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AI-Based Precision Transfusion Prediction Model in Critically Ill Patients

NCT07762131 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 2598

Last updated 2026-08-13

No results posted yet for this study

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 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.

Conditions

  • Critical Illness
  • Multiple Organ Dysfunction Syndrome
  • Blood Transfusion

Interventions

OTHER

Red Blood Cell Transfusion Exposure

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.

Sponsors & Collaborators

  • Second Affiliated Hospital, Zhejiang University, School of Medicine

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-09-01
Primary Completion
2029-05-01
Completion
2029-09-01

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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 NCT07762131 on ClinicalTrials.gov