Abstract
The Norwood surgical procedure restores functional systemic circulation in neonatal patients with single ventricle congenital heart defects, but this complex procedure carries a high mortality rate. In this study we address the need to provide an accurate patient specific risk prediction for one-year postoperative mortality or cardiac transplantation and prolonged length of hospital stay with the purpose of assisting clinicians and patients’ families in the preoperative decision making process. Currently available risk prediction models either do not provide patient specific risk factors or only predict in-hospital mortality rates. We apply machine learning models to predict and calculate individual patient risk for mortality and prolonged length of stay using the Pediatric Heart Network Single Ventricle Reconstruction trial dataset. We applied a Markov Chain Monte-Carlo simulation method to impute missing data and then fed the selected variables to multiple machine learning models. The individual risk of mortality or cardiac transplantation calculation produced by our deep neural network model demonstrated 89 ± 4% accuracy and 0.95 ± 0.02 area under the receiver operating characteristic curve (AUROC). The C-statistics results for prediction of prolonged length of stay were 85 ± 3% accuracy and AUROC 0.94 ± 0.04. These predictive models and calculator may help to inform clinical and organizational decision making.
Highlights
Hypoplastic left heart syndrome (HLHS) is one of several severe congenital cardiac defects involving a single ventricle physiology
Using the Pediatric Heart Network (PHN) Single Ventricle Reconstruction (SVR) trial data, we developed and tested multiple machine learning algorithms to predict the individualized risk of one-year mortality or cardiac transplantation and prolonged hospital length of stay for patients undergoing the Norwood procedure
The best results for the individual risk of mortality or cardiac transplantation calculation were produced by the deep neural networks (DNN) model, which demostrated 89% ± 4% accuracy, F-score of 0.89 ± 0.03, and area under the receiver operating characteristic curve (AUROC) 0.95 ± 0.02
Summary
Hypoplastic left heart syndrome (HLHS) is one of several severe congenital cardiac defects involving a single ventricle physiology. With the increasing www.nature.com/scientificreports national emphasis on efficient medical practice and standardization of care, surgeons, operating room staff, and hospital administrators are in need of a way to predict comorbidities, adverse events and length of stay for hospital patients Such information is helpful in optimization of resource utilization and is used for prioritizing quality improvement. Tabbutt et al.[9], analyzed time to death following hospital discharge after Norwood procedure using Kaplan-Meier estimation and Cox proportional hazards regression They concluded that factors such as the use of Extra Corporeal Membrane Oxygenation and center/surgeon volume are important in predicting the postoperative risk of mortality limited to Norwood hospitalization, but they did not provide patient specific risk of mortality. Our study aims to address the need to provide an accurate patient specific risk score for long-term (one-year) postoperative mortality or cardiac transplantation and prolonged length of hospital stay, based on the data available at the time the prediction would be most clinically useful. In the cases with a very high risk of mortality, this may be agreed upon by clinicians and the patient’s family as the most appropriate course of action
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