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Development of an Adverse Event Surveillance Model for Outpatient Surgery in the Veterans Health Administration

Objective: Develop and validate a surveillance model to identify outpatient surgical adverse events (AEs) based on previously developed electronic triggers.

Data Sources: Veterans Health Administration's Corporate Data Warehouse.

Study Design: Six surgical AE triggers, including postoperative emergency room visits and hospitalizations, were applied to FY2012–2014 outpatient surgeries (n = 744,355). We randomly sampled triggerflagged and unflagged cases for nurse chart review to document AEs and measured positive predictive value (PPV) for triggers. Next, we used chart review data to iteratively estimate multilevel logistic regression models to predict the probability of an AE, starting with the six triggers and adding in patient, procedure, and facility characteristics to improve model fit. We validated the final model by applying the coefficients to FY2015 outpatient surgery data (n = 256,690) and reviewing charts for cases at high and moderate probability of an AE.

Principal Findings: Of 1,730 FY2012–2014 reviewed surgeries, 350 had an AE (20 percent). The final surveillance model cstatistic was 0.81. In FY2015 surgeries with >0.8 predicted probability of an AE (n = 405, 0.15 percent), PPV was 85 percent; in surgeries with a 0.4–0.5 predicted probability of an AE, PPV was 38 percent.

Conclusions: The surveillance model performed well, accurately identifying outpatient surgeries with a high probability of an AE.

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