An empirically based tool for analyzing morbidity associated with operations for congenital heart disease.

OBJECTIVE: Congenital heart surgery outcomes analysis requires reliable methods of estimating the risk of adverse outcomes. Contemporary methods focus primarily on mortality or rely on expert opinion to estimate morbidity associated with different procedures. We created an objective, empirically based index that reflects statistically estimated risk of morbidity by procedure. METHODS: Morbidity risk was estimated using data from 62,851 operations in the Society of Thoracic Surgeons Congenital Heart Surgery Database (2002-2008). Model-based estimates with 95% Bayesian credible intervals were calculated for each procedure's average risk of major complications and average postoperative length of stay. These 2 measures were combined into a composite morbidity score. A total of 140 procedures were assigned scores ranging from 0.1 to 5.0 and sorted into 5 relatively homogeneous categories. RESULTS: Model-estimated risk of major complications ranged from 1.0% for simple procedures to 38.2% for truncus arteriosus with interrupted aortic arch repair. Procedure-specific estimates of average postoperative length of stay ranged from 2.9 days for simple procedures to 42.6 days for a combined atrial switch and Rastelli operation. Spearman rank correlation between raw rates of major complication and average postoperative length of stay was 0.82 in procedures with n greater than 200. Rate of major complications ranged from 3.2% in category 1 to 30.0% in category 5. Aggregate average postoperative length of stay ranged from 6.3 days in category 1 to 34.0 days in category 5. CONCLUSIONS: Complication rates and postoperative length of stay provide related but not redundant information about morbidity. The Morbidity Scores and Categories provide an objective assessment of risk associated with operations for congenital heart disease, which should facilitate comparison of outcomes across cohorts with differing case mixes.

Duke Scholars

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