CompletedNot Applicable

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

1,137 participantsStarted 2015-01-01

Plain-language 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.

Who can participate

Age range18 Years – 100 Years

SexALL

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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.

What they're measuring

All-cause Mortality at 30 Days

Timeframe: 30 days from the date of ICU admission.

Trial details

NCT IDNCT07408661

SponsorTongji University

Sponsor typeOTHER

Study typeOBSERVATIONAL

Primary completion2024-10-01

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