulation inflammation index, and age data are input into a predictive model constructed from clinical data to calculate the risk value.

[0087] The following is a detailed introduction to each component of the processor:

[0088] In this embodiment, the processor is an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0089] Optionally, the processor can perform various functions, such as the above-mentioned functions, by running or executing software programs stored in memory and by calling data stored in memory. Figure 1 The method shown.

[0090] In a specific implementation, as one example, the processor may include one or more microprocessors.

[0091] The memory is used to store the software program that executes the solution of this application, and the execution is controlled by the processor. The specific implementation method can be referred to the above method embodiment, which will not be repeated here.

[0092] Optionally, the memory can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory can be integrated with the processor or exist independently and coupled to the processing unit through the processor's interface circuitry; this application embodiment does not specifically limit this.

[0093] It should be noted that the processor structure shown in this embodiment does not constitute a limitation on the device. The actual device may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0094] Furthermore, the technical effects of the processor can be referred to the technical effects of the methods described in the above-described method embodiments, and will not be repeated here.

[0095] It should be understood that the processor in the embodiments of this application may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0096] It should also be understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0097] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0098] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0099] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0100] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0101] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0102] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0103] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0104] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0105] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0106] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for predicting the risk of developing primary hypertension, characterized in that, The method includes: Obtain platelet data, fibrinogen data, white blood cell data, age data, weight data, height data, and prothrombin time data from patients to be diagnosed; A coagulation inflammation index is constructed based on the platelet data, fibrinogen data, and white blood cell data; a body mass index is constructed based on the weight data and height data. The fibrinogen data, prothrombin time data, body mass index, coagulation inflammation index, and age data are input into a predictive model constructed from clinical data to calculate the risk value; the predictive model is a nomogram model.

2. The method for predicting the risk of primary hypertension according to claim 1, characterized in that, The coagulation inflammation index is constructed based on the following formula: SCI = PLT * Fib / WBC, where PLT is platelet count, Fib is fibrinogen count, and WBC is white blood cell count.

3. The method for predicting the risk of primary hypertension according to claim 2, characterized in that, The step of inputting the fibrinogen data, prothrombin time data, body mass index, coagulation inflammation index, and age data into a predictive model constructed from clinical data to calculate the risk value includes: The fibrinogen data, prothrombin time data, body mass index, coagulation inflammation index, and age data are input into the prediction model to obtain corresponding sub-scores. A total score is obtained based on multiple sub-scores, and a corresponding risk value is obtained based on the total score.

4. The method for predicting the risk of primary hypertension according to claim 3, characterized in that, The total score is obtained based on multiple sub-scores, including: The fibrinogen data of the patient to be diagnosed is compared with the first score distribution interval to obtain the first score. Based on the first score, a linear comparison is performed through a preset score distribution to obtain the first score value. The prothrombin time data is compared with the second score distribution interval to obtain the second score. Based on the second score, a linear comparison is performed through a preset score distribution to obtain the second score value. The third score is obtained by comparing the body mass index with the third score distribution interval, and the third score value is obtained by linearly comparing the third score with the preset score distribution. The fourth score is obtained by comparing the coagulation inflammation index with the fourth score distribution interval, and the fourth score value is obtained by linear comparison based on the fourth score distribution through a preset score distribution. The age data is compared with the fifth score distribution interval to obtain the fifth score, and a fifth score value is obtained by linearly comparing the fifth score with a preset score distribution. The total score is obtained by summing the first score, the second score, the third score, the fourth score, and the fifth score.

5. The method for predicting the risk of primary hypertension according to claim 4, characterized in that, The first score distribution interval includes three sub-intervals and two corresponding first scores. The first score is obtained by comparing the fibrinogen data of the patient to be diagnosed with the first score distribution interval, including: determining the distribution of the fibrinogen data within the three sub-intervals and determining the corresponding first score based on the distribution. The second score distribution interval includes two sub-intervals and two corresponding second scores. The second score is obtained by comparing the prothrombin time data with the second score distribution interval, including: determining the distribution of the prothrombin time data within the two sub-intervals and determining the corresponding second score based on the distribution. The third score distribution interval includes three sub-intervals and three corresponding third scores. The third score distribution interval is obtained by comparing the body mass index with the third score distribution interval. The third score is obtained by comparing the distribution intervals of the values, including: determining the distribution of the body mass index in three sub-intervals, and determining the corresponding third score based on the distribution; the fourth score distribution interval includes two sub-intervals and two corresponding fourth scores, and the fourth score is obtained by comparing the coagulation inflammation index with the fourth score distribution interval, including: determining the distribution of the coagulation inflammation index in two sub-intervals, and determining the corresponding fourth score based on the distribution; the fifth score distribution interval includes three sub-intervals and three corresponding fifth scores, and the fifth score is obtained by comparing the age data with the fifth score distribution interval, including: determining the distribution of the age data in three sub-intervals, and determining the corresponding fifth score based on the distribution.

6. A system for predicting the risk of developing primary hypertension, characterized in that, The system includes: The data acquisition device is used to acquire platelet data, fibrinogen data, white blood cell data, age data, weight data, height data, and prothrombin time data of patients to be diagnosed. An index calculation device is used to construct a coagulation inflammation index based on the platelet data, the fibrinogen data, and the white blood cell data, and to construct a body mass index based on the weight data and the height data; A risk prediction device is used to input the fibrinogen data, the prothrombin time data, the body mass index, the coagulation inflammation index, and the age data into a prediction model constructed from clinical data to calculate a risk value.

7. The primary hypertension risk prediction system according to claim 6, characterized in that, The risk prediction device includes a sub-score calculation unit, a total score calculation unit, and a risk value calculation unit. The sub-score calculation unit is used to determine multiple sub-scores corresponding to multiple sub-data. The total score calculation unit sums the multiple sub-scores to obtain a total score. The risk value calculation unit obtains the final risk value based on the total score.

8. The primary hypertension risk prediction system according to claim 7, characterized in that, The sub-score calculation unit includes a first score calculation unit, a second score calculation unit, a third score calculation unit, a fourth score calculation unit, and a fifth score calculation unit.

9. The primary hypertension risk prediction system according to claim 8, characterized in that, The first score calculation unit is used to compare the fibrinogen data of the patient to be diagnosed with the first score distribution interval to obtain a first score, and to obtain a first score value by performing a linear comparison based on the first score distribution through a preset score distribution; The second score calculation unit is used to compare the prothrombin time data with the second score distribution interval to obtain a second score, and to obtain a second score value by performing a linear comparison based on the second score distribution through a preset score distribution; The third score calculation unit is used to compare the body mass index with the third score distribution interval to obtain a third score, and to obtain a third score value by performing a linear comparison based on the third score distribution through a preset score distribution. The fourth score calculation unit is used to compare the coagulation inflammation index with the fourth score distribution interval to obtain the fourth score, and to obtain the fourth score value by performing a linear comparison based on the fourth score distribution through a preset score distribution. The fifth score calculation unit is used to compare the age data with the fifth score distribution interval to obtain the fifth score, and to obtain the fifth score value by performing a linear comparison based on the fifth score distribution through a preset score distribution.

10. The primary hypertension risk prediction system according to claim 9, characterized in that, The total score calculation unit sums the scores corresponding to the first score calculation unit, the second score calculation unit, the third score calculation unit, the fourth score calculation unit, and the fifth score calculation unit to obtain the total score.

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