Electrocardiographic predictors of atrial fibrillation in patients with cryptogenic stroke
Bibliographic record
Abstract
BACKGROUND: Empiric anticoagulation is not routinely indicated in patients with cryptogenic stroke without documentation of atrial fibrillation (AF). Therefore, identification of patients at increased risk of AF from this vulnerable group is vital. OBJECTIVES: To identify electrocardiographic (ECG) predictors of AF in patients with cryptogenic stroke or transient ischemic attack (TIA) undergoing insertion of an implantable cardiac monitor (ICM). METHODS: In this single-center study, 48 patients with cryptogenic stroke or TIA had an ICM implanted for detection of AF between January 2013 and September 2019. Patients with and without AF were compared in terms of p-wave duration and a novel index (MVP score). RESULTS: During a mean follow-up of 16 ± 14 months, AF was detected in seven patients (15%). Diagnosis of AF was made after a mean of 10 ± 14 months, with time to first AF detection ranging between 1 and 40 months. Patients with AF had a longer p-wave duration (136 ± 9 ms vs. 116 ± 10 ms; p = .0001) and a higher MVP score (4.5 ± 1.2 vs. 2.0 ± 0.9, p = .0001) than those without AF. Advanced interatrial block (IAB) was observed in 43% of patients with ICM evidence of AF and 0% of those without AF (p = .002). Age, LA size or LVEF were not predictors of AF. CONCLUSION: An increased p-wave duration, advanced IAB and high MVP score are associated with AF occurrence in patients with cryptogenic stroke. Identifying patients with these markers may be helpful as they may benefit from more exhaustive and prolonged monitoring.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".