Efficacy of Desmopressin in Reducing Bleeding in High-Risk Native Kidney Biopsy: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Kidney biopsy is an essential diagnostic procedure in nephrology, but it carries a bleeding risk, especially in patients with impaired kidney function. Desmopressin (DDAVP) is sometimes used prophylactically to mitigate this risk, though its efficacy remains uncertain. This study aimed to evaluate the effectiveness of DDAVP in reducing bleeding complications in high-risk patients undergoing native kidney biopsy through a systematic review and meta-analysis. Methods: We searched PubMed, EMBASE, Cochrane, ClinicalTrials.gov, and conference proceedings up to April 2025 for randomized and observational studies comparing DDAVP to placebo or no treatment in patients with impaired kidney function (eGFR <60 mL/min/1.73 m2). Risk of bias was assessed using the RoB-2 and Newcastle-Ottawa Scale. The primary outcome was the incidence of bleeding events. Pooled risk ratios (RR) were calculated using a random-effects model. Subgroup and sensitivity analyses, including leave-one-out diagnostics, were performed. Results: Nine studies with 2,470 patients were included. The overall pooled RR for bleeding was 0.61 [95% CI: 0.33–1.11], with high heterogeneity (I2 = 73.6%). Observational studies showed a significant reduction in bleeding (RR = 0.52 [95% CI: 0.44–0.61], I2 = 0%), while randomized controlled trials did not (RR = 0.87 [95% CI: 0.10–7.69], I2 = 89.3%). A leave-one-out analysis identified a single outlier study that, when excluded, significantly reduced heterogeneity (I2 = 18.3%) and rendered the pooled effect statistically significant (p < 0.0001). No consistent difference was found in major or minor bleeding rates in RCTs. Egger’s test did not indicate publication bias. Conclusion: DDAVP may reduce bleeding risk in high-risk patients undergoing native kidney biopsy, particularly in real-world observational settings. The benefit became statistically significant after removing a single outlier study. These findings support the cautious use of DDAVP in selected patients and underscore the need for well-powered RCTs to confirm its efficacy.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".