Characteristics Of The Target Population And Control Group

Published Date: 02 Nov 2017

Collected data consisted of all the available 73 ISS patients as our target group and a random sample of 73 PCI patients was required as our control group to compare for risk factors. Descriptive statistics and visual interpretations were applied to the ISS population in order to summarise data and to have a better grasp of the sample batch. Inferential statistics was applied to the whole sample of 146 patients in order to test for the associated risk factors and to draw conclusion.

3.2 Descriptive analysis

This discipline demonstrates the main features of the sample population where data is summarized.

3.2.1 Characteristics of the target population and control group.

Several features of the study population have been displayed in Table 1.

Table 1: Demographic and clinical characteristics of the

target population and control group

Characteristics

ISS group

Control group

Frequency

(%)

Frequency

(%)

Gender

Male

61

83.6

48

65.8

Female

12

16.4

25

34.2

DM

Yes

61

83.6

18

24.7

No

12

16.4

55

75.3

DM Treatment

Oral

57

78.1

14

19.2

IM

4

5.5

4

5.5

DM negative

12

16.4

55

75.3

HBP

Yes

50

68.5

59

80.8

No

23

31.5

14

19.2

Hypercholesterolemia

Yes

43

58.9

16

21.9

No

30

41.1

57

78.1

Smoker

Yes

46

63.0

25

34.2

No

27

37.0

48

65.8

CRF

Yes

5

6.8

6

8.2

No

68

93.2

67

91.8

Cardiac events

Angina

55

75.3

55

75.3

MI

18

24.7

18

24.7

Target vessel

LAD

51

69.9

33

45.2

LCx

7

9.6

13

17.8

RCA

15

20.5

27

37.0

No of stents

One

57

78.1

72

98.6

Two

14

19.2

1

1.4

Three

2

2.7

0

0.0

Mean Age

57.2

58.7

Std Deviation (Age)

±9.8

±10.7

3.2.2 Age distribution

The sample consisted of patients aged mostly above 50 years (Figure 11) with a mean age of 57.2 (± 9.8) years (Table 1)

Figure 12: Age distribution of target population

3.2.3 Gender distribution

There were 61 males and 12 females subjects in the study population

Figure 13: Gender distribution of target population

3.2.4 Clinical variables distribution

CRF: Chronic Renal Failure HBP: High Blood Pressure Hyperchol: Hypercholesterolemia

Figure 14: Distribution of some of the clinical

variables in the ISS population

3.2.5 Diabetes Mellitus treatment modality

Out of the sample of 73 patients, 57 were on oral drugs, 4 were on subcutaneous insulin injection while 12 did not have the disease.

DM: Diabetes Mellitus

Figure 15: DM treatment option

3.2.6 Cardiac events

Of the 73 stented patients, 18 presented with an acute MI while 55 of them had angina.

Figure 16: Cardiac events of patients upon presentation with the occluded stent

3.2.7 Target vessel

The majority of ISS subjects had stenting done in the left anterior descending coronary artery amounting to 51 patients. 15 patients had stents in their right coronary artery and 7 of them were stented in the left circumflex artery.

RCA: Right Coronary Artery LAD: Left Anterior Descending Artery lCx: Left Circumflex Artery

Figure 17: Stented target vessel being occluded

3.2.8 Percentage ISS

A visual estimation of the stented artery diameter is made by the interventional cardiologist on angiography and the stent is classified as occluded when the circulation has been compromised by more than 50 percent. The chart below (Figure 17) shows the degree of ISS of the target population.

Figure 18: Percentage of ISS in patients upon admission

3.2.9 Stented length

The stented length depends on the lesion length of the stenosed artery. Longer stented lengths imply two or three stents placed in series to each other while shorter stented lengths usually consist of only one stent.

Figure 19: Stented length of the vessels

3.2.10 Number of stents

This demonstrates the number of occluded stents aligned in series to each other in the target vessel being studied.

Figure 20: Number of stents in the target vessel

3.2.11 Management option

18 patients had to undergo a coronary artery bypass surgery, 25 of them did a post operative balloon angioplasty and 30 of them were restented.

Figure 21: Management options of ISS patients

3.2.12 Balloon pressure

This is the pressure exerted to deploy the stent into the vessel wall and is at the discretion of the interventional cardiologist.

Figure 22: Balloon pressure during PCI

3.2.13 Timing of balloon pressure

This variable is controlled by the interventional cardiologist and represents the duration of application of the balloon pressure during the stenting procedure.

Figure 23: Timing of balloon pressure

3.3 Incidence of ISS in diabetic and non-diabetic patients

Since ISS represents a negligible percentage of the total diabetic and non-diabetic population, a better visual display of the incidence was to show the number of diabetic and non-diabetic patients in the cohort of ISS patients for the last 3 years.

Table 2: Incidence of ISS in the PCI population

Time period

ISS patients

PCI patients

Incidence rate

Oct 2009 – Sept 2010

32

500

6.40 %

Oct 2010 – Sept 2011

17

399

4.26 %

Oct 2011 – Sept 2012

24

543

4.42 %

Cumulative Incidence

5.06 %

From October 2009 to September 2010, 29 DM and 3 non-DM patients presented with ISS. The year 2010/2011 showed a decrease in incidence with 13 DM and 4 non-DM patients presenting with ISS. In addition, 2011/2012 saw an increase in ISS patients with 19 DM and 5 non-DM patients together with a slightly higher incidence value compared to 2010/2011. It is to be noted that for any of the 3 years, DM patients have a higher incidence towards ISS compared to non-DM patients.

Figure 24: Incidence of ISS in diabetic and non-diabetic patients

3.3.1 Time interval between PCI and ISS

Out of the 61 diabetic patients, 43 had ISS within one year and 18 of them took more than one year to develop ISS. For any time interval indicated in Figure 24, DM patients are more prone to develop ISS quickly. Those patients who developed ISS within one month might probably be associated with procedural problems as a causative factor during the intervention.

Figure 25: Time interval between PCI and ISS

3.4 Inferential analysis

Usually, inferential statistics are used to make inferences and judgments from collected data to more general conditions (Trochim, 2006). This statistic was used to test for association between ISS and possible risk factors such that concrete conclusions might be reached. In addition, we also inferred from the sample data in order to be enlightened about how the population may behave towards certain factors. A p value of less than 0.05 was considered significant and a linear regression model was considered to determine the load of the different significant risk factors towards ISS.

3.4.1 Association between DM and ISS

Table 3: DM * ISS/ Control Crosstabulation

ISS/ Control

Total

Control

ISS

DM

No

55

12

67

Yes

18

61

79

Total

73

73

146

Table 4: Chi-Square Tests

Value

df

Asymp. Sig. (2-sided)

Pearson Chi-Square

51.002

1

.000

Continuity Correction

48.657

1

.000

Likelihood Ratio

54.622

1

.000

Since the Pearson Chi-Square test gave a p-value of 0.000 (which is less than 0.05), it can be deduced that there is a strong correlation between DM and ISS.

3.4.2 Association between HBP and ISS

Table 5: HBP * ISS/ Control Crosstabulation

ISS/ Control

Total

Control

ISS

HBP

No

14

23

37

Yes

59

50

109

Total

73

73

146

Table 6: Chi-Square Tests

Value

df

Asymp. Sig. (2-sided)

Pearson Chi-Square

2.932

1

.087

Continuity Correction

2.317

1

.128

Likelihood Ratio

2.955

1

.086

Since a p-value of 0.087 is obtained from the 2 test (>0.05), there is absence of a significant link between HBP and ISS.

3.4.3 Association between Hypercholesterolemia and ISS

Table 7: Hypercholesterolemia * ISS/ Control Crosstabulation

ISS/ Control

Total

Control

ISS

Hypercholesterolemia

No

57

30

87

Yes

16

43

59

Total

73

73

146

Table 8: Chi-Square Tests

Value

df

Asymp. Sig. (2-sided)

Pearson Chi-Square

20.735

1

.000

Continuity Correction

19.228

1

.000

Likelihood Ratio

21.347

1

.000

Since a p-value of 0.000 is obtained from the 2 test, there is a strong relationship between hypercholesterolemia and ISS.

3.4.4 Association between smoking and ISS

Table 9: Smoker * ISS/ Control Crosstabulation

ISS/ Control

Total

Control

ISS

Smoker

No

48

27

75

Yes

25

46

71

Total

73

73

146

Table 10: Chi-Square Tests

Value

df

Asymp. Sig. (2-sided)

Pearson Chi-Square

12.091

1

.001

Continuity Correction

10.967

1

.001

Likelihood Ratio

12.265

1

.000

Since a p-value of 0.001 (<0.05) is obtained from the 2 test, it can be affirmed that cigarette smoking is strongly associated with the development of ISS.

3.4.5 Association between Male gender and ISS

Table 11: Gender * ISS/ Control Crosstabulation

ISS/ Control

Total

Control

ISS

Gender

F

25

12

37

M

48

61

109

Total

73

73

146

Table 12: Chi-Square Tests

Value

df

Asymp. Sig. (2-sided)

Pearson Chi-Square

6.118

1

.013

Continuity Correction

5.213

1

.022

Likelihood Ratio

6.221

1

.013

Since the 2 test gave a p-value of 0.013 (which is <0.05), there is a strong association between male gender and ISS

3.4.6 Significance of risk factors towards ISS

Table 13: Significance of evaluated risk factors

Risk factors

2 value

p-value

Significance

Diabetes Mellitus

51.002

0.000

Significant

Hypertension

2.932

0.087

Not Significant

Hypercholesterolemia

20.735

0.000

Significant

Smoking

12.091

0.001

Significant

Male gender

6.118

0.013

Significant

3.5 Regression model

Table 14: Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.673a

.453

.438

.376

a. Predictors: (Constant), Smoker, Hyperchol, DM, Gender

Table 15: ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

16.548

4

4.137

29.236

.000a

Residual

19.952

141

.142

Total

36.500

145

a. Predictors: (Constant), Smoker, Hyperchol, DM, Gender

b. Dependent Variable: ISS/ Control

Table 16: Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

.014

.156

.087

.931

DM

.520

.065

.518

8.046

.000

Gender

.124

.077

.108

1.615

.109

Hyperchol

.244

.066

.239

3.708

.000

Smoker

.121

.068

.121

1.790

.076

a. Dependent Variable: ISS/ Control

Linear equation; cY = mX + cc

The linear regression model formed above follows the equation below;

ISS = 0.520 DM + 0.244 Hypercholesterolemia + 0.121 Smoker + 0.014

Hence, the percentage contribution towards ISS for DM is 52%, 24% for Hypercholesterolemia and 12% for cigarette smoking.