Atrial Cardiopathy in the Absence of Atrial Fibrillation Increases Risk of Ischemic Stroke, Incident Atrial Fibrillation, and Mortality and Improves Stroke Risk Prediction
Bibliographic record
Abstract
Background Atrial fibrillation ( AF ) is a major, often undetected, cardiac cause of stroke. Markers of atrial cardiopathy, including left atrial enlargement ( LAE ) or excessive atrial ectopy ( EAE ) increase the risk of AF and have shown associations with stroke. We sought to determine whether these markers improve stroke risk prediction beyond traditional vascular risk factors (eg CHA 2 DS 2 ‐ VAS c score). Methods and Results Retrospective longitudinal cohort of 32 454 consecutive community‐dwelling adults aged ≥65 years referred for outpatient echocardiogram or Holter in Ontario, Canada (2010–2017). Moderate‐severe LAE was defined as men >47 mm and women >43 mm, and EAE was defined as >30 APB s per hour. Cause‐specific competing risks Cox proportional hazards used to estimate risk of ischemic stroke (primary), incident AF , and death (secondary). C‐statistics, incremental discrimination improvement and net reclassification were used to compare CHA 2 DS 2 ‐ VAS c with LAE and EAE to CHA 2 DS 2 ‐ VAS c alone. Each 10 mm increase in left atrial diameter increased 2‐ and 5‐year adjusted cause‐specific stroke hazard almost 2‐fold ( LAE : 2‐year hazard ratio (HR), 1.72; P =0.007; 5‐year HR , 1.87; P <0.0001), while EAE showed no significant associations with stroke (2‐year HR , 1.00; P =0.99; 5‐year HR, 1.08, P =0.70), adjusting for incident AF . Stroke risk estimation improved significantly at 2 (C‐statistics=0.68–0.75, P= 0.008) and 5 years (C‐statistics=0.70–0.76, P =0.003) with LAE and EAE . Conclusions LAE was independently associated with an increased risk of ischemic stroke in the absence of AF and both LAE and EAE improved stroke risk prediction. These findings have implications for stroke risk stratification, AF screening, and stroke prevention before the onset of AF .
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".