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Machine‐learning–based early prediction of end‐stage renal disease in patients with diabetic kidney disease using clinical trials data
Author(s) -
Belur Nagaraj Sunil,
Pena Michelle J.,
Ju Wenjun,
Heerspink Hiddo L.
Publication year - 2020
Publication title -
diabetes, obesity and metabolism
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.445
H-Index - 128
eISSN - 1463-1326
pISSN - 1462-8902
DOI - 10.1111/dom.14178
Subject(s) - medicine , creatinine , end stage renal disease , kidney disease , proportional hazards model , diabetic nephropathy , diabetes mellitus , clinical trial , receiver operating characteristic , disease , machine learning , kidney , computer science , endocrinology
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.