An integrated machine learning predictive scheme for longitudinal laboratory data to evaluate the factors determining renal function changes in patients with different chronic kidney disease stages

Ming-Hsien Tsai; Mao-Jhen Jhou; Tzu-Chi Liu; Yu-Wei Fang; Chi-Jie Lu

doi:10.3389/fmed.2023.1155426

An integrated machine learning predictive scheme for longitudinal laboratory data to evaluate the factors determining renal function changes in patients with different chronic kidney disease stages

Front Med (Lausanne). 2023 Oct 4:10:1155426. doi: 10.3389/fmed.2023.1155426. eCollection 2023.

Authors

Ming-Hsien Tsai^{1

2}, Mao-Jhen Jhou³, Tzu-Chi Liu³, Yu-Wei Fang^{1

2}, Chi-Jie Lu^{3

4

5}

Affiliations

¹ Division of Nephrology, Department of Medicine, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan.
² Department of Medicine, School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan.
³ Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan.
⁴ Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City, Taiwan.
⁵ Department of Information Management, Fu Jen Catholic University, New Taipei City, Taiwan.

Abstract

Background and objectives: Chronic kidney disease (CKD) is a global health concern. This study aims to identify key factors associated with renal function changes using the proposed machine learning and important variable selection (ML&IVS) scheme on longitudinal laboratory data. The goal is to predict changes in the estimated glomerular filtration rate (eGFR) in a cohort of patients with CKD stages 3-5.

Design: A retrospective cohort study.

Setting and participants: A total of 710 outpatients who presented with stable nondialysis-dependent CKD stages 3-5 at the Shin-Kong Wu Ho-Su Memorial Hospital Medical Center from 2016 to 2021.

Methods: This study analyzed trimonthly laboratory data including 47 indicators. The proposed scheme used stochastic gradient boosting, multivariate adaptive regression splines, random forest, eXtreme gradient boosting, and light gradient boosting machine algorithms to evaluate the important factors for predicting the results of the fourth eGFR examination, especially in patients with CKD stage 3 and those with CKD stages 4-5, with or without diabetes mellitus (DM).

Main outcome measurement: Subsequent eGFR level after three consecutive laboratory data assessments.

Results: Our ML&IVS scheme demonstrated superior predictive capabilities and identified significant factors contributing to renal function changes in various CKD groups. The latest levels of eGFR, blood urea nitrogen (BUN), proteinuria, sodium, and systolic blood pressure as well as mean levels of eGFR, BUN, proteinuria, and triglyceride were the top 10 significantly important factors for predicting the subsequent eGFR level in patients with CKD stages 3-5. In individuals with DM, the latest levels of BUN and proteinuria, mean levels of phosphate and proteinuria, and variations in diastolic blood pressure levels emerged as important factors for predicting the decline of renal function. In individuals without DM, all phosphate patterns and latest albumin levels were found to be key factors in the advanced CKD group. Moreover, proteinuria was identified as an important factor in the CKD stage 3 group without DM and CKD stages 4-5 group with DM.

Conclusion: The proposed scheme highlighted factors associated with renal function changes in different CKD conditions, offering valuable insights to physicians for raising awareness about renal function changes.

Keywords: chronic kidney disease; diabetes; estimated glomerular filtration; feature selection; machine learning.

Grants and funding

This study was supported by the Shin Kong Wu Ho-Su Memorial Hospital (Grant No. 2022SKHADR035) and partially supported by the Fu Jen Catholic University (Grant No. A0111181) and the National Science and Technology Council, Taiwan (Grant No. 111-2221-E-030-009).