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

Transplant International, Journal Year: 2025, Volume and Issue: 38

Published: April 3, 2025

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

Language: Английский

The effect of pulmonary rehabilitation on quality of life and functional capacity after chemotherapy in patients with small cell lung cancer

Supportive Care in Cancer, Journal Year: 2024, Volume and Issue: 32(10)

Published: Sept. 21, 2024

Language: Английский