Suppr超能文献

通过胸部计算机断层扫描评估新型冠状病毒肺炎的范围和严重程度

Estimating COVID-19 Pneumonia Extent and Severity From Chest Computed Tomography.

作者信息

Carvalho Alysson Roncally Silva, Guimarães Alan, Garcia Thiego de Souza Oliveira, Madeira Werberich Gabriel, Ceotto Victor Fraga, Bozza Fernando Augusto, Rodrigues Rosana Souza, Pinto Joana Sofia F, Schmitt Willian Rebouças, Zin Walter Araujo, França Manuela

机构信息

Cardiovascular R&D Centre (UnIC), Department of Surgery and Physiology, Faculty of Medicine, University of Porto, Porto, Portugal.

Laboratory of Pulmonary Engineering, Biomedical Engineering Program, Alberto Luiz Coimbra Institute of Post-Graduation and Research in Engineering, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.

出版信息

Front Physiol. 2021 Feb 15;12:617657. doi: 10.3389/fphys.2021.617657. eCollection 2021.

Abstract

BACKGROUND

COVID-19 pneumonia extension is assessed by computed tomography (CT) with the ratio between the volume of abnormal pulmonary opacities (PO) and CT-estimated lung volume (CT). CT-estimated lung weight (CT) also correlates with pneumonia severity. However, both CT and CT depend on demographic and anthropometric variables.

PURPOSES

To estimate the extent and severity of COVID-19 pneumonia adjusting the volume and weight of abnormal PO to the predicted CT (pCT) and CT (pCT), respectively, and to evaluate their possible association with clinical and radiological outcomes.

METHODS

Chest CT from 103 COVID-19 and 86 healthy subjects were examined retrospectively. In controls, predictive equations for estimating pCT and pCT were assessed. COVID-19 pneumonia extent and severity were then defined as the ratio between the volume and the weight of abnormal PO expressed as a percentage of the pCT and pCT, respectively. A ROC analysis was used to test differential diagnosis ability of the proposed method in COVID-19 and controls. The degree of pneumonia extent and severity was assessed with Z-scores relative to the average volume and weight of PO in controls. Accordingly, COVID-19 patients were classified as with limited, moderate and diffuse pneumonia extent and as with mild, moderate and severe pneumonia severity.

RESULTS

In controls, CT could be predicted by sex and height (adjusted = 0.57; < 0.001) while CT by age, sex, and height (adjusted = 0.6; < 0.001). The cutoff of 20% (AUC = 0.91, 95%CI 0.88-0.93) for pneumonia extent and of 50% (AUC = 0.91, 95%CI 0.89-0.92) for pneumonia severity were obtained. Pneumonia extent were better correlated when expressed as a percentage of the pCT and pCT ( = 0.85, < 0.001), respectively. COVID-19 patients with diffuse and severe pneumonia at admission presented significantly higher CRP concentration, intra-hospital mortality, ICU stay and ventilatory support necessity, than those with moderate and limited/mild pneumonia. Moreover, pneumonia severity, but not extent, was positively and moderately correlated with age ( = 0.46) and CRP concentration ( = 0.44).

CONCLUSION

The proposed estimation of COVID-19 pneumonia extent and severity might be useful for clinical and radiological patient stratification.

摘要

背景

通过计算机断层扫描(CT)评估新冠病毒肺炎的扩展情况,采用异常肺部实变(PO)体积与CT估算肺体积(CT)的比值。CT估算的肺重量(CT)也与肺炎严重程度相关。然而,CT和CT均依赖于人口统计学和人体测量学变量。

目的

分别将异常PO的体积和重量调整为预测的CT(pCT)和CT(pCT),以估算新冠病毒肺炎的范围和严重程度,并评估它们与临床和放射学结果的可能关联。

方法

回顾性检查了103例新冠病毒肺炎患者和86例健康受试者的胸部CT。在对照组中,评估了估算pCT和pCT的预测方程。然后将新冠病毒肺炎的范围和严重程度分别定义为异常PO的体积和重量与pCT和pCT的百分比比值。采用ROC分析来测试所提出方法在新冠病毒肺炎患者和对照组中的鉴别诊断能力。用相对于对照组中PO平均体积和重量的Z分数评估肺炎范围和严重程度。据此,将新冠病毒肺炎患者分为肺炎范围有限、中度和弥漫性,以及肺炎严重程度为轻度、中度和重度。

结果

在对照组中,CT可通过性别和身高预测(调整后 = 0.57; < 0.001),而CT可通过年龄、性别和身高预测(调整后 = 0.6; < 0.001)。获得了肺炎范围的截断值为20%(AUC = 0.91,95%CI 0.88 - 0.93),肺炎严重程度的截断值为50%(AUC = 0.91,95%CI 0.89 - 0.92)。当分别表示为pCT和pCT的百分比时,肺炎范围的相关性更好( = 0.85, < 0.001)。入院时患有弥漫性和重度肺炎的新冠病毒肺炎患者的CRP浓度、院内死亡率、ICU住院时间和机械通气支持需求显著高于患有中度和有限/轻度肺炎的患者。此外,肺炎严重程度与年龄( = 0.46)和CRP浓度( = 0.44)呈正相关且为中度相关,但肺炎范围与年龄和CRP浓度无相关性。

结论

所提出的新冠病毒肺炎范围和严重程度的估算方法可能有助于临床和放射学对患者进行分层。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ebe/7917083/7834db36fb2e/fphys-12-617657-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验