Relationship between care speciality and atrial fibrillation outcomes in the GARFIELD-AF registry
Notice bibliographique
Résumé
Abstract Background Atrial fibrillation (AF) is associated with cardio-embolic stroke. It remains unclear whether AF outcomes are related to the care speciality at AF diagnosis. Purpose To explore associations between the care speciality at AF diagnosis and the risks of clinical outcomes in newly diagnosed AF patients. Methods GARFIELD-AF is an international registry of consecutively recruited newly diagnosed AF patients with ≥1 stroke risk factors. Participants were divided based on the care specialty at AF diagnosis: primary care, cardiology, or other medical specialties (internal medicine, neurology, or geriatrics). The follow-up period was from the date of enrolment, truncated at first event occurrence, death, loss to follow-up, or two years after enrolment, whichever occurred first. Hazard ratios for the associations of care speciality with selected clinical outcomes were estimated using Cox proportional-hazards models adjusted for the confounding factors, which included demographics, AF type, medical history, baseline comorbidities, and treatment information. Results The study population comprised 52,011 prospectively enrolled GARFIELD-AF patients with available care speciality and follow-up information. Most participants were diagnosed by a cardiology specialist (n=34,172, 65.7%), and fewer by other medical specialists (n=10,443, 20.1%) or primary care practitioners (n=7,396, 14.2%). Patients diagnosed by cardiologists were on average younger, had lower BMI, and were more likely to have paroxysmal AF when first diagnosed. These patients also received NOAC more frequently (30.1%), compared to patients diagnosed by other medical specialties (24.2%) or primary care practitioners (20.4%, Table 1). CHA2DS2-VASc and HAS-BLED scores were similar across care specialities. The proportion of patients treated by a cardiologist differed substantially between countries, ranging from 9% in Finland to 97% in Egypt. Patients cared for by non-cardiology medical specialties had a greater risk of all-cause mortality (HR 1.23, 95%CI 1.08 to 1.39), non-cardiovascular mortality (HR 1.31, 1.12 to 1.53) and non-haemorrhagic stroke/systemic embolism (HR 1.45, 1.18 to 1.80) compared with participants cared for by a cardiologist. Patients treated by primary care practitioners had a lower all-cause mortality risk compared with those diagnosed by cardiologists (HR 0.86, 0.74 to 0.99) (Figure 1). Conclusions Overall, patients developing new AF were most often diagnosed by cardiologists, but substantial regional variation existed. Patients diagnosed by cardiologists received NOACs more frequently compared to patients diagnosed from other care specialties. Patients treated by non-cardiology medical specialities experienced a comparatively greater risk of death and non-haemorrhagic stroke. Cardiology expertise could have important implications for the care of newly diagnosed AF patients.Figure 1Table 1
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 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 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 ».