Quality of life, medication adherence and satisfaction with anticoagulant treatment (dabigatran vs vitamin K antagonists) according to thromboembolic risk. Data from the CAPANA study
Notice bibliographique
Résumé
It has recently shown that amongst patients with non-valvular atrial fibrillation (NVAF) treated in cardiology setting, adherence, satisfaction and quality of life were higher for those patients treated with dabigatran than with vitamin K antagonists (VKA).1 However, it is uncertain whether these results could be different along the thromboembolic risk. The CAPANA study1 was an observational, prospective and multicentre study including outpatients with NVAF attended in Cardiology clinics in Spain, who started treatment with dabigatran or VKA within the previous month. Quality of life was assessed using the validated questionnaire AF-QOL 18 (0: minimum; 100: maximum), adherence with the Morisky-Green test and the perception of the cardiologist with a specific ad hoc questionnaire (0: completely unsatisfied; 10: completely satisfied). In this study, data were compared according to thromboembolic risk. A total of 1,015 patients (73.3 ± 9.4 years; CHA2DS2VASc 3.4 ± 1.5; CHA2DS2VASc >2: 71.0%; 74.7% treated with dabigatran and 25.3% with VKA) were included. Mean AF QOL 18 score decreased as CHA2DS2-VASc increased at baseline and at month 6 of treatment, particularly with VKA. Both, at baseline and at month 6, mean AF QOL 18 score was significantly higher amongst those patients taking dabigatran, compared with VKA, particularly in patients with CHA2DS2-VASc >2 (48.4 ± 22.9 vs 39.5 ± 20.8 and 50.4 ± 24.4 vs 38.5 ± 21.3, respectively; both P < .001) (Table 1). After 6 months, good adherence was significantly higher with dabigatran, compared with VKA (89.1% vs 81.0%; P = .003), particularly in patients with CHA2DS2-VASc >2 (87.9% vs 79.2%; P = .005) (Table 1). The overall satisfaction with anticoagulant treatment was higher with dabigatran than with VKA (9.0 ± 1.2 vs 6.6 ± 2.2; P < .001), regardless of thromboembolic risk. The CAPANA study showed that amongst patients starting treatment with either dabigatran or VKA, quality of life was better amongst those patients taking dabigatran.1 Our study showed that the overall quality of life worsened as thromboembolic risk increased. This is important, because some patients could withdraw anticoagulant therapy due to impaired quality of life, and this could be more frequent in the patients who may benefit more from anticoagulation, those with highest thromboembolic risk. Importantly, with VKA whereas quality of life worsened as CHA2DS2-VASc increased, quality of life remained stable with dabigatran. Therefore, dabigatran may assure a better quality of life than VKA, regardless of thromboembolic risk. Good adherence to anticoagulant therapy is associated with a significant reduction of ischemic stroke, without a substantial increase of major bleeding.2 Good adherence at month 6 was higher with dabigatran than with VKA, particularly in those patients with CHA2DS2-VASc ≥3. Different studies have shown that adherence to dabigatran is high.3 Despite that, more efforts are needed to improve the adherence with dabigatran.4 Physicians considered that patients’ general satisfaction with anticoagulant therapy with dabigatran was high, regardless of thromboembolic risk and greater than with treatment with VKA. This may be related to the advantages of dabigatran over VKA.5 In conclusion, in NVAF outpatients with a high thromboembolic risk, compared with VKA, dabigatran is associated with a higher quality of life, greater adherence and better satisfaction, particularly in those patients with the highest thromboembolic risk. Drs. Vivencio Barrios, Carlos Escobar and Juanjo Gómez Doblas have received honoraria for consultancy/honoraria from Bayer, Boehringer-Ingelheim, BMS Pfizer and Daiichi Sankyo. Dr. Gonzalo Baron has received honoraria for consultancy/honoraria from Bayer, Biotronik, BMS-Pfizer, Boehringer-Ingelheim, Daiichi-Sankyo, Novartis and Rovi. The other authors do not have conflicts of interest.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».