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Risk factors based vessel-specific prediction for stages of coronary artery disease using Bayesian quantile
Hyung-Bok Park1,2, Jina Lee1,3, Yongtaek Hong1
1CONNECT-AI Research Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea.
Insights
Bayesian quantile regression (BQR) effectively analyzes cardiovascular risk factors and coronary artery disease (CAD) stages. Specific risk factors like diabetes and dyslipidemia correlate with higher coronary artery stenosis severity.
Area of Science:
- Cardiovascular medicine
- Machine learning in healthcare
- Biostatistics
Background:
- Coronary artery disease (CAD) involves complex relationships between cardiovascular (CV) risk factors and disease severity.
- The Bayesian quantile regression (BQR) machine-learning method offers a novel approach to analyze these intricate associations.
Purpose of the Study:
- To apply the BQR model to analyze the relationship between multiple CV risk factors and different stages of CAD.
- To investigate these relationships in a vessel-specific manner for the left anterior descending (LAD), left circumflex (LCx), and right coronary artery (RCA).
Main Methods:
- Utilized data from 1,463 patients in the PARADIGM registry.
- Developed BQR models to predict lumen diameter stenosis (DS) and DS changes using baseline CV risk factors, symptoms, and lab results.
- Estimated conditional quantile functions (10th to 90th percentiles) for maximum DS and DS change in the LAD, LCx, and RCA.
Main Results:
- The 90th percentiles for DS and maximum DS change were 41%-50% and 5.6%-7.3%, respectively.
- Anginal symptoms, diabetes, and dyslipidemia were associated with higher quantiles of DS in specific coronary arteries.
- High-density lipoprotein cholesterol demonstrated a dynamic association with DS change.
Conclusions:
- The BQR model is clinically useful for comprehensively evaluating the relationship between CV risk factors and CAD.
- This method aids in understanding both baseline CAD severity and its progression.
Background And Hypothesis:
The recently introduced Bayesian quantile regression (BQR) machine-learning method enables comprehensive analyzing the relationship among complex clinical variables. We analyzed the relationship between multiple cardiovascular (CV) risk factors and different stages of coronary artery disease (CAD) using the BQR model in a vessel-specific manner.
Methods:
From the data of 1,463 patients obtained from the PARADIGM (NCT02803411) registry, we analyzed the lumen diameter stenosis (DS) of the three vessels: left anterior descending (LAD), left circumflex (LCx), and right coronary artery (RCA). Two models for predicting DS and DS changes were developed. Baseline CV risk factors, symptoms, and laboratory test results were used as the inputs. The conditional 10%, 25%, 50%, 75%, and 90% quantile functions of the maximum DS and DS change of the three vessels were estimated using the BQR model.
Results:
The 90th percentiles of the DS of the three vessels and their maximum DS change were 41%-50% and 5.6%-7.3%, respectively. Typical anginal symptoms were associated with the highest quantile (90%) of DS in the LAD; diabetes with higher quantiles (75% and 90%) of DS in the LCx; dyslipidemia with the highest quantile (90%) of DS in the RCA; and shortness of breath showed some association with the LCx and RCA. Interestingly, High-density lipoprotein cholesterol showed a dynamic association along DS change in the per-patient analysis.
Conclusions:
This study demonstrates the clinical utility of the BQR model for evaluating the comprehensive relationship between risk factors and baseline-grade CAD and its progression.
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