Patients’ Knowledge Of Anticoagulation and Its Association With Clinical Characteristics, INR Control and Warfarin-Related Adverse Events
Notice bibliographique
Résumé
Abstract Background Whether level of knowledge of anticoagulation (AC) among patients on warfarin plays a role in maintenance of therapeutic INR or in warfarin-related adverse events is controversial. Most studies conducted on this subject had small patient sample sizes and did not use validated questionnaires to assess patients’ knowledge of AC. Objectives To use the validated Oral Anticoagulation Knowledge (OAK) test (Zeolla MM, 2006) to assess knowledge of AC among patients attending a busy AC clinic, and to examine associations between level of knowledge, INR control and adverse events. We hypothesized that patients with higher OAK test scores (i.e. greater knowledge) would have better INR control (primary outcome) and fewer bleeding and thrombosis events (secondary outcomes). Methods Consecutive patients who had been followed in our AC clinic (tertiary care, university-affiliated hospital, 20,000 patient-visits per year) for at least one year and consented to participate were asked to complete the OAK test. The OAK test is a 20-question multiple-choice questionnaire that assesses patients’ knowledge of warfarin AC. A passing score is ≥15 correct responses. Patient charts were reviewed to obtain data on clinical and demographic characteristics, and information on INR values and any thrombosis or bleeding events during the preceding 1 year period. Associations between OAK scores and patient characteristics, INR control and bleeding/thrombosis events were assessed by chi-square and t-tests, as appropriate. Results Among 252 patients screened for participation, 225 met the inclusion criteria and completed the OAK test. Mean (SD) age was 70 (13.4) years, 53% were male and 75% were on warfarin for >3 years. Indications for AC were atrial fibrillation in 65%, VTE in 8%, mechanical heart valve in 10%, and other in 19%. The mean OAK score was 12/20, and 64% failed the OAK test. Predictors of a pass score on the OAK test were younger age (p= 0.01) and higher level of education (p=0.03). Over the preceding year, 57.3% of INRs were therapeutic, 25.1% subtherapeutic and 17.4% supratherapeutic, and there were 22 bleeding events and 5 thrombosis events. There was no association between OAK score and INR control, or OAK score and bleeding or thrombosis events. Conclusion To our knowledge, this is the first study to use the validated OAK test to assess patients’ AC knowledge. We found that younger and more educated patients were more likely to pass the OAK test; however, OAK test result did not predict INR control or occurrence of bleeding or thrombotic events. The OAK test may not be sensitive enough to capture the standard of care practiced in different anticoagulation clinics (e.g. differences in teaching material, frequency of INR checks in stable patients). Also, for some patients, AC knowledge among their caretakers may be more important than self-knowledge. Further research is needed to assess the relationship between AC knowledge, INR control and adverse clinical outcomes. Disclosures: No relevant conflicts of interest to declare.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».