Abstract
Venous thromboembolism (VTE) is closely relevant to head and neck cancer (HNC) prognosis, but little data exist on the risk prediction of VTE in patients with HNC. To study the risk factors regarding VTE in HNC patients and construct a nomogram model for its prediction. A cross-sectional retrospective study was implemented to comparatively analyze 220 HNC patients from January 2018 to December 2021. The Lasso algorithm was used to optimize the selection of variables. A nomogram model for predicting HNC-associated VTE was established using multivariate logistic regression analysis. Internal validation of the model was performed by bootstrap resampling (1000 times). Calibration plot and decision curve analysis (DCA) were applied to evaluate the calibration capability of the prediction model. The demographics, medical history, blood biochemical indicators, and modalities of treatment were included for analysis. The incidence of HNC-associated VTE was 2.8% (55/1967) in authors' affiliation. Five variables of risk factors, including surgery, radiochemotherapy, D-dimer, aspartate transaminase, and globulin, were screened and selected as predictors by Lasso algorithm. A prediction model that incorporated these independent predictors was developed and presented as the nomogram. The model showed good discrimination with a C-index of 0.972 (95% CI: 0.934-0.997), and had an area under the receiver operating characteristic curve value of 0.981 (p < 0.001, 95% CI: 0.964-0.998). The calibration curve displayed good agreement of the predicted probability with the actual observed probability for HNC-associated VTE. The DCA plot showed that the application of this nomogram was associated with net benefit gains in clinical practice. The high-performance nomogram model developed in this study may help early diagnose the risk of VTE in HNC patients and to guide individualized decision-making on thromboprophylaxis.
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