Prediction of Clinical Course in COVID19 Patients Using Unsupervised Classification Approaches of Clinical, Biological and the Multiparametric Signature of the Chest CT Scan Performed at Admission
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 826
- 试验地点
- 1
- 主要终点
- diagnostic of COVID disease composite
研究概览
简要总结
In the context of the COVID19 pandemic and containment, chest CT is currently frequently performed on admission, looking for suggestive signs and basic abnormalities of COVID19 compatible viral pneumonitis pending confirmation of identification of viral RNA by reverse-transcription polymerase chain reaction(PCR), with a reported sensitivity of 56-88% in the first few days, slightly higher than PCR (60%) (1). Nevertheless, currently established radiological abnormalities are not specific for COVID19 and the specificity of the chest CT is ~25% when PCR is used as a reference (1). Deconfinement and its consequences will complicate the triage of COVID patients and the role of the scanner, with the expected impact of a decrease in the prevalence of infection in the emergency department and an increase in the number of "all-round" patients, including patients with non-COVID viral infiltrates or pneumopathies.
In addition, there are currently no imaging criteria to complement the clinical and biological data that can predict the progression of lung disease from the initial data.
详细描述
In image processing, computational medical imaging has demonstrated its ability to predict a therapeutic response or a particular evolution after extracting relevant anatomical, functional or even non-visually perceptible information from the volume of images, making it possible to construct a powerful radiomic signature or to use robust anatomical/functional measurements to provide estimates of ventilation or vascular state. By combining these data extracted from the scanner with the standard clinical-biological data produced at admission during triage, our ambition is to build a predictive model using unsupervised classification approaches capable of helping predict clinical evolution with the aim of optimizing the management of the resource.
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
- •age ≥ 18 years
- •clinical suspicion of COVID-19 confirmed by RT-PCR
- •CT scan at ER admission
- •RT-PCR sampling
排除标准
- •CT scan failure or loss of CT data
- •RT-PCR initial results unavailable
结局指标
主要结局
diagnostic of COVID disease composite
时间窗: On admission to the hospital
The diagnostoc of COVID disease is composite of: * CT features wich will include presence/location/laterality of morphological CT abonormal densities (ground glass opacities, consolidations, reticulations), * pulmonary vessels size, * distribution and abnormalities, * local / global CT-ventilation index (CT-VI) severity, * radiomic features (shape features, 1st-order and 2nd order statistics) Analysis of CT-Scan results.
次要结局
未报告次要终点