Ignite Creation Date:
2025-12-24 @ 9:26 PM
Ignite Modification Date:
2026-01-01 @ 8:03 PM
Study NCT ID:
NCT07019532
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-06-13
First Post:
2025-06-05
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Machine Learning-Based Risk Stratification for Fistula Formation After Perianal Abscess Drainage
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D012003', 'term': 'Rectal Fistula'}], 'ancestors': [{'id': 'D007412', 'term': 'Intestinal Fistula'}, {'id': 'D016154', 'term': 'Digestive System Fistula'}, {'id': 'D004066', 'term': 'Digestive System Diseases'}, {'id': 'D007410', 'term': 'Intestinal Diseases'}, {'id': 'D005767', 'term': 'Gastrointestinal Diseases'}, {'id': 'D012002', 'term': 'Rectal Diseases'}, {'id': 'D005402', 'term': 'Fistula'}, {'id': 'D020763', 'term': 'Pathological Conditions, Anatomical'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 450}, 'targetDuration': '6 Months', 'patientRegistry': True}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-07-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-06', 'completionDateStruct': {'date': '2026-06-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-06-05', 'studyFirstSubmitDate': '2025-06-05', 'studyFirstSubmitQcDate': '2025-06-05', 'lastUpdatePostDateStruct': {'date': '2025-06-13', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-06-13', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-12-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Fistula formation within 6 months', 'timeFrame': '6 months', 'description': 'Confirmed by clinical exam, surgical findings, or MR imaging'}], 'secondaryOutcomes': [{'measure': 'Correlation between drainage location and fistula rate', 'timeFrame': '6 months'}, {'measure': 'Correlation between provider experience and fistula complexity', 'timeFrame': '6 months'}, {'measure': 'Diagnostic accuracy of AI-based MR analysis vs radiologist', 'timeFrame': '6 months'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Artificial Intelligence', 'MR Imaging', 'Colorectal Surgery', 'Surgical Outcomes', 'Drainage', 'Fistula Prediction'], 'conditions': ['Perianal Abscess', 'Anal Fistula']}, 'descriptionModule': {'briefSummary': 'This prospective cohort study investigates the influence of provider experience and drainage location on fistula formation within 6 months following perianal abscess drainage. Additionally, the study explores the role of artificial intelligence (AI)-based interpretation of magnetic resonance (MR) images in early identification of fistula development.', 'detailedDescription': 'Perianal abscess drainage is a common surgical procedure. However, subsequent fistula formation remains a significant complication. This study aims to determine whether the procedure setting (operating room, emergency department, or outpatient clinic) and the experience level of the performing clinician affect fistula development rates.\n\nFurthermore, the study evaluates the use of AI-assisted analysis of selected MR images to identify early signs of fistula formation. Selected image slices will be labeled based on radiological reports, and a machine learning model will be trained to predict fistula risk. The study will also compare AI-generated interpretations with expert radiologist assessments to validate performance.\n\nThe ultimate goal is to create a risk stratification tool to support clinical decision-making in surgical management of perianal abscesses.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': "Adult patients (≥18 years old) undergoing surgical drainage for their first perianal abscess at a university or state hospital. Participants must not have a history of anal fistula, Crohn's disease, or be under immunosuppressive therapy. Follow-up duration is 6 months after drainage.", 'healthyVolunteers': False, 'eligibilityCriteria': "Inclusion Criteria:\n\n* Age ≥ 18\n* First-time perianal abscess\n* Surgical drainage performed\n\nExclusion Criteria:\n\n* Existing anal fistula history\n* Crohn's disease\n* Immunosuppressive treatment\n* Incomplete 6-month follow-up"}, 'identificationModule': {'nctId': 'NCT07019532', 'acronym': 'PRISM', 'briefTitle': 'Machine Learning-Based Risk Stratification for Fistula Formation After Perianal Abscess Drainage', 'organization': {'class': 'OTHER_GOV', 'fullName': 'Gumushane State Hospital'}, 'officialTitle': 'A Prospective Cohort Study for Machine Learning-Based Prediction of Anal Fistula Formation After Perianal Abscess Drainage Based on Drainage Setting, Provider Experience, and MRI Interpretation (PRISM)', 'orgStudyIdInfo': {'id': 'FISTUL-ML-01'}}, 'contactsLocationsModule': {'centralContacts': [{'name': 'Kayahan Eyüboğlu, MD', 'role': 'CONTACT', 'email': 'kayahaneyuboglu@gmail.com', 'phone': '+905546813327'}]}, 'ipdSharingStatementModule': {'infoTypes': ['STUDY_PROTOCOL', 'ICF', 'CSR'], 'ipdSharing': 'YES', 'description': 'De-identified MR images and anonymized patient-level data will be available for collaborative analysis upon request.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Gumushane State Hospital', 'class': 'OTHER_GOV'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'General Surgery Specialist', 'investigatorFullName': 'Kayahan Eyüboğlu', 'investigatorAffiliation': 'Gumushane State Hospital'}}}}