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Congenital Diaphragmatic Hernia (CDH) is characterized by an incomplete diaphragm formation, resulting in poor lung development (pulmonary hypoplasia), associated with altered vascularization of the lung (pulmonary hypertension), with respiratory and cardiovascular insufficiency at birth. Mortality and morbidity are extremely variable. Several efforts have been done to identify possible prenatal and postnatal indicators which could accurately predict patients' prognosis and to promote an individualized management. However, to date the accuracy of these factors with respect to the prediction of survival and disease severity still has limits. In the last years, there has been an impressive development of new research methodologies based on the artificial intelligence, also in the neonatal field. The Machine Learning (ML) method explores the possibility of building algorithms starting from the acquisition of relevant clinical data, and using them to make predictions or take decisions. Nevertheless, the ML method has never been applied to predict patient's outcome in newborns with CDH so far. Moreover, with the available tools, a reliable prediction on patient's risk of developing severe postnatal PH is not feasible. Our hypothesis is that the use of ML approach, based on multivariate analysis of different clinical pre- and postnatal variables, could allow the development of algorithms able to accurately predict patient's outcome.
The investigators will collect clinical and instrumental data regarding prenatal history as well as the medical and surgical postnatal course. In particular, the investigators will record data from a prenatal ultrasound performed between 25+0 and 30+6 weeks of gestation (before "Fetoscopic Endotracheal Occlusion" (FETO) procedure, in case of prenatal treatment): estimated fetal weight (EFW), amniotic fluid, Doppler velocimetry of umbilical artery, defect side, herniated organs, observed/expected lung-to-head ratio tracing (O/E LHR%), grading of hernia severity, Doppler velocimetry of contralateral pulmonary artery. Gestational age at diagnosis, details about FETO procedure, and the course of pregnancy will be also recorded.
On fetal MRI, the investigators will calculate: observed/expected total fetal lung volume (O/E TFLV%), percentage of liver herniation (%LH), signal intensity of lung and liver on T2 sequences, mediastinal shift angle, apparent diffusion coefficient (ADC) on diffusion-weighted sequences (DWI).
The radiographic pulmonary area will be calculated on digital chest x-ray performed within 24 hours after birth, by tracing the perimeter of the lung outlined by the rib cage and the diaphragm, excluding the mediastinal structures and the herniated organs.
Regarding the neonatal course, the investigators will focus on pulmonary hypertensive status, need for ECMO, and deaths. In particular, pulmonary hypertension will be evaluated based on clinical parameters (such as systemic pressure, heart rate, oxygen saturation, and oxygen supplementation, inotropic drugs, vasopressors, pulmonary vasodilators) as well as echocardiographic parameters (systolic pulmonary artery pressure (PAPs) from tricuspid valve regurgitation, mean pulmonary artery pressure from pulmonary valve regurgitation, pulmonary artery flow, characteristics of the interventricular sept, shunts, cardiac anomalies). Echocardiograms in our NICU are performed bedside throughout the hospital stay. The investigators will consider one exam per day from birth to 48 hours after surgery, one exam per week in the following 4 weeks, one exam per months until discharge. Other relevant data, like neurologic complications, metabolic disorders or infections, will be recorded as well.
Finally, the investigators will record data regarding the surgical course: day of intervention, type of surgical repair, use of patch, intra- or post-operative complications.
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| data collection | Other | retrospective data collection |
| Measure | Description | Time Frame |
|---|---|---|
| Prediction of suprasystemic pulmonary hypertension | The main objective of the study is to develop a model to identify prenatally CDH patients who will develop suprasystemic PH, assessed in the time frame from birth to 48 hours after surgery and at discharge from the NICU. | from birth to 48 hours after birth |
| Measure | Description | Time Frame |
|---|---|---|
| Prediction of death | To develop a model to identify the risk of death | from birth up to 24 weeks |
| Prediction of extracorporeal membrane oxygenation (ECMO) | To develop a model to identify the need for ECMO |
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Inclusion Criteria:
Exclusion Criteria:
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Patients with CDH born between January 2016 and April 2020 will be considered for the study and will be enrolled according to the inclusion/exclusion criteria reported.
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| Name | Affiliation | Role |
|---|---|---|
| Giacomo Cavallaro, MD, PhD | Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico | Principal Investigator |
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| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 40067512 | Derived | Conte L, Amodeo I, De Nunzio G, Raffaeli G, Borzani I, Persico N, Griggio A, Como G, Colnaghi M, Fumagalli M, Cascio D, Cavallaro G. A machine learning approach to predict mortality and neonatal persistent pulmonary hypertension in newborns with congenital diaphragmatic hernia. A retrospective observational cohort study. Eur J Pediatr. 2025 Mar 11;184(4):238. doi: 10.1007/s00431-025-06073-0. | |
| 38416256 |
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| ID | Term |
|---|---|
| D065630 | Hernias, Diaphragmatic, Congenital |
| D006976 | Hypertension, Pulmonary |
| ID | Term |
|---|---|
| D000013 | Congenital Abnormalities |
| D009358 | Congenital, Hereditary, and Neonatal Diseases and Abnormalities |
| D006548 | Hernia, Diaphragmatic |
| D000082122 | Internal Hernia |
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| ID | Term |
|---|---|
| D003625 | Data Collection |
| ID | Term |
|---|---|
| D004812 | Epidemiologic Methods |
| D008919 | Investigative Techniques |
| D017531 | Health Care Evaluation Mechanisms |
| D011787 | Quality of Health Care |
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| from birth up to 24 weeks |
| Prediction of favorable response to extracorporeal membrane oxygenation (ECMO) | To develop a model to identify the favorable response to the treatment in those requiring ECMO | from birth up to 24 weeks |
| Prediction of favorable response to Fetoscopic Endotracheal Occlusion (FETO) | To develop a model to identify the favorable response to the treatment in those patients undergoing FETO procedure | from birth up to 24 weeks |
| Derived |
| Conte L, Amodeo I, De Nunzio G, Raffaeli G, Borzani I, Persico N, Griggio A, Como G, Cascio D, Colnaghi M, Mosca F, Cavallaro G. Congenital diaphragmatic hernia: automatic lung and liver MRI segmentation with nnU-Net, reproducibility of pyradiomics features, and a machine learning application for the classification of liver herniation. Eur J Pediatr. 2024 May;183(5):2285-2300. doi: 10.1007/s00431-024-05476-9. Epub 2024 Feb 28. |
| 34752491 | Derived | Amodeo I, De Nunzio G, Raffaeli G, Borzani I, Griggio A, Conte L, Macchini F, Condo V, Persico N, Fabietti I, Ghirardello S, Pierro M, Tafuri B, Como G, Cascio D, Colnaghi M, Mosca F, Cavallaro G. A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study. PLoS One. 2021 Nov 9;16(11):e0259724. doi: 10.1371/journal.pone.0259724. eCollection 2021. |
| D006547 | Hernia |
| D020763 | Pathological Conditions, Anatomical |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D006973 | Hypertension |
| D014652 | Vascular Diseases |
| D002318 | Cardiovascular Diseases |
| D017530 | Health Care Quality, Access, and Evaluation |
| D011634 | Public Health |
| D004778 | Environment and Public Health |