Machine and Deep Learning for Congenital Diaphragmatic Hernia (CLANNISH)

NCT ID: NCT04609163

Last Updated: 2024-11-12

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 50 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2012-01-01
• Study Completion Date: 2022-12-01

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.

Detailed Description

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.

Conditions

Congenital Diaphragmatic Hernia

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Interventions

data collection

retrospective data collection

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• 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.

Exclusion Criteria

• 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.

• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico

OTHER

Sponsor Role: lead

Responsible Party

Giacomo Cavallaro

Principal Investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Giacomo Cavallaro, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico

References

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.

Reference Type: DERIVED

PMID: 40067512 (View on PubMed)

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.

Reference Type: DERIVED

PMID: 38416256 (View on PubMed)

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.

Reference Type: DERIVED

PMID: 34752491 (View on PubMed)

Other Identifiers

1790

Identifier Type: -

Identifier Source: org_study_id