A machine learning approach to predict mortality and neonatal persistent pulmonary hypertension in newborns with congenital diaphragmatic hernia. A retrospective observational cohort study
• Machine learning (ML) models, particularly XGBoost, have been shown to accurately forecast mortality and the severity of PPHN in infants with CDH based on prenatal and early postnatal clinical and imaging data. • ML-based predictive models can enhance prenatal counseling, optimize birth planning, and tailor postnatal care for patients with CDH, enabling real-time risk assessment and adaptive management strategies.
Authors
- Donato Cascio (ORCID: https://orcid.org/0000-0001-6522-1259)
- Alice Griggio (ORCID: https://orcid.org/0000-0002-2947-4627)
- Nicola Persico (ORCID: https://orcid.org/0000-0001-9028-9597)
- Mariarosa Colnaghi
- Luana Conte (ORCID: https://orcid.org/0000-0002-8741-3478)
- Giorgio De Nunzio (ORCID: https://orcid.org/0000-0002-1998-0286)
- Irene Borzani (ORCID: https://orcid.org/0000-0003-0386-4712)
- Ilaria Amodeo (ORCID: https://orcid.org/0000-0002-2650-0084)
- Monica Fumagalli (ORCID: https://orcid.org/0000-0002-0186-0710)
- Giacomo Cavallaro (ORCID: https://orcid.org/0000-0002-4921-1437)
- Genny Raffaeli (ORCID: https://orcid.org/0000-0001-9175-9394)
- Giuseppe Como (ORCID: https://orcid.org/0009-0009-2120-0427)
Institutions
- University of Salento (IT)
- University of Milan (IT)
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (IT)
- Istituto Nazionale di Fisica Nucleare, Sezione di Lecce (IT)
- ASST Fatebenefratelli Sacco (IT)
- University of Palermo (IT)
Publication Details
- Journal
- European Journal of Pediatrics
- Published
- 2025-03-11
- DOI
- https://doi.org/10.1007/s00431-025-06073-0
- Citations
- 6
- Primary Topic
- Congenital Diaphragmatic Hernia Studies
- Type
- article
- Field-Weighted Citation Impact
- 12.04
Funders
- Ministero della Salute