Background: Whereas no global severity score exists for congenital heart defects (CHD), risk (Risk Adjusted Cardiac Heart Surgery-1: RACHS-1) and/or complexity (Aristotle Basic Complexity: ABC) scores have been developed for those who undergo surgery. Population-based studies for assessing the predictive ability of these scores are lacking.
Objective: To assess the predictive ability of RACHS-1 and ABC scores for the risk of infant mortality using population-based cohort (EPICARD) data for newborns with structural CHD.
Methods: The study population comprised 443 newborns who underwent curative surgery. We assessed the predictive ability of each score alone and in conjunction with an a priori selected set of predictors of infant mortality. Statistical analysis included logistic regression models for which we computed model calibration, discrimination (ROC), and a rarely used but clinically meaningful measure of variance explained (Tjur's coefficient of discrimination).
Results: The risk of mortality increased with increasing RACHS-1 and the ABC scores and models based on both scores had adequate calibration. Model discrimination was higher for the RACHS-1-based model (ROC 0.68, 95% CI, 0.58-0.79) than the ABC-based one (ROC 0.59, 95% CI, 0.49-0.69), P = 0.03. Neither score had the good predictive ability when this was assessed using Tjur's coefficient.
Conclusions: Even if the RACHS-1 score had better predictive ability, both scores had low predictive ability using a variance-explained measure. Because of this limitation and the fact that neither score can be used for newborns with CHD who do not undergo surgery, it is important to develop new predictive models that comprise all newborns with structural CHD.
Keywords: congenital heart defects; infant mortality; severity scores; thoracic surgery.
© 2021 The Authors. Health Science Reports published by Wiley Periodicals LLC.