Clinical trial NCT06389058: Using NLP and Neural Networks to Autonomously Identify Severe Asthma and Determine Study Eligibility in a Large Healthcare System
Official title: Using NLP and Neural Networks to Autonomously Identify Severe Asthma and Determine Study Eligibility in a Large Healthcare System
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Severe Asthma
Lead sponsor: San Diego State University
Sponsor class: OTHER
Collaborators: name: GlaxoSmithKline
name: Scripps Health
name: Modena Allergy + Asthma, La Jolla, CA
name: University of California, San Diego
First posted: 2024-04-29
Enrollment: 31795
Sex: ALL
Minimum age (years): 6
Maximum age (years): 85
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
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.

Brief 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. 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.

Eligibility criteria: Inclusion Criteria:

- Demographics: Males \~ 40%, Blacks \~ 5-10%, Hispanic \~15-30%, Urban \~80-90%

Exclusion Criteria:

* None

Primary outcomes:
measure: Identification of Patients with Severe Asthma
timeFrame: 4 years
Secondary outcomes:
Other outcomes:
Locations:
city: San Diego
country: United States
facility: San Diego State University
state: California
Study officials:
affiliation: San Diego State University
name: yusuf Ozturk, Ph.D.
role: PRINCIPAL_INVESTIGATOR
References: