Comparative adherence between DOAC and VKA in patients with atrial fibrillation: a 23-year retrospective observational study in Canada
Notice bibliographique
Résumé
Abstract Background A recent systematic review highlighted significant gaps in the evidence on atrial fibrillation (AF) patients' adherence to oral anticoagulants (OAC). Current evidence suffers from short follow-up times, focuses on the first OAC and does not take switching into account. There is also lack of observational data on adherence to warfarin due to its varying dose that complicates the calculations. As such there is lack of evidence on comparative adherence between VKAs and DOACs and whether the convenience of DOACs translates into better adherence in AF patients. Purpose Our objective was to measure AF patients' long-term OAC adherence and compare the impact of taking direct oral anticoagulants (DOAC) versus vitamin K antagonists (VKA) on adherence, while accounting for switching. Methods Using linked, population-based administrative data containing physician billings, hospitalization and prescription records of 4.8 million British Columbians (1996–2019), incident adult cases of AF were identified. The primary measure of adherence was proportion of days covered (PDC). Consecutive rolling 90-day windows were created for each patient starting from their first OAC prescription fill date until the end of their follow-up. The PDC for each 90-day rolling window was calculated and averaged to yield mean adherence over the follow-up period for each patient. Permanent medication discontinuation resulted in a PDC of 0 for all subsequent rolling windows after their supply ran out. As such, both poor execution and non-persistence were measured simultaneously. The association between drug class and adherence was assessed using generalized mixed effect linear regression models with drug class treated as time-varying covariate to account for switching. Results The study cohort was 30,264 AF patients [mean age 72.2 years (SD11.0), 44.6% female, mean CHA2DS2-VASc 2.94 (SD1.4)] with mean follow-up of 7.7 (SD 4.8) years. The mean PDC was 0.71 (SD 0.27) with 51% of the cohort having mean PDC values below the conventional threshold of adherence (PDC<0.8). Adherence dropped over time with the greatest decline in the first two years after therapy initiation. After controlling for all other confounders and accounting for switching, taking VKA compared to DOAC was, on average, associated with a 1-day decrease in number of days of medication-taking per year. Conclusion AF patients' OAC adherence was below the conventional threshold of 0.8, and dropped over time, particularly in the first two years. Drug class had no clinically meaningful impact on medication adherence. Our study highlights the need for effective adherence interventions particularly early in OAC therapy. Our findings also emphasizes that prescribers should not assume inherently better adherence for DOACs and should instead choose OAC in conversation with the patient and in accordance with their values and preferences. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Canadian Institutes of Health Research grant
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,010 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».