Comparison of Quadruple Therapy Sequencing Strategies for Heart Failure With Reduced Ejection Fraction
Bibliographic record
Abstract
BACKGROUND: Guidelines unanimously recommend quadruple therapy for patients with heart failure with reduced ejection fraction (HFrEF), yet direct comparative evidence is lacking to recommend a specific order of initiating and titrating these agents (or "sequencing strategy"). In the absence of randomized evidence, we aimed to compare the 1-year efficacy and harms of proposed HFrEF quadruple therapy sequencing strategies using a microsimulation model. METHODS: We conducted a microsimulation study to compare 6 different HFrEF medication sequencing strategies (emulating the range of conventional, 2-drug, and rapid or simultaneous strategies), each with 2 versions (twice-weekly/weekly medication adjustments), applied to treatment-naïve outpatients with HFrEF. We modeled death; total heart failure hospitalization (including recurrent events); the composite of death or first heart failure hospitalization; and adverse drug events (bradycardia, hyperkalemia, hypotension, and renal impairment) at 1 year. RESULTS: At 1 year, an estimated 15.3 per 100 patients died without treatment compared with 6.9 per 100 patients with the conventional sequence adjusted biweekly, and 5.2 to 6.4 per 100 patients with other sequencing strategies. The incidence of heart failure hospitalization decreased from 31.5 per 100 patients without treatment to 11.4 per 100 patients with conventional sequencing adjusted biweekly, and 7.3 to 9.4 per 100 patients with other strategies. The cumulative incidence of total adverse drug events per 100 patients was 14.2 to 16.0 across sequencing strategies. CONCLUSIONS: For treatment-naïve outpatients with HFrEF, sequencing strategies that started 2 to 4 medications on the first visit, followed by titration biweekly or weekly, were associated with lower risk of death and heart failure hospitalization, and similar risk of treatment-related adverse events, at 1 year compared with conventional sequencing.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".