CancerNCT07408661
Application of Artificial Intelligence and Iron Metabolism Markers in Predicting ICU Outcomes for Critically Ill Cancer Patients
An observational study of Cancer, run by Tongji University.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 100 years
- Conditions the study is for
- Cancer
- Type of treatment
- Injection or infusion
- How many people
- 1,137 participants
- Where
- No sites listed yet
- Study dates
- 1 Jan 2015to 1 Dec 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 100 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study population consists of adult cancer patients who were critically ill, requiring intensive care unit admission. This retrospective cohort is derived from the MIMIC-IV database, comprising patients with a cancer diagnosis who had their first ICU stay during hospitalization and for whom complete data on the key predictors and outcome are available.
Who may be able to join
- 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).
Who cannot join
- 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.
About this study
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.
Read the 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
- Keywords
- Critical care, Cancer, Artificial Intelligence, Risk stratification, Ferritin
- Medical subject headings
- Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Critically Ill Cancer Patients | 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
All-cause Mortality at 30 Days
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.
Measured: 30 days from the date of ICU admission.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Feb 2026
- Study start1 Jan 2015
- Primary completion1 Oct 2024
- Study completion1 Dec 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 1,137 participants (actual)
Sponsor and investigators
- Lead sponsor
- Tongji University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Linlin Liu, Tongji University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07408661
- Sponsor's protocol ID
- MIMIC-IRON-CANCER-2025
- Status last verified
- Feb 2026
- First submitted
- 6 Feb 2026
- Last updated
- 13 Feb 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 13 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Application of Artificial Intelligence and Iron Metabolism Markers in Predicting ICU Outcomes for Critically Ill Cancer Patients
- ClinicalTrials.gov ID
- NCT07408661
- Acronym / sponsor ID
- MIMIC-IRON-CANCER-2025
- Conditions
- Cancer
- Keywords
- Critical care, Cancer, Artificial Intelligence, Risk stratification, Ferritin
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,137 participants (actual)
- Lead sponsor
- Tongji UniversityUniversity, hospital or other
- Study start
- 1 Jan 2015 (actual)
- Primary completion
- 1 Oct 2024 (actual)
- Study completion
- 1 Dec 2025 (actual)
- First posted
- 13 Feb 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Critically Ill Cancer Patients | – | –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. |
Who may qualify
- Ages
- 18 to 100 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population consists of adult cancer patients who were critically ill, requiring intensive care unit admission. This retrospective cohort is derived from the MIMIC-IV database, comprising patients with a cancer diagnosis who had their first ICU stay during hospitalization and for whom complete data on the key predictors and outcome are available.
Key inclusion criteria (3 listed)
- 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).
Key exclusion criteria (2 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Feb 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Cancer, run by Tongji University.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 13 Feb 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
All-cause Mortality at 30 Days (time frame: 30 days from the date of ICU admission.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Feb 2026.