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Record W3006384650 · doi:10.1161/circep.119.007676

Genetic Susceptibility for Atrial Fibrillation in Patients Undergoing Atrial Fibrillation Ablation

2020· article· en· W3006384650 on OpenAlexaff
M. Benjamin Shoemaker, Daniela Husser, Carolina Roselli, Meelad Al Jazairi, Jonathan Chrispin, Michael Kühne, Benjamin Neumann, Stacey Knight, Han Sun, Sanghamitra Mohanty, Christian M. Shaffer, Sébastien Thériault, Lauren Lee Rinke, Joylene E. Siland, Diane M. Crawford, Laura Ueberham, Omeed Zardkoohi, Petra Büttner, Bastiaan Geelhoed, Steffen Blum, Stefanie Aeschbacher, Jonathan D. Smith, David R. Van Wagoner, Rebecca Freudling, Martina Müller‐Nurasyid, Jay A. Montgomery, Zachary T. Yoneda, Quinn S. Wells, Tariq Z. Issa, Peter Weeke, Victoria Jacobs, Isabelle C. Van Gelder, Gerhard Hindricks, John Barnard, Hugh Calkins, Dawood Darbar, Greg Michaud, Stefan Kääb, Patrick T. Ellinor, Andrea Natale, Mina K. Chung, Saman Nazarian, Michael J. Cutler, Moritz F. Sinner, David Conen, Michiel Rienstra, Andreas Bollmann, Dan M. Roden, Steven A. Lubitz

Bibliographic record

VenueCirculation Arrhythmia and Electrophysiology · 2020
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversité LavalPopulation Health Research Institute
FundersNational Center for Advancing Translational SciencesNational Center for Research ResourcesNational Heart, Lung, and Blood InstituteSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsMedicineAtrial fibrillationCardiologyInternal medicineAblationAblation of atrial fibrillationCatheter ablation

Abstract

fetched live from OpenAlex

Background: Ablation is a widely used therapy for atrial fibrillation (AF); however, arrhythmia recurrence and repeat procedures are common. Studies examining surrogate markers of genetic susceptibility to AF, such as family history and individual AF susceptibility alleles, suggest these may be associated with recurrence outcomes. Accordingly, the aim of this study was to test the association between AF genetic susceptibility and recurrence after ablation using a comprehensive polygenic risk score for AF. Methods: Ten centers from the AF Genetics Consortium identified patients who had undergone de novo AF ablation. AF genetic susceptibility was measured using a previously described polygenic risk score (N=929 single-nucleotide polymorphisms) and tested for an association with clinical characteristics and time-to-recurrence with a 3 month blanking period. Recurrence was defined as >30 seconds of AF, atrial flutter, or atrial tachycardia. Multivariable analysis adjusted for age, sex, height, body mass index, persistent AF, hypertension, coronary disease, left atrial size, left ventricular ejection fraction, and year of ablation. Results: Four thousand two hundred seventy-six patients were eligible for analysis of baseline characteristics and 3259 for recurrence outcomes. The overall arrhythmia recurrence rate between 3 and 12 months was 44% (1443/3259). Patients with higher AF genetic susceptibility were younger ( P <0.001) and had fewer clinical risk factors for AF ( P =0.001). Persistent AF (hazard ratio [HR], 1.39 [95% CI, 1.22–1.58]; P <0.001), left atrial size (per cm: HR, 1.32 [95% CI, 1.19–1.46]; P <0.001), and left ventricular ejection fraction (per 10%: HR, 0.88 [95% CI, 0.80–0.97]; P =0.008) were associated with increased risk of recurrence. In univariate analysis, higher AF genetic susceptibility trended towards a higher risk of recurrence (HR, 1.08 [95% CI, 0.99–1.18]; P =0.07), which became less significant in multivariable analysis (HR, 1.06 [95% CI, 0.98–1.15]; P =0.13). Conclusions: Higher AF genetic susceptibility was associated with younger age and fewer clinical risk factors but not recurrence. Arrhythmia recurrence after AF ablation may represent a genetically different phenotype compared to AF susceptibility.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.041
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.281
Teacher spread0.252 · 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 teacher head, 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

Citations51
Published2020
Admission routes1
Has abstractyes

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