Changes in Adherence to Evidence-Based Medications in the First Year After Initial Hospitalization for Heart Failure
Bibliographic record
Abstract
BACKGROUND: The use of evidence-based medications in patients with heart failure has increased over the past 10 years. We aimed to determine whether adherence to these medications has also increased during this time. METHODS AND RESULTS: A retrospective cohort was created using administrative databases from the province of Saskatchewan, Canada. Subjects discharged alive from their first hospitalization for heart failure between 1994 and 2003 were eligible. Those filling a prescription for a beta-blocker (BB), angiotensin-converting enzyme inhibitor (ACEI), or angiotensin receptor blocker (ARB) within 6 months of discharge were followed for 1 year after the initial prescription. Of 8805 eligible patients, 67% of BB users (941/1414) and 74% of ACEI/ARB users (4441/5991) exhibited optimal adherence at 1 year (defined as >or=80% adherence calculated from pharmacy refill records). When grouped by year of initial heart failure hospitalization, the proportion of optimally adherent patients improved from 54% to 75% with BB and from 67% to 80% with ACEI/ARBs between 1994/1995 and 2002/2003 (P for trend <0.001 for both). Mean 1-year adherence improved from 71% to 83% for BB and 80% to 88% for ACEI/ARBs. After adjustment using multivariable logistic regression, subjects discharged in 2003 were significantly more likely to exhibit optimal adherence to a BB (odds ratio, 2.04; 95% CI, 1.21 to 3.44) or an ACEI/ARB (odds ratio, 1.65; 95% CI, 1.30 to 2.08) than those prescribed therapy in 1994/1995. CONCLUSIONS: One-year adherence to BB and ACEI/ARB is improving over time in patients discharged after first heart failure hospitalization. Patients taking multiple cardiac medications were not any less likely to exhibit optimal adherence than patients taking only 1 medication.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".