南充市新型冠状病毒感染的高分辨率CT特征:不同临床类型的初、随访变化

Yuting Jiang, Dandan Guo, Chunping Li, Tianwu Chen , Rui Li
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引用次数: 24

摘要

目的探讨冠状病毒病2019 (COVID-19)不同临床类型患者的高分辨率计算机断层扫描(HRCT)初、随访表现。方法选取川北医学院附属医院住院的7例新冠肺炎患者。所有患者均进行了初始和随访胸部HRCT检查。评估不同临床类型的主要CT表现及表征疾病严重程度的半定量评分。结果CT初诊时,普通和重型病例的主要异常分别为纯磨玻璃混浊(GGOs)和GGOs周围的斑片状实变。危重病例多处实变,周围有大范围ggo分布于整个肺野。普通病例(4.5分和5分)、重度病例(9.5分和9.5分)和危重病例(19分和12分)的范围和密度评分呈梯度递增。随访CT表现为病灶密度降低、吸收实变、GGOs。危重病例显示病情恶化。普通和重症患者的程度评分和进展评分均显著降低,而危重症患者的范围评分最高,并伴有密度评分升高。结论ct扫描可以准确评估COVID-19的严重程度,并有助于监测不同临床情况下随访期间的疾病转化情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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High-resolution CT features of the COVID-19 infection in Nanchong City: Initial and follow-up changes among different clinical types

Objective

To discuss the high resolution computed tomography (HRCT) manifestations of coronavirus disease 2019 (COVID-19) patients among different clinical types on initial and follow-up CT.

Methods

Seven COVID-19 patients admitted to the Affiliated Hospital of North Sichuan Medical Collage were enrolled. All patients underwent initial and follow-up chest HRCT. The main CT features and semi-quantitative score which represent disease severity among different clinical types were evaluated.

Result

On initial CT, the main abnormalities observed in common and severe cases respectively were pure ground glass opacities (GGOs) and patchy consolidation surrounded by GGOs. Critical cases had multiple consolidation surrounded by wide range of GGOs distributed in the whole lung fields. The scope and density score in common (4.5 and 5), severe (9.5 and 9.5) and critical (19 and 12) cases were increased by gradient. On follow-up CT, common and severe types manifested as decreasing density of lesion, absorbed consolidation and GGOs. Critical cases showed progression of the disease. The extent and progression scores in common and severe patients were significantly decreased, while the range score of patients with critical disease reached the highest points, accompanied with an increase in the density score.

Conclusion

CT scanning can accurately assess the severity of COVID-19, and help to monitor disease transformation during follow-up among different clinical conditions.

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