Hydralazine–Isosorbide Dinitrate Use in Patients With End-Stage Kidney Disease on Dialysis
Bibliographic record
Abstract
Introduction: The combination of hydralazine-isosorbide dinitrate (H-ISDN) has potential as a heart failure (HF) therapy in the setting of maintenance dialysis. Methods: In this retrospective study, we analyzed the efficacy of H-ISDN using United States Renal Data System (USRDS) data. We identified all adult patients with a history of HF on maintenance dialysis between January 1, 2011, and December 31, 2016, with at least 1 prescription for H-ISDN. Baseline characteristics, prescriptions, and outcomes were retrieved from institutional and physician claims. The primary outcome was death from any cause. Additional outcomes included cardiovascular death, sudden cardiac death, hospitalization for HF, an inpatient diagnosis of myocardial infarction (MI), or new-onset atrial fibrillation. Stabilized inverse probability weights were estimated using relevant baseline characteristics and were used in Cox proportional hazards regression. Results: We identified 6306 patients who were treated with H-ISDN and 75,509 patients who did not receive H-ISDN. The crude all-cause mortality rate was lower in patients treated with H-ISDN (16.0 events/100 patient years [PYs]) than in nonusers (27.9/100-PY). H-ISDN use was independently associated with lower mortality: hazard ratio (HR) 0.48 (95% CI 0.43-0.54). Cardiovascular death and sudden cardiac death were less common among H-ISDN users than nonusers, Weighted HR was 0.62 (95% CI 0.53-0.71) and 0.62 (95% CI 0.52-0.73), respectively. In contrast, HF admission and MI were more frequent in patients treated with H-ISDN (195.5 and 18.0 events/100-PY) compared with nonusers (73.4 and 10.2 events/100-PY). Conclusion: H-ISDN therapy may improve cardiovascular outcomes in maintenance dialysis patients with HF.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.004 | 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".