Recurrent Neural Network to Predict Renal Function Impairment in Diabetic Patients via Longitudinal Routine Check-up Data

Abstract

People affected by diabetes are at a high risk of developing diabetic nephropathy, which, in turn, is the leading cause of end-stage chronic kidney disease worldwide. Predicting the onset of renal complications as early as possible, when kidney function is still intact, is of paramount importance for therapy selection due to existence of a class of antidiabetic agents (SGLT2 inhibitors) with known nephroprotective properties.

In the present work, we study the anthropometric and laboratory data of 28,955 diabetic patients followed for a median of 6.6 years (IQR 4.7–7.8) by 14 Italian diabetes outpatient clinics. We develop a deep learning model, based on the incorporation of variable-length longitudinal baseline data via recurrent layers, to predict the onset of impaired kidney function (KDOQI stage ≥ 3). We adopt a multi-label output-coding system to address the irregularity and sparsity in the sampling of endpoints induced by the real-life structure of the data.

Using the cumulative/dynamic AUROC with respect to a variable prediction horizon of 1 to 7 years, we compare the proposed model against the predictor of imminent deterioration of kidney function used in clinical practice, i.e., the estimated glomerular filtration rate (eGFR), and a set of year-specific logistic regressions trained on a single baseline visit.

The proposed deep learning model generally outperforms both benchmarks, especially in the medium-to-long term, with AUROC ranging from 0.841 to 0.895. Supplementary analyses confirm the effective encoding of sequence data within the network.