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.
Highlights
Chronic kidney disease (CKD) is a major health problem, both worldwide and in Korea.When CKD progresses to end stage kidney disease, it causes a heavy socioeconomic burden on both individual patients and communities [1,2]
The purpose of this study is to develop a predictive model using machine learning techniques that can screen high-risk patients with coronary artery disease among young chronic kidney disease, and we compared the performance of machine learning techniques and traditional logistic regression
We found that traditional logistic regression has limitations in predicting the CAC score of young CKD patients and classifying them into high-risk groups
Summary
Chronic kidney disease (CKD) is a major health problem, both worldwide and in Korea. When CKD progresses to end stage kidney disease, it causes a heavy socioeconomic burden on both individual patients and communities [1,2]. Among the various complications of CKD, cardiovascular disease (CVD) is at least the second most common cause of death for all stages of CKD patients, and it is the most common cause of death for CKD patients in stages 3–5 [3]. In CKD patients, CVD risk assessment and timely intervention may improve the prognosis for CKD patients. The evaluation of CVD risk in younger patients is important. Because younger patients often are more involved in socioeconomic activities than older patients, the development of CVD in young patients has a greater adverse effect on society
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