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Record W4388600655 · doi:10.1093/eurheartj/ehad655.551

Relationship between care speciality and atrial fibrillation outcomes in the GARFIELD-AF registry

2023· article· en· W4388600655 on OpenAlexaff
C J F Camm, Saverio Virdone, Ramón Corbalán, Seil Oh, John W. Eikelboom, Ali̇ Oto, Keith A.A. Fox, A. John Camm, Karen S. Pieper, Shinichi Goto, Ajay Kakkar

Bibliographic record

VenueEuropean Heart Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineAtrial fibrillationStroke (engine)Hazard ratioSpecialtyInternal medicinePopulationProportional hazards modelEmergency medicineNeurologyGeriatricsPediatricsConfidence intervalFamily medicine

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.208
GPT teacher head0.397
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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