Enhancing individual glomerular filtration rate assessment : can we trust the equation? Development and validation of machine learning models to assess the trustworthiness of estimated GFR compared to measured GFR
Background: Creatinine-based estimated glomerular filtration rate (eGFR) equations are widely used in clinical practice but exhibit inherent limitations. On the other side, measuring GFR is time consuming and not available in routine clinical practice. We developed and validated machine learning models to assess the trustworthiness (i.e. the ability of equations to estimate measured GFR (mGFR) wit
