Estimating the incidence of atrial fibrillation in single‐chamber implantable cardioverter defibrillator patients
Bibliographic record
Abstract
BACKGROUND: Atrial arrhythmias are associated with major adverse cardiovascular events. Recent reports among implantable cardioverter defibrillator (ICD) patients have demonstrated a high prevalence of atrial fibrillation (AF), predominantly in dual-chamber recipients. AF incidence among patients with single-chamber systems (approximately 50% of all ICDs) is currently unknown. The objective was to estimate the prevalence of new-onset AF among single-chamber ICD patients by observing the rates of new atrial tachycardia (AT)/AF among a propensity scoring matched cohort of dual-chamber ICD patients from the PainFree SmartShock technology study, to better inform screening initiatives. METHODS: Among 2770 patients enrolled, 1862 single-chamber, dual-chamber, and cardiac resynchronization therapy subjects with no prior history of atrial tachyarrhythmias were included. Daily AT/AF burden was estimated using a propensity score weighted model against data from dual-chamber ICDs. RESULTS: Over 22 ± 9 months of follow-up, the estimated incidence of AT/AF-lasting at least 6 min, 6 h, and 24 h per day -in the single-chamber cohort was 22.0, 9.8, and 6.3%, whereas among dual-chamber patients, the prevalence was 26.6, 13.1, and 7.1%, respectively. Initiation of oral anticoagulation was estimated to occur in 9.8% of the propensity matched single-chamber cohort, which was higher than the actual observed rate of 6.0%. Stroke and transient ischemic attack occurred at low rates in all device subgroups. CONCLUSIONS: Atrial arrhythmias occur frequently, and significant underutilization of anticoagulation is suggested in single-chamber ICD recipients. Routine screening for AF should be considered among single-chamber ICD recipients.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".