Infarct Topography and Detection of Atrial Fibrillation in Cryptogenic Stroke: Results from CRYSTAL AF
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Bibliographic record
Abstract
BACKGROUND: Insertable cardiac monitors (ICM) have been shown to detect atrial fibrillation (AF) at a higher rate than routine monitoring methods in patients with cryptogenic stroke (CS). However, it is unknown whether there are topographic patterns of brain infarction in patients with CS that are particularly associated with underlying AF. If such patterns exist, these could be used to help decide whether or not CS patients would benefit from long-term monitoring with an ICM. METHODS: In this retrospective analysis, a neuro-radiologist blinded to clinical details reviewed brain images from 212 patients with CS who were enrolled in the ICM arm of the CRYptogenic STroke And underLying AF (CRYSTAL AF) trial. Kaplan-Meier estimates were used to describe rates of AF detection at 12 months in patients with and without pre-specified imaging characteristics. Hazard ratios (HRs), 95% confidence intervals (CIs), and p values were calculated using Cox regression. RESULTS: We did not find any pattern of acute brain infarction that was significantly associated with AF detection after CS. However, the presence of chronic brain infarctions (15.8 vs. 7.0%, HR 2.84, 95% CI 1.13-7.15, p = 0.02) or leukoaraiosis (18.2 vs. 7.9%, HR 2.94, 95% CI 1.28-6.71, p < 0.01) was associated with AF detection. There was a borderline significant association of AF detection with the presence of chronic territorial (defined as within the territory of a first or second degree branch of the circle of Willis) infarcts (20.9 vs. 10.0%, HR 2.37, 95% CI 0.98-5.72, p = 0.05). CONCLUSIONS: We found no evidence for an association between brain infarction pattern and AF detection using an ICM in patients with CS, although patients with coexisting chronic, as well as acute, brain infarcts had a higher rate of AF detection. Acute brain infarction topography does not reliably predict or exclude detection of underlying AF in patients with CS and should not be used to select patients for ICM after cryptogenic stroke.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it