Suppr超能文献

胸部计算机断层扫描和肺泡-动脉氧分压差作为诊断和分流轻度症状COVID-19肺炎患者的快速工具。

Chest computed tomography and alveolar-arterial oxygen gradient as rapid tools to diagnose and triage mildly symptomatic COVID-19 pneumonia patients.

作者信息

de Roos Marlise P, Kilsdonk Iris D, Hekking Pieter-Paul W, Peringa Jan, Dijkstra Nynke G, Kunst Peter W A, Bresser Paul, Reesink Herre J

机构信息

Dept of Respiratory Medicine, OLVG, Amsterdam, The Netherlands.

Dept of Radiology, OLVG, Amsterdam, The Netherlands.

出版信息

ERJ Open Res. 2021 Mar 8;7(1). doi: 10.1183/23120541.00737-2020. eCollection 2021 Jan.

Abstract

BACKGROUND

In the coronavirus disease 2019 (COVID-19) pandemic, rapid clinical triage is crucial to determine which patients need hospitalisation. We hypothesised that chest computed tomography (CT) and alveolar-arterial oxygen tension ratio (A-a) gradient may be useful to triage these patients, since they reflect the severity of the pneumonia-associated ventilation/perfusion abnormalities.

METHODS

A retrospective analysis was performed in 235 consecutive patients suspected for COVID-19. The diagnostic protocol included low-dose chest CT and arterial blood gas analysis. In patients with CT-based COVID-19 pneumonia, the association between "need for hospitalisation" and A-a gradient was investigated by a multivariable logistic regression model. The A-a gradient was tested as a predictor for need for hospitalisation using receiver operating characteristic curve analysis and a logistic regression model.

RESULTS

72 out of 235 patients (mean±sd age 55.5±14.6 years, 40% female) screened by chest CT showed evidence for COVID-19 pneumonia. In these patients, A-a gradient was shown to be a predictor of need for hospitalisation, with an optimal decision level (cut-off) of 36.4 mmHg (95% CI 0.70-0.91, p<0.001). The A-a gradient was shown to be independently associated with need for hospitalisation (OR 1.97 (95% CI 1.23-3.15), p=0.005; A-a gradient per 10 points) from CT severity score (OR 1.13 (95% CI 0.94-1.36), p=0.191), National Early Warning Score (OR 1.19 (95% CI 0.91-1.57), p=0.321) or peripheral oxygen saturation (OR 0.88 (95% CI 0.68-1.14), p=0.345).

CONCLUSION

Low-dose chest CT and the A-a gradient may serve as rapid and accurate tools to diagnose COVID-19 pneumonia and to select mildly symptomatic patients in need for hospitalisation.

摘要

背景

在2019冠状病毒病(COVID-19)大流行期间,快速临床分诊对于确定哪些患者需要住院治疗至关重要。我们推测胸部计算机断层扫描(CT)和肺泡-动脉氧分压差(A-a)梯度可能有助于对这些患者进行分诊,因为它们反映了肺炎相关通气/灌注异常的严重程度。

方法

对235例连续怀疑患有COVID-19的患者进行回顾性分析。诊断方案包括低剂量胸部CT和动脉血气分析。在基于CT诊断为COVID-19肺炎的患者中,通过多变量逻辑回归模型研究“住院需求”与A-a梯度之间的关联。使用受试者工作特征曲线分析和逻辑回归模型,将A-a梯度作为住院需求的预测指标进行测试。

结果

在235例接受胸部CT筛查的患者中,有72例(平均年龄±标准差55.5±14.6岁,40%为女性)显示有COVID-19肺炎的证据。在这些患者中,A-a梯度被证明是住院需求的预测指标,最佳决策水平(临界值)为36.4 mmHg(95%置信区间0.70-0.91,p<0.001)。A-a梯度被证明与住院需求独立相关(比值比1.97(95%置信区间1.23-3.15),p=0.005;每10分的A-a梯度),与CT严重程度评分(比值比1.13(95%置信区间0.94-1.36),p=0.191)、国家早期预警评分(比值比1.19(95%置信区间0.91-1.57),p=0.321)或外周血氧饱和度(比值比0.88(95%置信区间0.68-1.14),p=0.345)无关。

结论

低剂量胸部CT和A-a梯度可作为快速、准确的工具,用于诊断COVID-19肺炎,并筛选出需要住院治疗的轻症患者。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5477/7938045/326e3efbced0/00737-2020.01.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