Introduction Machine learning has been increasingly used to develop predictive models to
diagnose different disease conditions. The heterogeneity of the kidney transplant population …

Risk prediction models are useful for identifying kidney recipients at high risk of graft failure,
thus optimizing clinical care. Our objective was to systematically review the models that have …

Background Kidney allograft failure is a common cause of end-stage renal disease. We
aimed to develop a dynamic artificial intelligence approach to enhance risk stratification for …

BACKGROUND: Tools are needed to predict which individuals with diabetes will develop
kidney disease and its complications. STUDY DESIGN: An observational analysis of a …

Kidney transplant recipients usually have low vitamin D levels, especially in the early
posttransplantation period, but the association between vitamin D status with renal …

In kidney transplant recipients, late graft failure is often multifactorial. In addition, primary
endpoints in kidney transplantation studies seek to demonstrate the short-term efficacy and …

Background Identifying biomarkers to predict kidney transplant failure and to define new
therapeutic targets requires more comprehensive understanding of the immune response to …

Chronic renal allograft injury (CRAI) is a multifactorial clinical/pathological entity
characterised by a progressive decrease in glomerular filtration rate, generally associated …

Background Accurate prediction of kidney transplant failure remains imperfect. The objective
of this study was to develop and validate risk scores predicting 5-year transplant failure …

Background Most current scoring tools to predict allograft and patient survival upon kidney
transplantion are based on variables collected posttransplantation. We developed a novel …