Increasing tendency of urine protein is a risk factor for rapid eGFR decline in patients with CKD: A machine learning-based prediction model by using a big database

• Published in: PLoS ONE Vol. 15; no. 9; p. e0239262
• Main Authors: Inaguma, Daijo, Kitagawa, Akimitsu, Yanagiya, Ryosuke, Koseki, Akira, Iwamori, Toshiya, Kudo, Michiharu, Yuzawa, Yukio
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science (PLoS) 17.09.2020; Public Library of Science
• Subjects: Algorithms; Artificial intelligence; Biology and Life Sciences; Blood pressure; Chronic kidney failure; Comorbidity; Complications and side effects; Computer and Information Sciences; Diabetes; Electronic health records; Electronic medical records; End-stage renal disease; Epidermal growth factor receptors; Glomerular filtration rate; Health aspects; Hemoglobin; Hospitals; Hypertension; Internal medicine; Kidney diseases; Kidneys; Laboratories; Learning algorithms; Machine learning; Medical records; Medicine; Medicine and Health Sciences; Nephrology; Patients; Physical Sciences; Prediction models; Prognosis; Proteins; Proteinuria; Risk analysis; Risk factors; Science; Statistics; Urine; Japan
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0239262

Artificial intelligence is increasingly being adopted in medical fields to predict various outcomes. In particular, chronic kidney disease (CKD) is problematic because it often progresses to end-stage kidney disease. However, the trajectories of kidney function depend on individual patients. In this study, we propose a machine learning-based model to predict the rapid decline in kidney function among CKD patients by using a big hospital database constructed from the information of 118,584 patients derived from the electronic medical records system. The database included the estimated glomerular filtration rate (eGFR) of each patient, recorded at least twice over a period of 90 days. The data of 19,894 patients (16.8%) were observed to satisfy the CKD criteria. We characterized the rapid decline of kidney function by a decline of 30% or more in the eGFR within a period of two years and classified the available patients into two groups-those exhibiting rapid eGFR decline and those exhibiting non-rapid eGFR decline. Following this, we constructed predictive models based on two machine learning algorithms. Longitudinal laboratory data including urine protein, blood pressure, and hemoglobin were used as covariates. We used longitudinal statistics with a baseline corresponding to 90-, 180-, and 360-day windows prior to the baseline point. The longitudinal statistics included the exponentially smoothed average (ESA), where the weight was defined to be 0.9*(t/b), where t denotes the number of days prior to the baseline point and b denotes the decay parameter. In this study, b was taken to be 7 (7-day ESA). We used logistic regression (LR) and random forest (RF) algorithms based on Python code with scikit-learn library (