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      Predicting the occurrence of venous thromboembolism: construction and verification of risk warning model


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          The onset of venous thromboembolism is insidious and the prognosis is poor. In this study, we aimed to construct a VTE risk warning model and testified its clinical application value.


          Preliminary construction of the VTE risk warning model was carried out according to the independent risk warning indicators of VTE screened by Logistic regression analysis. The truncated value of screening VTE was obtained and the model was evaluated. ROC curve analysis was used to compare the test of Caprini risk assessment scale and VTE risk warning model. The cut-off value of the VTE risk warning model was used to evaluate the test effectiveness of the model for VTE patients with validation data set.


          The VTE risk warning model is p = e x / (1+ e x), x = − 4.840 + 2.557 • X 10(1) + 1.432 • X 14(1) + 2.977 • X 15(1) + 3.445 • X 18(1) + 1.086 • X 25(1) + 0.249 • X 34 + 0.282 • X 41. ROC curve results show that: AUC (95%CI), cutoff value, sensitivity, specificity, accuracy, Youden index, Caprini risk assessment scale is 0.596 (0.552, 0.638), 5, 26.07, 96.50, 61.3%, 0.226, VTE risk warning model is 0.960 (0.940, 0.976), 0.438, 92.61, 91.83, 92.2%, 0.844, respectively, with statistically significant differences (Z = 14.521, P < 0.0001). The accuracy and Youden index of VTE screening using VTE risk warning model were 81.8 and 62.5%, respectively.


          VTE risk warning model had high accuracy in predicting VTE occurrence in hospitalized patients. Its test performance was better than Caprini risk assessment scale. It also had high test performance in external population.

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          Most cited references 35

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          Epidemiology of cancer-associated venous thrombosis.

          Cancer-associated venous thrombosis is a common condition, although the reported incidence varies widely between studies depending on patient population, start and duration of follow-up, and the method of detecting and reporting thrombotic events. Furthermore, as cancer is a heterogeneous disease, the risk of venous thrombosis depends on cancer types and stages, treatment measures, and patient-related factors. In general, cancer patients with venous thrombosis do not fare well and have an increased mortality compared with cancer patients without. This may be explained by the more aggressive type of malignancies associated with this condition. It is hypothesized that thromboprophylaxis in cancer patients might improve prognosis and quality of life by preventing thrombotic events. However, anticoagulant treatment leads to increased bleeding, particularly in this patient group, so in case of proven benefit of thromboprophylaxis, only patients with a high risk of venous thrombosis should be considered. This review describes the literature on incidence of and risk factors for cancer-associated venous thrombosis, with the aim to provide a basis for identification of high-risk patients and for further development and refinement of prediction models. Furthermore, knowledge on risk factors for cancer-related venous thrombosis may enhance the understanding of the pathophysiology of thrombosis in these patients.
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            Epidemiology of venous thromboembolism.

             John Heit (2015)
            Thrombosis can affect any venous circulation. Venous thromboembolism (VTE) includes deep-vein thrombosis of the leg or pelvis, and its complication, pulmonary embolism. VTE is a fairly common disease, particularly in older age, and is associated with reduced survival, substantial health-care costs, and a high rate of recurrence. VTE is a complex (multifactorial) disease, involving interactions between acquired or inherited predispositions to thrombosis and various risk factors. Major risk factors for incident VTE include hospitalization for surgery or acute illness, active cancer, neurological disease with leg paresis, nursing-home confinement, trauma or fracture, superficial vein thrombosis, and-in women-pregnancy and puerperium, oral contraception, and hormone therapy. Although independent risk factors for incident VTE and predictors of VTE recurrence have been identified, and effective primary and secondary prophylaxis is available, the occurrence of VTE seems to be fairly constant, or even increasing.
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              Cancer-associated thrombosis.

              Cancer-associated thrombosis accounts for almost one-fifth of all cases of venous thromboembolism (VTE) and is a leading cause of death, morbidity, delays in care, and increased costs. Our understanding of risk factors for cancer-associated thrombosis has expanded in recent years, and investigators have begun to use biomarkers and clinical prediction models to identify those cancer patients at greatest risk for VTE. The Khorana Risk Model, which is based on easily obtained biomarkers and clinical factors, has now been validated in several studies. Recent clinical trials of prophylaxis and treatment of VTE in cancer patients are reviewed here. In addition, consensus guidelines and expert opinion regarding management of VTE in specific challenging situations are presented.

                Author and article information

                BMC Cardiovasc Disord
                BMC Cardiovasc Disord
                BMC Cardiovascular Disorders
                BioMed Central (London )
                27 May 2020
                27 May 2020
                : 20
                [1 ]GRID grid.440642.0, ISNI 0000 0004 0644 5481, Department of Nursing, , Affiliated Hospital of Nantong University, ; 20 Xisi Road, Nantong City, 226000 Jiangsu China
                [2 ]GRID grid.488140.1, School of Nursing, Suzhou Vocational Health College, ; 28 Kehua Road, Suzhou City, 215009 Jiangsu China
                [3 ]GRID grid.440642.0, ISNI 0000 0004 0644 5481, Department of Information, , Affiliated Hospital of Nantong University, ; 20 Xisi Road, Nantong City, 226000 Jiangsu China
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                Funded by: Social and People's Livelihood Science and Technology Projects of Nantong
                Award ID: MS12018084
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                © The Author(s) 2020


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