An empirically based tool for analyzing mortality associated with congenital heart surgery

Abstract

Objective: Analysis of congenital heart surgery results requires a reliable method of estimating the risk of adverse outcomes. Two major systems in current use are based on projections of risk or complexity that were predominantly subjectively derived. Our goal was to create an objective, empirically based index that can be used to identify the statistically estimated risk of in-hospital mortality by procedure and to group procedures into risk categories.

Methods: Mortality risk was estimated for 148 types of operative procedures using data from 77,294 operations entered into the European Association for Cardiothoracic Surgery (EACTS) Congenital Heart Surgery Database (33,360 operations) and the Society of Thoracic Surgeons (STS) Congenital Heart Surgery Database (43,934 patients) between 2002 and 2007. Procedure-specific mortality rate estimates were calculated using a Bayesian model that adjusted for small denominators. Each procedure was assigned a numeric score (the STS-EACTS Congenital Heart Surgery Mortality Score [2009]) ranging from 0.1 to 5.0 based on the estimated mortality rate. Procedures were also sorted by increasing risk and grouped into 5 categories (the STS-EACTS Congenital Heart Surgery Mortality Categories [2009]) that were chosen to be optimal with respect to minimizing within-category variation and maximizing between-category variation. Model performance was subsequently assessed in an independent validation sample (n = 27,700) and compared with 2 existing methods: Risk Adjustment for Congenital Heart Surgery (RACHS-1) categories and Aristotle Basis Complexity scores.

Results: Estimated mortality rates ranged across procedure types from 0.3% (atrial septal defect repair with patch) to 29.8% (truncus plus interrupted aortic arch repair). The proposed STS-EACTS score and STS-EACTS categories demonstrated good discrimination for predicting mortality in the validation sample (C-index = 0.784 and 0.773, respectively). For procedures with more than 40 occurrences, the Pearson correlation coefficient between a procedure's STS-EACTS score and its actual mortality rate in the validation sample was 0.80. In the subset of procedures for which RACHS-1 and Aristotle Basic Complexity scores are defined, discrimination was highest for the STS-EACTS score (C-index = 0.787), followed by STS-EACTS categories (C-index = 0.778), RACHS-1 categories (C-index = 0.745), and Aristotle Basic Complexity scores (C-index = 0.687). When patient covariates were added to each model, the C-index improved: STS-EACTS score (C-index = 0.816), STS-EACTS categories (C-index = 0.812), RACHS-1 categories (C-index = 0.802), and Aristotle Basic Complexity scores (C-index = 0.795).

Conclusion: The proposed risk scores and categories have a high degree of discrimination for predicting mortality and represent an improvement over existing consensus-based methods. Risk models incorporating these measures may be used to compare mortality outcomes across institutions with differing case mixes.