Ignite Creation Date:
2025-12-25 @ 12:10 AM
Ignite Modification Date:
2025-12-25 @ 10:11 PM
Study NCT ID:
NCT06389058
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2025-11-18
First Post:
2024-04-24
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Using NLP and Neural Networks to Autonomously Identify Severe Asthma and Determine Study Eligibility in a Large Healthcare System
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D001249', 'term': 'Asthma'}], 'ancestors': [{'id': 'D001982', 'term': 'Bronchial Diseases'}, {'id': 'D012140', 'term': 'Respiratory Tract Diseases'}, {'id': 'D008173', 'term': 'Lung Diseases, Obstructive'}, {'id': 'D008171', 'term': 'Lung Diseases'}, {'id': 'D012130', 'term': 'Respiratory Hypersensitivity'}, {'id': 'D006969', 'term': 'Hypersensitivity, Immediate'}, {'id': 'D006967', 'term': 'Hypersensitivity'}, {'id': 'D007154', 'term': 'Immune System Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'CROSS_SECTIONAL', 'observationalModel': 'OTHER'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 31795}, 'targetDuration': '2 Years', 'patientRegistry': True}, 'statusModule': {'overallStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2023-05-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-11', 'completionDateStruct': {'date': '2026-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-11-14', 'studyFirstSubmitDate': '2024-04-24', 'studyFirstSubmitQcDate': '2024-04-24', 'lastUpdatePostDateStruct': {'date': '2025-11-18', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2024-04-29', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Identification of Patients with Severe Asthma', 'timeFrame': '4 years', 'description': 'Identify patients with severe asthma and compare diagnoses to that of medical professionals'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Severe Asthma']}, 'descriptionModule': {'briefSummary': '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.\n\nPrimary 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.\n\n* 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.\n* Aim 1b: Use NLP applied to unstructured text to predict SA. Determine model accuracy as above in Aim 1a.\n* Aim 1c: Use a combination of ML applied to structured data to predict SA. Determine model accuracy as above in Aim 1a.', 'detailedDescription': "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.\n\nNovel 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.\n\nThe 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.\n\nThe 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.\n\nPrimary 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.\n\n* 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.\n* Aim 1b: Use NLP applied to unstructured text to predict SA. Determine model accuracy as above in Aim 1a.\n* Aim 1c: Use a combination of ML applied to structured data to predict SA. Determine model accuracy as above in Aim 1a."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'maximumAge': '85 Years', 'minimumAge': '6 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': '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.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n\\- Demographics: Males \\~ 40%, Blacks \\~ 5-10%, Hispanic \\~15-30%, Urban \\~80-90%\n\nExclusion Criteria:\n\n* None'}, 'identificationModule': {'nctId': 'NCT06389058', 'briefTitle': 'Using NLP and Neural Networks to Autonomously Identify Severe Asthma and Determine Study Eligibility in a Large Healthcare System', 'organization': {'class': 'OTHER', 'fullName': 'San Diego State University'}, 'officialTitle': 'Using NLP and Neural Networks to Autonomously Identify Severe Asthma and Determine Study Eligibility in a Large Healthcare System', 'orgStudyIdInfo': {'id': 'G00014538'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Severe Asthma', 'description': 'Patients with Severe or Uncontrolled Asthma', 'interventionNames': ['Other: Recommendation for the diagnoses and treatment of Severe Asthma']}], 'interventions': [{'name': 'Recommendation for the diagnoses and treatment of Severe Asthma', 'type': 'OTHER', 'description': 'No intervention planned in this phase for the patients. Recommendations to be developed for healthcare and condition.', 'armGroupLabels': ['Severe Asthma']}]}, 'contactsLocationsModule': {'locations': [{'zip': '92182-1309', 'city': 'San Diego', 'state': 'California', 'country': 'United States', 'facility': 'San Diego State University', 'geoPoint': {'lat': 32.71571, 'lon': -117.16472}}], 'overallOfficials': [{'name': 'yusuf Ozturk, Ph.D.', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'San Diego State University'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'San Diego State University', 'class': 'OTHER'}, 'collaborators': [{'name': 'GlaxoSmithKline', 'class': 'INDUSTRY'}, {'name': 'Scripps Health', 'class': 'OTHER'}, {'name': 'Modena Allergy + Asthma, La Jolla, CA', 'class': 'UNKNOWN'}, {'name': 'University of California, San Diego', 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR'}}}}