Predictive index for risk stratification of venous thromboembolic disease in patients with hematologic malignancies
Keywords:
venous thromboembolic disease, hematological malignancies, prediction index, efficacyAbstract
Introduction: venous thromboembolic disease is a frequent complication in hematologic malignancies and has a significant impact on morbidity and mortality. Despite the existence of multiple well-validated scores to stratify the risk of this disease in solid tumors, hematological malignancies are underrepresented in these models.Objective: to design a predictive index for the stratification of the thrombotic risk in patients with hematologic malignancies.
Methods: an analytical observational study of cases and controls was carried out in the “Arnaldo Milián Castro” Hospital from Villa Clara Province, during the period from October 2016 to January 2019, in 285 hospitalized patients with hematological malignancies (94 with thromboembolic disease and 191 without). For univariate analysis, Chi-square test, Odds Ratio for risk estimation and Cramer's V for strength of association were applied. Logistic regression and ROC curve were applied in the multivariate analysis.
Results: the predictive index of thromboembolic disease was composed of five predictors: hypercholesterolemia, tumor activity, immobility, use of thrombogenic drugs and diabetes mellitus. High risk of thrombosis was defined as a patient who scored four points or more and low risk as a patient who scored less than four points. The index correctly classified 81.10% of the patients, for a sensitivity of 59.57% and a specificity of 92.15%. The positive and negative predictive values were 78.87% and 82.24%, respectively.
Conclusions: the developed index represented a specific and effective tool for the prediction of thromboembolic disease in hospitalized patients with hematologic malignancy.
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References
1.Centros para el Control y la Prevención de Enfermedades. Tromboembolismo venoso (coágulos de sangre). ¿En riesgo de tener coágulos de sangre? [Internet]. Atlanta: CDC; 2021 [citado 20/03/2021]. Disponible en: https://www.cdc.gov/ncbddd/spanish/dvt/features/trombosisvenosaprofunda.html
2.Salvador C, Segura Á. Modelos predictivos de riesgo de enfermedad tromboembólica venosa [Internet]. Madrid: SEOM; 2019 [citado 20/03/2021]. Disponible en: https://seom.org/images/Modelos_predictivos_de_riesgo_ETV.pdf
3.Oficina Nacional de Estadística e Información. Anuario Estadístico de Cuba 2018. La Habana: ONEI; 2019 [citado 01/08/2019]. Disponible en: http://www.onei.gob.cu/sites/default/files/aec_2019_0.pdf
4.López-Sacerio A, Álvarez-Basulto N, Batista-Hernández NE, Álvarez-Acosta M. Factores predictivos de trombosis en pacientes con hemopatías malignas. Rev Cubana Hematol Inmunol Hemoter [Internet]. 2017 [citado 11/02/2019];33(Supl. 1):[aprox. 6 p.]. Disponible en: http://www.revhematologia.sld.cu/index.php/hih/article/viewFile/814/626
5.Grupo de Coordinación de Expertos. Trombosis asociada al cáncer (TAC), una causa de muerte muchas veces ignorada en pacientes con cáncer: medidas necesarias para mejorar los resultados en salud y reducir la mortalidad. (Adaptación española del White Paper original) [Internet]. Dinamarca: LEO Pharma; 2017 [citado 01/08/2018]. Disponible en: https://trombo.info/wp-content/uploads/2017/11/Trombosis-asociada-al-cancer-TAC.pdf
6.Heit JA, Spencer FA, White RH. The epidemiology of venous thromboembolism. J Thromb Thrombolysis [Internet]. 2016 [citado 03/01/2019];41(1):3-14. Disponible en: https://link.springer.com/article/10.1007%2Fs11239-015-1311-6. https://doi.org/10.1007/s11239-015-1311-6
7.Khorana AA, Kamphuisen PW, Meyer G, Bauersachs R, Janas MS, Jarner MF, et al. Tissue factor as a predictor of recurrent venous thromboembolism in malignancy: Biomarker analyses of the CATCH trial. J Clin Oncol [Internet]. 2017 [citado 03/01/2019];35(10):1078-85. Disponible en: https://pubmed.ncbi.nlm.nih.gov/28029329/. https://doi.org/10.1200/jco.2016.67.4564
8.Stuck AK, Spirk D, Schaudt J, Kucher N. Risk assessment models for venous thromboembolism in acutely ill medical patients. A systematic review. Thromb Haemost [Internet]. 2017 [citado 01/03/2018];117(4):801-8. Disponible en: https://www.ncbi.nlm.nih.gov/pubmed/28150851. https://doi.org/10.1160/th16-08-0631
9.Silveira G, López I, Carlomagno A, De Andrés F, Ventura V, Baccelli A, et al. Evaluación de la prescripción de trombo-profilaxis farmacológica y valoración del impacto que generan distintas estrategias para mejorar su indicación. Rev Urug Med Int [Internet]. 2017 [citado 01/03/2018];2(1):21-24. Disponible en: http://www.scielo.edu.uy/scielo.php?script=sci_arttext&pid=S2393-67972017000100021
10.van Es N, Di Nisio M, Cesarman G, Kleinjan A, Otten H-M, Mahé I, et al. Comparison of risk prediction scores for venous thromboembolism in cancer patients: a prospective cohort study. Haematologica [Internet]. 2017 [citado 01/03/2018];102(9):1494-1501. Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5685240/. https://haematologica.org/article/view/8184
11.Van Es N, Louzada M, Carrier M, Tagalakis V, Gross PL, Shivakumar S, et al. Predicting the risk of recurrent venous thromboembolism in patients with cancer: A prospective cohort study. Thromb Res [Internet]. 2018 [citado 11/02/2019];163:41-46. Disponible en: https://pubmed.ncbi.nlm.nih.gov/29353682/. https://doi.org/10.1016/j.thromres.2018.01.009
12.Crous-Bou M, Harrington LB, Kabrhel C. Environmental and genetic risk factors associated with venous thromboembolism. Semin Thromb Hemost [Internet]. 2016 [citado 01/03/2019];42(8):808-820. Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5146955/. https://dx.doi.org/10.1055/s-0036-1592333
13.Antic D, Milic N, Nikolovski S, Todorovic M, Bila J, Djurdjevic P, et al. Development and validation of multivariable predictive model for thromboembolic events in lymphoma patients. Am J Hematol [Internet]. 2016 [citado 03/01/2019];91(10):1014-1019. Disponible en: http://onlinelibrary.wiley.com/doi/10.1002/ajh.24466/full/. https://doi.org/10.1002/ajh.24466
14.Garcia-Raso A, Ene GS, Llamas Sillero P. Alterations of lipid profile are a risk factor for venous thromboembolism and thrombotic complications. Eur J Lipid Sci Technol [Internet]. 2014 [citado 21/04/2018];116(5):514-520. Disponible en: http://onlinelibrary.wiley.com/doi/10.1002/ejlt.201300414/full. https://doi.org/10.1002/ejlt.201300414
15.Bravo-Grau S, Cruz JP. Estudios de exactitud diagnóstica: Herramientas para su interpretación. Rev Chil Radiol [Internet]. 2015 [citado 01/03/2019]; 21(4):158-164. Disponible en: https://scielo.conicyt.cl/pdf/rchradiol/v21n4/art07.pdf
16.Moratalla Rodríguez G. Lectura crítica de artículos de pruebas diagnósticas II: análisis de resultados. Radiol [Internet]. 2015 [citado 21/04/2018];57(Supl. 1):22-8. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S0033833814001775. https://doi.org/10.1016/j.rx.2014.11.004
17.Chen XL, Pan L, Wang Y. Validity of Padua risk assessment scale for assessing the risk of deep venous thrombosis in hospitalized patients. Zhonghua Nei Ke Za Zhi [Internet]. 2018 [citado 11/12/2018];57(7):514-7. Disponible en: https://pubmed.ncbi.nlm.nih.gov/29996271/. https://doi.org/10.3760/cma.j.issn.0578-1426.2018.07.009
18.Antic D, Milic N, Nikolovski S, Todorovic M, Bila J, Djurdjevic P, et al. Comparative analysis of predictive models for thromboembolic events in lymphoma patients. Hematol Oncol [Internet]. 2017 [citado 11/12/2018]; 35(52):416. Disponible en: https://onlinelibrary.wiley.com/doi/full/10.1002/hon.2439_198. https://doi.org/10.1002/hon.2439_198
19.Mulder FI, Candeloro M, Kamphuisen PW, Di Nisio M, Bossuyt PM, Guman N, et al. The Khorana score for prediction of venous thromboembolism in cancer patients: a systematic review and meta-analysis. Haematologica [Internet]. 2019 [citado 11/12/2018];104(6):1277-1287. Disponible en: http://www.haematologica.org/content/early/2019/01/02/haematol.2018.209114. https://doi.org/10.3324/haematol.2018.209114
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