Asthma in ChildrenAsthmoscopeNCT04528342
Personalized Digital Health and Artificial Intelligence in Childhood Asthma
An observational study of Asthma in Children, run by Isabelle Ruchonnet-Métrailler at 2 sites in Switzerland.
Trial summary
Asthma is a chronic inflammatory disease of the airways that causes recurrent episodes of wheezing, breathing difficulties and coughing. The prevalence of asthma is 8% in school-aged children and 30% in preschoolers, making asthma the first chronic disease in children. Read more about this study
- Who is it for?
- Ages 2 to 16 years
- Conditions the study is for
- Asthma in Children
- How many people
- 290 participants
- Where
- 2 sitesSwitzerland
- Study dates
- 1 Mar 2020to 1 Apr 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 2 to 16 years
- All sexes
- No healthy volunteers
- Child
Study population: 290 patients presenting with an acute asthma exacerbation within 70 severe asthma (clinical PRAM score > 7) within the PED. For the hospitalized patients, we estimate 70 patients needed during two years. At least 150 patients up to 7 years of age, within DS measurements and spirometer measures evaluation 6 to 8 weeks after acute episode by the pulmonlogist.
Who may be able to join
- Patients with clinical diagnosis of acute asthma exacerbations
- age > 2 years and < 16 years
- information and written consent of a legal representative
Who cannot join
- Chronic lung diseases other than asthma (cystic fibrosis, bronchopulmonary Dysplasia),
- Congenital heart disease
- Refusal of consent.
Trial locations and contacts
2 sites in 1 country.
Isabelle Ruchonnet-Metrailler, M.D., PhD contact
Switzerland2 sites, 2 recruiting
- Recruiting
Geneva University Hospital
Geneva, 1205
Isabelle Ruchonnet-Métrailler, MD PhD, +41 79 553 41 69, Isabelle.ruchonnet-metrailler@hcuge.ch
Isabelle Ruchonnet-Métrailler, M.D PhD
- Recruiting
Geneva University Hospital
Geneva, 1205
Mohamed-Rida Benissa, M.D., M.Sc., M.P.H., +33.62.866.40.47, mrbenissa@gmail.com
Isabelle Ruchonnet-Métrailler, MD PhD, Isabelle.ruchonnet-metrailler@hcuge.ch
About this study
Asthma is a chronic inflammatory disease of the airways that causes recurrent episodes of wheezing, breathing difficulties and coughing. The prevalence of asthma is 8% in school-aged children and 30% in preschoolers, making asthma the first chronic disease in children. Symptoms are due to diffuse but variable airway obstruction, reversible spontaneously or after inhalation of beta2 agonists (β-2a) such as salbutamol. Exacerbations of asthma are frequent and difficult to assess by parents and the patient himself. It is estimated that approximately 2.5% of children with asthma are hospitalized annually. The global burden caused by asthma can thus be reduced by improving early detection of bronchial obstruction, prescribing immediate treatment with the appropriate background therapy, and reliably and objectively assess response to treatment.
The natural history of asthma symptoms in children shows a great intra and inter-individual variability. The difficulty of assessing the severity of an attack by the parents or the child himself, when he is old enough to control his chronic disease, is a key element in the management of asthma and allows the treatment to be adapted quickly, sometimes avoiding hospitalization. Healthcare professionals can assess the severity of the episode using the Pediatric Respiratory Assesment Measure (PRAM) score, which has the advantage of being adaptable at any age. The Global Alliance against Chronic Respiratory Diseases (GARD) integrates in its diagnostic strategy for chronic respiratory diseases, the lung function test, which allows the quantification of respiratory function in the context of diagnosis and long-term follow-up. Although spirometry are non-invasive tests, they still require a high level of patient cooperation, which remains problematic before the age of 7 years.
The digital stethsocope integrates a capacity for recording auscultations and data transmission to high-performance software. This has made it possible to extend auscultation beyond what was audible to the human ear alone (over 20-20,000 Hertz).Auscultatory sounds analysis, particularly those most often associated with obstructive syndrome could be simple, reproducible and a reliable method of assessing the severity and response to treatment in children's asthma. Major advances in signal processing and unsupervised learning in artificial intelligence research provide the potential for high-performance analysis of physiological measures.
Read the detailed description
Aim:
Develop an artificial intelligence based algorithm for unsupervised diagnostic and classification of childhood asthma exacerbation.
Methodology: A Longitudinal prospective monocentric observational study will be performed in the Pediatric Emergency Division (PED) and the Pediatric Respiratory Unit (PRU) of the Geneva University Hospitals (HUG) during 24 months. This clinical study will include patients aged from 2 to16 years with acute asthma exacerbations. The intervention consists in recording auscultation of asthmatic patients at rest, during acute exacerbation and after treatment by bronchodilatators (β-2 agonists) inhalation in the PED, with a Digital Stethoscope (DS). Auscultation will be recorded during hospitalization every day, at home 7 days after the acute episode, combining intdoor and outdoor measures, and evaluating the exposome. A last record will be done at 6 to 8 weeks after the acute episode, with a lung function test if the patient is up to 7 years. A validation and training audio database will be constituted for the development of Artificial Intelligence (AI) algorithms, allowing analysis of respiratory rate, inspiratory/expiratory time ratio, PRAM score, wheezing variation of intensity and unsupervised diagnosis.
Expected results:
Creation of a performant AI algorithm for unsupervised acute asthma exacerbation diagnosis, with > 70 % of Sensitivity and > 70% of Specificity compared to the expert. Response to treatment will improve patient empowerment and personalized medicine in childhood asthma management.
- Conditions
- Asthma in Children
- Keywords
- Digital stethoscope;, digital health, machine learning, Asthma
- Medical subject headings
- Asthma
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic performance of an algorithm compared to the physician in asthma attack
To evaluate the diagnostic performance of an algorithm in the asthma crisis in children aged between 2 and 16 years old, presenting to the Reception Service, and to Pediatric Emergencies compared to the physician.
Measured: Assessment before inhalation of bronchodilators
Diagnostic performance of an algorithm compared to the physician in asthma attack
To evaluate the diagnostic performance of an algorithm in the asthma crisis in children aged between 2 and 16 years old, presenting to the Reception Service, and to Pediatric Emergencies compared to the physician.
Measured: Assessment 20 minutes after inhalation of bronchodilators
Secondary outcomes (12)
Artificial intelligence algorithm evaluation in treatment response
To evaluate the diagnostic performance of an artificial intelligence algorithm in response to treatment as compared to the physician.
Measured: Assessment before inhalation of bronchodilators
Artificial intelligence algorithm evaluation in treatment response
To evaluate the diagnostic performance of an artificial intelligence algorithm in response to treatment as compared to the physician.
Measured: Assessment 20 minutes after inhalation of bronchodilators
Asthma attack severity
Automated assessment of asthma attack severity comparing PRAM score and auscultation
Measured: Assessment before inhalation of bronchodilators
Asthma attack severity
Automated assessment of asthma attack severity comparing PRAM score and auscultation
Measured: Assessment 20 minutes after inhalation of bronchodilators
Analysis of different parameters in asthma attack
Automated assessment of respiratory rate
Measured: Assessment before inhalation of bronchodilators
Analysis of different parameters in asthma attack
Automated assessment of respiratory rate
Measured: Assessment 20 minutes after inhalation of bronchodilators
Analysis of breathing times during auscultation
Automated Inspiratory Time (TI) measurement
Measured: Assessment before inhalation of bronchodilators
Analysis of breathing times during auscultation
Automated Inspiratory Time (TI) measurement
Measured: Assessment 20 minutes after inhalation of bronchodilators
Analysis of breathing times during auscultation
Automated expiratory Time (TE) measurement.
Measured: Before inhalation of bronchodilators
Analysis of breathing times during auscultation
Automated expiratory Time (TE) measurement.
Measured: 20 minutes after inhalation of bronchodilators
Auscultatory wheezing evaluation
Automated wheezing auscultation analysis before β-2agonist.
Measured: Before inhalation of bronchodilators
Auscultatory wheezing evaluation
Automated wheezing auscultation analysis after β-2agonist.
Measured: 20 minutes after inhalation of bronchodilators
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Aug 2020
- Study start1 Mar 2020
- Primary completion30 Nov 2021 (estimated)
- Study completion1 Apr 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Asthma in ChildrenStudies of the same condition, open and under way.
- 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
- Prospective
- Enrolment
- 290 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Isabelle Ruchonnet-Métrailler
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Isabelle Ruchonnet-Métrailler, University Hospital, Geneva
- Investigators
- Alain Gervaix, M.D, Study director, University of Geneva, SwitzerlandConstance Barazzone Argiroffo, M.D, Study chair, University of Geneva, SwitzerlandIsabelle Ruchonnet-Metrailler, M.D., PhD, Principal investigator, University of Geneva, Switzerland
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04528342
- Sponsor's protocol ID
- 2019-01238
- Other IDs
- 2019
- Acronym
- Asthmoscope
- Status last verified
- Feb 2021
- First submitted
- 12 May 2020
- Last updated
- 4 Feb 2021
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 4 Feb 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Personalized Digital Health and Artificial Intelligence in Childhood Asthma
- ClinicalTrials.gov ID
- NCT04528342
- Acronym / sponsor ID
- Asthmoscope / 2019-01238
- Conditions
- Asthma in Children
- Keywords
- Digital stethoscope;, digital health, machine learning, Asthma
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 290 participants (estimated)
- Lead sponsor
- Isabelle Ruchonnet-MétraillerUniversity, hospital or other
- Study start
- 1 Mar 2020 (actual)
- Primary completion
- 30 Nov 2021 (estimated)
- Study completion
- 1 Apr 2022 (estimated)
- First posted
- 27 Aug 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.
Asthma is a chronic inflammatory disease of the airways that causes recurrent episodes of wheezing, breathing difficulties and coughing. The prevalence of asthma is 8% in school-aged children and 30% in preschoolers, making asthma the first chronic disease in children. Symptoms are due to diffuse but variable airway obstruction, reversible spontaneously or after inhalation of beta2 agonists (β-2a) such as salbutamol. Exacerbations of asthma are frequent and difficult to assess by parents and the patient himself. It is estimated that approximately 2.5% of children with asthma are hospitalized annually. The global burden caused by asthma can thus be reduced by improving early detection of bronchial obstruction, prescribing immediate treatment with the appropriate background therapy, and reliably and objectively assess response to treatment.
The natural history of asthma symptoms in children shows a great intra and inter-individual variability. The difficulty of assessing the severity of an attack by the parents or the child himself, when he is old enough to control his chronic disease, is a key element in the management of asthma and allows the treatment to be adapted quickly, sometimes avoiding hospitalization. Healthcare professionals can assess the severity of the episode using the Pediatric Respiratory Assesment Measure (PRAM) score, which has the advantage of being adaptable at any age. The Global Alliance against Chronic Respiratory Diseases (GARD) integrates in its diagnostic strategy for chronic respiratory diseases, the lung function test, which allows the quantification of respiratory function in the context of diagnosis and long-term follow-up. Although spirometry are non-invasive tests, they still require a high level of patient cooperation, which remains problematic before the age of 7 years.
The digital stethsocope integrates a capacity for recording auscultations and data transmission to high-performance software. This has made it possible to extend auscultation beyond what was audible to the human ear alone (over 20-20,000 Hertz).Auscultatory sounds analysis, particularly those most often associated with obstructive syndrome could be simple, reproducible and a reliable method of assessing the severity and response to treatment in children's asthma. Major advances in signal processing and unsupervised learning in artificial intelligence research provide the potential for high-performance analysis of physiological measures.
Detailed description
Aim:
Develop an artificial intelligence based algorithm for unsupervised diagnostic and classification of childhood asthma exacerbation.
Methodology: A Longitudinal prospective monocentric observational study will be performed in the Pediatric Emergency Division (PED) and the Pediatric Respiratory Unit (PRU) of the Geneva University Hospitals (HUG) during 24 months. This clinical study will include patients aged from 2 to16 years with acute asthma exacerbations. The intervention consists in recording auscultation of asthmatic patients at rest, during acute exacerbation and after treatment by bronchodilatators (β-2 agonists) inhalation in the PED, with a Digital Stethoscope (DS). Auscultation will be recorded during hospitalization every day, at home 7 days after the acute episode, combining intdoor and outdoor measures, and evaluating the exposome. A last record will be done at 6 to 8 weeks after the acute episode, with a lung function test if the patient is up to 7 years. A validation and training audio database will be constituted for the development of Artificial Intelligence (AI) algorithms, allowing analysis of respiratory rate, inspiratory/expiratory time ratio, PRAM score, wheezing variation of intensity and unsupervised diagnosis.
Expected results:
Creation of a performant AI algorithm for unsupervised acute asthma exacerbation diagnosis, with > 70 % of Sensitivity and > 70% of Specificity compared to the expert. Response to treatment will improve patient empowerment and personalized medicine in childhood asthma management.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 2 to 16 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child
- Study population
- 290 patients presenting with an acute asthma exacerbation within 70 severe asthma (clinical PRAM score > 7) within the PED. For the hospitalized patients, we estimate 70 patients needed during two years. At least 150 patients up to 7 years of age, within DS measurements and spirometer measures evaluation 6 to 8 weeks after acute episode by the pulmonlogist.
Key inclusion criteria (3 listed)
- Patients with clinical diagnosis of acute asthma exacerbations
- age > 2 years and < 16 years
- information and written consent of a legal representative
Key exclusion criteria (3 listed)
- Chronic lung diseases other than asthma (cystic fibrosis, bronchopulmonary Dysplasia),
- Congenital heart disease
- Refusal of consent.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Diagnostic performance of an algorithm compared to the physician in asthma attack
Time frame: Assessment before inhalation of bronchodilators
To evaluate the diagnostic performance of an algorithm in the asthma crisis in children aged between 2 and 16 years old, presenting to the Reception Service, and to Pediatric Emergencies compared to the physician.
Diagnostic performance of an algorithm compared to the physician in asthma attack
Time frame: Assessment 20 minutes after inhalation of bronchodilators
To evaluate the diagnostic performance of an algorithm in the asthma crisis in children aged between 2 and 16 years old, presenting to the Reception Service, and to Pediatric Emergencies compared to the physician.
12 secondary outcomes
Artificial intelligence algorithm evaluation in treatment response
Time frame: Assessment before inhalation of bronchodilators
Artificial intelligence algorithm evaluation in treatment response
Time frame: Assessment 20 minutes after inhalation of bronchodilators
Asthma attack severity
Time frame: Assessment before inhalation of bronchodilators
Asthma attack severity
Time frame: Assessment 20 minutes after inhalation of bronchodilators
Analysis of different parameters in asthma attack
Time frame: Assessment before inhalation of bronchodilators
Analysis of different parameters in asthma attack
Time frame: Assessment 20 minutes after inhalation of bronchodilators
Analysis of breathing times during auscultation
Time frame: Assessment before inhalation of bronchodilators
Analysis of breathing times during auscultation
Time frame: Assessment 20 minutes after inhalation of bronchodilators
Analysis of breathing times during auscultation
Time frame: Before inhalation of bronchodilators
Analysis of breathing times during auscultation
Time frame: 20 minutes after inhalation of bronchodilators
Auscultatory wheezing evaluation
Time frame: Before inhalation of bronchodilators
Auscultatory wheezing evaluation
Time frame: 20 minutes after inhalation of bronchodilators
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Feb 2021.
- Individual participant dataSharing planned: No
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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.
Isabelle Ruchonnet-Metrailler, M.D., PhD contact
Alain Gervaix, M.D study director
University of Geneva, Switzerland
Constance Barazzone Argiroffo, M.D study chair
University of Geneva, Switzerland
Isabelle Ruchonnet-Metrailler, M.D., PhD principal investigator
University of Geneva, Switzerland
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Asthma in Children, run by Isabelle Ruchonnet-Métrailler at 2 sites in Switzerland.
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 3 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: status not confirmed, as of the sponsor's last update on 4 Feb 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Switzerland. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Isabelle Ruchonnet-Metrailler, M.D., PhD (+41.79.553.41.69, Isabelle.Ruchonnet-Metrailler@hcuge.ch), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic performance of an algorithm compared to the physician in asthma attack (time frame: Assessment before inhalation of bronchodilators). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 4 Feb 2021.