ArticleThrombosis journal2024
Establishment and validation of a predictive model for lower extremity deep vein thrombosis in patients with traumatic pelvic fractures.
Article in Thrombosis journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning prediction models for deep vein thrombosis in hospitalized patients: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Risk Factors for Deep Vein Thrombosis in Hospitalized Patients With Periarticular Knee Fractures Caused by High-Energy Injuries.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundPatients with traumatic pelvic fracture (TPF) are at high risk for developing deep vein thrombosis (DVT). However, there is still no unified standard on how to distinguish high-risk groups for DVT in patients with TPF and how to accurately use anticoagulants at present.
objectivesThis observational study aimed to establish a DVT risk nomogram score (DRNS) model for TPF patients, and to explore the value of the DRNS model as a clinical guideline in the prevention of DVT with low molecular weight heparin (LMWH).
methodsIndependent risk factors of lower extremity DVT were screened through Lasso regression and logistic regression. A DRNS model was established per this.
resultsThe independent risk factors of DVT included combined femoral fractures, age ≥ 40 years old, BMI (body mass index) ≥ 24 kg/m
conclusionsThe DRNS model based on independent risk factors of DVT could stratify the risk of DVT for TPF patients, and it was able to provide more precise DVT drug prevention plans for clinicians.
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