Consultant Radiologist, Tawam Hospital, Tawam Roundabout, Khalifa Ibn Zayed Street, Al Ain, Abu Dhabi, UAE.
Department of Medical Imaging, American Hospital, Oud Metha, Dubai.
Curr Med Imaging. 2023;19(13):1533-1540. doi: 10.2174/1573405619666230210143430.
Developing a reliable predictive tool of disease severity in COVID-19 infection is important to help triage patients and ensure the appropriate utilization of health-care resources.
To develop, validate, and compare three CT scoring systems (CTSS) to predict severe disease on initial diagnosis of COVID-19 infection.
One hundred and twenty and 80 symptomatic adults with confirmed COVID-19 infection who presented to emergency department were evaluated retrospectively in the primary and validation groups, respectively. All patients had non-contrast CT chest within 48 hours of admission. Three lobarbased CTSS were assessed and compared. The simple lobar system was based on the extent of pulmonary infiltration. Attenuation corrected lobar system (ACL) assigned further weighting factor based on attenuation of pulmonary infiltrates. Attenuation and volume-corrected lobar system incorporated further weighting factor based on proportional lobar volume. The total CT severity score (TSS) was calculated by adding individual lobar scores. The disease severity assessment was based on Chinese National Health Commission guidelines. Disease severity discrimination was assessed by the area under the receiver operating characteristic curve (AUC).
The ACL CTSS demonstrated the best predictive and consistent accuracy of disease severity with an AUC of 0.93(95%CI:0.88-0.97) in the primary cohort and 0.97 (95%CI:0.91.5-1) in the validation group. Applying a TSS cut-off value of 9.25, the sensitivities were 96.4% and 100% and the specificities were 75% and 91% in the primary and validation groups, respectively.
The ACL CTSS showed the highest accuracy and consistency in predicting severe disease on initial diagnosis of COVID-19. This scoring system may provide frontline physicians with a triage tool to guide admission, discharge, and early detection of severe illness.
开发一种可靠的 COVID-19 感染疾病严重程度的预测工具对于帮助分诊患者和确保医疗资源的合理利用非常重要。
开发、验证和比较三种 CT 评分系统（CTSS），以预测 COVID-19 感染初始诊断时的严重疾病。
回顾性分析了在初级组和验证组中分别就诊于急诊的 120 例和 80 例症状性成人确诊 COVID-19 感染患者。所有患者在入院后 48 小时内均进行了非对比 CT 胸部检查。评估和比较了三种基于肺叶的 CTSS。简单的肺叶系统基于肺部浸润的范围。衰减校正肺叶系统（ACL）根据肺部浸润的衰减进一步分配加权因素。衰减和体积校正的肺叶系统根据肺叶体积的比例进一步分配加权因素。总 CT 严重程度评分（TSS）通过加单个肺叶评分计算。疾病严重程度评估基于中国国家卫生健康委员会的指南。疾病严重程度的区分通过接收者操作特征曲线（AUC）下的面积来评估。
ACL CTSS 在初级队列中的 AUC 为 0.93（95%CI：0.88-0.97），在验证组中的 AUC 为 0.97（95%CI：0.91-1），显示出对疾病严重程度的最佳预测和一致准确性。在初级组和验证组中，TSS 截断值为 9.25 时，敏感性分别为 96.4%和 100%，特异性分别为 75%和 91%。
ACL CTSS 在预测 COVID-19 初始诊断时的严重疾病方面具有最高的准确性和一致性。该评分系统可为一线医生提供一种分诊工具，以指导入院、出院和早期发现重症。
