Although non-stroke outcomes are more common, stroke risk scores can be used for prediction in patients with atrial fibrillation
Bibliographic record
Abstract
Background We investigated whether cardiovascular outcome patterns differ across atrial fibrillation (AF) subgroups defined by age, valvular status, newly diagnosed vs. prevalent cases, or anticoagulation status, and whether stroke risk models can accurately predict non-stroke outcomes. Methods and results We performed a retrospective cohort study of all 147,952 adults with AF in Alberta, Canada between January 2008 and March 2014: 23,095 (15.6%) had at least one thromboembolic event (stroke, TIA, or systemic embolism) and 52,618 (35.6%) had a non-stroke major adverse cardiovascular events (NS-MACE = all-cause mortality, new heart failure, new acute coronary syndrome) during follow-up (median 46 months). NS-MACE were 2–3 times more frequent than stroke in all subgroups. Newly diagnosed patients had higher rates of all outcomes in the first year than those with prevalent AF (and those with valvular AF had the highest rates): incident vs. prevalent NS-MACE rates per 100 patient years were 53.1 vs. 23.2 for anticoagulated valvular AF patients, 32.8 vs. 11.0 for non-anticoagulated NVAF patients, and 29.6 vs. 14.6 for anticoagulated NVAF patients. In non-anticoagulated NVAF patients, the stroke risk models exhibited similar accuracy for prediction of NS-MACE as they did for stroke prediction: C-statistics 0.66 [0.66–0.66] vs. 0.67 [0.66–0.68] for ATRIA-STROKE, 0.66 [0.66–0.67] vs. 0.62 [0.61–0.62] for CHADS 2 , and 0.62 [0.61–0.62] vs. 0.52 [0.51–0.52] for CHA 2 DS 2 -VASc. Conclusions Non-stroke cardiovascular outcomes are more common than stroke in all AF subgroups but current stroke risk scores exhibit similar (modest) ability to predict risk for NS-MACE as for stroke, allowing identification of high-risk individuals for intervention.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".