摘要

Introduction: Meaningful evaluation of the quality of care must account for the variations in the population of patients receiving treatment (%26quot;case-mix%26quot;). In order to analyze mortality after congenital heart surgery over 16 years, we used four complexity stratification tools: Aristotle Basic Complexity Score (ABC Score), Risk Adjustment for Congenital Heart Surgery-1 Categories (RACHS-1 Categories), The Society of Thoracic Surgeons - European Association for Cardio-Thoracic Surgery Congenital Heart Surgery Mortality Score (STAT Mortality Score), and STAT Mortality Categories. Our goal was not only to analyze our institutional results, but also to evaluate the ability of each tool to predict mortality. %26lt;br%26gt;Material and methods: Complete and verified data on hospital mortalities that occurred after 8404 operations over 16 years in our institution were included in the study. For evaluating the statistical predictability of each tool, we included only those procedures that were scored by that tool. %26lt;br%26gt;Results: Mean hospital mortality was 4.38%, ranging from 0% to 33%. The STAT Mortality Score had the highest discrimination for predicting mortality (C-index = 0.768). The Pearson correlation coefficient between a procedure%26apos;s STAT Mortality Score and its actual mortality rate was r = 0.84. In the subset of procedures which could be classified by all four complexity stratification tools (33 procedures), discrimination was highest for the STAT Mortality Score (C-index = 0.776). %26lt;br%26gt;Conclusions: In this single-institution analysis, the STAT Mortality Score had the strongest association with actual mortality. This analysis demonstrates a strategy for the application of complexity stratification tools, based on multi-institutional data, to single-institution results.

  • 出版日期2013-6