Establishment of Prediction Models for Venous Thromboembolism in Non-Oncological Urological Inpatients – A Single-Center Experience

Abstract

Purpose: Venous thromboembolism (VTE) comprises deep venous thrombosis (DVT) and pulmonary embolism (PE), which can lead to death. VTE is an insidious disease with no specific symptoms and overlooked readily. We aimed to establish prediction models for VTE in non-oncological urological inpatients to aid urologists to better identify VTE patients. Patients and Methods: A retrospective analysis of 1453 inpatients was carried out. The risk factors for VTE had been clarified in our previous study. A stepwise regression method was used to screen the relevant influencing factors for VTE and construct a logistic regression prediction model to predict VTE. To validate the accuracy of the model, data from 291 patients from another cohort were used for external validation. Results: A total of 1453 inpatients were enrolled. Five potential risk factors (previous VTE; treatment with anticoagulants or anti-platelet agents before hospital admission; D-dimer ≥0.89 μg/mL; lower-extremity swelling; chest symptoms) were selected by multi-variable analysis with p < 0.05. These five risk factors were used to build a logistic regression prediction model. When p < 0.1 in the multivariable logistic regression model, two additional risk factors were added: Caprini score ≥5 and complications, and all seven risk factors were used to build another prediction model. Internal verification showed the cutoff values, sensitivity, and specificity of the two models to be 0.02474, 0.941, 0.816 (model 1) and 0.03824, 0.941, and 0.820 (model 2), respectively. Both models had good predictive ability, but prediction accuracy was 43.0% for both when using the data of the additional 291 inpatients in the two models. Conclusion: Two novel prediction models were built to predict VTE in non-oncological urological inpatients. This is a new method for VTE screening, and internal validation showed a good performance. External validation results were suboptimal but may provide clues for subsequent VTE screening.