Published online Oct 16, 2023. doi: 10.12998/wjcc.v11.i29.7004
Peer-review started: July 5, 2023
First decision: July 18, 2023
Revised: August 1, 2023
Accepted: September 11, 2023
Article in press: September 11, 2023
Published online: October 16, 2023
Processing time: 94 Days and 6.5 Hours
The incidence of chronic kidney disease (CKD) has dramatically increased in recent years, with significant impacts on patient mortality rates. Previous studies have identified multiple risk factors for CKD, but they mostly relied on the use of traditional statistical methods such as logistic regression and only focused on a few risk factors.
To determine factors that can be used to identify subjects with a low estimated glomerular filtration rate (L-eGFR < 60 mL/min per 1.73 m2) in a cohort of 1236 Chinese people aged over 65.
Twenty risk factors were divided into three models. Model 1 consisted of demographic and biochemistry data. Model 2 added lifestyle data to Model 1, and Model 3 added inflammatory markers to Model 2. Five machine learning methods were used: Multivariate adaptive regression splines, eXtreme Gradient Boosting, stochastic gradient boosting, Light Gradient Boosting Machine, and Categorical Features + Gradient Boosting. Evaluation criteria included accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), F-1 score, and balanced accuracy.
A trend of increasing AUC of each was observed from Model 1 to Model 3 and reached statistical significance. Model 3 selected uric acid as the most important risk factor, followed by age, hemoglobin (Hb), body mass index (BMI), sport hours, and systolic blood pressure (SBP).
Among all the risk factors including demographic, biochemistry, and lifestyle risk factors, along with inflammation markers, UA is the most important risk factor to identify L-eGFR, followed by age, Hb, BMI, sport hours, and SBP in a cohort of elderly Chinese people.
Core Tip: This is a retrospective study that used five machine learning methods to evaluate the impact of lifestyle and chronic inflammation in identifying subjects with abnormal estimated glomerular rates among elderly Chinese subjects. Our results showed that uric acid is the most important risk factor (inflammatory marker), followed by age, hemoglobin, body mass index, sport hours, and systolic blood pressure.