Development and validation of chest CT-based imaging biomarkers for early stage COVID-19 screening

Mao, Xuanyu; Li, Xiaoping; Xiong, Miao; Yang, Xu; Jin, Xiaoqing; Li, Zhiqiang; Zhou, Shuang; Chang, Hang

doi:10.1101/2020.05.15.20103473

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…To date, many studies have reported the detection of COVID-19 in patients suspected of infection. 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 For instance, Menni et al. 16 reported the efficiency of self-reported symptoms in early identification of potential COVID-19.…”

Section: Discussionmentioning

confidence: 99%

“…To date, many studies have reported the detection of COVID-19 in patients suspected of infection. [15][16][17][18][19][20][21][22][23][24][25] For instance, Menni et al 16 reported the efficiency of self-reported symptoms in early identification of potential COVID-19. This allows medical providers to timely admit all potential COVID-19 cases to hospitals in countries and territories where medical resources are relatively adequate, such as China.…”

Section: Accessmentioning

confidence: 99%

“…Some studies have reported how to predict deterioration and mortality during hospitalization, [8][9][10][11][12][13][14][15] and even predict the probability of SARS-CoV-2 infections, which enables the timely quarantine of high-risk infections and prevents spread. [15][16][17][18][19][20][21][22][23][24][25] But none of these studies provide a solution for rational triage of patients in places where medical resources are limited.…”

Section: Introductionmentioning

confidence: 99%

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A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics

Zheng¹,

Zhu²,

Ji³

et al. 2020

Patterns

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The emergence of the novel coronavirus disease 2019 (COVID-19) is placing an increasing burden on healthcare systems. Although the majority of infected patients experience non-severe symptoms and can be managed at home, some individuals develop severe symptoms and require hospital admission. Therefore, it is critical to efficiently assess the severity of COVID-19 and identify hospitalization priority with precision. In this respect, a four-variable assessment model, including lymphocyte, lactate dehydrogenase, C-reactive protein, and neutrophil, is established and validated using the XGBoost algorithm. This model is found to be effective in identifying severe COVID-19 cases on admission, with a sensitivity of 84.6%, a specificity of 84.6%, and an accuracy of 100% to predict the disease progression toward rapid deterioration. It also suggests that a computation-derived formula of clinical measures is practically applicable for healthcare administrators to distribute hospitalization resources to the most needed in epidemics and pandemics.

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Section: Discussionmentioning

confidence: 99%

Section: Accessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics

Zheng¹,

Zhu²,

Ji³

et al. 2020

Patterns

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Thoracic imaging tests for the diagnosis of COVID-19

Ebrahimzadeh,

Islam,

Dawit

et al. 2022

Cochrane Database of Systematic Reviews

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Background Our March 2021 edition of this review showed thoracic imaging computed tomography (CT) to be sensitive and moderately specific in diagnosing COVID‐19 pneumonia. This new edition is an update of the review. Objectives Our objectives were to evaluate the diagnostic accuracy of thoracic imaging in people with suspected COVID‐19; assess the rate of positive imaging in people who had an initial reverse transcriptase polymerase chain reaction (RT‐PCR) negative result and a positive RT‐PCR result on follow‐up; and evaluate the accuracy of thoracic imaging for screening COVID‐19 in asymptomatic individuals. The secondary objective was to assess threshold effects of index test positivity on accuracy. Search methods We searched the COVID‐19 Living Evidence Database from the University of Bern, the Cochrane COVID‐19 Study Register, The Stephen B. Thacker CDC Library, and repositories of COVID‐19 publications through to 17 February 2021. We did not apply any language restrictions. Selection criteria We included diagnostic accuracy studies of all designs, except for case‐control, that recruited participants of any age group suspected to have COVID‐19. Studies had to assess chest CT, chest X‐ray, or ultrasound of the lungs for the diagnosis of COVID‐19, use a reference standard that included RT‐PCR, and report estimates of test accuracy or provide data from which we could compute estimates. We excluded studies that used imaging as part of the reference standard and studies that excluded participants with normal index test results. Data collection and analysis The review authors independently and in duplicate screened articles, extracted data and assessed risk of bias and applicability concerns using QUADAS‐2. We presented sensitivity and specificity per study on paired forest plots, and summarized pooled estimates in tables. We used a bivariate meta‐analysis model where appropriate. Main results We included 98 studies in this review. Of these, 94 were included for evaluating the diagnostic accuracy of thoracic imaging in the evaluation of people with suspected COVID‐19. Eight studies were included for assessing the rate of positive imaging in individuals with initial RT‐PCR negative results and positive RT‐PCR results on follow‐up, and 10 studies were included for evaluating the accuracy of thoracic imaging for imagining asymptomatic individuals. For all 98 included studies, risk of bias was high or unclear in 52 (53%) studies with respect to participant selection, in 64 (65%) studies with respect to reference standard, in 46 (47%) studies with respect to index test, and in 48 (49%) studies with respect to flow and timing. Concerns about the applicability of the evidence to: participants were high or unclear in eight (8%) studies; index test were high or unclear in seven (7%) studies; and reference standard were high or uncle...

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Development and validation of chest CT-based imaging biomarkers for early stage COVID-19 screening

Cited by 2 publications

References 19 publications

A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics

A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics

Thoracic imaging tests for the diagnosis of COVID-19

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