Medical Therapy Doses at Hospital Discharge in Patients with Existing and <i>De Novo</i> Heart Failure
Bibliographic record
Abstract
AIMS: Uptitrating angiotensin-converting enzyme inhibitors or angiotensin receptor blockers (ACE-I/ARBs), beta-blockers, and mineralocorticoid receptor antagonists (MRAs) to optimal doses in heart failure with reduced ejection fraction (HFrEF) is associated with improved outcomes and recommended in guidelines. Studies of ambulatory patients found that a minority are prescribed optimal doses. However, dose at hospital discharge has rarely been reported. This information may guide quality improvement initiatives during and following discharge. METHODS AND RESULTS: We assessed 370 consecutive patients with HFrEF hospitalized at two centres in Vancouver, Canada. Of those without contraindications, 86.4%, 93.4%, and 44.7% were prescribed an ACE-I/ARB/sacubitril-valsartan, beta-blocker, or MRA, respectively. The proportion of eligible patients prescribed target dose was respectively 28.6%, 31.7%, and 4.1%. Forty-two of 248 eligible patients (16.9%) were prescribed ≥50% of target dose, and only three patients received target dosing of all three medication classes. In multivariate regression models, cardiologist involvement in care was independently associated with increased dose and prescription of ≥50% of target dose for all medications, whereas a history of HF was only predictive for beta-blockers. CONCLUSIONS: In a single-region experience of hospitalized HFrEF patients, a high proportion of eligible patients were discharged on ACE-I/ARB or beta-blocker. Less than half were prescribed MRAs, and few were prescribed ≥50% or target dosing of all medications. Further exploration into barriers to medication uptitration, and improvement in processes of care, is needed.
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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 | 0.000 | 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 | 0.001 | 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".