Incidence of Acute Ischemic Stroke in Hospitalized Patients With Atrial Fibrillation Who Had Anticoagulation Interruption: A Retrospective Study
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) is one of the leading causes of acute ischemic stroke requiring anticoagulation. Many patients experience treatment interruption in the hospital setting. The aim of this study was to evaluate the effect of anticoagulation interruption on short-term risk of ischemic stroke in hospitalized patients with AF. Methods: We performed a retrospective medical record review using the Hospital Corporation of America (HCA) database. We included patients admitted to our institution between December 2015 and December 2018 who had a prior history of AF. Patients were excluded if they had ischemic stroke, hemorrhagic stroke, history venous thromboembolism or mechanical valve on admission. We compared the incidence of ischemic stroke in patients in whom anticoagulation was interrupted for more than 48 h to those who continued anticoagulation. Results: A total of 2,277 patients with history of AF were included in the study. In this cohort, 79 patients (3.47%) had anticoagulation interruption of more than 48 h during their hospital stay. There was no difference in incidence of stroke between the interruption and no interruption groups (1.27% (n = 1) vs. 0.23% (n = 5), P = 0.19). Interruption of anticoagulation did not associate with a significant increase in the risk of in-hospital ischemic stroke. CHA 2 DS 2 VASc score was a strong predictor of in-hospital stroke risk regardless of anticoagulation interruption (odds ratio: 7.199, 95% confidence interval: 2.920 - 17.751). Conclusion: In this study, the in-hospital incidence of ischemic stroke in patients with AF did not significantly increase by short-term anticoagulation interruption. Cardiol Res. 2021;12(4):225-230 doi: https://doi.org/10.14740/cr1263
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".