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Emerging literature suggests that measures of social vulnerability should be incorporated into surgical risk calculators. The Social Vulnerability Index (SVI) is a measure designed by the CDC that encompasses 15 socioeconomic and demographic variables at the census tract level. We examined whether adding the SVI into a parsimonious surgical risk calculator would improve model performance.The eight-variable Surgical Risk Preoperative Assessment System (SURPAS), developed using the entire American College of Surgeons (ACS) NSQIP database, was applied to local ACS-NSQIP data from 2012 to 2018 to predict 12 postoperative outcomes. Patient addresses were geocoded and used to estimate the SVI, which was then added to the model as a ninth predictor variable. Brier scores and c-indices were compared for the models with and without the SVI.The analysis included 31,222 patients from five hospitals. Brier scores were identical for eight outcomes and improved by only one to two points in the fourth decimal place for four outcomes with addition of the SVI. Similarly, c-indices were not significantly different (p values ranged from 0.15 to 0.96). Of note, the SVI was associated with most of the eight SURPAS predictor variables, suggesting that SURPAS may already indirectly capture this important risk factor.The eight-variable SURPAS prediction model was not significantly improved by adding the SVI, showing that this parsimonious tool functions well without including a measure of social vulnerability.

Present at the time of surgery (PATOS) is an important measure to collect in postoperative complication surveillance systems because it may affect a patient's risk of a subsequent complication and the estimation of postoperative complication rates attributed to the healthcare system. The American College of Surgeons (ACS) NSQIP started collecting PATOS data for 8 postoperative complications in 2011, but no one has used these data to quantify how this may affect unadjusted and risk-adjusted postoperative complication rates.This study was a retrospective observational study of the ACS NSQIP database from 2012 to 2018. PATOS data were analyzed for the 8 postoperative complications of superficial, deep, and organ space surgical site infection; pneumonia; urinary tract infection; ventilator dependence; sepsis; and septic shock. Unadjusted postoperative complication rates were compared ignoring PATOS vs taking PATOS into account. Observed to expected ratios over time were also compared by calculating expected values using multiple logistic regression analyses with complication as the dependent variable and the 28 nonlaboratory preoperative variables in the ACS NSQIP database as the independent variables.In 5,777,108 patients, observed event rates for each outcome were reduced by between 6.1% (superficial surgical site infection) and 52.5% (sepsis) when PATOS was taken into account. The observed to expected ratios were similar each year for all outcomes, except for sepsis and septic shock in the early years.Taking PATOS into account is important for reporting unadjusted event rates. The effect varied by type of complication-lowest for superficial surgical site infection and highest for sepsis and septic shock. Taking PATOS into account was less important for risk-adjusted outcomes (observed to expected ratios), except for sepsis and septic shock.

The objective of this study was to determine the effects of using the Surgical Risk Preoperative Assessment System (SURPAS) on patient satisfaction and surgeon efficiency in the surgical informed consent process, as compared to surgeons' "usual" consent process.Patient perception of the consent process was assessed via survey in 2 cohorts: 10 surgeons in different specialties used their "usual" consent process for 10 patients; these surgeons were then taught to use SURPAS, and they used it during the informed consent process of 10 additional patients. The data were compared using Fisher's exact test and the Cochran-Mantel-Haenszel test.One hundred patients underwent the "usual" consent process (USUAL), and 93 underwent SURPAS-guided consent (SURPAS). Eighty-two percent of SURPAS were "very satisfied" and 18% were "satisfied" with risk discussion vs 16% and 72% of USUAL, respectively. Of those who used SURPAS, 75.3% reported the risk discussion made them "more comfortable" with surgery vs 19% of USUAL, and 90.3% of SURPAS users reported "somewhat" or "greatly decreased" anxiety vs 20% of USUAL. All p values were0.0001. Among SURPAS patients, 97.9% reported "enough time spent discussing risks" vs 72.0% of USUAL patients.The SURPAS tool improved the informed consent process for patients compared with the "usual" consent process, in terms of patient satisfaction, ie making patients feel more comfortable and less anxious about their impending operations. Providers should consider integrating the SURPAS tool into their preoperative consent process.

Background Annual institution resection volume has been proposed for defining centers of excellence, with various cut-offs for defining "high-volume" centers used. This study aimed to define an objective, evidence-based operative volume threshold associated with improved postoperative outcomes after pancreatic resection. Study Design This retrospective analysis of patients who underwent pancreatic resection in the Nationwide Inpatient Sample, a 20% representative sample of patients in the US between 1998 and 2003, was performed using multivariable logistic regression. Different models of annual hospital resection volume were analyzed and the goodness of fit of each "high-volume" model to postoperative mortality was compared through use of the pseudo r2. Results Based on analysis of 7,558 patients who underwent pancreatic resection, median annual institution resection volume was 15 (range 1 to 254), and overall in-hospital mortality was 7.6%. The best model of "high-volume" centers was an annual institution resection volume of 19 or more, as determined by goodness of fit (r2 of 5.29%). But there was little difference in data variance explained between this best model and other "high-volume" models. The model without any volume variable had a goodness-of-fit r2 of 3.57%, suggesting that volume explains less than 2% of data variance in perioperative death after pancreatic resection. Conclusions Very little difference was observed in the explanatory powers of models of "high-volume" centers. Although volume has an important impact on mortality, volume cut-off is necessary but insufficient for defining centers of excellence. Volume appears to function as an imperfect surrogate for other variables, which may better define centers of excellence.

RESULTS: No significant differences in preoperative comorbidities were identified between the two groups. Median length of stay was 12 days for both groups. (p 0.42) There were no differences in sepsis (p 0.47), pneumonia (p 0.19), prolonged intubation (p 0.27), anastomotic leak (p 0.56), reintubation (p 1.0), and readmission (p 0.37) between the two groups. The open group had a decreased incidence of atrial arrhythmia requiring treatment (p 0.04). Overall 30 day mortality was 2% and was similar in both groups. (p 1.0).