Literature DB >> 32074287

Latent Classes of Adherence to Oral Anticoagulation Therapy Among Patients With a New Diagnosis of Atrial Fibrillation.

Nemin Chen1, Maria M Brooks1, Inmaculada Hernandez2.   

Abstract

Importance: Less than half of US patients with a diagnosis of atrial fibrillation (AF) receive oral anticoagulation.
Objectives: To identify patients with similar patterns of adherence to regimens of warfarin and direct oral anticoagulants (DOACs) in the first year after AF diagnosis and to evaluate associations between patient characteristics and membership in latent classes of adherence. Design, Setting, and Participants: This retrospective cohort study used 2013 to 2016 Medicare claims data to identify 7491 patients with a new diagnosis of AF in 2014 to 2015 who initiated warfarin after AF diagnosis and 9478 patients with a new diagnosis of AF in 2014 to 2015 who initiated DOAC treatment after AF diagnosis, for a total of 16 969 Medicare beneficiaries. Participants were followed up for 12 months after AF diagnosis. Statistical analysis was performed from February 1 to November 30, 2018. Exposures: Treatment with warfarin or DOAC after AF diagnosis. Main Outcomes and Measures: The main outcome was the proportion of days that patients received warfarin or DOAC, measured in 30-day intervals after AF diagnosis. Independent variables included patient demographic characteristics, socioeconomic status, region of residence, and clinical characteristics. Latent class mixed models were used to identify latent classes of warfarin and DOAC adherence, and polytomous logistic regression was used to assess the association between patient characteristics and membership in each latent class.
Results: Among the 7491 patients receiving warfarin (4348 women), the mean (SD) age was 76.0 (10.0) years; among the 9478 patients receiving DOAC (5496 women), the mean (SD) age was 77.0 (8.5) years. Four latent classes of patients were identified based on warfarin adherence: late initiators (980 [13%]), early initiators who discontinued therapy at months 1 to 3 (1297 [17%]) or at months 5 to 10 (735 [10%]), and continuously adherent patients (4479 [60%]). Four latent classes of patients were also identified based on DOAC adherence: patients who initiated DOAC in months 1 to 5 (1368 [14%]) or months 6 to 11 (800 [8%]), patients with suboptimal and decreasing adherence (2267 [24%]), and continuously adherent patients (5043 [53%]). Membership in latent classes of warfarin adherence was significantly associated with sex, eligibility for Medicaid and income subsidy, region of residence, CHA2DS2-VASc (cardiac failure or dysfunction, hypertension, age 65-74 [1 point] or ≥75 years [2 points], diabetes, and stroke, transient ischemic attack or thromboembolism [2 points]-vascular disease, and sex category [female]) risk score, and HAS-BLED (hypertension, abnormal renal and liver function, stroke, bleeding, labile international normalized ratio, elderly, and drugs or alcohol) score. Membership in latent classes of DOAC adherence was significantly associated with race/ethnicity, region of residence, HAS-BLED score, and use of antiarrhythmic medications. Conclusions and Relevance: This study found that, among patients who initiated anticoagulation therapy, 40% of those who initiated warfarin therapy and 47% of those who initiated DOAC treatment did not continuously adhere to therapy in the first year after AF diagnosis. Identifying longitudinal patterns of warfarin and DOAC adherence and the factors associated with them provides suggestions for the design of targeted strategies to mitigate suboptimal oral anticoagulation use.

Entities:  

Year:  2020        PMID: 32074287     DOI: 10.1001/jamanetworkopen.2019.21357

Source DB:  PubMed          Journal:  JAMA Netw Open        ISSN: 2574-3805


  8 in total

1.  Latent Class Analysis of Prescribing Behavior of Primary Care Physicians in the Veterans Health Administration.

Authors:  Alexis K Barrett; John P Cashy; Carolyn T Thorpe; Jennifer A Hale; Kangho Suh; Bruce L Lambert; William Galanter; Jeffrey A Linder; Gordon D Schiff; Walid F Gellad
Journal:  J Gen Intern Med       Date:  2022-01-06       Impact factor: 6.473

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

Authors:  Nemin Chen; Nico Gabriel; Maria M Brooks; Inmaculada Hernandez
Journal:  Am J Cardiovasc Drugs       Date:  2021-04-12       Impact factor: 3.571

3.  Adherence with direct oral anticoagulants in patients with atrial fibrillation: Trends, risk factors, and outcomes.

Authors:  Anat Arbel; Zomoroda Abu-Ful; Meir Preis; Shai Cohen; Walid Saliba
Journal:  J Arrhythm       Date:  2021-11-18

4.  Contemporary clinical and economic outcomes among oral anticoagulant treated and untreated elderly patients with atrial fibrillation: Insights from the United States Medicare database.

Authors:  Muhammad Bilal Munir; Patrick Hlavacek; Allison Keshishian; Jennifer D Guo; Rajesh Mallampati; Mauricio Ferri; Cristina Russ; Birol Emir; Matthew Cato; Huseyin Yuce; Jonathan C Hsu
Journal:  PLoS One       Date:  2022-02-17       Impact factor: 3.240

5.  Long-Term Medication Adherence Trajectories to Direct Oral Anticoagulants and Clinical Outcomes in Patients With Atrial Fibrillation.

Authors:  Jaejin An; Zoe Bider; Tiffany Q Luong; T Craig Cheetham; Daniel T Lang; Heidi Fischer; Kristi Reynolds
Journal:  J Am Heart Assoc       Date:  2021-10-29       Impact factor: 5.501

6.  Use of oral anticoagulants among individuals with cancer and atrial fibrillation in the United States, 2010-2016.

Authors:  Shirin Ardeshirrouhanifard; Huijun An; Ravi K Goyal; Mukaila A Raji; Jodi B Segal; G Caleb Alexander; Hemalkumar B Mehta
Journal:  Pharmacotherapy       Date:  2022-04-14       Impact factor: 6.251

7.  Comparison of Direct Oral Anticoagulant Use for the Treatment of Non-Valvular Atrial Fibrillation in Pivotal Clinical Trials vs. the Real-World Setting: A Population-Based Study from Southern Italy.

Authors:  Ylenia Ingrasciotta; Andrea Fontana; Anna Mancuso; Valentina Ientile; Janet Sultana; Ilaria Uomo; Maurizio Pastorello; Paolo Calabrò; Giuseppe Andò; Gianluca Trifirò
Journal:  Pharmaceuticals (Basel)       Date:  2021-03-25

8.  Association between behavioral phenotypes and sustained use of smartphones and wearable devices to remotely monitor physical activity.

Authors:  Sarah J Fendrich; Mohan Balachandran; Mitesh S Patel
Journal:  Sci Rep       Date:  2021-11-02       Impact factor: 4.379

  8 in total

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