• Caprice K Christian, Michael L Gustafson, Rebecca A Betensky, Jennifer Daley, and Michael J Zinner.
• Department of Surgery, Brigham and Women's Hospital, Boston, MA 021115, USA.
• Ann. Surg. 2003 Oct 1;238(4):447-55; discussion 455-7.

ObjectiveThe original Leapfrog Initiative recommends selective referral based on procedural volume thresholds (500 coronary artery bypass graft [CABG] surgeries, 30 abdominal aortic aneurysm [AAA] repairs, 100 carotid endarterectomies [CEA], and 7 esophagectomies annually). We tested the volume-mortality relationship for these procedures in the University HealthSystem Consortium (UHC) Clinical DatabaseSM, a database of all payor discharge abstracts from UHC academic medical center members and affiliates. We determined whether the Leapfrog thresholds represent the optimal cutoffs to discriminate between high- and low-mortality hospitals.MethodsLogistic regression was used to test whether volume was a significant predictor of mortality. Volume was analyzed in 3 different ways: as a continuous variable, a dichotomous variable (above and below the Leapfrog threshold), and a categorical variable. We examined all possible thresholds for volume and observed the optimal thresholds at which the odds ratio is the highest, representing the greatest difference in odds of death between the 2 groups of hospitals.ResultsIn multivariate analysis, a relationship between volume and mortality exists for AAA in all 3 models. For CABG, there is a strong relationship when volume is tested as a dichotomous or categorical variable. For CEA and esophagectomy, we were unable to identify a consistent relationship between volume and outcome. We identified empirical thresholds of 250 CABG, 15 AAA, and 22 esophagectomies, but were unable to find a meaningful threshold for CEA.ConclusionsIn this group of academic medical centers and their affiliated hospitals, we demonstrated a significant relationship between volume and mortality for CABG and AAA but not for CEA and esophagectomy, based on the Leapfrog thresholds. We described a new methodology to identify optimal data-based volume thresholds that may serve as a more rational basis for selective referral.

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