Relationship between wait times and postatrial fibrillation ablation outcomes: A population-based study
Bibliographic record
Abstract
BACKGROUND: Rhythm control is a cornerstone of atrial fibrillation (AF) management. Shorter time between diagnosis of AF and receipt of catheter ablation is associated with greater rates of therapy success. Previous work considered diagnosis-to-ablation time as a binary or categorical variable and did not consider the unique risk profile of patients after a referral for ablation was made. OBJECTIVE: The purpose of this study was to comprehensively assess the impact of diagnosis-to-ablation and referral-to-ablation time on postprocedural outcomes at a population level. METHODS: This observational cohort study included patients who received catheter ablation to treat AF in Ontario, Canada. Patient demographics, medical comorbidities, AF diagnosis date, ablation referral date, and ablation date were collected. The primary outcomes of interest included a composite of death and hospitalization/emergency department visit for AF, heart failure, or ischemic stroke. Multivariable Cox models assessed the impact of diagnosis-to-ablation and referral-to-ablation times on the primary outcome. RESULTS: Our cohort included 7472 patients who received ablation for de novo AF between April 1, 2016, and March 31, 2022. Median [interquartile range] diagnosis-to-ablation time was 718 [399-1274] days and median referral-to-ablation time was 221 [117-363] days. Overall, 911 patients (12.2%) had the composite endpoint within 1 year of ablation. Increasing diagnosis-to-ablation time was associated with a greater incidence for the primary outcome (hazard ratio [HR]1.02; 95% confidence interval [CI] 1.01-1.02 per month). Increasing referral-to-ablation time did not impact the primary outcome (HR 1.00; 95% CI 0.98-1.01 per month). CONCLUSION: Delays between AF diagnosis and ablation referral may contribute to adverse postprocedural outcomes and provide an opportunity for health system quality improvements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".