Luana Conte, Ilaria Amodeo, Giorgio De Nunzio, Genny Rafaeli, Irene Borzani, Nicola Persico, Alice Griggio, Giuseppe Como, Mariarosa Colnaghi, Monica Fumagalli, Donato Cascio, Giacomo Cavallaro
Università Degli Studi Di Palermo. Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico. Laboratory of Interdisciplinary Research Applied to Medicine (DReAM). “E. De Giorgi”, University of Salento. Università Degli Studi Di Milano. Ospedale Macedonio Melloni.
Italy
European Journal of Pediatrics
Eur J Pediatr 2025; 184:
DOI: 10.1007/s00431-025-06073-0
Abstract
Congenital diaphragmatic hernia (CDH) has high morbidity and mortality rates. This study aimed to develop a machine learning (ML) algorithm to predict outcomes based on prenatal and early postnatal data. This retrospective observational cohort study involved infants with left-sided CDH, born from 2012 to 2020. We analyzed clinical and imaging data using three classification algorithms: XGBoost, Support Vector Machine, and K-Nearest Neighbors. Medical records of 165 pregnant women with CDH fetal diagnosis were reviewed. According to inclusion criteria, 50 infants with isolated left-sided CDH were enrolled. The mean o/eLHR was 37.32%, and the average gestational age at delivery was 36.5 weeks. Among these infants, 26 (52%) had severe persistent neonatal pulmonary hypertension (PPHN), while 24 (48%) had moderate or mild form; 37 survived (74%), and 13 did not (26%). The XGBoost model achieved 88% accuracy and 95% sensitivity for predicting mortality using ten features and 82% accuracy for PPHN severity with 14 features. The area under the ROC curve was 0.87 for mortality and 0.82 for PPHN severity.
Category
Class III. Pulmonary Hypertension Associated with Lung Hypoplasia
Diagnostic Testing for Pulmonary Vascular Disease. Risk Stratification
Age Focus: Pediatric Pulmonary Vascular Disease
Fresh or Filed Publication: Fresh (PHresh). Less than 1-2 years since publication
Article Access
Free PDF File or Full Text Article Available Through PubMed or DOI: Yes