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Chest CT for triage during COVID-19 on the emergency department: myth or truth?

ABSTRACT: PURPOSE:We aimed to investigate the diagnostic performance of chest CT compared with first RT-PCR results in adult patients suspected of COVID-19 infection in an ED setting. We also constructed a predictive machine learning model based on chest CT and additional data to improve the diagnostic accuracy of chest CT. METHODS:This study's cohort consisted of 319 patients who underwent chest CT and RT-PCR testing at the ED. Patient characteristics, demographics, symptoms, vital signs, laboratory tests, and chest CT results (CO-RADS) were collected. With first RT-PCR as reference standard, the diagnostic performance of chest CT using the CO-RADS score was assessed. Additionally, a predictive machine learning model was constructed using logistic regression. RESULTS:Chest CT, with first RT-PCR as a reference, had a sensitivity, specificity, PPV, and NPV of 90.2%, 88.2%, 84.5%, and 92.7%, respectively. The prediction model with CO-RADS, ferritin, leucocyte count, CK, days of complaints, and diarrhea as predictors had a sensitivity, specificity, PPV, and NPV of 89.3%, 93.4%, 90.8%, and 92.3%, respectively. CONCLUSION:Chest CT, using the CO-RADS scoring system, is a sensitive and specific method that can aid in the diagnosis of COVID-19, especially if RT-PCR tests are scarce during an outbreak. Combining a predictive machine learning model could further improve the accuracy of diagnostic chest CT for COVID-19. Further candidate predictors should be analyzed to improve our model. However, RT-PCR should remain the primary standard of testing as up to 9% of RT-PCR positive patients are not diagnosed by chest CT or our machine learning model.

SUBMITTER: Hermans JJR

PROVIDER: S-EPMC7369539 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Chest CT for triage during COVID-19 on the emergency department: myth or truth?

Hermans Joep J R JJR Groen Joost J Zwets Egon E Boxma-De Klerk Bianca M BM Van Werkhoven Jacob M JM Ong David S Y DSY Hanselaar Wessel E J J WEJJ Waals-Prinzen Lenneke L Brown Vanessa V

Emergency radiology 20200720 6

<h4>Purpose</h4>We aimed to investigate the diagnostic performance of chest CT compared with first RT-PCR results in adult patients suspected of COVID-19 infection in an ED setting. We also constructed a predictive machine learning model based on chest CT and additional data to improve the diagnostic accuracy of chest CT.<h4>Methods</h4>This study's cohort consisted of 319 patients who underwent chest CT and RT-PCR testing at the ED. Patient characteristics, demographics, symptoms, vital signs, ...[more]

PMID: 32691211

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Project description:BackgroundTriage implementation in resource-limited emergency departments (EDs) has traditionally relied on intensive in-person training. This study sought to evaluate the impact of a novel digital-based learning strategy focused on the Interagency Integrated Triage Tool, a three-tier triage instrument recommended by the World Health Organization.MethodsA mixed methods study utilising pre-post intervention methods was conducted in two EDs in Papua New Guinea. The primary outcome was the mean change in knowledge before and after completion of a voluntary, multimodal training program, primarily delivered through a digital learning platform accessible via smartphone. Secondary outcomes included the change in confidence to perform selected clinical tasks, and acceptability of the learning methods.FindingsAmong 136 eligible ED staff, 91 (66.9%) completed the digital learning program. The mean knowledge score on the post-training exam was 87.5% (SD 10.4), a mean increase of 12.9% (95% CI 10.7-15.1%, p < 0.0001) from the pre-training exam. There were statistically significant improvements in confidence for 13 of 15 clinical tasks, including undertaking a triage assessment and identifying an unwell patient.In an evaluation survey, 100% of 30 respondents agreed or strongly agreed the online learning platform was easy to access, use and navigate, and that the digital teaching methods were appropriate for their learning needs. In qualitative feedback, respondents reported that limited internet access and a lack of dedicated training time were barriers to participation.InterpretationThe use of digital learning to support triage implementation in resource-limited EDs is feasible and effective when accompanied by in-person mentoring. Adequate internet access is an essential pre-requisite.FundingDevelopment of the Kumul Helt Skul learning platform was undertaken as part of the Clinical Support Program (Phase II), facilitated by Johnstaff International Development on behalf of the Australian Government Department of Foreign Affairs and Trade through the PNG-Australia Partnership. RM is supported by a National Health and Medical Research Council Postgraduate Scholarship and a Monash Graduate Excellence Scholarship, while PC is supported by a Medical Research Future Fund Practitioner Fellowship. Funders had no role in study design, results analysis or manuscript preparation.

Dataset Information

Chest CT for triage during COVID-19 on the emergency department: myth or truth?

Publications

Chest CT for triage during COVID-19 on the emergency department: myth or truth?

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