Ignite Creation Date:
2025-12-24 @ 7:59 PM
Ignite Modification Date:
2025-12-25 @ 5:33 PM
Study NCT ID:
NCT05610904
Status:
UNKNOWN
Last Update Posted:
2022-11-09
First Post:
2022-10-30
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Evaluation of AL Prediction for Rectal Cancer
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D057868', 'term': 'Anastomotic Leak'}, {'id': 'D012004', 'term': 'Rectal Neoplasms'}], 'ancestors': [{'id': 'D011183', 'term': 'Postoperative Complications'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}, {'id': 'D015179', 'term': 'Colorectal Neoplasms'}, {'id': 'D007414', 'term': 'Intestinal Neoplasms'}, {'id': 'D005770', 'term': 'Gastrointestinal Neoplasms'}, {'id': 'D004067', 'term': 'Digestive System Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D004066', 'term': 'Digestive System Diseases'}, {'id': 'D005767', 'term': 'Gastrointestinal Diseases'}, {'id': 'D007410', 'term': 'Intestinal Diseases'}, {'id': 'D012002', 'term': 'Rectal Diseases'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'SINGLE', 'whoMasked': ['PARTICIPANT']}, 'primaryPurpose': 'PREVENTION', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 418}}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2022-12-10', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2022-11', 'completionDateStruct': {'date': '2025-10-10', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2022-11-03', 'studyFirstSubmitDate': '2022-10-30', 'studyFirstSubmitQcDate': '2022-11-03', 'lastUpdatePostDateStruct': {'date': '2022-11-09', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2022-11-09', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-10-10', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Accuracy of stoma implementation', 'timeFrame': '1 months after surgery', 'description': 'Accuracy of stoma implementation: the number of anastomotic leakage patients with stoma and none anastomotic leakage patients without stoma to the number of total patients.'}], 'secondaryOutcomes': [{'measure': 'Sensitivity and specificity in the prediction of anastomotic leakage', 'timeFrame': '1 months after surgery'}, {'measure': 'Grade C leakage rate', 'timeFrame': '1 months after surgery'}, {'measure': 'Preventive stoma rate', 'timeFrame': '1 months after surgery'}, {'measure': 'Rate of stoma reverse', 'timeFrame': '3-6 months after surgery'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Anastomotic Leak', 'rectal cancer', 'machine learning'], 'conditions': ['Anastomotic Leak Rectum']}, 'descriptionModule': {'briefSummary': 'Anastomotic leakage is one of the most serious postoperative complications of low rectal cancer, with an incidence of 3%-21%. The occurrence of anastomotic leakage is related to many factors, and the occurrence of anastomotic leakage can be predicted by building a prediction model. Most of the anastomotic leakage prediction models constructed in the past are nomograms, which have limitations in the fitting of model creation. In the previous study, the center took the lead in building a random forest anastomotic leakage prediction model based on machine learning. This study intends to prospectively enroll patients with rectal cancer undergoing anterior abdominal resection and use their clinical data to prospectively verify the efficacy of the anastomotic leakage prediction model, and further improve and promote the prediction model.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '75 Years', 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Patients aged 18-75 years\n2. Adenocarcinoma confirmed by pathology\n3. Colonoscopy or imaging examination confirmed that the distance between the lower edge of the tumor and the anal edge was less than or equal to 12cm\n4. Preoperative imaging diagnosis was cTxNxM0\n5. No local complications (no obstruction, incomplete obstruction, no massive active bleeding, no perforation, abscess formation, and no invasion of adjacent organs)\n6. The hematopoietic functions of heart, lung, liver, kidney and bone marrow meet the requirements of surgery and anesthesia\n7. Voluntarily sign the informed consent form\n\nExclusion Criteria:\n\n1. Previous history of malignant tumor\n2. Simultaneous multiple primary colorectal cancer\n3. Previous multiple abdominal and pelvic surgeries or extensive abdominal adhesions\n4. Patients with intestinal obstruction, intestinal perforation, intestinal bleeding, etc., requiring emergency surgery\n5. Patients with familial adenomatous polyposis and active inflammatory bowel disease\n6. A history of severe mental illness\n7. pregnant or lactating women\n8. Patients with uncontrolled infection before operation\n9. The investigator did not consider the patient to be eligible for the trial'}, 'identificationModule': {'nctId': 'NCT05610904', 'briefTitle': 'Evaluation of AL Prediction for Rectal Cancer', 'organization': {'class': 'OTHER', 'fullName': 'Changhai Hospital'}, 'officialTitle': 'Evaluation of a Machine Learning Based Anastomotic Leakage Prediction Model After Anterior Resection for Rectal cancer-a Multicenter, Prospective, Randomized Controlled Study', 'orgStudyIdInfo': {'id': 'CHALP001'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'NO_INTERVENTION', 'label': 'Surgeon evaluation'}, {'type': 'EXPERIMENTAL', 'label': 'Surgeon combining with model evaluation', 'interventionNames': ['Diagnostic Test: Prediction model evaluation']}], 'interventions': [{'name': 'Prediction model evaluation', 'type': 'DIAGNOSTIC_TEST', 'description': 'a machine learning based anastomotic leakage prediction model', 'armGroupLabels': ['Surgeon combining with model evaluation']}]}, 'contactsLocationsModule': {'locations': [{'zip': '200433', 'city': 'Shanghai', 'state': 'Shanghai Municipality', 'country': 'China', 'facility': 'Department of Colorectal Surgery in Changhai Hospital', 'geoPoint': {'lat': 31.22222, 'lon': 121.45806}}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Changhai Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'professor', 'investigatorFullName': 'Wei Zhang', 'investigatorAffiliation': 'Changhai Hospital'}}}}