ArticleFrontiers in surgery2023
Development and validation of a newly developed nomogram for predicting the risk of deep vein thrombosis after surgery for lower limb fractures in elderly patients.
Article in Frontiers in surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 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
- Development, internal testing, and external validation of an interpretable machine-learning model for predicting imaging-confirmed in-hospital postoperative deep vein thrombosis in older patients with patellar fractures.BMC medical informatics and decision making · 2026Article
- Correlation between red blood cell distribution width and deep vein thrombosis in patients with lower limb fractures.Frontiers in cardiovascular medicine · 2026Article
- Dynamic Evolution Characteristics and Nursing Intervention Windows of Perioperative Deep Vein Thrombosis in Patients with Lower Extremity Traumatic Fractures: A Prospective Cohort Study.Therapeutics and clinical risk management · 2026Article
- Article
- Predictive Value of Centered Clinical Asymmetric Lower Limb Edema in Diagnosing Deep Vein Thrombosis in Puerperium.Journal of clinical medicine · 2025Article
- New Predictive Model and Predictors for Perioperative Deep Vein Thrombosis Formation in Fractures of the Lower Extremity Versus Traditional Scoring.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
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Authors and funding
13 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Prevention of deep vein thrombosis (DVT) is indispensable in the treatment of lower limb fractures during the perioperative period. This study aimed to develop and validate a novel model for predicting the risk of DVT in elderly patients after orthopedic surgeries for lower limb fractures. Methods: This observational study included 576 elderly patients with lower limb fractures who were surgically treated from January 2016 to December 2018. Eleven items affecting DVT were optimized by least absolute shrinkage and selection operator regression analysis. Multivariable logistic regression analysis was performed to construct a predictive model incorporating the selected features. C-index was applied to evaluate the discrimination. Decision curve analysis was employed to determine the clinical effectiveness of this model and calibration plot was applied to evaluate the calibration of this nomogram. The internal validation of this model was assessed by bootstrapping validation. Results: Predictive factors that affected the rate of DVT in this model included smoking, time from injury to surgery, operation time, blood transfusion, hip replacement arthroplasty, and D-dimer level after operation. The nomogram showed significant discrimination with a C-index of 0.919 (95% confidence interval: 0.893-0.946) and good calibration. Acceptable C-index value could still be reached in the interval validation. Decision curve analysis indicated that the DVT risk nomogram was useful within all possibility threshold. Conclusion: This newly developed nomogram could be used to predict the risk of DVT in elderly patients with lower limb fractures during the perioperative period.
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