Acute Lung/ Acute Respiratory Distress Syndrome and Extra-pulmonary Organ Injury Clinical Sub-phenotyping Study
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 1,500
- 试验地点
- 1
- 主要终点
- ICU mortality
研究概览
简要总结
- Construct a structured clinical data and biosample information platform for Chinese patients with acute lung injury/ acute respiratory distress syndrome.
- By deciphering the heterogeneity of patients with acute lung injury/ acute respiratory distress syndrome, achieve clinical, longitudinal physiological, and biological sub-phenotyping to guide individualized precision treatment and improve prognosis.
详细描述
Acute lung injury/ acute respiratory distress syndrome is one of the most common and complex critical illnesses in clinical practice, with a high mortality rate of 45% to 50%. Currently, effective therapeutic strategies for this condition are still lacking. Increasing evidence suggests that the significant heterogeneity of this disease plays a crucial role in the poor treatment outcomes and high mortality rates observed in patients. Therefore, this study aims to analyze the heterogeneity of acute lung injury/ acute respiratory distress syndrome patients and establish a clinical classification system for acute lung and extrapulmonary organ injuries.
The objectives of this study include establishing a nationwide clinical database and biobank for acute lung injury / acute respiratory distress syndrome by collecting clinical data and biological samples from various provinces. By overcoming the barriers posed by diverse and heterogeneous data sources, mathematical and machine learning models will be utilized to construct clinical, physiological, and biological classification systems for acute lung and extrapulmonary organ injuries. The proposed classification model will be validated multiple times using international public databases and prospective acute lung injury/acute respiratory distress syndrome cohorts to ensure its stability and generalizability. The mapping relationship between different classifications and patient prognosis as well as treatment responsiveness will be explored.
Moreover, a machine learning-based supervised technique will be applied to develop a bedside simplified model (Point-of-Care model) and establish a bedside clinical classification decision system. Ultimately, this research aims to provide a foundation for standardized and precision-guided clinical diagnostic and therapeutic pathways, promoting improved treatment outcomes and overall prognosis in acute lung injury/ acute respiratory distress syndrome.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
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
- •Meet the diagnostic criteria for Acute Respiratory Distress Syndrome (ARDS) according to the updated global definition in
- •The patient or their legal representative signs an informed consent form.
排除标准
- •Individuals aged less than 18 years old.
- •Those who refuse to participate in the study.
结局指标
主要结局
ICU mortality
时间窗: up to 12 weeks
In ICU mortality
hospital mortality
时间窗: up to 24 weeks
In hospital mortality
次要结局
- 28 days without mechanical ventilation(up to 28 days)
- length of stay in the ICU(up to 12 weeks)
- Total length of hospital stay(up to 24 weeks)
- Mortality at 1 year after discharge(through study completion, an average of 1 year)
研究者
Jingen Xia
Clinical Professor
China-Japan Friendship Hospital