Comparison of Pediatric Cardiac Surgical Mortality Rates From National Administrative Data to Contemporary Clinical Standards

BackgroundDespite the superior coding and risk adjustment of clinical data, the ready availability, national scope, and perceived unbiased nature of administrative data make it the choice of governmental agencies and insurance companies for evaluating quality and outcomes. We calculated pediatric cardiac surgery mortality rates from administrative data and compared them with widely quoted standards from clinical databases.MethodsPediatric cardiac surgical operations were retrospectively identified by ICD-9-CM diagnosis and procedure codes from the Nationwide Inpatient Sample (NIS) 1988–2005 and the Kids' Inpatient Database (KID) 2003. Cases were grouped into Risk Adjustment for Congenital Heart Surgery, version 1 (RACHS-1) categories. In-hospital mortality rates and 95% confidence intervals were calculated.ResultsA total of 55,164 operations from the NIS and 10,945 operations from the KID were placed into RACHS-1 categories. During the 18-year period, the overall NIS mortality rate for pediatric cardiac surgery decreased from 8.7% (95% confidence interval, 8.0% to 9.3%) to 4.6% (95% confidence interval, 4.3% to 5.0%). Mortality rates by RACHS-1 category decreased significantly as well. The KID and NIS mortality rates from comparable years were similar. Overall mortality rates derived from administrative data were higher than those from contemporary national clinical data, The Society of Thoracic Surgeons Congenital Heart Surgery Database, or published data from pediatric cardiac specialty centers. Although category-specific mortality rates were higher in administrative data than in clinical data, a minority of the relationships reached statistical significance.ConclusionsDespite substantial improvement, mortality rates from administrative data remain higher than those from clinical data. The discrepancy may be attributable to several factors: differences in database design and composition, differences in data collection and reporting structures, and variation in data quality. Despite the superior coding and risk adjustment of clinical data, the ready availability, national scope, and perceived unbiased nature of administrative data make it the choice of governmental agencies and insurance companies for evaluating quality and outcomes. We calculated pediatric cardiac surgery mortality rates from administrative data and compared them with widely quoted standards from clinical databases. Pediatric cardiac surgical operations were retrospectively identified by ICD-9-CM diagnosis and procedure codes from the Nationwide Inpatient Sample (NIS) 1988–2005 and the Kids' Inpatient Database (KID) 2003. Cases were grouped into Risk Adjustment for Congenital Heart Surgery, version 1 (RACHS-1) categories. In-hospital mortality rates and 95% confidence intervals were calculated. A total of 55,164 operations from the NIS and 10,945 operations from the KID were placed into RACHS-1 categories. During the 18-year period, the overall NIS mortality rate for pediatric cardiac surgery decreased from 8.7% (95% confidence interval, 8.0% to 9.3%) to 4.6% (95% confidence interval, 4.3% to 5.0%). Mortality rates by RACHS-1 category decreased significantly as well. The KID and NIS mortality rates from comparable years were similar. Overall mortality rates derived from administrative data were higher than those from contemporary national clinical data, The Society of Thoracic Surgeons Congenital Heart Surgery Database, or published data from pediatric cardiac specialty centers. Although category-specific mortality rates were higher in administrative data than in clinical data, a minority of the relationships reached statistical significance. Despite substantial improvement, mortality rates from administrative data remain higher than those from clinical data. The discrepancy may be attributable to several factors: differences in database design and composition, differences in data collection and reporting structures, and variation in data quality.