S98 Surgical Forum Abstracts J Am Coll Surg

adjustment. We sought to identify the patient- and procedure-related risk factors for iAEs.

METHODS: Our 2007-2012 institutional American College of

Surgeons (ACS) NSQIP and administrative databases werematched then screened for iAEs using the ICD-9-CM based Pa-tient Safety Indicator “Accidental Puncture/Laceration”. Theoccurrence of iAEs was confirmed by systematic review of opera-

tive reports. Patient co-morbidities were assessed using the ACSNSQIP variables. Previous abdominal surgery as a variable wasdetermined using the CPT codes for adhesiolysis. Operative

complexity was determined using relative value units (RVUs).Multivariate models were constructed to identify independentpredictors of iAEs. Sensitivity analyses were performed in a uni-

form sample of operations.

RESULTS: Of a total of 9292 general surgery procedures, 218

iAEs were confirmed. The median patient age was 56 years; 54%were female. In multivariable regression analyses, previous surgery(adjusted OR¼2.34, 95% CI: 1.71-3.21, p<0.001), higher opera-

tive complexity (3rd vs 1st RVU IQR: OR¼3.36 [1.66-6.78],p<0.001; 4th vs 1st RVU IQR: OR¼5.97 [3.01-11.86],p<0.001), and “open” surgical approach (vs laparoscopic;

OR¼2.04 [1.39-3.01], p<0.001) independently predicted theoccurrence of iAEs. Sensitivity analyses confirmed previous surgeryand higher operative complexity as independent predictors of iAEs.

CONCLUSIONS: Previous surgery and higher operativecomplexity significantly increase the risk of iAEs. Any attempts at

benchmarking the quality of intraoperative surgical care needs torisk adjust for these factors.

Evaluating the Surgical Apgar Score in the VeteransAdministration (VA)Joshua S Richman, MD, PhD, William Henderson, PhD,Michael Bronsert, PhD, Terri Monk, MD,Mary T Hawn, MD, MPH, FACSBirmingham VA Medical Center, Birmingham, AL

INTRODUCTION: The Surgical Apgar Score (SAS) provides a sim-ple means to risk-stratify patients for postoperative death or com-plications. This study examined the overall and specialty-stratified

discrimination of the SAS in a broad surgical cohort comparedto traditional multivariable models.

METHODS: VA Surgical Quality Improvement Program (VAS-QIP) data was merged with electronic intraoperative Anesthesia In-formation Management System (AIMS) data at six VA medical

centers from 2001 to 2008. The SAS was constructed using intra-operative mean arterial pressure and heart rate AIMS data and esti-mated blood loss calculated from VASQIP using a validated

algorithm. Logistic regression models were constructed using 1)best fit VASQIP variables (V-Model), and 2) the V-Model plusSAS components (V+SAS). Discrimination was estimated by the

C-statistic for the outcomes of mortality and any VASQIP compli-cation assessed 30 days postoperatively. Pairwise comparisons of

the C-statistics were made by bootstrapping. All analyses were con-ducted both overall and by surgical specialty.

RESULTS: The SAS was calculated for 11,115 patients with he-

matocrits in VASQIP. The overall C-statistics for mortality wereSAS: 0.69; V-Model: 0.84; and V+SAS: 0.86 (all pairwisep<0.05); ranges by specialty were SAS: 0.36 to 0.76; V-Model:

0.76 to 0.92; and V+SAS: 0.81 to 0.92. Model Results were similarfor the outcome of any complication with all approaches showingreduced discrimination.

CONCLUSIONS: The Surgical Apgar’s discrimination is less than

VASQIP-based models. However, adding the SAS improved theVASQIP model suggesting that dynamic risk prediction models us-ing patient risk factors updated with clinical data better identifythose most at risk for complications after surgery.

Do Past Mortality Rates Predict Future HospitalMortality?Taylor M Coe, BS, Samuel E Wilson, MD, FACS,David C Chang, PhD, MPH, MBAUniversity of California, San Diego, San Diego, CA, Universityof California, Irvine, Irvine, CA

INTRODUCTION: Surgical outcomes are a combination of healthsystem (including hospitals and surgeons) and patient factors, yet

most quality and outcomes analyses only consider patient factorsin the risk adjustment. The purpose of this study is to determinewhether non-patient factors have an independent impact on patient

outcomes. Specifically, we hypothesize that hospitals with higherhistorical mortality rates would be independently associated withworse patient outcomes, even after accounting for patient

confounders.

METHODS: Observational study of in-hospital mortality in openAAA repairs, aortic valve replacement (AVR), and coronary arterybypass graft surgery (CABG) in an in-patient database from the

California Office of Statewide Health Planning and Development.Hospitals’ historical mortality rates for each year between 1998 and2010 were calculated based on three years of data prior to each in-dex year. Results were adjusted for race, sex, age, hospital teaching

status, admission year, insurance status, Charlson comorbidityindex.

RESULTS: Hospitals were divided into quartiles based on histor-ical mortality rates and 142449 patients were analyzed. For AAA,

the odds ratio (OR) for in-hospital mortality for hospitals withinthe highest quartile of prior mortality rates was 1.30 comparedto the lowest quartile (95%CI:1.03-1.63). For AVR, the OR was

1.41 for the 3rd quartile (95%CI:1.15-1.73) and 1.54 for the high-est quartile (95%CI:1.27-1.87). For CABG, the OR was 1.33 forthe 3rd (95%CI:1.2-1.49) and 1.58 for the highest (95%

CI:1.41-1.76).

CONCLUSIONS: Patients presenting to hospitals with high histor-ical mortality rates have a 30% to 60% increased mortality risk

Multivariable Model: Risk Factors for SSI in VHR Patients

Variable Odds ratio95% Confidence

interval p Value

MRSA+ 3.5 1.8-6.9 <0.001

Smoking 2.0 1.1-3.8 0.028

Diabetes 1.5 0.8-2.8 0.179

BMI 1.0 1.0-1.1 0.004

Myofascial release 6.0 3.5-10.5 <0.001

Wound classification 1.7 1.3-2.3 <0.001

Vol. 219, No. 3S, September 2014 Surgical Forum Abstracts S99

compared to patients presenting to hospitals with low historicalmortality rates.

Does Hospital Proficiency Vary for Melanoma SentinelLymph Node Biopsies? An Evaluation of Hospital-LevelAdjusted Node Positivity Rates and OutcomesChristine V Kinnier, MD, Jeanette W Chung, PhD,Jennifer L Paruch, MD, Jeffrey D Wayne, MD, Merrick I Ross, MD,David P Winchester, MD, FACS,Karl Y Bilimoria, MD, MS, FACSNorthwestern University, Chicago, IL, Massachusetts General

Hospital, Boston, MA

INTRODUCTION: The proficiency of performing SLNB for mel-anoma may vary among hospitals and may be reflected by a hospi-tal’s SLNB positivity rate. Our objectives were to (1) examinewhether SLNB positivity rates vary from hospital-to-hospital, (2)

identify hospital characteristics associated with lower- and higher-than-expected SLNB positivity rates, and (3) examine whether hos-pitals with lower-than-expected SLNB positivity rates have worse

outcomes.

METHODS: Stage IA-III melanoma patients undergoing SLNB

were identified from the National Cancer Data Base (2004-2010). Hospital-level SLNB positivity rates were adjusted for de-mographics, histology, and T stage. Hospitals were dividedinto terciles of adjusted SLNB positivity rates. Hospital charac-

teristics (using multinomial logistic regression) and survival (us-ing Cox modeling) were examined across terciles of SLNBpositivity rates.

RESULTS: Of 36,739 patients (646 hospitals) who underwentSLNB, 3219 (8.8%) had at least one positive lymph node. The me-dian adjusted SLNB positivity rate was 7.3% (IQR 1.9%-15.2%).

Both low and high tercile hospitals were more likely to be low-vol-ume hospitals (low tercile: RRR¼2.57, p¼0.002; high tercile:RRR¼2.3, p¼0.004) when compared to hospitals in the middle

tercile. Stage I patients treated at low tercile hospitals had a worse5-year survival than those treated at middle tercile hospitals (90.0%vs 91.9%, p¼0.014; HR¼1.284, 95% CI¼1.052-1.566); survivaldifferences were not observed for Stage II or III.

CONCLUSIONS: Adjusted hospital SLNB positivity rates variedwidely. Surgery at hospitals with lower-than-expected SLNB posi-

tivity rates was associated with decreased survival. Hospital SLNBpositivity rates are a novel, unique measure to confidentially reportto hospitals for internal quality assessment.

History of MRSA Infection Considerably Increases Risk ofSurgical Site Infection in Ventral Hernia RepairJenny Ousley, BS, Rebeccah B Baucom, MD,Michael D Holzman, MD, MPH, FACS, Jesse M Ehrenfeld, MD,MPH, Kenneth W Sharp, MD, FACS, William H Nealon, MD,FACS, Benjamin K Poulose, MD, MPH, FACSVanderbilt University Medical Center, Nashville, TN

INTRODUCTION: Surgical site infections (SSIs) can lead to devas-tating complications after ventral hernia repair (VHR), including

mesh infections and hernia recurrence. We aimed to determinewhether a history of preoperative MRSA infection, regardless ofsite, confers an increased risk of 30-day SSI after VHR.

METHODS: A retrospective cohort study of patients undergoing

VHR between 2006-2012 was performed using Vanderbilt Univer-sity Medical Center’s Perioperative Data Warehouse and the elec-tronic medical record. Preoperative MRSA status and site of

infection were determined, as well as 30-day SSI. Univariate anal-ysis comparing proportions was performed, as well as multivariableanalysis adjusting for confounding factors.

RESULTS: Of the 797 VHR patients meeting inclusion criteria,47% were women. There were 63 preoperative MRSA infections

(MRSA+), of which 32% (n¼20) were soft tissue infections,17% (n¼11) bloodstream, and 6% (n¼4) pulmonary. OverallSSI rate was 11% (n¼88). The SSI rate in the MRSA+ group

was 37%, compared to 9% in the control group (p<0.001). Multi-variable analysis demonstrated that MRSA+ infection conferred athreefold higher odds of postoperative SSI (OR 3.5, 95% CI

1.8-6.9, p<0.001). Other contributors to postoperative SSI wereperformance of myofascial release, increasing body mass index(BMI), smoking, and higher CDC wound classification (Table).

CONCLUSIONS: Site-independent MRSA+ infection confers asignificant risk for the development of postoperative SSI afterVHR. Future studies should focus on perioperative treatment reg-

imens that might decrease the risk of SSI in VHR. Additionally,further investigation is needed to determine the optimal prosthetictype and technique for VHR in this population.

Understanding Quality Improvement: A Tale of TwoImplementations of Preoperative Chlorhexidine BathingElizabeth C Wick, MD, FACS, Deborah B Hobson, RN, BSN,Ronald Bleday, MD, FACS, Claro Pio Roda, Ira L Leeds, MDJohns Hopkins, Baltimore, MD, Brigham and Womens Hospital,

Boston, MA

INTRODUCTION: Surgical site infections (SSI) are a potentiallyavoidable cause of patient harm. Bundles of evidence-based processmeasures are effective for SSI reduction but for many hospitals

implementation has failed to translate to improved outcomes.We hypothesized that the different approaches to patient delivery