Effect of Chronic Digoxin Use on Mortality and Heart Failure Hospitalization in Pulmonary Arterial Hypertension
Bibliographic record
Abstract
Background Digoxin acutely increases cardiac output in patients with pulmonary arterial hypertension (PAH) and right ventricular failure; however, the effects of chronic digoxin use in PAH are unclear. Methods and Results Data from the Minnesota Pulmonary Hypertension Repository were used. The primary analysis used likelihood of digoxin prescription. The primary end point was a composite of all-cause mortality or heart failure (HF) hospitalization. Secondary end points included all-cause mortality, HF hospitalization, and transplant-free survival. Multivariable Cox proportional hazards analyses determined the hazard ratios (HR) and 95% CIs for the primary and secondary end points. Among 205 patients with PAH in the repository, 32.7% (n=67) were on digoxin. Digoxin was more often prescribed to patients with severe PAH and right ventricular failure. After propensity score-matching, 49 patients were digoxin users, and 70 patients were nonusers; of these 31 (63.3%) in the digoxin group and 41 (58.6%) in nondigoxin group met the primary end point during a median follow-up time of 2.1 (0.6-5.0) years. Digoxin users had a higher combined all-cause mortality or HF hospitalization (HR, 1.82 [95% CI, 1.11-2.99]), all-cause mortality (HR, 1.92 [95% CI, 1.06-3.49]), HF hospitalization (HR, 1.89 [95% CI, 1.07-3.35]), and worse transplant-free survival (HR, 2.00 [95% CI, 1.12-3.58]) even after adjusting for patient characteristics and severity of PAH and right ventricular failure. Conclusions In this retrospective, nonrandomized cohort, digoxin treatment was associated with greater all-cause mortality and HF hospitalization, even after multivariate correction. Future randomized controlled trials should assess the safety and efficacy of chronic digoxin use in PAH.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".