A Clinical Tool for Prediction of Bleeding Complications After Percutaneous Renal Biopsy
Bibliographic record
Abstract
Background: Kidney biopsy is a diagnostic procedure which may result in bleedingrelated complications. Developing a tool for risk stratification could help predict such risk and would benefit shared decision-making. The aim of the present study was to derive and verify such a tool from training and validation cohorts. Methods: This is a single-center study of 1450 ultrasound-guided native kidney biopsies performed for diagnosis of kidney disease between 2007 and 2020 at a tertiary academic hospital in Quebec, Canada. Major bleeds were defined by hematoma/hematuria with 1 g/dL drop in hemoglobin (Hb) and requiring transfusion, bleeding requiring hospitalization or transfer to ICU, hemorrhagic shock, angioembolization, nephrectomy, or death. Two thirds of the cohort was randomly selected and used as the training set (n=987) for the identification of the determinants of major bleeds, using univariate and multivariate logistic regression analysis, and the rest was used as validation cohort. Results: In the training cohort (59% male) the mean age, weight, Hb and platelet counts were 55±17 years, 79±20 kg, 11.4± 2.5 g/dL, and 243± 100 × 109/L, respectively, while the median eGFR was of 33 mL/min/1.73m2 (IQR: 12-62). In this group, major bleeding occurred in 57 patients (5.8%). Major bleeding was higher with younger age, lower pre-biopsy Hb, the use of anticoagulant within the week prior to the biopsy (defined as the use of direct-acting oral anticoagulants, warfarin, i.v. heparin, therapeutic doses of low molecular weight heparin), and higher INR at the time of kidney biopsy. The probability of risk was defined by the following equation: Probability of bleeding= e(-2.426054-0.017820*Age + 0.910106*Anti_Coag -0.026364*Hb + 3.142164*INR)/(1 + e(-2.426054-0.017820*Age + 0.910106*Anti_Coag -0.026364*Hb + 3.142164*INR)) The AUC of the equation was 0.741 (Min-Max: 0.740 - 0.742) in the training cohort. In our validation cohort, with similar characteristics, major bleedings occurred in 18 patients (3.9%). The AUC of the equation predicted well the risk in the validation cohort (AUC of 0.733 (Min-Max: 0.711 - 0.747)). Conclusions: This study proposes an equation for the estimation of the probability of major bleeding after percutaneous renal 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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".