A comparison of neural networks and regression-based approaches for estimating kidney function in pediatric chronic kidney disease: Practical predictive epidemiology for clinical management of a progressive disease

Purpose: Clinical management of pediatric chronic kidney disease requires estimation of glomerular filtration rate (eGFR). Currently, eGFR is determined by two endogenous markers measured in blood: serum creatine (SCr) and cystatin C (CysC). Machine learning methods show promise to potentially improve eGFR, but it is unclear if they can outperform regression-based approaches under clinical constraining requiring real time measurement and only two predictors. We constructed a neural network for eGFR (NNeGFR) and compared it to the clinical standard Under 25 (U25eGFR) equations using the same data for training and validation. Methods: The U25eGFR data comprised 1683 training and 843 validation observations that included iohexol measured GFR (mGFR), SCr and CysC. Sex-stratified feed forward NNs included the same predictors as U25eGFR (i.e., age, height/SCr, CysC) with additional nonlinear transformations. Performance was evaluated by bias (for calibration), proportions within 10 % and 30 % of mGFR (P₁₀ and P₃₀, for accuracy), root mean square error (RMSE, for precision) and R² (for discrimination). Results: NNeGFR performed comparably to the U25eGFR equations on all metrics. Biases were minimal, slightly favoring U25eGFR. NNeGFR and U25eGFR had similar P₁₀ (>37 %), P₃₀ (>86 %) and RMSE. Conclusions: NNeGFR performed as well as established equations to estimate GFR. Without additional biomarkers related to kidney function, which are not currently clinically available in real time, NN methods are unlikely to substantially outperform regression derived GFR estimating equations. Implications for translation of these advanced epidemiologic methods to clinical practice are discussed.