key: cord-307279-1yei5ifs
authors: Nagra, Deepak; Russell, Mark; Yates, Mark; Galloway, James; Barker, Richard; Desai, Sujal R.; Norton, Sam
title: Covid-19: Opacification score is higher in the right lung and right lung involvement is a better predictor of ICU admission
date: 2020-10-02
journal: Eur Respir J
DOI: 10.1183/13993003.02340-2020
sha: 
doc_id: 307279
cord_uid: 1yei5ifs

In COVID-19 the right lung has higher degree of opacification on plain radiograph than the left lung. Right lung opacificiation is a stronger predictor for critical care admission and death.

Dear Editor,

The global response to COVID-19 has resulted in a wealth of research. The accrual of data through electronic health records (EHR) facilitates the efficient interrogation of datasets. Indeed, many issues of relevance to the COVID-19 response have been explored in this way, the impact of ethnicity or ACE inhibition on outcomes, to name but two (1, 2) .

Large teaching hospitals in the capital were at the forefront of the UK COVID-19 outbreak in the United Kingdom with over a thousand patients admitted in under one month. Research teams mobilised quickly to understand this new and unprecedented disease. We extracted data from our EHR to build a risk score that predicted critical care admission or death. The model included demographics, laboratory data and chest radiographic (CXR) severity (3).

The extent of CXR abnormality was scored using an adapted radiographic assessment of lung oedema for COVID-19, as proposed by Wong et al (4) . The severity score attributes a number between 0-4 to each lung depending on extent of consolidation or ground-glass opacification as follows: 0 = no disease, 1 = <25% extent, 2 = 25-49%, 3 = 50-75%, 4 = >75%).

Admission CXRs on 1,389 consecutive patients admitted with COVID-19 were evaluated. The first 200 radiographs were assessed by two independent scorers: there was high (90.5%) inter-rater concordance. Subsequent review of between lung scores demonstrated moderate agreement (r = 0.72; κ=0.52).

A polychoric correlation comparing the degree of opacification by lung showed significant differences (p<0.0001). The striking differences were in the most severe categories. The right lung was more likely to be assigned the maximum extent score of 4: 11% versus 6% in the left lung. In addition, opacification of the right lung was a stronger predictor of admission to critical care or die (see figure) . This finding has not been reported previously or with other imaging modalities. We acknowledge important limitations in our work. We did not account for projectional image quality (e.g. anterior or posterior views). The scoring was done by acute physicians rather than radiologists.

The explanation for the apparent differential lung involvement in COVID-19 is unclear. If the finding is confirmed, it may offer insights into the pathobiology of COVID-19 in the lungs. The explanation may lie in anatomy: the right lung is anatomically larger than the left, with a larger main bronchus diameter and more segmental bronchi, possibly increasing viral delivery to respiratory epithelial surfaces. Conversely, it is also possible that the lung scoring is subject to perception bias with the cardiac silhouette distracting from left lung abnormalities. To our knowledge asymmetrical radiographic involvement in interstitial lung disease has not been previously reported.

OpenSAFELY: factors associated with COVID-19-related hospital death in the linked electronic health records of 17 million adult NHS patients

Use of renin-angiotensin-aldosterone system inhibitors and risk of COVID-19 requiring admission to hospital: a case-population study

A clinical risk score to identify patients with COVID-19 at high risk of critical care admission or death: An observational cohort study

Frequency and Distribution of Chest Radiographic Findings in COVID-19 Positive Patients

As a research group with an interest in machine learning, it is interesting to reflect on the power of human observation. We look forward to this pattern being explored in other cohorts using tools such as volumetric CT.

c.