Using NLP and Neural Networks to Autonomously Identify Severe Asthma and Determine Study Eligibility in a Large Healthcare System

Severe Asthma Clinical Trial

Official title:

Using NLP and Neural Networks to Autonomously Identify Severe Asthma and Determine Study Eligibility in a Large Healthcare System

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT06389058
• Other study ID #: G00014538
• Status: Active, not recruiting
• Start date: May 1, 2023
• Est. completion date: November 2026

Study information

• Verified date: April 2024
• Source: San Diego State University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical 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. 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.

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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 31795
• Est. completion date: November 2026
• Est. primary completion date: July 1, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 6 Years to 85 Years

Eligibility

Inclusion Criteria:

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

Exclusion Criteria:

• None

Related Conditions & MeSH terms

• Asthma
• Severe Asthma

Intervention

Other:
• Recommendation for the diagnoses and treatment of Severe Athma
No intervention planned in this phase for the patients. Recommendations to be developed for healthcare and condition.

Locations

Country	Name	City	State
United States	San Diego State University	San Diego	California

Sponsors (5)

Lead Sponsor	Collaborator
San Diego State University	GlaxoSmithKline, Modena Allergy + Asthma, La Jolla, CA, Scripps Health, University of California, San Diego

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Identification of Patients with Severe Asthma	Identify patients with severe asthma and compare diagnoses to that of medical professionals	4 years