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History of Changes for Study: NCT05140889
Integrating Deep Learning CT-scan Model, Biological and Clinical Variables to Predict Severity of Asthma in Children (BREATHE)
Latest version (submitted January 20, 2022) on ClinicalTrials.gov
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Study Record Versions
Version A B Submitted Date Changes
1 November 17, 2021 None (earliest Version on record)
2 January 20, 2022 Recruitment Status, Study Status, Contacts/Locations and Oversight
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Study NCT05140889
Submitted Date:  November 17, 2021 (v1)

Open or close this module Study Identification
Unique Protocol ID: 08073521
Brief Title: Integrating Deep Learning CT-scan Model, Biological and Clinical Variables to Predict Severity of Asthma in Children (BREATHE)
Official Title: Integrating Deep Learning CT-scan Model, Biological and Clinical Variables to Predict Severity of Asthma in Children
Secondary IDs:
Open or close this module Study Status
Record Verification: November 2021
Overall Status: Not yet recruiting
Study Start: December 1, 2021
Primary Completion: June 30, 2024 [Anticipated]
Study Completion: June 30, 2024 [Anticipated]
First Submitted: October 13, 2021
First Submitted that
Met QC Criteria:
November 17, 2021
First Posted: December 2, 2021 [Actual]
Last Update Submitted that
Met QC Criteria:
November 17, 2021
Last Update Posted: December 2, 2021 [Actual]
Open or close this module Sponsor/Collaborators
Sponsor: IRCCS Policlinico S. Matteo
Responsible Party: Principal Investigator
Investigator: Amelia Licari
Official Title: MD
Affiliation: IRCCS Policlinico S. Matteo
Collaborators: Istituto per la Ricerca e l'Innovazione Biomedica
Università Ca' Foscari Venezia
Open or close this module Oversight
U.S. FDA-regulated Drug: No
U.S. FDA-regulated Device: No
Data Monitoring: No
Open or close this module Study Description
Brief Summary:

Artificial intelligence (AI) offers substantial opportunities for healthcare, supporting better diagnosis, treatment, prevention and personalized care. Analysis of health images is one of the most promising fields for applying AI in healthcare, contributing to better prediction, diagnosis and treatment of diseases.

Deep learning (DL) is currently one of the most powerful machine learning techniques. DL algorithms are able to learn from raw (or with little pre-processing) input data and build by themselves sophisticated abstract feature representations (useful patterns) that enable very accurate task decision making. Recently, DL has shown promising results in assisting lung disease analysis using computed tomography (CT) images.

Current severe asthma guidelines recommend high-resolution and multidetector CT as a tool for disease evaluation. CT scans contain prognostic information, as the presence of bronchial wall thickening, air trapping, bronchial luminal narrowing, and bronchiectasis are associated with longer disease duration and disease severity in adults. Only a small number of studies have reported chest CT findings in children with severe asthma, and their relationship to clinical and pathobiological parameters yielded inconsistent results. Thus, to which extent CT scans add prognostic information beyond what can be inferred from clinical and biological data is still unresolved in children.

The project is expected to build an DL-severity score to prognoses severe evolution for children with asthma, using a DL model to capture CT scan prognosis information.

Detailed Description:

The aims of this project are:

  • to build a large database of clinical, biological and radiological data collected from pediatric patients with severe asthma;
  • to design and train a forecaster model based on DL techniques to predict asthma severity in children;
  • to estimate transition probabilities between asthma severity levels using a multi-state Markov model taking into account qualitative and quantitative information obtained from CT imaging.

Our evaluation of DL-severity and existing clinical scores in childhood asthma is expected to reveal that emerging methodologies assisted by DL techniques can provide accurate severity predictions, when compared with existing clinical scores. Such an accurate prediction model would allow pediatricians to identify features that are the most indicative of severity and progression of asthma and would be employed to formulate intervention strategies and early medical attention for children.

Open or close this module Conditions
Conditions: Asthma in Children
Keywords: Asthma
Children
Severe Asthma
Artificial Intelligence
Deep Learning
Computed Tomography
Open or close this module Study Design
Study Type: Observational [Patient Registry]
Observational Study Model: Case-Control
Time Perspective: Prospective
Biospecimen Retention:
Biospecimen Description:
Enrollment: 25 [Anticipated]
Number of Groups/Cohorts 2
Target Follow-Up Duration: 36 Months
Open or close this module Groups and Interventions
Groups/Cohorts Interventions
Group 1
Children with severe asthma
Group 2
Children who undergo chest CT scan for other reasons than asthma
Open or close this module Outcome Measures
Primary Outcome Measures:
1. Prediction of asthma severity in children
[ Time Frame: 3 years ]

To build a severity score to prognoses evolution for children with asthma, using a deep-learning model to capture CT scan prognosis information and integrate with clinical and laboratory data obtained from medical records.
Open or close this module Eligibility
Study Population:

Eligible participants will be identified among children referred to our Pediatric Clinic for severe asthma by their general practitioner or by their primary care pediatrician.

Children who undergo chest CT scan for other reasons than asthma will be selected as controls.

Sampling Method: Probability Sample
Minimum Age: 6 Years
Maximum Age: 17 Years
Sex: All
Gender Based:
Accepts Healthy Volunteers: No
Criteria:

Inclusion Criteria:

  • age 6-17 years
  • confirmed diagnosis of severe asthma according to ERS/ATS guidelines

Exclusion Criteria:

  • other diseases that may mimic asthma according to ERS/ATS guidelines (i.e., cystic fibrosis, primary ciliary dyskinesia, tracheobronchomalacia, etc)
Open or close this module Contacts/Locations
Central Contact Person: Amelia Licari, MD
Telephone: +39(0)382502629
Email: a.licari@smatteo.pv.it
Study Officials: Amelia Licari, MD
Principal Investigator
IRCCS Policlinico San Matteo
Locations:
Open or close this module IPDSharing
Plan to Share IPD: No
Open or close this module References
Citations:
Links:
Available IPD/Information:

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