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Record W2981952679 · doi:10.1093/eurheartj/ehz746.1126

P6536Implementation of a mass atrial fibrillation screening program in Canadian community practice

2019· article· en· W2981952679 on OpenAlexaffabout
Richard Godin, Isabelle Nault, Joaquim Andrade

Bibliographic record

VenueEuropean Heart Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsVancouver Coastal Health Research InstituteInstitut universitaire de cardiologie et de pneumologie de QuébecBristol-Myers Squibb (Canada)
Fundersnot available
KeywordsMedicineAtrial fibrillationStroke (engine)Clinical PracticeEmergency medicineIntensive care medicineMedical emergencyPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Atrial fibrillation is the most common arrhythmia seen in clinical practice, and is an important cause of stroke. In up to 10% of patients, stroke is the first clinical manifestation of undiagnosed atrial fibrillation (AF). Early detection of AF is therefore desirable, as it would provide an opportunity to initiate appropriate stroke prevention with anticoagulation. While European guidelines recommend screening for AF as part of routine care in patients with risk factors, the optimal modality has not yet been determined. Smartphone enabled single lead ECG devices have been shown to be highly accurate and could prove to be an effective point of care tool for uncovering AF in the community and may facilitate implementation of screening recommendations. Purpose We sought to describe the integration of a smartphone enabled single lead ECG device into the practice of selected Canadian community-based physicians and its impact on patient care. Methods Canadian community-based physicians were provided with a smartphone enabled single lead ECG device for a period of six months. Physicians were instructed to perform a single 30 second ECG recording in patients over the age of 65 who present for a regular follow-up visit. Patients with previous AF diagnosis were excluded. Outcomes related to screening, and stroke prevention-treatment initiation were prospectively ascertained and documented using a patient flow tracker. Results Among 612 physicians who were invited to participate in the program, 315 (51%) agreed to track and report the use of the device that was provided to them. During this program, 315 physicians screened a total of 15,538 patients. AF was detected 1103 patients (7.1%). The majority of patients with a positive screen underwent a subsequent 12-lead ECG confirmatory exam (79%). AF could not be confirmed in 277 patients (31.7% of patients undergoing 12-lead ECG). A total of 677 patients were initiated on oral anticoagulation (OAC), with 262 patients (24%) being initiated on OAC prior to ECG confirmation. Conclusion Undiagnosed AF is common in patients >65 years of age in community practice. The results of this program indicate that single lead mobile ECG devices can be integrated into routine point of care use for Canadian physicians and, can be a valuable tool to detect previously undiagnosed AF. While this approach identified a large number of undiagnosed AF patients eligible for OAC therapy, undertreatment with OAC remains a concern. Acknowledgement/Funding Bristol-Myers Squibb/Pfizer Alliance

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)0.001
metaresearch head score (Gemma)0.007
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 score0.057
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.132
GPT teacher head0.416
Teacher spread0.284 · 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
Published2019
Admission routes2
Has abstractyes

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