• Medicine · Sep 2024

    Observational Study

    Construction and validation of a predictive model for preoperative lower extremity deep vein thrombosis risk in elderly hip fracture patients: An observational study.

    • Chang-Song Yang and Zhe Tan.
    • Ya'an Mingshan District Hospital of Traditional Chinese Medicine, Ya'an, China.
    • Medicine (Baltimore). 2024 Sep 20; 103 (38): e39825e39825.

    AbstractThe aim of this study was to identify independent risk factors for preoperative lower extremity deep vein thrombosis (DVT) in elderly hip fracture patients and to construct a nomogram prediction model based on them. We collected clinical data from elderly hip fracture patients from Ya'an Hospital of Traditional Chinese Medicine (2021-2023), and used univariate and multivariate logistic regression analyses to identify independent risk factors for preoperative DVT. In this way, a nomogram prediction model was established. In addition, external validation of the model was performed by patient data from Ya'an Mingshan District Hospital of Traditional Chinese Medicine. Receiver operating characteristic curve analysis was used to calculate the area under the curve, and calibration and decision curves were plotted to assess the predictive performance of the model. Of the 223 elderly hip fracture patients, 23 (10.31%) developed DVT of the lower extremities before surgery. A total of 6 variables were identified as independent risk factors for preoperative lower extremity DVT in elderly hip fracture patients by logistic regression analysis: age > 75 years (OR = 1.932; 95% CI: 1.230-3.941), diabetes mellitus (OR = 2.139; 95% CI: 1.149-4.342), and prolonged duration of disease (OR. 2.535; 95% CI: 1.378-4.844), surgical treatment (OR = 1.564; 95% CI: 1.389-3.278), D-dimer > 0.5 mg/L (OR = 3.365; 95% CI: 1.229-7.715) fibrinogen > 4 g/L (OR = 3.473; 95% CI: 1.702-7.078). The constructed nomogram model has high accuracy in predicting the risk of preoperative DVT in elderly hip fracture patients, providing an effective tool for clinicians to identify high-risk patients and implement early intervention.Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.

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