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Predictive Model for High Coronary Artery Calcium Score in Young Patients with Non-Dialysis Chronic Kidney Disease

Authors
 Tae Ryom Oh  ;  Su Hyun Song  ;  Hong Sang Choi  ;  Sang Heon Suh  ;  Chang Seong Kim  ;  Ji Yong Jung  ;  Kyu Hun Choi  ;  Kook-Hwan Oh  ;  Seong Kwon Ma  ;  Eun Hui Bae  ;  Soo Wan Kim 
Citation
 JOURNAL OF PERSONALIZED MEDICINE, Vol.11(12) : 1372, 2021-12 
Journal Title
JOURNAL OF PERSONALIZED MEDICINE
Issue Date
2021-12
Keywords
artificial intelligence ; chronic kidney disease ; coronary artery calcification ; machine learning ; prediction ; random forest
Abstract
Cardiovascular disease is a major complication of chronic kidney disease. The coronary artery calcium (CAC) score is a surrogate marker for the risk of coronary artery disease. The purpose of this study is to predict outcomes for non-dialysis chronic kidney disease patients under the age of 60 with high CAC scores using machine learning techniques. We developed the predictive models with a chronic kidney disease representative cohort, the Korean Cohort Study for Outcomes in Patients with Chronic Kidney Disease (KNOW-CKD). We divided the cohort into a training dataset (70%) and a validation dataset (30%). The test dataset incorporated an external dataset of patients that were not included in the KNOW-CKD cohort. Support vector machine, random forest, XGboost, logistic regression, and multi-perceptron neural network models were used in the predictive models. We evaluated the model's performance using the area under the receiver operating characteristic (AUROC) curve. Shapley additive explanation values were applied to select the important features. The random forest model showed the best predictive performance (AUROC 0.87) and there was a statistically significant difference between the traditional logistic regression model and the test dataset. This study will help identify patients at high risk of cardiovascular complications in young chronic kidney disease and establish individualized treatment strategies.
Files in This Item:
T202126251.pdf Download
DOI
10.3390/jpm11121372
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Choi, Kyu Hun(최규헌) ORCID logo https://orcid.org/0000-0003-0095-9011
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/190657
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