ECG predictors of AF: a systematic review (Predicting AF in Ischaemic Stroke-PrAFIS)
Bibliographic record
Abstract
Abstract In 25% of patients presenting with embolic stroke, a cause is not determined. Atrial fibrillation (AF) is a commonly identified mechanism of stroke in this population, particularly in older patients. Conventional investigations are used to detect AF, but can we predict AF in this population and generally? We performed a systematic review to identify potential predictors of AF on 12-lead electrocardiogram (ECG). Method We conducted a search of EMBASE and Medline databases for prospective and retrospective cohorts, meta-analyses or case-control studies of ECG abnormalities in sinus rhythm predicting subsequent atrial fibrillation. We assessed quality of studies based on the Newcastle-Ottawa scale and data were extracted according to PRISMA guidelines. Results We identified 42 studies based on our criteria. ECG patterns that predicted the risk of developing AF included interatrial block, P-wave terminal force lead V1, P-wave dispersion, abnormal P-wave-axis, abnormal P-wave amplitude, prolonged PR interval, left ventricular hypertrophy, QT prolongation, ST-T segment abnormalities and atrial premature beats. Furthermore, we identified that factors such as increased age, high CHADS-VASC, chronic renal disease further increase the positive-predictive value of some of these parameters. Several of these have been successfully incorporated into clinical scoring systems to predict AF. Conclusion There are several ECG abnormalities that can predict AF both independently, and with improved predictive value when combined with clinical risk factors, and if incorporated into clinical risk scores. Improved and validated predictive models could streamline selection of patients for cardiac monitoring and initiation of oral anticoagulants.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".