Bleeding Risk Prediction in Patients Treated with Antithrombotic Drugs According to the Anatomic Site of Bleeding, Indication for Treatment, and Time Since Treatment Initiation

Vinai Bhagirath; Tanya Kovalova; Jia Wang; Lizhen Xu; Shrikant I. Bangdiwala; Martin O'Donnell; Ashkan Shoamanesh; Jackie Bosch; Rosa Coppolecchia; Tatsiana Vaitsiakhovich; Frank Kleinjung; Hardi Mundl; John Eikelboom

doi:10.1055/a-2259-1134

TH Open, Table of Contents

CC BY 4.0 · TH Open 2024; 08(01): e121-e131
DOI: 10.1055/a-2259-1134

Original Article

Bleeding Risk Prediction in Patients Treated with Antithrombotic Drugs According to the Anatomic Site of Bleeding, Indication for Treatment, and Time Since Treatment Initiation

Vinai Bhagirath

¹Population Health Research Institute, Hamilton, Ontario, Canada

²McMaster University, Hamilton, Ontario, Canada

,

Tanya Kovalova

¹Population Health Research Institute, Hamilton, Ontario, Canada

,

Jia Wang

¹Population Health Research Institute, Hamilton, Ontario, Canada

,

Lizhen Xu

¹Population Health Research Institute, Hamilton, Ontario, Canada

,

Shrikant I. Bangdiwala

¹Population Health Research Institute, Hamilton, Ontario, Canada

²McMaster University, Hamilton, Ontario, Canada

,

Martin O'Donnell

¹Population Health Research Institute, Hamilton, Ontario, Canada

³University of Galway, Galway, Galway, Ireland

,

Ashkan Shoamanesh

¹Population Health Research Institute, Hamilton, Ontario, Canada

²McMaster University, Hamilton, Ontario, Canada

,

Jackie Bosch

²McMaster University, Hamilton, Ontario, Canada

,

Rosa Coppolecchia

⁴Bayer US LLC, Whippany, New Jersey, United States

,

Tatsiana Vaitsiakhovich

⁵Bayer AG, Berlin, Germany

,

Frank Kleinjung

⁵Bayer AG, Berlin, Germany

,

Hardi Mundl

⁶Bayer AG, Wuppertal, Germany

,

John Eikelboom

¹Population Health Research Institute, Hamilton, Ontario, Canada

²McMaster University, Hamilton, Ontario, Canada

› Author Affiliations

Abstract

Background Reasons for the relatively poor performance of bleeding prediction models are not well understood but may relate to differences in predictors for various anatomical sites of bleeding.

Methods We pooled individual participant data from four randomized controlled trials of antithrombotic therapy in patients with coronary and peripheral artery diseases, embolic stroke of undetermined source (ESUS), or atrial fibrillation. We examined discrimination and calibration of models for any major bleeding, major gastrointestinal (GI) bleeding, and intracranial hemorrhage (ICH), according to the time since initiation of antithrombotic therapy, and indication for antithrombotic therapy.

Results Of 57,813 patients included, 1,948 (3.37%) experienced major bleeding, including 717 (1.24%) major GI bleeding and 274 (0.47%) ICH. The model derived to predict major bleeding at 1 year from any site (c-index, 0.69, 95% confidence interval [CI], 0.68–0.71) performed similarly when applied to predict major GI bleeding (0.71, 0.69–0.74), but less well to predict ICH (0.64, 0.61–0.69). Models derived to predict GI bleeding (0.75, 0.74–0.78) and ICH (0.72, 0.70–0.79) performed better than the general major bleeding model. Discrimination declined over time since the initiation of antithrombotic treatment, stabilizing at approximately 2 years for any major bleeding and major GI bleeding and 1 year for ICH. Discrimination was best for the model predicting ICH in the ESUS population (0.82, 0.78–0.92) and worst for the model predicting any major bleeding in the coronary and peripheral artery disease population (0.66, 0.65–0.69).

Conclusion Performance of risk prediction models for major bleeding is affected by site of bleeding, time since initiation of antithrombotic therapy, and indication for antithrombotic therapy.

Keywords

bleeding - risk prediction - antithrombotic - atrial fibrillation - ESUS, CAD, PAD

Full Text

References

References
1 Piccolo R, Oliva A, Avvedimento M. et al. Mortality after bleeding versus myocardial infarction in coronary artery disease: a systematic review and meta-analysis. EuroIntervention 2021; 17 (07) 550-560
2 Gao X, Cai X, Yang Y, Zhou Y, Zhu W. Diagnostic accuracy of the HAS-BLED bleeding score in VKA- or DOAC-treated patients with atrial fibrillation: a systematic review and meta-analysis. Front Cardiovasc Med 2021; 8: 757087
3 Rosenberg RD, Aird WC. Vascular-bed–specific hemostasis and hypercoagulable states. N Engl J Med 1999; 340 (20) 1555-1564
4 Eikelboom JW, Connolly SJ, Bosch J. et al; COMPASS Investigators. Rivaroxaban with or without aspirin in stable cardiovascular disease. N Engl J Med 2017; 377 (14) 1319-1330
5 Hart RG, Sharma M, Mundl H. et al; NAVIGATE ESUS Investigators. Rivaroxaban for stroke prevention after embolic stroke of undetermined source. N Engl J Med 2018; 378 (23) 2191-2201
6 Connolly SJ, Ezekowitz MD, Yusuf S. et al; RE-LY Steering Committee and Investigators. Dabigatran versus warfarin in patients with atrial fibrillation. N Engl J Med 2009; 361 (12) 1139-1151
7 Connolly SJ, Eikelboom J, Joyner C. et al; AVERROES Steering Committee and Investigators. Apixaban in patients with atrial fibrillation. N Engl J Med 2011; 364 (09) 806-817
8 Yuan F, Bosch J, Eikelboom J. et al. A hybrid automated event adjudication system for clinical trials. Clin Trials 2023; 20 (02) 166-175
9 Thygesen K, Alpert JS, Jaffe AS. et al; Joint ESC/ACCF/AHA/WHF Task Force for Universal Definition of Myocardial Infarction, Authors/Task Force Members Chairpersons, Biomarker Subcommittee, ECG Subcommittee, Imaging Subcommittee, Classification Subcommittee, Intervention Subcommittee, Trials & Registries Subcommittee, Trials & Registries Subcommittee, Trials & Registries Subcommittee, Trials & Registries Subcommittee, ESC Committee for Practice Guidelines (CPG), Document Reviewers. Third universal definition of myocardial infarction. J Am Coll Cardiol 2012; 60 (16) 1581-1598
10 Meier-Hirmer C, Ortseifen C. Sauerbrei, Willi. Multivariable fractional polynomials in SAS: an algorithm for determining the transformation of continuous covariates and selection of covariates. [Internet]. 2003 [cited 2023 Apr 27]. Accessed February 12, 2024: http://people.musc.edu/~hille/2009BMTRY755_Website/LectureNotes/Diagnostics2/beschreibung.pdf
11 Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 1999; 94: 496-509
12 Mickey RM, Greenland S. The impact of confounder selection criteria on effect estimation. Am J Epidemiol 1989; 129 (01) 125-137
13 Fibrinogen Studies Collaboration. Measures to assess the prognostic ability of the stratified Cox proportional hazards model. Stat Med 2009; 28 (03) 389-411
14 Austin PC, Harrell Jr FE, van Klaveren D. Graphical calibration curves and the integrated calibration index (ICI) for survival models. Stat Med 2020; 39 (21) 2714-2742
15 Steyerberg EW, Harrell Jr FE, Borsboom GJ, Eijkemans MJ, Vergouwe Y, Habbema JD. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol 2001; 54 (08) 774-781
16 Pisters R, Lane DA, Nieuwlaat R, de Vos CB, Crijns HJGM, Lip GYH. 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 (05) 1093-1100
17 O'Brien EC, Simon DN, Thomas LE. et al. The ORBIT bleeding score: a simple bedside score to assess bleeding risk in atrial fibrillation. Eur Heart J 2015; 36 (46) 3258-3264
18 Fox KAA, Lucas JE, Pieper KS. et al; GARFIELD-AF Investigators. Improved risk stratification of patients with atrial fibrillation: an integrated GARFIELD-AF tool for the prediction of mortality, stroke and bleed in patients with and without anticoagulation. BMJ Open 2017; 7 (12) e017157
19 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. J Am Coll Cardiol 2011; 58 (04) 395-401
20 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 (03) 713-719
21 Hijazi Z, Oldgren J, Lindbäck J. et al; ARISTOTLE and RE-LY Investigators. The novel biomarker-based ABC (age, biomarkers, clinical history)-bleeding risk score for patients with atrial fibrillation: a derivation and validation study. Lancet 2016; 387 (10035): 2302-2311
22 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. BMJ 2014; 349: g4606
23 Makam RCP, Hoaglin DC, McManus DD. et al. Efficacy and safety of direct oral anticoagulants approved for cardiovascular indications: systematic review and meta-analysis. PLoS One 2018; 13 (05) e0197583
24 Zeng Z, Chen J, Qian J, Ma F, Lv M, Zhang J. Risk factors for anticoagulant-associated intracranial hemorrhage: a systematic review and meta-analysis. Neurocrit Care 2023; 38 (03) 812-820
25 Hearnshaw SA, Logan RFA, Lowe D, Travis SPL, Murphy MF, Palmer KR. Acute upper gastrointestinal bleeding in the UK: patient characteristics, diagnoses and outcomes in the 2007 UK audit. Gut 2011; 60 (10) 1327-1335
26 Fernando SM, Qureshi D, Talarico R. et al. Intracerebral hemorrhage incidence, mortality, and association with oral anticoagulation use: a population study. Stroke 2021; 52 (05) 1673-1681
27 Eikelboom JW, Bosch JJ, Connolly SJ. et al. Major bleeding in patients with coronary or peripheral artery disease treated with rivaroxaban plus aspirin. J Am Coll Cardiol 2019; 74 (12) 1519-1528
28 Best JG, Ambler G, Wilson D. et al; Microbleeds International Collaborative Network. Development of imaging-based risk scores for prediction of intracranial haemorrhage and ischaemic stroke in patients taking antithrombotic therapy after ischaemic stroke or transient ischaemic attack: a pooled analysis of individual patient data from cohort studies. Lancet Neurol 2021; 20 (04) 294-303

Supplementary Material

Supplementary Material