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Record W3216821101 · doi:10.1016/j.hroo.2021.11.012

Diagnosis-to-ablation time predicts recurrent atrial fibrillation and rehospitalization following catheter ablation

2021· article· en· W3216821101 on OpenAlexaff
Derek S. Chew, Kelley A. Jones, Zak Loring, Eric Black‐Maier, Peter A. Noseworthy, Derek V. Exner, Douglas L. Packer, Jennifer S. Grant, Daniel B. Mark, Jonathan P. Piccini

Bibliographic record

VenueHeart Rhythm O2 · 2021
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicineAtrial fibrillationHazard ratioCatheter ablationAblationCardiologyInternal medicineProportional hazards modelConfidence intervalCohortHeart failure

Abstract

fetched live from OpenAlex

BackgroundWait times for catheter ablation in patients with symptomatic atrial fibrillation (AF) may influence clinical outcomes.ObjectiveThis study examined the relationship between the duration from AF diagnosis to ablation, or diagnosis-to-ablation time (DAT), on the clinical response to catheter ablation in a large nationwide cohort of patients.MethodsWe identified patients with new AF who underwent catheter ablation between January 2014 and December 2017 using the IBM MarketScan databases. Cox proportional hazard models were used to estimate the strength of the association between DAT and the outcomes of AF recurrence and hospitalization at 1 year postablation.ResultsAmong 11,143 AF patients who underwent ablation, the median age was 59 years, 31% were female, and the median CHA2DS2-VASc score was 2. Median DAT was 5.5 (2.6, 13.1) months. At 1 year postablation, 10.0% (n = 1116) developed recurrent AF. For each year increase in DAT, the risk of AF recurrence increased by 20% after adjustment for baseline comorbidities and medications (hazard ratio [HR] 1.20, 95% confidence interval [CI] 1.11–1.30). A longer DAT was associated with an increased risk of hospitalization (HR 1.08 per DAT year, 95% CI 1.02–1.15). DAT was a stronger predictor of AF recurrence postablation than traditional clinical risk factors, including age, prior heart failure, or renal failure.ConclusionIncreasing duration between AF diagnosis and catheter ablation is associated with higher AF recurrence rates and all-cause hospitalization. Our findings are consistent with a growing body of evidence supporting the benefits of prioritizing early restoration of sinus rhythm. Wait times for catheter ablation in patients with symptomatic atrial fibrillation (AF) may influence clinical outcomes. This study examined the relationship between the duration from AF diagnosis to ablation, or diagnosis-to-ablation time (DAT), on the clinical response to catheter ablation in a large nationwide cohort of patients. We identified patients with new AF who underwent catheter ablation between January 2014 and December 2017 using the IBM MarketScan databases. Cox proportional hazard models were used to estimate the strength of the association between DAT and the outcomes of AF recurrence and hospitalization at 1 year postablation. Among 11,143 AF patients who underwent ablation, the median age was 59 years, 31% were female, and the median CHA2DS2-VASc score was 2. Median DAT was 5.5 (2.6, 13.1) months. At 1 year postablation, 10.0% (n = 1116) developed recurrent AF. For each year increase in DAT, the risk of AF recurrence increased by 20% after adjustment for baseline comorbidities and medications (hazard ratio [HR] 1.20, 95% confidence interval [CI] 1.11–1.30). A longer DAT was associated with an increased risk of hospitalization (HR 1.08 per DAT year, 95% CI 1.02–1.15). DAT was a stronger predictor of AF recurrence postablation than traditional clinical risk factors, including age, prior heart failure, or renal failure. Increasing duration between AF diagnosis and catheter ablation is associated with higher AF recurrence rates and all-cause hospitalization. Our findings are consistent with a growing body of evidence supporting the benefits of prioritizing early restoration of sinus rhythm.

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 score0.183
Threshold uncertainty score0.636

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 head0.029
GPT teacher head0.305
Teacher spread0.276 · 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".

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Citations55
Published2021
Admission routes1
Has abstractyes

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