Effect of Co-Morbidities on Anticoagulation Rates Among Chronic Atrial Fibrillation Patients
Bibliographic record
Abstract
P50 Background: Anticoagulation (AC) with warfarin among patients with chronic atrial fibrillation (AF) continues to be underutilized despite proven effectiveness in stroke prevention and mortality reduction. The increased benefits of AC among patients with specific co-morbidities is also well established. However, concerns about negative health outcomes may be a barrier to AC in patients with co-morbidities. This study examines the effect of co-morbidities on AC rates. Methods: A random sample of 2,450 California Medicare fee-for-service inpatient records with a discharge date in 1998 and a diagnosis of AF was selected. We analyzed rates of co-morbidities associated with an increased risk for stroke among chronic AF patients (congestive heart failure (CHF), angina pectoris, myocardial infarction, diabetes, hypertension, and prior stroke/TIA). AC rates were analyzed by chi-square and multivariate logistic regression. Results: Data was available for 2369 records (96.7%). Cases were excluded for contraindications to AC (n=731), non-chronic AF (n=717), death (n=200) and age < 65 years (n=24). After exclusions, there were 697 (29.4%) cases for analysis. The majority of patients with chronic AF (56.8%, 396/697) were not prescribed warfarin at discharge. Crude rates of co-morbidities were: hypertension 70%, angina pectoris 63%, CHF 47%, myocardial infarction 37%, prior stroke/TIA 27%, diabetes 25%. Almost all patients (94%) had at least one co-morbidity. Multivariate analysis controlling for age, gender, and all co-morbidities identified prior stroke/TIA (OR 2.1, p<.01) and CHF (OR 1.9, p<.01) to be associated with increased AC rate. Conclusion: Although it is encouraging that AC rates were higher in high risk groups with prior stroke/TIA and CHF, prescription rates were still only about 50%, indicating many missed opportunities for stroke prevention. The markedly high prevalence of all co-morbidities in a randomized sample highlights the importance of continued education emphasizing the increased benefits of AC in patients with co-morbidities.
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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 | 0.002 | 0.012 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".