Abstract TP286: Atrial Fibrillation Predictors on Insertable Cardiac Monitor: The ANTARCTICA Study
Bibliographic record
Abstract
Introduction: Atrial Fibrillation (AF) is detected in nearly 30% of patients undergoing cardiac monitoring after ischemic stroke. Studies investigating predictors of AF showed mixed results. In this study, we aim to identify predictors of AF on insertable cardiac monitors (ICMs) and compare rates between cryptogenic stroke patients and controls. Methods: The ANT icoagulation A nd St R oke Re C urrence in A T rial F I brillation Dete C ted A fter Stroke (ANTARCTICA) study is an individual patient data meta-analysis of prospective observational studies of cryptogenic ischemic stroke and control patients (non-cryptogenic ischemic stroke and non-ischemic stroke) who underwent an ICM implantation. The search included prospective observational studies and randomized controlled trials of patients with non-cardioembolic ischemic stroke or transient ischemic attack or non-ischemic stroke controls who underwent prolonged cardiac monitoring with an ICM after the index event. We performed multiple imputations to derive missing covariates such as left atrial volume index. We used multivariable multi-level logistic regression models to identify clinical, imaging, and echocardiographic factors associated with AF detection. We compared AF rates and charecterisctis between cryptogenic stroke and controls. Results: We identified 14 studies (2 RCTs and 12 observational) that included 2036 patients (1562 cryptogenic stroke and 474 non-cryptogenic stroke and non stroke controls); AF was detected in 30.7% of cryptogenic stroke patients and 29.1% of non-cryptogenic stroke patients. In multivariable logistic regression analyses, factors associated with AF were age (OR per year increase 1.05 95% CI 1.04-1.06), left atrial volume index (OR per unit increase 1.03 95% CI 1.02-1.05), and cryptogenic stroke (adjusted OR 1.89, 95% CI 1.20-2.98, p = 0.006). When compared to controls, the time to AF detection was significantly shorter in cryptogenic stroke (median 65 days vs. 169 days, p < 0.001) and AF duration was non-significantly longer (median 90 minutes vs. 120 minutes, p = 0.144). Results remained unchanged when the control group was limited to patients with non-cryptogenic ischemic stroke. Conclusions: In this large, individual patient data meta-analysis of patients undergoing ICM, there is increased detection and burden of AF after cryptogenic stroke compared to controls, suggesting a likely pathogenicity of device-detected AF in cryptogenic stroke.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".