Volume 53 | Number 6 | December 2018

Abstract List

Hillary J. Mull Ph.D., Kamal M. F. Itani M.D., Steven D. Pizer Ph.D., Martin P. Charns M.B.A., D.B.A., Peter E. Rivard, Nathalie McIntosh Ph.D., Mary T. Hawn M.D., M.P.H., Amy K. Rosen Ph.D.


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

Data Sources

Veterans Health Administration's Corporate Data Warehouse.

Study Design

Six surgical triggers, including postoperative emergency room visits and hospitalizations, were applied to 2012–2014 outpatient surgeries ( = 744,355). We randomly sampled trigger‐flagged and unflagged cases for nurse chart review to document s and measured positive predictive value () for triggers. Next, we used chart review data to iteratively estimate multilevel logistic regression models to predict the probability of an , 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 2015 outpatient surgery data ( = 256,690) and reviewing charts for cases at high and moderate probability of an .

Principal Findings

Of 1,730 2012–2014 reviewed surgeries, 350 had an (20 percent). The final surveillance model c‐statistic was 0.81. In 2015 surgeries with >0.8 predicted probability of an ( = 405, 0.15 percent), was 85 percent; in surgeries with a 0.4–0.5 predicted probability of an , was 38 percent.


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