Impact of Digoxin Use on Guideline-Directed Medical Therapy in Patients With Heart Failure With Reduced Ejection Fraction
Bibliographic record
Abstract
Background: Digoxin was one of the first agents used in the management of heart failure with reduced ejection fraction (HFrEF). Concerns over its safety, efficacy, and the introduction of guideline-directed medical therapy (GDMT) have relegated it to a secondary role. The efficacy of digoxin is still under debate, and its use in patients on GDMT remains unclear. We aim to evaluate whether patients with HFrEF on digoxin can tolerate higher doses of a β-blocker (BB), angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blocker (ARB), mineralocorticoid receptor antagonists (MRAs), and angiotensin receptor-neprilysin inhibitor (ARNI). Methods: A retrospective chart review was performed on 233 patients with HFrEF managed at a tertiary care center in Cleveland, Ohio. A bivariate analysis was performed to compare patients on digoxin with patients not on digoxin in terms of ability to progress the dosing of BB, ACEI, MRA, ARB, or ARNI. Results: Thirty-four (14.6%) of our 233 patients were receiving digoxin at baseline visit. The digoxin group was more likely to have lower initial and last systolic blood pressure, initial diastolic blood pressure, and left ventricular ejection fraction. Mean follow-up duration and baseline sodium level were higher in the digoxin group. There was no significant difference between the two groups in terms of patients receiving higher doses of BB (P = 0.235), ACEI/ARB (P = 0.903), MRA (P = 0.331), or ARNI (P = 0.717). Conclusions: There was no significant difference between the doses of BB, ACEI, ARB, MRA, or ARNI among HFrEF patients on digoxin compared to those that were not. Randomized control trials with a larger sample are needed to establish our findings of digoxin not significantly affecting the ability to up titrate GDMT in HFrEF patients.
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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 | 0.004 |
| 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".