Frequency, Timing, and Prediction of Major Bleeding Complications From Percutaneous Renal Biopsy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The risk and timing of bleeding events following ultrasound-guided percutaneous renal biopsy are not clearly defined. DESIGN SETTING PARTICIPANTS AND MEASUREMENTS: We performed a retrospective study of 617 consecutive adult patients who underwent kidney biopsy between 2012 and 2017 at a tertiary academic hospital in London, Canada. We assessed frequency and timing of minor (not requiring intervention) and major (requiring blood transfusion, surgery, or embolization) bleeds and developed a personalized risk calculator for these. RESULTS: Bleeding occurred in 79 patients (12.8%; 95% confidence interval [CI]: 10.4%-15.7%). Minor bleeding occurred in 67 patients (10.9%; 95% CI: 8.6%-13.6%). Major bleeding occurred in 12 patients (1.9%; 95% CI: 1.1%-3.4%); 2 required embolization or surgery (0.3%; 95% CI: 0.09%-1.2%) and 10 required blood transfusion (1.6%; 95% CI: 0.9%-3.0%). Seventy-three of 79 events were identified immediately on post-procedure ultrasound (92.4% of cases; 95% CI: 84.4%-96.5%). Four of 617 patients experienced a minor event not detected immediately (0.6%; 95% CI: 0.3%-1.7%). Two patients (0.3%; 95% CI: 0.09%-1.2%) suffered a major complication that was not recognized immediately; both required blood transfusions only. There were no deaths or nephrectomies. A risk calculator using age, body mass index, platelet count, hemoglobin concentration, size of the target kidney, and whether the kidney is native, or an allograft predicted minor (C-statistic, 0.70) and major bleeding (C-statistic, 0.83). CONCLUSIONS: This retrospective study of 617 patients who had percutaneous ultrasound-guided renal biopsies supports the safety of short post-biopsy monitoring for most patients. A risk calculator can further personalize estimates of complication risk (http://perioperativerisk.com/kbrc).
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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 | 0.001 | 0.010 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".