Abstract
<title>Abstract</title> <p> <bold>Objective:</bold> This study aims to develop a diagnostic model to accurately distinguish between venous thromboembolism (VTE) and non-VTE in elderly patients using basic clinical and laboratory parameters. <bold>Materials and methods:</bold> Elderly inpatients at high risk (Padua score) admitted to Cixi People's Hospital from January 2025 to June 2025 were retrospectively enrolled and grouped according to VTE occurrence. A total of 420 patients (227 with VTE and 203 without VTE) were enrolled and randomly divided into training and validation cohorts at a 7:3 ratio.LASSO regression was employed to select optimal predictors, which were then incorporated into a multivariate logistic regression model. The model's performance, including its discriminative ability, calibration, and clinical utility, was assessed using ROC curves, calibration plots, and decision curve analysis (DCA). <bold>Results:</bold> Multivariate logistic regression identified Uric Acid (UA), age adjusted D-dimer, chronic obstructive pulmonary disease (COPD), and coronary heart disease (CHD) as independent predictors of VTE. The model demonstrated good discrimination (area under the receiver operating characteristic curve [AUC]) and good calibration in both cohorts. Clinical decision curve analysis confirmed the model's applicability in clinical practice, suggesting it could benefit the overall patient population. <bold>Conclusion:</bold> This multivariate diagnostic model can assist clinicians in the early differentiation between VTE and non-VTE in elderly patients using simple clinical and laboratory parameters. </p>