Machine-learning-based early prediction of end-stage renal disease in patients with diabetic kidney disease using clinical trials data

Abstract

Aim: To predict end-stage renal disease (ESRD) in patients with type 2 diabetes by using machine-learning models with multiple baseline demographic and clinical characteristics.

Materials and methods: In total, 11 789 patients with type 2 diabetes and nephropathy from three clinical trials, RENAAL (n = 1513), IDNT (n = 1715) and ALTITUDE (n = 8561), were used in this study. Eighteen baseline demographic and clinical characteristics were used as predictors to train machine-learning models to predict ESRD (doubling of serum creatinine and/or ESRD). We used the area under the receiver operator curve (AUC) to assess the prediction performance of models and compared this with traditional Cox proportional hazard regression and kidney failure risk equation models.

Results: The feed forward neural network model predicted ESRD with an AUC of 0.82 (0.76-0.87), 0.81 (0.75-0.86) and 0.84 (0.79-0.90) in the RENAAL, IDNT and ALTITUDE trials, respectively. The feed forward neural network model selected urinary albumin to creatinine ratio, serum albumin, uric acid and serum creatinine as important predictors and obtained a state-of-the-art performance for predicting long-term ESRD.

Conclusions: Despite large inter-patient variability, non-linear machine-learning models can be used to predict long-term ESRD in patients with type 2 diabetes and nephropathy using baseline demographic and clinical characteristics. The proposed method has the potential to create accurate and multiple outcome prediction automated models to identify high-risk patients who could benefit from therapy in clinical practice.

Keywords: clinical trial, cohort study, diabetes complications, diabetic nephropathy, type 2 diabetes.

FIGURE 1

Architecture of the proposed ESRD prediction system. Rigorous cross‐validation was performed to identify optimal model to predict renal risk in the testing set. CV, cross‐validation; ESRD, end‐stage renal disease; k‐NN, k nearest neighbour; SMOTE, synthetic minority oversampling technique

FIGURE 2

The distribution of AUC (mean [95% CI]) to predict ESRD using individual variables in all three clinical trials. Solid vertical black line corresponds to the mean AUC and rectangular box represents the standard deviation. Albumin, serum albumin; ACR, urine albumin‐creatinine ratio; AUC, area under the receiver operator characteristic curve; BMI, body mass index; CVD, history of cardiovascular diseases; DBP, diastolic blood pressure; Hb, haemoglobin; Phos, phosphorous; SBP, systolic blood pressure; Scr, serum creatinine; smoking, current/past smoker; SP, serum potassium; UA, serum uric acid

FIGURE 3

Plot showing the distribution of the predicted ESRD risk probability in patients with and without ESRD events for all three clinical trials. Jittering was performed for the ESRD event for better visualization. The best performing machine‐learning model (FNN) is compared with the best performing traditional KFRE model. To quantify the separation between two clusters, we estimated the mean Euclidean distance between the probability scores <0.5 (without ESRD) and probability scores ≥0.5 (with ESRD). The mean Euclidean distance for FNN and KFRE models were 0.66 and 0.5, respectively. ESRD, end‐stage renal disease; FNN, feed‐forward neural network; KFRE, kidney failure risk equation

FIGURE 4

Risk calibration plots for FNN and KFRE models to predict ESRD events in RENAAL and IDNT trials. The calibration plot of FNN model is closer to the identity (or diagonal) when compared with the KFRE model. ESRD, end‐stage renal disease; FNN, feed‐forward neural network; KFRE, kidney failure risk equation