Objectives: To create and validate a prediction model to assess outcomes associated with the Norwood operation.
Methods: The public-use dataset from a multicenter, prospective, randomized single-ventricle reconstruction trial was used to create this novel prediction tool. A Bayesian lasso logistic regression model was used for variable selection. We used a hierarchical framework by representing discrete probability models with continuous latent variables that depended on the risk factors for a particular patient. Bayesian conditional probit regression and Markov chain Monte Carlo simulations were then used to estimate the effects of the predictors on the means of these latent variables to create a score function for each of the study outcomes. We also devised a method to calculate the risk of outcomes associated with the Norwood operation before the actual heart operation. The 2 study outcomes evaluated were in-hospital mortality and composite poor outcome.
Results: The training dataset used 520 patients to generate the prediction model. The model included patient demographics, baseline characteristics, cardiac diagnosis, operation details, site volume, and surgeon experience. An online calculator for the tool can be accessed at https://soipredictiontool.shinyapps.io/NorwoodScoreApp/. Model validation was performed on 520 observations using an internal 10-fold cross-validation approach. The prediction model had an area under the curve of 0.77 for mortality and 0.72 for composite poor outcome on the validation dataset.
Conclusions: Our new prognostic tool is a promising first step in creating real-time risk stratification in children undergoing a Norwood operation; this tool will be beneficial for the purposes of benchmarking, family counseling, and research.
Keywords: Markov chain Monte Carlo; Norwood operation; prediction tool; risk predictors—Bayesian; severity of illness.
Copyright © 2017 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.