Quality of life, medication adherence and satisfaction with anticoagulant treatment (dabigatran vs vitamin K antagonists) according to thromboembolic risk. Data from the CAPANA study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".