Abstract 97: Acute Ischemic Stroke Despite Anticoagulant Use: The Neuro-AFib Study
Bibliographic record
Abstract
Background: Acute ischemic stroke (AIS) admissions in patients with atrial fibrillation (AF) using anticoagulants (AIS-despite-AC) are commonly seen in stroke units but data on their frequency, causes, and outcomes are scarce. Methods: The Neuro-AFib study is a multicenter effort geared toward elucidating the causes and consequences of strokes in a contemporary AF cohort. Detailed clinical, laboratory and multimodal imaging data from known AF patients consecutively admitted to 20 stroke centers with an IS between 1/2018-12/2019 were used to define characteristics of AIS-despite-AC and compared to AIS-off-AC. Results: Out of 4456 patients with known AF prior to the AIS, 2051 (46%) were using anticoagulants. Patients who had AIS-despite-AC were younger (76.8 + 11 vs 77.8 + 12, p=0.007), had higher mean CHA 2 DS 2 -VASc scores (4.57 + 1.7 vs 4.21 + 1.7, p<0.001), and they were more likely to have permanent AF (23.5% vs 19.6%, p=0.002) and a past-history of stroke/TIA (36% vs 25%, p<0.001) when compared to non-AC group. Chronic embolic (non-lacunar) infarcts on imaging were more common among AIS-despite-AC (34.5% vs 26.7%, p<0.001). Acute large vessel occlusion (LVO) was common among AIS-despite-AC (48%). The pattern of the acute infarcts (numbers, embolic features) was not different. The calculated acute infarct volume was large (34ml) and mean NIHSS was 10.4 for AIS-despite-AC. Intravenous thrombolysis was used much less commonly for AIS-despite-AC (10.5% vs 32.1%, p<0.001). Symptomatic carotid disease was suspected in 2.4%, a hypercoagulable condition in 5.7%, whereas acute lacunar infarct was found in 6.5% of all patients. Death or significant disability (mRS 3-5) at hospital discharge were common in AIS on vs off AC (70.6% vs 75.2%, p<0.001). Conclusions: Based on a large multicenter cohort, AF patients who have AIS-despite-AC have higher AF load and risk scores, most don't have a concurrent etiology and they are more likely to have recurrent embolic events. The stroke severity, common presence of LVO, infarct size, and poor outcomes are alarming features, and most patients are unable to receive thrombolysis because of AC use. Overall, detection of AF patients at risk of AIS-despite-AC and development of specific preventive modalities should be priorities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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".