Predictors of Bleeding Following Inpatient Percutaneous Kidney Biopsy
Bibliographic record
Abstract
Background: Percutaneous diagnostic kidney biopsy is important in managing kidney disease, but less is known about predictors of bleeding following biopsy. The purpose of this study was to determine clinical risk factors for minor/major bleeding following percutaneous diagnostic kidney biopsy among admitted patients. Methods: We analyzed a cohort of all adults that received an in-patient diagnostic kidney biopsy at a tertiary care center from 2014-2019. Biopsies for the diagnosis of kidney tumors were excluded. Outcomes of interest were minor bleeding (any of hemoglobin drop >10 g/L within 24 hours post biopsy, macrohematuria, hematoma on ultrasound) and major bleeding (need for blood transfusion or surgical intervention post biopsy). Predictors of major bleeding were analyzed using logistic regression; those factors significantly associated with bleeding using a P<0.05 were included in a multivariable model and reported using odds ratios (OR) with 95% confidence intervals (CI). Results: Between 2014-2019, a total of 380 in-patient biopsies were performed. 221 (58.2%) of the patients were male, and mean age of the population was 57.3 ± 15.8 years. A minor bleed occurred in 131 (34.5%) of patients, and a major bleed occurred in 56 (14.7%) of patients. Risk factors for major bleeding are noted in Table 1. Factors significantly associated with major bleed included: creatinine 400-600 (OR. 7.46; 95%CI. 2.13-26.14); creatinine >600 or on dialysis (OR. 13.82; 95%CI. 4.04-47.25); structural heart disease (OR. 9.40; 95%CI. 1.33-66.47) and cerebrovascular disease (OR. 6.62; 95%CI. 1.47-29.78). Table 1. - Risk factors for major bleeding following kidney biopsy (adjusted model; N=325) Variable Odds ratio 95% Confidence interval Lab values Creatinine <200 umol/L Reference Reference 200-400 umol/L 2.95 0.87-9.92 400-600 umol/L 7.46 2.13-26.14 >600 13.82 4.04-47.25 White blood cell count <11.00 Reference Reference ≥11.00 1.89 0.86-4.14 Platelet count <150 uL Reference Reference ≥150 uL 0.57 0.26-1.29 Albumin (each 1 unit increase in g/L) 0.96 0.91-1.00 Comorbidities Anemia (Hemoglobin <100 g/L) 2.57 1.06-6.25 Active cancer 1.93 0.55-6.74 Chronic Obstructive Lung Disease 1.16 0.37-3.66 Structural heart disease 9.40 1.33-66.47 Cerebrovascular disease 6.62 1.47-29.78 Ace inhibitor use (at baseline) 0.66 0.29-1.52 Anticoagulant (at baseline)* 1.03 0.48-2.19 *Anticoagulants were discontinued prior to biopsy Conclusions: This study highlights risk factors associated with bleeding after inpatient percutaneous kidney biopsy which is of clinical importance for health care professionals. In future study we will derive and validate a risk prediction model for major and minor bleeding following biopsy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".