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Integrating Deep Learning CT-scan Model, Biological and Clinical Variables to Predict Severity of Asthma in Children (BREATHE)

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ClinicalTrials.gov Identifier: NCT05140889
Recruitment Status : Recruiting
First Posted : December 2, 2021
Last Update Posted : February 3, 2022
Sponsor:
Collaborators:
Istituto per la Ricerca e l'Innovazione Biomedica
Università Ca' Foscari Venezia
Information provided by (Responsible Party):
Amelia Licari, IRCCS Policlinico S. Matteo

Tracking Information
First Submitted Date October 13, 2021
First Posted Date December 2, 2021
Last Update Posted Date February 3, 2022
Actual Study Start Date January 20, 2021
Estimated Primary Completion Date June 30, 2024   (Final data collection date for primary outcome measure)
Current Primary Outcome Measures
 (submitted: November 17, 2021)
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.
Original Primary Outcome Measures Same as current
Change History
Current Secondary Outcome Measures Not Provided
Original Secondary Outcome Measures Not Provided
Current Other Pre-specified Outcome Measures Not Provided
Original Other Pre-specified Outcome Measures Not Provided
 
Descriptive Information
Brief Title Integrating Deep Learning CT-scan Model, Biological and Clinical Variables to Predict Severity of Asthma in Children
Official Title Integrating Deep Learning CT-scan Model, Biological and Clinical Variables to Predict Severity of Asthma in Children
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.

Study Type Observational [Patient Registry]
Study Design Observational Model: Case-Control
Time Perspective: Prospective
Target Follow-Up Duration 36 Months
Biospecimen Not Provided
Sampling Method Probability Sample
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.

Condition Asthma in Children
Intervention Not Provided
Study Groups/Cohorts
  • Group 1
    Children with severe asthma
  • Group 2
    Children who undergo chest CT scan for other reasons than asthma
Publications * Not Provided

*   Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
 
Recruitment Information
Recruitment Status Recruiting
Estimated Enrollment
 (submitted: November 17, 2021)
25
Original Estimated Enrollment Same as current
Estimated Study Completion Date June 30, 2024
Estimated Primary Completion Date June 30, 2024   (Final data collection date for primary outcome measure)
Eligibility 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)
Sex/Gender
Sexes Eligible for Study: All
Ages 6 Years to 17 Years   (Child)
Accepts Healthy Volunteers No
Contacts
Contact: Amelia Licari, MD +39(0)382502629 a.licari@smatteo.pv.it
Listed Location Countries Italy
Removed Location Countries  
 
Administrative Information
NCT Number NCT05140889
Other Study ID Numbers 08073521
Has Data Monitoring Committee No
U.S. FDA-regulated Product
Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
IPD Sharing Statement
Plan to Share IPD: No
Current Responsible Party Amelia Licari, IRCCS Policlinico S. Matteo
Original Responsible Party Same as current
Current Study Sponsor IRCCS Policlinico S. Matteo
Original Study Sponsor Same as current
Collaborators
  • Istituto per la Ricerca e l'Innovazione Biomedica
  • Università Ca' Foscari Venezia
Investigators
Principal Investigator: Amelia Licari, MD IRCCS Policlinico San Matteo
PRS Account IRCCS Policlinico S. Matteo
Verification Date January 2022