Clinical trial · Observational
Application of Artificial Intelligence and Iron Metabolism Markers in Predicting ICU Outcomes for Critically Ill Cancer Patients
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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. The 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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Cancer | Malignant Neoplasm | ALIAS | 0.90 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (1)
- 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.
Primary outcomes (1)
- 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.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 100 Years
Show eligibility criteria text
Inclusion Criteria: 1. Adult patients (age ≥ 18 years). 2. Diagnosis of any type of cancer, as recorded in the hospital database. 3. First ICU admission during the hospital stay (only the first ICU stay is considered for patients with multiple admissions). Exclusion Criteria: 1. Length of ICU stay less than 24 hours. 2. 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.
References
Publications (0)
Data not yet available