Predictors of Sternal Wound Infection Post Cardiac Surgery in a Saudi Centre: A Case Control Study

doi:10.21203/rs.3.rs-1968984/v1

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Research Article

Predictors of Sternal Wound Infection Post Cardiac Surgery in a Saudi Centre: A Case Control Study

https://doi.org/10.21203/rs.3.rs-1968984/v1

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Journal Publication

published 16 Jan, 2023

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Background: Post Cardiac Surgery Sternal Wound Infection (SWI) remains an important cause of extra morbidity, mortality and cost. The objective was to identify risk factors of SWI in a Cardiac Centre in Eastern Saudi Arabia as part of investigation into Surveillance variance in the local rates of SWI.

Methods: We included Cases and controls from a cross section of patients who underwent major Cardiac surgery between 2017–2020 matched for age, gender and time of surgery. Explanatory Logistic Regression Model was fitted to estimate the risk factors.

Results: N = 204 (51 cases and 153 controls matching ratio 1:3, from a source population of 985 patients). factors significantly associated with SWI in the final Multivariate

Model: Hospital stay OR(1.05, Cl 1.01–1.10), Graft Conduit BIMA vs No Graft OR(10.94,Cl 1.60-74.63), transfusion of both packed cells plus other blood products vs no transfusion OR(3.53, Cl .93-13.44), HbA1c OR(1.09 Cl 0.84–1.41), BMI OR(1.25, Cl 1.04–1.50), perioperative Blood Glucose OR(1.02, Cl 1.004–1.03), surgery time OR(1.19, Cl 1.00-1.58).

Conclusions: the diverse aetiology, cross disciplinary nature of SWI prevention, and despite improved prevention and control practices including related care bundles with their proven value, SWI remain a serious challenge in Cardiac Surgery. Multidisciplinary consensus guidelines are well overdue.

sternal wound infection

mediastinitis

sternotomy

cardiac surgery

case control

Saudi Arabia

Sternal wound infection following Cardiac Surgery is relatively rare but serious complication associated with increased morbidity, mortality and cost.[1, 2] There is little, generally accepted, consensus prevention guidelines for this potentially fatal surgical complication, and the clinical practice is often not always supported by high quality data. [3]

Reported incidence of SWI varies widely (2–10%), with low incidence tend to be in studies of deep SWI excluding the superficial and donor site infections. Variation in incidence is probably due to the various case definitions, classifications, geographical locations, hospitals settings, duration of follow up and time of publication [4, 5, 6, 7]. In Saudi and the MENA region, the few published reports revealed incidence between 4–10% [8, 9, 10].

Published predictors of SWI encompassed all stages of surgical care: preoperative, intraoperative and post operative. This includes risk factors such as pre morbidities, elective versus urgent procedures, standard of operation room aseptic and surgical techniques, type of CABG graft conduit, and environment of both operation and recovery rooms. And post operative course, need for re operation and blood transfusion, length of intensive care stay and duration of mechanical ventilation. Also, and as most SWI cases are diagnosed post discharge from hospital, the quality of home and outpatient wound care is a reported predictor. [11, 12] The best pre operative predictive index is a 3-points score published by Friedman and colleagues utilizing a large data base of 4987 patients who underwent CABG surgery [13].

Recently, Emilo Bouza and associates published a multidisciplinary consensus guideline which included the following proven intervention to reduce SWI : glycaemic control, smoking cessation, weight reduction, incision site preparation with depilatory cream or electric razor to remove the hair and chlorhexidine rather than povidone-based products, screening and eradication of S.aureus, and systematic decontamination when time or other reasons preclude screening. Eradication of S.aureus should be with nasal mupirocin and chlorhexidine for the skin [3].

Modern Infection prevention and control practices, including care bundles have reduced the rates of SWI but their implementation and enforcement is an ongoing challenge. Despite the above-mentioned consensus preventive interventions, we noted variance in our rates of SWI and the process was out of control in the SWI Control Chart. Following implementation of several quality improvement projects, we conducted this focused study to look further into the local risk factors and quantify their relative impact and prioritize accordingly.

Our objective was to identify possible preventive interventions, and to evaluate the effectiveness of the current ones in reducing incidence of SWI. The prespecified hypothesis was that a range of specific preoperative and environmental factors would explain the incidence of SWI in our setting. It is generally accepted that SWI rates above 2% is indication of inadequate surgical practice. This is particularly important in deep sternal wound infections (mediastinitis) given their high mortality rate 10–40%. The heralding era of Value Based Purchasing, and potential non reimbursement of SWI, which already happened elsewhere, will add to the provider’s costs associated with long hospital stay and complex therapy[7].

Study design

We used a matched case-control design to compare the exposure of patients who underwent major cardiac surgery to a range of risk factors, chosen based on hypothesis, and who went on to develop SWI (cases) with those from the same source population who did not (controls). The source population is an open cohort of patients who had cardiac surgery during the defined time period 2017-2020.The matching ratio was 1:3 and cases were matched to control for unmeasured confounders. Explanatory Covariates included a range of preoperative, intraoperative and postoperative risk factors, which were chosen from root cause analysis, brain storming and published literature of known predictors of SWI. Institutional approval to conduct the study was obtained.

Setting

Saud Al-Babtain Cardiac Centre serve the Eastern Province of Saudi Arabia and neighbouring areas since 2001 with a total capacity of 64 beds, and it is located within the 500 bed Dammam Medical Complex.

The Cardiac Surgery data base (Dendrite version 1.7) was interrogated in November 2021 and all patients who underwent major cardiac surgery including CABG, valve replacement, and repairs of all kinds (ICD-10: PCS 012, 02R, 02Q) between 1^st January 2017-31^st December 2020 were recruited as the source population. Both exposure and outcome data were available at the time of data collection. We had no comorbid or gender restrictions. Exclusion criteria: age <18 years, isolated pericardial surgery, short procedure, and cardiac surgery without full median sternotomy.

Participants

Cases of SWI between 2017-2020 were ascertained through the Health Care associated Infection Surveillance records. The cases were diagnosed according to the CDC/NHSN case definition which include total SWI: superficial, and deep infections. The controls were selected from the same source population. Hospital-based controls were deemed appropriate and convenient as they best represent the catchment population of the hospital, and the exposures variables collection would be more convenient. The case definition is robust, and collection of variables from hospital records reduced the likelihood of measurement and recall bias respectively.

We initially planned to select 4 controls who did not develop SWI after their cardiac surgery for every case who did. This ratio and the matching was chosen to maximize the power and efficiency of detecting the effect estimates of the exposure covariates’ odds ratio. Also, matching allowed control of unmeasured confounders and convenient sampling of the controls. However, we obtained only randomly selected controls to cases at ratio of 3:1 because of lack of enough matched female controls in our study population. Independently of exposure, we selected and individually matched controls for gender, age and time of surgery. Gender was randomly matched first, then age +/- 2 years, and finally time of surgery +/- 2 years [14,15].

Variables sources and measurement

The outcome variable was binary, being a case of SWI or Control coded as indicator variable one and zero respectively. Our candidate explanatory variables included 23 covariates, both continuous, and categorical. Some are derived variables from the raw data. In addition, we scaled some continuous variables to clinically relevant groups. Also, we added two design variable, strata and time, to allow conditional logistic regression analysis. The continuous variables were age, inpatient days, HbA1c, weight, BMI, EuroSCORE II, perioperative blood glucose, haemoglobin, and white cell count and surgery time. And the categorical variable included gender, mortality, procedure, graft conduit, diabetes, smoking, pre morbidity, Antibiotic prophylaxis, Infection control supplies status, enrolment of an on-going antibiotic prophylaxis study, environmental issues in the operating room, surgeon ID code, blood transfusion, and re exploration.

The sources and measurements of our variables included the dendrite data base where data was retrieved directly from medical and laboratory records, and infection control surveillance records was obtained separately. There is dedicated personnel who enter clinical and laboratory data into the database. Laboratory data was obtained from a single inhouse laboratory, observing the CLSI standards, which did not change its methods within the study period. Also, the infection control staff is well trained in surveillance methods and use a robust inpatient and outpatient surveillance system based on the NHSN case definition. There were no differences between cases and controls in the way of measuring the outcome and exposure variables. Potential confounders included: HA1c, weight, BMI, pre morbidities, blood transfusion and re exploration. And the anticipated effect modifiers were age and gender.

Mitigation of potential sources of bias

Cases of SWI were ascertained within the continuous surveillance system for hospital acquired infections using internationally recognized methods and documentation guidance (NHSN). The latter uses both clinical and microbiological criteria to confirm the diagnosis, and is concurrent during hospital stay with outpatient follow up. This make misclassification unlikely. Also, the exposures variables consist of clinical and laboratory data documented in the participants’ medical records and hence not subject to recall bias. Furthermore, we chose cases and controls completely independent of exposure. In addition, the design of our study and source of variables did not have issues of potential participation and non-participation as we had no questionaries and, of course, we had no follow related potential sources of bias in this case control design. So, overall, the potential for selection and misclassification bias is little. Thus, we assume neither the external or infernal validity of our study are likely to be compromised.

Sample size

The sample size was determined by the number of cases during the study period. The cases were 51 and the matched control 153, which made our total study size 204. Also, we entered this sample number in the CDC EpiInfo sample size calculator. It calculated detection ability of OR difference of 2.60, for population exposure of 20%, and 6.06 for exposure of 2% at 80% power and 5% alpha.

Statistical Methods

Statistical Analysis Software- (SAS)–version 9.4 was used to analyze all the data after importation from the final excel workbook where it was stored after collection from the sources. We used t-test, ChiSq-test and conditional logistic regression to compare patients with SWI and their matched controls for selected perioperative risk factors.

We visualized, cleaned, corrected, recoded, scaled, and filled-in missing values by Multiple imputation as data was assumed Missed at Random (MAR). After, initially, filling-in missing values by reviewing the sources, the remaining missing were HbA1c (39/204,19%) and Surgery time (25/204,12%). No missing outcome variables. Multiple Imputation was used due to the relatively large missing values. Sensitivity testing with Incomplete data, single imputation, and full information maximum likelihood (FIML) was used for comparison.

Then, we obtained descriptive statistics, correlation matrix, and compared the distribution of covariate between cases and controls. Finally, we fitted several univariate and a final multivariate logistic regression model. We used conditional analysis in all model.

Descriptive statistics included summaries and plots of all the exposure variables using univariate and frequency procedures for continuous, and categorical variables respectively. Also, differences of exposure between cases and controls were compared with t-test and Chi-sq test. Furthermore, a correlation matrix was obtained to detect any potential colinear variables, cut-off (r=>0.7).

Univariate analysis yielded variables associated with SWI, and those with p-value <.10 were selected to progress to the final multivariate logistic model. We entered all determinant variables individually in 3 blocks. Confounding and interactions were identified. The total covariates were 23, the final complete multivariate model included 12 significant covariates with p<0.5 from univariate analysis including confounders. Two colinear variables were removed. Model diagnostics were used to assess and compare the models, and their goodness of fit (GOF).

Among 985 eligible patients who underwent major cardiac surgery with median sternotomy, two were excluded because of age under 18 years. There were 51 cases of SWI, no exclusive donor site infection, and 153 randomly selected matched controls from the remaining 932 patients who met the inclusion criteria. Thus, the total sample size was 204 patients. The distribution of missing data was stable among groups and the maximum was 16% and 19% in the HbA1c variable among cases and controls respectively while the surgery time missing values were identical in each group at 12%. The clinical and demographic characteristics of participant are summarized in Table 1.

We initially considered the following variables: preoperative, and total inpatient days, mortality status, Surgical procedure, graft conduit, diabetes status, HbA1c, BMI, blood glucose within 24-hours of surgery, weight, smoker status, premorbid, EuroSCORE-II, participation of an ongoing antibiotic prophylaxis study, shortage of Chlorhexdine Gluconate skin prep (CHG), operating room (OR) environmental issues, surgeon ID, Hb, WBC, blood transfusion, re exploration, and duration of surgery. The variables associated with SWI in univariates analysis, with p-value < .10, unadjusted estimates are shown in Table 2.

Eight variables were removed at this stage: pre operative hospital days, mortality status, smoking status, surgeon ID, Hb and WBC, all had P-value > .10. And procedure and operating room environment were excluded because of collinearity with graft conduit and the shortage of CHG respectively.

In Table 3. we display the final adjusted estimates associated with SWI. Eleven variables were included in the global final multivariate Conditional Logistic Model: inpatient days, graft conduit, HbA1c, weight, BMI, EuroSCORE II, shortage of CGH, perioperative blood glucose, blood transfusion, re exploration and surgery time.

Scaled continuous variables along clinically relevant groups: HbA1c percentage < 6 > 6 < 10 and > 10%, BMI > 30Kg/M² and < 30 Kg/M², EuroSCORE < 2, >2 < 6 and > 6 were entered in the model. they didn’t not reveal more information than their respective scale forms except BMI > 30.

We included the following confounders in the final model: weight is a confounder of BMI, blood transfusion confounded re exploration, all at > 10% change of the regression coefficient size compared to the univariate values. Also, the matching variables age and gender significantly modified the effects of graft and procedure variables.

Eighty-five Microbiology samples obtained from SWI cases revealed skin commensal (44/52%), gut bacteria and Pseudomonas aeruginosa (28/33%), and no growth in (13/15%). The commensal were mainly Staphylococcus species including a few cases of MRSA while the gut bacteria were, mainly, Klebsiella pneumoniae, Escherichia choli plus Pseudomonas aeruginosa as detailed in Table 4.

Hosmer and Lemeshaw test was insignificant at P-value of < .26. Also, Cox & Snell and Nagelkerke GOF R square were .28 and .41 respectively. All suggest that our final logistic model is a good fit for the data. In addition, the overall classifying accuracy of the model is good with the ROC being .84 as shown in Fig. 1.

As the missing values were relatively high for HbA1c (60/204, 19), and Operation time (25/204,12%) variables, and distributed between cases and control 16% and 20% for HbA1c, and 12% each for operation time respectively. Non the less we obtained 2 models using single, mean/median imputed data sets and FIML analyses. All produced comparable models to our final model shown in Table 3. Alternative GOF tests were used for comparison due to well-recognized short comings of the Hosmer Lemeshaw GOF test as, described by (Allison,2014). Skutel, deviance and Pearson, all did not reject our model at: Skutel 0.28, McFadden R-square 0.25, Deviance 0.27 and Pearson 0.45. The model did not need adding non-linearity or interaction terms.[16]

Our study revealed that inpatient days, graft conduit, HbA1c, weight, BMI, EuroSCORE II score, perioperative blood glucose, blood transfusion, re exploration and surgery time are associated with developing SWI.

Our results are consistent with previously published data elsewhere. Despite implementation of the surgical wound infection prevention bundle, modifiable risk factors still figured as predictors in our model. For example, perioperative blood glucose predicted SWI despite being within the recommended lrange140- 180mg/dl (mean 174.7 mg/dl in SWI cases, and 155.7mg/dl in controls), which suggest tighter control, rather than moderate, is required in selected patient groups, than recommended for perioperative level by authoritative guidelines. In addition, innovative multidisciplinary implementation of proven intervention is required. [5, 6, 10, 17, 18]

Risk factors not reaching statistical significance, which were previously consistently reported in the literature as risk factors, were most likely not identified because of underpower of our study and or bias towards the null due to matching with 3 variables. These include duration of preoperative admission, smoker and diabetes status, premorbid conditions. In addition, we chose 3 variables: shortage of CHG skin prep products, participation of an ongoing surgical antibiotic prophylaxis, and presence of operation room pressure, temperature and or humidity concerns around the time of surgery (within 72 hours), all did not predict SWI in the final model. An exploratory analysis using unsplit imputation data set (not shown) revealed that all these factors were significant except smoking, and operation room environmental issues with a larger sample size.

Pathogens isolated in SWI were in line with previously published data from Saudi Arabia and elsewhere, and the prevalence of multidrug resistant organisms is small.[9]

The strength of this study is evaluating 23 potential risk factors of SWI in a good fit logistic regression model. Twelve were associated with SWI in univariate regression analysis, and 8 of which remained significant, or trending, in the final model. This is similar to the risk factors published in Western European countries. In addition, shortages in CHG skin prep supplies, and hence use Bovidone-base alternatives during the COVID-19 pandemic, was associated with 16-fold increase in SWI (OR 17.73), McNamar’s test p < .000 but the 95%CI is too wide. Our study is an addition to the few published literature on determinants of SWI in the MENA region, and is likely to be generalizable.

Weaknesses of this work include the relatively small sample size (determined by the study period needed). The low sensitivity of the OR environment and CHG shortage variables as they are not strictly individual level variables, and hence did not show significant effects. Also, we did not study important predictors of SWI due to non-availability, which happened to be less studied generally. The latter include variation in operative techniques, hemostasis materials, diathermy use and post discharge home wound are. These are likely to be sources of residual confounding.

Practically, this study helped to inform an ongoing quality improvement project in our setting by prioritizing the tacking of certain risk factors. Also, we highlighted the need for improved perioperative glycemic control, and review selection for bilateral internal memory grafts and rationalize blood transfusion all recognized causes of delayed sternal healing and risk of SWI. Some centers established dedicated team and multidisciplinary protocols to supervise perioperative glycemic control. This should, however, be individualized and not impact critically ill patient who would suffer more from hypoglycemia as shown in the NICE-SUGAR study, (Finer et al,2009). However, the real challenge here is likely to be the implementation of the multidisciplinary protocols whatever the blood glucose target is, which remain controversial. [19, 20, 21]

In conclusion, the advent of value-based contracting, and non-enumeration of surgical wound infections, should focus the healthcare providers’ minds on reduction of SWI and hence the need for more robust multidisciplinary research, and evidence-based prevention efforts, especially on the, so far, poorly studied potential risks mentioned above. Our study suggested that implementation of preventive interventions of SWI need to be improved.

Ethical Approval

Internal Review Board of the SBCC approval was obtained.

Consent to participate and publish

Not applicable, as data was retrieved from an on-going primary cohort in which consent was already obtained in the past.

Competing interests

Non

Authors' contributions

A.I. conceived the study, obtained he data, did the analysis and wrote the manuscript.

Funding

No funding

Availability of data and materials

At request from the corresponding author.

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Table 1. Clinical and Demographic Characteristics of the Study Participants

	SWI Cases (N=51)	Controls (N=153)	Overall (N=204)
Variable Age(years)	Mean(sd) N(%) 58.5(9.00)	Mean(sd) N(%) 58.6(8.95)	p-value 58.57
Gender Male Female Mortality Smoker Premorbid (1) (2) Diabetes	36(17) 15(7) 4(7.8) 11(21) 37(73) 6(12) 42(82)	108(53) 45(23) 6(3.4) 37(24) 110(72) 10(7) 113(74)	144(70) 60(30) 10(5) 48(24) 147(72) 16(8) 155(76)
BMI	32.2(5.77)	29.4(5.81)	30.1(5.92) <.05
HbA1c^a	8.2(2.16)	7.3(2.05)	7.5(2.12) <.05
Procedure CABG CABG+Vlave Valve only Other	39(19) 3(2) 6(3) 4(2)	110(54) 7(3) 30(15) 6(3)	162(73) 10(5) 36(18) 50(4)
Graft Conduit LIMA+SVG LIMA+RIMA Other grafts No Graft	33(16) 10(5) 1(0.5) 7(3)	73(36) 16(13) 16(8) 38(19)	<.05 106(52) 26(18) 17(8) 41(22)
Inpatient Days* EuroSCOREII* Abx Study CHG shortage OR Environment	14(22) 3.4(2.94) 35(69) 41(80) 41(80)	12.0(8) 3.4(3.86) 93(61) 110(72) 110(72)	12(10) <.05 3.4(3.36) 128(63) 151(74) <.05
Periop Blood Glucose	174.7(53.43)	155.7(32.30)	160.(39.52) <.05
Transfusion RBCs FFP+/-Platelets RBCs +FFP/Platelets No Transfusion	8(4) 9(4) 24(12) 10(5)	20(10) 28(14) 43(21) 62(30)	<.05 28(14) 37(18) 67(33) 72(35)
Re exploration	14(7)	18(9)	32(16) <.05
Surgery Time^a	6.1(1.33)	5.6(1.8)	5.7(1.71) <.05

Note: P-value given when significant at <.05.*Median(IQR).Premorbid (1)=one premorbid condition, and (2)=two conditions. CHG=Chlorhexidine Gluconate skin prep.

^aMissing data: HbA1c in cases 16%, controls 19%, Surgery time 12% in each group.

Table 2. Univariate Conditional Logistic Regression Analysis

	Sig.	(OR)	95.0% CI for (OR)
	Sig.	(OR)	Lower	Upper
Inpatient Days	.001	1.046	1.019	1.074
Graft Conduit used	.071
Glycosylated Hb	.010	1.198	1.044	1.374
Weight	.196	1.013	.993	1.034
Body Mass Index	.004	1.091	1.029	1.118
EuroSCORE II score	.084	.908	.814	1.013
CHG Shortage	.076	.148	.018	1.221
Periop. Blood Glucose	.004	1.014	1.004	1.023
Blood Transfusion	.004
Re exploration	.009	.323	.139	.751
Surgery Time	.071	1.174	.986	1.398

Note: 95% CI given for continuous and binary variable.

Table 3. The Final Multivariate Conditional Logistic Regression Model

	B	. Sig	OR	95% CI for (OR)
Inpatient Days	.061	.024	1.054	1.007	1.104
Graft 1 vs 0	2.327	.166	3.456	0.865	13.816
Graft 2 vs 0	3.456	.062	10.936	1.602	74.636
Graft 3 vs 0	-1.073	.473	0.179	0.012	2.670
HbA1c	.084	.546	1.087	0.840	1.405
Wt.	-.071	.037	0.937	0.877	1.000
BMI	.243	.021	1.249	1.039	1.501
Periop Blood glucose	-.105	.234	1.018	1.004	1.032
EuroSCORE II	2.681	.154	0.926	0.796	1.077
CHG 1 vs 0	.017	.020	17.729	0.041	753.750
Transfusion 1 vs 0	1.256	.218	3.366	0.600	18.888
Transfusion 2 vs 0	1.272	.115	2.878	0.706	11.737
Transfusion 3 vs 0	1.168	.096	3.529	0.927	13.442
Rexploration 1 vs 0	.849	.233	1.850	0.513	6.669
Surgery Time	.240	.172	1.187	0.890	1.584

Note: Graft Conduit:0=No Graft (Ref), 1=LIMA+SVG, 2=LIMA+RIMA, 3=Other

Diabetes: 0=non-Diabetic, 1=Diabetic. 0=No Blood Transfusion (Ref),1=Packed

RBCs, 2=FFP and /or Platelets,3=RBCs+FFP‎/Platelets. ^awithin 24-hours of surgery.

CHG=Chlorhexidine Gluconate antiseptic shortage.

Table 4. Microbiology of cases of Sl wound Infection

	Frequency	Percent	Cumulative Percent
No Growth^a	13	15.3	15.3
Skin Commensals^b	44	51.8	67.1
Gut Bacteria^c	28	32.9	100.0
Total	85	100.0

Notes: ^aWound culture negative. ^bS.aureus, S.epidermidis and few MRSA.

^c Mainly K. pneumoniae, E. choli, and P. aeruginosa -.few MDR.

No competing interests reported.

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Journal Publication

published 16 Jan, 2023

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Editorial decision: Major revision
05 Nov, 2022
Reviews received at journal
23 Aug, 2022
Reviewers agreed at journal
22 Aug, 2022
Reviewers agreed at journal
22 Aug, 2022
Reviewers invited by journal
22 Aug, 2022
Editor assigned by journal
22 Aug, 2022
Submission checks completed at journal
22 Aug, 2022
First submitted to journal
16 Aug, 2022

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Predictors of Sternal Wound Infection Post Cardiac Surgery in a Saudi Centre: A Case Control Study

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Status:

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Version 1