An observational study in Severe Asthma, sponsored by San Diego State University. Active, not recruiting at 1 site in United States. Open to participants aged 6 Years to 85 Years. Per ClinicalTrials.gov, last updated 2026-04-22.
Sponsored by San Diego State University · Observational
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.
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.
3,921 studies on the registry are indexed under Asthma; 507 are open to participants now.
This study's enrollment of 31,795 is above the median of 150 across 970 observational studies indexed under Asthma.
Browse Asthma studies →San Diego State University is the lead sponsor of 145 studies on the registry; 41 are open to participants now.
Counted across the registry records on this site, refreshed daily.
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.
- Demographics: Males \~ 40%, Blacks \~ 5-10%, Hispanic \~15-30%, Urban \~80-90%
Exclusion Criteria:
Patients with Severe or Uncontrolled Asthma
Other: Recommendation for the diagnoses and treatment of Severe Asthma
No intervention planned in this phase for the patients. Recommendations to be developed for healthcare and condition.
Identification of Patients with Severe Asthma
Identify patients with severe asthma and compare diagnoses to that of medical professionals
Time frame: 4 years
Plan to share: No
No publications or documents are linked to this record.
This study is active, not recruiting, as verified in Apr 2026. You cannot join it, but the record below documents what was studied.
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San Diego State University