. 2022 Sep 29.
doi: 10.1002/ppul.26172. Online ahead of print. https://pubmed.ncbi.nlm.nih.gov/36174997/
Development of Mortality Prediction Models for Infants with Isolated, Left-Sided Congenital Diaphragmatic Hernia Before and After Birth
Kota Yoneda 1, Shoichiro Amari 1, Masashi Mikami 2, Keiichi Uchida 3, Akiko Yokoi 4, Manabu Okawada 5, Taizo Furukawa 6, Katsuaki Toyoshima 7, Noboru Inamura 8, Tadaharu Okazaki 9, Masaya Yamoto 10, Kouji Masumoto 11, Keita Terui 12, Hiroomi Okuyama 13, Masahiro Hayakawa 14, Tomoaki Taguchi 15, Noriaki Usui 16, Tetsuya Isayama 17
- PMID: 36174997
- DOI: 10.1002/ppul.26172
Background: Mortality prediction of congenital diaphragmatic hernia 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 congenital diaphragmatic hernia (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 This article is protected by copyright. All rights reserved.
Keywords: Cross-validation; Infant Mortality; Logistic Models; Machine Learning.
This article is protected by copyright. All rights reserved.