Abstract WP264: Early Rehospitalization for Stroke Higher With Aspirin or No Antithrombotic Therapy Than With Oral Anticoagulation in an Unselected Population of Patients With Atrial Fibrillation
Bibliographic record
Abstract
Introduction: Rehospitalization after discharge for a wide variety of medical conditions is common and is associated with increased morbidity and cost. We investigated the rates of rehospitalization for stroke in a population of patients hospitalized with atrial fibrillation (AF) who were at risk of stroke. Methods: Using the Premier Healthcare Database (representing 1 in 5 US hospital discharges), we identified 1,147,866 patients hospitalized from January 1, 2011 through June 30, 2015 with AF and a guideline indication for an oral anticoagulant based on a CHA 2 DS 2 -VaSC score ≥ 2. The primary outcome was readmission within 90 days due to acute ischemic stroke (AIS) or intracranial hemorrhage (ICH). Multivariable logistic regression was used to compare rehospitalization rates for patients receiving various oral antithrombotic therapies, while controlling for numerous patient risk factors, reasons for current hospitalization, and hospital characteristics. Confidence intervals and p-values were adjusted for multiple comparisons using Holm-Bonferroni. Results: Patients were 53% female with a mean age of 77 (±11) years. The distribution of patients on various antithrombotic agents at discharge was 23% with no therapy, 20% ASA only, 1% dual-antiplatelet therapy (DAPT), 17% non-vitamin K oral anticoagulants (NOAC), and 39% warfarin. Table 1 displays rehospitalization rates, unadjusted (therapy only), and adjusted for all covariates using logistic regression analysis. Conclusions: AF patients discharged on ASA alone or no antithrombotic therapy, compared to warfarin, were more likely to have early rehospitalization for acute ischemic stroke. Patients treated with NOACs had early ischemic stroke rehospitalization rates similar to warfarin, but were less likely to be have early rehospitalization for ICH when compared to warfarin. Table 1: 90-Day Rehospitalization Rates by Antithrombotic Agents
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".