Severe AsthmaNCT06389058
Using NLP and Neural Networks to Autonomously Identify Severe Asthma and Determine Study Eligibility in a Large Healthcare System
An observational study of Severe Asthma, run by San Diego State University at 1 site in United States.
Trial summary
The study aims to to use new technologies (ML, AI, NLP), to autonomously identify moderate to severe asthma populations within an EHR system, describe differences in treatment patterns across different populations, and determine trial eligibility. Primary Objectives Please ensure you detail primary objectives Aim 1. Read more about this study
- Who is it for?
- Ages 6 to 85 years
- Conditions the study is for
- Severe Asthma
- What is being tested?
- Recommendation for the diagnoses and treatment of Severe Asthma
- How many people
- 31,795 participants
- Where
- 1 siteUnited States
- Study dates
- 1 May 2023to Dec 2026
- How long will I take part?
- 2 Years
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 6 to 85 years
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: De-identified EHR data from N=31,795 patients diagnosed with asthma at Scripps Health (San Diego, CA) were filtered and processed, adhering to strict inclusion and exclusion criteria designed to accurately isolate cases of asthma.
Who may be able to join
- Demographics: Males ~ 40%, Blacks ~ 5-10%, Hispanic ~15-30%, Urban ~80-90%
Who cannot join
- None
Trial locations and contacts
1 site in 1 country.
United States1 site
San Diego State University
San Diego, California, 92182-1309
About this study
The study aims to to use new technologies (ML, AI, NLP), to autonomously identify moderate to severe asthma populations within an EHR system, describe differences in treatment patterns across different populations, and determine trial eligibility.
Primary Objectives Please ensure you detail primary objectives Aim 1. Determine and validate a diagnosis of severe asthma (SA) using predictive features obtained from the Scripps Health EHR.
- Aim 1a: Use ML applied to structured EHR data to predict SA. Use the opinion of 2 specialty-trained physicians and ATS guidelines to determine model accuracy.
- Aim 1b: Use NLP applied to unstructured text to predict SA. Determine model accuracy as above in Aim 1a.
- Aim 1c: Use a combination of ML applied to structured data to predict SA. Determine model accuracy as above in Aim 1a.
Read the detailed description
Asthma is a heterogeneous disease. The heterogeneity of asthma is supported by clinical observations and genome wide association studies (GWASs) that have identified over 200 asthma susceptibility loci in the DNA. These genetic 'hot spots' are near inflammatory cytokines, growth factors, and other inflammatory proteins knowingly linked to airway inflammation, including cytokines IL-4, -5, -13, -25, -33, and TSLP.
Novel monoclonal antibody therapies have drastically changed the treatment of moderate-to-severe asthma. Novel monoclonal antibody therapies introduced in the last 7 years have greatly advanced treatment options for moderate-to-severe asthma patients. These therapies effectively reduce or eliminate severe exacerbations, prevent hospitalizations, and improve patients' quality of life. However, many severe asthma patients, particularly those living in underserved areas, are still being overtreated with steroids and undertreated with monoclonal antibodies.
The 21st Century Cures Act will Change the Landscape of Research. The 21st Century Cures Act reinforced the use of real-world data (RWD) and real-world evidence (RWE) to support clinical trials, aid in drug coverage decisions, develop national treatment guidelines as well as standardized decision support tools. An underutilized source of RWE/D are electronic health records (EHR). Machine Learning (ML), AI, and natural language processing (NLP) are developing technologies that will greatly advance our ability to leverage data in EHR systems.
The study aims to use new technologies (ML, AI, NLP), to autonomously identify moderate to severe asthma populations within an EHR system, describe differences in treatment patterns across different populations, and determine trial eligibility.
Primary Objectives Please ensure you detail primary objectives Aim 1. Determine and validate a diagnosis of severe asthma (SA) using predictive features obtained from the Scripps Health EHR.
- Aim 1a: Use ML applied to structured EHR data to predict SA. Use the opinion of 2 specialty-trained physicians and ATS guidelines to determine model accuracy.
- Aim 1b: Use NLP applied to unstructured text to predict SA. Determine model accuracy as above in Aim 1a.
- Aim 1c: Use a combination of ML applied to structured data to predict SA. Determine model accuracy as above in Aim 1a.
- Conditions
- Severe Asthma
- Being tested
- Recommendation for the diagnoses and treatment of Severe Asthma
- Medical subject headings
- Asthma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Recommendation for the diagnoses and treatment of Severe AsthmaOther
No intervention planned in this phase for the patients. Recommendations to be developed for healthcare and condition.
| Study group | Role | What they receive |
|---|---|---|
| Severe Asthma | Patients with Severe or Uncontrolled Asthma Receives: Other: Recommendation for the diagnoses and treatment of Severe Asthma |
What is being measured
The primary outcome is the main question the study is designed to answer.
Identification of Patients with Severe Asthma
Identify patients with severe asthma and compare diagnoses to that of medical professionals
Measured: 4 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Apr 2024
- Study start1 May 2023
- Primary completionDec 2026 (estimated)
- Study completionDec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Severe AsthmaStudies of the same condition, open and under way.
- More trials run by San Diego State UniversityThe 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
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 31,795 participants (actual)
- Target duration
- 2 Years
Sponsor and investigators
- Lead sponsor
- San Diego State University
- Sponsor type
- University, hospital or other
- Collaborators
- GlaxoSmithKline, Scripps Health, Modena Allergy + Asthma, La Jolla, CA, University of California, San Diego
- Responsible party
- Sponsor
- Investigators
- yusuf Ozturk, Ph.D., Principal investigator, San Diego State University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06389058
- Sponsor's protocol ID
- G00014538
- Status last verified
- Apr 2026
- First submitted
- 24 Apr 2024
- Last updated
- 22 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- 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 22 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Using NLP and Neural Networks to Autonomously Identify Severe Asthma and Determine Study Eligibility in a Large Healthcare System
- ClinicalTrials.gov ID
- NCT06389058
- Acronym / sponsor ID
- G00014538
- Conditions
- Severe Asthma
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 31,795 participants (actual)
- Lead sponsor
- San Diego State UniversityUniversity, hospital or other
- Collaborators
- GlaxoSmithKline, Scripps Health, Modena Allergy + Asthma, La Jolla, CA, University of California, San Diego
- Study start
- 1 May 2023 (actual)
- Primary completion
- Dec 2026 (estimated)
- Study completion
- Dec 2026 (estimated)
- First posted
- 29 Apr 2024 (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.
The study aims to to use new technologies (ML, AI, NLP), to autonomously identify moderate to severe asthma populations within an EHR system, describe differences in treatment patterns across different populations, and determine trial eligibility.
Primary Objectives Please ensure you detail primary objectives Aim 1. Determine and validate a diagnosis of severe asthma (SA) using predictive features obtained from the Scripps Health EHR.
- Aim 1a: Use ML applied to structured EHR data to predict SA. Use the opinion of 2 specialty-trained physicians and ATS guidelines to determine model accuracy.
- Aim 1b: Use NLP applied to unstructured text to predict SA. Determine model accuracy as above in Aim 1a.
- Aim 1c: Use a combination of ML applied to structured data to predict SA. Determine model accuracy as above in Aim 1a.
Detailed description
Asthma is a heterogeneous disease. The heterogeneity of asthma is supported by clinical observations and genome wide association studies (GWASs) that have identified over 200 asthma susceptibility loci in the DNA. These genetic 'hot spots' are near inflammatory cytokines, growth factors, and other inflammatory proteins knowingly linked to airway inflammation, including cytokines IL-4, -5, -13, -25, -33, and TSLP.
Novel monoclonal antibody therapies have drastically changed the treatment of moderate-to-severe asthma. Novel monoclonal antibody therapies introduced in the last 7 years have greatly advanced treatment options for moderate-to-severe asthma patients. These therapies effectively reduce or eliminate severe exacerbations, prevent hospitalizations, and improve patients' quality of life. However, many severe asthma patients, particularly those living in underserved areas, are still being overtreated with steroids and undertreated with monoclonal antibodies.
The 21st Century Cures Act will Change the Landscape of Research. The 21st Century Cures Act reinforced the use of real-world data (RWD) and real-world evidence (RWE) to support clinical trials, aid in drug coverage decisions, develop national treatment guidelines as well as standardized decision support tools. An underutilized source of RWE/D are electronic health records (EHR). Machine Learning (ML), AI, and natural language processing (NLP) are developing technologies that will greatly advance our ability to leverage data in EHR systems.
The study aims to use new technologies (ML, AI, NLP), to autonomously identify moderate to severe asthma populations within an EHR system, describe differences in treatment patterns across different populations, and determine trial eligibility.
Primary Objectives Please ensure you detail primary objectives Aim 1. Determine and validate a diagnosis of severe asthma (SA) using predictive features obtained from the Scripps Health EHR.
- Aim 1a: Use ML applied to structured EHR data to predict SA. Use the opinion of 2 specialty-trained physicians and ATS guidelines to determine model accuracy.
- Aim 1b: Use NLP applied to unstructured text to predict SA. Determine model accuracy as above in Aim 1a.
- Aim 1c: Use a combination of ML applied to structured data to predict SA. Determine model accuracy as above in Aim 1a.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
- Target follow-up
- 2 Years
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Severe Asthma | – | Other: Recommendation for the diagnoses and treatment of Severe AsthmaPatients with Severe or Uncontrolled Asthma |
Who may qualify
- Ages
- 6 to 85 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- De-identified EHR data from N=31,795 patients diagnosed with asthma at Scripps Health (San Diego, CA) were filtered and processed, adhering to strict inclusion and exclusion criteria designed to accurately isolate cases of asthma.
Key inclusion criteria (1 listed)
- Demographics: Males ~ 40%, Blacks ~ 5-10%, Hispanic ~15-30%, Urban ~80-90%
Key exclusion criteria (1 listed)
- None
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 |
|---|---|---|
| Recommendation for the diagnoses and treatment of Severe AsthmaNo intervention planned in this phase for the patients. Recommendations to be developed for healthcare and condition. | Other | Severe Asthma |
Primary outcome
Identification of Patients with Severe Asthma
Time frame: 4 years
Identify patients with severe asthma and compare diagnoses to that of medical professionals
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Apr 2026.
- Individual participant dataSharing planned: No
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Severe Asthma
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.
yusuf Ozturk, Ph.D. principal investigator
San Diego State University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Severe Asthma, run by San Diego State University at 1 site in United States.
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 1 inclusion and 1 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: under way, not looking for participants, as of the sponsor's last update on 22 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. 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?
This study is still under way. Results are not available in the source record yet.
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?
Identification of Patients with Severe Asthma (time frame: 4 years). 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 22 Apr 2026; AzTrials last refreshed its copy on 6 Nov 2026.