NCT07408661

Application of Artificial Intelligence and Iron Metabolism Markers in Predicting ICU Outcomes for Critically Ill Cancer Patients

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 1,137 (actual)
• Sponsor: Tongji University · Academic / Other
• Sex: All
• Age: 18 Years – 100 Years
• Healthy volunteers: Not accepted

Summary

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.

Detailed description

Revised Protocol Description (Study Plan): This 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. Data 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. Multiple 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. To 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.

Conditions

• Cancer

Timeline

• Start date: 2015-01-01
• Primary completion: 2024-10-01
• Completion: 2025-12-01
• First posted: 2026-02-13
• Last updated: 2026-02-13