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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 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 teacher head, 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".