Computed-tomography-based predictive scores of surgical complications to help decision-making in enrolling obese patients in kidney transplantation list

Abstract

This study aimed at developing scores predicting surgical complications in obese transplant recipients, based on preoperative computed tomography (CT) parameters. All consecutive patients with a body mass index (BMI) ≥ 30 kg/m² who underwent kidney transplantation between 2012 and 2019 were included. The preoperative CT parameters were assessed: total fatty surface (TFS), subcutaneous fatty surface (SFS), iliac vessel to skin distance (VSD), and abdominal perimeter (AP). Per- and postoperative complications (vascular, urinary, parietal, and digestive complications) within 30 days were listed. Predictive models of surgical complications were generated based on the results of the logistic regression. Among the 163 patients included, 53 (32.5%) experienced surgical complications. The AP was a risk factor for complications in multivariate analysis (OR: 1.050; 95% CI: 1.016–1.087; p = 0.03). Two predictive models of complications were created based on the statistical analysis: a one-variable model based on AP (sensitivity 86.8%, specificity 41.8%, area under the curve (AUC) 65.3, with a cutoff value of 107 cm) and a five-variable model based on BMI, TFS, SFS, VSD, and AP (sensitivity 73.6%, specificity 57.3%, AUC 66.2). These models, based on patient morphometric measurements, could allow predicting the occurrence of surgical complications in obese candidates for kidney transplantation.