Method for evaluating venous thromboembolism risk of Chinese population based on VTE-PRS-15 model

Abstract

The invention relates to the field of medical diagnosis, in particular to a risk assessment model suitable for venous thromboembolism (VTE) of Chinese population and application of the risk assessment model. The model is based on genetic characteristics of Chinese population, comprises 15 genetic variables (SNP) related to a VTE pathophysiology mechanism, and covers five aspects of an anticoagulation system, a blood coagulation system, a fibrinolytic system, a platelet system and a vascular endothelial system. According to the model, the VTE genetic risk of a patient is evaluated by calculating a VTE-PRS-15 value, and the comprehensive risk level of the patient is determined by combining with a Caprini clinical evaluation scale. The invention also provides a corresponding gene polymorphism detection kit. Clinical data verifies that the model improves the accuracy and practicability of VTE risk assessment.

Description

Technical Field

[0001] This invention relates to the field of medical diagnostics, specifically to a risk assessment model for venous thromboembolism in the Chinese population and its application. Background Technology

[0002] Venous thromboembolism (VTE) is the abnormal clotting of blood within veins, causing complete or partial blockage of the vessel. It is a venous return disorder, including deep vein thrombosis (DVT) and pulmonary thromboembolism (PTE). VTE is characterized by its insidious onset, atypical clinical symptoms, and susceptibility to misdiagnosis. Once it occurs, it has a high mortality and disability rate, making it the third leading cause of death from circulatory systemic diseases after myocardial infarction and stroke. DVT can occur in veins throughout the body, but is most common in the lower extremities. In the acute phase of DVT, thrombus detachment can lead to pulmonary embolism (PE), with a mortality rate of up to 30%. Clinically, approximately 80% of DVT patients are asymptomatic, and its high mortality and disability rates have earned it the nickname "silent killer."

[0003] Clinically, from an etiological perspective, venous thromboembolism (VTE) can be divided into three main categories: familial VTE, VTE with complex disease characteristics, and secondary VTE. Among these, VTE with complex disease characteristics is the most common type clinically, determined by a combination of genetic and acquired factors or conditions. Risk assessment is the prerequisite and foundation for standardized VTE prevention. It involves quantitatively assessing the likelihood of various risk factors influencing VTE occurrence before and during the development of VTE. Currently, the commonly used VTE risk assessment tool recommended in Chinese guidelines such as the "Guidelines for Quality Evaluation and Management of Prevention and Treatment of Venous Thromboembolism in Hospitals (2022)," the "Guidelines for Prevention and Treatment of Thrombotic Diseases in China (2018)," the "Guidelines for Prevention of Venous Thromboembolism in Major Orthopedic Surgery in China (2016)," and the "Guidelines for Prevention of Perioperative Venous Thromboembolism in Trauma Orthopedic Patients in China (2021)" is the Caprini scale, which includes 37 risk factor items covering various aspects such as the patient's basic information, medical history, surgical history, medication history, and genetic factors.

[0004] The Caprini risk assessment scale is a comprehensive assessment scale integrating genetic and clinical risk factors, and it is currently widely used globally for VTE risk assessment. Numerous studies and clinical practices have confirmed its value in VTE risk assessment and guiding prevention decisions. However, the genetic factors F5 Leiden mutation and F2 G20210A mutation, as identified in the scale, are more common in European and American populations, while their mutation rate is almost zero in the Chinese population. Therefore, in current clinical applications of the Caprini scale in my country, genetic risk factors are calculated as zero, and only clinical risk factors are included. The assessment of genetic factors, which contribute 40-60% to VTE development, is missing. This inability to provide complete and ethnically appropriate predictive results affects the accuracy of clinical VTE risk assessment, preventing the standardized assessment of the risk of VTE in all potential VTE patients from achieving the desired outcome.

[0005] Studies have confirmed that no dominant genes or major-effect loci similar to the F5 Leiden mutation and F2G20210A mutation in Caucasians have been found in the Chinese population. The most common type of vegetative-tetany (VTE) in Chinese clinical practice is VTE, which is characterized by multiple genes and complex disease features. Genetically, VTE is controlled by multiple genes and loci, influencing different pathophysiological mechanisms in various ways. The effects of single or a few gene loci are weak and cannot accurately assess the genetic risk of VTE. Therefore, it is necessary to use the internationally recognized Polygenic Risk Score (PRS) modeling technique to construct a VTE-PRS model based on the genetic characteristics of the Chinese population. This model quantifies the cumulative effect of multiple gene polymorphisms, calculates the polygenic variation information into a score measuring an individual's susceptibility to VTE, assesses the genetic risk of VTE, and combines this with Caprini risk assessment results for a comprehensive clinical risk assessment.

[0006] Although PRS technology has yielded numerous research findings in China, three key technical issues remain unresolved in its clinical application. First, most models require the detection of dozens or even hundreds of SNPs, leading to high costs, clinical validation and registration expenses, and difficulties in clinical rollout. This necessitates both scientific simplification of SNPs and a clearer understanding of the pathophysiological mechanisms of VTE at the molecular level to ensure both cost-effectiveness and scientific risk assessment in clinical applications. Second, existing research largely relies on simulated data to construct training and validation sets for modeling, lacking validation of model effectiveness and predictive performance stability based on real clinical case data, and failing to address the crucial technical indicator of risk stratification thresholds based on real clinical data. Third, common VTE is a complex disease caused by the combined effects of genetic and non-genetic factors (such as environmental and acquired factors). Therefore, the practical application of PRS models in clinical practice must incorporate comprehensive risk assessment methods, such as the Caprini scale. However, resolving the methods and criteria for comprehensive risk assessment remains an unsolved problem. Summary of the Invention

[0007] To address the limitations of existing technologies, this invention develops a predictive model for assessing the risk of venous thromboembolism in the Chinese population. Based on this model, a gene polymorphism detection kit and a method for comprehensive risk assessment fused with Caprini are designed. A prospective case-control study was conducted, collecting real clinical data from 2038 patients to validate the predictive performance and clinical value of the model. The model is simple to operate and cost-effective, and for the first time, it solves the problem of clinical application of the global precision medicine technology, the polygenic risk score (PRS). Specifically, this invention provides the following technical solution: The first aspect of the present invention provides a method for assessing the risk of venous thromboembolism (VTE) in the Chinese population based on a multigene risk score (VTE-PRS-15), comprising the following steps: (1) assessing the risk of VTE in the following genes: SERPINC1 rs2227589, PROCrs146922325, PROC rs199469469, PROS1 rs6795524, THBD rs16984852, APOH rs52797880, F11 rs2289252, F11 rs2036914, F2 rs3136516, FGG rs2066865, PAI-1 rs1799762, ABOrs8176719, MTHFR rs1801133, NOS3 rs1799983 and HIVEP1. (1) Genotyping of 15 SNPs in rs169713; (2) VTE-PRS-15 value was calculated using the VTE-PRS-15 model formula; (3) VTE-PRS-15 risk threshold (Cut-off value) calculated using the K-means clustering algorithm was used to determine the patient’s VTE genetic risk; (4) The patient’s comprehensive VTE risk was determined by combining clinical risk (Caprini score results).

[0008] In one embodiment, the gene polymorphism detection employs PCR-fluorescent probe technology.

[0009] In one implementation, the VTE-PRS-15 value is calculated using the following formula: Where i = 1 to 15, βi represents the effect value of the i-th genetic locus (i.e., the Ln of the odds ratio [OR]), and Gi represents the copy number of the risk allele (0, 1, or 2).

[0010] In one implementation, the risk threshold (cut-off value) of VTE-PRS-15 is determined using the K-means clustering algorithm: (1) The cut-off value of VTE-PRS-15 using binary classification results is 2.335. When VTE-PRS-15 ≥ 2.335, it is genetically high risk; when VTE-PRS-15 < 2.335, it is genetically low risk; (2) When VTE-PRS-15 uses tri-class classification results, the cut-off values ​​are 2.230 and 1.730, respectively. When VTE-PRS-15 ≥ 2.230, it is genetically high risk; when 1.730 < VTE-PRS-15 < 2.230, it is genetically intermediate risk; and when VTE-PRS-15 ≤ 1.730, it is genetically low risk.

[0011] The second aspect of the present invention provides a comprehensive assessment method for evaluating the VTE risk in the Chinese population. The method includes the following steps: (1) performing the above-mentioned gene polymorphism detection on a patient to calculate the VTE-PRS-15 value; (2) simultaneously performing a clinical risk assessment using the Caprini risk assessment scale; (3) comprehensively determining whether the patient is at high risk, medium risk, or low risk based on the VTE-PRS-15 value and the Caprini score.

[0012] In one embodiment, when the Caprini scale assesses as high risk or medium risk, and the VTE-PRS-15 value ≥ 2.335, it is determined as high risk; when the Caprini scale assesses as medium risk or low risk, and the VTE-PRS-15 value < 2.335, it is determined as low risk, and in other cases, it is determined as medium risk.

[0013] In one embodiment, when the VTE-PRS-15 value ≥ 2.230, it is determined as high risk; when the Caprini scale assesses as high risk and 1.730 < VTE-PRS-15 value < 2.230, it is determined as medium risk; in other cases, it is determined as low risk.

[0014] The third aspect of the present invention provides a gene polymorphism detection kit for VTE risk assessment in the Chinese population. The kit includes specific primers and fluorescent probes for the 15 SNPs.

[0015] In one embodiment, the specific primers and probes include the sequences shown in SEQ ID NO.1 to SEQ ID NO.63.

[0016] The fourth aspect of the present invention provides the application of the above method in the preparation of a diagnostic reagent or product for evaluating the genetic risk of individual venous thromboembolism.

[0017] A fifth aspect of the present invention provides a method for constructing a risk assessment model for venous thromboembolism (VTE) in the Chinese population, the method comprising the following steps: (1) collecting clinical data of the target population and constructing a case group and a control group; (2) performing gene polymorphism detection on the case group and the control group respectively, detecting SERPINC1 rs2227589, PROCrs146922325, PROC rs199469469, PROS1 rs6795524, THBD rs16984852, APOH rs52797880, F11 rs2289252, F11 rs2036914, F2 rs3136516, FGG rs2066865, PAI-1 rs1799762, ABOrs8176719, MTHFR rs1801133, NOS3 (3) Calculate the VTE-PRS-15 value of each sample based on the test results; (4) Analyze the calculated VTE-PRS-15 value using statistical methods to determine the threshold for risk assessment.

[0018] In one embodiment, the gene polymorphism detection refers to the use of PCR fluorescent probe technology.

[0019] In one implementation, the VTE-PRS-15 value is calculated using the following formula: , where i = 1 to 15, βi represents the effect value of the i-th genetic locus (i.e., the odds ratio [OR] Ln), and Gi represents the copy number of the risk allele (0, 1, or 2).

[0020] In one implementation, K-means clustering analysis is used to determine the risk assessment threshold of the VTE-PRS-15 value, classifying it into three levels: high genetic risk, medium genetic risk, and low genetic risk.

[0021] Compared with the prior art, the present invention achieves the following beneficial technical effects: (1) Based on the principles of evidence-based medicine, statistics and genomics, this invention scientifically screens 15 SNPs that comprehensively cover the five major systems of VTE pathophysiology (anticoagulation system, coagulation system, fibrinolytic system, platelet system and vascular endothelial system), prospectively collects case-control data from 2038 cases, verifies that VTE-PRS-15 value is an independent risk factor for VTE, verifies the effectiveness of the VTE-PRS-15 model in stratifying VTE genetic risk, fills the gap in VTE clinical risk assessment that does not include genetic factors characteristic of the Chinese population, and improves the interpretability and preventive guidance of the test results.

[0022] (2) Based on real clinical data mining, this invention calculates the optimal risk threshold of the VTE-PRS-15 model and the comprehensive risk assessment criteria of VTE-PRS-15B+Caprini and VTE-PRS-15T+Caprini, and verifies the effectiveness, safety and reliability of VTE-PRS-15B+Caprini and VTE-PRS-15T+Caprini with real clinical data.

[0023] (3) This invention overcomes the core pain points of existing technologies, such as gene locus redundancy, lack of data validation, disconnect from clinical application, and high promotion costs, providing a safe and effective innovative tool for the precise prevention and control of VTE. Simultaneously, considering clinical practice, this invention, while maintaining the use of the Caprini scale, significantly improves the accuracy and safety of the original Caprini scale's risk assessment by adding the genetic assessment results of the VTE-PRS-15 model. For the first time, it systematically integrates genetic risk factors (VTE-PRS-15) with the clinically commonly used risk prediction model (Caprini), providing a more comprehensive, convenient, and reliable VTE risk assessment tool for clinical use. This reduces the economic burden on patients, increases the standardized prevention rate of VTE, and lowers the incidence of VTE during clinical application. Attached Figure Description

[0024] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 The distribution of VTE-PRS-15 values ​​in the case group and the control group; Figure 2 ROC curve for VTE-PRS-15; Figure 3 ROC curve for the VTE-PRS-15 model after 10 resampling cycles; Figure 4 The ROC curve for the Caprini scale in the target data. Detailed Implementation

[0025] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0026] This embodiment uses a prospective case-control study (National Medical Research Registration and Filing Information System Project Filing No.: MR-61-23-019694) to construct the target data, collecting clinical data from 18 multi-center hospitals. This data forms the target data for the model study.

[0027] Example 1: A prospective case-control study Research Methods: A prospective case-control study is a clinical research method in epidemiology that combines the characteristics of prospective cohort studies and traditional case-control studies. Its core is to first determine the study groups (case group and control group), and then prospectively collect or observe outcome-related data to explore the association between exposure factors and disease (or other outcomes).

[0028] Sample size calculation: The sample size was calculated based on the number of variables (SNPs) in the VTE-PRS-15 model, which is 15. Each SNP has 3 genotypes. According to the rule of thumb that "the sample size should be 20 times the number of variables to meet the most stringent requirements," the sample size calculation formula for both the case and control groups is: 15 (SNPs) × 3 (genotypes) × 20 = 900 cases. Considering the typical sample collection failure rate of 10-30% and the complexity of the Caprini clinical assessment scale for VTE, a sample collection failure rate of 20% was used: 900 + 900 × 20% = 1080 cases. Therefore, a total of 2200 multicenter clinical samples were collected, including 1100 cases in the case group and 1100 cases in the control group, to ensure that the sample size included in the statistical analysis was no less than 1800 cases, with at least 900 cases in the case group and at least 900 cases in the control group.

[0029] Outcome marker: Deep vein thrombosis (DVT).

[0030] Diagnostic methods and criteria for DVT: This study used color Doppler ultrasound examination of the veins in both lower extremities as the diagnostic criterion for lower extremity DVT. For cases of clinically high probability of DVT, with negative or indeterminate results from color Doppler ultrasound of both lower extremities, further magnetic resonance angiography or angiography may be performed according to relevant guidelines.

[0031] Observation period and indicators: from admission to the third month after surgery. Observation indicators included Caprini score, genetic information (VTE-PRS-15 value), laboratory indicators, and imaging examination results.

[0032] Data grouping: Patients diagnosed with DVT during the observation period were included in the case group; patients who did not develop DVT during the observation period were included in the control group.

[0033] Inclusion and Exclusion Criteria: Inclusion Criteria: Age 18-80 years; orthopedic inpatients with a hospital stay ≥3 days; surgical patients or patients with perioperative bone trauma; patients with fresh fractures; all patients had undergone bilateral lower extremity venous color Doppler ultrasound examination; no contraindications to anticoagulation; consent to participate in the study and signed informed consent form (provided by the patient or their legal guardian). Exclusion Criteria: Patients with pathological fractures; patients with cirrhosis, liver cancer, or who have undergone hepatectomy; patients with prolonged bed rest, hemiplegia, or other limited mobility before admission; patients in the end stage of various diseases with an expected survival of <1 year; patients with renal impairment or hepatic dysfunction; patients with hematologic disorders or coagulation disorders; patients with a history of thrombotic diseases and currently receiving treatment; patients with poor adherence, or known mental illness or intellectual disability; patients who do not receive regular anticoagulation prophylaxis.

[0034] Sample collection process: For inpatients in the orthopedics department (a high-risk department for VTE), patients meeting the inclusion and exclusion criteria were screened and signed informed consent forms. Patients who developed deep vein thrombosis (DVT) during hospitalization were included in the case group, and whole blood samples were collected for VTE-PRS-15 testing, with information completed on the Clinical Observation Form. Patients who did not develop DVT during hospitalization had whole blood samples collected before discharge for VTE-PRS-15 testing, and information completed on the Clinical Observation Form. They were followed up for three months after discharge. Patients who developed DVT during the follow-up period were included in the case group, and those who did not develop DVT were included in the control group.

[0035] This study collected 2,620 clinical samples from February 2023 to March 2025. After excluding cases that did not meet the inclusion or exclusion criteria, such as those who dropped out during follow-up, those with missing ultrasound diagnostic results, or those with a hospital stay of less than three days, a total of 2,038 cases that met the inclusion and exclusion criteria and could be used for statistical analysis were included. Among them, there were 1,013 cases in the case group and 1,025 cases in the control group, which met the sample size of 900 cases in each group required for the study analysis.

[0036] The target data is divided into case group data and control group data, consisting of six parts: general patient information data, Caprini score scale index data, laboratory index test data, imaging data, genetic test data, and DVT prevention data.

[0037] Based on our previous research findings, “A set of biomarkers, kits and their applications for predicting the genetic risk of venous thromboembolism in the Chinese Han population (ZL2022101197350)” and the academic paper “Construction and optimization of a polygenic risk model for venous thromboembolism in the Chinese population” published in the internationally renowned vascular surgery journal J Vasc Surg Venous Lymphat Disord. 2024 Jan;12(1):101666., we established the screening principles and grading standards for disease-related gene variant sites in the Chinese population according to the concept of evidence-based medicine, screened genetic variables that conform to the characteristics of the Chinese population, and optimized the model variables (detection sites) using a stepwise forward selection method. Starting from the SNP with the highest OR value (PROC, rs146922325) among 53 SNPs, we added SNPs to the PRS one by one in descending order of OR value and calculated the AUC of the PRS after each SNP was added. With the addition of SNPs, the AUC of PRS increased rapidly until the 10th SNP was included in PRS, at which point the AUC no longer increased significantly. Finally, 10 SNPs associated with the risk of VTE and which were repeatedly validated were identified (PROC gene rs146922325 (C>T), PROC gene rs199469469 (AAG>-), THBD gene rs16984852 (C>A), ABO gene rs8176719 (->C), FGG gene rs2066865 (G>A), PAI-1 gene rs1799762 (4G>5G), APOH gene rs8178847 (A>G), F11 gene rs2289252 (C>T), F11 gene rs2036914 (T>C), and MTHFR gene rs1801133 (G>A).

[0038] Then we analyzed the molecular mechanisms of disease development at the systemic level, combined with the influence of genetic factors on the pathophysiological mechanism of VTE, and comprehensively optimized the model from the perspective of "genomics". We added 5 important SNPs related to the pathophysiological mechanism of VTE from the patents and published papers of previous research results, namely: SERPINC1 rs2227589 (C>T), PROS1 rs6795524 (A>G), F2 rs3136516 (G>A), HIVEP1 rs169713 (T>C) and NOS3 rs1799983 (T>G).

[0039] Meanwhile, considering the primer and probe synthesis issues in the development of detection reagents, the APOH gene rs8178847 (A>G) was replaced with the fully linked APOH gene rs52797880 (A>G) with the same function.

[0040] Based on the above 15 loci, we constructed the VTE-PRS-15 model, as shown in the following formula: Where: i represents the i-th SNP (i = 1, 2, ..., 15); βi represents the effect size of the i-th SNP (i.e., the Ln of the odds ratio [OR]); Gi represents the copy number of the risk allele of the i-th SNP, represented by (0, 1 or 2).

[0041] The VTE-PRS-15 model contains 15 VTE-related SNPs that comprehensively cover the five major pathophysiological mechanisms of VTE development. It maintains both optimization and simplicity while improving the model's coverage of VTE genetic risk factors and its clinical applicability. This provides a basis for elucidating the pathogenesis at the molecular level and enabling precise prevention. Specific information on the 15 SNPs and their pathophysiological mechanism classifications are shown in Table 1.

[0042] Table 1. Classification of 15 SNPs and their pathophysiological mechanisms in VTE-PRS-15

[0043] Based on the 15 SNPs related to venous thromboembolism identified by the VTE-PRS-15 model, 15 sets of specific primers and probes were designed using PCR-fluorescent probe technology (Table 2).

[0044] Table 2 Primer and probe sequences for 15 SNPs related to VTE

[0045]

[0046] Example 2: Determination of the threshold for the VTE-PRS-15 model The cut-off value of the VTE-PRS-15 model in risk assessment is a crucial transformation point from "continuous" to "discrete" and from "quantitative" to "qualitative" data. Its core value lies in simplifying complex data into decision-making classification results, and it is a key technical indicator for the clinical application of the model. In this embodiment, the K-means clustering analysis algorithm is used to calculate and optimize the optimal risk stratification threshold of the VTE-PRS-15 model based on the actual results of the target data.

[0047] 2.1 K-means clustering algorithm:

[0048] S1 defines the ideal points as follows: a. 100% DVT identification rate in high-risk cases (1013 cases); b. 0% non-DVT identification rate in high-risk cases; c. 100% non-DVT identification rate in low-risk cases (1025 cases); d. 0% DVT identification rate in low-risk cases.

[0049] S2 Data Loading and Integration: The program reads the file line by line, extracting the values ​​from the first four columns of each line: the number of high-risk DVT patients, the number of high-risk non-DVT patients, the number of low-risk non-DVT patients, and the number of low-risk DVT patients, forming a four-dimensional data point. To locate the original source of the data, the program creates a custom ClusterPoint to store the four-dimensional numerical data (point) and its original row number (originalRowNum). All these ClusterPoint objects are added to a master list, forming a complete dataset available for analysis.

[0050] S3 Data Standardization: The program first calculates the mean and standard deviation for each dimension (i.e., each column) of the dataset. Then, the program iterates through each data point and applies the standardization formula to each value: New value = (Original value - Mean) / Standard deviation. P' = [(x1-μ1) / σ1, (x2-μ2) / σ2, (x3-μ3) / σ3, (x4-μ4) / σ4], where μ is the mean of each column and σ is the standard deviation of each column.

[0051] S4 K-means clustering: The algorithm uses a common heuristic rule k = sqrt(n / 2) (where n is the total number of data points) to automatically determine the number of clusters k to be formed.

[0052] The program uses an optimized K-means initialization method, KMeansPlusPlusClusterer, to intelligently select initial cluster centers (centroids) so that they are as dispersed as possible from each other.

[0053] The K-means algorithm begins by iterating, assigning each data point to the nearest cluster center and recalculating the center of each cluster (i.e., taking the average of all data points in that cluster as the new cluster center). This process is repeated until the cluster assignments no longer change, and all data points are divided into k distinct clusters, each consisting of a set of data points that are close to each other in the feature space.

[0054] S5 Optimal Data Point Precise Location: a. The algorithm first calculates the distance between the center point of each cluster and the preset "ideal point", finds the cluster whose center point is closest to the "ideal point", and this cluster is called the "optimal cluster"; b. The algorithm directly searches within the "optimal cluster", traverses all ClusterPoint objects in this cluster, calculates the distance between each point and the "ideal point", and finally determines the point with the smallest distance as the "optimal data point".

[0055] S6 Result Output: The program outputs detailed information about the found "optimal data point". From the metadata stored in the ClusterPoint object, the program not only displays the four-dimensional value of this optimal point, but also accurately provides the original row number (originalRowNum) in which the data point was found, thus making the analysis results traceable and practically valuable.

[0056] 2.2 Threshold and Risk Stratification of VTE-PRS-15 Model Based on Binary Classification Results

[0057] The VTE-PRS-15 value range calculated based on the target data is 0.680-5.780. A threshold (Cut-off value) is set within this range. Values ​​greater than or equal to the Cut-off value indicate a high genetic risk, while values ​​less than the Cut-off value indicate a low genetic risk.

[0058] According to the "Guidelines for Quality Evaluation and Management of Prevention and Treatment of Intravenous Thromboembolism in Hospitals (2022 Edition)," the Caprini score is divided into three risk levels: high-risk, intermediate-risk, and low-risk. A Caprini score ≥5 indicates high risk, a Caprini score of 3-4 indicates intermediate risk, and a Caprini score of 1-2 indicates low risk.

[0059] We used a combination of the three-category risk assessment results of the Caprini scale and the two-category risk assessment results of the VTE-PRS-15 to conduct a comprehensive risk assessment. We named this comprehensive assessment model "VTE-PRS-15B+Caprini", where B represents binary classification. The assessment results are defined as three risk levels: high risk, medium risk, and low risk. We obtained four sets of results according to the risk assessment criteria listed in Table 3.

[0060] Table 3. Overall Risk Assessment and Results for Each Group of VTE-PRS-15B+Caprini

[0061] Based on the target data, the VTE-PRS-15 value ranged from 0.680 to 5.780. The step size was set to 0.005. The cut-off value corresponding to each increment of the step size was calculated. Then, according to the four groups of comprehensive risk assessment results, the number of DVT and non-DVT patients in the high, medium and low risk categories under each group of results was counted.

[0062] An automated data processing program for the VTE-PRS-15B+Caprini integrated risk assessment was developed. The program traversed a total of 4085 data sets, with each set corresponding to a cut-off value. Four criteria were set: a. The number of DVT patients predicted as high-risk must be higher than the Caprini scale prediction result; b. The number of non-DVT patients predicted as low-risk must be higher than the Caprini scale prediction result; c. The number of non-DVT patients predicted as high-risk must be less than the Caprini scale prediction result; d. The number of DVT patients predicted as low-risk must be less than the Caprini scale prediction result. The computer program's calculations showed that 67 sets of results met the above four criteria, and all of them were assessment results from group 2 in Table 3.

[0063] For the 67 results, K-means clustering was used to calculate the optimal data point, which corresponds to a VTE-PRS-15 binary cut-off value of 2.335. A VTE-PRS-15 ≥ 2.335 indicates a high genetic risk, while a VTE-PRS-15 < 2.335 indicates a low genetic risk.

[0064] 2.3 Threshold and Risk Stratification of VTE-PRS-15 Model Based on Three-Class Classification Results

[0065] Within the VTE-PRS-15 value range of 0.680-5.780 calculated based on target data, two thresholds were set to classify the VTE-PRS-15 risk assessment results into three types: high genetic risk, medium genetic risk, and low genetic risk.

[0066] According to the "Guidelines for Quality Evaluation and Management of Prevention and Treatment of Intravenous Thromboembolism in Hospitals (2022 Edition)," Caprini scores are classified into three risk levels: high-risk, intermediate-risk, and low-risk. A Caprini score ≥5 indicates high risk, a Caprini score of 3-4 indicates intermediate risk, and a Caprini score of 1-2 indicates low risk.

[0067] We conducted a comprehensive risk assessment using the three-category risk assessment results of the Caprini scale and the three-category risk assessment results of the VTE-PRS-15. We named this comprehensive assessment model "VTE-PRS-15T+Caprini", where T represents three-category classification. The assessment results were defined as three risk levels: high risk, medium risk, and low risk. We obtained 432 sets of results according to the risk assessment criteria listed in Table 4.

[0068] Table 4. Comprehensive Risk Assessment and Results for Each Group of VTE-PRS-15T+Caprini

[0069] Based on the target data, the VTE-PRS-15 value ranged from 0.680 to 5.780. The step size was set to 0.01. The cut-off value corresponding to each increment of the step size was calculated. Then, according to the comprehensive risk assessment results of 432 groups, the number of DVT and non-DVT patients in the high, medium and low risk categories of each group was counted.

[0070] An automated data processing program for the VTE-PRS-15T+Caprini integrated risk assessment was developed. The program traversed a total of 2,246,400 data sets, with each set corresponding to a cut-off value. Then, the same four criteria as those used in the calculations were established: a. The number of DVT patients predicted as high-risk must be higher than the Caprini scale prediction result; b. The number of non-DVT patients predicted as low-risk must be higher than the Caprini scale prediction result; c. The number of non-DVT patients predicted as high-risk must be less than the Caprini scale prediction result; d. The number of DVT patients predicted as low-risk must be less than the Caprini scale prediction result. The computer program's calculations showed that 14,529 sets of results met the above four criteria.

[0071] For the 14,529 results, K-means clustering was used to calculate the optimal data point, with VTE-PRS-15 cut-off values ​​of 2.230 and 1.730 for the three categories, respectively. A VTE-PRS-15 value ≥ 2.230 indicates high genetic risk, 1.730 < VTE-PRS-15 < 2.230 indicates intermediate genetic risk, and VTE-PRS-15 ≤ 1.730 indicates low genetic risk.

[0072] Example 3: Confirmation of the Judgment Criteria for Comprehensive Risk Assessment Using the VTE-PRS-15 Model Combined with the Caprini Scale Based on real clinical data collected from prospective case-control studies, the K-means clustering algorithm was used to calculate and determine the optimal thresholds for the two classification results of the VTE-PRS-15 model. Simultaneously, the risk assessment criteria for VTE-PRS-15B+Caprini comprehensive assessment and VTE-PRS-15T+Caprini comprehensive assessment were calculated, as shown in Tables 5 and 6. VTE-PRS-15B refers to genetic risk assessment using a binary classification result, and VTE-PRS-15T refers to genetic risk assessment using a tri-class classification result.

[0073] Table 5 Risk Assessment Criteria for VTE-PRS-15B+Caprini Comprehensive Assessment

[0074] Table 6 Risk Assessment Criteria for VTE-PRS-15T+Caprini Comprehensive Evaluation

[0075] Example 4: Clinical application validation of the VTE-PRS-15 model

[0076] 4.1 Validation and efficacy of VTE-PRS-15 in predicting genetic risk ①Based on the detection results of 15 VTE-related SNPs in the case group and control group samples in the target data, the VTE-PRS-15 value of each sample was calculated.

[0077] ② The calculated VTE-PRS-15 values ​​for the two groups of patients were as follows: mean 2.91 (95% CI 2.88-2.95), difference 0.72, maximum 5.78, minimum 0.68; mean VTE-PRS-15 values ​​for the control group were as follows: mean 2.66 (95% CI 2.61-2.69), standard deviation 0.66, minimum 0.68, maximum 4.53, median 2.64; mean VTE-PRS-15 values ​​for the case group were as follows: mean 3.18 (95% CI 3.13-3.22), standard deviation 0.69, minimum 1.29, maximum 5.78, median 3.08.

[0078] ③ To visually demonstrate the distribution of VTE-PRS-15 scores in the two groups, based on the VTE-PRS-15 values ​​in the study population ranging from a minimum of 0.68 to a maximum of 5.78 (range 5.10), with a group interval of 0.2, the number of cases and controls within different score intervals was calculated. A bar chart was plotted with VTE-PRS-15 values ​​on the x-axis and the percentage of individuals on the y-axis (e.g., Figure 2 The results showed that the proportion of cases in each interval increased with the increase of VTE-PRS-15 value.

[0079] ④ An independent samples t-test was used to assess the association between VTE-PRS-15 values ​​and DVT. The results showed that the mean VTE-PRS-15 value in the case group was significantly higher than that in the control group (3.18±0.69 vs 2.66±0.67, p<0.001), with a mean difference of 0.52 (95% CI: 0.46~0.58). The Levin test for homogeneity of variance indicated no significant difference in variance between the two groups (F=1.120, p=0.290), supporting the t-test results using the assumption of equal variance. A high t-value (t=17.38) and the confidence interval not crossing zero further validated the robustness of the results. The results indicate that VTE-PRS-15 can serve as an effective indicator for DVT genetic risk stratification. The results of the independent samples t-test are shown in Table 7.

[0080] Table 7 Independent Samples t-Test

[0081] ⑤ Using VTE-PRS-15 as a continuous independent variable and the occurrence of DVT as a binary dependent variable, the area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the model's predictive and discriminative ability. The results showed that the AUC of the model was 0.702 (95% confidence interval: 0.680–0.725). The ROC curve is shown below. Figure 3 .

[0082] Conclusion: The AUC of VTE-PRS-15 was 0.702 (95% CI: 0.680–0.725, P<0.001), indicating that it has good predictive ability for the risk of VTE (AUC>0.7), which is significantly better than random classification (AUC=0.5).

[0083] ⑥ Bootstrap method to verify the discrimination of VTE-PRS-15 The Bootstrap method, a clinical prediction model validation approach, was employed. A Bootstrap resampling software program was developed, and target data (2038 cases from a prospective case-control study) was used. A Bootstrap resampling validation set of the same sample size (1013 cases and 1025 controls) was constructed using resampling with replacement. The discriminative value (AUC) of the VTE-PRS-15 model was validated. This process was repeated 10 times, resulting in 10 validation sets. The AUC of the model in each validation set was calculated, and the mean was determined to evaluate the stability of the VTE-PRS-15 model performance.

[0084] The area under the receiver operating characteristic (ROC) curve (AUC) of the VTE-PRS-15 model was plotted based on the 10-resampled validation set. The results are shown in Table 8. The results show that the AUC range of the VTE-PRS-15 model in the 10-resampled sample is 0.675-0.728, and the mean AUC is 0.701. Compared with the AUC of 0.702 calculated based on the target data, the two results are highly consistent, reflecting the stability of the model's discrimination ability in different datasets.

[0085] Table 8. VTE-PRS-15 Resampling Validation AUC

[0086] The area under the ROC curve (AUC) of the VTE-PRS-15 model was plotted based on a 10-times resampled validation set. Figure 4 As shown.

[0087] 4.2 Clinical value validation of VTE-PRS-15 First, using VTE-PRS-15 value as the independent variable and the occurrence of deep vein thrombosis (DVT) as the dependent variable, a one-way binary logistic regression analysis was conducted to analyze the effect of VTE-PRS-15 value on DVT (see Table 9). For every 1-point increase in VTE-PRS-15 value, the risk of DVT increased by 218.2% (OR=3.182, 95%CI: 2.740~3.694, p<0.001); expressed as a standard deviation (SD=0.68), each increase of 1 SD corresponded to a 119.8% increase in the risk of DVT (OR=2.198, 95%CI: 1.934~2.499). Therefore, VTE-PRS-15 is a significant risk factor for DVT.

[0088] Table 9 Univariate binary logistic regression analysis of the effect of VTE-PRS-15 on DVT

[0089] Further univariate analysis was performed on all risk factors of the Caprini scale in the target data. Chi-square test (χ² test) was used for inter-group comparisons. All tests were two-tailed with a significance level of α=0.05, and P<0.05 was considered statistically significant. Statistical results are shown in Table 10.

[0090] Table 10. Results of Caprini Quantitative Single Factor Analysis

[0091]

[0092] As shown in Table 10, the risk factors with statistically significant differences (P<0.001) include: age, BMI>25, severe lung disease (such as pneumonia, duration of illness <1 month), bedridden patients, pregnancy or postpartum, major open surgery (>45 minutes), elective joint replacement surgery, bed rest for >72 hours, plaster cast immobilization, and hip / pelvic or lower extremity fractures.

[0093] The common orthopedic clinical risk factors that were statistically significant (P < 0.001) in the above Caprini quantitative scalar factor analysis (BMI ≥ 25 kg / m², major open surgery (> 45 minutes), elective joint replacement surgery, hip / pelvic or lower limb fractures, age (continuous variable), and acute spinal cord injury (duration < 1 month) and VTE-PRS-15 value were included in the multivariate logistic regression analysis. The results are shown in Table 11.

[0094] Table 11 Multivariate Logistic Regression Analysis of VTE

[0095] The results showed that the correlation between VTE-PRS-15 value and DVT remained statistically significant (P<0.001), meaning it significantly contributed to the occurrence of DVT. This demonstrates that VTE-PRS-15 value is an independent risk factor for DVT (statistically speaking, if a variable significantly contributes to the outcome in a statistical model that includes known risk factors, it is called an independent risk factor). VTE-PRS-15 (OR=3.330, 95%CI: 2.836-3.911) suggests that each 1-point increase in VTE-PRS-15 value can increase the risk of DVT by 233%.

[0096] 4.3 Clinical application validation of VTE-PRS-15 ①Caprini scale data analysis This invention adopts the Caprini scale and risk assessment standard recommended by the Expert Committee of the National Project for the Prevention and Control of Pulmonary Embolism and Deep Vein Thrombosis, in the "Guidelines for Quality Evaluation and Management of Prevention and Control of Intravenous Thromboembolism in Hospitals (2022 Edition)". The risk assessment results based on the target data are shown in Table 12.

[0097] Table 12 Target Data Caprini Scale Risk Assessment Results

[0098] A total of 2038 cases were collected for the target data collection. Of these, 1691 cases (82.97%) were assessed as high-risk using the Caprini scale. Among these, 806 cases did not develop deep vein thrombosis (DVT), and 885 cases did. DVT accounted for 52.34% of the 1691 high-risk cases. 60 cases (2.94%) were assessed as low-risk using the Caprini scale. Of these, 47 cases did not develop DVT, and 13 cases did. Non-DVT accounted for 78.33% of the 60 low-risk cases.

[0099] The Caprini scale was defined as follows: high risk was defined as positive, and intermediate and low risk were combined as negative. The sensitivity of the Caprini scale was calculated to be 87.36%, and the specificity was 21.37%. The area under the ROC curve (AUC) of the Caprini scale was 0.569 (95% CI: 0.544–0.592, P<0.001), reflecting the current clinical context where the Caprini scale has limited ability to differentiate VTE risk populations. The Caprini scale ROC curve is shown below. Figure 4 .

[0100] In the current clinical setting in China, the accuracy of the Caprini scale assessment is significantly limited due to factors such as racial differences, lack of genetic risk factors, time-consuming and complex operation, and varying degrees of misunderstanding of certain items.

[0101] ② Analysis of the accuracy of clinical prediction after incorporating the genetic risk factor VTE-PRS-15 Statistical analysis was performed on the predictive results of the Caprini scale, VTE-PRS-15B+Caprini, and VTE-PRS-15T+Caprini. First, high risk or high-risk was assigned as the positive class, and intermediate or low risk as the negative class. The sensitivity, specificity, positive predictive value, and negative predictive value of Caprini, VTE-PRS-15B+Caprini, and VTE-PRS-15T+Caprini were calculated. Calculation formula:

[0102] Table 13 Accuracy Results of Risk Assessment for Three Models

[0103] The results show: The Caprini scale had a sensitivity of 87.36%, a specificity of 21.37%, a positive predictive value of 52.34%, and a negative predictive value of 63.11%. The VTE-PRS-15B+ Caprini model, while exhibiting high sensitivity (90.52%), also showed significantly higher specificity than other models and the Caprini scale itself. This indicates that this model, while possessing good DVT risk identification capabilities, can significantly reduce the misjudgment rate of the traditional Caprini scale. The VTE-PRS-15T+ Caprini model had even higher sensitivity (93.88%), and its specificity (27.63%) was improved compared to the Caprini scale. This indicates that this model, while possessing good DVT risk identification capabilities, can also reduce the misjudgment rate of the traditional Caprini scale to some extent.

[0104] Using the VTE-PRS-15B+ Caprini model, the sensitivity of the Caprini scale was increased from 87.36% to 90.52%, while the specificity increased from 21.37% to 33.85%. In other words, the Caprini scale would misclassify 79 out of 100 individuals as high-risk, while the VTE-PRS-15B+ Caprini scale would misclassify 66 out of 100 individuals, resulting in approximately 13 fewer misclassifications per 100 individuals. Furthermore, specificity is directly correlated with positive predictive value (PPV). With a constant prevalence, improved specificity leads to a corresponding increase in PPV, meaning that when the model predicts a "positive" result, the actual probability of having the disease is higher, thus improving the reliability of positive results from the VTE-PRS-15B+ Caprini scale.

[0105] The Net Reclassification Improvement Index (NRI) for the three models was further calculated. NRI measures the improvement in risk stratification accuracy between the new predictive model (or newly added indicator) and the old model. Using the Caprini scale as the old model and VTE-PRS-15B+Caprini and VTE-PRS-15T+Caprini as the new models, the calculation formula is as follows: NRI = (sensitivity) new +Specificity new ) — (Sensitivity) old +Specificity old ) The calculation results are shown in Table 14.

[0106] Table 14. Improved NRI based on the predictive power of the two new models

[0107] The results showed that both new models achieved a positive improvement in NRI (Negative Risk Index) greater than zero, indicating a substantial improvement in their risk stratification ability compared to the traditional Caprini scale. Both clinical application models incorporating VTE-PRS-15 demonstrated better risk assessment results than those using the Caprini scale alone. Specifically: The NRI (Negative RI) of the VTE-PRS-15B+Caprini scale compared to the traditional Caprini scale was 15.65% (95% CI, 11.73%, 19.57%; p<0.001), meaning the proportion of "correct reclassification" increased by 15.65% compared to the traditional Caprini scale. Among DVT patients, 3.16% were correctly reclassified from the low-to-intermediate risk group to the high-risk group, and among non-DVT patients, 12.49% were correctly reclassified from the high-risk group to the low-to-intermediate risk group. This demonstrates the significant clinical value of this new model in improving upon the old model. The NRI (Negative RI) of the VTE-PRS-15T+Caprini scale compared to the traditional Caprini scale was 11.88% (95% CI, 7.57%, 16.19%; p<0.001), meaning the proportion of "correct reclassification" increased by 11.88% compared to the traditional Caprini scale. Among DVT patients, 6.52% were correctly reclassified from the low-to-intermediate risk group to the high-risk group, and among non-DVT patients, 5.37% were correctly reclassified from the high-risk group to the low-to-intermediate risk group. This indicates that the new model also has significant clinical value in improving upon the old model.

[0108] 4.4 Validation of the clinical predictive accuracy after incorporating the genetic risk factor VTE-PRS-15

[0109] Ten validation sets were constructed from the target data using bootstrap resampling. The various indicators of the model in each validation set were calculated, and the means were calculated to evaluate the stability of the VTE-PRS-15B+Caprini and VTE-PRS-15T+Caprini models. See Tables 15, 16, 17, and 18 for details. The validation results show that in the 10 bootstrap resampling sets, the comprehensive risk assessment model incorporating genetic factors exhibited stable performance across all indicators, showing high consistency with the results calculated based on the target data. This validates that incorporating the genetic factor VTE-PRS-15 into clinical assessment results in higher accuracy for risk assessment in both DVT and non-DVT patients compared to the traditional Caprini scale, and also validates the good stability of the method's clinical predictive performance.

[0110] Table 15 Resampling Validation Results of the VTE-PRS-15B+Caprini Integrated Risk Assessment

[0111] Table 16 Validation Results of VTE-PRS-15T+Caprini Integrated Risk Assessment

[0112] Table 17 Validation Results of Accuracy Indicators for Risk Assessment of New and Old Models

[0113] Table 18. Validation results of the improved predictive ability of the two new models using NRI.

[0114] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for assessing the risk of venous thromboembolism in the Chinese population based on the VTE-PRS-15 model, characterized in that, Includes the following steps: (1) For SERPINC1 rs2227589, PROC rs146922325, PROC rs199469469, PROS1rs6795524, THBD rs16984852, APOH rs52797880, F11 rs2289252, F11 rs2036914, F2rs3136516, FGG rs2066865, PAI-1 rs1799762, ABO rs8176719, MTHFR rs1801133, NOS3rs1799983 and HIVEP1 (1) Genotyping of 15 SNPs in rs169713; (2) Calculation of VTE-PRS-15 value using VTE-PRS-15 model formula; (3) Determination of VTE genetic risk of patients using VTE-PRS-15 cut-off value calculated by K-means clustering algorithm; (4) Determination of comprehensive VTE risk of patients by combining Caprini score results.

2. The method according to claim 1, characterized in that, In step (1), SNP detection is performed using PCR-fluorescent probe technology.

3. The method according to claim 1, characterized in that, The formula for calculating the VTE-PRS-15 value is as follows: , where i = 1 to 15, βi represents the effect value of the i-th genetic locus, and Gi represents the copy number of the risk allele.

4. The method according to claim 1 or 3, characterized in that, The cut-off value of VTE-PRS-15 was determined by K-means clustering algorithm: (1) The cut-off value of VTE-PRS-15 with binary classification result is 2.

335. When VTE-PRS-15≥2.335, it is genetically high risk, and when VTE-PRS-15<2.335, it is genetically low risk; (2) When VTE-PRS-15 with tri-class classification result, the cut-off values ​​are 2.230 and 1.730, respectively. When VTE-PRS-15≥2.230, it is genetically high risk, when 1.730<VTE-PRS-15<2.230, it is genetically medium risk, and when VTE-PRS-15≤1.730, it is genetically low risk.

5. The use of the method of claim 1 in the preparation of diagnostic reagents or products for assessing the genetic risk of venous thromboembolism in individuals.

6. A comprehensive assessment method for evaluating VTE risk in the Chinese population, characterized in that, It includes the following steps: (1) Genotyping 15 SNPs including SERPINC1 rs2227589, PROC rs146922325, PROC rs199469469, PROS1 rs6795524, THBD rs16984852, APOH rs52797880, F11 rs2289252, F11 rs2036914, F2 rs3136516, FGG rs2066865, PAI-1 rs1799762, ABO rs8176719, MTHFR rs1801133, NOS3 rs1799983 and HIVEP1 rs169713 in a patient sample, and calculating the VTE-PRS−15 value through the VTE-PRS−15 model formula; (2) Simultaneously performing a clinical risk assessment using the Caprini risk assessment scale; (3) Based on the VTE-PRS−15 value and the Caprini score, comprehensively determining the VTE risk of the patient as high risk, medium risk or low risk.

7. The method according to claim 6, characterized in that, When the Caprini scale assessment is high risk or medium risk and the VTE-PRS−15 value ≥ 2.335, it is determined as high risk. When the Caprini scale assessment is medium risk or low risk and the VTE-PRS−15 value < 2.335, it is determined as low risk. In other cases, it is determined as medium risk.

8. The method according to claim 6, characterized in that, When the VTE-PRS−15 value ≥ 2.230, it is determined as high risk. When the Caprini scale assessment is high risk and 1.730 < VTE-PRS−15 value < 2.230, it is determined as medium risk. In other cases, it is determined as low risk.

9. A gene polymorphism detection kit for VTE risk assessment in the Chinese population, characterized in that, It includes specific primers and fluorescent probes for the 15 SNPs described in claim 1.

10. The reagent kit according to claim 9, characterized in that, The specific primers and probes include the sequences shown in SEQ ID NO.1 to SEQ ID NO.63.

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