Validation of a Risk Calculator for Predicting Major Bleeding Complications from Percutaneous Kidney Biopsy
Bibliographic record
Abstract
Background: Percutaneous kidney biopsy (PKB) is valuable for the diagnosis of kidney disease but may be associated with adverse events including major bleeding (the most concerning complication of PKB). A prior study developed a risk calculator for the prediction of major bleeding following PKB, however this calculator has not been externally validated. Methods: We analyzed all adults that received diagnostic PKB at a large, tertiary care center from 2012-2019. Predictors of major bleeding (defined as the need for blood transfusion, surgical intervention/embolization following PKB or death) included age, body mass index (BMI), platelets, hemoglobin, kidney length and transplant versus native kidney biopsy (the same factors included in the “Risk of bleeding complications after kidney biopsy” calculator; https://perioperativerisk.com/kbrc). Similar to the derivation study, continuous variables were converted to cubic splines and the risk of major bleeding complications were analyzed using logistic regression. Discrimination was assessed using the concordance statistic and calibration was analyzed using the Hosmer-Lemeshow test. Results: Of 714 patients, 568 with complete data for each predictor were included. 82% of biopsies were from native kidneys. Mean age was 56±16 years, mean BMI was 29.3±6.9 and mean kidney length was 11.3±1.5 cm. Mean platelet and hemoglobin count was 248X109/L and 109 g/L, respectively. A model incorporating these variables demonstrated excellent discrimination (area under the curve 0.858, figure 1a) and good calibration (P=0.9170, figure 1b). Conclusion: In this external validation study, a risk calculator for patients receiving diagnostic PKB demonstrated excellent calibration and discrimination emphasizing its utility in predicting the risk of major bleeding following PKB.Figure 1: External validation of “Risk of bleeding complications after kidney biopsy calculator” – receiver operating characteristic curve (1A) and calibration plot (1B)
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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 | 0.027 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".