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

NCT07408661 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 1137

Last updated 2026-02-13

No results posted yet for this study

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.

Conditions

Sponsors & Collaborators

  • Tongji University

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
100 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2015-01-01
Primary Completion
2024-10-01
Completion
2025-12-01

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Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07408661 on ClinicalTrials.gov