To investigate whether preoperative body morphometry analysis can identify patients at risk of parastomal hernia (PH), which is a common complication after radical cystectomy (RC). All patients who underwent RC between 2010 and 2020 with available cross-sectional imaging preoperatively and at 1 and 2 years postoperatively were included. Skeletal muscle mass and total fat mass (FM) were determined from preoperative axial computed tomography images obtained at the level of the L3 vertebral body using Aquarius Intuition software. Sarcopenia and obesity were assigned based on consensus definitions of skeletal muscle index (SMI) and FM index (FMI). PH were graded using both the Moreno-Matias and European Hernia Society criteria. Binary logistic regression and recursive partitioning were used to identify patients at risk of PH. The Kaplan-Meier method with log-rank and Cox proportional hazards models included clinical and image-based parameters to identify predictors of PH-free survival. A total of 367 patients were included in the final analysis, with 159 (43%) developing a PH. When utilising binary logistic regression, high FMI (odds ratio [OR] 1.63, P < 0.001) and low SMI (OR 0.96, P = 0.039) were primary drivers of risk of PH. A simplified model that only relied upon FMI, SMI, and preoperative albumin improved the classification of patients at risk of PH. On Kaplan-Meier analysis, patients who were obese or obese and sarcopenic had significantly worse PH-free survival (P < 0.001). Body morphometry analysis identified FMI and SMI to be the most consistent predictors of PH after RC.
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