Introduction:~Prevention of surgical site infection (SSI) continues to be a major challenge for the health care system since it incurs a substantial toll on public health and significantly inflates health care costs. SSIs are now the leading cause of health care related infections, complicating about 2-5 % of all surgeries(1-3). SSIs affects about 125,000 cases annually accounting for nearly a million excess hospital days and just under $1.6 billion in additional health care costs(4). It is estimated that half of the SSIs are preventable(5) and not surprisingly, the prevention of health care-associated infections has been a priority objective of the U.S. Department of Health and Human Services (HHS)(6) over the past several years. Public reporting of SSI outcomes is now mandatory and reimbursement for management of SSIs is being reduced or denied(7,8) in an effort to curb its incidence.~Despite the institution of stringent measures and surveillance programs, surgical registries continue to show SSI rates of about 2-5%(9,10) and SSIs remain a key cause of prolonged hospitalization, morbidity and death. The continued health care burden caused by SSI calls for closer scrutiny of the current clinical practices especially pertaining to perioperative antibiotic coverage. Although the institution of timely perioperative antibiotic prophylaxis is now a National Quality Anesthesia Care Measure(11), much remains to be known about antibiotic redosing, weight based adjustments and completion of antibiotic infusion prior to skin incision(7).~This study will seek to describe current practice of perioperative antibiotic prophylaxis among MPOG institutions, and in the subset of MPOG centers contributing NSQIP data, to identify the effect of appropriate guideline-based perioperative antimicrobial coverage - specifically regarding selection, timing, weight-based dose adjustments, and redosing - on SSI. The applicable guidelines against which adherence will be assessed are those of the Infectious Disease Society of American (IDSA). To understand the effects of IDSA guideline adherence, the investigators propose to utilize the American College of Surgeons - National Surgical Quality Improvement Program (ACS-NSQIP) data collection methodology, and to integrate these prospectively collected outcome data from 6 centers within MPOG with intraoperative anesthesia electronic health record (EHR) data available within MPOG. Beyond the descriptive aim to describe current practice, our primary inferential hypothesis is that adherence to IDSA guidelines regarding appropriate antibiotic selection, timely antibiotic redosing, weight based dose adjustments, and appropriate timing of infusions to ensure completion of administration prior to skin incision will be associated with a lower incidence of SSIs when considered both individually and as a basket of practices, while controlling for common confounders available within the MPOG and NSQIP datasets.~Methods Approval has been obtained from the Yale IRB for this multicenter, observational retrospective study. Data have previously been collected under an umbrella IRB protocol within the University of Michigan. The ACS-NSQIP methodology has been described in detail elsewhere(12). For the NSQIP/MPOG portion of the study, data collected from 01/01/2011 to 07/04/2018 will be extracted. Since the IDSA guidelines were proposed in 02/2013, for the descriptive portion of the study looking at predictors of guideline adherence, data from 01/01/2014 to 07/04/2018 will be extracted from the MPOG database.~Patient population All patients equal or greater than 18 years of age undergoing non-emergent non-cardiac surgical procedures involving a skin incision will potentially be included in the study. For the NSQIP/MPOG portion of the study, patients with conditions that could confound the analysis of SSI risk factors including emergency surgery, open wound with or without infection, current active infection, ongoing preoperative antibiotic therapy, missing perioperative antibiotic/medication documentation, ventilator dependence within 48 hours of surgery, ophthalmic surgeries, organ transplants, prior operation within 30 days, Organ harvesting surgeries, and ASA 5 or 6; will be excluded. A complete list of the exclusion criteria from ACS-NSQIP variables is documented in Supplement 1. For the descriptive study of MPOG antibiotic practices, exclusions are listed in Supplement 2.~Exploratory Factors:~The following MPOG and ACS-NSQIP preoperative clinical variables will be evaluated for its relationship with the occurrence of SSI in the primary inferential analyses (parentheses indicate the source database): age (MPOG), male sex (MPOG), body mass index (MPOG), diabetes mellitus (NSQIP, current smoker within 1 year (NSQIP), severe COPD (NSQIP), congestive heart failure within 30 days (NSQIP), history of myocardial infarction (NSQIP), hypertension (NSQIP), history of peripheral vascular disease (MPOG), ongoing dialysis requirements (NSQIP), transient ischemic attacks or stroke (NSQIP), disseminated cancer (NSQIP), loss of 10% of body weight in 6 months (NSQIP), steroid use for a chronic condition (NSQIP), chemotherapy within 30 days (NSQIP), and ASA physical status (MPOG).~Body mass index will be transformed into categorical variables based upon the clinically relevant World Health Organization classification scheme (< 20, 20-25, 25-30, 30-35, 35-40, 40-50, and > 50 kg/m2). ASA physical status will be transformed into three categorical dummy variables: ASA 1, 2, 3 or 4. Diabetes mellitus will be transformed into two dummy variables: diabetes mellitus requiring oral hypoglycemic treatment without insulin (NSQIP), and diabetes mellitus requiring insulin treatment with or without oral hypoglycemic (NSQIP).~Intraoperative variables including hypotension, hypothermia, transfusion volume, the need for vasopressor / inotrope infusion, median fiO2 and surgery duration will be included.~For intraoperative variables, hypotension will be calculated as the time in minutes below MAP 55mmHg. Transfusion volume will be calculated as the number of pRBC units transfused between surgery start and surgery end. The need for infusions of vasopressors and/or inotropes will be coded as yes/no based on the intraoperative anesthetic record and including only the need for infusions without regard for isolated bolus dosing. Duration of surgery will be calculated as the period of time from incision to surgery end. Median FiO2 utilized during the surgeries will be calculated.~Although there are a number of studies reporting the effect of hypothermia on SSI after certain surgeries, a consensus on a metric to measure the magnitude of hypothermia associated with SSI is lacking. In addition, intraoperative temperature measurement is subject to numerous artifacts such as dislodgment of the temperature measuring device from the patient. To minimize this, the investigators will utilize the artifact reducing algorithm. After artifact removal, the median temperature will be calculated for use in the relevant models.~Endpoints:~The primary end point to which the investigators will attempt to associate guideline-adherent antibiotic prophylaxis will be occurrence of a NSQIP-adjudicated SSI during the period from 01/01/2011 to 07/04/2018. SSIs will be a composite of superficial (only skin or subcutaneous tissue of the incision), deep (deep soft tissues), and organ space (any part of the anatomy other than the incision, which has been opened and manipulated during the operation), as provided by NSQIP.~Appropriate antibiotic prophylaxis:~Definition for appropriate antibiotic prophylaxis will be used per the Infectious Diseases Society of America (IDSA), the Surgical Infection Society (SIS) and American Society of Health-System Pharmacists (ASHP) guidelines(13). Data on timing, dose, redosing and choice of antibiotics will be obtained from MPOG.~Choice of antibiotics:~The IDSA guidelines will be utilized to assess choice of antibiotics (Supplement 3). Appropriate antibiotics will be decided a priori for all the CPT codes based on these guidelines. Patients will then be classified into 2 groups based on the choice of antibiotics. Under certain patient/hospital-based scenarios, the guidelines recommend additional antibiotics or a preference towards a certain class among the listed antibiotics in the category. The plan is to consider the antibiotic choice as appropriate if any antibiotic from the listed procedural category is utilized for the surgery. In case more than one antibiotic is administered, at least one antibiotic or a combination of antibiotics should match the recommendations.~Timing of antibiotics with respect to surgical incision will be coded in two ways, first as a continuous variable to assess the nonlinear association between antibiotic timing and SSI(14). Second, timing of antibiotics will be dichotomized whether it fits in the time period of existing guidelines and assessed as a categorical variable for its association with SSI. For antibiotic infusions, the start of antibiotic will be considered as time of administration.~Dosing with respect to weight adjustment will be considered in reference to the same guidelines and considered adherent if the dose of the appropriate antibiotics meets the minimum requirement for weight-based adjustment. For antibiotics with weight based guidelines in mg/kg (example vancomycin), dosing up to 10% below the calculated dose will be considered as guideline adherent.~Redosing will be considered in a dichotomous fashion and will be coded as adherent if the surgical duration necessitated a guideline-indicated redosing interval and such a dose was administered prior to that interval. In cases in which more than one redosing episode should have occurred, redosing adherence will be considered in an all-or nothing fashion whereby a lack of any timely guideline-adherent redosing will be coded as non-adherent.~Trends in guideline-adherent antibiotic usage:~The investigators will also investigate the trends in guideline-adherent antibiotic practices within the MPOG database including those institutions not contributing NSQIP data as per exclusions in supplement 2. This analysis will consider within-institution temporal trends and will examine the possible association of candidate patient-level and institution-level factors. More specifically, the rates of guideline-adherent antibiotic practices will be modelled using the mixed-effects multiple logistic regression method that include fix effects such as time (and polynomial terms of it if non-linearity is confirmed), institution-level factors (e.g., institution type, size, etc.) and patient-level variables, and random institution effect. The significance (i.e., p < 0.05) of coefficient for the time variable will be indicative of a significant overall trend effect. The estimates of adherence rate and their 95% confidence intervals (CIs) will be calculated.~Statistical analysis:~Statistical analysis will be performed using SAS version 9.4 (Cary, NC). A two-sided p-value <0.05 will be considered statistically significant, if not otherwise noted. Appropriate effect sizes (e.g., odds ratio), and their corresponding 95% confidences intervals (CIs) will be reported. Descriptive statistics (means, medians, frequencies) will be calculated to characterize demographics and all extracted clinical variables. Histograms and box plots will be constructed to evaluate distributions of continuous variables and identify potential outliers. Each outlier will be reviewed carefully and verified. Categorical items with more than two categories that do not exhibit sufficient variability across response levels will be dichotomized accordingly.~For the descriptive aim in parallel with the above analysis, practice patterns across MPOG institutions in relation to antibiotic selection, dosing, redosing, and timing will be examined. The distribution of adherence to these practices will be examined, and patient, provider, and institution level predictors of adherence to these practices, individually and as a bundle will be examined. Box-plots, caterpillar plots, and funnel plots will be generated to visualize the patterns/variability of SSI rates and potentially point out unusual performers at both local (i.e. institution) and national levels. In a typical funnel plot, the institution-specific rates can be plotted against the institution case volume with 95% and 99% confidence limits (corresponding to 2 and 3 standard deviations) superimposed around the rates. Institutions and providers with rates out of these limits will be marked as outliers and subject to further scrutinization to under the reason for the abnormal variability.~For the primary inferential aim, univariate analyses will be first performed using Pearson Chi-Square, Fisher's Exact Test, Student's t-test, and Mann Whitney U Test as appropriate to investigate the association of all preoperative and intraoperative variables with the outcome of NSQIP-adjudicated SSI. Generally, only the factors with p 0.1 from univariate analysis will be included in the multivariable regression model. However, Clinical variables with shown evidences affecting the risk of SSI will also be included in the model. Collinearity, the linear assumption, and the additivity assumption of the predictors will be checked, and nonlinear modeling of continuous predictors (e.g., infusion time) will be investigated. If necessary, highly correlated groups of predictors will be examined and dimensionality will be reduced either by subject matter knowledge (i.e., principal components), or by simple point scores.~After examining the prevalence or patterns of SSI by different center or surgery types, four distinct clustered or mixed-effects multiple logistic regression models to will be developed using SAS GLIMMIX procedure to associate the SSI outcome with each component of intraoperative antibiotic management domains: choice, redosing interval, weight-based adjustment, and time of administration criteria. Specifically, the investigators propose to test the hypothesis that correct antibiotic choice, timely antibiotic dosing, redosing, weight-based dose adjustments in accordance with guidelines, appropriate timing of infusions to ensure completion of administration prior to skin incision will be independently associated with a lower incidence of SSIs while controlling for significant confounders. Random effects for hospitals and anesthesia providers will be included to address the clustering of different surgical cases. The investigators will examine the modification effects of other specific factors, adding them into the model as fixed factors, which include patient level demographics such as age, health of patient (ASA class), BMI, gender, race/ethnicity, and ACS-NSQIP preoperative and other operative variables. In addition to p-values, as the measures of effect sizes, the investigators will also report adjusted odds ratios and 95% confidence intervals for each independent variable in the final model, comparing the likelihood of SSI among patients with and without the risk factor.~A dummy variable will be created that is coded as 'Yes' if adherence to guidance for all four intraoperative antibiotic management domains are met or 'No' otherwise, then the association of this dummy variable with the likelihood of SSI will be tested. This would help quantitate a composite effect for adherence to guidance on the SSI. Finally, an overall model incorporating all domains, preoperative and operative ACS-NSQIP variables, and the surgical complexity score will be developed using the same methodology described above.~For the purpose of model performance diagnosis, the amount of variability in the SSI outcome that is explained by each regression model will be quantified by the adjusted-R2 statistic, and the discrimination performance of the model will be assessed by C-statistic (i.e. AUC). The Hosmer-Lemeshow goodness-of-fit (GOF) test will be used to check if the final model fits the data well. A GOF P-value > 0.05 will generally indicate whether a model is a good fit or well-calibrated. The model will be internally validated using a resampling bootstrap technique to assess for the possibility of overfitting.~It is worth noting that there were approximately 9.3 million unique cases in MPOG as of June 2018 (and growing monthly), with adequate numbers of patients to develop a descriptive regression model with a number of variables. Given the rule of thumb of maintaining at 10 events per variable (EPV) in the multivariable logistic regression model, the investigators will have more than sufficient numbers to precisely estimate up to hundreds of predictors (when applicable, different categories for a discrete variable is counted as a predictor) in the final model. That is, overfitting will likely not a concern in the current study. However, the investigators will closely evaluate the issue when developing our models. If EPV >= 10 can't not seem to be guaranteed, the investigators will choose to use the penalized method-the least absolute shrinkage and selection operator (LASSO) for variable (feature) selection to first create a subset of potential important predictors, which then will be subject to our standard variable selection procedure described above to select the final specification of list.~Power analysis:~Although this is an observational analysis that does not involve recruitment of patients, a power analysis to establish that the database can detect a clinically meaningful and statistically significant difference is important. Previous SSI prevention interventions such as normothermia, antibiotic prophylaxis, and chlorhexidine surgical prep have demonstrated relative risk reduction rates ranging from 40% to 70%. For purposes of this power analysis, it will be assumed that a conservative benefit of only 20% for each of the intraoperative interventions, or the group as a bundle. Review of literature demonstrates a composite SSI incidence of about 4%. A 20% relative reduction would result in an observed SSI rate of 3.2%. Assuming the rate of appropriate antibiotic usage is 92%, a chi square test with a 0.05 two-sided significance level will have 80% power to detect the difference between these two rates when a total sample size is 55,637. In aggregate, the institutions presented in this proposal already offer sufficient ACS-NSQIP cases with integrated anesthesia EHR data.~Prespecified sensitivity analyses:~A sensitivity analysis will be conducted in which an attempt to create a propensity-score matched cohort of patients receiving vs. not receiving guideline adherent antibiotic prophylaxis to measure the possible association of such adherence to the same SSI outcome as above.~In this sensitivity analysis, instead of regression covariate adjustment in our primary analysis, the investigators will use the propensity score method for covariate adjustment of potential confounding. The propensity scores will be developed to predict those receiving vs. not receiving guideline adherent antibiotic prophylaxis to address potential issues of selection bias. Propensity scores will be developed using logistic regression models to predict exposure group using a dichotomous outcome indicator variable for exposure (1= receiving guideline-based antibiotics, 0 = not receiving guideline based antibiotics). The investigators will select a non-parsimonious set of covariates as listed in the primary analytic modelling description. Then, patients in two exposure groups will be matched, first via exact matching by institution, patient age in years, and anesthesia CPT code, followed by propensity score matching using the greedy method implemented in the %GMATCH SAS macro (Mayo Clinic, Rochester, Minnesota),or a similar algorithm based on the proximity of individual propensity scores. To assess whether appropriate balance on covariates has been achieved between each grouping, standardized difference (d) for each covariate will be calculated. If this meets a threshold value < 10%(15) the covariates will be considered to be generally well balanced. If residual imbalance exists and is deemed significant, iterative recalculation of propensity scores with additional candidate covariates will be considered. Last, simpler mixed-effects multiple logistic regression models of the SSI outcome with the fixed effect of exposure variable will be fit. If exact matching within institution causes diminution of successfully matched samples, the investigators will consider removing it from the exact match and including a random institution effect within the final propensity-score matched analysis.~Considering the issue of diminution of sample size during matching, another sensitivity analysis will be conducted in which the method of the inverse probability of treatment (exposure) weighting using derived propensity scores will be used to compare the SSI outcome between two groups (1= receiving guideline-adherent antibiotic prophylaxis, 0 = not receiving), as the weighting method will enable us to include all patients into the final analysis.~In the case of a significant association between guideline adherent antibiotic administration and SSI, we will explore how prevalent and powerful an unmeasured confounder would have needed to be able to erase the observed difference. That is, model the robustness of an observed association in the face of a hypothetical unmeasured confounder as described by Lin, et al(16). For this analysis a model of the characteristics of a hypothetical unmeasured binary confounder that could have accounted for observed differences in odds of SSI between patients with adherent vs. non-adherent antibiotic dosing, using a broad range of plausible values for the effect size and prevalence of such an unmeasured confounder.~As mentioned above, for antibiotics with weight-based guidelines in mg/kg (example vancomycin), dosing within 10% of the calculated dose will be considered as guideline adherent. To assess the correlation of dosing of these antibiotics on SSI, also a sensitivity analysis will be performed by categorizing patients within 25% of the calculated dose in guideline adherent group.
This study will seek to describe current practice of antibiotic prophylaxis to identify the effect of appropriate perioperative antimicrobial coverage - specifically regarding timing, dose adjustments, and redosing - on surgical site infections (SSI).