Effectiveness and safety of chronic diuretic use in older adults: an umbrella review of recently published systematic reviews and meta-analyses of randomized-controlled trials
Bibliographic record
Abstract
BACKGROUND: Healthcare providers should balance the potential risks and benefits of chronic diuretic use, particularly in older adults, as with age, diuretic benefits may decline and risks increase. A comprehensive synthesis and critical evaluation of the available evidence on chronic diuretic treatment effects is currently lacking. METHODS: We conducted an umbrella review of systematic reviews and meta-analyses published since 2018 on health outcomes associated with diuretic use in randomized-controlled trials (RCTs). We conducted random-effects meta-analysis for pooled effect estimates and narratively summarized data that could not be pooled. RESULTS: We included 741 effect estimations from 117 systematic reviews (SRs) on 1566 RCTs in individuals aged 62 ± 6 years. Of our 33 meta-analyses, 11 provided convincing, high-quality evidence: finerenone reduced the risk of cardiovascular (CV) mortality and end-stage kidney disease in individuals with chronic kidney disease (CKD) and/or type 2 diabetes (T2D). Torasemide reduced the risk of heart failure-related hospitalization (HFH) more than furosemide in individuals with HF. Thiazides reduced CV events in individuals with hypertension. Mineralocorticoid receptor antagonists (MRAs) reduced HFH, but also increased hyperkalemia risk in individuals with HF. MRAs also reduced the risk of atrial fibrillation in those with HF or CVD, and reduced HFH, major adverse cardiovascular events (MACEs), > 40% eGFR decrease, and composite kidney outcomes in individuals with CKD and/or T2D. Lower quality evidence suggests that in older (≥ 65 years), but not in younger adults, diuretics may reduce CV mortality, but also increase adverse event (AE) risk. CONCLUSIONS: Our umbrella review offers a comprehensive and up-to-date evaluation of the benefits and harms of diuretics. However, further research is needed to establish their efficacy and safety in populations commonly seen in clinical practice, especially older adults living with multimorbidity and frailty.
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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 | 0.039 | 0.117 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".