Derivation and validation of a novel bleeding risk score for elderly patients with venous thromboembolism on extended anticoagulation

Summary

Existing clinical scores do not perform well in predicting bleeding in elderly patients with acute venous thromboembolism (VTE). We sought to derive an easy-to-use clinical score to help physicians identify elderly patients with VTE who are at high-risk of bleeding during extended anticoagulation (>3 months). Our derivation sample included 743 patients aged ≥65 years with VTE who were enrolled in a prospective multicenter cohort study. All patients received extended anticoagulation with vitamin K antagonists. We derived our score using competing risk regression, with the time to a first major bleeding up to 36 months of extended anticoagulation as the outcome, and 17 candidate variables as predictors. We used bootstrapping methods for internal validation. Sixty-six (9%) patients suffered major bleeding. The clinical score is based on seven clinical factors (previous bleeding, active cancer, low physical activity, anemia, thrombocytopenia, antiplatelet drugs/NSAIDs, and poor INR control). Overall, 48% of patients were classified as low-risk, 37% as moderate-risk, and 15% as high-risk of bleeding. The rate of major bleeding was 1.4 events in low-risk, 5.0 events in moderate-risk, and 12.2 events per 100 patientyears in high-risk patients. The c-statistic was 0.78 at 3 months and 0.71 at 36 months of extended anticoagulation. Model calibration was excellent (p=0.93). Internal validation showed similar results. This simple clinical score accurately identified elderly patients with VTE who are at high risk of major bleeding and who may not benefit from extended anticoagulation. Further validation of the score is important before its implementation into practice. The study is registered to https://clinicaltrials.gov as NCT00973596.

This work was carried out at the Department of General Internal Medicine in the Bern University Hospital, Switzerland.

Keywords

Anticoagulation - bleeding risk - elderly

• References

• 1 Linkins L-A, Choi PT, Douketis JD. Clinical Impact of Bleeding in Patients Taking Oral Anticoagulant Therapy for Venous Thromboembolism. A Meta-Analysis. Ann Intern Med 2003; 139: 893-900.
• 2 Naess IA, Christiansen SC, Romundstad P. et al. Incidence and mortality of venous thrombosis: a population-based study. J Thromb Haemost 2007; 05: 692-699.
• 3 Spencer FA, Gore JM, Lessard D. et al. Venous thromboembolism in the elderlyA community-based perspective. Thromb Haemost 2008; 100: 780-788.
• 4 Kearon C, Akl EA, Ornelas J. et al. Antithrombotic Therapy for VTE disease: Chest Guideline and Expert Panel Report. Chest 2016; 149: 315-352.
• 5 Konstantinides SV, Torbicki A, Agnelli G. et al. 2014 ESC guidelines on the diagnosis and management of acute pulmonary embolism. Eur Heart J 2014; 35: 3033-3069 69a-69k.
• 6 Scherz N, Mean M, Limacher A. et al. Prospective, multicenter validation of prediction scores for major bleeding in elderly patients with venous thromboembolism. J Thromb Haemost 2013; 11: 435-443.
• 7 Poli D, Antonucci E, Testa S. et a The predictive ability of bleeding risk stratification models in very old patients on vitamin K antagonist treatment for venous thromboembolism: results of the prospective collaborative EPICA study. J Thromb Haemost 2013; 11: 1053-1058.
• 8 Kearon C, Ginsberg JS, Kovacs MJ. et al. Comparison of low-intensity warfarin therapy with conventional-intensity warfarin therapy for long-term prevention of recurrent venous thromboembolism. N Engl J Med 2003; 349: 631-639.
• 9 Kuijer PM, Hutten BA, Prins MH. et al. Prediction of the risk of bleeding during anticoagulant treatment for venous thromboembolism. Arch Intern Med 1999; 159: 457-460.
• 10 Pisters R, Lane DA, Nieuwlaat R. et al. A novel user-friendly score (HAS-BLED) to assess 1-year risk of major bleeding in patients with atrial fibrillation: the Euro Heart Survey. Chest 2010; 138: 1093-1100.
• 11 Ruiz-Gimenez N, Suarez C, Gonzalez R. et al. Predictive variables for major bleeding events in patients presenting with documented acute venous thromboembolism. Findings from the RIETE Registry. Thromb Haemost 2008; 100: 26-31.
• 12 Beyth RJ, Quinn LM, Landefeld CS. Prospective evaluation of an index for predicting the risk of major bleeding in outpatients treated with warfarin. Am J Med 1998; 105: 91-99.
• 13 Gage BF, Yan Y, Milligan PE. et al. Clinical classification schemes for predicting hemorrhage: results from the National Registry of Atrial Fibrillation (NRAF). Am Heart J 2006; 151: 713-719.
• 14 Fang MC, Go AS, Chang Y. et al. A new risk scheme to predict warfarin-associated hemorrhage: The ATRIA (Anticoagulation and Risk Factors in Atrial Fibrillation) Study. Am Coll Cardiol 2011; 58: 395-401.
• 15 Shireman TI, Mahnken JD, Howard PA. et al. Development of a contemporary bleeding risk model for elderly warfarin recipients. Chest 2006; 130: 1390-1396.
• 16 Mean M, Righini M, Jaeger K. et al. The Swiss cohort of elderly patients with venous thromboembolism (SWITCO65+): rationale and methodology. J Thromb Thrombolysis 2013; 36: 475-483.
• 17 Hendriksen JM, Geersing GJ, Moons KG. et al. Diagnostic and prognostic prediction models. J Thromb Haemost 2013; 11 (01) 129-141.
• 18 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.
• 19 Reilly BM, Evans AT. Translating clinical research into clinical practice: impact of using prediction rules to make decisions. Ann Intern Med 2006; 144: 201-209.
• 20 Collins GS, Reitsma JB, Altman DG. et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Br Med J 2015; 350: g7594.
• 21 Hippisley-Cox J, Coupland C. Predicting risk of upper gastrointestinal bleed and intracranial bleed with anticoagulants: cohort study to derive and validate the QBleed scores. Br Med J 2014; 349: g4606.
• 22 Frey PM, Mean M, Limacher A. et al. Physical activity and risk of bleeding in elderly patients taking anticoagulants. J Thromb Haemost 2015; 13: 197-205.
• 23 Leiss W, Mean M, Limacher A. et al. Polypharmacy is associated with an increased risk of bleeding in elderly patients with venous thromboembolism. J Gen Intern Med 2015; 30: 17-24.
• 24 Higashi MK, Veenstra DL, Kondo LM. et al. Association between CYP2C9 genetic variants and anticoagulation-related outcomes during warfarin therapy. J Am Med Assoc 2002; 287: 1690-1698.
• 25 Rieder MJ, Reiner AP, Gage BF. et al. Effect of VKORC1 haplotypes on transcriptional regulation and warfarin dose. N Engl J Med 2005; 352: 2285-2293.
• 26 McMahan DA, Smith DM, Carey MA. et al. Risk of major hemorrhage for outpatients treated with warfarin. J Gen Intern Med 1998; 13: 311-316.
• 27 Olesen JB, Lip GY, Hansen PR. et al. Bleeding risk in ’real world’ patients with atrial fibrillation: comparison of two established bleeding prediction schemes in a nationwide cohort. J Thromb Haemost 2011; 09: 1460-1467.
• 28 Veeger NJ, Piersma-Wichers M, Tijssen JG. et al. Individual time within target range in patients treated with vitamin K antagonists: main determinant of quality of anticoagulation and predictor of clinical outcome. A retrospective study of 2300 consecutive patients with venous thromboembolism. Br J Haematol 2005; 128: 513-519.
• 29 Kaatz S. Determinants and measures of quality in oral anticoagulation therapy. J Thromb Thrombolysis 2008; 25: 61-66.
• 30 Schulman S, Kearon C. Definition of major bleeding in clinical investigations of antihemostatic medicinal products in non-surgical patients. J Thromb Haemost 2005; 03: 692-694.
• 31 Jakobsson C, Jimenez D, Gomez V. et al. Validation of a clinical algorithm to identify low-risk patients with pulmonary embolism. J Thromb Haemost 2010; 08: 1242-1247.
• 32 Fine JP, Gray RJ. A Proportional Hazards Model for the Subdistribution of a Competing Risk. J Am Stat Assoc 1999; 94: 496-509.
• 33 May S, Hosmer DW. Hosmer and Lemeshow type goodness-of-fit statistics for the Cox proportional hazards model. Advances in Survival Analysis: Handbook of Statistics Volume 23. Amsterdam: Elsevier; 2004
• 34 Justice AC, Covinsky KE, Berlin JA. Assessing the generalizability of prognostic information. Ann Intern Med 1999; 130: 515-524.
• 35 Steyerberg E. Clinical prediction models: A Practical Approach to Development, Validation, and Updating. New York: Springer; 2009
• 36 Levi M, Hovingh GK, Cannegieter SC. et al. Bleeding in patients receiving vitamin K antagonists who would have been excluded from trials on which the indication for anticoagulation was based. Blood 2008; 111: 4471-4476.
• 37 Aujesky D, Smith KJ, Roberts MS. Oral anticoagulation strategies after a first idiopathic venous thromboembolic event. Am J Med 2005; 118: 625-635.
• 38 Wiener RS, Schwartz LM, Woloshin S. Time trends in pulmonary embolism in the United States: evidence of overdiagnosis. Arch Intern Med 2011; 171: 831-837.
• 39 Mertz KJ, Lee DC, Sui X. et al. Falls among adults: the association of cardiorespiratory fitness and physical activity with walking-related falls. Am J Prev Med 2010; 39: 15-24.
• 40 Penning-van Beest FJ, Geleijnse JM, van Meegen E. et al. Lifestyle and diet as risk factors for overanticoagulation. J Clin Epidemiol 2002; 55: 411-417.
• 41 Shibata Y, Hashimoto H, Kurata C. et al. Influence of physical activity on warfarin therapy. Thromb Haemost 1998; 80: 203-204.
• 42 Fihn SD, McDonell M, Martin D. et al. Risk factors for complications of chronic anticoagulation. A multicenter study. Warfarin Optimized Outpatient Follow-up Study Group. Ann Intern Med 1993; 118: 511-520.
• 43 Palareti G, Leali N, Coccheri S. et al. Bleeding complications of oral anticoagulant treatment: an inception-cohort, prospective collaborative study (ISCOAT). Lancet 1996; 348: 423-428.