DOPPS data suggest a possible survival benefit of renin angiotensin-aldosterone system inhibitors and other antihypertensive medications for hemodialysis patients
Bibliographic record
Abstract
The benefits of renin angiotensin-aldosterone system inhibitors (RAASi) are well-established in the general population, particularly among those with diabetes, congestive heart failure (CHF), or coronary artery disease (CAD). However, conflicting evidence from trials and concerns about hyperkalemia limit RAASi use in hemodialysis patients, relative to other antihypertensive agents, including beta blockers and calcium channel blockers. Therefore, we investigated prescription patterns and associations with mortality for RAASi and other antihypertensive agents using data from the international Dialysis Outcomes and Practice Patterns Study (DOPPS). Cox regression was used to estimate the effect of the prescription of RAASi and other antihypertensive agents at study entry on mortality in 11,421 incident (120 days or less) hemodialysis and 37,124 prevalent (over 120 days) hemodialysis patients from DOPPS phases 2-5 (2002-2015). Over 95% of RAASi were angiotensin-converting enzyme inhibitors or angiotensin receptor blockers. RAASi prevalence was 39% and varied minimally by CHF and CAD. The adjusted hazard ratio for RAASi (vs. no RAASi) was 0.89 (95% confidence interval 0.80-0.99) among incident and 0.94 (0.90-0.99) among prevalent hemodialysis patients, with no convincing evidence of interaction with diabetes, CAD or CHF. Inverse associations with mortality were also observed for beta blockers and calcium channel blockers, and were stronger for angiotensin receptor blockers than angiotensin-converting enzyme inhibitors, but this latter finding requires further study. Thus, our observations suggest a relatively small survival benefit of RAASi and other antihypertensive agents in hemodialysis patients, though randomized prospective studies are needed to potentially change prescribing criteria.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".