Impact of Patient Characteristics on Hospital-Level Outcomes Assessment in Congenital Heart Surgery.

BACKGROUND: Accurate hospital outcome measures in congenital heart surgery are important to multiple initiatives. While methods have been developed to account for differences in procedural case-mix, characteristics patients bring into the operation that may also vary across hospitals and influence outcome have received less attention. We evaluated the impact of these characteristics in a large cohort. METHODS: Patients undergoing congenital heart surgery at centers participating in The Society of Thoracic Surgeons Congenital Heart Surgery Database (2010 to 2013) with adequate data quality were included. Variation across hospitals in important patient characteristics was examined, and hospital operative mortality rates were compared with and without adjustment for patient characteristics. RESULTS: Overall, 86 centers (52,224 patients) were included. There was greater than twofold variation across hospitals for nearly all patient characteristics examined. For example, the proportion of a center's surgical population comprised of neonates ranged from 12.8% to 26.6% across hospitals; the proportion with a non-cardiac anomaly ranged from 0.7% to 5.0%. When hospital mortality rankings were evaluated based on "standard" (adjustment for differences in procedural case-mix alone) versus "full" models (adjustment for both differences in procedural case-mix and patient characteristics), 14.0% changed their ranking for mortality by 20 or greater positions, 34.9% of centers changed which mortality quartile they were classified in, and 14.0% changed their statistical classification (statistically higher, lower, or same-as-expected mortality). CONCLUSIONS: Characteristics of patients undergoing congenital heart surgery vary across centers and impact hospital outcomes assessment. Methods to assess outcomes and relative performance should account for these characteristics.

Duke Scholars

Author Sean Michael O'Brien Biostatistics & Bioinformatics, Division of Biostatistics