Abstract
Cancer frequently causes venous thromboembolism (VTE), a leading cause of cancer-related mortality. Primary liver cancer (PLC) is prevalent and highly fatal, with an increased risk of venous thrombotic complications. Thus, we aimed to develop a nomogram model for predicting VTE in patients with PLC. We retrospectively analyzed 1,565 patients diagnosed with PLC between January 2018 and December 2022 at Chongqing University Cancer Hospital. Univariate logistic analysis and multivariate logistic regression identified eight significant risk factors: activated partial thromboplastin time (APTT) ≤ 32.20 s, D-dimer > 1.44 mg/L, lymphocyte count (LYM) ≤ 1.18 × 10(9)/L, monocyte count (MONO) > 0.42 × 10(9)/L, transarterial chemoembolization (TACE), surgical intervention, immunotherapy, and β2-microglobulin. The nomogram model exhibited strong discriminatory power, with C indices of 0.753 and 0.710 for the training and validation cohorts, respectively. The calibration curve showed a strong correlation between predicted and actual probabilities. Additionally, decision curve analysis (DCA) and clinical impact curves (CIC) confirmed the model's clinical utility. This nomogram facilitates the identification of high-risk PLC patients, allowing for timely preventive and therapeutic interventions to reduce the risk of thrombosis.