Literature DB >> 27380861

Development and validation of multivariable predictive model for thromboembolic events in lymphoma patients.

Darko Antic1,2, Natasa Milic3,4, Srdjan Nikolovski5, Milena Todorovic5,6, Jelena Bila5,6, Predrag Djurdjevic7,8, Bosko Andjelic5,6, Vladislava Djurasinovic5, Aleksandra Sretenovic5, Vojin Vukovic5, Jelena Jelicic5, Suzanne Hayman9, Biljana Mihaljevic5,6.   

Abstract

Lymphoma patients are at increased risk of thromboembolic events but thromboprophylaxis in these patients is largely underused. We sought to develop and validate a simple model, based on individual clinical and laboratory patient characteristics that would designate lymphoma patients at risk for thromboembolic event. The study population included 1,820 lymphoma patients who were treated in the Lymphoma Departments at the Clinics of Hematology, Clinical Center of Serbia and Clinical Center Kragujevac. The model was developed using data from a derivation cohort (n = 1,236), and further assessed in the validation cohort (n = 584). Sixty-five patients (5.3%) in the derivation cohort and 34 (5.8%) patients in the validation cohort developed thromboembolic events. The variables independently associated with risk for thromboembolism were: previous venous and/or arterial events, mediastinal involvement, BMI>30 kg/m(2) , reduced mobility, extranodal localization, development of neutropenia and hemoglobin level < 100g/L. Based on the risk model score, the population was divided into the following risk categories: low (score 0-1), intermediate (score 2-3), and high (score >3). For patients classified at risk (intermediate and high-risk scores), the model produced negative predictive value of 98.5%, positive predictive value of 25.1%, sensitivity of 75.4%, and specificity of 87.5%. A high-risk score had positive predictive value of 65.2%. The diagnostic performance measures retained similar values in the validation cohort. Developed prognostic Thrombosis Lymphoma - ThroLy score is more specific for lymphoma patients than any other available score targeting thrombosis in cancer patients. Am. J. Hematol. 91:1014-1019, 2016.
© 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

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Year:  2016        PMID: 27380861     DOI: 10.1002/ajh.24466

Source DB:  PubMed          Journal:  Am J Hematol        ISSN: 0361-8609            Impact factor:   10.047


  17 in total

1.  SEOM clinical guideline of venous thromboembolism (VTE) and cancer (2019).

Authors:  A J Muñoz Martín; E Gallardo Díaz; I García Escobar; R Macías Montero; V Martínez-Marín; V Pachón Olmos; P Pérez Segura; T Quintanar Verdúguez; M Salgado Fernández
Journal:  Clin Transl Oncol       Date:  2020-01-24       Impact factor: 3.405

2.  Risk of venous thromboembolism in patients with non-Hodgkin lymphoma surviving blood or marrow transplantation.

Authors:  Radhika Gangaraju; Yanjun Chen; Lindsey Hageman; Jessica Wu; Liton Francisco; Michelle Kung; Emily Ness; Mariel Parman; Daniel J Weisdorf; Stephen J Forman; Mukta Arora; Saro H Armenian; Smita Bhatia
Journal:  Cancer       Date:  2019-08-30       Impact factor: 6.860

3.  HIGH-2-LOW risk model to predict venous thromboembolism in allogeneic transplant patients after platelet engraftment.

Authors:  Kylee L Martens; Wilson L da Costa; Christopher I Amos; Chris Davis; Madeline Kesten; Stephanie J Lee; Neil A Zakai; David A Garcia; Ang Li
Journal:  Blood Adv       Date:  2021-01-12

Review 4.  Thrombotic complications in patients with cancer: Advances in pathogenesis, prevention, and treatment-A report from ICTHIC 2021.

Authors:  Anna Falanga; Benjamin Brenner; Alok A Khorana; Charles W Francis
Journal:  Res Pract Thromb Haemost       Date:  2022-07-01

5.  Venous-thromboembolism and associated health care utilization in elderly patients with diffuse large B cell lymphoma.

Authors:  Radhika Gangaraju; Elizabeth S Davis; Smita Bhatia; Kelly M Kenzik
Journal:  Cancer       Date:  2022-04-01       Impact factor: 6.921

6.  Immune activation and inflammatory biomarkers as predictors of venous thromboembolism in lymphoma patients.

Authors:  Vladimir Otasevic; Biljana Mihaljevic; Natasa Milic; Dejana Stanisavljevic; Vojin Vukovic; Kristina Tomic; Jawed Fareed; Darko Antic
Journal:  Thromb J       Date:  2022-04-19

7.  Prediction of venous thromboembolism in newly diagnosed patients treated for lymphoid malignancies: validation of the Khorana Risk Score.

Authors:  Joanna Rupa-Matysek; Lidia Gil; Maciej Kaźmierczak; Marta Barańska; Mieczysław Komarnicki
Journal:  Med Oncol       Date:  2017-12-04       Impact factor: 3.064

8.  Risk factors for venous thromboembolism in patients with lymphoma requiring hospitalization.

Authors:  Stefan Hohaus; Maria Chiara Tisi; Francesca Bartolomei; Annarosa Cuccaro; Elena Maiolo; Eleonora Alma; Francesco D'Alò; Silvia Bellesi; Elena Rossi; Valerio De Stefano
Journal:  Blood Cancer J       Date:  2018-06-07       Impact factor: 11.037

9.  The Application of the Lymphoma International Prognostic Index to Predict Venous Thromboembolic Events in Diffuse Large B-Cell Lymphoma Patients.

Authors:  Hikmat Abdel-Razeq; Mohammad Ma'koseh; Rashid Abdel-Razeq; Rula Amarin; Alaa Abufara; Razan Mansour; Mohammad Manasrah; Mohammad Al-Rwashdeh; Rayan Bater
Journal:  Front Oncol       Date:  2021-05-28       Impact factor: 6.244

10.  Mean platelet volume as a predictive marker for venous thromboembolism in patients treated for Hodgkin lymphoma.

Authors:  Joanna Rupa-Matysek; Lidia Gil; Marta Barańska; Dominik Dytfeld; Mieczysław Komarnicki
Journal:  Oncotarget       Date:  2018-04-20
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