Use of Desmopressin Prior to Kidney Biopsy in Patients With High Bleeding Risk
Bibliographic record
Abstract
Introduction To prevent bleeding after native kidney biopsy (NKB), nephrologists often prescribe desmopressin, especially for patients with reduced estimated glomerular filtration rate (eGFR) at risk of uremia-related platelet dysfunction. However, only 1 randomized study has suggested a beneficial effect for desmopressin in patients with eGFR ≥60 ml/min per 1.73 m 2 . This retrospective cohort study aimed to evaluate desmopressin effect on postbiopsy bleeding in all patients, regardless of eGFR and other comorbidities. Methods In this retrospective cohort study, all adult patients who underwent an NKB from April 1, 2013, to April 30, 2018, in a tertiary hospital were identified. The association between desmopressin use and bleeding complications, including hemoglobin fall, transfusion, hematoma, symptomatic hematoma, urgent radiologic study, and hypotension, was analyzed using multivariable logistic regression models. Results A total of 413 native kidney biopsies were studied, 79% of which were performed after receiving desmopressin. Patients receiving desmopressin had worse chronic kidney disease (eGFR 28 vs. 45 ml/min per 1.73 m 2 ; P < 0.001) and were more often hospitalized (48% vs. 32%; P = 0.009). Despite higher bleeding risk, patients using desmopressin had a similar likelihood of symptomatic hematomas (odds ratio [OR], 0.39; 95% confidence interval [CI], 0.13–1.14) and a lower need for urgent radiologic studies (OR, 0.33; 95% CI, 0.11–0.98). Conclusion Patients at higher risk of bleeding using desmopressin before kidney biopsy had bleeding complications similar to those not using desmopressin. These results highlight potential important clinical and financial benefits of desmopressin use before kidney biopsy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".