1. Fencl, Jennifer L. DNP, RN, CNS-BC, CNOR
  2. Wood, Felecia G. PhD, RN, CNL
  3. Gupta, Sat PhD
  4. Swofford, Vangela BSN, RN, ASQ-CSSBB, CPHQ
  5. Morgan, Melissa BSN, RN, CIC
  6. Green, Debbie DNP, RN, CENP

Article Content

For any patient about to undergo a surgical procedure, the possibility of developing a surgical site infection (SSI) is an ever-present risk. SSIs continue to represent the most common type of harm for the surgical population, estimated to occur in 2%-5% of all surgical procedures performed in the United States.1-9 SSIs also represent 14% to 31% of all hospital-acquired infections and account for almost 77% of all deaths in patients with a hospital-acquired infection.3,7, 10-12 The consequences of acquiring an SSI for the patient and family can be overwhelming, as an SSI significantly impacts the patient's morbidity and mortality.1,4,5,7,9,11,13-19 As professional and regulatory agencies challenge and hold organizations accountable for a critical assessment of their prevention efforts, SSIs are a true public health concern and their elimination must be a priority for organizations to improve patient safety and the quality of care delivered.8,20

Figure. No caption a... - Click to enlarge in new windowFigure. No caption available.


Organizations can answer the challenge to improve patient safety and the quality of care by decreasing length of stay and readmissions, decreasing ICU admissions, and improving mortality for the surgical population, which includes decreasing or eliminating the incidence of SSIs. Patients who develop an SSI add, on average, 2 weeks to their hospital length of stay, are at increased risk to be admitted to an ICU by 60%, and are 2 to11 times more likely to die compared with patients who do not develop an SSI.1,7,14,21,22 In addition, billions of dollars are spent annually to treat this undesirable surgical outcome.3,15,23


The focus of this study was to critically examine neurosurgical spinal SSIs and was initiated to answer the following questions in the literature:


* What are the SSI risk factors for the neurosurgical spinal patient population?


* What evidence exists to describe SSIs in the neurosurgical spinal patient population?


* Is there a tool specifically designed for the neurosurgical spinal patient to assess SSI risk?



Literature review

Neurosurgical spinal procedure risk factors

The literature does identify additional risk factors contributing to an SSI that are unique to patients who will undergo a neurosurgical spinal procedure. These risk factors include: bowel and/or bladder incontinence; surgical approach (for example, posterior versus anterior); region of surgery (such as sacrum); history of previous spinal surgery; history of previous infection; intraoperative corticosteroid usage; utilization of fibrin glue or sealant, paste, or cement during surgery to repair a dural tear; instrumentation (such as implanting hardware); advanced age; blood loss during surgery; and fusions.1,2,4,8,11,14,15,17,18, 24-29


Although these risk factors specific to neurosurgical spinal surgery have been reported in the literature, there is not complete agreement due to relatively small sample sizes in the studies, small numbers of potential risk factors included in analyses, utilization of nonstandard definitions and variations in time frames for surveillance.2,8,14,27,29,30 This lack of agreement adds complexity to accurately assessing the patient's true risk for an SSI in this population.


Assessing for risk

There are several SSI prediction tools available from organizations, such as the National Healthcare Safety Network (NHSN), formerly known as National Nosocomial Infections Surveillance (NNIS) risk index, the Surgical Invasiveness Index for spinal surgery,28 and the American College of Surgeons' National Surgical Quality Improvement Program (NSQIP) web-based surgical risk calculator,32 but these tools have limitations.


A tool typically utilized for SSI prediction is the NNIS risk index (currently known as NHSN), which stratifies patient risk for SSI utilizing the American Society of Anesthesiologists (ASA) Physical Status Classification System, surgical wound classification, and length of surgery.6,14,19,31 Although the NNIS risk index has long been used for prediction, its limitations must be discussed and explored. Of particular interest for the patient having spinal surgery, limitations of the NNIS risk index include: the uncertainty of equally weighing all of the three elements of risk (such as a healthy and unhealthy patient are both assigned at the same level of risk for a wound class IV procedure), the inability to stratify risk based on a specific surgical procedure, the failure to account for inherent patient risk and intraoperative factors (other than wound classification) influencing SSI, and the final score limiting the discriminatory abilities since it reflects a small number.6,14,19,31


Cizik and colleagues examined the degree of surgical invasiveness in spinal surgery and compared to the risk of developing an SSI utilizing the Surgical Invasiveness Index.28 This index takes into consideration the vertebral level, type of surgery (for example, arthrodesis or fusion), instrumentation, and approach (such as posterior or anterior) and points are assigned based on those factors. Cizik and colleagues found those patients with a higher Surgical Invasiveness Index score assigned, correlated with the strongest risk to develop an SSI, which differs from other studies found in the literature.28 The index does not take into consideration the patient's comorbidities for developing an SSI, but rather focuses on the technical aspect of the surgery itself; whereas both are important in identifying those patients at highest risk for developing an SSI.


Also available to assess risk is the American College of Surgeons, NSQIP surgical risk calculator, a web-based tool.32 This surgical risk calculator was developed utilizing data from 393 participating NSQIP hospitals.32 The risk calculator takes into consideration 21 patient risk factors (such as age, ASA score, body mass index [BMI]) and the planned surgical procedure (for example, Current Procedural Terminology or CPT code) to assess the risk of not only SSI, but also eight other outcomes such as urinary tract infections, venous thromboembolism, and kidney failure.32 Although this risk calculator is well developed, it does not include risk factors specific to a surgical population (for example, neurosurgical-specific risk factors) and cannot consider intraoperative risk factors that may influence the development of SSIs including: dural tear, use of glue or cement, drain placement, blood transfusion, and appropriate antibiotic redosing.9



The ability to accurately assess and predict those patients at highest risk for an SSI and translate this information into prevention efforts would be a powerful tool for any organization. With the ability to correctly identify patients at highest risk for SSI prior to surgery, decisions regarding treatment and preventive strategies could be implemented with the ultimate goal to eliminate these devastating outcomes.6,31


The literature identifies risks specifically focused on patients undergoing spinal surgery, but does not demonstrate complete agreement regarding those risk factors. Nor is there a specific evidence-based practice (EBP) tool that can be utilized preoperatively and intraoperatively to assess a patient's risk of developing an SSI for patients having spinal surgery. Thus, the purpose of this study was twofold:


1. To identify the specific SSI risk factors for patients undergoing spinal surgery, and


2. To develop a risk assessment tool based on variables identified in the literature and results from this study that may contribute to prevention of an SSI.



Study setting

This project was conducted at an acute care hospital in the southeastern United States performing approximately 13,217 surgical cases per year with approximately 2,579 of those being neurosurgical spinal cases. This project was also approved by the appropriate institutional review board prior to data collection.


Study design

To examine specific SSI risk factors for the patient undergoing spinal surgery, a retrospective chart review was conducted utilizing similar methodology as discussed in previous studies focusing on neurosurgery spinal SSI.1,2,14,17,24,25 A detailed drill down tool was created reflecting specific risk factors identified in the literature contributing to SSI in the neurosurgery spinal population.1,2,14,17,24,25 This tool was completed for every patient identified as developing an SSI who underwent a neurosurgery spinal procedure (case patient) occurring in a 1-year time frame from June 2012 to June 2013.


For each case patient, the tool was also completed for three randomly selected noninfected patients (match control) who underwent a neurosurgery spinal procedure during the same time frame as the case patient (June 2012-June 2013) that were matched based on type of surgery and ASA score (for example, fusion, ASA 3). The number of case patient/match control patient ratio (1:3) for this project was determined by averaging the case patient/match control patient ratios described in previous studies examining neurosurgical risk factors for SSI.1,2,14,17,24,25


Inclusion criteria for both case patient and match control included 18 years of age or older and underwent a neurosurgery spinal procedure occurring during the time frame of June 2012 through June 2013. The CDC definition of an SSI was utilized in the identification of a confirmed SSI.10


Data collection

Information regarding patients who developed an SSI during the time frame of June 2012-June 2013 was provided by the organization's infection prevention department that had already established a program for tracking patients who developed an SSI for targeted surgical procedures, one of which was spinal cases.


The organization went live with a new electronic medical record (EMR) in November 2012. Data collection prior to the EMR was difficult and challenging, specifically surrounding the elements of illegible handwriting found in the documentation and the difficulty locating documents related to the nonspecific labeling of important documents as they were scanned into the patient's new EMR (for example, the operative record was not always labeled operative record). For this reason, the match control patients were pulled from procedures occurring from November 2012-June 2013, the time frame after EMR implementation.


To confirm reliability of data collection, 10% of the sample was randomly selected to assess for interrater reliability. The detailed audit tool was completed by another member of the team to demonstrate reliability in the data collection process.


Statistical analysis

Utilizing SPSS version 21, the first statistical analysis performed was computation of summary information for various variables. The main statistical procedure used was binary logistic regression, a common tool to model binary response variables that in this project is the incidence of SSI.


Logistic regression was utilized in two stages. In the first stage, only the individual predictors were analyzed to identify if any of the predictors would show a p-value of less than 0.15. In the final analysis, predictors were categorized as significant only with a p-value less than 0.05. Once this first stage or initial screening was completed, a short list of the predictors was identified. Using the short list of predictors, a binary logistic regression was again performed utilizing the forward selection option to account for mutual correlations/associations among various predictors.



Between June 2012 through June 2013, 18 patients who underwent neurosurgical spinal surgery developed an SSI. The final sample size consisted of 73 patients; reflecting both patients who developed a SSI (n = 18), and patients who did not develop a SSI (n = 55). Most of the patients in this project's sample were White (81%) and female (60%) with a mean age of 58.9 (with a standard deviation of 14.05 years). (See Examining neurosurgical SSI patient characteristics.) From this sample, 58% (n = 42) of the patients were current or past smokers, 22% (n = 16) had diabetes, 85% (n = 62) received appropriate antibiotic administration during the surgical procedure, 90% (n = 66) had at least one chlorhexidine gluconate (CHG) bath (either the night before surgery, day of surgery, or both), and had a mean BMI of 31.67 (with a standard deviation of 6.72). Three percent (n = 2) of the surgeries were thoracic procedures, with the majority of the surgical procedures, 89% (n = 65), occurring at the lumbar level, followed by 8% (n = 6) at the cervical level. For those patients who developed an SSI (n = 18), 39% (n = 7) were classified as organ space, 33% (n = 6) as deep, and 28% (n = 5) as superficial infections with the strongest percentage of causative organism being methicillin-sensitive Staphylococcus aureus (MSSA) or methicillin-resistant Staphylococcus aureus (MRSA) at 39%. (See Type of SSI and specific organism.)


Binary logistic regression was used to identify significant predictors of SSI. In Stage 1 screening utilizing binary logistic regression, the following variables had a p-value of less than 0.15: skin closure with suture and/or skin adhesive as compared to staples (p = 0.006; significant), type of intraoperative prepping agent used (p = 0.002; significant), placement of drains (p = 0.002; significant), a specific OR had a greater risk to develop a SSI as compared with the other OR rooms (p = 0.038; significant), BMI score (p = 0.063), patient age over 65 (p = 0.065), utilization of glue during the procedure to repair dural tears (or potential dural tears) (p = 0.082), person who performed the surgical prep (RN compared with surgeon) (p = 0.103), anesthesia administration of a preoperative corticosteroid prior to the incision (p = 0.123), and history of previous SSI (p = 0.129). Only the significant predictors (p-value less than 0.05) were reported in the binary logistic regression significant variables stage 1. (See Binary logistic regression.)

Table Examining neur... - Click to enlarge in new windowTable Examining neurosurgical SSI patient characteristics
Table Examining neur... - Click to enlarge in new windowTable Examining neurosurgical SSI patient characteristics (continued)

The next analysis included a collective assessment of all of these risk factors, including those where the p-value was between 0.05 and 0.15. Sometimes individual predictors may have significant correlation with SSIs; however, some of these predictors may be filtered out in a group analysis because their contribution to the development of SSI may be better explained by some other predictors. By utilizing this approach, one can account for cross-correlation among predictors. In this patient sample, the strongest predictors of SSI included the type of intraoperative prepping solution utilized and how skin closure was performed.


Patients who received iodine povacrylex in isopropyl alcohol as the intraoperative prepping agent as compared to povidone-iodine had a decreased risk of infection by 83% (p = 0.009 with an odds ratio of 0.17). Patients who had their skin closed with suture and/or skin adhesive as compared to staples had a decreased risk of infection by 92.4% (p = 0.006 with an odds ratio of 0.076). Based on chi-square test of association (Likelihood ratio test p-value = 0.026), the sample also demonstrated an association with decreasing risk for SSI with appropriate antibiotic administration,4,7,9,29 which included: antibiotic administration prior to surgery, appropriate redosing as indicated by length of surgery, and presurgical antibiotic selection. (See Appropriate antibiotic administration chi-square test of association.)


Since the surgical team can control the intraoperative prepping agent and type of skin closure with standardization of practice, data was assessed in the absence of the intraoperative prepping agent used and skin closure for those variables demonstrating significance in the stage 1 analysis. The following risks related to SSI in the absence of skin prep and skin closure were identified: patients whose dural tears (or potential dural tears) were repaired with glue were 8.3 times more likely to develop an SSI as compared with patients whose tears were not repaired with glue (p = 0.041 with an odds ratio of 8.31). Patients who had drains placed were 7.7 times more likely to develop an SSI as compared with patients who did not (p = 0.001 with an odds ratio of 7.66).


No statistical significance was found with the following risk factors in this project's sample: gender, diabetes, blood glucose level prior to surgery, smoking, age, lowest patient temperature during surgery, polymerase chain reaction (PCR) positive for Staphylococcus aureus and/or MRSA, application of CHG baths prior to surgery, multilevel surgery, bladder and/or bowel incontinence, previous spinal surgery, or length of surgery.



The patient sample in this project demonstrated similar risk factors for developing an SSI as reported in some studies, but is not in agreement with all the risk factors previously reported in the literature. For this sample, intraoperative actions and interventions demonstrated the greatest link to SSI risk, rather than dependent variables of patient risk, such as diabetes, BMI, and history of smoking. Risk factors such as intraoperative prepping solution used, closing the skin with staples, the placement of drains, glue used to repair a dural tear (or potential dural tear), and timing of antibiotic administration demonstrated the greatest risks for SSI in this sample.


High BMI, smoking history, and diabetes are well known, widely accepted risk factors for the development of SSI. Similar to some recently published studies focusing on this same patient population, this project did not find a statistical link with these risk factors.8,9,19,26,27 Recognizing that these risk factors do pose an increased risk for SSI, could this lack of significance found in this project, as well as other studies, be related to the positive impact of best practice bundles such as the surgical care improvement project (SCIP), PCR testing, and following established guidelines to mitigate those risks for developing an SSI?


Nursing implications

The results of this project support the development of an EBP risk assessment tool that accurately reflects not only preoperative risk factors, but also includes an assessment of intraoperative risk factors for SSI as well. The NNIS risk index assessment tool takes into account only limited data to assess the patient's true risk for developing an SSI (ASA score, wound classification, and length of surgery). Although the NSQIP risk assessment tool is more robust than the traditional NNIS risk index, it fails to account for specific risk factors occurring in a surgical patient population (for example, in neurosurgical patients, specific risk factors may include bladder and/or bowel incontinence or previous spinal surgery). In addition, the NSQIP risk assessment tool also fails to account for risk introduced during the intraoperative phase of patient care that may be unknown prior to the incision (such as intraoperative prepping agent, appropriate antibiotic redosing, dural tear, glue used to repair an actual or potential dural tear, drain placement, and blood transfusion). The Surgical Invasiveness Index does not take into consideration the patient's comorbidities for developing an SSI, but rather focuses on the technical aspect of the surgery itself; whereas both are important in identifying those patients at highest risk for developing an SSI. Thorough and accurate assessment of a patient's risk for developing an SSI should consider and include both intraoperative risk factors as well as preoperative risk factors.


This study did have limitations. Although this project was conducted utilizing similar methodology as discussed in previous studies focusing on neurosurgery spinal SSI, the sample size was relatively small, potentially compromising the results due to the lack of statistical power. With that said, the results of this study strongly support the development of a risk assessment tool for SSI prediction that specifically includes intraoperative risk factors and warrants a larger well-designed study to fully explore this phenomenon. In addition, this project also only looked at neurosurgical spinal patients. Future projects should assess the patient population with orthopedic spinal surgery.

Table Binary logisti... - Click to enlarge in new windowTable Binary logistic regression
Table Appropriate an... - Click to enlarge in new windowTable Appropriate antibiotic administration chi-square test of association

A second limitation was the timing of the project, which occurred during implementation of a newly integrated EMR. Finding information in the old "paper" chart was very challenging related to illegible handwriting and nonspecific labeling of important scanned documents (for example, the operative record was not always labeled "operative record"). In addition, the new EMR posed unique challenges of its own. With the new EMR system, it was challenging finding all the different areas (such as screens) where data for this project could be documented, and not all areas of documentation automatically communicated and transferred information. For example, CHG baths preoperatively could be located in three different areas, two of which automatically communicated with each other and transferred information, but one of the areas stood alone. This is a common problem with efforts to make improvements in this rapidly changing healthcare environment, and one that many organizations will likely experience as they attempt to strengthen the quality of their care.

Table Risk assessmen... - Click to enlarge in new windowTable Risk assessment tool for neurosurgical spinal cases

An important implication of this study is for the departments of nursing, medicine, and infection prevention to collaboratively develop an EBP SSI risk assessment tool to identify patients at highest risk for SSI during the preoperative and intraoperative phases of patient care. An assessment tool was developed utilizing risk factors for spinal cases described in the literature and results from this study, in an effort to identify patients at a higher risk to develop an SSI.1,2,4,8,11,14,15,17,18,24-27,29 (See Risk assessment tool for neurosurgical spinal cases.) The next step will be to validate this tool through additional research.


Moving forward

By developing and validating a tool to help identify patients at risk for SSI, organizations could standardize practices based on published evidence such as the healthcare provider who preps patients, prepping agent, and wound closure. In addition, treatments such as irrigation of the wound with povidone-iodine or another solution prior to wound closure,5,8,27 application of specialty dressings (such as silver impregnated dressing) could also be implemented in an effort to prevent an SSI. This same methodology could be utilized to further explore and expand on SSI risk factors for other specific surgical populations (for example, colon surgery) and create EBP SSI risk assessment tools based on that information.


Being able to proactively identify patients at highest risk for a neurosurgical (or any) SSI is powerful information for an organization to help drive quality and safe patient care. Healthcare organizations must take measures to identify their patients at highest risk for poor outcomes and thoughtfully implement strategies that ultimately improve care and eliminate the occurrence of these potentially devastating infections. This forward thinking supplements best practices for SSI prevention already established in the literature that include: continued vigilance regarding proper dress attire, strict adherence to sterile technique, appropriate skin prep, thorough cleaning of the OR in-between procedures and terminal cleaning, minimizing/eliminating immediate use steam sterilization (also known as flash sterilization),4,8,12 and continued assessment of best practice bundles such as the SCIP, CHG preoperative baths, and decolonizing patients prior to surgery.5,20




1. Olsen MA, Mayfield J, Lauryssen C, et al. Risk factors for surgical site infection in spinal surgery. J Neurosurg. 2003;98(2 suppl):149-155. [Context Link]


2. Olsen MA, Nepple JJ, Riew KD, et al. Risk factors for surgical site infection following orthopaedic spinal operations. J Bone Joint Surg Am. 2008;90(1):62-69. [Context Link]


3. Harrop JS, Styliaras JC, Ooi YC, Radcliff KE, Vaccaro AR, Wu C. Contributing factors to surgical site infections. J Am Acad Orthop Surg. 2012;20(2):94-101. [Context Link]


4. Gruskay J, Kepler C, Smith J, Radcliff K, Vaccaro A. Is surgical case order associated with increased infection rate after spine surgery. Spine. 2012;37(13):1170-1174. [Context Link]


5. Savage JW, Anderson PA. An update on modifiable factors to reduce the risk of surgical site infections. Spine J. 2013;13(9):1017-1029. [Context Link]


6. van Walraven C, Musselman R. The Surgical Site Infection Risk Score (SSIRS): a model to predict the risk of surgical site infections. PLoS One. 2013;8(6):e67167. [Context Link]


7. McHugh SM, Hill AD, Humphreys H. Intraoperative technique as a factor in the prevention of surgical site infection. J Hosp Infect. 2011;78(1):1-4. [Context Link]


8. Schuster JM, Rechtine G, Norvell DC, Dettori JR. The influence of perioperative risk factors and therapeutic interventions on infection rates after spine surgery: a systematic review. Spine. 2010;35(9 suppl):S125-S137. [Context Link]


9. Boston KM, Baraniuk S, O'Heron S, Murray KO. Risk factors for spinal surgical site infection, Houston, Texas. Infect Control Hosp Epidemiol. 2009;30(9):884-889. [Context Link]


10. Surgical site infection event. Centers for Disease Control and Prevention website. [Context Link]


11. Xing D, Ma JX, Ma XL, et al. A methodological, systematic review of evidence-based independent risk factors for surgical site infections after spinal surgery. Eur Spine J. 2013;22(3):605-615. [Context Link]


12. Mangram AJ, Horan TC, Pearson ML, Silver LC, Jarvis WR. Guideline for prevention of surgical site infection, 1999. Hospital Infection Control Practices Advisory Committee. Infect Control Hosp Epidemiol. 1999;20(4):247-278. [Context Link]


13. Anthony T, Murray BW, Sum-Ping JT, et al. Evaluating an evidence-based bundle for preventing surgical site infection: a randomized trial. Arch Surg. 2011;146(3):263-269. [Context Link]


14. Maragakis LL, Cosgrove SE, Martinez EA, Tucker MG, Cohen DB, Perl TM. Intraoperative fraction of inspired oxygen is a modifiable risk factor for surgical site infection after spinal surgery. Anesthesiology. 2009;110(3):556-562. [Context Link]


15. Ming DY, Chen LF, Miller BA, Sexton DJ, Anderson DJ. The impact of depth of infection and postdischarge surveillance on rate of surgical-site infections in a network of community hospitals. Infect Control Hosp Epidemiol. 2012;33(3):276-282. [Context Link]


16. Owens CD, Stoessel K. Surgical site infections: epidemiology, microbiology and prevention. J Hosp Infect. 2008;70(suppl 2):3-10.


17. Rao SB, Vasquez G, Harrop J, et al. Risk factors for surgical site infections following spinal fusion procedures: a case-control study. Clin Infect Dis. 2011;53(7):686-692. [Context Link]


18. Meyer D, Klarenbeek R, Meyer F. Current concepts in perioperative care for the prevention of deep surgical site infections in elective spinal surgery. Cent Eur Neurosurg. 2010;71(3):117-120. [Context Link]


19. Abdul-Jabbar A, Takemoto S, Weber MH, et al. Surgical site infection in spinal surgery: description of surgical and patient-based risk factors for postoperative infection using administrative claims data. Spine. 2012;37(15):1340-1345. [Context Link]


20. Zinn J. Patient safety first: surgical patients: a vulnerable population. AORN J. 2013;98(6):647-652. [Context Link]


21. Swenson BR, Hedrick TL, Metzger R, Bonatti H, Pruett TL, Sawyer RG. Effects of preoperative skin preparation on postoperative wound infection rates: a prospective study of 3 skin preparation protocols. Infect Control Hosp Epidemiol. 2009;30(10):964-971. [Context Link]


22. Rothrock J. Preoperative skin cleansing with chlorhexidine gluconate. Medscape. 2010. [Context Link]


23. Edmiston CE Jr, Okoli O, Graham MB, Sinski S, Seabrook GR. Evidence for using chlorhexidine gluconate preoperative cleansing to reduce the risk of surgical site infection. AORN J. 2010;92(5):509-518. [Context Link]


24. Apisarnthanarak A, Jones M, Waterman BM, Carroll CM, Bernardi R, Fraser VJ. Risk factors for spinal surgical-site infections in a community hospital: a case-control study. Infect Control Hosp Epidemiol. 2003;24(1):31-36. [Context Link]


25. Friedman ND, Sexton DJ, Connelly SM, Kaye KS. Risk factors for surgical site infection complicating laminectomy. Infect Control Hosp Epidemiol. 2007;28(9):1060-1065. [Context Link]


26. Schimmel JJ, Horsting PP, de Kleuver M, Wonders G, van Limbeek J. Risk factors for deep surgical site infections after spinal fusion. Eur Spine J. 2010;19(10):1711-1719. [Context Link]


27. Watanabe M, Sakai D, Matsuyama D, Yamamoto Y, Sato M, Mochida J. Risk factors for surgical site infection following spine surgery: efficacy of intraoperative saline irrigation. J Neurosurg Spine. 2010;12(5):540-546. [Context Link]


28. Cizik AM, Lee MJ, Martin BI, et al. Using the spine surgical invasiveness index to identify risk of surgical site infection: a multivariate analysis. J Bone Joint Surg Am. 2012;94(4):335-342. [Context Link]


29. Pullter Gunne AF, Cohen DB. Incidence, prevalence, and analysis of risk factors for surgical site infection following adult spinal surgery. Spine. 2009;34(13):1422-1428. [Context Link]


30. Pullter Gunne AF, Hosman AJ, Cohen DB, et al. A methodological systematic review on surgical site infections following spinal surgery: part 1: risk factors. Spine. 2012;37(24):2017-2033. [Context Link]


31. Anaya DA, Cormier JN, Xing Y, et al. Development and validation of a novel stratification tool for identifying cancer patients at increased risk of surgical site infection. Ann Surg. 2012;255(1):134-139. [Context Link]


32. NSQIP risk assessment calculator. American College of Surgeons, National Quality Improvement Project. 2007-2014. [Context Link]