Gresser Eva, Rueckel Johannes, Puhr-Westerheide Daniel, Schwarze Vincent, Fink Nicola, Kunz Wolfgang G, Wassilowsky Dietmar, Irlbeck Michael, Ricke Jens, Ingrisch Michael, Sabel Bastian O
Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany.
Department of Anaesthesiology, University Hospital, LMU Munich, 81377 Munich, Germany.
Diagnostics (Basel). 2020 Dec 19;10(12):1108. doi: 10.3390/diagnostics10121108.
(1) Background: To assess the value of chest CT imaging features of COVID-19 disease upon hospital admission for risk stratification of invasive ventilation (IV) versus no or non-invasive ventilation (non-IV) during hospital stay. (2) Methods: A retrospective single-center study was conducted including all patients admitted during the first three months of the pandemic at our hospital with PCR-confirmed COVID-19 disease and admission chest CT scans ( = 69). Using clinical information and CT imaging features, a 10-point ordinal risk score was developed and its diagnostic potential to differentiate a severe (IV-group) from a more moderate course (non-IV-group) of the disease was tested. (3) Results: Frequent imaging findings of COVID-19 pneumonia in both groups were ground glass opacities (91.3%), consolidations (53.6%) and crazy paving patterns (31.9%). Characteristics of later stages such as subpleural bands were observed significantly more often in the IV-group (52.2% versus 26.1%, = 0.032). Using information directly accessible during a radiologist's reporting, a simple risk score proved to reliably differentiate between IV- and non-IV-groups (AUC: 0.89 (95% CI 0.81-0.96), < 0.001). (4) Conclusions: Information accessible from admission CT scans can effectively and reliably be used in a scoring model to support risk stratification of COVID-19 patients to improve resource and allocation management of hospitals.
（1）背景：评估新冠病毒病（COVID-19）入院时胸部CT影像特征对住院期间有创通气（IV）与无创通气或不进行通气（非IV）风险分层的价值。（2）方法：进行一项回顾性单中心研究，纳入在我院疫情爆发的前三个月期间收治的所有经PCR确诊为COVID-19且有入院胸部CT扫描的患者（n = 69）。利用临床信息和CT影像特征，制定了一个10分的序贯风险评分，并测试其区分疾病严重病程（IV组）和较中度病程（非IV组）的诊断潜力。（3）结果：两组中COVID-19肺炎常见的影像表现为磨玻璃影（91.3%）、实变（53.6%）和铺路石样改变（31.9%）。IV组中观察到更多后期特征，如胸膜下条索影（52.2%对26.1%，P = 0.032）。利用放射科医生报告时可直接获取的信息，一个简单的风险评分被证明能可靠地区分IV组和非IV组（AUC：0.89（95%CI 0.81 - 0.96），P < 0.001）。（4）结论：入院CT扫描获得的信息可有效且可靠地用于评分模型，以支持COVID-19患者的风险分层，改善医院的资源和分配管理。
