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Published on: June 2, 2022
Deep learning-based prediction of coronary artery calcium scoring in hemodialysis patients using radial artery
Yuankai Xu1, Wen Li2, Yanli Yang2
1Department of Nephrology, Zhejiang Hospital, Hangzhou City, China.
Insights
Radial artery calcification can predict coronary artery calcification (CAC) in hemodialysis patients. A random forest model showed high accuracy, offering a potential tool for CAC screening.
Area of Science:
- Cardiovascular Medicine
- Nephrology
- Medical Imaging
Background:
- Coronary artery calcification (CAC) is a significant predictor of cardiovascular events in hemodialysis patients.
- Early detection and risk stratification are crucial for managing cardiovascular disease in this population.
Purpose of the Study:
- To evaluate the feasibility of using radial artery calcification (RAC) to predict CAC in hemodialysis patients.
- To compare the predictive performance of a random forest model versus a logistic regression model for CAC.
Main Methods:
- Enrolled 118 hemodialysis patients undergoing ultrasound for RAC index and CT scans for coronary artery calcification scores (CACS).
- Developed and compared random forest and logistic regression models to predict CACS.
- Identified risk factors for RAC using logistic regression.
Main Results:
- The random forest model, incorporating RAC index, age, C-reactive protein, BMI, diabetes, and hypertension, achieved an area under the receiver operating characteristic curve (AUC) of 0.869.
- The logistic regression model achieved an AUC of 0.742 for CACS prediction.
- Identified sex, BMI, smoking, hypertension, diabetes, and serum calcium as risk factors for RAC.
Conclusions:
- Radial artery calcification is a feasible predictor of coronary artery calcification in hemodialysis patients.
- The random forest model demonstrates superior performance in predicting CACS compared to logistic regression.
- RAC assessment offers a potential non-invasive method for rapid screening and prediction of CAC.
Objective:
This study used random forest model to explore the feasibility of radial artery calcification in prediction of coronary artery calcification in hemodialysis patients.
Material And Methods:
We enrolled hemodialysis patients and performed ultrasound examinations on their radial arteries to evaluate the calcification status using a calcification index. All involved patients received coronary artery computed tomography scans to generate coronary artery calcification scores (CACS). Clinical variables were collected from all patients. We constructed both a random forest model and a logistic regression model to predict CACS. Logistic regression model was used to identify the risk factors of radial artery calcification.
Results:
One hundred eighteen patients were included in our analysis. In random forest model, the radial artery calcification index, age, serum C-reactive protein, body mass index (BMI), diabetes, and hypertension history were related to CACS based on the average decrease of the Gini coefficient. The random forest model achieved a sensitivity of 76.9%, specificity of 75.0%, and area under receiver operating characteristic of 0.869, while the logistic regression model achieved a sensitivity of 75.2%, specificity of 68.7%, and area under receiver operating characteristic of 0.742 in prediction of CACS. Sex, BMI index, smoking history, hypertension history, diabetes history, and serum total calcium were all the risk factors related to radial artery calcification.
Conclusions:
A random forest model based on radial artery calcification could be used to predict CACS in hemodialysis patients, providing a potential method for rapid screening and prediction of coronary artery calcification.
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