DISCUSSION
coronary stenosis, deteriorating stage, CAD, coronary artery stenosis, Hypertension, dyslipidemia, Shortness of breath, stenosis, diabetes
CORONARY STENOSIS, STABLE ANGINA, DISEASE, CORONARY ARTERY STENOSIS, HYPERTENSION, DYSLIPIDEMIA, REGRESSION, STENOSIS, CAD, DIABETES
In the present study, we demonstrated the clinical utility of the Bayesian truncated quantile regression machine learning method to evaluate the comprehensive relationship between CV risk factors and baseline‐graded subclinical to clinical coronary artery stenosis and its progression.First, while HDL‐C showed a consistent negative association with most DS levels, interestingly, the dynamic relationship was revealed for DS change severity, from positive relation to low‐level DS change and a negative association with high‐level DS change in our data set. The empirical results suggest that high HDL‐C has a preventive effect on CAD progression only for patients at a rapidly deteriorating stage. Hypertension is another CV risk factor exhibiting dynamic relation to DS change, from positive to negative, along with DS change severity. Typical angina symptoms were only associated with a high quantile of stenosis in the LAD and not in the LCx or RCA. Likewise, diabetes was strongly associated with LCx, and dyslipidemia was associated with RCA. Shortness of breath showed some relationship with a certain degree of stenosis in the LCx and RCA.The empirical results from the BQR model provide clinical evidence supporting the implicit relationships among the risk factors. It has been known by clinical experience that LAD lesions are associated with typical anginal symptoms owing to their considerable accountability in the entire coronary perfusionIn addition, HDL‐C showed a dynamic interrelationship with graded coronary stenosis and stenosis progression, which was the most distinctive utility of the BQR model that could not be achieved in any other standard regression models. Our empirical results from the BQR analysis might provide valuable clinical clues for enabling targeted management of CAD patients, especially since low HDL‐C levels could be an aggravating factor for rapid CAD progression.Since Koenker and Bassett first introduced quantile regression models, they have been used in various research areas, such as investment, economics, and engineering, due to their multiple advantages over standard regression analysis.Although Wehby et al.Although clinical models for estimating the pretest probability of CAD based on age, sex, and symptom typicality in patients with stable angina have been developed,To the best of our knowledge, the present study is the first to apply BQR analysis to the prediction of CAD and especially for CAD progression, by exploring the comprehensive association between CV risk factors and various stages of CAD. This pilot study can provide a framework for the cost‐efficient utilization of previously overlooked clinical information, thereby facilitating the development of a more accurate CAD pretest probability model. Furthermore, applying BQR analysis to complex clinical data will provide a hidden pattern of certain clinical risk factors for dynamically impacting certain targeted populations with specific stages of the disease and thus will be utilized in personalized therapy.Recent studies have shown the possibility of deep learning‐based novel methods for detecting CAD in its early stage utilizing a conventional twelve‐lead electrocardiogram (ECG).This study has several limitations. First, we only included 1463 patients with complete clinical information; most had LAD lesions and the LCx and RCA lesions were only on 465 and 340 vessels, respectively. Thus, there were insufficient data for the evaluation of the LCx or RCA. Second, although we included major CV risk factors for CAD, further specified and various CV risk factors should be included to enhance the performance of this model. Lastly, this study could not present an elaborate CAD prediction model. To develop an advanced CAD prediction analysis, balanced vessel numbers and complete clinical data are needed.In conclusion, we introduced the BQR machine learning method in the CV field to evaluate the complex interrelationship between CV risk factors and the different stages of CAD and its progression. Using this innovative method, we comprehensively determined the dominant association of each coronary vessel with symptoms or CV risk factors, which is clinically useful.