Artificial Intelligence-Based Precision Transfusion Prediction Model for Prevention of Multiple Organ Dysfunction Syndrome in Critically Ill Patients: A Multicenter Observational Study
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 2,598
- 主要终点
- Occurrence of Multiple Organ Dysfunction Syndrome (MODS)
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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.
排除标准
- •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.
研究组 & 干预措施
Critically Ill Patient Cohort
干预措施: Red Blood Cell Transfusion Exposure (Other)
结局指标
主要结局
Occurrence of Multiple Organ Dysfunction Syndrome (MODS)
时间窗: 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.
次要结局
- ICU Mortality(Up to Day 28 after ICU admission)
- Hospital Mortality(Up to Day 28 after ICU admission)