Development and validation of a nomogram for predicting venous thromboembolism risk in colorectal cancer patients undergoing postoperative chemotherapy.

Authors

Xue Min, Cui Chen, Qianjie Xu, Yuliang Yuan, Zuhai Hu, Haike Lei, Bin Peng

Published in

Medical oncology (Northwood, London, England). Volume 43. Issue 9. Aug 07, 2026. Epub Aug 07, 2026.

Abstract

Cancer-associated venous thromboembolism (VTE) is a common and life-threatening complication in patients with colorectal cancer (CRC) following surgery. This study aims to develop and validate a nomogram for accurately predicting the risk of VTE in CRC patients undergoing postoperative chemotherapy. This study included a total of 1,397 CRC patients who received postoperative chemotherapy at Chongqing University Cancer Hospital. The patients were randomly divided into training cohort and validation cohort in a 7:3 ratio. LASSO regression and multiple logistic regression were employed to identify independent risk factors for VTE and construct a nomogram. The model performance was evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration curves and decision curve analysis (DCA). Among the patients, 116 cases (8.30%) developed VTE. Six independent predictors included: Age, Karnofsky performance status (KPS), Surgical level, leukocyte count (WBC), lymphocyte count (LYM), and D-dimer levels. The nomogram demonstrated excellent discriminative ability, with AUCs of 0.868 (95% CI: 0.820-0.915) and 0.831 (95% CI: 0.748-0.914) in the training and validation cohorts, respectively. Calibration curves showed strong agreement between predicted and actual probabilities in both cohorts. DCA confirmed the model's clinical net benefit across a wide range of threshold probabilities. We developed and validated a nomogram incorporating six available clinical variables, which accurately assesses the risk of VTE in CRC patients undergoing postoperative chemotherapy, while demonstrating significant clinical value.

PMID:
42566095
Bibliographic data and abstract were imported from PubMed on 07 Aug 2026.

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