Abstract
Background
To investigate the predictive role of a risk assessment model constructed using Caprini score combined with D‐dimer in gynecological postoperative patients for the occurrence of deep vein thrombosis.
Methods
Patients scheduled for gynecological surgery at our hospital between January 2018 and April 2024 were included. This study included 136 patients, with 35 cases in the DVT group and 101 cases in the non‐DVT group. General information, intraoperative parameters, D‐dimer levels, Caprini Score, lower extremity Doppler ultrasonography, and intervention methods were collected. Logistic regression analysis and a combined model were employed to analyze the factors influencing the occurrence of DVT.
Results
Compared to non‐DVT patients, the DVT group had a significantly older age (p = 0.035), higher hypertension prevalence (p = 0.025), and more complex surgeries (p = 0.004). Pre‐discharge D‐dimer levels and pre‐/postoperative Caprini scores were markedly elevated in DVT patients (p < 0.05). Critically, logistic regression identified pre‐discharge D‐dimer levels (p < 0.001), preoperative Caprini score (p = 0.003), and postoperative Caprini score (p < 0.001) as independent risk factors for DVT. The combined prediction model integrating these factors achieved an AUC of 0.812, demonstrating high discriminative power for postoperative DVT occurrence.
Conclusion
The predictive value of the DVT prediction model constructed using the Caprini score in combination with D‐dimer for the occurrence of DVT is high. The combined predictive model can be further promoted in clinical practice to take appropriate preventive measures to reduce the likelihood of DVT occurrence and to intervene promptly in existing risk factors.
Keywords: Caprini score, D‐dimer, deep vein thrombosis, gynecological, risk prediction model
INTRODUCTION
Venous thromboembolism (VTE) is a common vascular disease comprising deep vein thrombosis (DVT) and pulmonary embolism (PE). 1 , 2 , 3 DVT refers to the abnormal coagulation of blood within the deep veins, leading to partial or complete blockage of the vein lumen, causing venous stasis. 4 , 5 , 6 Without timely treatment, dislodged thrombi can travel to the lungs through the bloodstream, resulting in pulmonary embolism, which can be fatal or non‐fatal. 7 Patients with gynecological diseases are at high risk for DVT and PE, with DVT being a common and serious postoperative complication in gynecological surgeries, potentially leading to significant health issues. 8 DVT often presents with subtle symptoms, with only 10%–17% of DVT patients exhibiting obvious clinical manifestations, leading to potential underdiagnosis due to self‐neglect and clinical oversight. 9 Despite advancements in modern medical technology and postoperative care, some patients still develop DVT after surgery, especially in certain high‐risk populations. 10 , 11 , 12 Therefore, prompt identification of high‐risk patients and implementation of effective preventive measures are crucial in reducing the occurrence of DVT.
The Caprini score is widely used to assess the risk of venous thrombosis and has been proven effective in various clinical settings. 13 The American College of Chest Physicians (ACCP) guidelines recommend using the Caprini risk assessment model for thrombotic risk assessment. 14 However, the use of the Caprini score alone may not fully capture individual patient characteristics and physiological status. D‐dimer is a degradation product of crosslinked fibrin formed during fibrinolysis and serves as a biomarker for hemostasis and fibrinolysis, commonly used as a clinical indicator in the early assessment of suspected acute thrombosis due to its elevation during thrombus formation. 15 In the past two decades, D‐dimer testing has been widely used in the exclusion of DVT and PE. 16 Therefore, this study aims to construct a predictive model based on the Caprini score and D‐dimer levels to assess the risk of DVT in postoperative gynecological patients. By combining clinical scoring and biochemical indicators, we aim to enhance the predictive ability for patients at risk of DVT and provide clinicians with better decision support tools to develop personalized preventive strategies.
METHODS
Study design and population
We adhered to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines for training, validation, and reporting of the proposed nomination chart. Patients scheduled for gynecological surgery at our hospital from January 2018 to May 2024 were included and categorized into non‐DVT and DVT groups based on the occurrence of DVT postoperatively. Inclusion criteria for the DVT group were: (1) diagnosis of DVT by vascular color Doppler ultrasound within 60 days postoperatively; (2) complete clinical information of the patient; (3) normal mental and cognitive function of the patient and their family members. Exclusion criteria for the DVT group were: (1) age < 18 years; (2) hospital stay <2 days; (3) pre‐existing long‐term anticoagulant therapy (e.g., for prior DVT, cardiac conditions) or documented history of preoperative DVT; (4) history of peripheral vascular disease; (5) concomitant significant organ diseases such as cardiac, cerebral, or renal conditions; (6) pregnant or lactating women. In addition, inclusion criteria for the control group were the absence of DVT observed in venous color Doppler ultrasound within 60 days postoperatively. The exclusion criteria for the control group were the same as those for the DVT group. This study obtained approval from the Ethics Committee of Taiyuan Maternal and Child Health Care Hospital (No. 2024‐1013‐01), and the principles of the Helsinki Declaration were followed.
Assessment and intervention of preoperative and postoperative deep vein thrombosis
Preoperative evaluation included D‐dimer testing, lower extremity venous ultrasound, and risk stratification using the Caprini score. Patients with normal D‐dimer levels, ultrasound findings of chronic thrombus, and low‐risk Caprini scores received conservative management with compression stockings. For patients with elevated D‐dimer levels, ultrasound evidence of superficial vein thrombosis, or moderate‐to‐high risk Caprini scores, prophylactic measures included compression stockings and subcutaneous administration of low‐molecular‐weight heparin (5000 IU) or bemiparin (3000 IU) twice daily. Surgery was performed after normalization of D‐dimer levels; postoperative anticoagulation was implemented to reduce thrombotic risk.
Postoperative thrombosis prevention strategies included the use of compression stockings, subcutaneous administration of low‐molecular‐weight heparin (5000 IU), bemiparin (3000 IU), or enoxaparin starting 24 h post‐surgery, and transitioning to oral rivaroxaban upon discharge. Additional measures included intermittent pneumatic compression therapy (24 h post‐surgery) and early mobilization. D‐dimer levels were monitored, with lower extremity venous ultrasound performed as needed. For patients with postoperative thrombosis (commonly intramuscular venous thrombi), treatment included continued use of compression stockings (with attention to lower limb circulation), subcutaneous low‐molecular‐weight heparin or bemiparin (5000 IU or 3000 IU, twice daily), and transitioning to oral rivaroxaban. In cases of large, refractory thrombi in the lower extremity veins, inferior vena cava filter placement was considered to prevent pulmonary embolism.
Diagnostic criteria for DVT
Patients presenting with symptoms such as swelling and skin color changes underwent duplex lower extremity vascular color Doppler ultrasound, while other patients underwent B‐mode ultrasound before discharge to confirm the occurrence of DVT. As per the diagnostic criteria, 17 color Doppler ultrasonography revealed disappearance of color filling defects, loss of Doppler signals, and inability to compress the veins due to thrombus, aiding in determining the location and extent of the thrombus. The diagnosis was conclusively established by integrating patient history and clinical manifestations. Follow‐up within 60 days postoperatively was conducted to exclude or diagnose DVT.
Collection of clinical data
Patient data including age, height, weight, BMI, preoperative comorbidities, gynecological disease type, surgery duration, type of surgery, intraoperative position, pre‐ and postoperative interventions, and length of hospital stay were collected.
D‐dimer assay
Prior to surgery, at 24 h postoperatively, and before discharge on an empty stomach, 4 mL of venous blood was collected from the patients and added to sodium citrate anticoagulant tubes. The samples were centrifuged at 2500 r/min for 10 min, and quantitative measurement of D‐dimer was performed. A D‐dimer level >550 μg/L was considered positive.
Caprini score
The Caprini risk assessment model was employed both before and after surgery to assess the risk and grade of DVT in the selected patients. This model encompasses 39 risk factors associated with DVT, each assigned a specific score. The patient's total DVT score was calculated based on the presence of relevant factors. Patients were then graded for DVT risk as follows: low risk: 0–1 point; moderate risk: 2–3 points; high risk: 4–5 points; very high risk: ≥6 points.
Lower extremity color doppler ultrasound
Before and after the surgery, bilateral measurements of the diameters of the femoral artery, popliteal artery, anterior tibial artery, posterior tibial artery, and dorsalis pedis artery were performed. Additionally, peak systolic flow velocity and blood flow were measured. The assessment also included observation of intimal thickness, presence of plaques, and the presence of stenosis in the arterial lumen.
Statistical analyses
Data analysis was conducted using SPSS and R. Statistical significance was set at p < 0.05 for two‐tailed tests. For the comparison of continuous variables, the t‐test (normal distribution) and Mann–Whitney U test (skewed distribution) were utilized, with results presented as mean ± standard deviation and median (interquartile range, IQR) respectively. Categorical variables were expressed as numbers (percentages) and compared using the chi‐square test. Potential high‐risk factors for DVT were screened using univariate and multivariate logistic regression. Variables with a univariate p < 0.05 were selected and included in the multivariate logistic regression analysis. The “regplot” R software package was employed to scale each regression coefficient from the multivariate logistic regression to a 0–100 scale, constructing a nomogram. Calibration curves were used to assess the predictive performance of the model. Model stability was evaluated using 1000 bootstrapping resamples. Decision curve analysis (DCA) and clinical impact curve (CIC) analysis were performed to evaluate the clinical utility of the model.
RESULTS
General information
Comparison of the general data of the two patient groups revealed that, compared to the DVT group, the non‐DVT group had significantly lower numbers of individuals with hypertension (p = 0.025) and lower extremity venous thrombosis (p = 0.021), as well as a younger age (p = 0.035) (Table 1). However, there were no significant differences between the two groups in terms of height, weight, BMI, diabetes, hyperlipidemia, coronary heart disease, chronic nephritis, fractures, cysts, anemia, and gynecological disease type (p > 0.05).
TABLE 1.
General information.
| Parameter | DVT (n = 35) | Non‐DVT (n = 101) | p |
|---|---|---|---|
| Age (years) | 53.60 ± 11.12 | 48.90 ± 11.15 | 0.035 |
| Height (m) | 1.60 ± 0.06 | 1.60 ± 0.05 | 0.647 |
| Weight (kg) | 64.99 ± 9.84 | 62.88 ± 8.90 | 0.268 |
| BMI (kg/m2) | 25.45 ± 3.25 | 24.63 ± 3.25 | 0.207 |
| Preoperative complications | |||
| Hypertension | 12 (34.29%) | 15 (14.85%) | 0.025 |
| Diabetes | 2 (5.71%) | 7 (6.93%) | 1.000 |
| Hyperlipidemia | 0 (0.00%) | 1 (0.99%) | 1.000 |
| Coronary heart disease | 1 (2.86%) | 0 (0.00%) | 0.257 |
| Chronic nephritis | 1 (2.86%) | 0 (0.00%) | 0.257 |
| Fractures | 2 (5.71%) | 3 (2.97%) | 0.824 |
| Lower extremity venous thrombosis | 0 (0.00%) | 0 (0.00%) | 0.021 |
| Chocolate cyst | 1 (2.86%) | 1 (0.99%) | 1.000 |
| Anemia | 0 (0.00%) | 3 (2.97%) | 0.716 |
| Gynecological disease type | |||
| Benign | 0.122 | ||
| Uterine leiomyoma | 9 (25.7%) | 48 (47.52%) | |
| Adenomyosis | 3 (8.57%) | 7 (6.93%) | |
| Benign ovarian tumor | 2 (5.71%) | 6 (5.94%) | |
| Pelvic organ prolapse | 7 (20%) | 13 (12.87%) | |
| Endometrial polyp with stromal nodule | 1 (2.86%) | 0 (0.00%) | |
| Other benign diseases | 0 (0.00%) | 5 (4.95%) | |
| Malignant | 0.0062 | ||
| Cervical cancer | 6 (17.14%) | 7 (6.93%) | |
| Endometrial cancer | 3 (8.57%) | 15 (14.85%) | |
| Ovarian cancer | 3 (8.57%) | 0 | |
| Retroperitoneal malignancy | 1 (2.86%) | 0 | |
Intraoperative parameters
There were no significant differences between the two groups in terms of surgical time, total hysterectomy rate, uterine lesion resection rate, ovarian resection rate, and ovarian lesion resection rate (p > 0.05) (Table 2). In comparison with the DVT group, the non‐DVT group showed significantly lower rates of ovarian tumor cell reduction, bilateral adnexectomy, salpingectomy, lymph node dissection, and vaginal suspension/repair (p < 0.05). In the DVT group, surgeries were primarily performed in the lithotomy position (60.00%), while in the non‐DVT group, the lithotomy position was predominant (58.42%), indicating a significant difference (p < 0.001).
TABLE 2.
Intraoperative parameters.
| Parameter | DVT (n = 35) | Non‐DVT (n = 101) | p |
|---|---|---|---|
| Operation time (h) | 1.67 (1.21, 2.34) | 2.00 (1.50, 2.50) | 0.135 |
| Surgical type [n (%)] | |||
| Total hysterectomy | 22 (62.86%) | 60 (59.41%) | 0.874 |
| Uterine lesion resection | 5 (14.29%) | 28 (27.72%) | 0.247 |
| Ovarian tumor cell reduction | 3 (8.57%) | 0 (0.00%) | 0.021 |
| Bilateral adnexectomy | 13 (37.14%) | 0 (0.00%) | <0.001 |
| Salpingectomy | 6 (17.14%) | 0 (0.00%) | <0.001 |
| Lymph node dissection | 6 (17.14%) | 2 (1.98%) | 0.004 |
| Ovarian resection | 2 (5.71%) | 0 (0.00%) | 0.108 |
| Ovarian lesion resection | 0 (0.00%) | 4 (3.96%) | 0.539 |
| Vaginal suspension/repair | 6 (17.14%) | 7 (6.93%) | 0.012 |
| Position | |||
| Lithotomy | 13 (37.14%) | 42 (41.58%) | <0.001 |
| Supine | 22 (62.86%) | 59 (58.42%) | |
D‐dimer levels
The D‐dimer levels of the two groups of patients were compared preoperatively, at 24 h post‐operation, and prior to discharge. The results revealed that the DVT group had significantly higher D‐dimer levels compared to the non‐DVT group, particularly preoperatively (p = 0.012) and prior to discharge (p < 0.001) (Table 3).
TABLE 3.
Comparison of D‐dimer levels and Caprini score.
| Parameter | DVT (n = 35) | Non‐DVT (n = 101) | p |
|---|---|---|---|
| D‐dimer levels | |||
| Preoperative | 530.00 (190.00, 671.12) | 293.00 (161.00, 347.04) | 0.012 |
| Postoperative 24 h | 2269.00 (954.00, 3410.00) | 1486.00 (820.00, 1934.00) | 0.082 |
| Before discharge | 3250.22 (1415.00, 4555.50) | 1879.72 (1141.00, 1879.72) | <0.001 |
| Caprini score | |||
| Preoperative | 3.00 (2.00, 4.00) | 2.00 (1.00, 2.00) | <0.001 |
| Postoperative | 4.89 ± 1.84 | 3.72 ± 1.27 | 0.001 |
Caprini score
The risk of developing DVT in both groups of patients was evaluated using the Caprini score. The results revealed that, compared to the non‐DVT group, the DVT group had significantly higher scores both preoperatively and postoperatively (p < 0.001) (Table 3).
Doppler ultrasound of the lower extremities
Doppler ultrasound of the lower extremities in both the DVT and non‐DVT groups predominantly showed normal results preoperatively. However, the proportion of lower extremity superficial vein thrombosis was higher in the DVT group compared to the non‐DVT group, with a significant difference between the two groups (p < 0.001) (Table 4). Postoperatively, lower extremity Doppler ultrasound showed that in the DVT group, superficial vein thrombosis was predominant (77.14%), while the non‐DVT group predominantly showed normal results (97.03%), indicating a significant distinction.
TABLE 4.
Doppler ultrasound of the lower extremities.
| Parameter | DVT (n = 35) | Non‐DVT (n = 101) | p |
|---|---|---|---|
| Preoperative | |||
| Normal | 29 (82.86%) | 98 (97.03%) | <0.001 |
| Lower extremity superficial vein thrombosis | 6 (17.14%) | 0 (0.00%) | |
| Arterial plaque | 0 (0.00%) | 2 (1.98%) | |
| Varicose veins | 0 (0.00%) | 1 (0.99%) | |
| Postoperative | |||
| Normal | 7 (20%) | 98 (97.03%) | <0.001 |
| Lower extremity superficial vein thrombosis | 27 (77.14%) | 0 (0.00%) | |
| Arterial plaque | 1 (2.86%) | 0 (0.00%) | |
| Blood flow stasis | 0 (0.00%) | 3 (2.97%) | |
Perioperative intervention
Preoperative and postoperative interventions were conducted for both groups of patients. The results indicated that prior to surgery, the non‐DVT group had no interventions (100%), while the majority of the DVT group also had no interventions preoperatively (85.71%). The remaining patients underwent interventions, such as rivaroxaban or inferior vena cava filter placement (2.86%, 11.43%), showing a significant difference between the two groups (p < 0.001) (Table S1). After the operation, the majority of patients in both groups received treatment, with the DVT group predominantly receiving treatment with enoxaparin sodium (57.14%), and the non‐DVT group predominantly receiving treatment with low molecular weight heparin calcium (47.52%). Apart from the treatment with rivaroxaban, postoperative interventions including no intervention, low molecular weight heparin calcium, enoxaparin sodium, nadroparin calcium, and dual lower limb pneumatic compression therapy all showed significant differences between the two groups (p < 0.05). However, the length of hospital stay showed no significant difference between the two groups (p > 0.05) (Table S1).
Logistic regression analysis
Based on the significant variables mentioned above, a random logistic regression model was established (fitting the regression model to the training data through multiple sampling and ultimately selecting the most important features with high scores) to determine the most influential factors. Following the principle of EPV > 10, three independent variables were ultimately selected: pre‐discharge D‐dimer levels, preoperative Caprini score, and postoperative Caprini score, as the fitting variables affecting the occurrence of DVT. Logistic regression for these three variables revealed that the odds ratios for pre‐discharge D‐dimer, preoperative Caprini score, and postoperative Caprini score were all less than 1. Notably, pre‐discharge D‐dimer had the most significant impact on the occurrence of DVT, followed by postoperative Caprini score and preoperative Caprini score (Table 5). A line chart prediction model was constructed for the three variables (Figure 1), calculating the total sum of scores for different factors, with a higher score indicating a higher DVT occurrence rate.
TABLE 5.
Logistic regression analysis.
| Parameter | Odds ratio | 95%CI | p |
|---|---|---|---|
| Pre‐discharge D‐dimer | 0.098 | 0.039–0.231 | <0.001 |
| Preoperative Caprini score | 0.294 | 0.130–0.657 | 0.003 |
| Postoperative Caprini score | 0.174 | 0.074–0.393 | <0.001 |
FIGURE 1.
Line chart prediction model of DVT occurrence.
ROC analysis
A combined model predicting the occurrence of DVT was established, and the results indicated an AUC value of 0.812, demonstrating the substantial predictive value of this composite risk factor model for DVT (Figure 2a). The clinical impact curve further visually demonstrated the predictive accuracy of the model (Figure 2b). DCA revealed that within the range of net benefit >0, the model's effectiveness is acceptable (Figure 2c). The calibration curve illustrated the deviation between the predicted results from the line chart and the actual results, with an average absolute error of 0.038 for the actual and predicted occurrence rates (Figure 2d). In comparison, the ROC curve of D‐dimer, with an AUC of 0.642, showed that the combined model outperforms D‐dimer in predicting thrombosis (Figure S1).
FIGURE 2.
Combined model of risk factors for predicting DVT. (a) ROC analysis. (b) CIC nomogram. (c) DCA analysis. (d) Calibration curve.
DISCUSSION
DVT is a severe complication among gynecological surgical patients, particularly in those with tumors, with a greater likelihood of occurrence postoperatively. 18 , 19 , 20 The acute phase of DVT is associated with a high mortality rate; even with standardized anticoagulant therapy, some patients may experience long‐term complications. 21 Given that thrombosis can lead to pulmonary embolism and lower extremity dysfunction, early prevention and treatment of postoperative DVT in gynecological patients are crucial. Assessing the risk of thrombosis is the foremost step in its prevention. However, DVT manifestation lacks specificity in clinical presentation, often leading to misdiagnosis or underdiagnosis, and there is no unified gold standard for DVT prediction in clinical practice. 22 Therefore, selecting an effective, convenient, and rapid assessment tool to predict the risk of DVT occurrence and enable early intervention can effectively reduce the disability and mortality rates among DVT patients.
The Caprini thrombosis risk assessment model is a weighted risk evaluation tool widely utilized in clinical practice. 23 , 24 , 25 Its clinical significance lies in its ability to categorize patients into various risk levels for postoperative complications such as DVT. A higher Caprini score indicates a greater risk of developing thromboembolic events, which can guide clinicians in tailoring prevention strategies, such as the use of prophylactic anticoagulation or mechanical interventions. Previous studies have indicated a positive correlation between Caprini scores in surgical inpatients and the risk of DVT occurrence, demonstrating its strong predictive capability. 26 Research results have also confirmed the effectiveness of the Caprini thrombosis risk assessment model in evaluating the risk of DVT occurrence in hospitalized patients, with a significantly higher risk of DVT in patients with higher scores compared to those with lower scores. 27 In this study, significant differences were observed in the preoperative and postoperative Caprini scores between patients who experienced DVT and those who did not. Despite preoperative prevention and postoperative treatment for patients with higher scores, some patients still developed lower limb DVT, possibly attributed to various factors in gynecological diseases such as inflammatory factor expression, local compression of venous return, and physiological stress responses. Therefore, further research is needed to better differentiate the risk of lower limb DVT among patients and implement effective prevention strategies.
Early postoperative assessment of thrombotic risk is of paramount importance in improving the quality of life and prognosis of patients in this regard. D‐dimer is a specific degradation product of cross‐linked fibrin monomers, generated through fibrin degradation by plasmin, and serves as a biomarker for hypercoagulability and thrombus formation in the human body. It offers the advantages of being rapid, cost‐effective, and repeatable, thus holding high clinical utility. 28 , 29 , 30 An analysis from a study revealed a significant association between D‐dimer and the risk of DVT in patients. 31 The results of this study demonstrated a significant difference in D‐dimer levels before discharge between the two groups of patients, and logistic regression analysis indicated its significant association with lower limb DVT. This underscores the importance of using D‐dimer levels as a valuable reference for diagnosing lower limb DVT in patients. However, in the experiment, there was also a significant difference in preoperative D‐dimer levels between the two groups, possibly related to the presence of lower limb superficial vein thrombosis in some patients in the DVT group prior to surgery, which may have a potential impact on the occurrence of DVT as well. After conducting a comprehensive study on the significant differences in risk factors between the two groups, including age, hypertension, lower extremity venous thrombosis, surgical type, position, lower limb color Doppler ultrasound, and preoperative and postoperative intervention methods, it was found that the levels of D‐dimer before discharge, preoperative and postoperative Caprini scores had the most significant impact. Through the construction of a predictive model, it was demonstrated that the combined diagnosis of Caprini scores and serum D‐dimer has good early predictive diagnostic value for lower limb DVT. This approach can effectively prevent DVT in at‐risk populations, optimize the rational utilization of limited medical resources, reduce the incidence of DVT, and ensure patient safety. Using pre‐discharge D‐dimer levels in conjunction with the Caprini score offers a comprehensive approach to risk stratification, enabling clinicians to assess both the patient's underlying thrombotic risk (through the Caprini score) and ongoing risk of clot formation (through D‐dimer levels) before discharge. In this study, we integrated the postoperative Caprini score and pre‐discharge D‐dimer levels to refine VTE risk assessment and guide tailored interventions. Patients with low risk (low Caprini score and normal D‐dimer) were managed with early ambulation and compression stockings, while those with moderate to high risk (elevated Caprini score or D‐dimer) received LMWH prophylaxis, transitioning to oral rivaroxaban upon discharge if necessary. In cases of confirmed postoperative thrombosis, anticoagulation was intensified, and severe cases required IVC filter placement. Pre‐discharge D‐dimer testing helped identify patients needing extended anticoagulation and follow‐up ultrasound, while those with normal D‐dimer levels at discharge avoided unnecessary treatment. This dynamic risk assessment approach allows for individualized thromboprophylaxis, reducing both VTE incidence and bleeding complications associated with overtreatment.
We identified pre‐discharge D‐dimer levels, preoperative Caprini score, and postoperative Caprini score as the key independent variables influencing the occurrence of postoperative DVT. While malignancy, performance status (PS), and type of surgery (open vs. laparoscopic) are also well‐established risk factors for DVT, we believe that these factors may be captured, to some extent, within the selected variables of Caprini scores and D‐dimer. For instance, the Caprini score already integrates patient factors such as history of malignancy and other comorbidities, which may encompass the influence of tumor presence and PS score. Additionally, while the type of surgery (open vs. laparoscopic) is an important determinant in postoperative thrombosis risk, the postoperative Caprini score likely reflects this variable, as it considers the surgical approach and other patient‐specific factors that affect thrombotic risk. Therefore, our model, which includes the Caprini scores and D‐dimer, provides a comprehensive assessment of the key risk factors without adding additional independent variables.
Nonetheless, it is important to acknowledge that malignancy, PS, and surgical approach may still influence DVT risk; future studies could benefit from directly evaluating these factors in different patient cohorts to further refine DVT prediction models. We believe that our approach has identified the most influential and actionable predictors of DVT occurrence, which can guide clinical decision‐making and patient management.
This study still has some limitations. Firstly, as a single‐center retrospective study, the sample size is limited, especially with fewer DVT patients, resulting in some selection bias. Therefore, further validation is needed through large‐sample, multicenter, and prospective studies. Secondly, due to the small number of DVT cases in the study, there are fewer clinical impact factors that can be used for modeling. The study only focused on validating the significantly influential indicators, and in the future, it would be beneficial to expand to more indicators for a comprehensive assessment. Despite these limitations, this study provides substantial support for predicting DVT occurrence and offers a clinical theoretical basis for implementing relevant measures to prevent and treat DVT.
In conclusion, the predictive value of the DVT model constructed by combining Caprini scores and D‐dimer for predicting DVT occurrence is high and aligns well with actual occurrences. This combined predictive model can be further promoted in clinical practice, enabling the implementation of appropriate preventive measures, reducing the likelihood of DVT occurrence, and timely intervention in existing risk factors.
AUTHOR CONTRIBUTIONS
Jing Lu: Conceptualization; methodology; writing – original draft; writing – review and editing. Huihui Zhu: Data curation; formal analysis. Xilian Guo: Methodology; validation. Yan Jia: Formal analysis; visualization. Jian Liu: Project administration; supervision. Can Cui: Software; visualization. Yang Deng: Data curation; methodology; resources; writing – original draft; writing – review and editing.
FUNDING INFORMATION
This study was not supported by any sponsor or funder.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflict of interests for this article.
Supporting information
TABLE S1: Perioperative intervention.
FIGURE S1: ROC curve of D‐dimer.
ACKNOWLEDGMENTS
The authors are grateful to all participants in the present study.
Lu J, Zhu H, Guo X, Jia Y, Liu J, Cui C, et al. Construction and evaluation of a risk prediction model for postoperative deep vein thrombosis in gynecological patients. J Obstet Gynaecol Res. 2025;51(10):e70036. 10.1111/jog.70036
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. This study was conducted with the approval of the ethics committee. Any research that differs from the objectives and analysis methods of this study must be approved again by the ethics committee before data sharing.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
TABLE S1: Perioperative intervention.
FIGURE S1: ROC curve of D‐dimer.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. This study was conducted with the approval of the ethics committee. Any research that differs from the objectives and analysis methods of this study must be approved again by the ethics committee before data sharing.