Use of Warfarin at Discharge Among Acute Ischemic Stroke Patients With Nonvalvular Atrial Fibrillation in China
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Guidelines recommend oral anticoagulation for ischemic stroke patients with atrial fibrillation, and previous studies have shown the underuse of anticoagulation for these patients in China. We sought to explore the underlying reasons and factors that currently affect the use of warfarin in China. METHODS: From June 2012 to January 2013, 19 604 patients with acute ischemic stroke were admitted to 219 urban hospitals voluntarily participating in the China National Stroke Registry II. Multivariable logistic regression models using the generalized estimating equation method were used to identify patient/hospital factors independently associated with warfarin use at discharge. RESULTS: Among the 952 acute ischemic stroke patients with nonvalvular atrial fibrillation, 19.4% were discharged on warfarin. The risk of bleeding (52.8%) and patient refusal (31.9%) were the main reasons for not prescribing anticoagulation. Larger/teaching hospitals were more likely to prescribe warfarin. Older patients, heavy drinkers, patients with higher National Institutes of Health Stroke Scale score on admission were less likely to be given warfarin, whereas patients with history of heart failure and an international normalized ratio between 2.0 and 3.0 during hospitalization were significantly associated with warfarin use at discharge. CONCLUSIONS: The rate of warfarin use remains low among patients with ischemic stroke and known nonvalvular atrial fibrillation in China. Hospital size and academic status together with patient age, heart failure, heavy alcohol drinking, international normalized ratio in hospital, and stroke severity on admission were each independently associated with the use of warfarin at discharge. There is much room for improvement for secondary stroke prevention in nonvalvular atrial fibrillation patients in China.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".