Related Experiment Video
Updated: Oct 9, 2025
Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans
Published on: August 28, 2018
Predictive Model for High Coronary Artery Calcium Score in Young Patients with Non-Dialysis Chronic Kidney Disease
Tae Ryom Oh1, Su Hyun Song1, Hong Sang Choi1
1Department of Internal Medicine, Chonnam National University Hospital, Gwangju 61469, Korea.
Insights
Machine learning models can predict cardiovascular disease risk in younger chronic kidney disease patients with high coronary artery calcium scores. The random forest model demonstrated the best predictive performance, aiding in early risk identification and personalized treatment.
Area of Science:
- Nephrology
- Cardiology
- Artificial Intelligence
Background:
- Cardiovascular disease (CVD) is a significant complication in chronic kidney disease (CKD) patients.
- Coronary artery calcium (CAC) scoring is a key indicator for coronary artery disease (CAD) risk.
- Early identification of high-risk individuals is crucial for effective management.
Purpose of the Study:
- To predict cardiovascular outcomes in non-dialysis CKD patients under 60 with high CAC scores.
- To leverage machine learning techniques for enhanced risk prediction.
- To identify high-risk patients for individualized treatment strategies.
Main Methods:
- Utilized the Korean Cohort Study for Outcomes in Patients with Chronic Kidney Disease (KNOW-CKD) dataset.
- Developed and compared multiple machine learning models: Support Vector Machine, Random Forest, XGBoost, Logistic Regression, and Multi-Perceptron Neural Network.
- Validated models using a 70/30 training/validation split and an external test dataset.
- Assessed model performance using Area Under the Receiver Operating Characteristic (AUROC) curves and Shapley Additive Explanation (SHAP) values.
Main Results:
- The Random Forest model achieved the highest predictive performance with an AUROC of 0.87.
- A statistically significant difference was observed between the Random Forest model and the traditional logistic regression model on the test dataset.
- SHAP values were used to identify key predictive features for cardiovascular events.
Conclusions:
- Machine learning, particularly Random Forest, can effectively predict cardiovascular complications in younger CKD patients with high CAC scores.
- This approach facilitates early identification of at-risk individuals.
- Findings support the development of tailored treatment strategies to mitigate cardiovascular risk in this population.
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.
More Related Videos
11:30A Semi-Automated and Reproducible Biological-Based Method to Quantify Calcium Deposition In Vitro
Published on: June 2, 2022
06:57Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection
Published on: September 22, 2023
Related Concept Videos
01:25Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT
01:25Chronic Kidney Disease I: Introduction
01:28Chronic Kidney Disease III: Interprofessional Care
01:30Coronary Artery Disease I: Introduction
01:26Coronary Artery Disease IV: Preventive Measures
01:28Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration