BQR analysis for DS and DS change according to CV risk factors
hypertension, dyslipidemia, diabetes
REGRESSION, HYPERTENSION, DYSLIPIDEMIA, DIABETES
The quantile estimates of 10%, 25%, 50%, 75%, and 90% for the three vessels and their per‐patient values of the DS and DS changes are shown in Table In the per‐vessel analysis of DS, the typical anginal symptom was associated with the highest quantile (90%) of DS in the LAD; diabetes was associated with higher quantiles (75% and 90%) of DS in the LCx; dyslipidemia was associated with the highest quantile (90%) of DS in the RCA, whereas other symptoms showed some association with the LCx and RCA (Figure Bayesian quantile regression analysis for DS in the three vessels (LAD, LCx, and RCA). Error bar charts of the coefficient estimates with 95% confidence intervals for the selected risk factors in the three vessels are presented. Risk factors with at least one statistically significant estimate among the five quantiles (10%, 25%, 50%, 75%, and 90%) of DS were chosen using the Bayesian quantile regression model (Model Bayesian quantile regression analysis for DS in per‐patient. The error bar chart of the coefficient estimates with 95% confidence intervals for the selected risk factors per‐patient is presented. Risk factors with at least one statistically significant estimate among the five quantiles (10%, 25%, 50%, 75%, and 90%) of DS were chosen using the Bayesian quantile regression model (Model In the per‐vessel analysis of DS change, HDL‐C showed a clear and dynamic relationship, a positive association with a low level of DS change and a negative association with a high level of DS change in the LAD and RCA; hypertension also showed a dynamic relationship with DS change in the LCx and DS change severity (Figure Bayesian quantile regression analysis for DS change in the three vessels (LAD, LCx, and RCA). Error bar charts of the coefficient estimates with 95% confidence intervals for the selected risk factors in the three vessels are presented. Risk factors with at least one statistically significant estimate among the five quantiles (10%, 25%, 50%, 75%, and 90%) of DS change were chosen using the Bayesian quantile regression model (Model Bayesian quantile regression analysis for DS change in per‐patient. The error bar chart of the coefficient estimates with 95% confidence intervals for the selected risk factors per‐patient is presented. Risk factors with at least one statistically significant estimate among the five quantiles (10%, 25%, 50%, 75%, and 90%) of DS change were chosen using the Bayesian quantile regression model (Model 