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Construction of Risk Prediction Model for Lower Extremity Deep Vein Thrombosis in Patients with Decompensated Cirrhosis

Objective: To develop a prediction model for the risk of lower extremity deep venous thrombosis (DVT) in patients with decompensated liver cirrhosis. Methods: A retrospective study was conducted on 236 inpatients with decompensated cirrhosis who were admitted to the Department of Infectious Diseases of a tertiary grade A comprehensive hospital in Wenzhou from January 2018 to December 2021. A risk prediction model was established by univariate analysis and binary logistic regression, and the effectiveness of the model was verified by the area under the ROC curve. Results: The DVT risk prediction model of patients with decompensated liver cirrhosis included 5 predictors: age (OR= 4.377), BI score (OR= 0.946), bedridden time (OR=5.229), CRP value (OR=1.021) and D-dimer concentration (OR=1.216). Model formula: Z=1.227+1.476× age-0.056 ×BI score +1.654× bedridden time+ 0.020×CRP +0.196× D-dimer. The AUC is 0.921, the sensitivity is 0.797, the specificity is 0.949, and the Youden index is 0.746. Validation with 57 cases showed that the AUC is 0.866, the sensitivity is 0.807, the specificity is 0.842, and the accuracy rate is 81.58%, indicating satisfactory prediction effects. Conclusion: The risk assessment model constructed in this study shows good predictive performance, which can provide reference for clinical medical staff to assess the risk of DVT in patients with decompensated liver cirrhosis.

Liver Cirrhosis, Deep Venous Thrombosis, Risk Factors, Prediction Model

Lingling Lin, Zhongqiu Lu, Liyang Hu. (2023). Construction of Risk Prediction Model for Lower Extremity Deep Vein Thrombosis in Patients with Decompensated Cirrhosis. American Journal of Nursing Science, 12(4), 80-86.

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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