| Literature DB >> 32547772 |
Melissa Schorr1,2, Pavel S Roshanov1, Matthew A Weir1,3, Andrew A House1.
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.Entities:
Keywords: bleeding; complications; native kidney; percutaneous renal biopsy; risk prediction; timing; transplant kidney
Year: 2020 PMID: 32547772 PMCID: PMC7251654 DOI: 10.1177/2054358120923527
Source DB: PubMed Journal: Can J Kidney Health Dis ISSN: 2054-3581
Patient Characteristics Before and After Imputation of Missing Data.
| N (% of total) or median [IQR] | |
|---|---|
|
| 57.0 [46.0, 66.0] |
|
| 225 (36.5%) |
|
| 392 (63.5%) |
|
| 203.0 [157.0, 256.0][ |
|
| 108.0 [91.0, 125.0][ |
|
| 11.5 [10.7, 12.5][ |
|
| 27.9 [24.4, 32.0][ |
|
| 13.2 [8.9, 19.7][ |
|
| 201.0 [135.0, 347.0][ |
|
| 247 (40.0%)[ |
|
| 16 (2.6%)[ |
|
| 126 (20.4%)[ |
|
| 260 (42.1%) |
|
| 462 (74.9%) |
|
| 165 (26.7%) |
|
| |
| | 77 (12.5%) |
| | 46 (7.5%) |
| | 154 (25.0%) |
| | 87 (14.1%) |
| | 253 (41.0%) |
|
| |
| | 39 (6.3%) |
| | 116 (18.8%) |
| | 51 (8.3%) |
| | 324 (52.5%) |
| | 21 (3.4%) |
| | 11 (1.8%) |
| | 40 (6.5%) |
| | 10 (1.6%) |
| | 5 (0.8%) |
Note. Footnotes: Data are post imputation. Missing data were imputed where specified. BMI = body mass index; INR = international normalized ratio; UACR = urine albumin-to-creatinine ratio.
Missing data imputed for 2 patients (0.3%).
Missing data imputed for 2 patients (0.3%).
Missing data imputed for 59 patients (9.6%).
Missing data imputed for 64 patients (10.4%).
Missing data imputed for 22 patients (3.6%).
Missing data imputed for 1 patient (0.2%).
Missing data imputed for 3 patients (0.5%).
Missing data imputed for 19 patients (3.2%).
missing data imputed for 213 patients (34.5%); for missing data regarding proteinuria, we assumed that the value would have been less than 3+ on dipstick, <300 mg/mmol for urine albumin-to-creatinine ratio, and <3.5 g/day for a 24-hour urine collection.
Type and Timing of Kidney Biopsy Bleed.
| Bleeding characteristics | Number with events | % of all 617 patients | % of 79 patients with a bleed |
|---|---|---|---|
|
| |||
| | 79 | 12.8 | 100.0 |
| | 67 | 10.9 | 84.8 |
| | 10 | 1.6 | 12.7 |
| | 2 | 0.3 | 2.5 |
|
| |||
| | 73 | 11.8 | 92.4 |
| | 1 (major, transfusion) | 0.2 | 1.3 |
| | 1 (minor, no intervention) | 0.2 | 1.3 |
| | 1 (major, transfusion) | 0.2 | 1.3 |
| | 1 (minor, no intervention) | 0.2 | 1.3 |
| | 1 (minor, no intervention) | 0.2 | 1.3 |
| | 1 (minor, no intervention) | 0.2 | 1.3 |
Bleeding Events in Native vs. Transplant Kidney Biopsies.
|
|
|
| |||
|---|---|---|---|---|---|
| None | Minor | Major (requiring transfusion only) | Major (requiring surgery or embolization) | ||
|
| 247 | 214 (86.6%) | 26 (10.5%) | 5 (2.0%) | 2 (0.8%) |
|
| 370 | 324 (87.6%) | 41 (11.1%) | 5 (1.4%) | 0 |
Number of Biopsy Needle Passes and Bleeding Events.
|
|
|
| |||
|---|---|---|---|---|---|
| None | Minor | Major (requiring transfusion only) | Major (requiring surgery or embolization) | ||
|
| 10 | 6 (60.0%) | 3 (30.0%) | 1 (10.0%) | 0 (0%) |
|
| 407 | 356 (87.5%) | 44 (10.8%) | 6 (1.5%) | 1 (0.25%) |
|
| 149 | 133 (89.3%) | 14 (9.4%) | 2 (1.3%) | 0 (0%) |
|
| 35 | 31 (88.6%) | 4 (11.4%) | 0 (0%) | 0 (0%) |
|
| 16 | 12 (75.0%) | 2 (12.5%) | 1 (6.25%) | 1 (6.25%) |
Figure 1.Results of internal validation.
Note. Results of internal validation in 1,000 bootstrap samples drawn from each of 100 imputed datasets. Calibration curves closer to the diagonal indicate closer agreement between predicted and observed risk. Regions above the diagonal indicate that predictions that are too low; regions below the diagonal indicate predictions that are too high. The rug plot at the top of each graph indicates the distribution of patients across predicted risks derived from the first imputed dataset.
Figure 2.Adjusted associations between predictor variables and development of any bleeding after kidney biopsy.
Note. Continuous variables were fit using restricted cubic spline functions with 3 knots to allow for nonlinear relationships with the primary outcome. Shaded regions represent 95% CIs. Associations are from 100 imputed datasets and are adjusted for each other in a multivariable model using logistic regression with penalized maximum likelihood estimation.