Evaluation of the use of evidence-based angiotensin-converting enzyme inhibitor criteria for the treatment of congestive heart failure: opportunities for pharmacists to improve patient outcomes
Bibliographic record
Abstract
BACKGROUND: The under-utilization and under-dosing of angiotensin-converting enzyme inhibitors (ACEIs) in patients with congestive heart failure (CHF) continues to be a problem observed in clinical practice. OBJECTIVE: To develop and implement drug use evaluation (DUE) criteria for the use of ACEIs in patients with CHF which could be used by pharmacists to ensure that all eligible patients receive an ACEI at an appropriate dose. METHODS: A retrospective chart review of all patients discharged from the study institution with a diagnosis of CHF during the period of March 1 to July 31, 1998 was conducted using the DUE criteria developed. RESULTS: Of the 138 patients evaluated, only 68.6% were discharged on ACEI therapy. Additionally, only 40% of those discharged on an ACEI achieved target dose. Multiple regression analysis revealed that males were 2.43 times more likely to be discharged on an ACEI than females, while those on concomitant diuretics or digoxin were less likely to be discharged on an ACEI (25% and 18%, respectively). CONCLUSIONS: The application of these DUE criteria by pharmacists in hospital and community practice has the potential to improve utilization and dosing of this important class of medications for the management of the symptoms and progression of CHF.
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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 | 0.053 | 0.204 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".