Abstract WMP101: Potential Causes Of Anticoagulant Underuse In Patients With Atrial Fibrillation Presenting With Ischemic Strokes
Bibliographic record
Abstract
Background: Underuse of FDA-approved stroke prevention methods in atrial fibrillation (AF) remains a major problem. We aimed to explore the potential causes of oral anticoagulant (OAC) non-use and compare the frequency of these factors between AF patients with acute ischemic stroke (AIS) off and on oral anticoagulant (OAC). Methods: The Neuro-AFib study is a multicenter observational study aiming to clarify the causes of different stroke types in a contemporary AF cohort. Potential causes of OAC non-use were systematically collected from all enrolled patients based on the exclusion criteria of the Direct OAC (DOAC) studies. The frequency of potential causes of OAC non-use (AIS-off-OAC) are explored in AF patients consecutively admitted to 22 US academic stroke centers with an AIS between 1/2018-12/2019, and these rates are compared to AIS-on-OAC. Results: Among 4898 patients with known AF who had IS, 2694 (55%) were not using any OAC, and 45% were AIS-on-OAC. CHA2DS2-VASc <2, the cutoff representing low embolic risk until late 2019, was found in 7% of AIS-off-OAC group compared to 3.8% in AIS-on-OAC (p<0.0001). The most common factor in OAC non-use group was history of falls (26%) vs 18% in AIS-on-OAC group (p=0.004). History of bleeding [intracranial (2.3%) and extracranial (18.5%)] was found in 20.8% of AIS-off-OAC vs 10.4% of AIS-on-OAC group (p<0.0001). 82.6% of these hemorrhages were classified as major bleeds. Pre-stroke cognitive impairment was also common in AIS-off-OAC (21.7%). Renal failure (creatinine >2mg/dl) was found in 13% of AIS-off-OAC. Gait problems leading to limited mobility (10%) and excessive alcohol use (3.8%) were other potential factors for OAC non-use. Among OAC non-users, 64% had at least one risk factor defined above. Conclusions: In a large multicenter contemporary IS cohort with known AF, 55% of patients were not on OAC, and about two thirds of them had a reason that would exclude them from the phase 3 DOAC studies. Other than improving the accuracy of risk prediction algorithms, research should focus on identifying optimal management approaches for this large AF population who present challenges to lifelong OAC use. FDA-approved left atrial appendage closure procedures can be considered in such AF patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".