Adherence to medication in patients with heart failure : effect on mortality and hospitalization
Bibliographic record
Abstract
Heart failure is a chronic condition that increases the risk for death and disability. Beta blockers and ACE inhibitors have become standard treatments in heart failure because clinical trials have demonstrated their beneficial effect on mortality and morbidity in these patients. As not much is known about adherence to these medications, the main objectives of this project were to determine long term adherence to ACE inhibitors and beta blockers and determine how various degrees of adherence to a beta blocker can affect major health outcomes in patients with heart failure. Data was obtained from Saskatchewan health from January 1, 1994 to December 31, 2003 for all heart failure patients from their first hospitalization for heart failure. Adherence was calculated using the fill frequency measure of adherence, and all survival analyses were completed using the Cox proportional hazards model.Although 14, 000 patients were admitted to hospital for a first admission for heart failure, only 1143 subjects started a beta blocker and 5084 subjects started an ACE inhibitor within 3 months of the index hospitalization. Within the first year, adherence was excellent for both beta blockers (80.8 percent) and ACE inhibitors (82.5 percent). The proportion of patients remaining adherent slowly decreased to reach approximately 60 percent, for both medication classes, after 4 years. There was no significant difference in all-cause mortality between patients with high adherence and low adherence, but there appeared to be a trend towards decreased survival time in those remaining adherent throughout the study period [HR = 1.18 (95% CI: 0.98 to 1.43; p=0.07)].Since the overall rate of adherence to beta blockers was excellent in most patients during the first year, it is possible that non-adherence is not responsible for a significant burden of mortality in Saskatchewan heart failure patients, and perhaps and the focus of quality improvement should be optimal prescribing of evidence-based therapies, and continued adherence over time.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".