OBJECTIVES

: Institutions around the world have implemented Enhanced Recovery After Surgery (ERAS®) care pathways with numerous studies showing decreased rates of complications. However, most surgical risk calculators do not track whether a patient is on an ERAS® protocol. In this study we hypothesized that the addition of an ERAS® variable would improve the predictive ability of our established multivariate pancreaticoduodenectomy (PD) predictive models.

METHODS

: A retrospective analysis of a procedure-specific REDCap™ data repository was performed for patients who underwent PD from 2007-2017 (n=620). 470 patients underwent PD without ERAS® protocol, and 150 underwent PD on ERAS® protocol. ERAS® was added as an independent binary variable to the data set. Bi-variate analyses were conducted measuring associations between ERAS® protocol and postoperative outcomes. The ERAS® variable was also included in established institutional multivariate predictive models to test improvements in model discriminative ability using area under the curve (AUC) analysis.

RESULTS

: Significant associations were observed between the independent ERAS® variable and eight dependent postoperative outcomes. ERAS patients had significantly reduced odds for delayed gastric emptying, pancreatic fistula, and re-operation. In addition, ERAS patients had significantly increased odds for emesis/vomiting. hypotension, nausea, and urinary retention. Preliminary analysis demonstrates the addition of an ERAS® variable improved discriminative ability in predictive models for postoperative outcomes (e.g. re-operation model without ERAS® variable AUC: 0.6674 vs. re-operation model with ERAS® variable AUC: 0.7363, p=.002).

CONCLUSIONS

: In this study we demonstrate that the addition of an ERAS® variable to an established predictive model improves its ability to predict postoperative outcomes. Given the widespread adoption of ERAS® protocols over a variety of surgical sub-specialties, we propose that surgical risk calculators incorporate an ERAS® variable to more accurately predict risk for postoperative complications.