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

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Severe Asthma
• Intervention / Treatment: Other: Recommendation for the diagnoses and treatment of Severe Athma

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 Type: Observational
• Enrollment (Actual): 31795

Contacts and Locations

Study Locations

• United States
  • California
    • San Diego, California, United States, 92182-1309
      • San Diego State University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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.

Description

Inclusion Criteria:

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

Exclusion Criteria:

• None

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Severe Asthma; Patients with Severe or Uncontrolled Asthma	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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Identification of Patients with Severe Asthma	Identify patients with severe asthma and compare diagnoses to that of medical professionals	4 years

Collaborators and Investigators

• Sponsor: San Diego State University
• Collaborators: University of California, San Diego; GlaxoSmithKline; Scripps Health; Modena Allergy + Asthma, La Jolla, CA
• Investigators: Principal Investigator: yusuf Ozturk, Ph.D., San Diego State University

Study record dates

Study Major Dates

• Study Start (Actual): May 1, 2023
• Primary Completion (Estimated): July 1, 2024
• Study Completion (Estimated): November 1, 2026

Study Registration Dates

• First Submitted: April 24, 2024
• First Submitted That Met QC Criteria: April 24, 2024
• First Posted (Actual): April 29, 2024

Study Record Updates

• Last Update Posted (Actual): April 29, 2024
• Last Update Submitted That Met QC Criteria: April 24, 2024
• Last Verified: April 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Respiratory Tract Diseases; Immune System Diseases; Lung Diseases; Hypersensitivity, Immediate; Bronchial Diseases; Lung Diseases, Obstructive; Respiratory Hypersensitivity; Hypersensitivity; Asthma
• Other Study ID Numbers: G00014538

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No