Mortality Prediction Following Cardiac Surgery in Children - An STS Congenital Heart Surgery Database Analysis

The Society of Thoracic Surgeons' Congenital Heart Surgery Database (STS CHSD) provides risk-adjusted operative mortality rates to approximately 120 North American congenital heart centers. Optimal case-mix adjustment methods for operative mortality risk prediction in this population remain unclear. A panel created diagnosis-procedure (D-P) combinations of encounters in the CHSD. Models for operative mortality using the new D-P categories, procedure-specific risk factors, and syndromes/abnormalities included in the CHSD were estimated using Bayesian additive regression trees (BART) and lasso models. Performance of the new models was compared to the current STS-CHSD risk model. Of 98,825 operative encounters (69,063 training; 29,762 testing), 2,818 (2.85%) STS-defined operative mortalities were observed. Differences in sensitivity, specificity, true and false positive predicted values were negligible across models. Calibration for mortality predictions at the higher end of risk from the lasso and BART models was better than predictions from the STS-CHSD model, likely due to new models' inclusion of diagnosis-palliative procedure variables affecting < 1% of patients overall, but accounting for 27% of mortalities. Model discrimination varied across models for high-risk procedures, hospital volume, and hospitals. Overall performance of the new models did not differ meaningfully from the STS-CHSD risk model. Addition of procedure-specific risk factors and allowing diagnosis to modify predicted risk for palliative operations may augment model performance for very high-risk surgeries. Given the importance of risk adjustment in estimating hospital quality, a comparative assessment of surgical program quality evaluations using the different models is warranted.