Abstract 17022: The Gap Between Indicated and Prescribed Stroke Prevention Therapies in A High-risk Geriatric Population in Ontario
Bibliographic record
Abstract
Introduction: Atrial fibrillation (AF) is the most common cardiac dysrhythmia, with estimated risk of about 25% by 80 years of age. The use of oral anticoagulation (OAC) in the elderly population treated in long term care (LTC) facilities is inconsistent and poorly studied. Hypothesis: We performed a retrospective analysis to assess the magnitude and sources of the gap between indicated and prescribed use of OAC in the elderly with AF along with the prevalence of different therapies. Methods: We retrospectively scanned 25 LTC facilities in Ontario, Canada. The diagnosis of AF was drawn from electronic medical records. This was merged with a pharmacy database, which was the sole provider of all medications for each resident. Attributable risk factors for possible failure to prescribe use of OAC were: Advanced directives for no hospitalization, Do-Not-Resuscitate order, Dementia, Cognitive Performance Scale, Activities of Daily Living Self Performance Hierarchy scale and Changes in Health, End-Stage Disease, Signs, and Symptoms Scale. Results: In total 3378 active residents were examined in 25 LTC facilities. All patients were ≥ 65 years old with mean age 85 ± 8 years and 2449 (72%) were female. We identified 433 (13%) AF patients with mean age 87 ± 7 years and mean CHADS2 score 3 ± 1; all qualify for OAC therapy. Out of all AF patients, 273 (63%) patients were on OAC therapy. Patients were mostly treated with Vitamin K antagonists (N= 114 (42%)), rivaroxaban (N= 71 (26%)) or apixaban (N= 62 (23%)) followed by dabigatran (N= 26 (10%)). Antiplatelet drugs as the only stroke prevention therapy were used in 88 (20%) patients and 28 (6%) patients were on dual therapy (anticoagulation and antiplatelet drugs). Seventy-two (17%) patients were not on any antiplatelet or antithrombotic therapy. None of the attributable risks identified consistently correlated with the failure to prescribe indicated therapy. Conclusions: This real world data set suggests that 37% of eligible elderly LTC patients fail to receive recommended stroke prevention therapies. Vitamin K antagonists are the most common OAC therapy used in elderly patients in LTC facilities. Lack of use of indicated therapy appears to be idiosyncratic.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".