Impact of missed treatment opportunities on outcomes in hospitalised patients with heart failure
Bibliographic record
Abstract
Objective: Many patients with heart failure (HF) do not receive recommended treatments, resulting in suboptimal outcomes. We aimed to investigate the impact of implementing recommended HF therapies on health outcomes, and the costs and effectiveness of interventions for improving adherence. Methods: The health benefits of ACE inhibitor (ACEi), beta blockers and optimal therapy (ACEi and beta blockers if not contraindicated) following hospitalisation for HF were combined with evidence on uptake. The aim was to examine how much health was lost as a result of failure to follow guidelines, and how much could be gained using strategies to promote uptake.The net health benefits of different treatments (measured in quality-adjusted life-years (QALY)) were estimated using a decision-analytic model and treatment effectiveness from the literature. Data on the number of patients who would have benefitted from the additional treatments were estimated from 2010 to 2013 using the National Heart Failure Audit. Results: Each recommended treatment was associated with positive net health benefit. In 2010, up to 4019 (38.3%) patients would have benefitted from additional treatments rising to 4886 patients in 2013 (although falling to 25.2% of patients). Failure to follow guidelines resulted in large health losses. In 2010, if all patients had received optimal therapy, 1569 QALYs would have been gained, implying a maximum justifiable investment in interventions to promote uptake of £31.4 million. Conclusion: Current gaps in translation of evidence to practise in hospitals are associated with significant health losses. Strategies to encourage uptake of guidelines could be effective and cost-effective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".