| Objectives:1. To explore the distribution spectrum of coronary atherosclerosis plaque in suspected coronary heart disease by coronary angiography and achieve the aim of clinical diagnosis and treatment.2. Univariate and multivariate analyses were performed to evaluate the relationship between blood parameters and plaque burden scores including Total plaque score (TPS), segment stenosis score (SSS) and coronary artery disease severity (CADS), and an attempt to establish a no-invasive and simple screening model based on these indicators was made for coronary atherosclerosis, which will help to reduce the patients' economic burden and trauma, to reduce missed rate.Materials and Methods:A total of 1366 patients underwent coronary angiography in our hospital with symptoms were enrolled in this study. All the high risk factors were recognized. According to the role of American Heart Association (AHA), coronary system was divided into 16 segments. To analyze the degree of coronary artery stenosis and plaque via coronary angiography images, and a quantitative assessment for coronary atherosclerosis plaque burden was made by total plaque score (TPS), segment stenosis score (SSS) and coronary atherosclerosis degree score (CADS), respectively. Then we made a detailed description of the distribution spectrum of plaque in coronary system. The means and variable levels were described by mean±SD and median±interquartile range. Kruskal-Wallis test was used to analyze the differences between the different sex and age groups. Meanwhile, in order to describe the burden degree of coronary artery plaque directly, the incidence of plaque on each coronary segment and the degree of coronary artery stenosis were marked on tree-shaped model graph. Using logistic regression analysis with adjustment for sex and age, the independent predictive factor of plaque burden were determined directly. Then put the predictive factor into multivariate logistic model, the screening model for suspected patients with coronary heart disease was established via stepwise regression. Finally, to evaluate the discriminant effect of screening model by receiver operating characteristic cure (ROC).Results:1. According to coronary angiography,1105 patients with coronary stenosis were diagnosed and the outcome of 261 patients were negative. The average age of the group with no coronary artery stenosis was 55.6 years, the mean age of the other group with stenosis was 60.8 years. There were significant differences between two groups. The level of systolic and diastolic pressure, fasting glucose, serum total cholesterol and glycated hemoglobin of group with coronary stenosis were significantly higher than those of group with no coronary artery stenosis. But the level of high-density lipoprotein in group with coronary stenosis was much lower than those in group with no coronary artery stenosis. There were significant differences between two groups. There were no significant differences in the level of triglycerides and low-density lipoprotein between two groups.2. The distributions of TPS and SSS scores were both right-skewed. The overall trend is the more scores, the less people. The proportion of people with high scores was lower than those and the scores of most people focused on a relatively low level. The number of people with score of SSS> 20 accounted for 7.71% (98/1366). The percentage of people with score of TPS> 8 was 5.56% (76/1366). The number of people with score of SSS?5 accounted for 44.36% (606/1366). The percentage of people with score of TPS?5 was 76.57%(1046/1366).3. There were significant differences of TPS and SSS scores between different sex and age groups. In different sex groups, the average TPS score of females was 2.81±2.81, the median±interquartile range was 2±2. the average TPS score of males was 3.75±2.76, the median±interquartile range was 3±2.5. The TPS score of males was higher than that of females, There were significant differences between this two groups (x2=46.7659, P<0.0001). The average SSS score of females was 6.60±7.20, the median±interquartile range was 4±5.5.the average SSS score of males was 9.11±7.24, the median±interquartile range was 8±5.5. The SSS score of males was higher than that of females, There were significant differences between this two groups (x2=51.6603, P<0.0001). In different age groups, The average TPS score of group (age<52 year) was 2.45±2.52, the median±interquartile range was 2±2. The average TPS score of group (age range:52-59) was 2.85±2.69, the median±interquartile range was 2±2.5. The average TPS score of group (age range:60-67) was 3.51±2.86, the median±interquartile range was 3±2. The average TPS score of group (age?68 year) was 4.60±2.69, the median±interquartile range was 5±1.5. The outcome revealed the score increased with age. There were significant differences among these groups (x2=123.4456, P<0.0001). Similarly, the SSS score displayed the same trend. The average SSS score of group (age<52 year) was 5.76±6.23, the median±interquartile range was 4±4.5. The average SSS score of group (age range:52-59) was 6.64±6.90, the median±interquartile range was 4±5.5. The average SSS score of group (age range:60-67) was 8.43±7.56, the median±interquartile range was 7±5.5. The average TPS score of group (age?68 year) was 11.37±7.22, the median±interquartile range was 11±5.5. The outcome revealed the score increased with age. There were significant differences among these groups (x2=126.5659, P<0.0001).4. To mark definitely the frequency of plaque in each segment of coronary artery in TPS score graph and mark the percentage of different stenosis degree in each segment in SSS score graph. The TPS score graph revealed although plaque occured in each segment, the most common position was in the proximal LAD, which accounted for 51.39%. Then in the middle LAD(39.68%), proximal RCA(31.55%), middle RCA(28.92%) and proximal LCX(27.89%). In SSS score graph, the distribution of score in proximal LAD, which plaque was present highest, was 0:48.61%,1:10.32%, 2:9.15%,3:31.92%. Then the distribution of sore in middle LAD was 0:60.32%,1: 7.1%,2:8.86%,3:23.72%. Next the distribution of score in proximal RCA was 0: 68.45%,1:8.64%,2:5.93%,3:16.98%. The distribution of score in middle RCA was 0:71.08%,1:7.54%,2:5.49%,3:15.89%. Finally, the distribution of score in proximal LCX was 0:72.11%,1:7.03%,2:6.3%,3:14.57%.5. Using logistic regression analysis, we found the TPS score>5, SSS score>5 and CADS>0 were valuable indicator for systolic pressure, fasting glucose, triglycerides, HDL and glycated hemoglobin. In the model with TPS score>5, the area under ROC curve was 0.758(95%CI:0.718-0.794). In the model with SSS score>5, the area under ROC curve was 0.728(95%CI:0.687-0.766). In the model with CADS score>0, the area under ROC curve was 0.728(95%CI:0.687-0.766). The above three models can be used to predict the high-risk population.Conclusion:1. The tree-shaped graph of coronary artery revealed directly the distribution of plaque burden in patients with coronary heart disease. The proximal LAD was the most common site of major involvement, next was proximal RCA, middle LAD, proximal LCX and middle RCA.2. The predictive outcome of three models were definitely valuable and can predict without CTA or DSA, which can accomplish a no-invasive and simple screening and reduce the patients'economic burden and missed rate. |
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