Evaluation of Prescribing Adherence to Guideline-Directed Medical Therapy in Patients with Chronic Heart Failure. A Retrospective Study at The National Heart Centre in Oman
Bibliographic record
Abstract
Purpose: Guidelines-directed medical therapy (GDMT) may benefit patients with heart failure (HF) reduced ejection fraction ((HFrEF, ≤40%), however, there are significant gaps between guidelines and real-world practices. The aim of this study was to evaluated prescribing adherence to the recommended GDMT in HFrEF patients using the global guideline adherence score. Method: A retrospective study among HFrEF patients at the National Heart Centre in Muscat, Oman, was conducted between 1st January and 30th June 2022. The optimum target doses were identified according to the 2021 European Society of Cardiology HF guidelines. Thus, for eligible patients, prescribing all indicated GDMT in doses ≥50% of the target dose is considered good adherence; the use of more than half of the medications in doses ≥50% of the target dose, moderate adherence; and the use of less than half the recommended medications and/or in doses <50% of target dose, poor adherence. Univariate statistics were used for the analysis. Results: The overall mean age of the cohort was 57 ±13.6 years with a predominance of male patients (70%; n=180). The overall prescribing adherence to guideline-recommended HF medications was 71% good, 22% moderate, and 7% poor. There was a significate association between the sub-optimal dose of GDMT and patients with hypertension (P=0.004), dilated cardiomyopathy (P=0.015), older age (P=0.004) and chronic kidney disease (P=0.001). Conclusion: Prescribing adherence to recommended GDMT in Oman is similar to that of international studies. Furthermore, sub-optimal GDMT titration was significantly associated with older age and comorbidity, suggesting that frailty perception may have an impact on GDMT titration.
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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) | 0.001 | 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".