Outcomes of guideline-based medical therapy in patients with acute heart failure and reduced left ventricular ejection fraction
Bibliographic record
Abstract
ABSTRACT: This study aimed to report on the use, predictors and outcomes of guideline-based medical therapy (GBMT) in patients with acute heart failure (HF) with reduced ejection fraction of <40% (HFrEF), from seven countries in the Arabian Gulf.Patients with acute HFrEF (N = 2680), aged 18 years or older, and hospitalized February-November 2012 were recruited and data were collected post discharge at 3 months (n = 2477) and 1 year (n = 2418). The use and doses of GBMT were evaluated as per European, American and Canadian HF guidelines. Analyses were performed using multivariate logistic regression. This study was registered at clinicaltrials.gov (NCT01467973).The majority of patients were on dual (39%) and triple (39%) GBMT modalities, 14% received one GBMT medication, while 7.2% were not on any GBMT medications. On admission, 80% of patients were on renin-angiotensin system (RAS) blockers, 75% on b-blockers and 56% on mineralocorticoid receptor antagonists (MRAs), with a small proportion of these patients were taking target doses (RAS blockers 13%, b-blockers 7.3%, MRAs 14%). Patients taking triple GBMT were younger (P < .001), less likely to have comorbidities such as diabetes mellitus (P < .001) and CKD/dialysis (P < .001), less likely to receive in-hospital invasive treatments (P < .001), and more likely to be treated by a cardiologist (P < .001), than patients on a single medication. Patients taking triple GBMT showed significantly reduced all-cause mortality both at 3-months (P = .048), and at 12-months (P = .003), compared to patients taking no GBMT.Triple GBMT prescribing and dosing in patients with HFrEF were suboptimal in the Arabian Gulf. Further studies are required to investigate GBMT utilization and dosing in the outpatient setting.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".