Objective: By evaluating the relationship between deep vein thrombosis (DVT) in intensive care unit (ICU) non-surgical patients and Caprini venous thromboembolism risk assessment model (Caprini model for short), the predictive value of Caprini model in ICU non-surgical patients was analyzed. Methods: 200 ICU non-surgical inpatients in the first affiliated hospital of Jinan university from April to September 2019 were retrospectively analyzed. General data of patients and the number of new DVT events were collected, and Caprini model was used for scoring the risk of venous thromboembolism (VTE). Results: There were 31 patients with DVT, accounting for 15.50%, and 169 patients without new DVT (non-DVT). Caprini model score was 9.03±2.70 in patients with DVT, higher than that in patients without DVT (6.80±2.48, P<0.001). 24 (12.00%) non-surgical ICU patients were at high risk of VTE and 171 cases (85.50%) were at very high risk. Only one patient with DVT was at high risk of VTE (3.23%), while the other 30 patients were at very high risk of VTE (96.77%). There were 1 case in low risk of VTE (0.59%), 4 cases in medium risk (2.37%), 23 cases in high risk (13.61%) and 141 cases in very high risk (83.43%) in non-DVT group. There was no significant difference in VTE risk stratification between DVT patients and non-DVT patients (P=0.063). The receiver operating characteristic (ROC) curve was plotted by using Caprini model score to predict DVT. The area under the ROC curve was 0.731, and the 95% confidence interval was 0.663-0.791 (P<0.001). The optimal cut-off point was 7, the sensitivity was 74.19%, the specificity was 65.68% and Youden’s index was 0.3897. Conclusion: The incidence of high risk and very high risk of VTE in ICU non-surgical patients was high, and Caprini model could better predict the occurrence of DVT, so it was necessary to strengthen the nursing of ICU non-surgical patients and effectively prevent DVT.
| Published in | American Journal of Internal Medicine (Volume 8, Issue 1) |
| DOI | 10.11648/j.ajim.20200801.18 |
| Page(s) | 40-44 |
| 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), 2020. Published by Science Publishing Group |
Intensive Care Unit, Deep Vein Thrombosis, Venous Thromboembolism, Risk Assessment Model, Prediction
| [1] | SchItIman D, Ageno W, KonstanIinides SV. Venous hromboebolism: past, present and future [J]. Thromb Haemost, 2017, 117 (7): 1219-1229. |
| [2] | Caprini J A. Risk assessment as a guide to thrombosis prophylaxis [J]. Curr Opin Pulm Med, 2010, 16 (5): 448-452. |
| [3] | Sevens S M, Douketis J D. Deep vein thrombosis prophylaxis in hospitalized medical patients: current recommendations, general rates of implementation, and initiatives for improvement [J]. Clin Chest Med, 2010, 31 (4): 675-689. |
| [4] | Hostlerd C, Marxe S, Mooresl K, et al. Validation of the international medical prevention registry on venous thromboembolism bleeding risk score [J]. Chest, 2016, 149 (2): 372-379. |
| [5] | ObIa T, Pannuccl C J, Nackashl A, et al. Validation of the Caprini venous thromboembolism risk assessment model in critically ill surgical patients [J]. JAMA Surg, 2015, 150 (10): 941-948. |
| [6] | Miner C, Potton L, Bonadona A, et al. Venous thromboembolism in the ICU: main characteristics, diagnosis and thromboprophyIaxis [J]. Crit Care, 2015, 19: 287. |
| [7] | Geerts W H, Heit J A, Clagett G P, et al. Prevention of venous thromboembolism [J]. Chest, 2001, 119 (1 Suppl): 132S-175S. |
| [8] | Cullough M, Kholdani C, Zamanian RT. Prevention of deep vein thrombosis and pulmonary embolism in high-risk medical patients [J]. Clin Chest Med. 2018, 39 (3): 483-492. |
| [9] | Brien A, Redley B, Wood B, et aI. STOPDVTs: Development and testing of a clinical assessment tool to guide nursing assessment of postoperative patients for deep vein thrombosis [J]. J Clin Nurs, 2018, 27 (9): 1803-1811. |
| [10] | Qaseem A, Snow V, Barry P, et al. Current diagnosis of venous thromboembolism in primary care: A clinical practice guideline from the American academy of family physicians and the American college of physicians [J]. Ann Intern Med. 2007.146 (6): 454-458. |
| [11] | Bahl V, Hu HM, Henke PK, et al. A validation study of a retrospective venous thromboembolism risk scoring method [J]. Ann Surg, 2010, 251 (2): 344-350. |
| [12] | Liu X, Liu C, Chen X, et al. Comparison between Caprini and Padua risk assessment models for hospitalized medical patients at risk for venous thromboembolism: a retrospective study [J]. Interact Cardiovasc Thorac Surg, 2016, 23 (4): 538-543. |
| [13] | Zhou Hx, Peng LQ, Yan Y, et al. Validation of the Caprini risk assessment model in Chinese hospitalized patients with venous thmmboembo1ism [J]. Thromb Res, 2012, 130 (5): 735-740. |
| [14] | SIone J, Hangge P, AIbadawi H, et al. Deep vein thrombosis: pathogenesis, diagnosis, and medical management [J]. Cardiovasc Diagn Ther, 2017, 7 (3): 276-284. |
| [15] | Zhang H, Mao P, Wang C, eI al. Incidence and risk factor of deep vein thrombosis (DVT) after total hip or knee arthroplasty: a retrospective study with routinely applied venography [J]. Blood Coagul Fibrinolysis, 2017, 28 (2): 126-133. |
| [16] | Tongiputn S, Kunanusont N, Senjuntichai C, et al. Lower extremity deep venous thrombosis among that patients with stroke [J]. Neurol J Southeast Asia, 1999, 4: 13-18. |
| [17] | Alper EC, Ip Ik, Balthazar P, et al. Risk stratification model: lower extremity ultrosonography for hospitalized patients with suspected deep vein thrombosis [J]. J Gen Intern Med, 2018, 33 (1): 21-25. |
APA Style
Xin Zhang, Wanxian Lu, Miaohang Shan. (2020). Predictive Value of Caprini Venous Thromboembolism Risk Assessment Model for Deep Vein Thrombosis in Intensive Care Unit Non-surgical Patients. American Journal of Internal Medicine, 8(1), 40-44. https://doi.org/10.11648/j.ajim.20200801.18
ACS Style
Xin Zhang; Wanxian Lu; Miaohang Shan. Predictive Value of Caprini Venous Thromboembolism Risk Assessment Model for Deep Vein Thrombosis in Intensive Care Unit Non-surgical Patients. Am. J. Intern. Med. 2020, 8(1), 40-44. doi: 10.11648/j.ajim.20200801.18
AMA Style
Xin Zhang, Wanxian Lu, Miaohang Shan. Predictive Value of Caprini Venous Thromboembolism Risk Assessment Model for Deep Vein Thrombosis in Intensive Care Unit Non-surgical Patients. Am J Intern Med. 2020;8(1):40-44. doi: 10.11648/j.ajim.20200801.18
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Download@article{10.11648/j.ajim.20200801.18,
author = {Xin Zhang and Wanxian Lu and Miaohang Shan},
title = {Predictive Value of Caprini Venous Thromboembolism Risk Assessment Model for Deep Vein Thrombosis in Intensive Care Unit Non-surgical Patients},
journal = {American Journal of Internal Medicine},
volume = {8},
number = {1},
pages = {40-44},
doi = {10.11648/j.ajim.20200801.18},
url = {https://doi.org/10.11648/j.ajim.20200801.18},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajim.20200801.18},
abstract = {Objective: By evaluating the relationship between deep vein thrombosis (DVT) in intensive care unit (ICU) non-surgical patients and Caprini venous thromboembolism risk assessment model (Caprini model for short), the predictive value of Caprini model in ICU non-surgical patients was analyzed. Methods: 200 ICU non-surgical inpatients in the first affiliated hospital of Jinan university from April to September 2019 were retrospectively analyzed. General data of patients and the number of new DVT events were collected, and Caprini model was used for scoring the risk of venous thromboembolism (VTE). Results: There were 31 patients with DVT, accounting for 15.50%, and 169 patients without new DVT (non-DVT). Caprini model score was 9.03±2.70 in patients with DVT, higher than that in patients without DVT (6.80±2.48, P<0.001). 24 (12.00%) non-surgical ICU patients were at high risk of VTE and 171 cases (85.50%) were at very high risk. Only one patient with DVT was at high risk of VTE (3.23%), while the other 30 patients were at very high risk of VTE (96.77%). There were 1 case in low risk of VTE (0.59%), 4 cases in medium risk (2.37%), 23 cases in high risk (13.61%) and 141 cases in very high risk (83.43%) in non-DVT group. There was no significant difference in VTE risk stratification between DVT patients and non-DVT patients (P=0.063). The receiver operating characteristic (ROC) curve was plotted by using Caprini model score to predict DVT. The area under the ROC curve was 0.731, and the 95% confidence interval was 0.663-0.791 (P<0.001). The optimal cut-off point was 7, the sensitivity was 74.19%, the specificity was 65.68% and Youden’s index was 0.3897. Conclusion: The incidence of high risk and very high risk of VTE in ICU non-surgical patients was high, and Caprini model could better predict the occurrence of DVT, so it was necessary to strengthen the nursing of ICU non-surgical patients and effectively prevent DVT.},
year = {2020}
}
TY - JOUR T1 - Predictive Value of Caprini Venous Thromboembolism Risk Assessment Model for Deep Vein Thrombosis in Intensive Care Unit Non-surgical Patients AU - Xin Zhang AU - Wanxian Lu AU - Miaohang Shan Y1 - 2020/02/28 PY - 2020 N1 - https://doi.org/10.11648/j.ajim.20200801.18 DO - 10.11648/j.ajim.20200801.18 T2 - American Journal of Internal Medicine JF - American Journal of Internal Medicine JO - American Journal of Internal Medicine SP - 40 EP - 44 PB - Science Publishing Group SN - 2330-4324 UR - https://doi.org/10.11648/j.ajim.20200801.18 AB - Objective: By evaluating the relationship between deep vein thrombosis (DVT) in intensive care unit (ICU) non-surgical patients and Caprini venous thromboembolism risk assessment model (Caprini model for short), the predictive value of Caprini model in ICU non-surgical patients was analyzed. Methods: 200 ICU non-surgical inpatients in the first affiliated hospital of Jinan university from April to September 2019 were retrospectively analyzed. General data of patients and the number of new DVT events were collected, and Caprini model was used for scoring the risk of venous thromboembolism (VTE). Results: There were 31 patients with DVT, accounting for 15.50%, and 169 patients without new DVT (non-DVT). Caprini model score was 9.03±2.70 in patients with DVT, higher than that in patients without DVT (6.80±2.48, P<0.001). 24 (12.00%) non-surgical ICU patients were at high risk of VTE and 171 cases (85.50%) were at very high risk. Only one patient with DVT was at high risk of VTE (3.23%), while the other 30 patients were at very high risk of VTE (96.77%). There were 1 case in low risk of VTE (0.59%), 4 cases in medium risk (2.37%), 23 cases in high risk (13.61%) and 141 cases in very high risk (83.43%) in non-DVT group. There was no significant difference in VTE risk stratification between DVT patients and non-DVT patients (P=0.063). The receiver operating characteristic (ROC) curve was plotted by using Caprini model score to predict DVT. The area under the ROC curve was 0.731, and the 95% confidence interval was 0.663-0.791 (P<0.001). The optimal cut-off point was 7, the sensitivity was 74.19%, the specificity was 65.68% and Youden’s index was 0.3897. Conclusion: The incidence of high risk and very high risk of VTE in ICU non-surgical patients was high, and Caprini model could better predict the occurrence of DVT, so it was necessary to strengthen the nursing of ICU non-surgical patients and effectively prevent DVT. VL - 8 IS - 1 ER -
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