Background: Mortality prediction of congenital diaphragmatic hernia (CDH) is essential for developing treatment strategies, including fetal therapy. Several researchers have reported prognostic factors for this rare but life-threatening condition; however, the optimal combination of prognostic factors remains to be elucidated.
Objectives: This study aimed to develop the most discriminative prenatal and postnatal models to predict the mortality of infants with an isolated left-sided CDH.
Methods: This multi-institutional retrospective cohort study included infants with CDH born at 15 tertiary hospitals of the Japanese CDH Study Group between 2011 and 2016. We developed multivariable logistic models with every possible combination of predictors and identified models with the highest cross-validated area under the receiver operating characteristic curve (AUC) for prenatal and postnatal predictions.
Results: Among 302 eligible infants, 44 died before discharge. The prenatal mortality prediction model was based on the observed/expected lung area to head circumference ratio (O/E LHR), liver herniation, and stomach herniation (AUC, 0.830). The postnatal mortality prediction model was based on O/E LHR, liver herniation, and the lowest oxygenation index (AUC, 0.944).
Conclusion: Our models can facilitate the prenatal and postnatal mortality prediction of infants with isolated left-sided CDH.
Keywords: cross-validation; infant mortality; logistic models; machine learning.
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