ArticleBioMed research international2021
Nomogram for Predicting Deep Venous Thrombosis in Lower Extremity Fractures.
Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 23 citations in OpenAlex.
- The risk prediction models for deep venous thrombosis in perioperative patients with lower limb fractures: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2025Pooled it
- Risk Prediction Models for Preoperative Deep Vein Thrombosis in Older Patients with Hip Fracture: A Systematic Review and Meta-Analysis.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisPooled it
- Numerical simulation and in vitro experimental study of the hemodynamic performance of vena cava filters with helical forms.Scientific reports · 2024Article
- Prediction model of deep vein thrombosis risk after lower extremity orthopedic surgery.Heliyon · 2024Article
- Article
- A Nomogram Model to Predict Deep Vein Thrombosis Risk After Surgery in Patients with Hip Fractures.Indian journal of orthopaedics · 2024Article
- Risk factors analysis and the establishment of nomogram prediction model for PICC-related venous thrombosis in patients with lymphoma: a double-center cohort-based case-control study.Frontiers in oncology · 2024Article
- Nomogram for predicting postoperative deep vein thrombosis in patients with spinal fractures caused by high-energy injuries.Archives of orthopaedic and trauma surgery · 2024Article
- Retracted: Nomogram for Predicting Deep Venous Thrombosis in Lower Extremity Fractures.BioMed research international · 2024Article
- Analysis of risk factors for postoperative deep vein thrombosis after craniotomy and nomogram model construction.World journal of clinical cases · 2023Article
- A nomogram to predict postoperative deep vein thrombosis in patients with femoral fracture: a retrospective study.Journal of orthopaedic surgery and research · 2023Article
- Ten-Year Multicenter Retrospective Study Utilizing Machine Learning Algorithms to Identify Patients at High Risk of Venous Thromboembolism After Radical Gastrectomy.International journal of general medicine · 2023Article
- Red Cell Distribution Width-to-High-Density Lipoprotein Cholesterol Ratio (RHR): A Promising Novel Predictor for Preoperative Deep Vein Thrombosis in Geriatric Patients with Hip Fracture.Clinical interventions in aging · 2022Article
- A risk prediction model for evaluating thrombosis extension of muscle calf venous thrombosis after craniotomy.Frontiers in surgery · 2022Article
- Risk Factors of Deep Vein Thrombosis in Adults with Acute Compartment Syndrome Following Lower Extremity Fractures.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
Corrections and comments
- Retraction · 2024-03-20Computer-Aided Content or Computer-Generated Content · Concerns/Issues about Data · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
20 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep venous thrombosis (DVT) is a common complication in patients with lower extremity fractures, causing delays in recovery short-term and possible impacts on quality of life long-term. Early prediction and prevention of thrombosis can effectively reduce patient pain while improving outcomes. Although research on the risk factors for thrombosis is prevalent, there is a stark lack of clinical predictive models for DVT occurrence specifically in patients with lower limb fractures. In this study, we aim to propose a new thrombus prediction model for lower extremity fracture patients. Data from 3300 patients with lower limb fractures were collected from Wuhan Union Hospital and Hebei Third Hospital, China. Patients who met our inclusion criteria were divided into a thrombosis and a nonthrombosis group. A multivariate logistic regression analysis was carried out to identify predictors with obvious effects, and the corresponding formulas were used to establish the model. Model performance was evaluated using a discrimination and correction curve. 2662 patients were included in the regression analysis, with 1666 in the thrombosis group and 996 in the nonthrombosis group. Predictive factors included age, Body Mass Index (BMI), fracture-fixation types, energy of impact at the time of injury, blood transfusion during hospitalization, and use of anticoagulant drugs. The discriminative ability of the model was verified using the C-statistic (0.676). For the convenience of clinical use, a score table and nomogram were compiled. Data from two centers were used to establish a novel thrombus prediction model specific for patients with lower limb fractures, with verified predictive ability.
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