Skip to main content
Log in

Joint Latent Class Analysis of Oral Anticoagulation Use and Risk of Stroke or Systemic Thromboembolism in Patients with Atrial Fibrillation

  • Original Research Article
  • Published:
American Journal of Cardiovascular Drugs Aims and scope Submit manuscript

Abstract

Background

Oral anticoagulation (OAC) is recommended to reduce the risk of stroke or systemic thromboembolism (TE) in atrial fibrillation (AF). In this study, we applied novel joint latent class mixed models to identify heterogeneous patterns of trajectories of OAC use and determined how these trajectories are associated with risks of thromboembolic outcomes.

Methods and Results

We used 2013–2016 claims data from a 5% random sample of Medicare beneficiaries, obtained from the Centers for Medicare and Medicaid Services. Our study sample included 16,399 patients newly diagnosed with AF in 2014–2015 who were followed for 12 months after the first AF diagnosis and filled at least one OAC prescription in this time period. OAC use was defined as the number of days covered with OACs every 30-day interval after the first AF diagnosis. We used a joint latent class mixed model to simultaneously evaluate the longitudinal patterns of OAC use and time to stroke or TE, while adjusting for age, race, CHAD2S2-VASc score and HAS-BLED score.

Five classes of OAC use patterns were identified: late users (17.8%); late initiators (12.5%); early discontinuers (18.6%); late discontinuers (15.4%); and continuous users (35.6%). Compared with continuous users, the risk of stroke or TE was higher for participants in the late initiators (hazard ratio [HR] 1.73, 95% confidence interval [CI] 1.49–2.01) and late discontinuers (HR 1.23, 95% CI 1.04–1.45) classes.

Conclusion

Late initiators and late discontinuers had a higher risk of stroke or TE than continuous users. Early initiation and continuous OAC use is important in preventing stroke and TE among patients diagnosed with AF.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Freedman B, Potpara TS, Lip GYH. Stroke prevention in atrial fibrillation. Lancet. 2016;388(10046):806–17.

    Article  Google Scholar 

  2. Lip GYH, Banerjee A, Boriani G, Chiang CE, Fargo R, Freedman B, et al. Antithrombotic therapy for atrial fibrillation: CHEST Guideline and Expert Panel Report. Chest. 2018;154(5):1121–201.

    Article  Google Scholar 

  3. January CT, Wann LS, Calkins H, Chen LY, Cigarroa JE, Cleveland JC Jr, et al. 2019 AHA/ACC/HRS Focused Update of the 2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society in Collaboration With the Society of Thoracic Surgeons. Circulation. 2019;140(2):e125–51.

    Article  Google Scholar 

  4. Hernandez I, He M, Brooks MM, Saba S, Gellad WF. Adherence to anticoagulation and risk of stroke among medicare beneficiaries newly diagnosed with atrial fibrillation. Am J Cardiovasc Drugs. 2020;20(2):199–207.

    Article  CAS  Google Scholar 

  5. Hernandez I, He M, Chen N, Brooks MM, Saba S, Gellad WF. Trajectories of oral anticoagulation adherence among medicare beneficiaries newly diagnosed with atrial fibrillation. J Am Heart Assoc. 2019;8(12):e011427.

    Article  Google Scholar 

  6. Hernandez I, Zhang Y, Saba S. Comparison of the effectiveness and safety of apixaban, dabigatran, rivaroxaban, and warfarin in newly diagnosed atrial fibrillation. Am J Cardiol. 2017;120(10):1813–9.

    Article  CAS  Google Scholar 

  7. Hart RG, Pearce LA, Aguilar MI. Meta analysis antithrombotic therapy to prevent stroke in patients who have nonvalvular atrial fibrillation. Ann Intern Med. 2007;146(12):857–67.

    Article  Google Scholar 

  8. Clemens A, Haertter S, Friedman J, Brueckmann M, Stangier J, van Ryn J, et al. Twice daily dosing of dabigatran for stroke prevention in atrial fibrillation: a pharmacokinetic justification. Curr Med Res Opin. 2012;28(2):195–201.

    Article  CAS  Google Scholar 

  9. Clemens A, Noack H, Brueckmann M, Lip GY. Twice- or once-daily dosing of novel oral anticoagulants for stroke prevention: a fixed-effects meta-analysis with predefined heterogeneity quality criteria. PLoS ONE. 2014;9(6):e99276.

    Article  CAS  Google Scholar 

  10. Lang K, Bozkaya D, Patel AA, Macomson B, Nelson W, Owens G, et al. Anticoagulant use for the prevention of stroke in patients with atrial fibrillation: findings from a multi-payer analysis. BMC Health Serv Res. 2014;14(1):329.

    Article  Google Scholar 

  11. CMS Chronic Conditions Data Warehouse (CCW). CCW condition algorithms. 2019. https://www2.ccwdata.org/web/guest/condition-categories. Accessed 8 Apr 2021.

  12. January CT, Wann LS, Alpert JS, Calkins H, Cigarroa JE, Cleveland JC Jr, et al. 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the Heart Rhythm Society. J Am Coll Cardiol. 2014;64(21):e1-76.

    Article  Google Scholar 

  13. Sokal RR, Rohlf FJ. Statistical tables. 3rd ed. New York, NY, USA: MacMillan; 1995.

    Google Scholar 

  14. Skinner J, Weinstein JN, Sporer SM, Wennberg JE. Racial, ethnic, and geographic disparities in rates of knee arthroplasty among Medicare patients. N Engl J Med. 2003;349(14):1350–9.

    Article  CAS  Google Scholar 

  15. Duncan O, Duncan B. A methodological analysis of segregation indexes. Am Sociol Rev. 1955;20(2):210–7.

    Article  Google Scholar 

  16. Massey D, Denton N. The dimensions of residential segregation. Soc Forces. 1988;67(2):281–315.

    Article  Google Scholar 

  17. Lip GY, Nieuwlaat R, Pisters R, Lane DA, Crijns HJ. Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation. Chest. 2010;137(2):263–72.

    Article  Google Scholar 

  18. Pisters R, Lane DA, Nieuwlaat R, de Vos CB, Crijns HJ, Lip GY. 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(5):1093–100.

    Article  Google Scholar 

  19. Proust-Lima C, Séne M, Taylor JMG, Jacqmin-Gadda H. Joint latent class models for longitudinal and time-to-event data: a review. Stat Methods Med Res. 2012;23(1):74–90.

    Article  Google Scholar 

  20. Proust C, Jacqmin-Gadda H. Estimation of linear mixed models with a mixture of distribution for the random effects. Comput Methods Programs Biomed. 2005;78(2):165–73.

    Article  Google Scholar 

  21. Proust-Lima C, Philipps V, Liquet B. Estimation of extended mixed models using latent classes and latent processes: the R Package lcmm. J Stat Softw. 2017. https://doi.org/10.18637/jss.v078.i02

    Article  Google Scholar 

  22. Chen N, Brooks MM, Hernandez I. Latent classes of adherence to oral anticoagulation therapy among patients with a new diagnosis of atrial fibrillation. JAMA Netw Open. 2020;3(2):e1921357.

    Article  Google Scholar 

  23. Yao X, Abraham NS, Alexander GC, Crown W, Montori VM, Sangaralingham LR, et al. Effect of adherence to oral anticoagulants on risk of stroke and major bleeding among patients with atrial fibrillation. J Am Heart Assoc. 2016;5(2):e003074.

    Article  Google Scholar 

  24. Borne RT, O’Donnell C, Turakhia MP, Varosy PD, Jackevicius CA, Marzec LN, et al. Adherence and outcomes to direct oral anticoagulants among patients with atrial fibrillation: findings from the veterans health administration. BMC Cardiovasc Disord. 2017;17(1):236.

    Article  Google Scholar 

  25. Beunckens C, Molenberghs G, Verbeke G, Mallinckrodt C. A latent-class mixture model for incomplete longitudinal Gaussian data. Biometrics. 2008;64(1):96–105.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Inmaculada Hernandez.

Ethics declarations

Funding

This study was funded by the National Institutes of Health (Grant Number UL1TR001857), and the National Heart, Lung and Blood Institute (Grant Number K01HL142847).

Conflict of interest

Inmaculada Hernandez has received consulting fees from Bristol Myers Squibb and Pfizer. Nemin Chen, Nico Gabriel, and Maria M. Brooks have no conflicts of interest to declare.

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and material

The authors are unable to share the data since the data were obtained under a data user agreement that does not allow data sharing.

Code availability

Code can be made available upon request to the corresponding author.

Author contributions

IH and NC designed the study; NG undertook the statistical analysis; NC wrote the manuscript; and IH, NG, and MMB undertook critical revision of the manuscript.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 127 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, N., Gabriel, N., Brooks, M.M. et al. Joint Latent Class Analysis of Oral Anticoagulation Use and Risk of Stroke or Systemic Thromboembolism in Patients with Atrial Fibrillation. Am J Cardiovasc Drugs 21, 573–580 (2021). https://doi.org/10.1007/s40256-021-00476-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40256-021-00476-8

Navigation