Stroke risk prediction in patients with atrial fibrillation with and without rheumatic heart disease
Bibliographic record
Abstract
AIMS: Patients with atrial fibrillation (AF) and rheumatic heart disease (RHD), especially mitral stenosis, are assumed to be at high risk of stroke, irrespective of other factors. We aimed to re-evaluate stroke risk factors in a contemporary cohort of AF patients. METHODS AND RESULTS: We analysed data of 15 400 AF patients presenting to an emergency department and who were enrolled in the global RE-LY AF registry, representing 47 countries from all inhabited continents. Follow-up occurred at 1 year after enrolment. A total of 1788 (11.6%) patients had RHD. These patients were younger (51.4±15.7 vs. 67.8±13.6 years), more likely to be female (66.2% vs. 44.7%) and had a lower mean CHA2DS2-VASc score (2.1±1.7 vs. 3.7±2.2) as compared to patients without RHD (all P<0.001). Significant mitral stenosis (average mean transmitral gradient 11.5±6.5 mmHg) was the predominant valve lesion in those with RHD (59.6%). Patients with RHD had a higher baseline rate of anticoagulation use (60.4% vs. 45.2%, P<0.001). Unadjusted stroke rates at 1 year were 2.8% and 4.1% for patients with and without RHD, respectively. The performance of the CHA2DS2-VASc score was modest in both groups [stroke at 1 year, c-statistics 0.69, 95% confidence interval (CI) 0.60-0.78 and 0.63, 95% CI 0.61-0.66, respectively]. In the overall cohort, advanced age, female sex, prior stroke, tobacco use, and non-use of anticoagulation were predictors for stroke (all P<0.05). Mitral stenosis was not associated with stroke risk (adjusted odds ratio 1.07, 95% CI 0.67-1.72, P=0.764). CONCLUSION: The performance of the CHA2DS2-VASc score was modest in AF patients both with and without RHD. In this cohort, moderate-to-severe mitral stenosis was not an independent risk factor for stroke.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 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".