ArticleWorld journal of surgical oncology2024
Development and validation of a nomogram for predicting venous thromboembolism risk in post-surgery patients with cervical cancer.
Article in World journal of surgical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Biomarkers of Hypercoagulability and Thromboinflammation in Cervical Cancer-Associated Thrombosis: A Systematic Review with Translational Insights from Breast Cancer.International journal of molecular sciences · 2026Pooled it
- Development and validation of a nomogram for predicting venous thromboembolism risk in colorectal cancer patients undergoing postoperative chemotherapy.Medical oncology (Northwood, London, England) · 2026Article
- Development and validation of machine learning models for assessing the risk of postoperative venous thromboembolism in cervical cancer patients.Scientific reports · 2025Article
- A Predictive Model Based on Machine Learning Algorithm for Vein Thrombosis After Ovarian Cancer Resection.International journal of women's health · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivePostoperative 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.
methodsWe 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).
resultsThe 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.
conclusionsWe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.