AI-Based Precision Transfusion Prediction Model in Critically Ill Patients

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.

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Key information

Conditions

Critical Illness Blood Transfusion Disease Attributes Multiple Organ Dysfunction Syndrome Multiple Organ Failure Pathologic Processes Pathological Conditions, Signs and Symptoms Shock

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

About this study

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.

Who can participate

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

Inclusion criteria

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.

Exclusion criteria

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.

Treatment and study plan

Red Blood Cell Transfusion Exposure

Other

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.

Primary outcomes

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.

Secondary outcomes

ICU Mortality

Time frame: Up to Day 28 after ICU admission

Death occurring during the intensive care unit stay.
Hospital Mortality

Time frame: Up to Day 28 after ICU admission

Death occurring during the index hospitalization

Sponsors and collaborators

Lead sponsor

Second Affiliated Hospital, Zhejiang University, School of Medicine

Other

Registry information

Official study title

Artificial Intelligence-Based Precision Transfusion Prediction Model for Prevention of Multiple Organ Dysfunction Syndrome in Critically Ill Patients: A Multicenter Observational Study

Important dates

Study start: 2026
Primary completion: 2029
Study completion: 2029
First posted: Aug 13, 2026
Registry last updated: Aug 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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