Device-Detected Atrial Fibrillation in Patients With and Without Cryptogenic Ischemia: The ANTARCTICA Pooled Analysis
Bibliographic record
Abstract
BACKGROUND: Insertable cardiac monitoring (ICM) detects atrial fibrillation (AF) in substantial proportions of cryptogenic stroke, noncryptogenic ischemic stroke without known AF, and nonstroke patients who are at risk of underlying AF. Given differences in patient characteristics across studies, there may be differences in AF detection rates on ICM across these subgroups that have not been identified. We investigate whether AF detection rates on ICM are higher in cryptogenic stroke or transient ischemic attack (C-IS/TIA) patients compared with individuals with noncryptogenic stroke or without stroke, when accounting for differences in study populations. METHODS: This is an individual-participant data meta-analysis of prospective studies and randomized controlled trials of ICM in C-IS/TIA, noncryptogenic ischemic stroke, and nonstroke patients. Multilevel multivariable logistic regression models were used to test whether C-IS/TIA is associated with increased AF detection relative to other categories. We performed multiple imputation to derive values for variables with <20% missing data and used Rubin’s rules to estimate adjusted odds ratios by combining 100 postimputation data sets. The primary outcome was detection of AF. The attributable risk was derived by application of Bayes’ Theorem. RESULTS: Two randomized controlled trials and 12 prospective studies were included with a total of 1562 C-IS/TIA patients and 474 non-C-IS/TIA patients. In adjusted multilevel logistic regression analyses, AF detection was higher in C-IS/TIA patients (adjusted odds ratio, 1.90 [95% CI, 1.18–3.06]; P =0.009), indicating that 47% of AF detected in C-IS/TIA is pathogenic. Limiting the comparator group to ischemic stroke or history of stroke yielded similar results (adjusted odds ratio, 2.83 [95% CI, 1.47–5.44]; P =0.002). Days to AF detection were significantly shorter in C-IS/TIA patients (median 65 versus 169; P <0.001). CONCLUSIONS: In this individual-participant data meta-analysis of patients undergoing ICM, AF detection was higher in C-IS/TIA patients, with shorter time to AF detection compared with noncryptogenic/nonstroke individuals. These findings suggest that some of the AF detected in patients with C-IS/TIA may be pathogenic.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.045 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".