Abstract TP526: Can Electronic Alerts Improve Oral Anticoagulant Use in Elderly Atrial Fibrillation Patients?
Bibliographic record
Abstract
Background: Oral anticoagulant (OAC) for atrial fibrillation use remains underutilized despite evidence in favor of its utility. In a three-center study examining the efficacy of a technological intervention to improve OAC use, two sites were randomly selected to incorporate an embedded alert in the electronic health record (EHR) while the third site provided usual care. At the intervention sites, the EHR calculated each patient’s CHA 2 DS 2 -VASc score and alerted the clinician when OAC therapy was recommended. We aimed to investigate whether this system increased OAC use in elderly patients. Methods: Patient medication was tracked at the time of hospitalization, discharge, and within 30 days of discharge. Patients were categorized by age and study arm to assess medication use at last known follow-up via the Chi Square and Fisher’s Exact tests. Results: The control site contained 152 patients, 65 being 75+ years of age, while the two intervention sites contained 164 patients, also with 65 patients who were 75+ years of age. Those aged 75+ show statistically significant proclivity for prior strokes (<0.001), coronary artery disease (0.04), hypertension (0.004), and lower rate of diagnosed obstructive sleep apnea (0.001). Furthermore, they tend to be females (53.9%). The median CHA 2 DS 2 -VASC score was 4 in the elderly group and 2 in the younger group (p<0.001). Use of warfarin or OACs in these two populations did not vary at baseline. At follow-up, use of warfarin was statistically significantly higher in those 75+ (21.9% vs 12.8%, p-value=0.04) but not when partitioned by study arm or when all OAC use was considered. There was no difference in OAC use between the intervention and control sites (43% vs. 54% p=0.22). Conclusions: Despite increased CHA 2 DS 2 -VASc scores, we did not demonstrate the benefit of electronic alerts among elderly AF patients. Additional research is needed regarding methods to overcome therapeutic inertia in this area. Study support: Boehringer-Ingelheim
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".