Results and Discussions:
Descriptive statistics of the Quantitative variables:
Quantitative Variables  No. of Observation, N  Mean  Standard Deviation 
AGE  50  49.14  11.502 
BMI  50  26.48  3.3383 
LVEF  50  
QRS Score  50  6.88  3.842 
Vessel Score  50  1.78  0.764 
Friesinger Score  50  7.52  3.019 
Frequency Distribution of Qualitative Variables:
SEX
Frequency  Percent  
Male  44  88 
Female  6  12 
Total  50  100 
SMOKING
Frequency  Percent  
No  17  34 
Yes  33  66 
Total  50  100 
DM
Frequency  Percent  
No  31  62 
Yes  19  38 
Total  50  100 
HTN
Frequency  Percent  
No  24  48 
Yes  26  52 
Total  50  100 
DYSLIPIDEMIA
Frequency  Percent  
No  40  80 
Yes  10  20 
Total  50  100 
F/H of IHD
Frequency  Percent  
No  43  86 
Yes  7  14 
Total  50  100 
Site of MI
Frequency  Percent  
Anterior  27  54 
Inferior  23  46 
Total  50  100 
Relationship of Demographic Variables with QRS score:
The bivariate correlation analysis of Age with QRS score shows that (r=+0.075, pvalue=0.606) , which implies that there is a very week positive relationship between those variables and the pvalue suggests that the degree of relationship isn’t significant enough.
Again bivariate correlation analysis of BMI with QRS score shows that (r=+0.613, pvalue=0.00), which implies that there is a very strong positive relationship between those variables and the pvalue suggests that the degree of relationship is highly significant.
Relationship of different groups of SEX with QRS score (ANOVA):
SEX 
PValue 

MALE  FEMALE  
QRS Score  7.30 ± 3.837  3.83 ± 2.317  0.037 
There is significant difference in QRS score between Male and Female group of patients (F=4.603, P= 0.037).
ANOVA of QRS Score Among Different Groups of Cardiovascular Risk Factors:
#Data are analyzed using ANOVA statistics and are presented as mean ± SD;
SMOKING 
PValue 

NO  YES  
QRS Score  5.24 ± 3.345  7.73 ± 3.851  0.028(sig.) 
There is significant difference in QRS score between Smoker and Nonsmoker group of patients (F=5.117, P= 0.028).
DM 
PValue 

NO  YES  
QRS Score  6.42 ± 4.023  7.63 ± 3.499  0.283 
There is no significant difference in QRS score between two groups of patients who have DM and who don’t have DM (F=1.177, P= 0.283).
HTN 
PValue 

NO  YES  
QRS Score  6.79 ± 3.635  6.96 ± 4.094  0.878 
There is no significant difference in QRS score between two groups of patients who have HTN and who don’t have HTN (F=0.024, P= 0.878).
DYSLIPIDEMIA

PValue 

NO  YES  
QRS Score  6.63 ± 3.691  7.90 ± 4.458  0.353 
There is no significant difference in QRS score between two groups of patients who have DYSLIPIDEMIA and who don’t have (F=0.879, P= 0.353).
F/H of IHD 
PValue 

NO  YES  
QRS Score  6.81 ± 3.614  7.29 ± 5.376  0.767 
There is no significant difference in QRS score between two groups of patients who have F/H of IHD and who don’t have (F=0.089, P= 0.767).
ANOVA of QRS Score between Two Groups of Site of MI:
SITE of MI 
PValue 

Anterior  Inferior  
QRS Score  7.11 ± 4.182  6.61 ± 3.474  0.650 
There is no significant difference in QRS score between Anterior and Inferior SITE of MI patients (F=0.209, P= 0.650).
Correlation between QRS Score and Vessel Score:
QRS score and Vessel score exhibits a significantly positive correlation (r=+0.972, p=0.000).Which concludes that QRS score and Vessel score tend to increase and decrease in the same direction.
Correlation between QRS Score and Friesinger Score:
QRS score and Friesinger score exhibits a significantly positive correlation (r=+0.989, p=0.000).Which concludes that QRS score and Vessel score tend to increase and decrease in the same direction.
Regression Analysis of QRS score on Vessel score:
A simple regression analysis of QRS score has been performed on Vessel score which yields the following results:
Coefficients:
Model Summary:
R  R square  Adjusted R square 
0.972  0.944  0.940 
Which implies that the fitted regression model explains 94% of the total variation of Vessel score by QRS score.
The slope coefficient ẞ=0.193 implies that for per unit change in QRS score, the average probable change in Vessel score will be 0.193 unit.
Regression Analysis of QRS score on Friesinger score:
A simple regression analysis of QRS score has been performed on Friesinger score which yields the following results:
Coefficients:
Model Summary:
R  R square  Adjusted R square 
0.989  0.978  0.978 
Which implies that the fitted regression model explains 97.8% of the total variation of Vessel score by QRS score.
The slope coefficient ẞ=0.777 implies that for per unit change in QRS score, the average probable change in Vessel score will be 0.777 unit.
Conclusion:
In this cross sectional observational study, the association between QRS score with angiographic severity was studied through correlation and simple linear regression analysis. Also the mean difference among different groups of cardiovascular risk factors were studied through ANOVA (Analysis of Variance). Demographic characteristics were studied through descriptive analysis and also relationship of demographic variables with QRS score is studied through correlation analysis and ANOVA.
Descriptive analysis of the Quantitative variables reveals that the mean Age of 50 patients were 49.14 ± 11.502; the average BMI of the patients were 26.48 ± 2.2283; the mean LVEF was
; the mean QRS score was 6.88 ± 3.842; the mean Vessel score was 1.78 ± 0.764 and the mean Friesinger score was 7.52 ± 3.019.
Bivariate correlation analysis and ANOVA was performed to study the relationship of demographic variables with QRS score. Correlation analysis reveals that there is no significant relationship between Age and QRS score, but there is a strong positive relationship between BMI and QRS score which is highly significant.
Analysis of Variance (ANOVA) suggests that there is a significant difference in QRS score between male and female group of patients.
Frequency distribution of Qualitative variables shows that 88% of the patients were male and 12% were female; 66% of the patients have the habit of smoking; 38% patients have DM; 52% patients have Dyslipidemia; only 14% patients have F/H of IHD; 54% patients have Anterior site of MI and 46% of the patients have Inferior site of MI.
Analysis of Variance (ANOVA) shows that there is significant difference in QRS score only between smoker and nonsmoker group of patients, where there is no significant difference among other groups of cardiovascular risk factors. Also there is no significant difference between Anterior site of MI patients and Inferior site of MI patients.
To study the association between QRS score with angiographic severity we measure the correlation between QRS score and Vessel score, again the correlation between QRS score and Friesinger score. The correlation between QRS score and Vessel score (r= +0.972) indicates a significantly positive correlation between those scores, I.e, they tend to increase or decrease together. Similarly, the correlation between QRS score and Friesinger score (r=+0.989) indicates a significantly positive correlation between them, i.e, they tend to increase or decrease together.
To see the degree of dependence of angiographic severity on QRS score we fit two simple linear regression model i) regression model of QRS score on Vessel score ii) regression model of QRS score on Friesinger score. The slope coefficient,ẞ=0.193 (p=0.00) of model(i) implies that for per unit change in QRS score, the average probable change in Vessel score will be 0.193 unit. And the slope coefficient,ẞ=0.777 (p=0.00) of model(ii) implies that for per unit change in QRS score, the average probable change in Vessel score will be 0.777 unit. Thus the angiographic severity is significantly dependent on QRS score.