Ignite Creation Date:
2026-03-26 @ 3:14 PM
Ignite Modification Date:
2026-03-30 @ 9:32 PM
Study NCT ID:
NCT07408661
Status:
COMPLETED
Last Update Posted:
2026-02-13
First Post:
2026-02-06
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Application of Artificial Intelligence and Iron Metabolism Markers in Predicting ICU Outcomes for Critically Ill Cancer Patients
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D009369', 'term': 'Neoplasms'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 1137}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2015-01-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-02', 'completionDateStruct': {'date': '2025-12-01', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2026-02-12', 'studyFirstSubmitDate': '2026-02-06', 'studyFirstSubmitQcDate': '2026-02-12', 'lastUpdatePostDateStruct': {'date': '2026-02-13', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-02-13', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-10-01', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'All-cause Mortality at 30 Days', 'timeFrame': '30 days from the date of ICU admission.', 'description': 'The primary outcome is the incidence of death from any cause within 30 days following the date of ICU admission. Mortality status will be determined by a review of the hospital discharge records and associated death records in the MIMIC-IV database.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Critical care', 'Cancer', 'Artificial Intelligence', 'Risk stratification', 'Ferritin'], 'conditions': ['Cancer']}, 'descriptionModule': {'briefSummary': 'This study aimed to develop a more accurate way to predict the 30-day survival of cancer patients admitted to the intensive care unit (ICU). The researchers focused on markers of iron metabolism, as imbalances in iron are common in cancer and severe illness.\n\nThe study analyzed data from 1,137 critically ill cancer patients. Using artificial intelligence (AI), specifically a model called TabPFN, the study combined these iron markers with other routine clinical data (like blood cell counts and lactate levels) to create a new prediction tool.', 'detailedDescription': 'Revised Protocol Description (Study Plan):\n\nThis retrospective cohort study aims to evaluate whether the integration of artificial intelligence with iron metabolism markers can improve the prediction of 30-day all-cause mortality in critically ill adult cancer patients admitted to the ICU.\n\nData will be derived from the MIMIC-IV database. Eligible patients will be identified based on predefined inclusion and exclusion criteria. The study will assess the prognostic value of three iron metabolism markers-ferritin, serum iron, and total iron-binding capacity (TIBC)-both individually and in combination with other clinical variables.\n\nMultiple machine learning algorithms will be developed and compared. Feature selection will be performed using methods such as LASSO regression. Candidate models will include, but are not limited to, TabPFN, XGBoost, and Random Forest. Model performance will be evaluated in an independent test set using metrics including the area under the receiver operating characteristic curve (AUC), calibration plots, Brier score, and decision curve analysis.\n\nTo ensure model interpretability, SHAP (SHapley Additive exPlanations) analysis will be applied to the final model to identify the most influential predictors. The study protocol has been reviewed and approved by the relevant institutional review boards, and all methods will be conducted in accordance with relevant guidelines and regulations.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '100 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'The study population consists of adult cancer patients who were critically ill, requiring intensive care unit admission. This retrospective cohort is derived from the MIMIC-IV database, comprising patients with a cancer diagnosis who had their first ICU stay during hospitalization and for whom complete data on the key predictors and outcome are available.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Adult patients (age ≥ 18 years).\n2. Diagnosis of any type of cancer, as recorded in the hospital database.\n3. First ICU admission during the hospital stay (only the first ICU stay is considered for patients with multiple admissions).\n\nExclusion Criteria:\n\n1. Length of ICU stay less than 24 hours.\n2. Missing or unavailable data for the key study variables, specifically iron metabolism markers (ferritin, serum iron, total iron-binding capacity) or essential clinical parameters needed for analysis.'}, 'identificationModule': {'nctId': 'NCT07408661', 'briefTitle': 'Application of Artificial Intelligence and Iron Metabolism Markers in Predicting ICU Outcomes for Critically Ill Cancer Patients', 'organization': {'class': 'OTHER', 'fullName': 'Tongji University'}, 'officialTitle': 'Application of Artificial Intelligence and Iron Metabolism Markers in Predicting ICU Outcomes for Critically Ill Cancer Patients', 'orgStudyIdInfo': {'id': 'MIMIC-IRON-CANCER-2025'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Critically Ill Cancer Patients', 'description': 'This group comprises adult cancer patients who were admitted to the intensive care unit (ICU). The primary interest is in their 30-day all-cause mortality following ICU admission. The cohort includes 1,137 patients whose clinical data was extracted from the MIMIC-IV database. Key variables of interest include iron metabolism markers (ferritin, serum iron, TIBC), routine blood tests, and vital signs, all assessed at or near ICU admission. This retrospective observational study investigates the prognostic value of these markers and aims to develop a machine learning model for predicting mortality risk.'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Tongji University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Clinical Professor', 'investigatorFullName': 'Linlin Liu', 'investigatorAffiliation': 'Tongji University'}}}}