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Objective <p>Develop a nomogram to predict postoperative venous thromboembolism (VTE) risk in gynecologic malignancy patients.</p> Methods <p>This study retrospectively examined 2020 patients with gynaecological malignancies of Fujian Provincial Hospital and Fujian Provincial Hospital South Branch (Jinshan Hospital). Independent VTE predictors were identified via least absolute shrinkage and selection operator(LASSO) regression analysis and multivariate regression.A nomogram was constructed and validated. Performance was assessed using AUC, calibration curves, and decision curve analysis (DCA).</p> Results <p>The predictive nomogram model consists of the following six factors: Triglyceride, Arrhythmia, Varicose vein, Hypoproteinemia, History of VTE, Tumor types.The resulting model showed good predictive performance in the derivation group (AUC 0.804, 95% CI 0.760–0.848) and in the validation group(AUC 0.824, 95% CI 0.760–0.887).The calibration curve showed that predictive nomogram had good consistency.The decision curve analysis revealed that within the probability threshold range of 5–80% (training group) and 10–70%(validation group), the predictive model yielded higher net benefits.</p> Conclusion <p>The nomogram for predicting the risk of VTE after surgery for gynecological gynecologic malignancies demonstrated good identification accuracy and consistency. These predictors are easily accessible to clinicians, enabling them to individualize their assessment of patients.</p>

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Development and validation of a preoperative nomogram for predicting venous thromboembolism risk after gynecologic oncology surgery

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月9日

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  • Binbin Lin,
  • Xiaoya Wu,
  • Qiuhang Chen,
  • Lan Lin,
  • Zhiqin Chen

摘要

Objective

Develop a nomogram to predict postoperative venous thromboembolism (VTE) risk in gynecologic malignancy patients.

Methods

This study retrospectively examined 2020 patients with gynaecological malignancies of Fujian Provincial Hospital and Fujian Provincial Hospital South Branch (Jinshan Hospital). Independent VTE predictors were identified via least absolute shrinkage and selection operator(LASSO) regression analysis and multivariate regression.A nomogram was constructed and validated. Performance was assessed using AUC, calibration curves, and decision curve analysis (DCA).

Results

The predictive nomogram model consists of the following six factors: Triglyceride, Arrhythmia, Varicose vein, Hypoproteinemia, History of VTE, Tumor types.The resulting model showed good predictive performance in the derivation group (AUC 0.804, 95% CI 0.760–0.848) and in the validation group(AUC 0.824, 95% CI 0.760–0.887).The calibration curve showed that predictive nomogram had good consistency.The decision curve analysis revealed that within the probability threshold range of 5–80% (training group) and 10–70%(validation group), the predictive model yielded higher net benefits.

Conclusion

The nomogram for predicting the risk of VTE after surgery for gynecological gynecologic malignancies demonstrated good identification accuracy and consistency. These predictors are easily accessible to clinicians, enabling them to individualize their assessment of patients.