Adherence to Antithrombotic Therapy for Patients Attending a Multidisciplinary Thrombosis Service in Canada – A Cross-Sectional Survey
Bibliographic record
Abstract
Kwadwo Osei Bonsu,1 Stephanie Young,1,2 Tiffany Lee,1,2 Hai Nguyen,1 Rufaro S Chitsike3,4 1School of Pharmacy, Memorial University of Newfoundland and Labrador, St John’s, NL, A1B 3V6, Canada; 2Pharmacy Program, Eastern Region Health Authority, St John’s, NL, A1B 3V6, Canada; 3Division of Medicine (Hematology), Memorial University of Newfoundland and Labrador, St John’s, NL, A1B 3V6, Canada; 4Division of Hematology, Eastern Region Health Authority, St John’s, NL, A1B 3V6, CanadaCorrespondence: Kwadwo Osei Bonsu, School of Pharmacy, Memorial University of Newfoundland and Labrador, 300 Prince Philip Drive, St John’s, NL, A1B 3V6, Canada, Email koseibonsu@mun.caBackground: Poor medication adherence puts patients who require antithrombotic therapy at greater risk of complications. We started a multidisciplinary Adult Outpatient Thrombosis Service in 2017 in a Canadian health authority and were interested in the level of medication adherence in the population attending.Aim(S): The aim of this study is to assess adherence to antithrombotic medications for patients attending a multidisciplinary Thrombosis Service.Methods: We conducted a cross-sectional survey of outpatients seen at the Thrombosis Service between 2017 and 2019 using the 12-item validated Adherence to Refills and Medications Scale (ARMS) to assess adherence to antithrombotic (anticoagulants and antiplatelet) therapy. Linear regression analysis examined the factors associated with adherence to antithrombotic therapy.Results: Of 1058 eligible patients, 53.2% responded to the survey. Seventeen were excluded from the analysis for missing more than 6 responses to the 12 items on the ARMS. About 55% (n = 297) were on direct oral anticoagulants (DOACs), 19% (n = 102) on warfarin, 5.0% (n = 27) on low molecular weight heparin, 3.3% (n = 18) on antiplatelet therapy and 18% (n = 96) were no longer on antithrombotic therapy. Nearly half (47%, n = 253) had taken antithrombotic therapy for 1– 5 years while 28% (n = 150) and 25% (n = 137) had taken antithrombotic treatments for < 1 and > 5 years, respectively. Most patients (87%, n = 475) were ≥ 50 years and half (51%, n = 277) were male. The mean adherence score was 13.9 (SD± 2.2) and 88% (n = 481) of participants were adherent to antithrombotic treatment (ARMS = 12– 16). Multivariable linear regression showed that patients with post-graduate education had 0.4% lower adherence to antithrombotic therapy as compared with elementary education (β = 0.0039, p = 0.048). Patients with prior antithrombotic agent use > 5 years had 0.5% lower adherence to antithrombotic treatment compared to patients with < 1 year (β = 0.0047, p = 0.0244).Conclusion: Self-reported adherence to antithrombotic therapy was high (88%) within a multidisciplinary Thrombosis Service. Patients with advanced education and prolong duration of antithrombotic therapy were more likely to have lower self-reported adherence to antithrombotic treatment.Keywords: medication adherence, self-reported adherence, multidisciplinary care, thrombosis service, anticoagulation management program, antithrombotic therapy
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".