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

Abstract

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 ( https://scikit-learn.org/ ) for model creation. The areas under the curve for LR and RF were 0.71 and 0.73, respectively. The 7-day ESA of urine protein ranked within the first two places in terms of importance according to both models. Further, other features related to urine protein were likely to rank higher than the rest. The LR and RF models revealed that the degree of urine protein, especially if it exhibited an increasing tendency, served as a prominent risk factor associated with rapid eGFR decline.

Machine Learning Approaches for Electronic Health Records Phenotyping: A Methodical Review

“…Traditionally, gold-standard labels are annotated by manual review of patient records [37,85,116]. Labels have also been derived from registry data [33], laboratory results [61,112,117], diagnosis codes [30,57,58,118–120], and rule-based algorithms [59,121–123] to enable more rapid development of labeled datasets. The most commonly used methods for classifying a binary phenotype are random forest [26,28,35,37,56,57,60,62,70,81,84,117,119,120,124–126], logistic regression [36,37,57,58,60,67,82,84,93,116,117,119,125,127,128], and support vector machine (SVM) [31,35,37,58,60,81,82,84,92,97,104,116,125,126] (Supplementary Material Table S12 ).…”

Development of a machine learning-based prediction model for extremely rapid decline in estimated glomerular filtration rate in patients with chronic kidney disease: a retrospective cohort study using a large data set from a hospital in Japan

“…We created a prediction model for identifying patients with rapid eGFR decline among those with CKD in our previous study. 19 However, we could not adjust the models and stratify them according to eGFR. Machine learning enables the classification of trajectories of eGFR decline into nine patterns using eGFR at baseline and the rate of eGFR decline.…”

“…Contrary to expectations, proteinuria did not significantly influence the prediction, as opposed to our previous report on a prediction model for rapid eGFR decline. 19 We believe that this was because patients with extremely rapid eGFR decline who were at a higher risk were enrolled. A relatively high amount of proteinuria was already recognised in the patients in this study.…”

“…Our hospital has maintained a large database of more than 900 000 patients who were treated and followed up for various diseases since 2004. We previously demonstrated a prediction model for patients with an eGFR decline of ≥30% within 2 years using machine learning 19. However, we realised that the patterns of eGFR decline varied even among those patients.…”

“…We previously demonstrated a prediction model for patients with an eGFR decline of ≥30% within 2 years using machine learning. 19 However, we realised that the patterns of eGFR decline varied even among those patients. A more detailed prediction is crucial in the real-world clinical settings because of higher risks of progression to end-stage kidney disease and the incidence of CV disease.…”