Congenital Diaphragmatic HerniaCLANNISHNCT04609163
Machine and Deep Learning for Congenital Diaphragmatic Hernia (CLANNISH)
An observational study of Congenital Diaphragmatic Hernia, run by Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico.
Trial 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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Congenital Diaphragmatic Hernia
- What is being tested?
- data collection
- How many people
- 50 participants
- Where
- No sites listed yet
- Study dates
- 1 Jan 2012to 1 Dec 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
About this study
Official title: A Machine Learning Approach to Predict Pulmonary Hypertension in Newborns With Congenital Diaphragmatic Hernia: a Pilot Study
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.
Read the 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
- Being tested
- data collection
- Keywords
- congenital diaphragmatic hernia, pulmonary hypertension, big data, machine learning
- Medical subject headings
- Hernias, Diaphragmatic, Congenital, Hypertension, Pulmonary
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
data collectionOther
retrospective data collection
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: from birth to 48 hours after birth
Secondary outcomes (4)
Prediction of death
To develop a model to identify the risk of death
Measured: from birth up to 24 weeks
Prediction of extracorporeal membrane oxygenation (ECMO)
To develop a model to identify the need for ECMO
Measured: 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
Measured: 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
Measured: from birth up to 24 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Oct 2020
- Study start1 Jan 2012
- Primary completion1 Nov 2022
- Study completion1 Dec 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Congenital Diaphragmatic HerniaStudies of the same condition, open and under way.
- More trials run by Fondazione IRCCS Ca' Granda, Ospedale Maggiore PoliclinicoThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 50 participants (actual)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Giacomo Cavallaro, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico
- Investigators
- Giacomo Cavallaro, MD, PhD, Principal investigator, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04609163
- Sponsor's protocol ID
- 1790
- Acronym
- CLANNISH
- Status last verified
- Nov 2024
- First submitted
- 20 Oct 2020
- Last updated
- 12 Nov 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
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.
PubMed 40067512Conte 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.
PubMed 38416256Amodeo 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.
PubMed 34752491
Study at a glance
From the registry record, last updated by the sponsor on 12 Nov 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Machine Learning Approach to Predict Pulmonary Hypertension in Newborns With Congenital Diaphragmatic Hernia: a Pilot Study
- ClinicalTrials.gov ID
- NCT04609163
- Acronym / sponsor ID
- CLANNISH / 1790
- Conditions
- Congenital Diaphragmatic Hernia
- Keywords
- congenital diaphragmatic hernia, pulmonary hypertension, big data, machine learning
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 50 participants (actual)
- Lead sponsor
- Fondazione IRCCS Ca' Granda, Ospedale Maggiore PoliclinicoUniversity, hospital or other
- Study start
- 1 Jan 2012 (actual)
- Primary completion
- 1 Nov 2022 (actual)
- Study completion
- 1 Dec 2022 (actual)
- First posted
- 30 Oct 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (5 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| data collectionretrospective data collection | Other | – |
Primary outcome
Prediction of suprasystemic pulmonary hypertension
Time frame: from birth to 48 hours after birth
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.
4 secondary outcomes
Prediction of death
Time frame: from birth up to 24 weeks
Prediction of extracorporeal membrane oxygenation (ECMO)
Time frame: from birth up to 24 weeks
Prediction of favorable response to extracorporeal membrane oxygenation (ECMO)
Time frame: from birth up to 24 weeks
Prediction of favorable response to Fetoscopic Endotracheal Occlusion (FETO)
Time frame: from birth up to 24 weeks
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Nov 2024.
- Derived from this study: PubMed 40067512Conte 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.
- Derived from this study: PubMed 38416256Conte 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.
- Derived from this study: PubMed 34752491Amodeo 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.
Related trials for Congenital Diaphragmatic Hernia
Enrolling by invitationObservational study
North American Fetal Therapy Network Prospective Registry for Long-term Outcome Following Fetoscopic Endoluminal Tracheal Occlusion in Severe Left and Right Congenital Diaphragmatic Hernia
Congenital Diaphragmatic Hernia
Active, not recruiting
Fetoscopic Endoluminal Tracheal Occlusion in Severe Left Congenital Diaphragmatic Hernia
Congenital Diaphragmatic Hernia, Pulmonary Hypoplasia
Active, not recruitingPhase 2
Effect of a Parenteral Emulsion With Omega3 on Neonates With PPHN and CDH
Pulmonary Hypertension of Newborn, Diaphragm Defect
RecruitingPhase 4
De-implementing Inhaled Nitric Oxide for Congenital Diaphragmatic Hernia
Congenital Diaphragmatic Hernia
Active, not recruiting
Fetoscopic Endoluminal Tracheal Occlusion (FETO) for Severe Left Diaphragmatic Hernia (CDH)
Congenital Diaphragmatic Hernia
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Giacomo Cavallaro, MD, PhD principal investigator
Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico
Questions about this trial
What is this clinical trial studying?
An observational study of Congenital Diaphragmatic Hernia, run by Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 12 Nov 2024. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Prediction of suprasystemic pulmonary hypertension (time frame: from birth to 48 hours after birth). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04609163, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 Nov 2024.