a Retrospective Study of Neural Network Model to Dynamically Quantificate the Severity in COVID-19 Disease
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 1,000
- 试验地点
- 1
- 主要终点
- Calibration
研究概览
简要总结
The research aim to collect large samples of COVID-19 disease patients with clinical symptoms, laboratory and imaging examination data. Screening the biological indicators which are related to the occurrence of severe diseases. Then, investigators using artificial intelligence (AI) technology deep learning method to find a prediction model that can dynamically quantify COVID-19 disease severity.
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 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients of COVID-19 disease confirmed by virus nucleic acid RT-PCR and CT
排除标准
- •unconfirmed suspected cases
- •Patients during pregnancy and lactation
- •incomplete clinical data
- •inestigators considered patients ineligible for the trial
结局指标
主要结局
Calibration
时间窗: up to 3 months
The calibration curves analysis is used to show error between the predicted clinical phenotype with prediction model and actual clinical phenotype.
Net benefit
时间窗: up to 3 months
Decision curve analysis was used to determine whether the models could be considered useful tools for clinical decisionmaking by comparing the net benefits at any threshold.
discrimination
时间窗: up to 3 months
The performance of our prediction model is evaluated with the receiver operating characteristic (ROC) curves, areas under the curves (AUCs) and concordance index (c-index).
次要结局
未报告次要终点
研究者
Jianguo Sun
Deputy Director,Head of Oncology department, Principal Investigator, Clinical Professor
Xinqiao Hospital of Chongqing