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

Emerg Radiol. 2020 Dec;27(6):641-651. doi: 10.1007/s10140-020-01821-1. Epub 2020 Jul 20.


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.

Keywords: CO-RADS classification; COVID-19; Chest computed tomography; Emergency Department; Machine learning; Prediction model; Real-time reverse transcription polymerase chain reaction (RT-PCR).

Publication types

  • Multicenter Study

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Betacoronavirus
  • COVID-19
  • COVID-19 Testing
  • COVID-19 Vaccines
  • Clinical Laboratory Techniques
  • Coronavirus Infections / diagnosis
  • Coronavirus Infections / diagnostic imaging*
  • Coronavirus Infections / epidemiology
  • Emergency Service, Hospital*
  • Female
  • Humans
  • Machine Learning
  • Male
  • Middle Aged
  • Netherlands / epidemiology
  • Pandemics
  • Pneumonia, Viral / diagnostic imaging*
  • Pneumonia, Viral / epidemiology
  • Prospective Studies
  • Radiography, Thoracic / methods*
  • SARS-CoV-2
  • Sensitivity and Specificity
  • Tomography, X-Ray Computed / methods*
  • Triage*