Better Respiratory Function in Heart Failure Patients With Use of Central-Acting Therapeutics
Bibliographic record
Abstract
Background Diaphragm atrophy can contribute to dyspnea in patients with heart failure (HF) with its link to central neurohormonal over-activation. HF medications that cross the blood-brain barrier could act centrally and improve respiratory function potentially alleviating diaphragmatic atrophy. Therefore, we compared the benefit of central- vs peripheral-acting HF drugs on respiratory function as assessed by a single cardiopulmonary exercise test (CPET) and outcomes in HF patients. Methods A retrospective study of 624 (80% males) ambulatory adult HF patients with reduced left ventricular ejection fraction <40% and a complete CPET, followed at a single institution between 2001-2017. CPET parameters, and the outcomes all-cause death, a composite endpoint (all-cause death, need for left ventricular assist device, heart transplantation), and all-cause/HF-hospitalizations, were compared in patients receiving central-acting (n=550) vs peripheral-acting (n=74) drugs. Results Compared to peripheral-acting drugs, patients that receive central-acting drugs had better respiratory function (peak VO 2 ,p=0.020, FEV1,p=0.007), and ventilatory efficiency (VE/VCO 2 ,p<0.001, PETCO 2 ,p=0.015, trend for FVC,p=0.056). Many of the associations between the CPET parameters and drug type remained significant after multivariate adjustment. Moreover, patients receiving central-acting drugs had fewer composite events (p=0.023), and HF-hospitalizations (p=0.044), though, significance after multivariant correction was not achieved despite the hazard ratio being 0.664 and 0.757, respectively. Conclusion Central-acting drugs were associated with better respiratory function as measured by CPET parameters in HF patients. This could extend to clinically meaningful composite outcomes and hospitalizations but required more power to be definitive in linking to drug effect. Central-acting HF drugs show a role in mitigating diaphragm weakness.
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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 | 0.001 |
| 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) | 0.002 | 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".