The collected data was analyzed using the SPSS-25 (Statistical Package for the Social Sciences) software, and various statistical methods were employed. The study sample consisted of 83 cases.
Descriptive statistics:
Descriptive statistics involve analyzing and summarizing data to gain insight into the studied variables, such as age and gender. This can be done by calculating measures such as percentages, mean, median, and standard deviation, and presenting the results using graphical forms to enhance the understanding of the data.
The mean age in our study was 52 years, with a standard deviation of 10. (Table 1)
Table 1
Distribution of the study sample according to age.
Arithmetic Mean
|
52
|
Standard Deviation
|
10
|
Out of all cases, 69% were male, representing the highest percentage. (Table 2, Chart 1)
Table 2
Distribution of the study sample according to gender.
|
Number
|
Percentage (%)
|
Male
|
58
|
69
|
Female
|
25
|
31
|
Total
|
83
|
100
|
When considering the pharmacological history of the patients, it was found that 66% of all patients were taking angiotensin-converting enzyme inhibitors (ACEIs), 60% were taking beta-blockers (β-blockers), and 53% were taking statins. In addition, 45% of patients were taking blood thinners, 38% were taking calcium channel blockers, and 36% were taking diuretics. (Table 3, Chart 2)
Table 3
Distribution of the study sample according to pharmacological history.
Medication
|
Number
|
Percentage (%)
|
ACEIs
|
55
|
66
|
β-blockers
|
50
|
60
|
Statins
|
44
|
53
|
Blood thinners
|
38
|
45
|
Calcium channel blockers
|
32
|
38
|
Diuretics
|
30
|
36
|
Regarding smoking status, it was found that 45% of the patients were smokers. (Table 4, Chart 3)
Table 4
Distribution of the study sample according to smoking status.
|
Number
|
Percentage (%)
|
Smokers
|
37
|
45
|
Non-smokers
|
46
|
55
|
Total
|
83
|
100
|
When asked about the presence of a previous coronary vascular disease, 36% of patients reported having been diagnosed with the condition. (Table 5, Chart 4)
Table 5
Distribution of the study sample according to the presence of a previous coronary vascular disease.
|
Number
|
Percentage (%)
|
Presence
|
30
|
36
|
Absence
|
53
|
64
|
Total
|
83
|
100
|
Regarding echogenic left ventricular mass (according to Cube), 52% of male patients had normal left ventricular mass, while 48% (28 men) had LVH. (Table 6, Chart 5)
Table 6
Distribution of males according to echogenic left ventricular mass.
LVMI* (g/m2)
|
Number
|
Percentage (%)
|
Normal: 49–115
|
30
|
52
|
LVH: more than 115
|
28
|
48
|
Total
|
58
|
100
|
*LVMI: Left ventricular mass index = Left ventricular mass/Body surface area
|
Among female patients, 60% had normal left ventricular mass, while 40% (10 women) had LVH as determined by echogenic measurement (according to Cube). (Table 7, Chart 6)
Table 7
Distribution of females according to echogenic left ventricular mass.
LVMI* (g/m2)
|
Number
|
Percentage (%)
|
Normal: 43–95
|
15
|
60
|
LVH: more than 95
|
10
|
40
|
Total
|
25
|
100
|
*LVMI: Left ventricular mass index = Left ventricular mass/Body surface area
|
Regarding the distribution of signs of LVH in male patients according to ECG: (Table 8, Chart 7)
Table 8
Distribution of signs of LVH in males according to ECG.
|
Number
|
Percentage (%)
|
Presence
|
7
|
12
|
Absence
|
51
|
88
|
Total
|
58
|
100
|
While in females, the distribution of signs of LVH according to ECG was: (Table 9, Chart 8)
Table 9
Distribution of signs of LVH in females according to ECG.
|
Number
|
Percentage (%)
|
Presence
|
2
|
8
|
Absence
|
23
|
92
|
Total
|
25
|
100
|
The sensitivity and specificity of ECG in determining LVH according to gender, smoking status, BMI and age was evaluated in this study:
(Chart 9,10,11,12)
Inferential statistics:
We employed the Chi-square independence test to examine whether there is a relationship between two descriptive variables. This test involves calculating the Chi-square statistic and comparing it to a critical value based on the degrees of freedom and a significance level (commonly set at 0.05). If the calculated p-value is less than 0.05, we can reject the null hypothesis and conclude that there is a statistically significant relationship between the variables being studied.
The results of the experimental study indicate that the sensitivity of ECG was 25% in men, compared to 20% in women, with a p-value less than 0.001, indicating a statistically significant difference between the two groups. Additionally, the study found that the specificity of ECG was 87% in men, compared to 76% in women, with a p-value less than 0.001, which is also statistically significant.
Our study also found that the sensitivity of ECG in smokers was 27%, compared to 19% in non-smokers, with a p-value less than 0.001, indicating a statistically significant difference between the two groups. Furthermore, the specificity of ECG in smokers was 89%, compared to 76% in non-smokers, with a p-value less than 0.001, also indicating a statistically significant difference.
Regarding the distribution of sensitivity and specificity based on BMI, the study found that the sensitivity of the group with a BMI between 18 and 24.9 was 25%, compared to 23% for the group with a BMI between 25 and 29.9, and 19% for the group with a BMI between 30 and 34.9. The p-value for this comparison was 0.001, indicating a statistically significant difference between the groups. The study also found that the specificity of the group with a BMI between 18 and 24.9 was 88%, compared to 83% for the group with a BMI between 25 and 29.9, and 77% for the group with a BMI between 30 and 34.9. The p-value for this comparison was 0.001, indicating a statistically significant difference between the groups.
Finally, regarding the distribution of sensitivity and specificity based on age, the study found that the sensitivity was 26% in the age group of 40–49, 25% in the age group of 50–59, and 23% in the age group of 60–69. The p-value for this comparison was 0.53, indicating that the difference in sensitivity between the age groups was not statistically significant. The study also found that the specificity was 89% in the age group of 40–49, 87% in the age group of 50–59, and 87% in the age group of 60–69. The p-value for this comparison was 0.41, indicating that the difference in specificity between the age groups was not statistically significant.