Exploration of Diagnosis and Treatment Strategies and Prognostic Prediction Models for Acute Respiratory Distress Syndrome Based on Radiographic Evaluations Assessed by Artificial Intelligence
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 400
- 试验地点
- 1
- 主要终点
- Accuracy of ARDS severity classification
研究概览
简要总结
By using multi-center chest CT data, an intelligent assessment model for the severity of ARDS was constructed. Based on CT quantitative features and clinical characteristics, a prediction model for short-term critical events (such as mechanical ventilation decisions, prone position strategies, death, ECMO use, etc.) was established. The disease was staged and quantified, and a diagnosis and risk stratification model for ARDS was developed to assist in guiding the diagnosis and treatment strategies for ARDS.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 100 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Meets the diagnostic criteria for ARDS
- •Be admitted to the intensive care unit
- •There are chest CT images
排除标准
- •Age less than 18 years old
- •Missing medical records
- •No chest CT images
研究组 & 干预措施
Training group, testing group, validation group
The study adopts a stratified random sampling strategy with an 8:2 split to construct training and internal validation datasets, together with an independent external test cohort from a separate center. No randomization of clinical interventions or treatments is involved. The model will be developed and evaluated using observational data derived from real-world clinical pathways and outcomes, with the objectives of assessing performance in disease severity stratification, treatment recommendation, and mortality prediction. Model performance will be compared with established ICU severity scores and existing AI-based approaches according to a prespecified statistical analysis plan.
干预措施: CT scan (Diagnostic Test)
结局指标
主要结局
Accuracy of ARDS severity classification
时间窗: Baseline, defined as within 24 hours of index chest CT acquisition during ICU admission.
Accuracy of the artificial intelligence-based model in classifying ARDS severity (mild, moderate, or severe), using the reference clinical classification defined by the 2023 global ARDS criteria as the ground truth.
Treatment plan matching rate between model-recommended and actual clinical management.
时间窗: Baseline, defined as within 24 hours of index chest CT acquisition during ICU admission.
Concordance rate between model-recommended treatment strategies and actual clinical management decisions across five predefined intervention modalities: mechanical ventilation, high-flow nasal oxygen therapy, non-invasive ventilation, prone positioning, and neuromuscular blockade.
Accuracy of 28-day in-hospital mortality prediction.
时间窗: Up to 28 days from ICU admission, or until hospital discharge, whichever occurs first.
Accuracy of the model in predicting all-cause in-hospital mortality within 28 days, based on integrated chest CT imaging features and clinical variables.
次要结局
- Comparative performance improvement over baseline AI models.(Baseline for severity classification and treatment plan matching; up to 28 days from ICU admission for mortality prediction)
- Calibration performance of 28-day mortality prediction.(Up to 28 days from ICU admission, or until hospital discharge, whichever occurs first.)
- Model interpretability based on imaging and clinical feature contributions.(Baseline for feature extraction; up to 28 days from ICU admission for outcome association analysis.)
- Association between treatment concordance and 28-day in-hospital mortality.(Up to 28 days from ICU admission, or until hospital discharge, whichever occurs first.)