Ignite Creation Date:
2025-12-24 @ 12:47 PM
Ignite Modification Date:
2025-12-29 @ 7:54 PM
Study NCT ID:
NCT05167461
Status:
COMPLETED
Last Update Posted:
2023-10-23
First Post:
2021-11-05
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
AutoMated BUrn Diagnostic System for Healthcare (AMBUSH)
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D002056', 'term': 'Burns'}], 'ancestors': [{'id': 'D014947', 'term': 'Wounds and Injuries'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D000098408', 'term': 'Reinforcement Machine Learning'}], 'ancestors': [{'id': 'D000069550', 'term': 'Machine Learning'}, {'id': 'D001185', 'term': 'Artificial Intelligence'}, {'id': 'D000465', 'term': 'Algorithms'}, {'id': 'D055641', 'term': 'Mathematical Concepts'}]}}, 'protocolSection': {'designModule': {'bioSpec': {'retention': 'SAMPLES_WITH_DNA', 'description': 'discarded burn tissue will be taken'}, 'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'CASE_CROSSOVER'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 30}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2022-05-24', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2023-10', 'completionDateStruct': {'date': '2023-05-25', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2023-10-18', 'studyFirstSubmitDate': '2021-11-05', 'studyFirstSubmitQcDate': '2021-12-09', 'lastUpdatePostDateStruct': {'date': '2023-10-23', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2021-12-22', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2023-05-25', 'type': 'ACTUAL'}}, 'outcomesModule': {'otherOutcomes': [{'measure': 'Evaluate burn software accuracy', 'timeFrame': '2 yrs', 'description': 'Survey software evaluators - who are medical providers, residents, and medical students. The tertiary aim will be designed and conducted after the primary and secondary aims have completed, and that the protocol will be amended to include this design and all necessary elements at that time. The tertiary aim was presented to show how additional validity surrounding the ease of use and understanding of results produced by the program in real life situations will be established.'}], 'primaryOutcomes': [{'measure': 'Compare human assessment of burn depth to AI assessment', 'timeFrame': '2 year', 'description': 'Compare human assessment of burn depth to the technology output (Artificial Intelligence and TDI) as determined by need for surgery (time points include day 0 +/- 3 days). Biopsy collected from patients that go to OR (one-time collection) to verify the burn depth via histological analysis.'}], 'secondaryOutcomes': [{'measure': 'Confirm burn conversion', 'timeFrame': '2 years', 'description': ': Confirm burn conversion by the presence of infection as found in the electronic medical record (EMR). Confirm burn infection by sending one tissue biopsy from OR (one-time collection)'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['burns', 'thermal burn'], 'conditions': ['Burns Multiple', 'Burns', 'Thermal Burn', 'Burn Infection']}, 'descriptionModule': {'briefSummary': 'The primary objective of this study is to develop a high accuracy and automated system that can provide early assessment of burn injuries with at least 90% accuracy in absence of burn experts, using AI and FDA cleared harmonic ultrasound TDI data based on the analysis of mechanical and hemodynamic properties of the subcutaneous burned tissue. Data collected in this study will lead to the development of better diagnostic tools that could inform clinical burn practices by enabling doctors to determine burn depth and the need for surgery with greater speed and accuracy, resulting in better clinical outcomes.', 'detailedDescription': "Burn injuries are considered as one of the most complex type of traumatic injuries. In the United States (US), about 1.25 million people are treated each year for burns, and 40,000 are hospitalized for the treatment of these injuries resulting in high medical costs, approximately $7.9 billion per year. Early and accurate treatment of burns is critical to prevent infections and improve the possible outcomes of the patient, decreasing the mortality rate by about 36% In this regard, prioritizing burns that require a surgical procedure to heal, i.e. burn excision and skin grafting, is critical. This constitutes a challenging task because it involves the determination of the burn depth, for which experienced burn surgeons achieve an accuracy of 67-76%, value that decreases to 50% for inexperienced surgeons. Assessment of the burn depth is one of the most important aspects of burn care. It is a predictor of pathological scarring that occurs in 30%-91% of burn injuries. However, it continues to be an open clinical challenge for which an accurate solution has yet to be found. Burns are classified into three different categories: superficial burns, which involve only epidermis; partial-thickness burns, which affect epidermis and dermis; and full-thickness burns, which include deep structures such as subcutaneous, muscles, and bone. The first two are expected to heal without scarring by 14-21 days after the burn injury, while the last one will heal with scarring resulting in significant morbidity to the patient, including pain, loss of joint mobility, loss of function, and social isolation. For this reason, full-thickness burns are excised, and exposed body parts are covered with skin grafts to prevent further complications with scarring Currently, burn depth is determined by clinical assessment, based on appearance, blanching to pressure, sensation to pin prick, and bleeding on needle prick. This visual and tactile inspection approach introduces inter-subject variability, especially when partial-thickness burns are involved. Overestimation of burn depth leads to unnecessary surgery of excision of viable skin and replacement with skin grafts that look different from surrounding skin. These grafts are less pliable with lack of the ability to sweat for thermoregulation, while underestimation leads to surgical delay, long length of hospital stays, high treatment costs, scarring, and poor functional and aesthetic outcomes. This issue is exacerbated by a phenomenon known as burn conversion, which is not fully understood yet, and it is usually not accounted for in the assessment process or in the clinical decision support technologies. Burn injury is a dynamic process, the longer the delay in intervening, the more abundant the scar tissue formation and the more likely the patient is to have deformity with loss of mobility and function. Thus, early prediction of burn conversion is critical for surgical decision making and burn follow up. Several non-invasive light-based imaging technologies have been developed to support clinical assessment, which is currently the most common technique for burn depth assessment. They operate based on the existing correlation among blood perfusion, functional blood vessels, and burn depth. However, Laser Doppler Imaging (LDI) is the only one approved by FDA. It is the most widely adopted non-invasive technology in burn treatment facilities in the US, but it is used as the preferred modality in only 6% of them. Considering the many limitations of this technology, this is not unexpected. It uses light/optic principles to detect the active blood flow in the damaged tissue of the patient, and the results can be greatly altered by the curvature of tissues, ambient light and temperature, motion artifacts, topical wound dressings, non-debrided tissue, skin color, pigment from tattoos, and blisters. In addition, LDI performance is poor when burn injuries are less than 24 hours old and can be difficult to interpret for inexperienced users. To overcome the limitations of light-based technologies, the investigators will integrate novel techniques in Artificial Intelligence (AI), Computer Vision, with FDA approved Harmonic B-mode ultrasound (HUSD B-mode) and Harmonic Ultrasound Tissue Doppler Elastography imaging (TDI), medical expertise in the wound care, tissue injury management, and burn surgery domains. This will enhance the burn assessment accuracy, improving the patient's prognosis."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Subjects aged 18 years and above, male and female, with a thermal burn injury will be considered for participation in this study. Enrollment of 30 subjects is planned', 'healthyVolunteers': False, 'eligibilityCriteria': 'Subjects must meet all the following enrollment criteria to be eligible for participation in the study:\n\n1. Male or female patients aged 18 years and above.\n2. The patient has a thermal burn injury\n3. Antibiotic use allowed\n4. Patient does not need to have the ability to communicate\n5. Inpatient or outpatient\n6. Within 7 days of burn\n7. No prior surgical debridement\n8. Burn ≤ 75% of body surface\n9. The patient or their legal representative agrees to comply with all compulsory study procedures and visit schedule\n10. The patient or their legal representative agrees to abstain from enrollment in any other clinical trial for the duration of the study.\n11. In the opinion of the investigator, the patient or their legal representative must be able to:\n\n 1. Understand the full nature and purpose of the study, including possible risks and adverse events,\n 2. Understand instruction, and\n 3. Provide voluntary informed written consent/assent as appropriate for study participation.\n\nExclusion Criteria: Subjects who meet any of the following criteria are not eligible for participation in the study:\n\n1. Unable to provide informed consent\n2. Age \\<18 years\n3. Burn ≥ 75% of body surface\n4. Burns caused by chemicals, electricity or radiation.\n5. Patients presenting with only 3rd-degree/full-thickness wounds which require immediate autografting.\n6. Burn injury has had prior surgical treatment.\n7. Prisoners\n8. Pregnant individuals\n9. Unable to follow study schedule or understand study instructions'}, 'identificationModule': {'nctId': 'NCT05167461', 'acronym': 'AMBUSH', 'briefTitle': 'AutoMated BUrn Diagnostic System for Healthcare (AMBUSH)', 'organization': {'class': 'OTHER', 'fullName': 'Indiana University'}, 'officialTitle': 'AutoMated Burn Diagnostic System for Healthcare', 'orgStudyIdInfo': {'id': '12689'}}, 'armsInterventionsModule': {'interventions': [{'name': 'Collection of imaging by ultrasonography', 'type': 'OTHER', 'otherNames': ['deep machine learning'], 'description': 'The investigators will collect burn image data to be processed through the software combined with the deep machine learning to find automated diagnostic burn assessment with accuracy of \\>95% compared to human assessment'}]}, 'contactsLocationsModule': {'locations': [{'zip': '46202', 'city': 'Indianapolis', 'state': 'Indiana', 'country': 'United States', 'facility': 'Eskenazi Health', 'geoPoint': {'lat': 39.76838, 'lon': -86.15804}}], 'overallOfficials': [{'name': 'Gayle Gordillo, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Indiana University'}]}, 'ipdSharingStatementModule': {'infoTypes': ['STUDY_PROTOCOL', 'SAP', 'ICF'], 'timeFrame': '1 yr after all data collected until 3 yrs after study results', 'ipdSharing': 'YES', 'description': 'We will share the artificial intelligence data regarding how the AI performed in comparison to the human evaluators', 'accessCriteria': 'via email from statistician'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Indiana University', 'class': 'OTHER'}, 'collaborators': [{'name': 'Eskenazi Health', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor of Plastic Surgery', 'investigatorFullName': 'Gayle Gordillo', 'investigatorAffiliation': 'Indiana University'}}}}