Clinical and Radiological Fusion: A New Frontier in Predicting Post-Transplant Diabetes Mellitus.

Abstract

This study developed a predictive model for Post-Transplant Diabetes Mellitus (PTDM) by integrating clinical and radiological data to identify at-risk kidney transplant recipients. In a retrospective analysis across three Mayo Clinic sites, clinical metrics were combined with deep learning analysis of pre-transplant CT images, focusing on body composition parameters like adipose tissue and muscle mass instead of BMI or other biomarkers. Among 2,005 nondiabetic kidney recipients, 335 (16.7%) developed PTDM within the first year. PTDM patients were older, had higher BMIs, elevated triglycerides, and were more likely to be male and non-White. They exhibited lower skeletal muscle area, greater visceral adipose tissue (VAT), more intermuscular fat, and higher subcutaneous fat (all p < 0.001). Multivariable analysis identified age (OR: 1.05, 95% CI: 1.03-1.08, p < 0.0001), family diabetes history (OR: 1.55, CI: 1.14-2.09, p = 0.0061), White race (OR: 0.43, CI: 0.28-0.66, p < 0.0001), and VAT area (OR: 1.37, CI: 1.14-1.64, p = 0.0009) as predictors. The combined model achieved C-statistic of 0.724 (CI: 0.692-0.757), outperforming the clinical-only model (C-statistic 0.68). Patients with PTDM in the first year had higher mortality than those without PTDM. This model improves predictive precision, enabling accurate identification and intervention for at risk patients.