Critical IllnessNCT07762131
AI-Based Precision Transfusion Prediction Model in Critically Ill Patients
An observational study of Critical Illness and Multiple Organ Dysfunction Syndrome, run by Second Affiliated Hospital, Zhejiang University, School of Medicine.
Trial summary
This multicenter observational study aims to develop and validate an artificial intelligence-based precision transfusion prediction model for critically ill patients. The study will collect clinical characteristics, laboratory parameters, transfusion-related information, physiological data, and clinical outcomes from… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Critical Illness, Multiple Organ Dysfunction Syndrome, Blood Transfusion
- What is being tested?
- Red Blood Cell Transfusion Exposure
- How many people
- 2,598 participants
- Where
- No sites listed yet
- Study dates
- 1 Sep 2026to 1 Sep 2029
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 years and older
- All sexes
- Adult, Older adult
Study population: This study includes adult critically ill patients admitted to intensive care units. Clinical data including demographic characteristics, underlying diseases, severity indicators, laboratory parameters, transfusion-related information, and clinical outcomes will be collected from electronic medical records. The study population will be used to develop an artificial intelligence-based prediction model for precision transfusion management and risk assessment of multiple organ dysfunction syndrome in critically ill patients.
Who may be able to join
- Adult patients (aged ≥18 years) admitted to the intensive care unit.
- Patients with available clinical data, including demographic characteristics, laboratory parameters, transfusion-related information, and clinical outcomes.
- Patients meeting the requirements for model development and analysis.
Who cannot join
- Patients younger than 18 years.
- Patients with missing key clinical information required for analysis.
- Patients with repeated ICU admissions during the study period (only the first ICU admission will be included).
- Patients whose data cannot be used for research purposes according to ethical requirements.
About this study
Official title: Artificial Intelligence-Based Precision Transfusion Prediction Model for Prevention of Multiple Organ Dysfunction Syndrome in Critically Ill Patients: A Multicenter Observational Study
This multicenter observational study aims to develop and validate an artificial intelligence-based precision transfusion prediction model for critically ill patients. The study will collect clinical characteristics, laboratory parameters, transfusion-related information, physiological data, and clinical outcomes from critically ill patients admitted to intensive care units. An AI model will be developed using retrospective data and further evaluated using prospective observational data. The primary objective is to investigate factors associated with multiple organ dysfunction syndrome (MODS) and establish a predictive model to support individualized transfusion management in critically ill patients.
Read the detailed description
Critically ill patients frequently require red blood cell transfusion during intensive care. However, transfusion decisions based solely on conventional indicators may not fully reflect individual differences in disease severity, oxygen delivery, and risk of organ dysfunction. Unnecessary transfusion may increase the risk of adverse outcomes, whereas delayed transfusion may worsen tissue hypoxia.
This multicenter observational study aims to establish an artificial intelligence-based precision transfusion prediction model for critically ill patients. The study includes retrospective model development and prospective observational validation phases.
Clinical data including demographic characteristics, underlying diseases, laboratory parameters, physiological variables, transfusion records, severity scores, organ function indicators, and clinical outcomes will be collected. Machine learning approaches will be applied to identify important predictors associated with multiple organ dysfunction syndrome (MODS) and transfusion-related outcomes.
The developed model will be evaluated based on predictive performance, including discrimination, calibration, and clinical applicability. This study aims to provide an individualized risk assessment approach to improve transfusion decision-making and facilitate precision management in critically ill patients.
- Being tested
- Red Blood Cell Transfusion Exposure
- Keywords
- Artificial Intelligence, Precision Transfusion, Critically Ill Patients, Multiple Organ Dysfunction Syndrome
- Medical subject headings
- Critical Illness, Multiple Organ Failure
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Red Blood Cell Transfusion ExposureOther
Red blood cell transfusion exposure refers to the receipt of red blood cell transfusion during intensive care hospitalization. Transfusion-related information, including transfusion status, number of transfused units, and cumulative transfusion volume, will be collected from routine clinical care records. Transfusion decisions are not assigned by the study protocol, and no intervention is performed as part of this observational study.
| Study group | Role | What they receive |
|---|---|---|
| Critically Ill Patient Cohort | Not described Receives: Other: Red Blood Cell Transfusion Exposure |
What is being measured
The primary outcome is the main question the study is designed to answer.
Occurrence of Multiple Organ Dysfunction Syndrome (MODS)
The primary outcome is the occurrence of multiple organ dysfunction syndrome (MODS) during intensive care unit hospitalization. MODS will be defined according to clinical diagnostic criteria based on dysfunction of two or more organ systems.
Measured: During ICU hospitalization (up to 28 days after ICU admission)
Secondary outcomes (2)
ICU Mortality
Death occurring during the intensive care unit stay.
Measured: Up to Day 28 after ICU admission
Hospital Mortality
Death occurring during the index hospitalization
Measured: Up to Day 28 after 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 Aug 2026
- Study start1 Sep 2026 (estimated)
- Primary completion1 May 2029 (estimated)
- Study completion1 Sep 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Critical IllnessStudies 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
- 2,598 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Second Affiliated Hospital, Zhejiang University, School of Medicine
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07762131
- Sponsor's protocol ID
- 20260140
- Other IDs
- IR2025639
- Status last verified
- Aug 2026
- First submitted
- 5 Aug 2026
- Last updated
- 13 Aug 2026
- Results posted
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 13 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-Based Precision Transfusion Prediction Model for Prevention of Multiple Organ Dysfunction Syndrome in Critically Ill Patients: A Multicenter Observational Study
- ClinicalTrials.gov ID
- NCT07762131
- Acronym / sponsor ID
- 20260140
- Keywords
- Artificial Intelligence, Precision Transfusion, Critically Ill Patients, Multiple Organ Dysfunction Syndrome
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 2,598 participants (estimated)
- Lead sponsor
- Second Affiliated Hospital, Zhejiang University, School of MedicineUniversity, hospital or other
- Study start
- 1 Sep 2026 (estimated)
- Primary completion
- 1 May 2029 (estimated)
- Study completion
- 1 Sep 2029 (estimated)
- First posted
- 13 Aug 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 multicenter observational study aims to develop and validate an artificial intelligence-based precision transfusion prediction model for critically ill patients. The study will collect clinical characteristics, laboratory parameters, transfusion-related information, physiological data, and clinical outcomes from critically ill patients admitted to intensive care units. An AI model will be developed using retrospective data and further evaluated using prospective observational data. The primary objective is to investigate factors associated with multiple organ dysfunction syndrome (MODS) and establish a predictive model to support individualized transfusion management in critically ill patients.
Detailed description
Critically ill patients frequently require red blood cell transfusion during intensive care. However, transfusion decisions based solely on conventional indicators may not fully reflect individual differences in disease severity, oxygen delivery, and risk of organ dysfunction. Unnecessary transfusion may increase the risk of adverse outcomes, whereas delayed transfusion may worsen tissue hypoxia.
This multicenter observational study aims to establish an artificial intelligence-based precision transfusion prediction model for critically ill patients. The study includes retrospective model development and prospective observational validation phases.
Clinical data including demographic characteristics, underlying diseases, laboratory parameters, physiological variables, transfusion records, severity scores, organ function indicators, and clinical outcomes will be collected. Machine learning approaches will be applied to identify important predictors associated with multiple organ dysfunction syndrome (MODS) and transfusion-related outcomes.
The developed model will be evaluated based on predictive performance, including discrimination, calibration, and clinical applicability. This study aims to provide an individualized risk assessment approach to improve transfusion decision-making and facilitate precision management in critically ill patients.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Critically Ill Patient Cohort | – | Other: Red Blood Cell Transfusion Exposure |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- This study includes adult critically ill patients admitted to intensive care units. Clinical data including demographic characteristics, underlying diseases, severity indicators, laboratory parameters, transfusion-related information, and clinical outcomes will be collected from electronic medical records. The study population will be used to develop an artificial intelligence-based prediction model for precision transfusion management and risk assessment of multiple organ dysfunction syndrome in critically ill patients.
Key inclusion criteria (3 listed)
- Adult patients (aged ≥18 years) admitted to the intensive care unit.
- Patients with available clinical data, including demographic characteristics, laboratory parameters, transfusion-related information, and clinical outcomes.
- Patients meeting the requirements for model development and analysis.
Key exclusion criteria (4 listed)
- Patients younger than 18 years.
- Patients with missing key clinical information required for analysis.
- Patients with repeated ICU admissions during the study period (only the first ICU admission will be included).
- Patients whose data cannot be used for research purposes according to ethical requirements.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Red Blood Cell Transfusion ExposureRed blood cell transfusion exposure refers to the receipt of red blood cell transfusion during intensive care hospitalization. Transfusion-related information, including transfusion status, number of transfused units, and cumulative transfusion volume, will be collected from routine clinical care records. Transfusion decisions are not assigned by the study protocol, and no intervention is performed as part of this observational study. | Other | Critically Ill Patient Cohort |
Primary outcome
Occurrence of Multiple Organ Dysfunction Syndrome (MODS)
Time frame: During ICU hospitalization (up to 28 days after ICU admission)
The primary outcome is the occurrence of multiple organ dysfunction syndrome (MODS) during intensive care unit hospitalization. MODS will be defined according to clinical diagnostic criteria based on dysfunction of two or more organ systems.
2 secondary outcomes
ICU Mortality
Time frame: Up to Day 28 after ICU admission
Hospital Mortality
Time frame: Up to Day 28 after ICU admission
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Aug 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.
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 Critical Illness and Multiple Organ Dysfunction Syndrome, run by Second Affiliated Hospital, Zhejiang University, School of Medicine.
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 4 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: opening soon, as of the sponsor's last update on 13 Aug 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?
This study is still under way. Results are not available in the source record yet.
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?
Occurrence of Multiple Organ Dysfunction Syndrome (MODS) (time frame: During ICU hospitalization (up to 28 days after 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 Aug 2026.