It has been theorized that a tiered, regionalized system of care for emergency general surgery (EGS) patients—akin to regional trauma systems—would translate into significant survival benefits. Yet data to support this supposition are lacking. The aim of this study was to determine the potential number of lives that could be saved by regionalizing EGS care to higher-volume, lower-mortality EGS institutions.
Adult patients who underwent one of 10 common EGS operations were identified in the California Inpatient Database (2010–2011). An algorithm was constructed that “closed” lower-volume, higher-mortality hospitals and referred those patients to higher-volume, lower-mortality institutions (“closure” based on hospital EGS volume-threshold that optimized to 95% probability of survival). Primary outcome was the number of lives saved. Fifty thousand regionalization simulations were completed (5,000 for each operation) employing a bootstrap resampling method to proportionally redistribute patients. Estimates of expected deaths at the higher-volume hospitals were recalculated for every bootstrapped sample.
Of the 165,123 patients who underwent EGS operations over the 2-year period, 17,655 (10.7%) were regionalized to a higher-volume hospital. On average, 128 (48.8%) of lower-volume hospitals were “closed,” ranging from 68 (22.0%) hospital closures for appendectomy to 205 (73.2%) for small bowel resection. The simulations demonstrated that EGS regionalization would prevent 9.7% of risk-adjusted EGS deaths, significantly saving lives for every EGS operation: from 30.8 (6.5%) deaths prevented for appendectomy to 122.8 (7.9%) for colectomy. Regionalization prevented 4.6 deaths per 100 EGS patient-transfers, ranging from 1.3 for appendectomy to 8.0 for umbilical hernia repair.
This simulation study provides important new insight into the concept of EGS regionalization, suggesting that 1 in 10 risk-adjusted deaths could be prevented by a structured system of EGS care. Future work should expand upon these findings using more complex discrete-event simulation models.
LEVEL OF EVIDENCE
Therapeutic/Care Management, level IV.