Patients at High-Risk for Surgical Site Infection

Abstract

Background:

Surgical site infections (SSIs) are a significant healthcare quality issue, resulting in increased morbidity, disability, length of stay, resource utilization, and costs. Identification of high-risk patients may improve pre-operative counseling, inform resource utilization, and allow modifications in peri-operative management to optimize outcomes.

Methods:

Review of the pertinent English-language literature.

Results:

High-risk surgical patients may be identified on the basis of individual risk factors or combinations of factors. In particular, statistical models and risk calculators may be useful in predicting infectious risks, both in general and for SSIs. These models differ in the number of variables; inclusion of pre-operative, intra-operative, or post-operative variables; ease of calculation; and specificity for particular procedures. Furthermore, the models differ in their accuracy in stratifying risk. Biomarkers may be a promising way to identify patients at high risk of infectious complications.

Conclusions:

Although multiple strategies exist for identifying surgical patients at high risk for SSIs, no one strategy is superior for all patients. Further efforts are necessary to determine if risk stratification in combination with risk modification can reduce SSIs in these patient populations.

Surgical site infections (SSIs) are a significant healthcare problem in the United States and globally [1]. Such infections are associated with increased morbidity, mortality, hospital length of stay, resource utilization, and costs [2 –5]. A 2012 analysis using American College of Surgeons (ACS) National Surgical Quality Improvement Project (NSQIP) data found that SSIs were the most common reason for unplanned readmissions [4]. A 2013 meta-analysis noted that SSIs contribute the most to overall costs (33.7% of $9.8 billion per year, or $3.6 billion annually) for common healthcare-acquired infections [5]. The estimated cost of each SSI was estimated to be $20,785 [5]. Furthermore, a population-based study in Europe reported that the attributable burden of an SSI is 11.6 disability-adjusted life years per 100,000 population [6]. Thus, SSIs have a significant impact on healthcare costs and patient outcomes, both short- and long-term.

Multiple evidence-based interventions exist for the prevention of SSIs [7, 8]. Although a systematic review of healthcare-associated infection-prevention programs in general showed a favorable cost-benefit ratio [9], the number and quality of economic evaluations of specific interventions (surgical hand and skin antisepsis and wound dressings) to prevent SSIs are poor [10]. Furthermore, compliance with multi-faceted prevention programs or prevention bundles can be difficult [11, 12]. Thus, it may be more practical to reserve more expensive, complex, or resource-intensive interventions for patients at high risk of SSI.

In addition to optimizing resource utilization, other benefits of accurate risk stratification include better pre-operative counseling regarding expectations and risks, peri-operative risk modification in order to reduce SSIs, and increased post-operative surveillance to detect and treat SSIs early. Multiple methods have been used for stratifying patients' SSI risk. One method is to target patients having a specific risk factor. Another is to utilize risk calculators or scoring systems in order to predict the risk of SSI and other complications. A third method is to utilize biomarkers predictive of clinical outcomes such as SSIs. This paper reviews the evidence for and implications of using each of these methods to identify patients at high risk of SSIs.

Specific Risk Factors

Factors at the patient, operative, and institutional levels can affect a patient's risk of SSI (Table 1) [13]. All should be considered in determining a patient's overall risk and for devising strategies to mitigate that risk. Because this review is about the high-risk patient, only patient-level factors will be discussed. Furthermore, the focus is on whether these factors predict risk accurately and if so, how that risk can be reduced. Lastly, this is not intended to be a comprehensive review, so not all risk factors will be discussed in detail.

Table 1.

Examples of Risk Factors for Surgical Site Infection on the Basis of Patient, Operative, and Institutional-Level Factors

Patient-level factors
Characteristics
Sex
Age
Frailty
Patient dependence
Socioeconomic factors
Lifestyle
Smoking status
Alcohol abuse
Co-morbidities
Diabetes mellitus
Chronic obstructive pulmonary disease
Congestive heart failure
Acute myocardial infarction
Renal insufficiency
Hypertension
Osteoporosis
Multiple co-morbidities (i.e., Charlson Co-Morbidity Score)
Medications
Immunosuppression
Prior environment
Pre-operative length of stay
Admission from a long-term healthcare facility
Risk calculators or scoring systems
National Nosocomial Infections Surveillance
American Society of Anesthesiologists (ASA)

Operative-level factors
Procedure characteristics
Incision class
Type of surgery
Elective versus emergency procedure
Case complexity
Duration of operation
Blood loss/blood transfusions
Medical device implantation

Institutional-level factors
Current environment
Safety culture
Hospital characteristics
Size
Experience
Physician
Hospital

Patient characteristics

Multiple studies have identified patient characteristics such as age and sex as risk factors for SSI [13,14]. Although acknowledgement of these risk factors may improve pre-operative counseling and peri-operative management, many of these factors are not modifiable. Therefore, more recently, there has been greater emphasis on potentially modifiable patient characteristics such as frailty, which can be associated with age and disability but that is not a surrogate for either. Frailty is defined as increased vulnerability to everyday or acute stressors resulting from a decline in physiologic reserve and function across multiple organ systems [15]. The ACS NSQIP and the American Geriatrics Society have devised recommendations for optimal pre-operative assessment of geriatric patients [16]. They recommend that all geriatric patients have a baseline frailty assessment, given that it is a risk factor for poor surgical outcomes [17]. Multiple scoring systems for frailty have been developed, and several have been correlated with SSIs and other complications after surgery [18 –20]. Interventions that have been employed to prevent or reduce frailty have included physical activity, nutrition, home modifications, comprehensive geriatric assessment, or a combination of these [21]. However, not all of these methods are effective [21], nor is it known what the optimal methods are for implementing these interventions (i.e., for increasing physical activity) [22]. For example, current systematic reviews of the effect of pre-habilitation, which can include pre-operative physical or nutritional therapy or both, on surgical outcomes describe a paucity of high-quality studies, as well as inconclusive results [23,24]. Moreover, most of the trials have focused on overall complications, length of stay, and mortality rate rather than on specific complications such as SSI. Further research is required to determine whether short-term interventions that ameliorate frailty can reduce surgical complications, including SSIs.

Lifestyle characteristics

Smoking and alcohol abuse have both been reported to be risk factors for SSIs [13]. Smoking in particular has been found to be a risk factor for both impaired wound healing and SSIs for many procedures [8,13,25 –28]. Smoking is included in risk prediction models such as the Surgical Site Infection Risk Score (SSIRS) [29]. A systematic review of studies evaluating the pathophysiology of smoking suggests three mechanisms by which this practice impairs wound healing and increases infections: Reduction of tissue oxygenation and perfusion, impairment of the inflammatory response and bactericidal mechanisms, and decrease in site cell proliferation and remodeling [30]. A meta-analysis of almost 500,000 patients suggested that smoking increases the odds of SSI by 79% (odds ratio [OR] 1.79; 95% confidence interval [CI] 1.57–2.07) [31]. Furthermore, based on four randomized controlled trials, cessation of smoking is associated with a 57% relative risk reduction in SSIs (OR 0.43; 95% CI 0.21–0.85) [31]. The optimal timing of pre-operative smoking cessation and its impact on both incision healing and other problems, such as respiratory complications, is controversial [32, 33]. However, the ACS and Surgical Infection Society (SIS) 2016 guidelines recommend smoking cessation four to six wks prior to surgery as a way to prevent SSIs [8].

Patient co-morbidities

Multiple patient co-morbidities have been associated with SSIs (Table 1). In a scoping review of risk factors, diabetes mellitus was the co-morbidity most frequently studied in association with SSIs [13]. Poor glycemic control, both long and short term, has been linked to SSIs, and multiple studies have demonstrated an association between peri-operative hyperglycemia, regardless of diabetes mellitus status, and SSIs [34]. Furthermore, data from a multi-center quality-improvement initiative suggest that better glycemic control reduces SSIs [35]. However, whether tight glycemic control, traditionally defined as a serum glucose concentration below 110 mg/dL, is a feasible, effective, and safe method for reducing SSIs is still controversial [34,36]. The ACS/SIS guidelines recommend a target of 110–150 mg/dL or <180 mg/dL in patients undergoing cardiac surgery [8]. With regard to long-term glycemic control prior to surgery, the data are conflicting. Although multiple retrospective studies have demonstrated an association between an elevated hemoglobin A_1c (HbA_1c) and SSIs [37 –39], other investigators have reported no correlation [40]. Furthermore, a systematic review suggested that an elevated HbA_1c was not definitely associated with SSIs.(41) Other studies that have examined hemoglobin A_1c and hyperglycemia together suggest that both are associated with SSIs [42 –44]. However, high-quality, randomized trial data on whether targeting a specific HbA_1c value pre-operatively reduces SSIs are lacking. The ACS/SIS guidelines recommend that “optimal” blood glucose control be obtained pre-operatively but acknowledge the evidence gap [8].

Multiple co-morbidities clearly increase the risk of SSIs [13]. Therefore, another approach to identifying the high-risk patient is to use prediction models or risk calculators that incorporate multiple co-morbidities and other characteristics.

Models for Predicting SSI Risk

Multiple scores and models have been developed to predict the outcomes of surgery (Table 2), and choosing the right model can be challenging [45,46]. The ideal model should be accurate, objective, and easy to use. Although no model is perfect, these tools can be useful in risk-stratifying surgical patients in order to determine whether pre-operative modification of risk factors or changes in surgical approach should be considered. The available models differ in several respects. First, some models are specific to SSIs or infectious complications, whereas others predict all complications or death. Some models can be applied broadly across multiple types of surgical procedures, whereas others are procedure specific. Some models utilize only pre-operative variables, whereas others incorporate pre-operative and intra-operative factors. Lastly, models differ in the number of variables included. Several popular models for predicting SSI risk are discussed below. However, a comprehensive description of all published models is beyond the scope of this paper.

Table 2.

Scoring Systems

System	Year	No. of variables	Inclusion of intra-operative variables	Outcomes predicted
Pre-operative
American Society of Anesthesiologists (ASA)	1941	Unlimited	No	None
Charlson Comorbidity Index	1987	19	No	Death
Surgical Risk Scale(76)	2002	3	No	Death
Surgical Outcome Risk Tool (SORT)(77)	2014	6	No	Death
Post-operative
Portsmouth Physiological and Operative Severity Score for the enumeration of Mortality and morbidity (P-POSSUM)(78)	1998	18	6	Morbidity and death
American College of Surgeons National Surgical Quality Improvement Project (ACS NSQIP)(60)	1997	72	15	Morbidity and death
Surgical Apgar Score (SAS)(79)	2007	3	3	Morbidity and death
National Nosocomial Infection Surveillance (NNIS)(56)	1991	3	Yes	SSI
Contamination, Obesity, Laparotomy, and ASA (COLA)(80)	2012	4	Yes	Colorectal SSI
Preventie Ziekenhuisinfecties door Surveillance (PREZIES)(81)	2006	4	No^a	SSI
Ventral Hernia Risk Score(58)	2013	5	Yes	Ventral hernia SSI

Includes discharge diagnosis.

Multiple scoring systems exist to predict death, morbidity, and infectious complications such as SSIs. They differ in terms of variables included, specificity to procedures, and accuracy.

Modified from Sobol and Wunsch [45].

Incision class

The Association of periOperative Registered Nurses (AORN) incision classification system is used frequently to stratify patients on the basis of SSI risk [47]. There are four classes: Clean (I), clean-contaminated (II), contaminated (III), and dirty (IV). The risk of SSI increases with the extent of site contamination. However, recent analyses of multi-institutional datasets suggest that the absolute risk of infection with each incision class may be less than previously estimated [48,49]. Furthermore, results differ regarding whether incision class predicts overall SSI risk accurately [50,51]. The lack of association between incision class and SSI may be attributable in part to misclassification. Several studies in pediatric surgery have demonstrated inaccuracies in the documentation of incision class, even after multi-faceted educational interventions [48,52 –55]. Nonetheless, site classification continues to be used in many models predicting SSIs and other post-operative outcomes.

National Nosocomial Infections Surveillance (NNIS)

The National Nosocomial Infections Surveillance (NNIS) system builds on incision classification. Using this system, the American Society of Anesthesiologists (ASA) physical status classification, and the duration of the operative procedure beyond the 75^th percentile, the NNIS system stratifies patients into three classes of risk for SSI [56]. However, the predictive ability of NNIS is poor, with one study reporting only 57% accuracy for SSIs prediction after colorectal resections [57]. In another study, NNIs had 64% accuracy in predicting SSIs after open ventral hernia repair [58]. Lastly, in a study of valvular and coronary artery bypass grafting surgery, NNIS had accuracies of 62% and 60%, respectively, for predicting SSIs [59].

American College of Surgeons National Surgical Quality Improvement Project (ACS NSQIP)

The ACS NSQIP database provides risk- and reliability-adjusted data that allow hospitals and surgeons to compare their outcomes, including SSIs. They also have a risk calculator that utilizes multiple variables to calculate patients' morbidity and likelihood of death after surgery [60]. This calculator is not specific for types of surgery or complications. Several studies have suggested that the risk calculator is inaccurate for predicting SSIs [49,61,62]. A study by the Ventral Hernia Outcomes Collaborative compared multiple risk calculators, including the ACS NSQIP tool, for their ability to stratify patients for SSIs after open ventral hernia repair [49]. The investigators found that the ACS NSQIP risk calculator had modest ability to predict SSIs (73% accuracy). The accuracy of any of the calculators ranged from 55% to 81%. Despite the limitations of the risk calculator, ACS NSQIP can be used to identify risk factors for SSI in specific procedures or subspecialties [63 –66].

Other models

Many other models for predicting SSIs build on the incision classification and NNIS systems (Table 2).

Accuracy

The utility of predictive models, scoring systems, and risk calculators depends on their accuracy. As already noted, the accuracy of different models for predicting SSI after open ventral hernia repair is only modest [49]. This finding was replicated in a study comparing models for predicting SSI after colorectal surgery. Bergquist et al. compared the performance of four models in predicting SSIs in 2,300 colorectal surgery patients from a single institution: NNIS; Contamination, Obesity, Laparotomy, and ASA score (COLA); Preventie Ziekenhuisinfecties door Surveillance (PREZIES); and NSQIP [57]. The authors found that the c-statistic for all four models ranged from 0.57 to 0.61. The c-statistic is equivalent to the area under the receiver operating characteristic (ROC) curve and represents the predictive accuracy of a model; a c-statistic of 0.50 indicates that the model is no more accurate than flipping a coin. A systematic review of 16 risk scores for predicting SSIs or peri-prosthetic joint infections after joint arthroplasty found that only three scores had a discriminative ability greater than 0.70 [67]. These studies demonstrate the limited accuracy of the available models for predicting SSIs.

Biomarkers

Biomarkers are “objective, quantifiable characteristics of biological processes” that can correlate with patients' clinical outcomes [68]. Both pre-operative and post-operative markers have been evaluated for their correlation with the development of SSI or other infectious complications [69 –75]. However, if the intent is to modify treatment to prevent SSIs, pre-operative biomarkers would be more useful.

The pre-operative biomarkers most commonly studied include measures of nutritional status such as serum pre-albumin or albumin concentrations and pre-operative systemic inflammation markers such as C-reactive protein (CRP). Multiple studies of spine surgery patients have identified a low pre-albumin concentration as a predictor of SSI [73 –75]. Whereas the ACS/SIS guidelines do not mention pre-operative nutritional support to prevent SSIs, the World Health Organization states that oral or enteral multiple nutrient-enhanced formulas should be considered in underweight patients undergoing major surgical procedures [7]. The WHO performed their own meta-analyses and determined that multiple nutrient-enhanced nutritional formulas resulted in a 47% reduction in the odds of an SSI (OR 0.53; 95% CI 0.30–0.91) compared with standard formula in randomized controlled trials [7]. However, the quality of the evidence was rated very low. Single nutrient-enhanced formulas did not have any effect on SSIs [7]. Furthermore, it is not clear whether nutritional support yielding a specified pre-albumin target or a change in the pre-albumin concentration reduces SSIs.

Pre-operative inflammation, represented by elevated CRP and hypoalbuminemia, has been associated with more post-operative infections in patients undergoing gastrointestinal surgery [69 –71]. In a multi-center study of patients having elective gastrointestinal cancer surgery, the Glasgow Prognostic Score (GPS), which is a measure of systemic inflammation based on albumin and CRP, was associated with post-operative infection [70]. However, the GPS was not correlated with SSI specifically by univariate or multivariate analyses. In another multi-center prospective study of patients undergoing elective colorectal surgery, the areas under the ROC curves for pre-operative albumin and CRP concentrations were 0.62 and 0.57, respectively, for any infectious complication [69]. Further studies are necessary to determine if pre-operative systemic inflammation can be modified as a risk factor.

Pre-operative biomarkers are promising as adjuncts to other clinical tools for risk stratifying surgical patients. Risk scores utilizing only biomarkers, such as the GPS, are not specific for SSIs. Prospective trials are necessary to determine if modification of these pre-operative risk factors can reduce SSIs.

Implications

Although multiple studies have evaluated the prognostic accuracy of models and biomarkers to predict SSIs, many conclusions are based on a specific cohort of patients or on a single-center study. Only a few models have been validated externally [67]. Variations in defining risk factors and different risk-adjustment strategies can cause significant discordance between prediction methods. Misclassification and measurement errors can lead to inaccurate risk prediction even if the model is robust. Furthermore, unanswered questions remain regarding: (1) Whether changes in care based on risk stratification can reduce SSIs; (2) at what risk threshold(s) interventions should be implemented; and (3) at what risk threshold(s) it is cost-effective to recommend additional interventions to mitigate SSI risk.

Conclusions

Prevention of SSIs is a complex problem that requires a multi-faceted approach. Identification of high-risk patients and of other complications may result in interventions for pre-operative risk modification, change in operative strategy, triage of patients to a higher level of care post-operatively, or utilization of more resources for prevention and surveillance. However, no single approach for stratifying risk is accurate uniformly in all patients and for all procedures, and all methods of risk stratification have advantages and disadvantages. Furthermore, it is unknown at which threshold changes in management should occur to obtain effectiveness and cost-effectiveness.

Footnotes

Author Disclosure Statement

Neither of the authors has any competing financial interests.

References

Abbas

, Pittet

. Surgical site infection prevention: A global priority. J Hosp Infect, 2016; 93:319–332.

Graf

, Ott

, Vonberg

, Kuehn

, et al. Surgical site infections: Economic consequences for the health care system. Langenbecks Arch Surg, 2011; 396:453–459.

Kirkland

, Briggs

, Trivette

, et al. The impact of surgical-site infections in the 1990s: Attributable mortality, excess length of hospitalization, and extra costs. Infect Control Hosp Epidemiol, 1999; 20:725–730.

Merkow

, Ju

, Chung

, et al. Underlying reasons associated with hospital readmission following surgery in the United States. JAMA, 2015; 313:483–495.

Zimlichman

, Henderson

, Tamir

, et al. Health care-associated infections: A meta-analysis of costs and financial impact on the US health care system. JAMA Intern Med, 2013; 173:2039–2046.

Cassini

, Plachouras

, Eckmanns

, et al. Burden of six healthcare-associated infections on European population health: Estimating incidence-based disability-adjusted life years through a population prevalence-based modelling study. PLoS Med, 2016; 13:e1002150.

Allegranzi

, Bischoff

, de Jonge

, et al. New WHO recommendations on preoperative measures for surgical site infection prevention: An evidence-based global perspective. Lancet Infect Dis, 2016; 16:e276–e287.

Ban

, Minei

, Laronga

, et al. American College of Surgeons and Surgical Infection Society: Surgical site infection guidelines, 2016 update. J Am Coll Surg, 2017; 224:59–74.

Arefian

, Vogel

, Kwetkat

, Hartmann

. Economic evaluation of interventions for prevention of hospital acquired infections: A systematic review. PLoS One, 2016; 11:e0146381.

10.

Gillespie

, Chaboyer

, Erichsen-Andersson

, et al. Economic case for intraoperative interventions to prevent surgical-site infection. Br J Surg, 2017; 104:e55–e64.

11.

Schweizer

, Chiang

, Septimus

, et al. Association of a bundled intervention with surgical site infections among patients undergoing cardiac, hip, or knee surgery. JAMA, 2015; 313:2162–2171.

12.

Tanner

, Padley

, Assadian

, et al. Do surgical care bundles reduce the risk of surgical site infections in patients undergoing colorectal surgery? A systematic review and cohort meta-analysis of 8,515 patients. Surgery, 2015; 158:66–77.

13.

Korol

, Johnston

, Waser

, et al. A systematic review of risk factors associated with surgical site infections among surgical patients. PLoS One, 2013; 8:e83743.

14.

Alfonso-Sanchez

, Martinez

, Martin-Moreno

, et al. Analyzing the risk factors influencing surgical site infections: The site of environmental factors. Can J Surg, 2017; 60:17916.

15.

Fried

, Tangen

, Walston

, et al. Frailty in older adults: Evidence for a phenotype. J Gerontol A Biol Sci Med Sci, 2001; 56:M146–M156.

16.

Chow

, Rosenthal

, Merkow

, et al; American College of Surgeons National Surgical Quality Improvement P, et al. Optimal preoperative assessment of the geriatric surgical patient: A best practices guideline from the American College of Surgeons National Surgical Quality Improvement Program and the American Geriatrics Society. J Am Coll Surg, 2012; 215:453–466.

17.

Makary

, Segev

, Pronovost

, et al. Frailty as a predictor of surgical outcomes in older patients. J Am Coll Surg, 2010; 210:901–908.

18.

Ali

, Schwalb

, Nerenz

, et aI. Use of the modified frailty index to predict 30-day morbidity and mortality from spine surgery. J Neurosurg Spine, 2016; 25:537–541.

19.

Farhat

, Velanovich

, Falvo

, et al. Are the frail destined to fail? Frailty index as predictor of surgical morbidity and mortality in the elderly. J Trauma Acute Care Surg, 2012; 72:1526–1530.

20.

Karam

, Tsiouris

, Shepard

, et al. Simplified frailty index to predict adverse outcomes and mortality in vascular surgery patients. Ann Vasc Surg, 2013; 27:904–908.

21.

Puts

, Toubasi

, Andrew

, et al. Interventions to prevent or reduce the level of frailty in community-dwelling older adults: A scoping review of the literature and international policies. Age Ageing, 2017 doi: 10.1093/aging/afw247 (E-pub ahead of print).

22.

de Labra

, Guimaraes-Pinheiro

, Maseda

, et al. Effects of physical exercise interventions in frail older adults: A systematic review of randomized controlled trials. BMC Geriatr, 2015; 15:154.

23.

Looijaard

, Slee-Valentijn

, Otten

, Maier

. Physical and nutritional prehabilitation in older patients with colorectal carcinoma: A systematic review. J Geriatr Phys Ther, 2017 doi 10.1519/JPT.0000000000000125 (E-pub ahead of print).

24.

Moran

, Guinan

, McCormick

, et al. The ability of prehabilitation to influence postoperative outcome after intra-abdominal operation: A systematic review and meta-analysis. Surgery, 2016; 160:1189–1201.

25.

Singh

, Schleck

, Harmsen

, et al. Current tobacco use is associated with higher rates of implant revision and deep infection after total hip or knee arthroplasty: A prospective cohort study. BMC Med, 2015; 13:283.

26.

Hawn

, Houston

, Campagna

, et al. The attributable risk of smoking on surgical complications. Ann Surg, 2011; 254:914–920.

27.

Olsen

, Nickel

, Margenthaler

, et al. Development of a risk prediction model to individualize risk factors for surgical site infection after mastectomy. Ann Surg Oncol, 2016; 23:2471–2479.

28.

Sharma

, Deeb

, Iannuzzi

, Rickles

, et al. Tobacco smoking and postoperative outcomes after colorectal surgery. Ann Surg, 2013; 258:296–300.

29.

van Walraven

, Musselman

. The Surgical Site Infection Risk Score (SSIRS): A model to predict the risk of surgical site infections. PLoS One, 2013; 8:e67167.

30.

Sorensen

. Wound healing and infection in surgery: The pathophysiological impact of smoking, smoking cessation, and nicotine replacement therapy: A systematic review. Ann Surg, 2012; 255:1069–1079.

31.

Sorensen

. Wound healing and infection in surgery: The clinical impact of smoking and smoking cessation: A systematic review and meta-analysis. Arch Surg, 2012; 147:373–383.

32.

Myers

, Hajek

, Hinds

, McRobbie

. Stopping smoking shortly before surgery and postoperative complications: A systematic review and meta-analysis. Arch Intern Med, 2011; 171:983–989.

33.

Wong

, Lam

, Abrishami

, et al. Short-term preoperative smoking cessation and postoperative complications: A systematic review and meta-analysis. Can J Anaesth, 2012; 59:268–279.

34.

Kao

, Phatak

. Glycemic control and prevention of surgical site infection. Surg Infect, 2013; 14:437–444.

35.

Kwon

, Thompson

, Dellinger

, et al. Importance of perioperative glycemic control in general surgery: A report from the Surgical Care and Outcomes Assessment Program. Ann Surg, 2013; 257:8–14.

36.

Kao

, Meeks

, Moyer

, Lally

. Peri-operative glycaemic control regimens for preventing surgical site infections in adults. Cochrane Database Syst Rev. 2009(3):CD006806.

37.

Domek

, Dux

, Pinzur

, et al. Association between hemoglobin a1c and surgical morbidity in elective foot and ankle surgery. J Foot Ankle Surg, 2016; 55:939–943.

38.

Gatti

, Perrotti

, Reichart

, et al. Glycated hemoglobin and risk of sternal wound infection after isolated coronary surgery. Circ J, 2016; 81:36–43.

39.

Hikata

, Iwanami

, Hosogane

, et al. High preoperative hemoglobin A_1c is a risk factor for surgical site infection after posterior thoracic and lumbar spinal instrumentation surgery. J Orthop Sci, 2014; 19:223–228.

40.

Rawlins

, Rawlins

, Brown

, Schumacher

. Effect of elevated hemoglobin A_1c in diabetic patients on complication rates after Roux-en-Y gastric bypass. Surg Obes Relat Dis, 2013; 9:749–752.

41.

Rollins

, Varadhan

, Dhatariya

, Lobo

. Systematic review of the impact of HbA_1c on outcomes following surgery in patients with diabetes mellitus. Clin Nutr, 2016; 35:308–316.

42.

Faritous

, Ardeshiri

, Yazdanian

, et al. Hyperglycemia or high hemoglobin A_1C: Which one is more associated with morbidity and mortality after coronary artery bypass graft surgery?. Ann Thorac Cardiovasc Surg, 2014; 20:223–228.

43.

Goodenough

, Liang

, Nguyen

, et al. Preoperative glycosylated hemoglobin and postoperative glucose together predict major complications after abdominal surgery. J Am Coll Surg, 2015; 221:854–861

44.

Hwang

, Kim

, Bamne

, et al. Do glycemic markers predict occurrence of complications after total knee arthroplasty in patients with diabetes?. Clin Orthop Relat Res, 2015; 473:1726–1731.

45.

Sobol

, Wunsch

. Triage of high-risk surgical patients for intensive care. Crit Care, 2011; 15:217.

46.

Stonelake

, Thomson

, Suggett

. Identification of the high risk emergency surgical patient: Which risk prediction model should be used?. Ann Med Surg, 2015; 4:240–247.

47.

Mangram

, Horan

, Pearson

, et al. Guideline for prevention of surgical site infection, 1999. Hospital Infection Control Practices Advisory Committee. Infect Control Hosp Epidemiol, 1999; 20:250–278.

48.

Levy

, Lally

, Blakely

, et al. Surgical wound misclassification: A multicenter evaluation. J Am Coll Surg, 2015; 220:323–329.

49.

Ventral Hernia Outcome C, Mitchell

, Holihan

, et al. Do risk calculators accurately predict surgical site occurrences?. J Surg Res, 2016; 203:56–63.

50.

Mioton

, Jordan

, Hanwright

, et al. The relationship between preoperative wound classification and postoperative infection: A multi-institutional analysis of 15,289 patients. Arch Plast Surg, 2013; 40:522–529.

51.

Ortega

, Rhee

, Papandria

, et al. An evaluation of surgical site infections by wound classification system using the ACS-NSQIP. J Surg Res, 2012; 174:33–38.

52.

Levy

, Holzmann-Pazgal

, Lally

, et al. Quality check of a quality measure: surgical wound classification discrepancies impact risk-stratified surgical site infection rates in pediatric appendicitis. J Am Coll Surg, 2013; 217:969–773.

53.

Putnam

, Levy

, Blakely

, et al. A multicenter, pediatric quality improvement initiative improves surgical wound class assignment, but is it enough?. J Pediatr Surg, 2016; 51:639–644.

54.

Putnam

, Levy

, Holzmann-Pazgal

, et al. Surgical wound classification for pediatric appendicitis remains poorly documented despite targeted interventions. J Pediatr Surg, 2015; 50:915–918.

55.

Snyder

, Johnson

, Tice

, et al. Wound classification in pediatric general surgery: Significant variation exists among providers. J Am Coll Surg, 2013; 217:819–826.

56.

Emori

, Culver

, Horan

, et al. National Nosocomial Infections Surveillance System (NNIS): Description of surveillance methods. Am J Infect Control, 1991; 19:19–35.

57.

Bergquist

, Thiels

, Etzioni

, et al. Failure of colorectal surgical site infection predictive models applied to an independent dataset: Do they add value or just confusion?. J Am Coll Surg, 2016; 222:431–438.

58.

Berger

, Li

, Hicks

, et al. Development and validation of a risk-stratification score for surgical site occurrence and surgical site infection after open ventral hernia repair. J Am Coll Surg, 2013; 217:974–982.

59.

Figuerola-Tejerina

, Bustamante

, Tamayo

, et al. Ability to predict the development of surgical site infection in cardiac surgery using the Australian Clinical Risk Index versus the National Nosocomial Infections Surveillance-derived Risk Index. Eur J Clin Microbiol Infect Dis, 2017 Jan 19 doi: 10.1007.s10096-016-2889-0 (E-pub ahead of print).

60.

Bilimoria

, Liu

, Paruch

, Zhou

, Kmiecik

, Ko

, et al. Development and evaluation of the universal ACS NSQIP surgical risk calculator: A decision aid and informed consent tool for patients and surgeons. J Am Coll Surg, 2013; 217:833–842.

61.

Schneider

, Deig

, Prasad

, et al. Ability of the National Surgical Quality Improvement Program risk calculator to predict complications following total laryngectomy. JAMA Otolaryngol Head Neck Surg, 2016; 142:972–979.

62.

Adegboyega

, Borgert

, Lambert

, Jarman

. Applying the National Surgical Quality Improvement Program risk calculator to patients undergoing colorectal surgery: Theory vs reality. Am J Surg, 2017; 213:30–35.

63.

Sutton

, Miyagaki

, Bellini

, et al. Risk factors for superficial surgical site infection after elective rectal cancer resection: A multivariate analysis of 8880 patients from the American College of Surgeons National Surgical Quality Improvement Program database. J Surg Res, 2017; 207:205–214.

64.

Pugely

, Martin

, Gao

, et al. The Incidence of and risk factors for 30-day surgical site infections following primary and revision total joint arthroplasty. J Arthroplasty, 2015; 30(9 Suppl):47–50.

65.

Chung

, Wink

, Nelson

, et al. Surgical site infections after free flap breast reconstruction: An analysis of 2,899 patients from the ACS-NSQIP datasets. J Reconstr Microsurg, 2015; 31:434–441.

66.

McCutcheon

, Ubl

, Babu

, et al. Predictors of surgical site infection following craniotomy for intracranial neoplasms: An analysis of prospectively collected data in the American College of Surgeons National Surgical Quality Improvement Program database. World Neurosurg, 2016; 88:350–358.

67.

Kunutsor

, Whitehouse

, Blom

, Beswick

. Systematic review of risk prediction scores for surgical site infection or periprosthetic joint infection following joint arthroplasty. Epidemiol Infect, 2017:1–7.

68.

Strimbu

, Tavel

. What are biomarkers?. Curr Opin HIV AIDS, 2010; 5:463–466.

69.

De Magistris

, Paquette

, Orry

, et al. Preoperative inflammation increases the risk of infection after elective colorectal surgery: Results from a prospective cohort. Int J Colorectal Dis, 2016; 31:1611–1617.

70.

Mohri

, Miki

, Kobayashi

, et al. Correlation between preoperative systemic inflammation and postoperative infection in patients with gastrointestinal cancer: A multicenter study. Surg Today, 2014; 44:859–867.

71.

Aguilar-Nascimento

, Marra

, Slhessarenko

, Fontes

. Efficacy of National Nosocomial Infection Surveillance score, acute-phase proteins, and interleukin-6 for predicting postoperative infections following major gastrointestinal surgery. Sao Paulo Med J, 2007; 125:34–41.

72.

Facy

, Paquette

, Orry

, et al. Diagnostic accuracy of inflammatory markers as early predictors of infection after elective colorectal surgery: Results From the IMACORS study. Ann Surg, 2016; 263:961–966.

73.

Kudo

, Miyakoshi

, Hongo

, et al. Relationship between preoperative serum rapid turnover proteins and early-stage surgical wound infection after spine surgery. Eur Spine J, 2016 Nov 10 (E-pub ahead of print).

74.

Salvetti

, Tempel

, Gandhoke

, et al. Preoperative prealbumin level as a risk factor for surgical site infection following elective spine surgery. Surg Neurol Int, 2015; 6(Suppl 19):S500–S503.

75.

Tempel

, Grandhi

, Maserati

, et al. Prealbumin as a serum biomarker of impaired perioperative nutritional status and risk for surgical site infection after spine surgery. J Neurol Surg A Cent Eur Neurosurg, 2015; 76:139–143.

76.

Sutton

, Bann

, Brooks

, Sarin

. The Surgical Risk Scale as an improved tool for risk-adjusted analysis in comparative surgical audit. Br J Surg, 2002; 89:763–768.

77.

Protopapa

, Simpson

, Smith

, Moonesinghe

. Development and validation of the Surgical Outcome Risk Tool (SORT). Br J Surg, 2014; 101:1774–1783.

78.

Prytherch

, Whiteley

, Higgins

, et al. POSSUM and Portsmouth POSSUM for predicting mortality. Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity. Br J Surg, 1998; 85:1217–1220.

79.

Gawande

, Kwaan

, Regenbogen

, et al. An Apgar score for surgery. J Am Coll Surg, 2007; 204:201–208.

80.

Gervaz

, Bandiera-Clerc

, Buchs

, et al. Scoring system to predict the risk of surgical-site infection after colorectal resection. Br J Surg, 2012; 99:589–595.

81.

Geubbels

, Grobbee

, Vandenbroucke-Grauls

, et al. Improved risk adjustment for comparison of surgical site infection rates. Infect Control Hosp Epidemiol, 2006; 27:1330–1339.