Machine and Deep Learning for Congenital Diaphragmatic Hernia (CLANNISH)

NCT04609163 | Completed

• 1 other identifier: 1790
• Study Type: observational
• Target: 50
• Locations: 0 countries
• Sites: N/A

Brief Summary

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.

Conditions

Congenital Diaphragmatic Hernia

Keywords

congenital diaphragmatic hernia; pulmonary hypertension; big data; machine learning

Outcome Measures

Primary Outcomes (1)

• 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

Secondary Outcomes (4)

• Prediction of death

  from birth up to 24 weeks

• Prediction of extracorporeal membrane oxygenation (ECMO)

  from birth up to 24 weeks

• Prediction of favorable response to extracorporeal membrane oxygenation (ECMO)

  from birth up to 24 weeks

• Prediction of favorable response to Fetoscopic Endotracheal Occlusion (FETO)

  from birth up to 24 weeks

Interventions

data collection OTHER

retrospective data collection

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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.

You may qualify if:

• Inborn patients, born between 01/01/2012 and 31/12/2020, admitted to the NICU at birth;
• Prenatal diagnosis of CDH;
• Take charge of the mother with CDH fetus at a gestational age below or equal to 30+6 weeks at our Fetal Surgery Center.

You may not qualify if:

• Outborn patients;
• Lack of prenatal diagnosis of CDH;
• Mother with CDH fetus not taken in charge at our Fetal Surgery Center;
• Prenatal or postnatal diagnosis of non-isolated CDH, thus associated with genetic or malformative anomalies known to have an impact on patients' survival;
• Twin pregnancies.

Sponsors & Collaborators

• Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico: lead

Related Publications (3)

• 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.

  PMID: 40067512 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.

  PMID: 38416256 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.

  PMID: 34752491 DERIVED

MeSH Terms

Conditions

Hernias, Diaphragmatic, Congenital; Hypertension, Pulmonary

Interventions

Data Collection

Condition Hierarchy (Ancestors)

Congenital Abnormalities; Congenital, Hereditary, and Neonatal Diseases and Abnormalities; Hernia, Diaphragmatic; Internal Hernia; Hernia; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms; Lung Diseases; Respiratory Tract Diseases; Hypertension; Vascular Diseases; Cardiovascular Diseases

Intervention Hierarchy (Ancestors)

Epidemiologic Methods; Investigative Techniques; Health Care Evaluation Mechanisms; Quality of Health Care; Health Care Quality, Access, and Evaluation; Public Health; Environment and Public Health

Study Officials

• Giacomo Cavallaro, MD, PhD

  Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: October 20, 2020
• First Posted: October 30, 2020
• Study Start: January 1, 2012
• Primary Completion: November 1, 2022
• Study Completion: December 1, 2022
• Last Updated: November 12, 2024

Record last verified: 2024-11