Ghaneh Fananapazir1, Aleksandar Kitich2, Ramit Lamba2, Susan L Stewart2, Michael T Corwin2. 1. University of California Davis Medical Center, 4860 Y Street Suite 3100, Sacramento, CA, 95817, USA. fananapazir@ucdavis.edu. 2. University of California Davis Medical Center, 4860 Y Street Suite 3100, Sacramento, CA, 95817, USA.
Abstract
PURPOSE: To determine normal bladder wall thickness on CT in patients without bladder disease. MATERIALS AND METHODS: Four hundred and nineteen patients presenting for trauma with normal CTs of the abdomen and pelvis were included in our retrospective study. Bladder wall thickness was assessed, and bladder volume was measured using both the ellipsoid formula and an automated technique. Patient age, gender, and body mass index were recorded. Linear regression models were created to account for bladder volume, age, gender, and body mass index, and the multiple correlation coefficient with bladder wall thickness was computed. Bladder volume and bladder wall thickness were log-transformed to achieve approximate normality and homogeneity of variance. Variables that did not contribute substantively to the model were excluded, and a parsimonious model was created and the multiple correlation coefficient was calculated. Expected bladder wall thickness was estimated for different bladder volumes, and 1.96 standard deviation above expected provided the upper limit of normal on the log scale. RESULTS: Age, gender, and bladder volume were associated with bladder wall thickness (p = 0.049, 0.024, and < 0.001, respectively). The linear regression model had an R2 of 0.52. Age and gender were negligible in contribution to the model, and a parsimonious model using only volume was created for both the ellipsoid and automated volumes (R2 = 0.52 and 0.51, respectively). CONCLUSION: Bladder wall thickness correlates with bladder wall volume. The study provides reference bladder wall thicknesses on CT utilizing both the ellipsoid formula and automated bladder volumes.
PURPOSE: To determine normal bladder wall thickness on CT in patients without bladder disease. MATERIALS AND METHODS: Four hundred and nineteen patients presenting for trauma with normal CTs of the abdomen and pelvis were included in our retrospective study. Bladder wall thickness was assessed, and bladder volume was measured using both the ellipsoid formula and an automated technique. Patient age, gender, and body mass index were recorded. Linear regression models were created to account for bladder volume, age, gender, and body mass index, and the multiple correlation coefficient with bladder wall thickness was computed. Bladder volume and bladder wall thickness were log-transformed to achieve approximate normality and homogeneity of variance. Variables that did not contribute substantively to the model were excluded, and a parsimonious model was created and the multiple correlation coefficient was calculated. Expected bladder wall thickness was estimated for different bladder volumes, and 1.96 standard deviation above expected provided the upper limit of normal on the log scale. RESULTS: Age, gender, and bladder volume were associated with bladder wall thickness (p = 0.049, 0.024, and < 0.001, respectively). The linear regression model had an R2 of 0.52. Age and gender were negligible in contribution to the model, and a parsimonious model using only volume was created for both the ellipsoid and automated volumes (R2 = 0.52 and 0.51, respectively). CONCLUSION: Bladder wall thickness correlates with bladder wall volume. The study provides reference bladder wall thicknesses on CT utilizing both the ellipsoid formula and automated bladder volumes.
Authors: Ryan Pewowaruk; David Rutkowski; Diego Hernando; Bunmi B Kumapayi; Wade Bushman; Alejandro Roldán-Alzate Journal: PLoS One Date: 2020-11-19 Impact factor: 3.240