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OpenTrials
Completed

NCT Number: NCT07408661

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

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.

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Key information

Conditions

Cancer Neoplasms

Age range

18 year–100 year

Sex eligibility

All sexes

Study type

Observational

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Adult patients (age ≥ 18 years).
  • Diagnosis of any type of cancer, as recorded in the hospital database.
  • First ICU admission during the hospital stay (only the first ICU stay is considered for patients with multiple admissions).

Exclusion criteria

  • Length of ICU stay less than 24 hours.
  • 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.

Treatment and study plan

Primary outcomes

  1. All-cause Mortality at 30 Days

    Time frame: 30 days from the date of ICU admission.

    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.

Sponsors and collaborators

Lead sponsor

Tongji University

Other

Registry information

Important dates

Study start
2015
Primary completion
2024
Study completion
2025
First posted
Feb 13, 2026
Registry last updated
Feb 13, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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