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

Lingling Lin; Zhongqiu Lu; Liyang Hu

doi:doi:10.11648/j.ajns.20231204.12

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

Lingling Lin, Zhongqiu Lu, Liyang Hu

Published in American Journal of Nursing Science (Volume 12, Issue 4)

Received: 21 July 2023 Accepted: 5 August 2023 Published: 15 August 2023

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Abstract

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.

Published in	American Journal of Nursing Science (Volume 12, Issue 4)
DOI	10.11648/j.ajns.20231204.12
Page(s)	80-86
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

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

References

[1]	Chen H. The role of coagulation imbalance in visceral venous thrombosis and the safety and efficacy of anticoagulant therapy [D]. Shaanxi: The Fourth Military Medical University, 2016.
[2]	Carnevale R, Raparelli V, Nocella C, etc. Guutderived endotoxin stimulates factor VIII secretion from endothelial cells. Implications for hypercoagulability in cirrhosis [J]. Hepatol, 2017, 67 (5): 950-956.
[3]	Tripodi A, Primignani M, Mannucci PM, etc. Changing concepts of ciihotic coagulopathy [J]. Am J Gastroenterol, 2017112, (2): 274-281.
[4]	AMBROSINO P, TARANTINO L, DI MINNO G, et al. The risk of venous thromboembolism in patients with cirrhosis. A systematic review and meta-analysis [J]. Thrombosis and haemostasis, 2017, 117 (1): 139-148.
[5]	lesmana CR, Inggriani S, Cahyadinata L&lesmana LA. Deep vein thrombosis in patients with advanceded liver cirrhosis: a rare condition? Hepatol Int, 2010. 4 (1): 433-438.
[6]	Fikui H, Saito H, Ueno Y, Yto H, et al. Exidence-based clinical practice guidelines for liver cirrhosis 2015 [J]. J Gastroenterol, 2016, ploidy (7): 629-650.
[7]	Chinese Society of Hepatology. Guidelines for diagnosis and treatment of liver cirrhosis [J]. Chin J Hepatology, 2019, 27 (11): 846-865. (in Chinese).
[8]	Guidelines for the diagnosis and treatment of deep vein thrombosis (3rd Ed). Chin J General Surgery, 2017. 32 (9).
[9]	XU X Y, Ting H G, LI W G, et al. Guidelines for diagnosis and treatment of liver cirrhosis [J]. J Practical Hepatology, 2019, 22 (6) 770-786.
[10]	Kamath PS, Kim WR, Advanced Liver Disease Study Group. The model for end-stage liver disease (MELD) [J]. Hepatology, 2007, 45 (3): 797-805.
[11]	Chen Zhen, Gong Yangyang, Qi Hongbo. Study on the factors of hypercoagulable state in obstetrics [J]. Chinese Journal of Applied Obstetrics and Gynecology, 2022, 38 (4): 242.
[12]	WU H, NGUYEN GC. Liver cirrhosis ia associated with venous thromboembolism among hospitalized patients in a [J]. Clin Gastroentrrol Hepatol, 2010. 8: 800-805.
[13]	Zhao Jinli, Xu Pingping. Analysis of risk factors related to deep vein thrombosis after hip replacement in elderly patients [J]. Nursing and Rehabilitation, 2019, 20 (09): 22-25. (in Chinese).
[14]	Mahoney FI, Barthel DW. Functional Evaluation: The Narthel Index [J]. Md State Med J, 1965, 14: 61-65.
[15]	Li Zhong, Dong Lingwan, Zhu Jing, et al. Chen Y, Wang Y, Wang Y, et al. Pulmonary infection and deep vein thrombosis in elderly patients with stroke [J]. Chin J General Med, 2016, 14 (12): 2034-2036.
[16]	Satoshi Higuchi YK, Matsushita K, Taguchi H, et al. Barthel Index as a predictor of 1-year mortality in very elderly patients who underwent percutaneous coronary intervention for acute coronary syndrome: better activities of daily living, longer life [U]. Clin Cardiol, 2016, 39 (2): 83-89.
[17]	Ying Furong, Zhao Zhichao, Lu Jiayu. Application value of systemic inflammatory markers in patients with deep fungal infection [J]. Chin J Hygiene Lab, 2017, 27 (24): 3503-3504.
[18]	Wang Huafu, GUI Zhihong, Shang Zhenqiu, et al. Wang H F, GUI Z H, Shang Z Q, et al. Distribution of pathogenic bacteria and risk factors in 79 patients with hepatitis B cirrhosis complicated with pulmonary infection [J]. Chinese Rural Medicine, 2017, 24 (18): 59-60.
[19]	CAI Cunhui. To explore the clinical value of combined detection of hs-CRP, Fg, ALP and WBC levels in patients with infection after bone injury surgery [J]. Journal of Lab Medicine, 2019, 37 (1): 85-92.
[20]	Li P, Zhang Z K, Liu Y K, et al. Changes and clinical significance of biochemical markers of bone turnover in elderly patients with osteoporosis and hip fracture [J]. Journal of Experimental & Laboratory Medicine, 2019, 37 (4): 669-671.
[21]	Dempfle CE, Schraml M, Besenthal I, et al. Multicent reevaluation of a new point of care test for t he quantitive determinatiation of D-dimer [J]. Clin Chim Acta, 2001, 307 (1-2): 211-218.
[22]	Guo Xiaohui. Expression of plasma D-dimer in patients with post-hepatitis B cirrhosis and its relationship with prognosis [J]. Journal of Modern Integrated Traditional Chinese and Western Medicine, 2019, 28 (30): 3346-3348.
[23]	Expert Committee on Chinese Guidelines for the prevention and treatment of thrombotic Diseases. Chinese guidelines for the prevention and treatment of thrombotic diseases [J]. Chin J Med, 2018, 98 (36): 2861-2888.
[24]	Wang Yl-Lian. The role of thromboelastography in the evaluation of coagulation in patients with liver cirrhosis [D]. Nanjing: Southeast University, 2016. (in Chinese).
[25]	Forkin KT, Colquhoun DA, Nemergut EC, et al. The coagulation profile of end-stage liver disease and considerations for intraoperative management. Anesth Analg, 2018, 126: 46-61.
[26]	TRIPODI A, PRIMIGNANI M, LEMMA L, et al. Evidence that low protein C contributes to the procoagulant imbalance in cirrhosis [J]. J Hepatol, 2013, 59 (2): 265-270.
[27]	ROMERO GoMEZ M, SUaREZ GARCiA E, LoPEZ LACOMBA D, et al. Antiphospholipid antibodies are related to portal vein thrombosis in patients with liver cirrhosis [J]. J Clin Gastroenterol, 2000, 31 (3): 237-240.

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APA Style

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. https://doi.org/10.11648/j.ajns.20231204.12

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ACS Style

Lingling Lin; Zhongqiu Lu; Liyang Hu. Construction of Risk Prediction Model for Lower Extremity Deep Vein Thrombosis in Patients with Decompensated Cirrhosis. Am. J. Nurs. Sci. 2023, 12(4), 80-86. doi: 10.11648/j.ajns.20231204.12

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AMA Style

Lingling Lin, Zhongqiu Lu, Liyang Hu. Construction of Risk Prediction Model for Lower Extremity Deep Vein Thrombosis in Patients with Decompensated Cirrhosis. Am J Nurs Sci. 2023;12(4):80-86. doi: 10.11648/j.ajns.20231204.12

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@article{10.11648/j.ajns.20231204.12,
  author = {Lingling Lin and Zhongqiu Lu and Liyang Hu},
  title = {Construction of Risk Prediction Model for Lower Extremity Deep Vein Thrombosis in Patients with Decompensated Cirrhosis},
  journal = {American Journal of Nursing Science},
  volume = {12},
  number = {4},
  pages = {80-86},
  doi = {10.11648/j.ajns.20231204.12},
  url = {https://doi.org/10.11648/j.ajns.20231204.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajns.20231204.12},
  abstract = {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.},
 year = {2023}
}

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TY  - JOUR
T1  - Construction of Risk Prediction Model for Lower Extremity Deep Vein Thrombosis in Patients with Decompensated Cirrhosis
AU  - Lingling Lin
AU  - Zhongqiu Lu
AU  - Liyang Hu
Y1  - 2023/08/15
PY  - 2023
N1  - https://doi.org/10.11648/j.ajns.20231204.12
DO  - 10.11648/j.ajns.20231204.12
T2  - American Journal of Nursing Science
JF  - American Journal of Nursing Science
JO  - American Journal of Nursing Science
SP  - 80
EP  - 86
PB  - Science Publishing Group
SN  - 2328-5753
UR  - https://doi.org/10.11648/j.ajns.20231204.12
AB  - 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.
VL  - 12
IS  - 4
ER  -

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Author Information

Lingling Lin

Department of Infectious Diseases, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
Zhongqiu Lu

Emergency Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
Liyang Hu

Department of Gynecology, The First Affiliated Hospital of Middle Wenzhou Medical University, Wenzhou, China

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APA Style

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. https://doi.org/10.11648/j.ajns.20231204.12

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ACS Style

Lingling Lin; Zhongqiu Lu; Liyang Hu. Construction of Risk Prediction Model for Lower Extremity Deep Vein Thrombosis in Patients with Decompensated Cirrhosis. Am. J. Nurs. Sci. 2023, 12(4), 80-86. doi: 10.11648/j.ajns.20231204.12

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AMA Style

Lingling Lin, Zhongqiu Lu, Liyang Hu. Construction of Risk Prediction Model for Lower Extremity Deep Vein Thrombosis in Patients with Decompensated Cirrhosis. Am J Nurs Sci. 2023;12(4):80-86. doi: 10.11648/j.ajns.20231204.12

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@article{10.11648/j.ajns.20231204.12,
  author = {Lingling Lin and Zhongqiu Lu and Liyang Hu},
  title = {Construction of Risk Prediction Model for Lower Extremity Deep Vein Thrombosis in Patients with Decompensated Cirrhosis},
  journal = {American Journal of Nursing Science},
  volume = {12},
  number = {4},
  pages = {80-86},
  doi = {10.11648/j.ajns.20231204.12},
  url = {https://doi.org/10.11648/j.ajns.20231204.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajns.20231204.12},
  abstract = {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.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Construction of Risk Prediction Model for Lower Extremity Deep Vein Thrombosis in Patients with Decompensated Cirrhosis
AU  - Lingling Lin
AU  - Zhongqiu Lu
AU  - Liyang Hu
Y1  - 2023/08/15
PY  - 2023
N1  - https://doi.org/10.11648/j.ajns.20231204.12
DO  - 10.11648/j.ajns.20231204.12
T2  - American Journal of Nursing Science
JF  - American Journal of Nursing Science
JO  - American Journal of Nursing Science
SP  - 80
EP  - 86
PB  - Science Publishing Group
SN  - 2328-5753
UR  - https://doi.org/10.11648/j.ajns.20231204.12
AB  - 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.
VL  - 12
IS  - 4
ER  -

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