**5. Study results**

120 Aneurysm

**4. Statistic methodology** 

univariant methods were used

performed using this method.

and females seperately).

probability level p ≤ 0,01.

**Figure 12.**VR 3D cut option in coronary reconstuction in the antero-posterior direction (a) in 3 planes (b,c,d)

Considering the heterogeneity of the population included, as well as the number of

Univariant and multivariant staistical methods **–** for testing statistic significance of difference between parameters for qualitative variables, as well as quantitative variables,

ANOVA - one-sample analysis of variance- univariant analysis of the effect of one selected factor on dependent variable. Comparing mean values, standard deviations in development of aneurysm between races and in the same race compared to control subjects, was

Median Test, Kolmogorov-Smirnov Z-test analysis of the mutual influence (of the selected variable among the groups), testing the compatibility of controls and patients in terms of developing the aneurysm, between races, and in the same race compared to the controls.

We determined the correlation coefficient (Pearson correlation) related to groups, smoking habit, and smoking history of all the subjects included, subjects according to sex (in males

According to univariant logistic regression analysis (ULRA) we tested the influence of selected variable (risk factors) and their correlation on the aneurysm development at the

Regression analysis (logistic model) In the model of MLRA we included all variables (risk factors) that were confirmed by univariant logistic regression analysis (ULRA) to be connected to the aneurysm development at the level of p ≤ 0,01, so we determined the independent risk factors for development of aneurysm in all the subjects, and then seperately for male and

female groups. All the variables were additionally tested in terms of age.

analyzed variables, we used several statistical models for data analysis in this study:

χ² test –for testing the relationship between non-parametric variabes.

Distribution of patients according to the site of aneurysm, mean values ± SD in demographic and anthropological criteria and CT measurements between two races of the respondents suffering from AAA is shown in Table 1. The other criteria did not find statistically significant differences in relation to race in patients with AAA.


**Table 1.** Distribution of patients according to the site of aneurysm, mean values ± SD of age, height, body weight, surface area, BMI index, aneurysm neck length, aver.c.i.a.length, aver.distance a.a., Fda.a1, Fd-a.a2, Rd-a.a1, Rd-a.a2, Rd-a.a3, Rd-a.a4, Rd-a.a6 and volume c.i.a. - where the parameters are found statistical differences (\*, \*\*) and the difference is highly statistically differences (\*\*\*).

Distribution of respondents to the AP and EP aneurysm and control groups the same race by age, gender, BMI and body height is shown in Table 2.


Age χ2=13,322; p=0,0001 Gender χ2=0,337; p=0,561 BMI χ2=12,785; p<0,005 Body height χ2=28,57; p<0,0001

**Table 2.** Distribution of respondents to the AP and EP aneurysm and control groups the same race by age, gender, BMI and body height.

The presence of factors in patients and control subjects as well as univariant regression analysis (ULRA) for assessment risk-factors among patients and controls in AP and EP groups of patients is shown in Table 3a. The presence of risk-factors in patients and control subjects as well as multinivariant regression analysis (MLRA) for assessment risk-factors among patients and controls in AP and group of patients is shown in Table 3b and the presence of risk-factors in patients and control subjects as well as multinivariant regression analysis (MLRA) for assessment risk-factors among patients and controls in EP group of patients is shown in Table 3c.

Abdominal Aortic Aneurysm in Different Races Epidemiologic Features and Morphologic-Clinical Implications Evaluated by CT Aortography 123


122 Aneurysm

Age / Gender BMI, Body height

Age

Gender

BMI (kg/m2)

Body height (cm)

Age χ2=13,322; p=0,0001 Gender χ2=0,337; p=0,561 BMI χ2=12,785; p<0,005 Body height χ2=28,57; p<0,0001

age, gender, BMI and body height.

patients is shown in Table 3c.

Distribution of respondents to the AP and EP aneurysm and control groups the same race

patients with aneurysm patients without aneurysm

AP EP AP EP

No % No % No % No %

≤ 74 13 41,90 26 86,60 62 47,69 114 90.47 ≥74 18 58,10 4 13,40 68 52,31 12 9,53

Male 25 83,3 24 77,4 58 46,03 90 83,3

Female 5 16,7 7 22,6 68 53,97 40 16,7

< 18,4 0 0,00 3 9,68 3 2,38 7 5,38

18,5 -24,9 12 40,00 22 70,97 96 76,19 113 86,92 25-29,9 15 50,00 6 19,35 24 19,05 7 5,38

> 30 3 10,00 0 0,00 3 2,38 3 2,31

< 160 14 45,20 0 0,00 73 56,15 0 0,00 160-170 12 38,70 7 23,3 50 38,46 24 19,05

≥171 5 16,10 14 46,70 7 5,38 73 57,94

Total 31 100 30 100 126 100 130 100

**Table 2.** Distribution of respondents to the AP and EP aneurysm and control groups the same race by

The presence of factors in patients and control subjects as well as univariant regression analysis (ULRA) for assessment risk-factors among patients and controls in AP and EP groups of patients is shown in Table 3a. The presence of risk-factors in patients and control subjects as well as multinivariant regression analysis (MLRA) for assessment risk-factors among patients and controls in AP and group of patients is shown in Table 3b and the presence of risk-factors in patients and control subjects as well as multinivariant regression analysis (MLRA) for assessment risk-factors among patients and controls in EP group of

by age, gender, BMI and body height is shown in Table 2.

**Table 3.** The presence of risk-factors in patients and control subjects as well as multinivariant regression analysis (MLRA) for assessment risk-factors among patients and controls in EP group of patients.
