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Development and validation of a nomogram for predicting venous thromboembolism risk in post-surgery patients with cervical cancer

World Journal of Surgical Oncology volume 22, Article number: 354 (2024) Cite this article

Abstract

Objective

Postoperative venous thromboembolism (VTE) is a potentially life-threatening complication. This study aimed to develop a predictive model to identify independent risk factors and estimate the likelihood of VTE in patients undergoing surgery for cervical cancer.

Methods

We conducted a retrospective cohort study involving 1,174 patients who underwent surgery for cervical carcinoma between 2019 and 2022. The cohort was randomly divided into training and validation sets at 7:3. Univariate and multivariate logistic regression analyses were used to determine the independent factors associated with VTE. The results of the multivariate logistic regression were used to construct a nomogram. The nomogram's performance was assessed via the concordance index (C-index) and calibration curve. Additionally, its clinical utility was assessed through decision curve analysis (DCA).

Results

The predictive nomogram model included factors such as age, pathology type, FIGO stage, history of chemotherapy, the neutrophil–lymphocyte ratio (NLR), fibrinogen degradation products (FDP), and D-dimer levels. The model demonstrated robust discriminative power, achieving a C-index of 0.854 (95% CI: 0.799–0.909) in the training cohort and 0.757 (95% CI: 0.657–0.857) in the validation cohort. Furthermore, the nomogram showed excellent calibration and clinical utility, as evidenced by the calibration curve and decision curve analysis (DCA) results.

Conclusions

We developed a high-performance nomogram that accurately predicts the risk of VTE in cervical cancer patients undergoing surgery, providing valuable guidance for thromboprophylaxis decision-making.

Introduction

Gynecological cancers often result in venous thromboembolism (VTE), a severe and potentially life-threatening complication that encompasses pulmonary embolism (PE) and deep venous thrombosis (DVT). Previous studies have reported that the incidence of VTE in patients with gynecological malignancies ranges from 2.8% to 26.7% [1,2,3]. Patients with gynecological cancers have a higher incidence of postoperative VTE, significantly affecting the mortality rates of those who undergo surgery. Among gynecological malignancies, cervical cancer is particularly notable [4]. It is the fourth most common cancer among women, with high incidence and mortality rates, and poses a severe threat to women's health and quality of life [5]. Surgical intervention remains the primary treatment for improving the life expectancy of patients with cervical cancer. Matsuo et al. reported that in the United States, up to 12.3% of females diagnosed with cervical cancer experienced VTE [6]. Another study reported that the incidence of VTE among cervical cancer patients was 11.7% [7]. Various factors influence the occurrence of VTE. While several studies have explored VTE risk factors in this population, a comprehensive predictive model has yet to be developed [2, 8]. The Caprini score is commonly used for VTE risk assessment in surgical patients. However, although this tool indicates that patients undergoing cervical cancer surgery often receive high-risk scores, it does not offer detailed risk stratification specific to these patients. Therefore, considering the variations in disease characteristics and demographics, developing an accurate VTE risk assessment model for patients undergoing cervical cancer surgery is crucial [9, 10].

A nomogram is an intuitive predictive tool designed to assess and estimate the probability of clinical events for individual patients. This model improves prediction accuracy, allowing clinicians to visually evaluate patient conditions and customize their assessments accordingly [11, 12]. Several studies have shown that nomograms possess strong predictive capacity for assessing VTE risk in patients with malignant tumors. However, research on their ability to predict postoperative VTE, specifically in cervical cancer patients, remains limited. This study aimed to develop a nomogram model to evaluate the probability of VTE in patients who underwent surgery for cervical cancer.

Methods and participants

Study population

From January 2019 to December 2022, 1,174 patients diagnosed with cervical cancer underwent surgical treatment at Chongqing University Cancer Hospital in China. Participants were required to meet the following inclusion criteria: 1) confirmed diagnosis of cervical cancer via pathological examination, 2) underwent surgical intervention for cervical cancer at the hospital, and 3) provided comprehensive medical information, including preoperative status, surgical procedures, and results from any additional tests. The exclusion criteria were as follows: 1) age younger than 18 years, 2) a history of VTE from other causes prior to surgery, and 3) secondary cervical carcinoma or the presence of other primary malignancies. Figure 1 illustrates the incomplete clinical records.

Fig. 1
figure 1

Flowchart of inclusion and exclusion of subjects

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Data collection

Based on previous relevant studies and possible risk factors for VTE in clinic practice. Clinicopathological data were collected, including variables such as age; body mass index (BMI); marital status (married/others); pathological type (such as squamous cell carcinoma, adenocarcinoma, and others); FIGO tumor stage (I-IV); surgical approach (transabdominal, laparoscopic, or robotic); duration of surgery; and treatment history, including radiotherapy, chemotherapy, targeted therapy, and immunotherapy. In addition, several laboratory test results, including leukocyte count (WBC), platelet count (PLT), microglobulin, neutrophil–lymphocyte ratio (NLR), prothrombin time (PT), activated partial thromboplastin time (APTT), fibrinogen (FIB), fibrinogen degradation products (FDP), and D-dimer levels, were collected.

Diagnosis of VTE

Vascular ultrasound studies or computed tomography (CT) angiography were utilized to diagnose DVT. Computed tomography pulmonary angiography or enhanced chest CT scans were employed to diagnose PE. The diagnosis of VTE was confirmed by two experienced physicians.

Development and validation of the nomogram

The participants were randomly assigned to the training and internal validation cohorts at a 7:3 ratio. The training cohort was used to construct the predictive model, whereas the validation cohort was used to assess the model's accuracy. Univariate and multivariate logistic regression models were applied to the training cohort to analyze independent variables associated with VTE in cervical cancer patients undergoing surgery. Multivariate analysis included variables with a P value less than 0.05 from the univariate analysis, aiming to identify independent predictors for VTE. Odds ratios (ORs) and their respective 95% confidence intervals (CIs) were calculated to evaluate the impact of each variable on VTE risk. A nomogram was developed based on the multivariable analysis results to visually represent individual risk probabilities of VTE in postoperative cervical cancer patients. The nomogram's effectiveness in predicting risk probabilities was further assessed through discrimination and calibration within the validation cohort. The bootstrapping technique with 1,000 resamples was employed to evaluate the nomogram's reliability in both the training and validation cohorts. The performance of the nomogram was measured via the concordance index (C-index) and receiver operating characteristic (ROC) curve analysis. Additionally, calibration plots were used to assess the ability of the nomogram to predict postoperative VTE in cervical cancer patients, evaluating its discrimination ability and calibration proficiency. To explore the potential clinical value of the new nomogram, we conducted a decision curve analysis (DCA).

Statistical analysis

Data, including age were summarized using means and standard deviations for normally distributed variables, and categorical data were presented as counts and percentages. Group differences were tested via t-tests and Mann–Whitney U tests. Potential risk factors identified in crude analyses were further examined through multivariate logistic regression with stepwise selection. The Akaike information criterion (AIC) was employed to select the optimal model. The smaller the AIC value, the better the fit of the model. We choose the model with the lowest AIC value as the best model [13, 14]. The concordance index (C-index) was used to evaluate the nomogram's performance. Statistical analyses were performed via R software version 4.2.3, applying two-sided tests, with statistical significance set at a P value of less than 0.05. The following R packages were utilized to construct and evaluate the model and develop a web-based tool for assessing patient VTE risk: 'pROC (1.18.4)', 'ggDCA (1.2)', 'shiny (1.7.4.1)', and 'DynNom (5.0.2)'. This calculator offers customized and immediate VTE predictions based on the nomogram (available at https://www.shinyapps.io/).

Results

Patient characteristics

The study enrolled 1,174 individuals diagnosed with cervical cancer who underwent surgical intervention at Chongqing University Cancer Hospital between January 2019 and December 2022. Among these patients, 91 developed VTE post-surgery. 822 patients were assigned to the training cohort at a 7:3 ratio, while 352 randomly selected patients formed the validation cohort. VTEs were observed in 62 patients (7.54%) in the training cohort and 29 (8.24%) in the validation cohort. The incidence of VTE did not significantly differ between the two groups (P = 0.848). For more detailed information, please refer to Table 1.

Table 1 Demographics and clinicopathologic characteristics of the training and validation cohorts
Full size table

Creation and verification of the nomogram

Table 2 presents the findings from univariate and multivariate logistic regression analyses investigating factors associated with VTE. Univariate analysis revealed significant correlations (P < 0.05) between the increased incidence of VTE and various factors, including age, pathological type, FIGO stage, history of radiotherapy, history of chemotherapy, operation duration, D-dimer levels, microglobulin levels, PT, APTT, FIB, FDP, and NLR. A stepwise selection method was employed to incorporate all significant variables from the univariate analysis into the multivariate logistic regression model. The multivariate analysis identified six independent risk factors for VTE: age (OR, 1.04; 95% CI, 1.02–1.07; P = 0.001), pathological type (OR, 3.28; 95% CI, 1.56–6.92; P = 0.002), FIGO stage (OR, 1.92; 95% CI, 1.06–3.48; P = 0.032), history of chemotherapy (OR, 2.69; 95% CI, 1.47–4.89; P = 0.001), NLR (OR, 1.07; 95% CI, 1.01–1.13; P = 0.016), FDP (OR, 1.08; 95% CI, 1.02–1.16; P = 0.016), and D-dimer (OR, 4.57; 95% CI, 2.23–9.36; P < 0.001).

Table 2 Logistic regression analysis of the risk factors for VTE in the training cohort
Full size table

These independent variables were used to construct a nomogram (Fig. 2A) to predict VTE's probability in cervical cancer patients after surgery. Each variable was assigned a specific score on a point scale, and the overall risk score was calculated by summing the points across all the variables. To determine the likelihood of a VTE, users can draw a vertical line along the point axis. A web-based calculator based on a nomogram was also developed (https://cqcervical.shinyapps.io/cervical/) to estimate VTE risk in surgical patients diagnosed with cervical carcinoma (Fig. 2B). After the relevant parameters are entered, users can calculate the likelihood of VTE. For example, in the case of a 53-year-old cervical carcinoma patient diagnosed with stage IV SCC undergoing chemotherapy, with an NLR of 5, a D-dimer level below 0.5, and an FDP of 14 µg/ml, the estimated incidence of VTE was 10.9% (95% CI: 4.2–25.3%). This web-based calculator is a valuable tool for clinicians, aiding developing personalized prophylactic strategies.

Fig. 2
figure 2

Nomogram model. A The nomogram model was constructed on the basis of the independent risk factors identified by multivariate logistic regression analysis; B The interface of the web-based nomogram

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The AIC value of the Nomogram model constructed in this study is 348.45. The C-index of the nomogram for predicting VTE risk in cervical cancer patients was 0.854 (95% CI: 0.799–0.909) in the training cohort (Fig. 3A) and 0.757 (95% CI: 0.657–0.857) in the validation cohort (Fig. 3B), indicating effective predictive performance. Additionally, the calibration plot of the nomogram for predicting VTE in postoperative cervical cancer patients demonstrated strong agreement in both the training (Fig. 3C) and validation (Fig. 3D) cohorts. These findings underscore the model's reliable predictive ability. We further conducted 100 iterations of tenfold cross-validation to calculate the average C-index and model accuracy, confirming the model's robustness. The average C-index was 0.827, and the average accuracy was 92.6%.

Fig. 3
figure 3

ROC curves of the nomogram for VTE risk prediction in the training cohort (A) and validation cohort (B), along with the calibration plots of the nomogram for VTE risk in the training cohort (C) and validation cohort (D)

Full size image

Figures 4A and B illustrate the DCA results for the predictive nomogram. The effectiveness of the nomogram was evaluated via DCA, which calculates the net benefit at various threshold probabilities. The decision curve demonstrated that the predictive model offers a significantly more significant net benefit for thromboprophylaxis in cervical cancer patients at risk for VTE than the strategies for treating all or none. This advantage was evident within a probability threshold range of 2%—68% in the training cohort and 2% −56% in the validation cohort.

Fig. 4
figure 4

In the training cohort (A) and validation cohort (B), VTE was analyzed via decision curve analysis (DCA) to assess the decisions made

Full size image

Discussion

Surgical intervention is the primary approach for managing cervical cancer. A common complication associated with this surgery is VTE. Patients with gynecologic cancers face a VTE risk ranging from 2.8% to 26.7%, which is at least double the rate observed in individuals with benign gynecological conditions. Accurately identifying cervical cancer patients at elevated risk for VTE, coupled with the administration of preventive anticoagulant therapy, can significantly improve survival rates in these individuals. Therefore, examining the factors contributing to VTE and identifying patients at heightened risk is crucial to prevent its occurrence proactively.

Although many studies have investigated the risk of thrombus formation in individuals with gynecologic cancers, relatively few have focused on developing an accurate assessment model for determining individual VTE risk in cervical cancer patients [15,16,17]. To predict the likelihood of VTE effectively, we developed a predictive nomogram model based on clinicopathological data from 1,174 cervical cancer patients who underwent surgery at our institution. This model, designed to assess VTE risk, includes seven independent clinical variables: age, pathological type, FIGO stage, history of chemotherapy, NLR, FDP, and D-dimer. Internal validation of the nomogram indicated robust predictive ability, as reflected by a C-index of 0.854, underscoring its strong performance. Our nomogram model achieved satisfactory calibration, discrimination, and predictive accuracy regarding clinical outcomes, highlighting its potential utility in clinical decision-making. These results closely correlate with those of prior studies that investigated risk factors linked to postoperative VTE in patients with gynecological malignancies [16,17,18,19].

Several studies have explored the relationship between advanced age and VTE in cervical cancer patients. [15, 20,21,22]. One study revealed that age is a distinct factor influencing VTE risk in individuals with gynecological cancers. Furthermore, when comparing patients aged 60 to those under 30, the younger cohort demonstrated a nearly 50% lower incidence of VTE [16]. Wang et al. highlighted the importance of age in developing a nomogram to estimate VTE risk in patients with gynecological cancers [17]. A recent study revealed that being 60 years or older is a significant risk factor for VTE in patients with cervical cancer based on an examination of 2,086 patients diagnosed with gynecological cancer [22].

Patients diagnosed with adenocarcinoma or other pathological types of cervical cancer have a greater risk of VTE than those with squamous cell carcinoma. As a result, our nomogram includes pathological type as a distinct predictive factor, underscoring the importance of considering this variable when assessing the risk of postoperative VTE in cervical cancer patients. Among cancer-related factors, tumor stage has emerged as an independent risk factor for VTE in cervical cancer patients. A recent study involving 798 patients investigated the connection between VTE risk and cervical cancer risk. It revealed that advanced-stage disease is significantly associated with an increased probability of VTE, even after accounting for other factors [5]. Our findings are consistent with several previous studies that have also demonstrated the predictive significance of tumor staging in cervical cancer [6, 23, 24].

The administration of chemotherapeutic agents has been linked to an increased incidence of VTE in individuals diagnosed with gynecological malignancies. One prior study reported that approximately 16.7% of cervical cancer patients who underwent chemotherapy and radiotherapy experienced VTE [25]. Jacobson et al. reported an incidence of 11.7% for VTE in patients diagnosed with cervical cancer [7]. Another study revealed that cervical cancer patients receiving chemotherapy had a fourfold increased risk of VTE compared with untreated patients [26]. In our study, patients who underwent chemotherapy presented a significantly greater risk of developing VTE than those who did not receive chemotherapy.

The NLR is a biomarker that reflects the balance between systemic inflammation and immune activity [27]. The NLR has been evaluated as a prognostic biomarker for various diseases, including cancers and cardiovascular disorders, and is correlated with disease progression and patient outcomes [28, 29]. Recent studies have linked the NLR to an increased risk of VTE across various cancer types [30, 31], and it has also been recognized as a predictive indicator in cancer patients with VTE [32, 33]. Otasevic et al. reported that the NLR significantly influences the incidence of VTE in patients diagnosed with malignancies [30]. Additionally, another study revealed that bladder cancer patients with an NLR exceeding 3 faced an elevated risk of VTE [31]. Consistent with previous findings, our findings indicate that an elevated NLR is correlated with a greater probability of VTE in cervical cancer patients who have undergone surgery, as reflected in our nomogram.

FDPs are essential markers in clinical assessments. Recent findings suggest that elevated FDP levels are associated with an increased incidence of VTE in patients with gastric cancer [34]. Jiao et al. analyzed 488 patients with spinal fractures and revealed that plasma FDP levels equal to or exceeding 5.19 mg/L are a significant risk factor for VTE [35]. Additionally, a study involving 569 patients with femoral and pelvic fractures confirmed that elevated FDP levels are associated with an increased incidence of VTE during the perioperative period [36]. Thus, it can be inferred that FDP plays a critical role in the occurrence of VTE. Our research further indicated that FDP levels were significantly elevated in cervical cancer patients experiencing VTE.

Elevated serum D-dimer levels indicate activation of the fibrinolytic system and a hypercoagulable state, as D-dimer is a unique degradation product of crosslinked fibrin. Patients with gynecological cancers face an increased risk of postoperative VTE when they present with elevated D-dimer levels [15, 16, 18]. However, the low specificity and limited generalizability of D-dimer levels in cancer patients are influenced by various factors that cause fluctuations. This study developed a nomogram model integrating seven variables [37]. By combining D-dimer levels with additional factors, we were able to predict the risk of VTE in cervical cancer patients accurately. These findings are consistent with those of previous studies. The inclusion of D-dimer in the predictive nomogram for cervical cancer underscores its clinical relevance. Therefore, D-dimer represents a valuable marker for predicting postoperative VTE in cervical cancer patients.

It is essential to acknowledge the limitations of this study. First, this retrospective study was conducted at a single center, which may introduce selection bias and weaken the statistical power. Additionally, certain gene alterations might impact the occurrence of VTE. Due to the lack of such detailed information, the association of these genes with VTE could not be analyzed in our study. Although the results suggest that the nomogram demonstrates strong precision, a multicenter validation with a larger sample size is needed to determine its general applicability and validity.

Conclusion

In conclusion, we successfully developed and validated a new nomogram incorporating seven risk factors to estimate the likelihood of VTE in cervical cancer patients undergoing surgery. This model offers a simple and reliable approach to assessing blood clot formation risk, thereby enabling personalized prevention and treatment strategies.

Data availability

The datasets generated and analyzed during the study are not publicly available because of the privacy concerns raised by the research committee. However, they are available from the corresponding author upon reasonable request.

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Acknowledgements

We extend our gratitude to the individuals and specialists who assisted in every stage of this study.

Funding

Support for this work was provided by the Shapingba district Science Committee joint health committee project (2023SQKWLH014).

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Author notes

  1. Yue Chen, Xiaosheng Li and Li Yuan contributed equally to this work.

Authors and Affiliations

  1. Chongqing Cancer Multiomics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing, 400030, China

    Yue Chen, Yuliang Yuan, Qianjie Xu & Haike Lei

  2. Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, 400030, China

    Xiaosheng Li, Li Yuan & Wei Zhang

  3. Department of Health Statistics, School of Public Health, Chongqing Medical University, Chongqing, 400016, China

    Zuhai Hu

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  1. Yue Chen
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Contributions

Yue Chen and Xiaosheng Li: Conceptualization, Software, Writing-original draft. Yuliang Yuan and Qianjie Xu: Conceptualization, Software, Writing-original draft. Li Yuan and Zuhai Hu: Visualization, validation. Wei Zhang: Resources, supervision and project administration. Haike Lei: Conceptualization, methodology, writing - review & editing.

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Correspondence to Wei Zhang or Haike Lei.

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Chen, Y., Li, X., Yuan, L. et al. Development and validation of a nomogram for predicting venous thromboembolism risk in post-surgery patients with cervical cancer. World J Surg Onc 22, 354 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12957-024-03649-2

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12957-024-03649-2

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World Journal of Surgical Oncology

ISSN: 1477-7819

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