Angiotensin-Converting Enzyme Inhibitors and Angiotensin Receptor Blockers in Peritoneal Dialysis: Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) are widely used in clinical practice. The safety and efficacy of these agents in peritoneal dialysis (PD) patients are unclear. OBJECTIVES: We conducted a systematic review to study the safety and efficacy of ACEI and ARB use in PD patients. Primary outcome measures were mortality and cardiovascular (CV) events; secondary outcome measures were renal function, proteinuria, hyperkalemia, and erythropoietin requirement at 3 months. METHODS: We searched Medline, EMBASE, Cochrane Central Register of Controlled Trials, trial registry Web sites, reference lists of eligible and review articles, as well as abstracts from the American Society of Nephrology and Canadian Society of Nephrology meetings. To be eligible, studies had to be randomized controlled trials that allocated PD patients to ACEI and ARB use or to placebo or other antihypertensive medications, included adult patients, and reported on at least one of the outcome measures. RESULTS: 418 citations were identified. Four met the eligibility criteria. Three examined CV events and mortality, of which two studies did not have any events. The third showed no statistically significant difference between control and treatment groups in either CV events or mortality: odds ratio 1.56 [95% confidence interval (CI) 0.24 - 10.05] for mortality and odds ratio 1.00 (95% CI 0.19 - 5.40) for CV events. Two studies reported renal function at 12 months and the weighted mean difference was 0.91 mL/minute/1.73 m(2) (95% CI 0.14 - 1.68), favoring ACEI and ARB use. CONCLUSIONS: In PD patients, evidence for the use of ACEIs and ARBs for reduction of mortality and CV events is lacking. Limited data suggest that they slow the loss of residual renal function.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.026 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".