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Derivation of a risk assessment model for hospital-acquired venous thrombosis: the NAVAL score

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Abstract

Venous thrombosis (VT) is a preventable cause of death in hospitalized patients. The main strategy to decrease VT incidence is timely thromboprophylaxis in at-risk patients. We sought to evaluate the reliability of risk assessment model (RAM) data, the incremental usefulness of additional variables and the modelling of an adjusted score (the NAVAL score). We used the RAM proposed by Caprini for initial assessment. A 5 % systematic sample of data was independently reviewed for reliability. We evaluated the incremental usefulness of six variables for VT during the score modelling by logistic regression. We then assessed the NAVAL score for calibration, reclassification and discrimination performances. We observed 11,091 patients with 37 (0.3 %) VT events. Using the Caprini RAM, high-risk and moderate-risk patients were respectively associated with a 17.4 (95 % confidence interval [CI] 6.1–49.9) and 4.2 (95 % CI 1.6–11.0) increased VT risk compared with low-risk patients. Four independent variables were selected for the NAVAL score: “Age”, “Admission clinic”, “History of previous VT event” and “History of thrombophilia”. The area under the receiver-operating-characteristic curve for the NAVAL score was 0.72 (95 % CI 0.63–0.81). The Net Reclassification Index (NRI) for the NAVAL score compared with the Caprini RAM was −0.1 (95 % CI −0.3 to 0.1; p = 0.28). We conclude that the NAVAL score is a simplified tool for the stratification of VT risk in hospitalized patients. With only four variables, it demonstrated good performance and discrimination, but requires external validation before clinical application. We also confirm that the Caprini RAM can effectively stratify VT risk in hospitalized patients in our population.

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References

  1. Shojania KG, Duncan BW, McDonald KM, Wachter RM, Markowitz AJ (2001) Making health care safer: a critical analysis of patient safety practices. AHCRQ, Rockville, pp 332–346

    Google Scholar 

  2. Geerts WH, Bergqvist D, Pineo GF, Heit JA, Samama CM, Lassen MR et al (2008) American College of Chest Physicians. Prevention of venous thromboembolism. American College of Chest Physicians evidence-based clinical practice guidelines (8th Edition). Chest 133:381S–453S

    Article  CAS  PubMed  Google Scholar 

  3. Tapson VF, Decousus H, Pini M, Chong BH, Froehlich JB, Monreal M et al (2007) Venous thromboembolism prophylaxis in acutely ill hospitalized medical patients: findings from the International Medical Prevention Registry on Venous Thromboembolism. Chest 132:936–945

    Article  PubMed  Google Scholar 

  4. Scottish Intercollegiate Guideline Network (SIGN) (2010) Guideline 62. Prophylaxis of venous thromboembolism. A national clinical guideline. http://www.sign.ac.uk/pdf/sign62.pdf. Accessed 10 Dec 2012

  5. Bastos M, Barreto SM, Caiafa JS, Rezende SM (2011) Thromboprophylaxis: medical recommendations and hospital programs. Rev Assoc Med Bras 57(1):88–99

    Article  PubMed  Google Scholar 

  6. Gould MK, Garcia DA, Wren SM, Karanicolas PJ, Arcelus JI, Heit JA et al (2012) American College of Chest Physicians. Prevention of VT in nonorthopedic surgical patients: antithrombotic therapy and prevention of thrombosis, 9th ed: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines. Chest 141(2 Suppl):227S–277S

    Article  Google Scholar 

  7. Evans RS, Lloyd JF, Aston VT, Woller SC, Tripp JS, Elliott CG et al. Computer surveillance of patients at high risk for and with venous thromboembolism. In: AMIA Annual Symposium Proceedings, vol 2010. American Medical Informatics Association, Bethesda, pp 217–221

  8. Bagot C, Gohil S, Perrott R, Barsam S, Patel RK, Arya R (2010) The use of an exclusion-based risk-assessment model for venous thrombosis improves uptake of appropriate thromboprophylaxis in hospitalized medical patients. QJM 103(8):597–605

    Article  CAS  PubMed  Google Scholar 

  9. Caiafa JS, Bastos M (2002) Programa de profilaxia do tromboembolismo venoso do Hospital Naval Marcílio Dias: um modelo de educação continuada. J Vasc Br 1(2):103–112

    Google Scholar 

  10. Caiafa JS, de Bastos M, Moura LK, Raymundo S (2002) Brazilian Registry of venous thromboembolism prophylaxis. Managing venous thromboembolism in Latin American patients: emerging results from the Brazilian Registry. Semin Thromb Hemost 28(Suppl 3):47–50

    Article  PubMed  Google Scholar 

  11. Caprini JA, Arcelus JI, Hasty JH, Tamhane AC, Fabrega F (1991) Clinical assessment of venous thromboembolic risk in surgical patients. Semin Thromb Hemost 17(Suppl 3):304–312

    PubMed  Google Scholar 

  12. Arcelus JI, Candocia S, Traverso CI, Fabrega F, Caprini JA, Hasty JH (1991) Venous thromboembolism prophylaxis and risk assessment in medical patients. Semin Thromb Hemost 17(Suppl 3):313–318

    PubMed  Google Scholar 

  13. Streiff MB, Carolan HT, Hobson DB, Kraus PS, Holzmueller CG, Demski R et al (2012) Lessons from the Johns Hopkins Multi-Disciplinary Venous Thromboembolism (VTE) Prevention Collaborative. BMJ 344:e3935

    Article  PubMed  PubMed Central  Google Scholar 

  14. de Bastos M, Barreto SM, Caiafa JS, Bogutchi T, Rezende SM (2013) Assessment of characteristics associated with pharmacologic thromboprophylaxis use in hospitalized patients: a cohort study of 10,016 patients. Blood Coagul Fibrinolysis 24:691–697

    Article  PubMed  Google Scholar 

  15. Hirsh J, Hoak J (1996) Management of deep vein thrombosis and pulmonary embolism. A statement for healthcare professionals from the council on thrombosis (in consultation with the Council on Cardiovascular Radiology). Circulation 93:2212–2245

    Article  CAS  PubMed  Google Scholar 

  16. Petrie A, Sabin C (2009) Medical statistics at a glance, 3rd edn. Wiley-Blackwell, Oxford

    Google Scholar 

  17. Tripepi G, Jager KJ, Dekker FW, Zoccali C (2010) Statistical methods for the assessment of prognostic biomarkers (Part I): discrimination. Nephrol Dial Transplant 25:1399–1401

    Article  CAS  PubMed  Google Scholar 

  18. Tripepi G, Jager KJ, Dekker FW, Zoccali C (2010) Statistical methods for the assessment of prognostic biomarkers (part II): calibration and reclassification. Nephrol Dial Transplant 25:1402–1405

    Article  CAS  PubMed  Google Scholar 

  19. Zhou H, Wang L, Wu X, Tang Y, Yang J, Wang B et al (2014) Validation of a venous thromboembolism risk assessment model in hospitalized chinese patients: a case-control study. J Atheroscler Thromb 21:261–272

    Article  PubMed  Google Scholar 

  20. Caprini JA (2005) Thrombosis risk assessment as a guide to quality patient care. Dis Mon 51:70–78

    Article  PubMed  Google Scholar 

  21. Maynard G, Stein J (2010) Designing and implementing effective venous thromboembolism prevention protocols: lessons from collaborative efforts. J Thromb Thrombolysis 29:159–166

    Article  PubMed  PubMed Central  Google Scholar 

  22. Pannucci CJ, Bailey SH, Dreszer G, Fisher Wachtman C, Zumsteg JW, Jaber RM et al (2011) Validation of the Caprini risk assessment model in plastic and reconstructive surgery patients. J Am Coll Surg 212:105–112

    Article  PubMed  PubMed Central  Google Scholar 

  23. Bahl V, Hu HM, Henke PK, Wakefield TW, Campbell DA Jr, Caprini JA (2010) A validation study of a retrospective venous thromboembolism risk scoring method. Ann Surg 251(2):344–350

    Article  PubMed  Google Scholar 

  24. Spyropoulos AC, Anderson FA Jr, Fitzgerald G, Decousus H, Pini M, Chong BH et al (2011) Predictive and associative models to identify hospitalized medical patients at risk for VTE. Chest 140(3):706–714

    Article  PubMed  Google Scholar 

  25. Millar JA (2008) Rational thromboprophylaxis in medical inpatients: not quite there yet. Med J Aust 189(9):504–506

    PubMed  Google Scholar 

  26. Bikdeli B, Sharif-Kashani B, Shahabi P, Raeissi S, Shahrivari M, Shoraka AR et al (2013) Comparison of three risk assessment methods for venous thromboembolism prophylaxis. Blood Coagul Fibrinolysis 24(2):157–163

    PubMed  Google Scholar 

  27. Woller SC, Stevens SM, Jones JP, Lloyd JF, Evans RS, Aston VT et al (2011) Derivation and validation of a simple model to identify venous thromboembolism risk in medical patients. Am J Med 124(10):947–954

    Article  PubMed  Google Scholar 

  28. Rosenberg D, Eichorn A, Alarcon M, McCullagh L, McGinn T, Spyropoulos AC (2014) External validation of the risk assessment model of the International Medical Prevention Registry on Venous Thromboembolism (IMPROVE) for medical patients in a tertiary health system. J Am Heart Assoc. 3(6):e001152

    Article  PubMed  PubMed Central  Google Scholar 

  29. Huang W, Anderson FA, Spencer FA, Gallus A, Goldberg RJ (2013) Risk-assessment models for predicting venous thromboembolism among hospitalized non-surgical patients: a systematic review. J Thromb Thrombolysis 35(1):67–80

    Article  PubMed  Google Scholar 

  30. Spyropoulos AC, McGinn T, Khorana AA (2012) The use of weighted and scored risk assessment models for venous thromboembolism. Thromb Haemost 108(6):1072–1076

    Article  PubMed  Google Scholar 

  31. Psaty BM, Koepsell TD, Lin D, Weiss NS, Siscovick DS, Rosendaal FR et al (1999) Assessment and control for confounding by indication in observational studies. J Am Geriatr Soc 47(6):749–754

    Article  CAS  PubMed  Google Scholar 

  32. Concato J, Feinstein AF, Holford TR (1993) The risk of determining risk with multivariable models. Ann Intern Med 118:201–210

    Article  CAS  PubMed  Google Scholar 

  33. Courvoisier DS, Combescure C, Agoritsas T, Gayet-Ageron A, Perneger TV (2011) Performance of logistic regression modeling: beyond the number of events per variable, the role of data structure. J Clin Epidemiol 64(9):993–1000

    Article  PubMed  Google Scholar 

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Funding

This study was partially funded by CNPq (Grant number 474120/2008-2).

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Authors and Affiliations

  1. Instituto de Previdência dos Servidores do Estado de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil

    Marcos de Bastos

  2. Departamento de Medicina Preventiva e Social, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil

    Sandhi M. Barreto

  3. Departamento de Angiologia, Hospital Naval Marcílio Dias, Rio de Janeiro, Rio de Janeiro, Brazil

    Jackson S. Caiafa

  4. Serviço de Estatística, Departamento de Matemática e Estatística da Pontifícia, Universidade Católica de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil

    Tânia Boguchi

  5. Grupo de Estudos de Medicina Tropical, Pós-Graduação em Ciências da Saúde da Faculdade de Medicina da, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil

    José Luiz Padilha Silva

  6. Departamento de Clínica Médica, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil

    Suely M. Rezende

Authors
  1. Marcos de Bastos
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  2. Sandhi M. Barreto
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  3. Jackson S. Caiafa
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  4. Tânia Boguchi
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  5. José Luiz Padilha Silva
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  6. Suely M. Rezende
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Corresponding author

Correspondence to Marcos de Bastos.

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de Bastos, M., Barreto, S.M., Caiafa, J.S. et al. Derivation of a risk assessment model for hospital-acquired venous thrombosis: the NAVAL score. J Thromb Thrombolysis 41, 628–635 (2016). https://doi.org/10.1007/s11239-015-1277-4

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  • DOI: https://doi.org/10.1007/s11239-015-1277-4

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