Download PDF
Thromb Haemost 2014; 112(04): 692-699
DOI: 10.1160/TH14-03-0239
Blood Coagulation, Fibrinolysis and Cellular Haemostasis
Schattauer GmbH

External validation of a risk assessment model for venous thromboembolism in the hospitalised acutely-ill medical patient (VTE-VALOURR)

Charles E. Mahan
1   Presbyterian Healthcare Services, University of New Mexico, Albuquerque, New Mexico, USA
,
Yang Liu
2   McMaster Transfusion Research Program, McMaster University, Hamilton, Ontario, Canada
,
A. Graham Turpie
3   Department of Medicine, McMaster University, Hamilton, Ontario, Canada
,
Jennifer T. Vu
4   Texas Children’s Hospital, Houston, Texas, USA
,
Nancy Heddle
2   McMaster Transfusion Research Program, McMaster University, Hamilton, Ontario, Canada
,
Richard J. Cook
5   Professor of Statistical Methods for Health Research, University of Waterloo, Waterloo, Ontario, Canada
,
Undaleeb Dairkee
6   Department of Medicine, McMaster University, Hamilton, Ontario, Canada
,
Alex C. Spyropoulos
7   Anticoagulation Services and Clinical Thrombosis, North Shore-LIJ Health System at Lenox Hill Hospital, Hofstra North Shore-LIJ School of Medicine, Manhasset, New York, USA
› Author Affiliations Financial support: The study was funded in part by a traveling fellowship grant from North American Thrombosis Forum for Dr. Mahan. In addition, McMaster University funded partial research assistant resources.
Further Information

Publication History

Received: 17 March 2014

Accepted after major revision: 30 April 2014

Publication Date:
04 December 2017 (online)

Also available at
    Permissions and Reprints

Summary

Venous thromboembolic (VTE) risk assessment remains an important issue in hospitalised, acutely-ill medical patients, and several VTE risk assessment models (RAM) have been proposed. The purpose of this large retrospective cohort study was to externally validate the IMPROVE RAM using a large database of three acute care hospitals. We studied 41,486 hospitalisations (28,744 unique patients) with 1,240 VTE hospitalisations (1,135 unique patients) in the VTE cohort and 40,246 VTE-free hospitalisations (27,609 unique patients) in the control cohort. After chart review, 139 unique VTE patients were identified and 278 randomly-selected matched patients in the control cohort. Seven independent VTE risk factors as part of the RAM in the derivation cohort were identified. In the validation cohort, the incidence of VTE was 0.20%; 95% confidence interval (CI) 0.18–0.22, 1.04%; 95%CI 0.88–1.25, and 4.15%; 95%CI 2.79–8.12 in the low, moderate, and high VTE risk groups, respectively, which compared to rates of 0.45%, 1.3%, and 4.74% in the three risk categories of the derivation cohort. For the derivation and validation cohorts, the total percentage of patients in low, moderate and high VTE risk occurred in 68.6% vs 63.3%, 24.8% vs 31.1%, and 6.5% vs 5.5%, respectively. Overall, the area under the receiver-operator characteristics curve for the validation cohort was 0.7731. In conclusion, the IMPROVE RAM can accurately identify medical patients at low, moderate, and high VTE risk. This will tailor future thromboprophylactic strategies in this population as well as identify particularly high VTE risk patients in whom multimodal or more intensive prophylaxis may be beneficial.

Keywords

Venous thromboembolism risk assessment - prevention - risk assessment models - hospitalised medical patients - validation

 

  • References

  • 1 Anderson Jr., FA, Zayaruzny M, Heit JA. et al. Estimated annual numbers of US acute-care hospital patients at risk for venous thromboembolism. Am J Hematol 2007; 82: 777-782.
    CrossrefPubMedGoogle Scholar
  • 2 Piazza G, Fanikos J, Zayaruzny M. et al. Venous thromboembolic events in hospitalised medical patients. Thromb Haemost 2009; 102: 505-510.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 3 Cohen AT, Agnelli G, Anderson FA. et al. Venous thromboembolism (VTE) in Europe. The number of VTE events and associated morbidity and mortality. Thromb Haemost 2007; 98: 756-764.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 4 Cohen AT, Tapson VF, Bergmann JF. et al. Venous thromboembolism risk and prophylaxis in the acute hospital care setting (ENDORSE study): a multinational cross-sectional study. Lancet 2008; 371: 387-394.
    CrossrefPubMedGoogle Scholar
  • 5 Cohen AT, Spiro TE, Buller HR. et al. Rivaroxaban for thromboprophylaxis in acutely ill medical patients. N Engl J Med 2013; 368: 513-523.
    CrossrefPubMedGoogle Scholar
  • 6 Goldhaber SZ, Leizorovicz A, Kakkar AK. et al. Apixaban versus enoxaparin for thromboprophylaxis in medically ill patients. N Engl J Med 2011; 365: 2167-2177.
    CrossrefPubMedGoogle Scholar
  • 7 Hull RD, Schellong SM, Tapson VF. et al. Extended-duration venous thromboembolism prophylaxis in acutely ill medical patients with recently reduced mobility: a randomized trial. Ann Intern Med 2010; 153: 8-18.
    CrossrefPubMedGoogle Scholar
  • 8 Lutz L, Haas S, Hach-Wunderle V. et al. Venous thromboembolism in internal medicine: risk assessment and pharmaceutical prophylaxis: publication for the specialist forum. Med Welt 2002; 53: 231-234.
    PubMedGoogle Scholar
  • 9 Cohen AT, Alikhan R, Arcelus JI. et al. Assessment of venous thromboembolism risk and the benefits of thromboprophylaxis in medical patients. Thromb Haemost 2005; 94: 750-759.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 10 Gallardo Jimenez P, Guijarro Merino R, Vallejo Herrera V. et al. Assessment of venous thromboembolism risk in hospitalised medical patients. Concordance between PRETEMED guide and the recommendations of the viii conference of the American College of Chest Physicians. Med Clin 2012; 139: 467-472.
    CrossrefPubMedGoogle Scholar
  • 11 Kucher N, Koo S, Quiroz R. et al. Electronic alerts to prevent venous thromboembolism among hospitalised patients. N Engl J Med 2005; 352: 969-977.
    CrossrefPubMedGoogle Scholar
  • 12 Spyropoulos AC, Anderson Jr., FA. et al. Predictive and associative models to identify hospitalised medical patients at risk for VTE. Chest 2011; 140: 706-714.
    CrossrefPubMedGoogle Scholar
  • 13 Woller SC, Stevens SM, Jones JP. et al. Derivation and validation of a simple model to identify venous thromboembolism risk in medical patients. Am J Med 2011; 124: 947-54e2.
    CrossrefPubMedGoogle Scholar
  • 14 Barbar S, Noventa F, Rossetto V. et al. A risk assessment model for the identification of hospitalised medical patients at risk for venous thromboembolism: the Padua Prediction Score. J Thromb Haemost 2010; 8: 2450-2457.
    CrossrefPubMedGoogle Scholar
  • 15 Maynard GA, Morris TA, Jenkins IH. et al. Optimizing prevention of hospital-acquired venous thromboembolism (VTE): prospective validation of a VTE risk assessment model. J Hosp Med 2010; 5: 10-18.
    CrossrefPubMedGoogle Scholar
  • 16 Huang W, Anderson FA, Spencer FA. et al. Risk-assessment models for predicting venous thromboembolism among hospitalised non-surgical patients: a systematic review. J Thromb Thrombolysis 2013; 35: 67-80.
    CrossrefPubMedGoogle Scholar
  • 17 Altman DG, Royston P. What do we mean by validating a prognostic model?. Stat Med 2000; 19: 453-473.
    CrossrefPubMedGoogle Scholar
  • 18 Harrell Jr., FE, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996; 15: 361-387.
    CrossrefPubMedGoogle Scholar
  • 19 McGinn TG, Guyatt GH, Wyer PC. et al. Users’ guides to the medical literature: XXII: how to use articles about clinical decision rules. Evidence-Based Medicine Working Group. J Am Med Assoc 2000; 284: 79-84.
    CrossrefPubMedGoogle Scholar
  • 20 Tapson VF, Decousus H, Pini M. et al. Venous thromboembolism prophylaxis in acutely ill hospitalised medical patients: findings from the International Medical Prevention Registry on Venous Thromboembolism. Chest 2007; 132: 936-945.
    CrossrefPubMedGoogle Scholar
  • 21 Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 2007; 115: 928-935.
    CrossrefPubMedGoogle Scholar
  • 22 Spyropoulos AC. Upper vs lower extremity deep vein thrombosis: outcome definitions of venous thromboembolism for clinical predictor rules or risk factor analyses in hospitalised patients. J Thromb Haemost 2009; 7: 1041-1042.
    CrossrefPubMedGoogle Scholar
  • 23 Bahl V, Hu HM, Henke PK. et al. A validation study of a retrospective venous thromboembolism risk scoring method. Ann Surg 2010; 251: 344-350.
    CrossrefPubMedGoogle Scholar
  • 24 Nendaz M, Spirk D, Kucher N. et al. Multicentre validation of the Geneva Risk Score for hospitalised medical patients at risk of venous thromboembolism. Explicit ASsessment of Thromboembolic RIsk and Prophylaxis for Medical PATients in SwitzErland (ESTIMATE). Thromb Haemost 2014; 111: 531-538.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 25 Spyropoulos AC, McGinn T, Khorana AA. The use of weighted and scored risk assessment models for venous thromboembolism. Thromb Haemost 2012; 108: 1072-1076.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 26 Kakkar AK, Cimminiello C, Goldhaber SZ. et al. Low-molecular-weight heparin and mortality in acutely ill medical patients. N Engl J Med 2011; 365: 2463-2472.
    CrossrefPubMedGoogle Scholar
  • 27 Samama MM, Cohen AT, Darmon JY. et al. A comparison of enoxaparin with placebo for the prevention of venous thromboembolism in acutely ill medical patients. Prophylaxis in Medical Patients with Enoxaparin Study Group. N Engl J Med 1999; 341: 793-800.
    CrossrefPubMedGoogle Scholar
  • 28 Garcia DA, Baglin TP, Weitz JI. et al. Parenteral anticoagulants: Antithrombotic Therapy and Prevention of Thrombosis, 9th ed: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines. Chest 2012; 141 (02) Suppl e24S-43S.
    CrossrefPubMedGoogle Scholar
  • 29 Spyropoulos AC. Emerging strategies in the prevention of venous thromboembolism in hospitalised medical patients. Chest 2005; 128: 958-969.
    CrossrefPubMedGoogle Scholar