Predictive Performance and Clinical Application of PRS-15 for Venous Thromboembolism Risk in Chinese Orthopedic Patients: A Multicenter Prospective Case-Control Study

Predictive Performance and Clinical Application of PRS-15 for Venous Thromboembolism Risk in Chinese Orthopedic Patients: A Multicenter Prospective Case-Control Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Predictive Performance and Clinical Application of PRS-15 for Venous Thromboembolism Risk in Chinese Orthopedic Patients: A Multicenter Prospective Case-Control Study Shaoying Lu, Jiaxuan Hou, Jiawei Zhang, Chao Liu, Huijun Yuan, and 24 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8190841/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Background Venous Thromboembolism (VTE) is influenced by clinical and genetic factors. The Caprini score is widely used but does not account for population-specific genetic susceptibility in Chinese individuals. We previously developed a Chinese-specific 15-variant polygenic risk score (PRS-15) for VTE; however, its clinical performance and added value when combined with the Caprini score remain unclear. Methods We conducted a multicenter prospective case–control study at 18 hospitals in China (February 2023–March 2025), enrolling 1,013 postoperative VTE cases and 1,025 controls. Blood samples were genotyped for PRS-15, and clinical variables and outcomes were analyzed. Discrimination and calibration were assessed using AUC and the Hosmer–Lemeshow test. Two integrated models were constructed: Model 1, PRS-15T (tertiles: high/intermediate/low) plus Caprini; and Model 2, PRS-15B (binary: high/low) plus Caprini. K-means clustering identified optimal PRS-15 thresholds and integration strategies. Model performance was evaluated by sensitivity, specificity, and net reclassification improvement (NRI), with internal validation by bootstrap resampling. Results The PRS-15 model achieved an AUC of 0.702 (95% CI, 0.680–0.725; P < 0.001). For ternary classification (PRS-15T), the optimal thresholds were 2.230 and 1.730, while the optimal threshold for binary classification (PRS-15B) was 2.335. The sensitivities of Model 1 and Model 2 were 93.88% and 90.52%, respectively, both exceeding that of the Caprini score alone (87.36%). Model 2 demonstrated the highest specificity (33.85%), outperforming Model 1 (26.73%) and the Caprini score (21.37%), and achieved an NRI of 15.65%. Conclusion PRS-15 accurately discriminated VTE cases from controls and remained robust across age and sex subgroups. Adding PRS-15 to the Caprini score significantly improved risk assessment accuracy, supporting its clinical use for VTE risk evaluation in Chinese orthopedic surgical patients. Health sciences/Risk factors Health sciences/Biomarkers/Predictive markers Biological sciences/Genetics/Genetic association study/Genome-wide association studies Venous Thromboembolism Polygenic risk score Caprini score Risk assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Venous thromboembolism (VTE), comprising deep vein thrombosis (DVT) and pulmonary thromboembolism (PTE), is the third most common cardiovascular disease worldwide [ 1 ] . The incidence of VTE within four months after spinal orthopedic surgery was reported to be approximately 4.3%-4.8% [ 2 ] . Epidemiological studies following VTE prophylaxis in major orthopedic surgery have reported DVT incidence rates of 1.8%-2.9% [ 3 ] .A multi-center study across 90 hospitals reported a fivefold surge in VTE cases from 2007 to 2016 in China [ 4 ] . Therefore, preventing VTE has substantial clinical significance. VTE is a multifactorial and complex disease caused by a combination of genetic and acquired factors. Genetic predisposition plays a crucial and non-negligible role in the development of VTE. The heritability of VTE has been estimated to be 23%-30% in some studies [ 5 , 6 ] and 40%-60% based on family studies [ 7 , 8 ] . As a comprehensive evaluation tool, the Caprini risk assessment model is widely recommended in orthopedics, which incorporates two major genetic factors: factor V Leiden and prothrombin G20210A mutations [ 9 ] . However, these mutations are exceedingly rare in the Chinese population. Therefore, only acquired factors in the Caprini score can be evaluated in Chinese clinical practice, limiting its applicability [ 10 – 12 ] . As no major-effect genes or loci associated with VTE have been identified in Chinese populations, the genetic risk of VTE cannot be adequately explained by individual variants or the combination of a few genetic variants. Therefore, constructing a polygenic risk score (PRS) model that estimates the cumulative effects of multiple genetic polymorphisms and integrating it with the Caprini score is essential to enhance comprehensive risk prediction. PRS research has become a major focus in the field of genetic risk prediction for multifactorial and complex diseases. PRS provides significant predictive value for complex diseases, such as cancer [ 13 ] , cardiovascular diseases [ 14 , 15 ] , and VTE [ 16 ] . However, no clinical studies have yet reported PRS-based VTE risk prediction models specifically tailored to the Chinese population. In our previous work, we developed PRS models (PRS-53, PRS-10, and PRS-X 10−53 ) consisting of 10–53 single-nucleotide polymorphisms (SNPs) and their combinations to predict first-onset VTE risk in the Chinese population [ 17 ] . These models were evaluated using simulated data and exhibited comparable predictive performance. We constructed PRS-15 based on the streamlined PRS-10 model to maintain model simplicity while fully capturing the pathophysiological mechanisms of VTE, supplementing it with five additional SNPs (SERPINC1, PROS1, F2, F11, and HIVEP1) that are closely associated with the pathogenesis of VTE among the 53 VTE-associated SNPs identified in Chinese populations. We then conducted a multicenter prospective case-control study among Chinese orthopedic patients to evaluate PRS-15 performance in real-world clinical cases and assess its clinical benefits in evaluating the risk of VTE, thereby providing empirical support for its clinical application. 2. Methods 2.1 Study Design This study adopted a prospective case-control study design, using DVT, with or without PTE, as the outcome variable. The research groups (case and control groups) were defined prospectively, and clinical cases related to the outcome were collected to evaluate the predictive performance of the PRS-15 model. In total, 2,318 clinical cases were initially recruited. After excluding 280 patients who were lost to follow-up, had missing ultrasound results, or had a hospital stay of less than three days, 2,038 patients were included in the final analysis. Among them, 1,013 were assigned to the case group and 1,025 to the control group. The inclusion and exclusion criteria are as follows: Inclusion criteria Exclusion criteria Patients aged 18–80 years; Hospitalized in the orthopedic department with a length of stay of ≥3 days; Surgical or perioperative orthopedic trauma patients; Patients with fresh fractures; Completion of bilateral lower-extremity venous color Doppler ultrasonography; Absence of contraindications for treatment with anticoagulants; Provision of written informed consent by the patients or their legal guardians. Pathological fractures; cirrhosis, hepatocellular carcinoma, or previous hepatectomy; Pre-admission long-term immobility or hemiplegia; Terminal stage of any disease with an expected survival length of <1 year; Renal impairment or hepatic dysfunction; Hematologic disorders or coagulation abnormalities; History of thrombotic diseases under treatment; Poor compliance, known psychiatric diseases, or cognitive impairment; Refusing to receive standard anticoagulation prophylaxis Patients were screened based on the predefined inclusion and exclusion criteria. Bilateral lower-extremity venous color Doppler ultrasonography was conducted to diagnose DVT. Patients who developed DVT during hospitalization were included in the case group. Those without DVT during hospitalization were followed for three months postoperatively. Patients who developed DVT during follow-up were classified into the case group, while those who did not develop DVT were assigned to the control group. This was an observational study without any intervention beyond standard clinical practice. The study was initiated by the First Affiliated Hospital of Xi’an Jiaotong University and approved by the ethics committees of all participating centers. Written informed consent was obtained from all participants, and the study This study was approved by the the Ethics Committee of the First Affiliated Hospital of Xi’an Jiaotong University, China (NO. XJTUAF2023LSK-285) and registered in the Chinese Medical Research Registry System (NO. MR-61-23-019694). 2.2 Construction of the PRS-15 Model Based on previous findings, the PRS-15 model was developed from 53 SNPs highly associated with VTE in the Chinese population. The streamlined PRS-10 model served as the foundation, and five additional SNPs associated with the pathogenesis of VTE were incorporated: SERPINC1 (rs2227589), PROS1 (rs6795524), F2 (rs3136516), F11 (rs2289252), and HIVEP1 (rs169713) . The PRS-15 model formula was as follows: where i represents the i- th SNP (i = 1, 2, …, 15); βi denotes the effect size of the i- th SNP (the natural logarithm of the odds ratio [ln(OR)]); and Gi represents the number of risk alleles for the i- th SNP, coded as 0, 1, or 2. Table 1 presents the 15 SNPs and their classification based on the corresponding pathophysiological mechanisms associated with VTE. Table 1 . Classification of pathophysiological mechanisms linking the 15 SNPs in PRS-15 to VTE. Pathophysiology GENE SNP Chr OR Anticoagulant SERPINC1 rs2227589 1 1.2 PROC rs146922325 2 6.91 PROC rs199469469 2 2.9 PROS1 rs6795524 3 1.18 THBD rs16984852 20 2.8 APOH rs52797880 17 1.55 Coagulation F11 rs2289252 4 1.49 F11 rs2036914 4 1.49 F2 rs3136516 11 1.19 Fibrinolytic FGG rs2066865 4 1.56 PAI-1 rs1799762 7 1.55 Platelet ABO rs8176719 9 1.85 Vascular endothelial cell MTHFR rs1801133 1 1.3 NOS3 rs1799983 7 1.41 HIVEP1 rs169713 6 1.2 Chr, Chromosome; SNP, single nucleotide polymorphism. 2.3 Sample Size Calculation The PRS-15 model contains 15 SNPs as variables, each with three possible genotypes. Since the number of events per variable (EPV) should not be less than 10 in the development and validation of clinical prediction models [18] , the sample size was calculated to be 20 times the number of variables. Therefore, both the case and control groups required approximately 900 subjects each: 15 (SNPs) × 3 (genotypes) × 20 = 900 per group (total n = 1,800). 2.4 Sample and Data Collection Whole-blood samples were collected from all participants during hospitalization for PRS-15-related genetic testing. The following clinical data were simultaneously recorded: general information (study code, sex, age, height, weight, department, admission date, discharge date, operation date, and clinical diagnosis), Caprini score information (excluding factor V Leiden and prothrombin G20210A mutations), polygenic risk score (PRS-15 value), laboratory parameters (red blood cell count [RBC], platelet [PLT], D-dimer, hemoglobin, C-reactive protein [CRP], thrombin time [TT], fibrin degradation products [FDP], prothrombin time [PT], fibrinogen [FIB]), and imaging results. Genomic DNA was extracted from blood samples using DNA extraction kits (Batch Nos. A0613A, A0809A, A1112A; TIANGEN BIOTECH CO., LTD., Beijing, China), followed by quality control. Genotyping of 15 VTE-related SNPs was conducted using PCR-fluorescence probes (Batch Nos. 240501, 240502, 240701; Xi’an Agen Medicine Technology Co., Ltd., China). The results were validated via Sanger sequencing. The results of genotyping were then incorporated into the PRS-15 formula to calculate individual PRS-15 values. 2.5 Determination of Optimal PRS-15 Thresholds and Integrated Evaluation via K-Means Clustering Analysis First, two integrated risk assessment models were defined: Model 1 (PRS-15T + Caprini), consisting of PRS-15T tertiary-classification outcome (high/intermediate/low genetic risk) combined with the Caprini score (high/intermediate/low risk); and Model 2 (PRS-15B + Caprini), consisting of PRS-15B binary-classification outcome (high/low genetic risk) combined with the Caprini score (high/intermediate/low risk). Both models classified all patients into high-risk, intermediate-risk, or low-risk categories. Given the large computational load, two custom software programs, namely “VTE Comprehensive Risk Assessment Automated Data Processing Program Algorithm” and “Software Program for Mining Optimal Thresholds of K-Means Clustering Analysis and Comprehensive Risk Assessment Methods”, were developed to improve computational efficiency. The detailed algorithms and program codes are provided in the appendix to ensure full reproducibility of the data mining process. The results of the Caprini score were first summarized, followed by data processing and k -means clustering analysis. Data processing: The PRS-15 values of the study samples ranged from 0.680 to 5.780. Step intervals were set at 0.05 for PRS-15T and at 0.005 for PRS-15B. The program iteratively traversed all possible risk thresholds or threshold pairs and their corresponding risk classification combinations and outcomes, including 5,200 threshold sets, 19,683 combinations, and 102,341,600 results for PRS-15T, as well as 1,020 thresholds, 729 combinations, and 743,580 results for PRS-15B. The samples were automatically categorized into high-risk, intermediate-risk, and low-risk groups based on predefined PRS-15 and Caprini combination strategies. The number of patients with DVT and without DVT was calculated for each group. Four criteria were set for selecting optimal thresholds and combinations: (1) more patients with DVT were classified as high risk; (2) more patients without DVT were classified as low risk; (3) fewer patients without DVT were classified as high risk; and (4) fewer patients with DVT were classified as low risk. The program automatically filtered data combinations that met all four criteria for k-means clustering analysis. K-means clustering analysis: The program divided the dataset into K mutually exclusive clusters, ensuring that data within each cluster had the highest similarity, while differences between clusters were maximized. Four “ideal points” were defined: in the high-risk group, 100% of patients had DVT and 0% of patients did not experience DVT; in the low-risk group, 100% of patients did not experience DVT and 0% of patients experienced DVT. The processed and filtered data groups were then input into the program. Through continuous iteration and adjustment, the program identified the data row closest to the predefined ideal points. This data row represented the optimal PRS-15 risk thresholds and the comprehensive risk assessment method integrating PRS-15 with the Caprini score. The overall technical workflow of the study is illustrated in Scheme 1 . Scheme 1 . Flow chart of the study. Model 1 represents the integrated risk assessment of the Caprini score and PRS-15T; Model 2 represents the integrated risk assessment of the Caprini score and PRS-15B. SEN, sensitivity; SEP, specificity; PPV, positive predictive value, NPV, negative predictive value,; NRI, net reclassification improvement. 2.6 Statistical Analysis Continuous variables are expressed as mean ± standard deviation (SD) and were compared between groups using the t -test. Categorical variables are presented as frequencies and percentages. Differences in categorical variables between two or more groups were evaluated using the chi-square test. The discriminative ability of PRS-15 was assessed using the area under the receiver operating characteristic curve (AUC), and the Z-test was employed to compare differences in AUC values. The stability of PRS-15 discrimination across different sample sets was validated using the bootstrap method. Calibration of the PRS-15 model was assessed using the Hosmer-Lemeshow goodness-of-fit test. All statistical analyses and visualizations were conducted using R software (version 4.3.2) , with a two-sided significance level set at P < 0.05 . 3. Results 3.1 Baseline Characteristics of the Study Population Table 2 summarizes the demographic characteristics of the 2,038 patients included in this study, of whom 51.72% were male. The mean age of the case group (n = 1,013) was 60.28 ± 12.68 years, while that of the control group (n = 1,025) was 52.24 ± 14.93 years. The mean body mass index (BMI) of the case group was higher than that of the control group (24.18 ± 3.66 vs. 23.74 ± 3.00). The mean Caprini score of the case group was 8.58 ± 3.05, and that of the control group was 7.69 ± 3.28. Detailed Caprini score data for all 2,038 patients are provided in Table S1 . Table 2 . Demographic Characteristics of the Study Cohort. Characteristic Case (n=1013) Control (n=1025) Age a , years 60.28±12.68 52.24±14.93 Male b 455(44.92%) 554(54.04%) BMI a , kg/m 2 24.18±3.66 23.74±3.00 Caprini score a 8.58±3.05 7.69±3.28 ≥5 885(52.34%) 806(47.66%) 3-4 115(40.1%) 172(59.9%) 1-2 13(21.67%) 47(78.33%) a Median±standard deviation, b Number of patients(percentage). BMI, Body Mass Index. 3.2 Distribution of PRS-15 in the Study Population Figure 1 shows the distribution of PRS-15 values in the case and control groups. The PRS-15 values in the case group were significantly higher than those in the control group (3.18 ± 0.69 vs. 2.66 ± 0.67, P < 0.001), and the difference was statistically significant ( Figure 1A ). Subgroup analyses by sex and age exhibited the same trend. Among male participants, the mean PRS-15 value was 3.18 ± 0.65 among cases and 2.69 ± 0.69 among controls (P < 0.001). Among female participants, the mean PRS-15 value was 3.18 ± 0.71 among cases and 2.62 ± 0.64 among controls (P < 0.001) ( Figure 1B ). In participants aged ≤60 years, the mean PRS-15 value was 3.20 ± 0.67 among cases and 2.67 ± 0.67 among controls (P 60 years, the mean PRS-15 value was 3.16 ± 0.70 among cases and 2.63 ± 0.65 among controls (P < 0.001) ( Figure 1C ). These findings suggest that PRS-15 can serve as an effective indicator for distinguishing patients with DVT from those without DVT. Since all 15 genetic polymorphisms were germline variants located on autosomes, their discriminative power was not affected by age or sex. 3.3 Discrimination and Calibration of PRS-15 Using the individual PRS-15 value as an independent variable and the incidence of DVT as the dependent variable (coded as 1 for cases and 0 for controls), the AUC for PRS-15 was calculated to be 0.702 (95% CI, 0.680–0.725; P 0.05), indicating that PRS-15 maintained good predictive ability for DVT risk in real-world clinical settings (AUC > 0.7). Subgroup analyses also showed that the discriminative performance of PRS-15 did not significantly differ by sex, with an AUC of 0.692 (95% CI, 0.6601–0.7243) in males and 0.716 (95% CI, 0.6851–0.747) in females (P = 0.294). Similarly, the discriminative ability was comparable across age groups, with an AUC of 0.706 (95% CI, 0.6767–0.736) for participants aged ≤60 years and 0.706 (95% CI, 0.6712–0.7416) for those aged >60 years (P = 0.998). Two complementary methods were utilized to assess model calibration. The Hosmer-Lemeshow goodness-of-fit test yielded a χ² value of 18.08 with 8 degrees of freedom (P = 0.021), suggesting a negligible difference between predicted and observed probabilities ( Figure 2B ). However, the calibration curve adjusted using the bootstrap method (B = 100 repetitions; n = 2,038) exhibited good agreement between the predicted probabilities and observed incidence rates, with a mean absolute error of 0.023 ( Figure 2C ). These results suggest an acceptable overall calibration performance of the PRS-15 model. A bootstrap resampling procedure was applied to the entire cohort of 2,038 patients using random sampling with replacement to generate ten validation sets of equal size (case group, n = 1,013; control group, n = 1,025). The AUC values for the ten resampled validation sets ranged from 0.675 to 0.728, with a mean AUC of 0.701, indicating good stability of the discriminative ability of the PRS-15 model across different sample sets. The AUC values for all ten bootstrap samples are shown in Table 3 , and the corresponding ROC curves are presented in Figure 3 . Table 3 . Results of 10 bootstrap resamples Bootstrap samples AUC SE P 95% CI Sample 1 0.728 0.011 0.000 0.706 0.749 Sample 2 0.704 0.011 0.000 0.682 0.726 Sample 3 0.693 0.012 0.000 0.671 0.716 Sample 4 0.723 0.011 0.000 0.701 0.745 Sample 5 0.675 0.012 0.000 0.652 0.698 Sample 6 0.685 0.012 0.000 0.663 0.708 Sample 7 0.704 0.011 0.000 0.682 0.727 Sample 8 0.694 0.011 0.000 0.671 0.716 Sample 9 0.689 0.012 0.000 0.666 0.711 Sample 10 0.712 0.011 0.000 0.690 0.734 3.4 PRS-15 Risk Thresholds and Comprehensive Risk Assessment Methods K -means clustering data mining identified PRS-15 thresholds as follows: the thresholds were 2.230 and 1.730 for the tertiary-classification model (PRS-15T), corresponding to high genetic risk (PRS-15 ≥ 2.230), intermediate genetic risk (1.730 < PRS-15 < 2.230), and low genetic risk (PRS-15 ≤ 1.730). The optimal risk threshold was 2.335 for the binary-classification model (PRS-15B), corresponding to high genetic risk (PRS-15 ≥ 2.335) and low genetic risk (PRS-15 < 2.335). The risk classification criteria for the two integrated risk assessment models (Model 1: PRS-15T + Caprini; Model 2: PRS-15B + Caprini) are presented in Table 4 . Table 4 . Risk stratification of Caprini + PRS-15. Caprini PRS-15T Model 1 PRS-15B Model 2 Risk stratification Score Risk Score Risk stratification Risk Score Risk stratification High ≥5 High ≥2.230 High high ≥2.335 high Moderate 3-4 High ≥2.230 High high ≥2.335 high Low 1-2 High ≥2.230 High high ≥2.335 moderate High ≥5 Moderate 1.730-2.230 Moderate low ＜2.335 moderate Moderate 3-4 Moderate 1.730-2.230 Low low ＜2.335 low low 1-2 Moderate 1.730-2.230 Low low ＜2.335 low High ≥5 Low ≤1.730 Low - - - Moderate 3-4 Low ≤1.730 Low - - - Low 1-2 Low ≤1.730 Low - - - Note: Model 1 represents the integrated risk assessment of the Caprini score and PRS-15T. For example, when both were classified as high risk, the integrated Mode 1 was also defined as high risk, and so forth. Mode 2 represents the integrated risk assessment of the Caprini score and PRS-15B. Based on these thresholds and the risk classification criteria, Models 1 and 2 were applied to the study population, and the results were compared with those of the Caprini score ( Table 5 ). The number of patients with DVT increased in the high-risk groups defined by both models. The proportion of patients with DVT in the intermediate-risk groups of Model 1 (24.8%) and Model 2 (24.9%) was significantly lower than that in the Caprini intermediate-risk group (40.1%). Similarly, the proportion of patients with DVT in the low-risk groups of Model 1 (8.5%) and Model 2 (6.5%) was notably lower than that in the Caprini low-risk group (21.7%). Overall, both Model 1 and Model 2 showed improved discrimination between patients with and without DVT. Table 5 ． Risk Reclassification by the Caprini risk score, Model 1, and Model 2. Caprini Model 1 Model 2 Risk stratification Total DVT(%) OR(95%CI) P Total DVT(%) OR(95%CI) P Total DVT(%) OR(95%CI) P High 1691 885(52.3) 3.97 * (2.20-7.70) <0.001 * 1702 951(55.9) 13.70 * (7.68-27.12) <0.001 * 1595 917(57.5) 19.48 * (8.65-55.74) <0.001 * Intermedium 287 115(40.1) 2.41 # (1.29-4.83) <0.001 # 206 51(24.8) 3.56 # (1.84-7.47) <0.001 # 366 91(24.9) 4.77 # (2.05-13.90) <0.001 # Low 60 13(21.7) 130 11(8.5) 77 5(6.5%) Note. Model 1 represents the integrated risk assessment of the Caprini score and PRS-15T; Model 2 represents the integrated risk assessment of the Caprini score and PRS-15B. * indicates the comparison between the high-risk and low-risk groups; # indicates the comparison between the intermediate-risk and low-risk groups. Compared to the results of the Caprini score, patients reclassified by Models 1 and 2 into lower-risk categories showed lower incidence rates of DVT, whereas those reclassified into higher-risk categories exhibited higher incidence rates of DVT ( Figure 4 ). Specifically, Model 1 reclassified 206 patients (12.2%) from high to intermediate risk, among whom DVT incidence (24.8%) was significantly lower compared to the original high-risk group (52.3%). Additionally, 70 patients (4.1%) were downgraded from high to low risk, with a DVT incidence (12.9%) much lower compared to the original high-risk group. Meanwhile, 242 patients (84.3%) were upgraded from the intermediate-risk group to the high-risk group, with a DVT incidence (46.7%) higher than that observed in the original intermediate-risk group (40.1%). Model 2 reclassified 324 patients (19.2%) from high risk to intermediate risk, whose DVT incidence (24.1%) was significantly lower than that observed in the original high-risk group (52.3%). Moreover, 228 patients (79.4%) were upgraded from intermediate risk to high risk, with a DVT incidence (48.2%) higher than that observed in the original intermediate-risk group (40.1%). Using real-world data from 2,038 patients, risk assessments were conducted using the Caprini score, Model 1, and Model 2. Regarding high-risk classifications as positive and intermediate/low-risk classifications as negative, sensitivity (SEN), specificity (SEP), positive predictive value (PPV), negative predictive value (NPV), and net reclassification improvement (NRI) were calculated for each model ( Table 6 ). Both Models 1 and 2 exhibited improvement in all indices compared to the Caprini score. The sensitivity of Models 1 and 2 reached 93.88% and 90.52%, respectively, which was higher than that of the Caprini score (87.36%). The specificity of Model 2 (33.85%) was higher than that of Model 1 (26.73%) and the Caprini score (21.37%). The NRI of Model 2 was 15.65% (95% CI, 11.73%–19.57%; P < 0.001), indicating that 15.65% of patients were correctly reclassified when combining PRS-15B with the Caprini score. The NRI of Model 1 was 11.88% (95% CI, 7.57%–16.19%; P < 0.001), suggesting that 11.88% of patients were correctly reclassified using the PRS-15T + Caprini model. Table 6 . Comparison of predictive ability between the Caprini and integrated models. Model Sensitivity Specificity PPV NPV NRI P NRI- NRI+ Caprini 87.36 21.37 52.34 63.11 - - - - Model 1 93.88 26.73 55.88 81.55 11.88(7.57-16.19) ＜0.001 6.52 5.37 Model 2 90.52 33.85 57.49 78.33 15.65(11.73-19.57) ＜0.001 3.16 12.49 Note: Model 1 represents the integrated risk assessment of the Caprini score and PRS-15T; Model 2 represents the integrated risk assessment of the Caprini score and PRS-15B. 4. Discussion The risk factors of VTE are numerous and complex. Genetic factors determine individual susceptibility to thrombosis, which persists throughout life and may lead to thrombus formation in the presence of one or more acquired factors [19] . Therefore, risk assessment for VTE cannot rely solely on either genetic or acquired factors. The Caprini score system integrates validated clinical risk factors [20] and has been recommended by multiple thrombosis prevention guidelines [9] . However, in our study population, the overall discriminative ability of the Caprini score was limited (AUC: 0.569, 95% CI: 0.544–0.594), mainly because genetic risk factors were not included. In our previous study, we established evidence-based and statistically principled criteria and grading standards for identifying disease-related genetic variants in the Chinese population. By integrating data from genome-wide association studies (GWAS), meta-analyses, and candidate gene studies, we identified 53 SNPs associated with first-episode VTE among Chinese individuals and developed a full PRS-53 model. Using a forward stepwise selection method, we optimized model variables and identified ten repeatedly validated SNPs associated with VTE risk to construct the most concise PRS-10 model. We calculated the AUC based on the simulated data comprising 1,000 cases of VTE and 1,000 controls, which were generated using real SNP frequencies and linkage disequilibrium structures. The results showed no significant difference between PRS-10 and PRS-53 (p > 0.05), indicating that PRS-10 and PRS-53, as well as their combined models (PRS-X 10-53 ), had comparable predictive performance for VTE. The PRS-15 model was constructed in this study to fill the gap in genetic risk factors for assessing the risk of VTE in clinical settings in China. The optimized PRS-15 model maintained the simplicity of previous models while comprehensively covering the pathophysiological mechanisms involved in VTE. It provides a molecular-level foundation for elucidating disease etiology and achieving precision prevention. First, this study confirmed the strong discriminative power of the PRS-15 model for predicting genetic susceptibility to VTE from three perspectives. (1) The AUC of PRS-15 was 0.702. Although higher AUC values (e.g., 0.8–0.9) suggest excellent discrimination, an AUC of ≥ 0.7 is generally considered acceptable and reflects good discriminative ability, suggesting that the model can effectively differentiate between high-risk and low-risk individuals with significant clinical utility [21] . In multifactorial complex diseases, such discriminative performance for genetic risk prediction is within the acceptable range. (2) Age and sex are typically regarded as important factors associated with the incidence of VTE. Our results showed that in both male and female orthopedic surgical patients, the PRS-15 model effectively predicted DVT risk(P = 0.294). Similarly, in the age-stratified analysis, PRS-15 maintained consistent discriminative ability between middle-aged and elderly patients (P = 0.998), demonstrating that the 15 germline SNPs included in the PRS-15 model, carried from birth and persisting throughout life, can advance risk screening in the general population. The model may be particularly useful for identifying younger high-risk individuals who might be overlooked by conventional risk models. It therefore represents a lifelong genetic risk indicator, offering a new perspective for full-cycle VTE risk management and early warning in non-surgical populations. (3) Based on data from 2,038 patients, we conducted bootstrap resampling to generate ten validation datasets of equal sample size. The AUC values for these validation sets ranged from 0.675 to 0.728, with a mean of 0.701, indicating good stability in the discriminative ability of the PRS-15 model across different datasets. This finding provides key evidence supporting its clinical applicability. Since PRS-15 values in the study sample were continuous, ranging from 0.680 to 5.780, an infinite number of threshold cutoffs could theoretically be employed to classify risk into two or three categories. Thus, there will also be innumerable possible combinations for integrated risk assessment when combined with the Caprini score. We applied k-means clustering to real clinical outcome data to objectively identify optimal risk thresholds and integration methods. This data-driven approach allowed the discovery of inherent risk stratification patterns within the dataset. We first defined two integrated risk assessment models: PRS-15T + Caprini (Model 1) and PRS-15B + Caprini (Model 2). Using k-means clustering analysis based on actual results from this multicenter prospective case-control study, we systematically traversed all potential thresholds, risk classification combinations, and outcomes to mine the optimal PRS-15 thresholds and integrated assessment strategies for both models. The optimal thresholds were 2.230 and 1.730 for the tertiary-classification PRS-15T outcome, and 2.335 for the binary-classification PRS-15B outcome. Table 4 presents the corresponding risk classification criteria for Models 1 and 2. A comparative analysis of the comprehensive risk prediction results of Models 1 and 2 with those of the Caprini score revealed that Model 2 achieved a substantial improvement in specificity (33.85% vs. 21.37%) while maintaining a high level of sensitivity (90.52% vs. 87.36%). This finding indicates that the model not only preserved but also enhanced the high sensitivity of the Caprini score while significantly decreasing its false-positive rate. Model 1 further increased sensitivity (93.88% vs. 87.36%) and improved specificity (26.73% vs. 21.37%), suggesting that the PRS-15 genetic factor markedly improved the overall accuracy of the Caprini score. Both sensitivity and specificity are important in clinical prediction modeling, but their relative importance may vary depending on disease characteristics. For a potentially fatal condition, like VTE, the consequences of missed diagnosis (low sensitivity) are more severe than those of false positive results (low specificity) [22, 23] . The “high sensitivity and low specificity” nature of the Caprini score reflects a trade-off nature in a clinically oriented, broad-spectrum risk model. Although low specificity increases false-positive rates, this broad coverage minimizes the detection rate of patients at high risk of VTE, which is clinically justified given the fatal risk associated with VTE [24, 25] . Incorporation of the PRS-15 model not only enhanced the sensitivity of the Caprini score but also markedly improved its specificity, substantially improving its applicability to Chinese patients. The study showed that the NRI for PRS-15T + Caprini was 11.88%, while that for PRS-15B + Caprini was 15.65%, demonstrating that the combined models correctly reclassified a larger proportion of high-risk and low/intermediate-risk patients. This represents significant clinical value in improving the accuracy of risk assessment. In summary, this study provides two practical clinical approaches for applying the PRS-15 model to assess the risk of first-onset VTE in Chinese orthopedic patients and offers a data-driven method for identifying optimal PRS thresholds based on real clinical outcomes. The results showed that both accuracy and clinical benefit were significantly improved, with controlled risk. Model 2 demonstrated high sensitivity and specificity. Its integrated assessment method was also simpler, making it more convenient for clinical implementation. In terms of net reclassification improvement, Model 2 yielded the greatest clinical benefit. This study addressed key issues in translating the PRS-15 model from “research” to “clinical application”, including the determination of optimal risk thresholds and integrated risk assessment strategies. As a main limitation, this study included only orthopedic patients, whereas VTE occurs in multiple clinical departments. Since PRS-15 represents the aggregate expression of genetic predisposition, it should theoretically be applicable across specialties. Future studies involving diverse clinical populations are needed to explore the interactions of PRS-15 with disease-specific acquired factors and assess its broader clinical utility. Internal validation of the PRS-15 model was conducted in this study. Future studies should conduct external validation using independent datasets to evaluate the generalizability and transferability of the PRS-15 model. Finally, the underlying mechanisms linking PRS-15 to VTE pathogenesis and prevention remain important areas for future research. With the continuous expansion of genetic databases and the increasing number of GWAS discoveries, the PRS-15 model can be continuously refined by incorporating newly identified genetic markers, thereby optimizing its performance and maintaining its scientific rigor. Declarations Competing interests The authors declare no competing interests. Author contributions S.L. conceived and designed the study. J.H., J.Z., C.L. wrote and edited the manuscript. J.H., H.Y., L.Y., J.M., J.G., Z.H., H.Z., Y.T., H.S., X.M., H.Q., X.W., Y.Q., W.L., L.W., Q.Y., H.W., J.L., C.X., H.P.W., W.Y.Z., W.H. collected and assembled the data. J.H., W.Z. and Y.W. performed the statistical analysis. X.H.M, S.Y.Q. and S.L. supervised the study and performed critical revision of the manuscript. All authors read and approved the final manuscript. Acknowledgements We thank all the patients for participating this study. This study was supported by the National Key Research and Development Program of China (Key Project “Diagnostic and Therapeutic Equipment and Biomedical Materials”, Grant No. SQ2025YFC24000958) and the Shaanxi Provincial Key Research and Development Program (Key Project of “Dual-Chain Integration” Initiative, Grant No. 2025ZG-JBGS-022). Data availability The data analyzed during the current study are available from the corresponding author on reasonable request by emailing [email protected] . Supplementary figures and tables are in Supplementary Data 1. References KHAN F, TRITSCHLER T, KAHN S R, et al. Venous thromboembolism [J]. Lancet (London, England), 2021, 398(10294): 64–77. BAUMANN A N, TRAGER R J, CUTTICA N, et al. Risk of venous thromboembolism and bleeding complications for early enoxaparin versus heparin after same-day spine surgery for central cord syndrome: A propensity-matched retrospective cohort study [J]. The journal of spinal cord medicine, 2025: 1–10. ASSOCIATION O B O C M. Guidelines for the prevention of venous thromboembolism in major orthopedic surgery in China [J]. Chinese Journal of Orthopaedics, 2016, (2): 65–71. ZHANG Z, LEI J, SHAO X, et al. Trends in Hospitalization and In-Hospital Mortality From VTE, 2007 to 2016, in China [J]. Chest, 2019, 155(2): 342–53. LINDSTRöM S, WANG L, SMITH E N, et al. Genomic and transcriptomic association studies identify 16 novel susceptibility loci for venous thromboembolism [J]. Blood, 2019, 134(19): 1645–57. KLARIN D, BUSENKELL E, JUDY R, et al. Genome-wide association analysis of venous thromboembolism identifies new risk loci and genetic overlap with arterial vascular disease [J]. Nature Genetics, 2019, 51(11): 1574–9. HEIT J A, PHELPS M A, WARD S A, et al. Familial segregation of venous thromboembolism [J]. Journal of thrombosis and haemostasis: JTH, 2004, 2(5): 731–6. SOUTO J C, ALMASY L, BORRELL M, et al. Genetic susceptibility to thrombosis and its relationship to physiological risk factors: the GAIT study. Genetic Analysis of Idiopathic Thrombophilia [J]. American journal of human genetics, 2000, 67(6): 1452–9. KEARON C, AKL E A, COMEROTA A J, et al. Antithrombotic therapy for VTE disease: Antithrombotic Therapy and Prevention of Thrombosis, 9th ed: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines [J]. Chest, 2012, 141(2 Suppl): e419S-e96S. BAUDUER F, LACOMBE D. Factor V Leiden, prothrombin 20210A, methylenetetrahydrofolate reductase 677T, and population genetics [J]. Molecular genetics and metabolism, 2005, 86(1–2): 91 – 9. KO Y L, HSU T S, WU S M, et al. The G1691A mutation of the coagulation factor V gene (factor V Leiden) is rare in Chinese: an analysis of 618 individuals [J]. Human genetics, 1996, 98(2): 176–7. JUN Z J, PING T, LEI Y, et al. Prevalence of factor V Leiden and prothrombin G20210A mutations in Chinese patients with deep venous thrombosis and pulmonary embolism [J]. Clinical and laboratory haematology, 2006, 28(2): 111–6. BALIAKAS P, MUNTERS A R, KäMPE A, et al. Integrating a Polygenic Risk Score into a clinical setting would impact risk predictions in familial breast cancer [J]. Journal of medical genetics, 2024, 61(2): 150–4. SAMANI N J, BEESTON E, GREENGRASS C, et al. Polygenic risk score adds to a clinical risk score in the prediction of cardiovascular disease in a clinical setting [J]. European heart journal, 2024, 45(34): 3152–60. LI L, PANG S, STARNECKER F, et al. Integration of a polygenic score into guideline-recommended prediction of cardiovascular disease [J]. European heart journal, 2024, 45(20): 1843–52. ELLIOTT J, BODINIER B, BOND T A, et al. Predictive Accuracy of a Polygenic Risk Score-Enhanced Prediction Model vs a Clinical Risk Score for Coronary Artery Disease [J]. Jama, 2020, 323(7): 636–45. LIU C, HOU J, LI W, et al. Construction and optimization of a polygenic risk model for venous thromboembolism in the Chinese population [J]. Journal of vascular surgery Venous and lymphatic disorders, 2024, 12(1): 101666. PEDUZZI P, CONCATO J, KEMPER E, et al. A simulation study of the number of events per variable in logistic regression analysis [J]. Journal of clinical epidemiology, 1996, 49(12): 1373–9. HUANG XJ H H, HU Y. Hematology [M]. Beijing: People’s Medical Publishing House, 2021. CAPRINI J A. Thrombosis risk assessment as a guide to quality patient care [J]. Disease-a-month: DM, 2005, 51(2–3): 70 – 8. DAVID W. HOSMER JR. S L, RODNEY X. STURDIVANT. Applied Logistic Regression [M]. New York: John Wiley & Sons, Inc, 2000. LIEDERMAN Z, CHAN N, BHAGIRATH V. Current Challenges in Diagnosis of Venous Thromboembolism [J]. Journal of clinical medicine, 2020, 9(11). LIM W, LE GAL G, BATES S M, et al. American Society of Hematology 2018 guidelines for management of venous thromboembolism: diagnosis of venous thromboembolism [J]. Blood advances, 2018, 2(22): 3226–56. ZHANG Z, LI H, WENG H, et al. Genome-wide association analyses identified novel susceptibility loci for pulmonary embolism among Han Chinese population [J]. BMC medicine, 2023, 21(1): 153. LU X, ZENG W, ZHU L, et al. Application of the Caprini risk assessment model for deep vein thrombosis among patients undergoing laparoscopic surgery for colorectal cancer [J]. Medicine (Baltimore), 2021, 100(4): e24479. Additional Declarations There is NO Competing Interest. Supplementary Files SupplementPRS.docx Dataset 1 Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8190841","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":558349659,"identity":"18d4aa3f-8172-40a2-b8fc-74b1189f2e51","order_by":0,"name":"Shaoying Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYFACHhBhwcDPwJAAYjE2EKclQYJBsoEhsYE0LQYHIKoJazFv7z34uPKHROLmGwnPH/Mw2MhuOMD87AE+LTJnziUbnkmQSNx25kBiMw9DmvGGA2zmBvi0SEjkmEk2gLQcbwBpOZy44QAPmwQBLeY/QVo2NzOAtPwnSosZI0jLBnawLQeI0MJzLlmyIU3CeAbQLzPnGCQbzzzMZoZfC3vvwY8NNjay/TNyEj68qbCT7Tve/AyvFiTAk8DAAAoqZiLVAwH7AeLVjoJRMApGwYgCANPmSE8EbiEwAAAAAElFTkSuQmCC","orcid":"","institution":"the first affiliated hospital of Xi'an Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Shaoying","middleName":"","lastName":"Lu","suffix":""},{"id":558349660,"identity":"158ea149-5898-42bd-9c0b-dd69f4ee302c","order_by":1,"name":"Jiaxuan Hou","email":"","orcid":"","institution":"the first affiliated hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Jiaxuan","middleName":"","lastName":"Hou","suffix":""},{"id":558349661,"identity":"492bcb2e-aac3-48a4-afcd-b491d4d8f3d4","order_by":2,"name":"Jiawei Zhang","email":"","orcid":"","institution":"the first affiliated hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Jiawei","middleName":"","lastName":"Zhang","suffix":""},{"id":558349662,"identity":"7dd28320-4534-47df-a404-b42eab234ae4","order_by":3,"name":"Chao Liu","email":"","orcid":"","institution":"the first affiliated hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Liu","suffix":""},{"id":558349663,"identity":"430b0d94-7381-449f-9181-eef668f0b838","order_by":4,"name":"Huijun Yuan","email":"","orcid":"","institution":"Xi'an Honghui Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huijun","middleName":"","lastName":"Yuan","suffix":""},{"id":558349664,"identity":"4330d72d-bcf7-48e9-ac8e-a886b58e8683","order_by":5,"name":"Liming Yu","email":"","orcid":"","institution":"Xi'an Daxing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liming","middleName":"","lastName":"Yu","suffix":""},{"id":558349665,"identity":"e9680d5b-8cc1-4963-bf2c-87ffa1d32fee","order_by":6,"name":"Jiancang Ma","email":"","orcid":"","institution":"The Second Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Jiancang","middleName":"","lastName":"Ma","suffix":""},{"id":558349666,"identity":"ef20f7aa-ceaa-4fe1-b549-a63fcbd781a3","order_by":7,"name":"Jizhou Geng","email":"","orcid":"","institution":"Yan'an People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jizhou","middleName":"","lastName":"Geng","suffix":""},{"id":558349667,"identity":"08549994-85a0-4f3e-bddb-b8114814c37c","order_by":8,"name":"Zhipeng Hu","email":"","orcid":"","institution":"The General Hospital of NingXia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhipeng","middleName":"","lastName":"Hu","suffix":""},{"id":558349668,"identity":"41232670-46ae-44fd-a01d-0fcc8293713a","order_by":9,"name":"Hong Zhang","email":"","orcid":"","institution":"Shaanxi Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Zhang","suffix":""},{"id":558349669,"identity":"0f6bb124-3c7a-4c74-89ee-9c7eee16d68f","order_by":10,"name":"Ye Tian","email":"","orcid":"","institution":"the First Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Tian","suffix":""},{"id":558349670,"identity":"ac889c34-1963-4dde-bde5-69519e113934","order_by":11,"name":"Hui Shang","email":"","orcid":"","institution":"Taihe Hospital of Shiyan City","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Shang","suffix":""},{"id":558349671,"identity":"b59eadca-dcb3-4c1f-8904-ff4fbb26872a","order_by":12,"name":"Xuefei Mao","email":"","orcid":"","institution":"Xi'an Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xuefei","middleName":"","lastName":"Mao","suffix":""},{"id":558349672,"identity":"b23d1fa9-bde5-4373-a925-f53f26d3d6d0","order_by":13,"name":"Hui Quan","email":"","orcid":"","institution":"Baoji Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Quan","suffix":""},{"id":558349673,"identity":"016b52a4-5513-48a3-8449-db0531603de8","order_by":14,"name":"Xuegang Wang","email":"","orcid":"","institution":"Mianyang Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xuegang","middleName":"","lastName":"Wang","suffix":""},{"id":558349674,"identity":"f9f8253e-9542-479d-b748-cdab19e90abc","order_by":15,"name":"Yong Qiao","email":"","orcid":"","institution":"Sichuan Provincial Orthopedic Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Qiao","suffix":""},{"id":558349675,"identity":"28c82213-47b2-4118-8527-c7b5dc00d0b2","order_by":16,"name":"Wujun Li","email":"","orcid":"","institution":"the First Affiliated Hospital of Xi'an Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wujun","middleName":"","lastName":"Li","suffix":""},{"id":558349676,"identity":"3e13b518-046a-4431-99b1-6c4a5c2dffbe","order_by":17,"name":"Liang Wang","email":"","orcid":"","institution":"Chengdu Integrated TCM\u0026Western Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Wang","suffix":""},{"id":558349677,"identity":"35228789-0f1b-4a78-971e-0b80916277bc","order_by":18,"name":"Qiang Yin","email":"","orcid":"","institution":"the Second Affiliated Hospital of Xi'an Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Yin","suffix":""},{"id":558349678,"identity":"1df921f9-a058-4c8b-96c7-8ab3ea0488d4","order_by":19,"name":"Hao Wu","email":"","orcid":"","institution":"The First Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Wu","suffix":""},{"id":558349679,"identity":"29b61fa7-5c03-4292-a648-a13a2eb94328","order_by":20,"name":"Jiayan Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Jiayan","middleName":"","lastName":"Li","suffix":""},{"id":558349680,"identity":"0fd774be-fce7-43cb-ac6f-144321b43dac","order_by":21,"name":"Chuangzhou Xie","email":"","orcid":"","institution":"Shaanxi Forestry Workers' Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chuangzhou","middleName":"","lastName":"Xie","suffix":""},{"id":558349681,"identity":"8bcb0f06-827c-46bd-bb46-795ee0b650f3","order_by":22,"name":"Huaping Wu","email":"","orcid":"","institution":"Dazhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huaping","middleName":"","lastName":"Wu","suffix":""},{"id":558349682,"identity":"302963bc-08a9-432a-86ba-27cd20930816","order_by":23,"name":"Wenyong Zhang","email":"","orcid":"","institution":"Shangluo Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenyong","middleName":"","lastName":"Zhang","suffix":""},{"id":558349686,"identity":"0790c536-b5bd-4a84-9328-33f9a3d0f5ed","order_by":24,"name":"Wei Hong","email":"","orcid":"","institution":"Ankang Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Hong","suffix":""},{"id":558349688,"identity":"430ab13b-cb05-4637-923f-9da8e6d2a64f","order_by":25,"name":"Wei Zhang","email":"","orcid":"","institution":"Xi 'an Agen Medicine Technology Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zhang","suffix":""},{"id":558349692,"identity":"b9516d1c-f703-4c86-82ac-35dbb72a8148","order_by":26,"name":"Yu Wang","email":"","orcid":"","institution":"Xi 'an Agen Medicine Technology Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Wang","suffix":""},{"id":558349693,"identity":"c6df748c-e792-4eee-8aff-ab2ca7a0b471","order_by":27,"name":"Xiaohong Meng","email":"","orcid":"","institution":"Xi 'an Agen Medicine Technology Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Xiaohong","middleName":"","lastName":"Meng","suffix":""},{"id":558349694,"identity":"32862949-f96c-4b07-86b2-d731e5112bae","order_by":28,"name":"Shengying Qin","email":"","orcid":"https://orcid.org/0000-0002-8458-5960","institution":"Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Bio-X Institutes, Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Shengying","middleName":"","lastName":"Qin","suffix":""}],"badges":[],"createdAt":"2025-11-24 08:15:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8190841/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8190841/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102992001,"identity":"7c5b4964-bf57-4f41-b7fd-8053656401a4","added_by":"auto","created_at":"2026-02-19 11:37:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":72357,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of PRS-15 distributions between cases and controls. \u003c/strong\u003eA) Distribution of PRS-15 in all cases and controls. B) Gender-stratified distribution of PRS-15 in cases and controls. C) Age-stratified distribution of PRS-15 in cases and controls. (Mean PRS-15 values for each group are indicated by vertical lines.)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8190841/v1/d04456e4acefcd8192ae1a95.png"},{"id":103049868,"identity":"3753e3c2-2a42-4f60-9280-f710d5fd3067","added_by":"auto","created_at":"2026-02-20 07:46:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":89280,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePerformance and calibration of the PRS-15 model. \u003c/strong\u003eA) ROC curves for PRS-15 in the study cohort; B) Scatter plot of calibration for the PRS-15 model using the Hosmer-Lemeshow test; C) Calibration curve for the PRS-15 model using the bootstrap method. (The dashed line represents the ideal calibration (perfect agreement between predicted and observed probabilities). The solid line shows the actual calibration estimated by bootstrapping. The small vertical ticks along the top border indicate the distribution of predicted probabilities among participants.)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8190841/v1/7c9510c4b8393a8d7d7f506e.png"},{"id":103503977,"identity":"8c9c821c-2136-442c-9f45-fd2494f11ece","added_by":"auto","created_at":"2026-02-26 13:06:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":31498,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curves for each bootstrap sample are displayed in different colors to distinguish the 10 resampled validation sets.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8190841/v1/3bcbe25eb944170f1a45baad.png"},{"id":102992002,"identity":"a2ccc62f-fed5-4a80-97ce-63667dd48ee8","added_by":"auto","created_at":"2026-02-19 11:37:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":99446,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSankey diagram visualizing the reclassification results of Model 1 (PRS-15T + Caprini) and Model 2 (PRS-15B + Caprini).\u003c/strong\u003e The diagrams illustrate subgroup distributions of reclassification from original Caprini risk categories to new integrated risk categories. The left side of each Sankey diagram shows DVT incidence rates within the original Caprini risk categories, while the right side represents DVT incidence rates within the integrated model risk categories (IR: incidence rate).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8190841/v1/8163149716a06f44f4aeb985.png"},{"id":104409969,"identity":"1d1d8349-6ef3-4fa2-b4e3-096c5089d9a4","added_by":"auto","created_at":"2026-03-11 12:48:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1873524,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8190841/v1/5567f21d-e75b-4ffa-ad50-934324705f76.pdf"},{"id":104397488,"identity":"9c07e443-53b7-4b9a-9c81-bc1cacb179a5","added_by":"auto","created_at":"2026-03-11 11:49:35","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":39471,"visible":true,"origin":"","legend":"Dataset 1","description":"","filename":"SupplementPRS.docx","url":"https://assets-eu.researchsquare.com/files/rs-8190841/v1/e93ebc8059729a39f27684cc.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Predictive Performance and Clinical Application of PRS-15 for Venous Thromboembolism Risk in Chinese Orthopedic Patients: A Multicenter Prospective Case-Control Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eVenous thromboembolism (VTE), comprising deep vein thrombosis (DVT) and pulmonary thromboembolism (PTE), is the third most common cardiovascular disease worldwide \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The incidence of VTE within four months after spinal orthopedic surgery was reported to be approximately 4.3%-4.8% \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Epidemiological studies following VTE prophylaxis in major orthopedic surgery have reported DVT incidence rates of 1.8%-2.9% \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.A multi-center study across 90 hospitals reported a fivefold surge in VTE cases from 2007 to 2016 in China \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Therefore, preventing VTE has substantial clinical significance.\u003c/p\u003e \u003cp\u003eVTE is a multifactorial and complex disease caused by a combination of genetic and acquired factors. Genetic predisposition plays a crucial and non-negligible role in the development of VTE. The heritability of VTE has been estimated to be 23%-30% in some studies\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e and 40%-60% based on family studies \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. As a comprehensive evaluation tool, the Caprini risk assessment model is widely recommended in orthopedics, which incorporates two major genetic factors: factor V Leiden and prothrombin G20210A mutations \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. However, these mutations are exceedingly rare in the Chinese population. Therefore, only acquired factors in the Caprini score can be evaluated in Chinese clinical practice, limiting its applicability\u003csup\u003e[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. As no major-effect genes or loci associated with VTE have been identified in Chinese populations, the genetic risk of VTE cannot be adequately explained by individual variants or the combination of a few genetic variants. Therefore, constructing a polygenic risk score (PRS) model that estimates the cumulative effects of multiple genetic polymorphisms and integrating it with the Caprini score is essential to enhance comprehensive risk prediction.\u003c/p\u003e \u003cp\u003ePRS research has become a major focus in the field of genetic risk prediction for multifactorial and complex diseases. PRS provides significant predictive value for complex diseases, such as cancer \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, cardiovascular diseases\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, and VTE \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. However, no clinical studies have yet reported PRS-based VTE risk prediction models specifically tailored to the Chinese population. In our previous work, we developed PRS models (PRS-53, PRS-10, and PRS-X\u003csub\u003e10\u0026minus;53\u003c/sub\u003e) consisting of 10\u0026ndash;53 single-nucleotide polymorphisms (SNPs) and their combinations to predict first-onset VTE risk in the Chinese population \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. These models were evaluated using simulated data and exhibited comparable predictive performance. We constructed PRS-15 based on the streamlined PRS-10 model to maintain model simplicity while fully capturing the pathophysiological mechanisms of VTE, supplementing it with five additional SNPs (SERPINC1, PROS1, F2, F11, and HIVEP1) that are closely associated with the pathogenesis of VTE among the 53 VTE-associated SNPs identified in Chinese populations. We then conducted a multicenter prospective case-control study among Chinese orthopedic patients to evaluate PRS-15 performance in real-world clinical cases and assess its clinical benefits in evaluating the risk of VTE, thereby providing empirical support for its clinical application.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adopted a prospective case-control study design, using DVT, with or without PTE, as the outcome variable. The research groups (case and control groups) were defined prospectively, and clinical cases related to the outcome were collected to evaluate the predictive performance of the PRS-15 model. In total, 2,318 clinical cases were initially recruited. After excluding 280 patients who were lost to follow-up, had missing ultrasound results, or had a hospital stay of less than three days, 2,038 patients were included in the final analysis. Among them, 1,013 were assigned to the case group and 1,025 to the control group. The inclusion and exclusion criteria are as follows:\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExclusion criteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cul\u003e\n \u003cli\u003ePatients aged 18\u0026ndash;80 years;\u003c/li\u003e\n \u003cli\u003eHospitalized in the orthopedic department with a length of stay of \u0026ge;3 days;\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSurgical or perioperative orthopedic trauma patients;\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePatients with fresh fractures;\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCompletion of bilateral lower-extremity venous color Doppler ultrasonography;\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAbsence of contraindications for treatment with anticoagulants;\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eProvision of written informed consent by the patients or their legal guardians.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cul\u003e\n \u003cli\u003ePathological fractures;\u003c/li\u003e\n \u003cli\u003ecirrhosis, hepatocellular carcinoma, or previous hepatectomy;\u003c/li\u003e\n \u003cli\u003ePre-admission long-term immobility or hemiplegia;\u003c/li\u003e\n \u003cli\u003eTerminal stage of any disease with an expected survival length of \u0026lt;1 year;\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRenal impairment or hepatic dysfunction;\u003c/li\u003e\n \u003cli\u003eHematologic disorders or coagulation abnormalities;\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHistory of thrombotic diseases under treatment;\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePoor compliance, known psychiatric diseases, or cognitive impairment;\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRefusing to receive standard anticoagulation prophylaxis\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Patients were screened based on the predefined inclusion and exclusion criteria. Bilateral lower-extremity venous color Doppler ultrasonography was conducted to diagnose DVT. Patients who developed DVT during hospitalization were included in the case group. Those without DVT during hospitalization were followed for three months postoperatively. Patients who developed DVT during follow-up were classified into the case group, while those who did not develop DVT were assigned to the control group. This was an observational study without any intervention beyond standard clinical practice. The study was initiated by the First Affiliated Hospital of Xi\u0026rsquo;an Jiaotong University and approved by the ethics committees of all participating centers. Written informed consent was obtained from all participants, and the study This study was approved by the the Ethics Committee of the First Affiliated Hospital of Xi\u0026rsquo;an Jiaotong University, China (NO. XJTUAF2023LSK-285) and registered in the Chinese Medical Research Registry System (NO. MR-61-23-019694).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Construction of the PRS-15 Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on previous findings, the PRS-15 model was developed from 53 SNPs highly associated with VTE in the Chinese population. The streamlined PRS-10 model served as the foundation, and five additional SNPs associated with the pathogenesis of VTE were incorporated:\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003eSERPINC1 (rs2227589), PROS1 (rs6795524), F2 (rs3136516), F11 (rs2289252), and HIVEP1 (rs169713)\u003c/strong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PRS-15 model formula was as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"146\" height=\"55\" src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1771423045.png\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere \u003cem\u003ei\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003erepresents the \u003cem\u003ei-\u003c/em\u003eth SNP (i = 1, 2, \u0026hellip;, 15); \u003cem\u003e\u0026beta;i\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003edenotes the effect size of the \u003cem\u003ei-\u003c/em\u003eth SNP (the natural logarithm of the odds ratio [ln(OR)]); and \u003cem\u003eGi\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003erepresents the number of risk alleles for the \u003cem\u003ei-\u003c/em\u003eth SNP, coded as 0, 1, or 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e presents the 15 SNPs and their classification based on the corresponding pathophysiological mechanisms associated with VTE.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e. Classification of pathophysiological mechanisms linking the 15 SNPs in PRS-15 to VTE.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathophysiology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGENE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 30px;\"\u003e\n \u003cp\u003eAnticoagulant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cem\u003eSERPINC1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003ers2227589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cem\u003ePROC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003ers146922325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e6.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cem\u003ePROC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003ers199469469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cem\u003ePROS1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003ers6795524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cem\u003eTHBD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003ers16984852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cem\u003eAPOH\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003ers52797880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 30px;\"\u003e\n \u003cp\u003eCoagulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cem\u003eF11\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003ers2289252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cem\u003eF11\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003ers2036914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cem\u003eF2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003ers3136516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 30px;\"\u003e\n \u003cp\u003eFibrinolytic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cem\u003eFGG\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003ers2066865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cem\u003ePAI-1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003ers1799762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003ePlatelet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cem\u003eABO\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003ers8176719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 30px;\"\u003e\n \u003cp\u003eVascular endothelial cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cem\u003eMTHFR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003ers1801133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cem\u003eNOS3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003ers1799983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cem\u003eHIVEP1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003ers169713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eChr, Chromosome; SNP, single nucleotide polymorphism.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Sample Size Calculation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PRS-15 model contains 15 SNPs as variables, each with three possible genotypes. Since the number of events per variable (EPV) should not be less than 10 in the development and validation of clinical prediction models \u003csup\u003e[18]\u003c/sup\u003e, the sample size was calculated to be 20 times the number of variables. Therefore, both the case and control groups required approximately 900 subjects each: \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e15 (SNPs) \u0026times; 3 (genotypes) \u0026times; 20 = 900 per group (total n = 1,800).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Sample and Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhole-blood samples were collected from all participants during hospitalization for PRS-15-related genetic testing. The following clinical data were simultaneously recorded: general information (study code, sex, age, height, weight, department, admission date, discharge date, operation date, and clinical diagnosis), Caprini score information (excluding factor V Leiden and prothrombin G20210A mutations), polygenic risk score (PRS-15 value), laboratory parameters (red blood cell count [RBC], platelet [PLT], D-dimer, hemoglobin, C-reactive protein [CRP], thrombin time [TT], fibrin degradation products [FDP], prothrombin time [PT], fibrinogen [FIB]), and imaging results.\u003c/p\u003e\n\u003cp\u003eGenomic DNA was extracted from blood samples using DNA extraction kits (Batch Nos. A0613A, A0809A, A1112A; TIANGEN BIOTECH CO., LTD., Beijing, China), followed by quality control. Genotyping of 15 VTE-related SNPs was conducted using PCR-fluorescence probes (Batch Nos. 240501, 240502, 240701; Xi\u0026rsquo;an Agen Medicine Technology Co., Ltd., China). The results were validated via Sanger sequencing. The results of genotyping were then incorporated into the PRS-15 formula to calculate individual PRS-15 values.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Determination of Optimal PRS-15 Thresholds and Integrated Evaluation via K-Means Clustering Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, two integrated risk assessment models were defined: Model 1 (PRS-15T + Caprini), consisting of PRS-15T tertiary-classification outcome (high/intermediate/low genetic risk) combined with the Caprini score (high/intermediate/low risk); and Model 2 (PRS-15B + Caprini), consisting of PRS-15B binary-classification outcome (high/low genetic risk) combined with the Caprini score (high/intermediate/low risk). Both models classified all patients into high-risk, intermediate-risk, or low-risk categories. Given the large computational load, two custom software programs, namely \u0026ldquo;VTE Comprehensive Risk Assessment Automated Data Processing Program Algorithm\u0026rdquo; and \u0026ldquo;Software Program for Mining Optimal Thresholds of K-Means Clustering Analysis and Comprehensive Risk Assessment Methods\u0026rdquo;, were developed to improve computational efficiency. The detailed algorithms and program codes are provided in the appendix to ensure full reproducibility of the data mining process.\u003c/p\u003e\n\u003cp\u003eThe results of the Caprini score were first summarized, followed by data processing and \u003cem\u003ek\u003c/em\u003e-means clustering analysis.\u003c/p\u003e\n\u003cp\u003eData processing: The PRS-15 values of the study samples ranged from 0.680 to 5.780. Step intervals were set at 0.05 for PRS-15T and at 0.005 for PRS-15B. The program iteratively traversed all possible risk thresholds or threshold pairs and their corresponding risk classification combinations and outcomes, including 5,200 threshold sets, 19,683 combinations, and 102,341,600 results for PRS-15T, as well as 1,020 thresholds, 729 combinations, and 743,580 results for PRS-15B. The samples were automatically categorized into high-risk, intermediate-risk, and low-risk groups based on predefined PRS-15 and Caprini combination strategies. The number of patients with DVT and without DVT was calculated for each group. Four criteria were set for selecting optimal thresholds and combinations: (1) more patients with DVT were classified as high risk; (2) more patients without DVT were classified as low risk; (3) fewer patients without DVT were classified as high risk; and (4) fewer patients with DVT were classified as low risk. The program automatically filtered data combinations that met all four criteria for k-means clustering analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eK-means clustering analysis:\u003c/strong\u003e The program divided the dataset into \u003cem\u003eK\u003c/em\u003e mutually exclusive clusters, ensuring that data within each cluster had the highest similarity, while differences between clusters were maximized. Four \u0026ldquo;ideal points\u0026rdquo; were defined: in the high-risk group, 100% of patients had DVT and 0% of patients did not experience DVT; in the low-risk group, 100% of patients did not experience DVT and 0% of patients experienced DVT. The processed and filtered data groups were then input into the program. Through continuous iteration and adjustment, the program identified the data row closest to the predefined ideal points. This data row represented the optimal PRS-15 risk thresholds and the comprehensive risk assessment method integrating PRS-15 with the Caprini score.\u003c/p\u003e\n\u003cp\u003eThe overall technical workflow of the study is illustrated in\u003cstrong\u003e\u0026nbsp;Scheme 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"485\" height=\"415\" src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1771423046.jpg\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScheme\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e. \u003cstrong\u003eFlow chart of the study.\u003c/strong\u003e Model 1 represents the integrated risk assessment of the Caprini score and PRS-15T; Model 2 represents the integrated risk assessment of the Caprini score and PRS-15B. SEN, sensitivity; SEP, specificity; PPV, positive predictive value, NPV, negative predictive value,; NRI, net reclassification improvement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContinuous variables are expressed as mean \u0026plusmn; standard deviation (SD) and were compared between groups using the \u003cem\u003et\u003c/em\u003e-test. Categorical variables are presented as frequencies and percentages. Differences in categorical variables between two or more groups were evaluated using the chi-square test. The discriminative ability of PRS-15 was assessed using the area under the receiver operating characteristic curve (AUC), and the Z-test was employed to compare differences in AUC values. The stability of PRS-15 discrimination across different sample sets was validated using the bootstrap method. Calibration of the PRS-15 model was assessed using the Hosmer-Lemeshow goodness-of-fit test. All statistical analyses and visualizations were conducted using \u003cstrong\u003eR software (version 4.3.2)\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003ewith a two-sided significance level set at \u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Baseline Characteristics of the Study Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e summarizes the demographic characteristics of the 2,038 patients included in this study, of whom 51.72% were male. The mean age of the case group (n = 1,013) was 60.28 \u0026plusmn; 12.68 years, while that of the control group (n = 1,025) was 52.24 \u0026plusmn; 14.93 years. The mean body mass index (BMI) of the case group was higher than that of the control group (24.18 \u0026plusmn; 3.66 vs. 23.74 \u0026plusmn; 3.00). The mean Caprini score of the case group was 8.58 \u0026plusmn; 3.05, and that of the control group was 7.69 \u0026plusmn; 3.28. Detailed Caprini score data for all 2,038 patients are provided in\u003cstrong\u003e\u0026nbsp;Table S1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e \u003cstrong\u003eDemographic Characteristics of the Study Cohort.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCase (n=1013)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl (n=1025)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003csup\u003ea\u003c/sup\u003e, years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e60.28\u0026plusmn;12.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e52.24\u0026plusmn;14.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e455(44.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e554(54.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003csup\u003ea\u003c/sup\u003e, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e24.18\u0026plusmn;3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e23.74\u0026plusmn;3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCaprini score\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e8.58\u0026plusmn;3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e7.69\u0026plusmn;3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e885(52.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e806(47.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3-4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e115(40.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e172(59.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1-2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e13(21.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e47(78.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003eMedian\u0026plusmn;standard deviation, \u003csup\u003eb\u003c/sup\u003e Number of patients(percentage). BMI, Body Mass Index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Distribution of PRS-15 in the Study Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1\u003c/strong\u003e shows the distribution of PRS-15 values in the case and control groups. The PRS-15 values in the case group were significantly higher than those in the control group (3.18 \u0026plusmn; 0.69 vs. 2.66 \u0026plusmn; 0.67, P \u0026lt; 0.001), and the difference was statistically significant (\u003cstrong\u003eFigure 1A\u003c/strong\u003e). Subgroup analyses by sex and age exhibited the same trend. Among male participants, the mean PRS-15 value was 3.18 \u0026plusmn; 0.65 among cases and 2.69 \u0026plusmn; 0.69 among controls (P \u0026lt; 0.001). Among female participants, the mean PRS-15 value was 3.18 \u0026plusmn; 0.71 among cases and 2.62 \u0026plusmn; 0.64 among controls (P \u0026lt; 0.001) (\u003cstrong\u003eFigure 1B\u003c/strong\u003e). In participants aged \u0026le;60 years, the mean PRS-15 value was 3.20 \u0026plusmn; 0.67 among cases and 2.67 \u0026plusmn; 0.67 among controls (P \u0026lt; 0.001). In individuals aged \u0026gt;60 years, the mean PRS-15 value was 3.16 \u0026plusmn; 0.70 among cases and 2.63 \u0026plusmn; 0.65 among controls (P \u0026lt; 0.001) (\u003cstrong\u003eFigure 1C\u003c/strong\u003e). These findings suggest that PRS-15 can serve as an effective indicator for distinguishing patients with DVT from those without DVT. Since all 15 genetic polymorphisms were germline variants located on autosomes, their discriminative power was not affected by age or sex.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Discrimination and Calibration of PRS-15\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the individual PRS-15 value as an independent variable and the incidence of DVT as the dependent variable (coded as 1 for cases and 0 for controls), the AUC for PRS-15 was calculated to be 0.702 (95% CI, 0.680\u0026ndash;0.725; P \u0026lt; 0.001) (\u003cstrong\u003eFigure 2A\u003c/strong\u003e). No significant difference was observed compared to the AUC value of 0.729 obtained from previous simulation-based data \u003csup\u003e[7]\u003c/sup\u003e (P \u0026gt; 0.05), indicating that PRS-15 maintained good predictive ability for DVT risk in real-world clinical settings (AUC \u0026gt; 0.7). Subgroup analyses also showed that the discriminative performance of PRS-15 did not significantly differ by sex, with an AUC of 0.692 (95% CI, 0.6601\u0026ndash;0.7243) in males and 0.716 (95% CI, 0.6851\u0026ndash;0.747) in females (P = 0.294). Similarly, the discriminative ability was comparable across age groups, with an AUC of 0.706 (95% CI, 0.6767\u0026ndash;0.736) for participants aged \u0026le;60 years and 0.706 (95% CI, 0.6712\u0026ndash;0.7416) for those aged \u0026gt;60 years (P = 0.998).\u003c/p\u003e\n\u003cp\u003eTwo complementary methods were utilized to assess model calibration. The Hosmer-Lemeshow goodness-of-fit test yielded a \u0026chi;\u0026sup2; value of 18.08 with 8 degrees of freedom (P = 0.021), suggesting a negligible difference between predicted and observed probabilities (\u003cstrong\u003eFigure 2B\u003c/strong\u003e). However, the calibration curve adjusted using the bootstrap method (B = 100 repetitions; n = 2,038) exhibited good agreement between the predicted probabilities and observed incidence rates, with a mean absolute error of 0.023 (\u003cstrong\u003eFigure 2C\u003c/strong\u003e). These results suggest an acceptable overall calibration performance of the PRS-15 model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA bootstrap resampling procedure was applied to the entire cohort of 2,038 patients using random sampling with replacement to generate ten validation sets of equal size (case group, n = 1,013; control group, n = 1,025). The AUC values for the ten resampled validation sets ranged from 0.675 to 0.728, with a mean AUC of 0.701, indicating good stability of the discriminative ability of the PRS-15 model across different sample sets. The AUC values for all ten bootstrap samples are shown in \u003cstrong\u003eTable 3\u003c/strong\u003e, and the corresponding ROC curves are presented in \u003cstrong\u003eFigure 3\u003c/strong\u003e. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e. Results of 10 bootstrap resamples\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBootstrap samples\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.726\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample 6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample 7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample 8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample 9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample 10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 PRS-15 Risk Thresholds and Comprehensive Risk Assessment Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eK\u003c/em\u003e-means clustering data mining identified PRS-15 thresholds as follows: the thresholds were 2.230 and 1.730 for the tertiary-classification model (PRS-15T), corresponding to high genetic risk (PRS-15 \u0026ge; 2.230), intermediate genetic risk (1.730 \u0026lt; PRS-15 \u0026lt; 2.230), and low genetic risk (PRS-15 \u0026le; 1.730). The optimal risk threshold was 2.335 for the binary-classification model (PRS-15B), corresponding to high genetic risk (PRS-15 \u0026ge; 2.335) and low genetic risk (PRS-15 \u0026lt; 2.335). The risk classification criteria for the two integrated risk assessment models (Model 1: PRS-15T + Caprini; Model 2: PRS-15B + Caprini) are presented in \u003cstrong\u003eTable 4\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e. Risk stratification of Caprini + PRS-15.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCaprini\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePRS-15T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePRS-15B\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eRisk stratification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eScore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eRisk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eScore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eRisk stratification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003eRisk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eScore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eRisk stratification\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026ge;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026ge;2.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003ehigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ge;2.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003ehigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e3-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026ge;2.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003ehigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ge;2.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003ehigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026ge;2.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003ehigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ge;2.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026ge;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.730-2.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003elow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e＜2.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e3-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.730-2.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003elow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e＜2.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003elow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003elow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.730-2.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003elow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e＜2.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003elow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026ge;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026le;1.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e3-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026le;1.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026le;1.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Model 1 represents the integrated risk assessment of the Caprini score and PRS-15T. For example, when both were classified as high risk, the integrated Mode 1 was also defined as high risk, and so forth. Mode 2 represents the integrated risk assessment of the Caprini score and PRS-15B. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on these thresholds and the risk classification criteria, Models 1 and 2 were applied to the study population, and the results were compared with those of the Caprini score (\u003cstrong\u003eTable 5\u003c/strong\u003e). The number of patients with DVT increased in the high-risk groups defined by both models. The proportion of patients with DVT in the intermediate-risk groups of Model 1 (24.8%) and Model 2 (24.9%) was significantly lower than that in the Caprini intermediate-risk group (40.1%). Similarly, the proportion of patients with DVT in the low-risk groups of Model 1 (8.5%) and Model 2 (6.5%) was notably lower than that in the Caprini low-risk group (21.7%). Overall, both Model 1 and Model 2 showed improved discrimination between patients with and without DVT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e．\u003c/strong\u003e\u003cstrong\u003eRisk Reclassification by the Caprini risk score, Model 1, and Model 2.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCaprini\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk stratification\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eDVT(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eDVT(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eDVT(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003eP\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e885(52.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3.97\u003csup\u003e*\u003c/sup\u003e(2.20-7.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e951(55.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e13.70\u003csup\u003e*\u003c/sup\u003e(7.68-27.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e917(57.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e19.48\u003csup\u003e*\u003c/sup\u003e(8.65-55.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntermedium\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e115(40.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2.41\u003csup\u003e#\u003c/sup\u003e(1.29-4.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e51(24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3.56\u003csup\u003e#\u003c/sup\u003e(1.84-7.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e91(24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4.77\u003csup\u003e#\u003c/sup\u003e(2.05-13.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e13(21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e11(8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e5(6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote. Model 1 represents the integrated risk assessment of the Caprini score and PRS-15T; Model 2 represents the integrated risk assessment of the Caprini score and PRS-15B. \u003csup\u003e*\u003c/sup\u003e indicates the comparison between the high-risk and low-risk groups; \u003csup\u003e#\u003c/sup\u003e indicates the comparison between the intermediate-risk and low-risk groups.\u003c/p\u003e\n\u003cp\u003eCompared to the results of the Caprini score, patients reclassified by Models 1 and 2 into lower-risk categories showed lower incidence rates of DVT, whereas those reclassified into higher-risk categories exhibited higher incidence rates of DVT (\u003cstrong\u003eFigure 4\u003c/strong\u003e). Specifically, Model 1 reclassified 206 patients (12.2%) from high to intermediate risk, among whom DVT incidence (24.8%) was significantly lower compared to the original high-risk group (52.3%). Additionally, 70 patients (4.1%) were downgraded from high to low risk, with a DVT incidence (12.9%) much lower compared to the original high-risk group. Meanwhile, 242 patients (84.3%) were upgraded from the intermediate-risk group to the high-risk group, with a DVT incidence (46.7%) higher than that observed in the original intermediate-risk group (40.1%). Model 2 reclassified 324 patients (19.2%) from high risk to intermediate risk, whose DVT incidence (24.1%) was significantly lower than that observed in the original high-risk group (52.3%). Moreover, 228 patients (79.4%) were upgraded from intermediate risk to high risk, with a DVT incidence (48.2%) higher than that observed in the original intermediate-risk group (40.1%).\u003c/p\u003e\n\u003cp\u003eUsing real-world data from 2,038 patients, risk assessments were conducted using the Caprini score, Model 1, and Model 2. Regarding high-risk classifications as positive and intermediate/low-risk classifications as negative, sensitivity (SEN), specificity (SEP), positive predictive value (PPV), negative predictive value (NPV), and net reclassification improvement (NRI) were calculated for each model (\u003cstrong\u003eTable 6\u003c/strong\u003e). Both Models 1 and 2 exhibited improvement in all indices compared to the Caprini score. The sensitivity of Models 1 and 2 reached 93.88% and 90.52%, respectively, which was higher than that of the Caprini score (87.36%). The specificity of Model 2 (33.85%) was higher than that of Model 1 (26.73%) and the Caprini score (21.37%). The NRI of Model 2 was 15.65% (95% CI, 11.73%\u0026ndash;19.57%; P \u0026lt; 0.001), indicating that 15.65% of patients were correctly reclassified when combining PRS-15B with the Caprini score. The NRI of Model 1 was 11.88% (95% CI, 7.57%\u0026ndash;16.19%; P \u0026lt; 0.001), suggesting that 11.88% of patients were correctly reclassified using the PRS-15T + Caprini model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003cstrong\u003e. Comparison of predictive ability between the Caprini and integrated models.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNRI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNRI-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNRI+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCaprini\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e87.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e21.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e52.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e63.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e93.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e26.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e55.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e81.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e11.88(7.57-16.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e6.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e5.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e90.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e33.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e57.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e78.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e15.65(11.73-19.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e12.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Model 1 represents the integrated risk assessment of the Caprini score and PRS-15T; Model 2 represents the integrated risk assessment of the Caprini score and PRS-15B.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe risk factors of VTE are numerous and complex. Genetic factors determine individual susceptibility to thrombosis, which persists throughout life and may lead to thrombus formation in the presence of one or more acquired factors \u003csup\u003e[19]\u003c/sup\u003e. Therefore, risk assessment for VTE cannot rely solely on either genetic or acquired factors. The Caprini score system integrates validated clinical risk factors \u003csup\u003e[20]\u003c/sup\u003e and has been recommended by multiple thrombosis prevention guidelines \u003csup\u003e[9]\u003c/sup\u003e. However, in our study population, the overall discriminative ability of the Caprini score was limited (AUC: 0.569, 95% CI: 0.544\u0026ndash;0.594), mainly because genetic risk factors were not included. In our previous study, we established evidence-based and statistically principled criteria and grading standards for identifying disease-related genetic variants in the Chinese population. By integrating data from genome-wide association studies (GWAS), meta-analyses, and candidate gene studies, we identified 53 SNPs associated with first-episode VTE among Chinese individuals and developed a full PRS-53 model. Using a forward stepwise selection method, we optimized model variables and identified ten repeatedly validated SNPs associated with VTE risk to construct the most concise PRS-10 model. We calculated the AUC based on the simulated data comprising 1,000 cases of VTE and 1,000 controls, which were generated using real SNP frequencies and linkage disequilibrium structures. The results showed no significant difference between PRS-10 and PRS-53 (p \u0026gt; 0.05), indicating that PRS-10 and PRS-53, as well as their combined models (PRS-X\u003csub\u003e10-53\u003c/sub\u003e), had comparable predictive performance for VTE. The PRS-15 model was constructed in this study to fill the gap in genetic risk factors for assessing the risk of VTE in clinical settings in China. The optimized PRS-15 model maintained the simplicity of previous models while comprehensively covering the pathophysiological mechanisms involved in VTE. It provides a molecular-level foundation for elucidating disease etiology and achieving precision prevention.\u003c/p\u003e\n\u003cp\u003eFirst, this study confirmed the strong discriminative power of the PRS-15 model for predicting genetic susceptibility to VTE from three perspectives. (1) The AUC of PRS-15 was 0.702. Although higher AUC values (e.g., 0.8\u0026ndash;0.9) suggest excellent discrimination, an AUC of \u0026ge; 0.7 is generally considered acceptable and reflects good discriminative ability, suggesting that the model can effectively differentiate between high-risk and low-risk individuals with significant clinical utility \u003csup\u003e[21]\u003c/sup\u003e. In multifactorial complex diseases, such discriminative performance for genetic risk prediction is within the acceptable range. (2) Age and sex are typically regarded as important factors associated with the incidence of VTE. Our results showed that in both male and female orthopedic surgical patients, the PRS-15 model effectively predicted DVT risk(P = 0.294). Similarly, in the age-stratified analysis, PRS-15 maintained consistent discriminative ability between middle-aged and elderly patients (P = 0.998), demonstrating that the 15 germline SNPs included in the PRS-15 model, carried from birth and persisting throughout life, can advance risk screening in the general population. The model may be particularly useful for identifying younger high-risk individuals who might be overlooked by conventional risk models. It therefore represents a lifelong genetic risk indicator, offering a new perspective for full-cycle VTE risk management and early warning in non-surgical populations. (3) Based on data from 2,038 patients, we conducted bootstrap resampling to generate ten validation datasets of equal sample size. The AUC values for these validation sets ranged from 0.675 to 0.728, with a mean of 0.701, indicating good stability in the discriminative ability of the PRS-15 model across different datasets. This finding provides key evidence supporting its clinical applicability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSince PRS-15 values in the study sample were continuous, ranging from 0.680 to 5.780, an infinite number of threshold cutoffs could theoretically be employed to classify risk into two or three categories. Thus, there will also be innumerable possible combinations for integrated risk assessment when combined with the Caprini score. We applied k-means clustering to real clinical outcome data to objectively identify optimal risk thresholds and integration methods. This data-driven approach allowed the discovery of inherent risk stratification patterns within the dataset. We first defined two integrated risk assessment models: PRS-15T + Caprini (Model 1) and PRS-15B + Caprini (Model 2). Using k-means clustering analysis based on actual results from this multicenter prospective case-control study, we systematically traversed all potential thresholds, risk classification combinations, and outcomes to mine the optimal PRS-15 thresholds and integrated assessment strategies for both models. The optimal thresholds were 2.230 and 1.730 for the tertiary-classification PRS-15T outcome, and 2.335 for the binary-classification PRS-15B outcome. Table 4 presents the corresponding risk classification criteria for Models 1 and 2. A comparative analysis of the comprehensive risk prediction results of Models 1 and 2 with those of the Caprini score revealed that Model 2 achieved a substantial improvement in specificity (33.85% vs. 21.37%) while maintaining a high level of sensitivity (90.52% vs. 87.36%). This finding indicates that the model not only preserved but also enhanced the high sensitivity of the Caprini score while significantly decreasing its false-positive rate. Model 1 further increased sensitivity (93.88% vs. 87.36%) and improved specificity (26.73% vs. 21.37%), suggesting that the PRS-15 genetic factor markedly improved the overall accuracy of the Caprini score. Both sensitivity and specificity are important in clinical prediction modeling, but their relative importance may vary depending on disease characteristics. For a potentially fatal condition, like VTE, the consequences of missed diagnosis (low sensitivity) are more severe than those of false positive results (low specificity) \u003csup\u003e[22, 23]\u003c/sup\u003e. The \u0026ldquo;high sensitivity and low specificity\u0026rdquo; nature of the Caprini score reflects a trade-off nature in a clinically oriented, broad-spectrum risk model. Although low specificity increases false-positive rates, this broad coverage minimizes the detection rate of patients at high risk of VTE, which is clinically justified given the fatal risk associated with VTE \u003csup\u003e[24, 25]\u003c/sup\u003e. Incorporation of the PRS-15 model not only enhanced the sensitivity of the Caprini score but also markedly improved its specificity, substantially improving its applicability to Chinese patients. The study showed that the NRI for PRS-15T + Caprini was 11.88%, while that for PRS-15B + Caprini was 15.65%, demonstrating that the combined models correctly reclassified a larger proportion of high-risk and low/intermediate-risk patients. This represents significant clinical value in improving the accuracy of risk assessment.\u003c/p\u003e\n\u003cp\u003eIn summary, this study provides two practical clinical approaches for applying the PRS-15 model to assess the risk of first-onset VTE in Chinese orthopedic patients and offers a data-driven method for identifying optimal PRS thresholds based on real clinical outcomes. The results showed that both accuracy and clinical benefit were significantly improved, with controlled risk. Model 2 demonstrated high sensitivity and specificity. Its integrated assessment method was also simpler, making it more convenient for clinical implementation. In terms of net reclassification improvement, Model 2 yielded the greatest clinical benefit. This study addressed key issues in translating the PRS-15 model from \u0026ldquo;research\u0026rdquo; to \u0026ldquo;clinical application\u0026rdquo;, including the determination of optimal risk thresholds and integrated risk assessment strategies. As a main limitation, this study included only orthopedic patients, whereas VTE occurs in multiple clinical departments. Since PRS-15 represents the aggregate expression of genetic predisposition, it should theoretically be applicable across specialties. Future studies involving diverse clinical populations are needed to explore the interactions of PRS-15 with disease-specific acquired factors and assess its broader clinical utility. Internal validation of the PRS-15 model was conducted in this study. Future studies should conduct external validation using independent datasets to evaluate the generalizability and transferability of the PRS-15 model. Finally, the underlying mechanisms linking PRS-15 to VTE pathogenesis and prevention remain important areas for future research. With the continuous expansion of genetic databases and the increasing number of GWAS discoveries, the PRS-15 model can be continuously refined by incorporating newly identified genetic markers, thereby optimizing its performance and maintaining its scientific rigor.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eS.L. conceived and designed the study. J.H., J.Z., C.L. wrote and edited the manuscript. J.H., H.Y., L.Y., J.M., J.G., Z.H., H.Z., Y.T., H.S., X.M., H.Q., X.W., Y.Q., W.L., L.W., Q.Y., H.W., J.L., C.X., H.P.W., W.Y.Z., W.H. collected and assembled the data. J.H., W.Z. and Y.W. performed the statistical analysis. X.H.M, S.Y.Q. and S.L. supervised the study and performed critical revision of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe thank all the patients for participating this study. This study was supported by the National Key Research and Development Program of China (Key Project \u0026ldquo;Diagnostic and Therapeutic Equipment and Biomedical Materials\u0026rdquo;, Grant No. SQ2025YFC24000958) and the Shaanxi Provincial Key Research and Development Program (Key Project of \u0026ldquo;Dual-Chain Integration\u0026rdquo; Initiative, Grant No. 2025ZG-JBGS-022).\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe data analyzed during the current study are available from the corresponding author on reasonable request by emailing [email protected]. Supplementary figures and tables are in Supplementary Data 1.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKHAN F, TRITSCHLER T, KAHN S R, et al. Venous thromboembolism [J]. Lancet (London, England), 2021, 398(10294): 64\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBAUMANN A N, TRAGER R J, CUTTICA N, et al. Risk of venous thromboembolism and bleeding complications for early enoxaparin versus heparin after same-day spine surgery for central cord syndrome: A propensity-matched retrospective cohort study [J]. The journal of spinal cord medicine, 2025: 1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eASSOCIATION O B O C M. Guidelines for the prevention of venous thromboembolism in major orthopedic surgery in China [J]. Chinese Journal of Orthopaedics, 2016, (2): 65\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZHANG Z, LEI J, SHAO X, et al. Trends in Hospitalization and In-Hospital Mortality From VTE, 2007 to 2016, in China [J]. Chest, 2019, 155(2): 342\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLINDSTR\u0026ouml;M S, WANG L, SMITH E N, et al. Genomic and transcriptomic association studies identify 16 novel susceptibility loci for venous thromboembolism [J]. Blood, 2019, 134(19): 1645\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKLARIN D, BUSENKELL E, JUDY R, et al. Genome-wide association analysis of venous thromboembolism identifies new risk loci and genetic overlap with arterial vascular disease [J]. Nature Genetics, 2019, 51(11): 1574\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHEIT J A, PHELPS M A, WARD S A, et al. Familial segregation of venous thromboembolism [J]. Journal of thrombosis and haemostasis: JTH, 2004, 2(5): 731\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSOUTO J C, ALMASY L, BORRELL M, et al. Genetic susceptibility to thrombosis and its relationship to physiological risk factors: the GAIT study. Genetic Analysis of Idiopathic Thrombophilia [J]. American journal of human genetics, 2000, 67(6): 1452\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKEARON C, AKL E A, COMEROTA A J, et al. Antithrombotic therapy for VTE disease: Antithrombotic Therapy and Prevention of Thrombosis, 9th ed: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines [J]. Chest, 2012, 141(2 Suppl): e419S-e96S.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBAUDUER F, LACOMBE D. Factor V Leiden, prothrombin 20210A, methylenetetrahydrofolate reductase 677T, and population genetics [J]. Molecular genetics and metabolism, 2005, 86(1\u0026ndash;2): 91\u0026thinsp;\u0026ndash;\u0026thinsp;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKO Y L, HSU T S, WU S M, et al. The G1691A mutation of the coagulation factor V gene (factor V Leiden) is rare in Chinese: an analysis of 618 individuals [J]. Human genetics, 1996, 98(2): 176\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJUN Z J, PING T, LEI Y, et al. Prevalence of factor V Leiden and prothrombin G20210A mutations in Chinese patients with deep venous thrombosis and pulmonary embolism [J]. Clinical and laboratory haematology, 2006, 28(2): 111\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBALIAKAS P, MUNTERS A R, K\u0026auml;MPE A, et al. Integrating a Polygenic Risk Score into a clinical setting would impact risk predictions in familial breast cancer [J]. Journal of medical genetics, 2024, 61(2): 150\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSAMANI N J, BEESTON E, GREENGRASS C, et al. Polygenic risk score adds to a clinical risk score in the prediction of cardiovascular disease in a clinical setting [J]. European heart journal, 2024, 45(34): 3152\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLI L, PANG S, STARNECKER F, et al. Integration of a polygenic score into guideline-recommended prediction of cardiovascular disease [J]. European heart journal, 2024, 45(20): 1843\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eELLIOTT J, BODINIER B, BOND T A, et al. Predictive Accuracy of a Polygenic Risk Score-Enhanced Prediction Model vs a Clinical Risk Score for Coronary Artery Disease [J]. Jama, 2020, 323(7): 636\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLIU C, HOU J, LI W, et al. Construction and optimization of a polygenic risk model for venous thromboembolism in the Chinese population [J]. Journal of vascular surgery Venous and lymphatic disorders, 2024, 12(1): 101666.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePEDUZZI P, CONCATO J, KEMPER E, et al. A simulation study of the number of events per variable in logistic regression analysis [J]. Journal of clinical epidemiology, 1996, 49(12): 1373\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHUANG XJ H H, HU Y. Hematology [M]. Beijing: People\u0026rsquo;s Medical Publishing House, 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCAPRINI J A. Thrombosis risk assessment as a guide to quality patient care [J]. Disease-a-month: DM, 2005, 51(2\u0026ndash;3): 70\u0026thinsp;\u0026ndash;\u0026thinsp;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDAVID W. HOSMER JR. S L, RODNEY X. STURDIVANT. Applied Logistic Regression [M]. New York: John Wiley \u0026amp; Sons, Inc, 2000.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLIEDERMAN Z, CHAN N, BHAGIRATH V. Current Challenges in Diagnosis of Venous Thromboembolism [J]. Journal of clinical medicine, 2020, 9(11).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLIM W, LE GAL G, BATES S M, et al. American Society of Hematology 2018 guidelines for management of venous thromboembolism: diagnosis of venous thromboembolism [J]. Blood advances, 2018, 2(22): 3226\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZHANG Z, LI H, WENG H, et al. Genome-wide association analyses identified novel susceptibility loci for pulmonary embolism among Han Chinese population [J]. BMC medicine, 2023, 21(1): 153.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLU X, ZENG W, ZHU L, et al. Application of the Caprini risk assessment model for deep vein thrombosis among patients undergoing laparoscopic surgery for colorectal cancer [J]. Medicine (Baltimore), 2021, 100(4): e24479.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Venous Thromboembolism, Polygenic risk score, Caprini score, Risk assessment","lastPublishedDoi":"10.21203/rs.3.rs-8190841/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8190841/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eVenous Thromboembolism (VTE) is influenced by clinical and genetic factors. The Caprini score is widely used but does not account for population-specific genetic susceptibility in Chinese individuals. We previously developed a Chinese-specific 15-variant polygenic risk score (PRS-15) for VTE; however, its clinical performance and added value when combined with the Caprini score remain unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a multicenter prospective case\u0026ndash;control study at 18 hospitals in China (February 2023\u0026ndash;March 2025), enrolling 1,013 postoperative VTE cases and 1,025 controls. Blood samples were genotyped for PRS-15, and clinical variables and outcomes were analyzed. Discrimination and calibration were assessed using AUC and the Hosmer\u0026ndash;Lemeshow test. Two integrated models were constructed: Model 1, PRS-15T (tertiles: high/intermediate/low) plus Caprini; and Model 2, PRS-15B (binary: high/low) plus Caprini. K-means clustering identified optimal PRS-15 thresholds and integration strategies. Model performance was evaluated by sensitivity, specificity, and net reclassification improvement (NRI), with internal validation by bootstrap resampling.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe PRS-15 model achieved an AUC of 0.702 (95% CI, 0.680\u0026ndash;0.725; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For ternary classification (PRS-15T), the optimal thresholds were 2.230 and 1.730, while the optimal threshold for binary classification (PRS-15B) was 2.335. The sensitivities of Model 1 and Model 2 were 93.88% and 90.52%, respectively, both exceeding that of the Caprini score alone (87.36%). Model 2 demonstrated the highest specificity (33.85%), outperforming Model 1 (26.73%) and the Caprini score (21.37%), and achieved an NRI of 15.65%.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePRS-15 accurately discriminated VTE cases from controls and remained robust across age and sex subgroups. Adding PRS-15 to the Caprini score significantly improved risk assessment accuracy, supporting its clinical use for VTE risk evaluation in Chinese orthopedic surgical patients.\u003c/p\u003e","manuscriptTitle":"Predictive Performance and Clinical Application of PRS-15 for Venous Thromboembolism Risk in Chinese Orthopedic Patients: A Multicenter Prospective Case-Control Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-19 11:37:41","doi":"10.21203/rs.3.rs-8190841/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsmed","sideBox":"Learn more about [Communications Medicine](http://www.nature.com/commsmed)","snPcode":"43856","submissionUrl":"https://mts-commsmed.nature.com/cgi-bin/main.plex","title":"Communications Medicine","twitterHandle":"@commsmedicine","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d7192f25-e68d-4e2f-b860-1ec5cb84e340","owner":[],"postedDate":"February 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59435004,"name":"Health sciences/Risk factors"},{"id":59435005,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":59435006,"name":"Biological sciences/Genetics/Genetic association study/Genome-wide association studies"}],"tags":[],"updatedAt":"2026-02-19T11:37:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-19 11:37:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8190841","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8190841","identity":"rs-8190841","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}