Nursing Evaluation of the Risk Factors Associated with Surgical Site Infection among Coronary Artery by Pass Graft Patients at King Abdul Aziz University Hospital
Introduction
Through this chapter, the investigator aim to establish a relationship between the surgical site infections post CABG with the risk factors that are patient related, those related to the preoperative factors, the risks related to the intra-operative factors, as well as those related to the post operative factors.
Data Coding
Different coding systems were devised to categorize the raw materials represented in the checklists in an accessible manner for later analysis of the data. This was done as follows; the demographic information of the institutionalized elderly (e.g. age, gender, nationality, etc.) was categorized according to their response to each and every aspect explored using Nominal or Ordinal measurement level. For example, gender was coded as 1 for male and 2 for female.
Also each other variable included in the study was coded using the appropriate code; e.g. for the nominal variables, we used 0 for No and 1 for Yes.
The aim of coding this information is to have a descriptive analysis of the patients investigated in this study.
The data is analyzed in this chapter to provide solutions to the research questions and the objectives of the study. This chapter also will present the exploratory analyses.
Data Preparations
The data for analysis was collected retrospectively using the medical records of the patients of undergone CABG from years 2011-2013 at the King Abdul Aziz University hospital. A total of 120 CABG cases with modified surgical site infection bundle checklist.
To prepare the data for analysis, the raw data of the filled checklist were transformed into SPSS spread sheet can be easily manipulated statistically to help in verifying the research hypotheses and meet the research objectives. This was done by coding and entry.
Data Entry & analysis
Once the responses were coded, the data were transferred to SPSS form that fit to easily by a statistical computer program for storage and analysis. The investigator used the Statistical Package IBM – SPSS Version 22 to analyze the data statistically.
The data were explored both for their descriptive statistics (i.e. calculation of percentage distributions, frequency distributions and calculations of averages and suitable statistical measures such as the Standard Deviation SD or coefficient of variation C.V) and inferential statistics (i.e. Chi-square tests and suitable measures of correlation). The following sections describe the outcome of applying these analyses.
Research Objectives
The main objectives of the study was to identify the risk factors associated with surgical site infection among coronary artery by-pass graft patients and assess the compliance of the nurses to the bundle care surgical site infection (SSI).
Table 1.1 Percentage distribution of patient’s demographic characteristic:
| Gender | |||||||
| Total | Female | Male | |||||
| % | n | % | n | % | n | ||
| 10.0 | 12 | 0.0 | 0 | 11.5 | 12 | Saudi | Nationality
|
| 90.0 | 108 | 100 | 16 | 88.5 | 92 | Non- Saudi | |
| 00 | 120 | 13.3 | 16 | 86.7 | 104 | Total | |
A total of 120 CABG cases were identified. Among the identified cases, 104 cases, equivalent to 86.7% were males while 16 cases or 13.3% were females.
Saudi patient’s male were to be 11.5 %, and female 0.0%, the non Saudi patient’s male 88.5%, and females 100%.
Table 1.2 Mean and stander deviation related to gender:
|
| Female | Male | ||
| Mean | SD | Mean | SD | |
| Age | 60.94 | 11.17 | 59.17 | 9.61 |
| Body Mass Index (BMI) | 28.44 | 5.60 | 27.10 | 4.99 |
The mean age for female is 60.9 and standard deviation 11.17, while the mean age for male is 59.17 and standard deviation 9.61
The mean of Body Mass Index (BMI) for female is 28.44 and standard deviation 5.60, for male the mean of BMI is 27.10 and slandered deviation is 4.99
Table 2 Patient’s diagnosis related to gender:
| Diagnosis | Gender | Total | ||
| F | M | |||
| Angina Pectoris (AP) | n | 0 | 3 | 3 |
| % | 0.0% | 2.9% | 2.5% | |
| Ischemic Heart Disease (IHD) | n | 1 | 5 | 6 |
| % | 6.3% | 4.8% | 5.0% | |
| Myocardial Infarction (MI) | n | 1 | 8 | 9 |
| % | 6.3% | 7.7% | 7.5% | |
| Non–ST-Segment Elevation Myocardial Infarction (NSTEMI) | n | 2 | 22 | 22 |
| % | 12.5% | 18.3% | 20.0% | |
| NSTEMI and Unstable Angina (UA) | n | 0 | 1 | 1 |
| % | 0.0% | 1.0% | .8% | |
| Stable Angina (SA) | n | 1 | 3 | 4 |
| % | 6.3% | 2.9% | 3.3% | |
| ST-Segment Elevation Myocardial Infarction (STEMI) | n | 0 | 8 | 8 |
| % | 0.0% | 6.7% | 6.3% | |
| Unstable Angina (UA) | n | 11 | 53 | 64 |
| % | 68.8% | 51.0% | 53.3% | |
| UA with Congestive Heart Failure (CHF) and Myocardial Infarction (MI) | n | 0 | 1 | 1 |
| % | 0.0% | 1.0% | .8% | |
| Total | n | 16 | 104 | 120 |
| % | 100.0% | 100.0% | 100.0% | |
Angina pectoris were found to be 0.0% for female while among male it were 2.9%. And 6.3% female case diagnosed of ischemic heart disease while 4.8% among male. 6.3% female were diagnosed of Myocardial infarction cases versus 7.7 % for male. Types of non ST segment elevation myocardial infarction were found to be 12.5% among female cases versus 18.3% male . no diagnosed in female cases with non ST segment elevation myocardial infarction and unstable angina while 1.0% for male.
About 6.3% female cases diagnosed with Stable angina while 2.9% among male. No diagnosed in female cases with ST segment elevation myocardial infarction as it compared to 6.7% male. However 68.8% were diagnosed unstable angina in female versus 51.0% male.
No cases diagnosed of unstable angina with congestive heart failure and myocardial infarction was reported among female while 1.0% in male.
Table 3 Patient’s risk factors associated with gender
| Gender | Patient’s Risk Factors | ||||||||
| Total | Female | Male | |||||||
| % | n | % | n | % | n | ||||
| 89.2 | 107 | 100 | 16 | 87.5 | 91 | Hypertension | |||
| 25.8 | 31 | 6.3 | 1 | 25.8 | 30 | Smoking | |||
| 20.0 | 24 | 25.0 | 4 | 19.2 | 20 | Chronic Obstructive Pulmonary Disease (COPD) | |||
| 20.0 | 24 | 12.5 | 2 | 21.2 | 22 | Peripheral Vascular Diseases (PVD) | |||
| 2.5 | 3 | 0.0 | 0 | 2.9 | 3 | Diabetes Mellitus Type 1 (DM1) | |||
| 77.5 | 93 | 100 | 16 | 74.0 | 77 | Diabetes Mellitus Type 2 (DM2) | |||
Hypertension were measured and documented to be highest among female 100% as hypertension were versus 87.5% in male . Smoking as a risk factor for the development of SSI among CABG patients was more common among the males (25.8%) than in the females (6.3%). Chronic Obstructive Pulmonary Disease (COPD) is lower 19.2 % in male and 25% in female. , the study established that COPD is a preoperative risk factor for the development of the infections with 40% of the included patients having a history of COPD. The peripheral vascular diseases were prevalent among male 21.2% as compared to female 25%. PVD are diseases of the peripheral blood vessels such as the peripheral artery disease which comprises the atherosclerosis of the lowerextremity arteries, abdominal aorta and iliac(Olin & Sealove, 2010). No diabetic female cases type one reported versus male 2.9%. However all female cases heights diabetes type two than one as it compared 74% in male.
Table 4 Preoperative risk factors associated with gender
| Gender | Preoperative Risk Factors
| |||||
| Total | Female | Male | ||||
| % | n | % | n | % | n | |
| 100 | 120 | 100 | 16 | 100 | 102 | Methicillin-Resistant Staphylococcus Aurous (MRSA) |
| 74.2 | 89 | 87.5 | 14 | 72.1 | 75 | Pre bath |
| 50.0 | 60 | 37.5 | 6 | 51.9 | 54 | Clipper |
| 11.7 | 14 | 6.3 | 1 | 12.5 | 13 | Razor |
| 100 | 120 | 100 | 16 | 100 | 104 | Antibiotic |
All sample (male & female) involved in the study were sampled and cultured it’s found (negative) from methicilin-resistant staphylococcus aurous(MRSA) . 72% of male cases were pre bathed versus 87.5% in female. Pre-bath was found to be a risk factor causing the infections in 74.2% of the CABG patients. Clipper was to be highest in male 51.9% versus than female 37.5%. While Razor used by male 12.5% to be higher than female 6.3%. Antibiotic were administered to all cases (female and male).
Table 5 Intra operative risk factors associated with gender
| Gender | Intra Operative Risk Factors | ||||||
| Total | Female | Male | |||||
| % | n | % | n | % | n | ||
| 93.3 | 112 | 93.8 | 15 | 93.3 | 97 | Elective | Type of Surgery
|
| 2.5 | 3 | 6.3 | 1 | 1.9 | 2 | Urgent | |
| 4.2 | 5 | 0.0 | 0 | 4.8 | 5 | Emergent | |
| 100 | 120 | 100 | 16 | 100 | 104 | Antibiotic | |
| 35.8 | 43 | 56.3 | 9 | 32.7 | 34 | Inotropes | |
Table show elective surgeries were approximately equal in both male and female surgeries, while the urgent surgeries were to be 1.9% male versus 6.3% in female, no emergent surgeries while emergent surgeries were to be 4.8% in male.
The antibiotic was administered in both male and female about 100%. The inotrops were used higher among female 56.3% as male 32.7%.
Table 6 Post operative risk factors associated with gender
| Gender | Postoperative Risk Factors | |||||
| Total | Female | Male | ||||
| % | n | % | n | % | n | |
| 3.4 | 4 | 0.0 | 0 | 3.8 | 4 | Reoperation |
| 3.3 | 4 | 6.3 | 1 | 2.9 | 3 | Ventilation ≥24hr |
| 2.5 | 3 | 6.3 | 1 | 1.9 | 2 | Low cardiac out put |
| 11.7 | 14 | 6.3 | 1 | 12.5 | 13 | Antibiotic |
Reoperations were no performed among female while 3.8% male were re operated. The ventilators were applied to male 2.9% and 6.3% female. Low cardiac output was measured among male1.9 % while 6.3% female. The administer of antibiotic higher among male 12.5% versus to 6.3%. Female.
Table 7 Wound infection outcomes associated with gender
| Gender | Outcomes | |||||
| Total | Female | Male | ||||
| % | n | % | n | % | n | |
| 6.3 | 1 | 6.3 | 1 | 0.0 | 0 | Superficial Leg Infection |
| 5.8 | 6 | 6.3 | 1 | 4.8 | 5 | Superficial Sternum Infection |
| 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | Deep Leg Infection |
| 3.8 | 4 | 6.3 | 1 | 2.9 | 3 | Deep Sternum Infection |
Superficial sternum infection were measured in male 4.8% while measured in female 6.3% so the superficial sternum infection were measured in male 4.8 % while in female 6.3% .
The deep sternum infection were analysed 3% among male while 6.3% in female.
No any deep leg infection was measured for both male and female.
Table 8 Management outcomes associated with gender among CABG patients
| Gender | Outcomes | |||||
| total | Female | Male | ||||
| % | n | % | n | % | n | |
| 8.3 | 10 | 12.5 | 2 | 7.7 | 8 | Dressing |
| 8.3 | 10 | 12.5 | 2 | 7.7 | 8 | Antibiotic |
| 8.3 | .10 | 12.5 | 2 | 7.7 | 8 | Culture |
| 4.2 | 5 | 12.5 | 2 | 2.9 | 3 | Rewiring |
| 5.0 | 6 | 12.5 | 2 | 3.8 | 4 | Vacuum-assisted closure (VAC dressing) |
| 5.0 | 6 | 12.5 | 2 | 3.8 | 4 | Cured within 2-3 weeks |
| 3.3 | 4 | 0.0 | 0 | 3.8 | 4 | Still Infected after 2-3 weeks |
Table show the dressing were performed among male 7.7% while in female 12.5 % . the antibiotic was administered among male 9.6 % while in female 12.5%. the sample was cultured among male 7.7 % and 12.5 in female . Rewiring was done among male 2.9% while in female 12.5% .VAC dressing was performed among male 3.8% while in female 12.5% . The cured outcomes analysed 3.8% among male while in female 12.5% , and the still infected case were measured among male only 3.8% while no percentage in female .
No different between male and female as related to management.
Table 9 Relationship between infected case and non infected case related to demographic factors
| Characteristics
| Infected Cases))
| Non infected control)) | P– Value |
| Demographic | |||
| Age- mean, Standard Deviation | 58,12 | 60,10 | .74 |
| Males, n (Percentage) Female, n(percentage)
| 8(80) 2(11.8) | 96(87) 16(15.1) | .62 .396 |
| BMI, (n, mean, SD) | 10,32,4 | 110, 27, 5 | .016 |
*All percentages were calculated by using the denominators which excluded the missing values.
In the whole patients under investigation, only three SSI cases were females while 8 were males. A two sided Fisher’s exact tests reveal a weak relationship between the gender and SSIs, since the value of p=0.621>0.005. 70% of the infections were recorded among the patients with ages above 55 years, with the oldest patient with an infection recorded to be 80 years. However, young patients were also recorded to have been infected since there was a case of a 38 and 43 year old patients with infections.
Table 10 the relationship between infected case and non infected case related to selected patient’s factors
| Patient’s factors: | |||
| Characteristics | infected (Cases) n % | non infected Control)) n % | P– Value |
| Chronic Obstructive Pulmonary Disease (COPD), n (%) | 4(40) | 20(18) | .112 |
| Peripheral vein disease, n (%) | 4(40) | 20(18) | .112 |
| Diabetes Mellitus, n (%) | 10(100) | 86(78) | .005 |
| Current Smoking, n (%) | 3(30) | 28(26) | .503 |
| Hypertension, n (%) | 9(90) | 98(89) | .704 |
*P values are calculated using the Student t test and Fisher’s exact test.
All percentages were calculated by using the denominators which excluded the missing values.
Patient’s factors:
The analysis will investigate the relationship between the co morbid conditions such as Chronic Obstructive Pulmonary Disease (COPD), Peripheral vein disease, Diabetes Mellitus, Current Smoking, Hypertension, and the incidences of infection among the CABG patients of KAUH. According to the data from the KAUH CABG patients, the following outcomes can be implied.
About 27% of the infections were recorded among the patients who had also reported to be smokers. This is a considerable amount considering the percentage of smokers in the control population was 26%. However, there is no strong evidence to reveal a strong association between smoking and the SSIs. All the cases of SSIs also had reported to be diabetic. 20% of the infections were reported among the patients with Diabetes Mellitus Type I while 80% of the cases were reported to have Diabetes Mellitus type II. A two sided Fisher’s exact tests revealed a strong association between Diabetes and the SSIs.
With regard to Chronic Obstructive Pulmonary Disease (COPD), 40% of the patients with SSIs reported to have had COPD. However, the control population recorded only 18% for COPD. 80% of the participants of the study recorded a low ejection fraction of 55 and below. This portrays the low ejection fraction as a strong predictor of SSIs, considering that only 20% of the infections affected the patients with high ejection fraction. There was a significant evidence to show a strong relationship between low cardiac output and SSIs, with P = 0.018<0.05. The instances of low cardiac output among the SSI cases was 20% while among the control population, it was just 1%.
The peripheral vein disease was 40% prevalent among the SSI cases while its prevalence rates among the control patients was 18%. However, this did not imply a statistical association to the SSIs. 90% of the SSI cases were hypertension cases while the prevalence of hypertension among the control population was 89%. Their relationship for the prevalence of hypertension and SSIs was weakly presented by the data
Table 11 Relationship between infected and non infected cases related to selected preoperative factors
| Preoperative factors | Infected (Cases) n % | Non infected (control) n % | P– Value |
| Methicilin-resistant staphylococcus aurous, n (%) | 0(0) | 2(2) | .000 |
| Clipper, n (%) | 3(30) | 57(52) | .322 |
| Razor, n (%) | 1(10) | 13(12) | 1.000 |
| Pre bath, n (%) | 8(80) | 81(74) | 1.000 |
| Antibiotic, n (%) | 10(100) | 110(100) |
*P values are calculated using the Student t test and Fisher’s exact test.
All percentages were calculated by using the denominators which excluded the missing values.
Preoperative factors.
There were no cases of methicilin-resistant staphylococcus aurous among the SSI cases and only existed 2 cases in the control population. The analyses revealed a weak association between the use of the clipper and the SSIs among the KAUH CABG patients.
About 10% of the SSI cases reported to have used a razor while 12% of the control population was reported to have used the razor. Using Fisher’s exact tests, the relationship between the use of the razor and the use of razors was not statistically significant. This is probably due to the correct use of the razor approach to shave the patients.
80% of the SSI cases had recorded being bathed prior to the procedure and only 74% percent of the control population recorded pre bathing. Pre bathing using an antibacterial prior to the CABG procedure is a standard procedure for reducing the skin microflora. In this study, there was a weak association between these variable and the incidences of the SSIs.
The analyses revealed all CABG patients, both the SSI infected and non infected cases had in fact used antibiotics and therefore analyzing the statistical significance was not possible.
Table 12 Relationship between infected and non infected cases related to selected intraoperative risk factors
| Intraoperative factors | Infected (Cases) n % | Non infected (control) n % | P– Value |
| Urgency, n (%) | 1(10) | 2(2) | .134 |
| Elective ,n (%) | 8(80) | 104(95) | .13 |
| Emergency ,n(%) | 1(10) | 4(4) | .36 |
| Antibiotic, n (%) | 10(100) | 110(100) | |
*P values are calculated using the Student t test and Fisher’s exact test.
All percentages were calculated by using the denominators which excluded the missing values.
Urgency, Emergency and Elective Procedure
Whether the procedure is urgent or elective or an emergency was hypothesized to influence the SSIs. However, the analyses revealed that the relationship between the urgency of the procedure and the prevalence of the SSIs was not statistically significant (p=.23). Only 10% of the SSI cases underwent an urgent procedure and just 2 cases in the control population, constituting of about 2%.
The analysis revealed that the relationship between the elective procedure and the SSIs was not statistically significant, (p = .13). About 80 percent of the SSI cases were elective cases while about 95% of the control cases were elective cases. Whether the procedure was an emergency was also found to have little effect on the SSIs. The relationship between the emergency procedure and the SSIs was also not statistically significant, (p = .36).
Table 13 Relationship between infected and non infected cases related to selected post operative risk factors
| Post operative factors | Infected Cases)) n % | Non infected Control)) n % | P– Value |
| Re-operation for bleeding, n (%) | 1(10) | 3(3) | .299 |
| Rewiring, n (%) | 6(60) | 0(0) | .000 |
| Antibiotic, n (%) | 10(100) | 110(100) | |
| Dressing, n (%) | 10(100) | 0(0) | .000 |
| Ventilation, n (%) | 4(40) | 0(0) | .000 |
| Low Cardiac output, n (%) | 2(20) | 1(1) | .018 |
*All percentages were calculated by using the denominators which excluded the missing values.
Re-operation
Only 10% of the SSI cases had been re-operated for bleeding while just 3% of the control had been re-operated for bleeding. The p-value for the association was .299.
Rewiring
About 60% of the SSI cases had undergone the rewiring while among the control population, none underwent the procedure. Fisher’s exact tests reveal a strong association between rewiring and SSIs, with a p-value of .000.
Antibiotic
The analyses revealed all CABG patients, both the SSI cases and the control cases had in fact used antibiotics and therefore analyzing the statistical significance was not possible.
Dressing
It study hypothesizes that improper wound cleaning and dressing is a contributing factor towards the wound infection of the surgical sites. There is found significant in influencing the SSIs since 100% of the SSI cases were dressed and none of the control population had been dressed. The relationship here is strong and the P-value is .000<0.05.
Ventilation
40% of the SSI cases reported to have had ventilation, whereas none of the control population reported of the same. There was statistical significance of an association between ventilation and the prevalence of the SSIs, with the Fisher’s exact tests giving a p-value of .000.
Ventilation and dressing were found to have a strong association towards SSIs. The p-values for ventilation and dressing were .000 and .000 respectively. 40% of the SSI cases had ventilation whereas the control population had 0%.
80% of the participants of the study recorded a low ejection fraction of 55 and below. This portrays the low ejection fraction as a strong predictor of SSIs, considering that only 20% of the infections affected the patients with high ejection fraction.
Low cardiac output
There was a significant evidence to show a strong relationship between low cardiac output and SSIs, with P = 0.018<0.05. The instances of low cardiac output among the SSI cases was 20% while among the control population, it was just 1%.
