ArticleInternational journal of general medicine2022
Establishment of Prediction Models for Venous Thromboembolism in Non-Oncological Urological Inpatients - A Single-Center Experience.
Article in International journal of general medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 7 citations in OpenAlex.
- Loneliness-associated factors among older adults: Focus on friendship type and number of friends.Japan journal of nursing science : JJNS · 2025Article
- Identification of key risk factors for venous thromboembolism in urological inpatients based on the Caprini scale and interpretable machine learning methods.Thrombosis journal · 2024Article
- Establishment of Prediction Models for Venous Thromboembolism in Non-Oncological Urological Inpatients - A Single-Center Experience.International journal of general medicine · 2022Article
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Authors and funding
7 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
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 multivariable analysis with 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.
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