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Article

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

1
Department of Internal Medicine, Chonnam National University Hospital, Gwangju 61469, Korea
2
Department of Internal Medicine, Gachon University of Medicine and Science, Incheon 21565, Korea
3
Department of Internal Medicine, Institute of Kidney Disease Research, College of Medicine, Yonsei University, Seoul 03722, Korea
4
Department of Internal Medicine, College of Medicine, Seoul National University , Seoul 03080, Korea
*
Author to whom correspondence should be addressed.
J. Pers. Med. 2021, 11(12), 1372; https://doi.org/10.3390/jpm11121372
Received: 29 November 2021 / Revised: 11 December 2021 / Accepted: 13 December 2021 / Published: 15 December 2021
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. View Full-Text
Keywords: random forest; prediction; coronary artery calcification; machine learning; artificial intelligence; chronic kidney disease random forest; prediction; coronary artery calcification; machine learning; artificial intelligence; chronic kidney disease
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MDPI and ACS Style

Oh, T.R.; Song, S.H.; Choi, H.S.; Suh, S.H.; Kim, C.S.; Jung, J.Y.; Choi, K.H.; Oh, K.-H.; Ma, S.K.; Bae, E.H.; Kim, S.W. Predictive Model for High Coronary Artery Calcium Score in Young Patients with Non-Dialysis Chronic Kidney Disease. J. Pers. Med. 2021, 11, 1372. https://doi.org/10.3390/jpm11121372

AMA Style

Oh TR, Song SH, Choi HS, Suh SH, Kim CS, Jung JY, Choi KH, Oh K-H, Ma SK, Bae EH, Kim SW. Predictive Model for High Coronary Artery Calcium Score in Young Patients with Non-Dialysis Chronic Kidney Disease. Journal of Personalized Medicine. 2021; 11(12):1372. https://doi.org/10.3390/jpm11121372

Chicago/Turabian Style

Oh, Tae Ryom, 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, and Soo Wan Kim. 2021. "Predictive Model for High Coronary Artery Calcium Score in Young Patients with Non-Dialysis Chronic Kidney Disease" Journal of Personalized Medicine 11, no. 12: 1372. https://doi.org/10.3390/jpm11121372

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