Cumulative Radiation Exposure in Covid-19 Patients Admitted to the Intensive Care Unit.

Medical imaging plays a major role in coronavirus disease-2019 (COVID-19) patient diagnosis and management. However, the radiation dose received from medical procedures by these patients has been poorly investigated. We aimed to estimate the cumulative effective dose (CED) related to medical exposure in COVID-19 patients admitted to the intensive care unit (ICU) in comparison to the usual critically ill patients. We designed a descriptive cohort study including 90 successive ICU COVID-19 patients admitted between March and May 2020 and 90 successive non-COVID-19 patients admitted between March and May 2019. In this study, the CED resulting from all radiological examinations was calculated and clinical characteristics predictive of higher exposure risk identified. The number of radiological examinations was 12.0 (5.0-26.0) [median (interquartile range) in COVID-19 vs. 4.0 (2.0-8.0) in non-COVID-19 patient (P < 0.001)]. The CED during a four-month period was 4.2 mSv (1.9-11.2) in the COVID-19 vs. 1.2 mSv (0.13-6.19) in the non-COVID-19 patients (P < 0.001). In the survivors, the CED in COVID-19 vs. non-COVID-19 patients was ≥100 mSv in 3% vs. 0%, 10-100 mSv in 23% vs. 15%, 1-10 mSv in 56% vs. 30% and <1 mSv in 18% vs. 55%. The CED (P < 0.001) and CED per ICU hospitalization day (P = 0.004) were significantly higher in COVID-19 than non-COVID-19 patients. The CED correlated significantly with the hospitalization duration (r = 0.45, P < 0.001) and the number of conventional radiological examinations (r = 0.8, P < 0.001). To conclude, more radiological examinations were performed in critically ill COVID-19 patients than non-COVID-19 patients resulting in higher CED. In COVID-19 patients, contribution of strategies to limit CED should be investigated in the future.

Study of Thoracic CT in COVID-19: The STOIC Project.

Background There are conflicting data regarding the diagnostic performance of chest CT for COVID-19 pneumonia. Disease extent at CT has been reported to influence prognosis. Purpose To create a large publicly available data set and assess the diagnostic and prognostic value of CT in COVID-19 pneumonia. Materials and Methods This multicenter, observational, retrospective cohort study involved 20 French university hospitals. Eligible patients presented at the emergency departments of the hospitals involved between March 1 and April 30th, 2020, and underwent both thoracic CT and reverse transcription-polymerase chain reaction (RT-PCR) testing for suspected COVID-19 pneumonia. CT images were read blinded to initial reports, RT-PCR, demographic characteristics, clinical symptoms, and outcome. Readers classified CT scans as either positive or negative for COVID-19 based on criteria published by the French Society of Radiology. Multivariable logistic regression was used to develop a model predicting severe outcome (intubation or death) at 1-month follow-up in patients positive for both RT-PCR and CT, using clinical and radiologic features. Results Among 10 930 patients screened for eligibility, 10 735 (median age, 65 years; interquartile range, 51-77 years; 6147 men) were included and 6448 (60%) had a positive RT-PCR result. With RT-PCR as reference, the sensitivity and specificity of CT were 80.2% (95% CI: 79.3, 81.2) and 79.7% (95% CI: 78.5, 80.9), respectively, with strong agreement between junior and senior radiologists (Gwet AC1 coefficient, 0.79). Of all the variables analyzed, the extent of pneumonia at CT (odds ratio, 3.25; 95% CI: 2.71, 3.89) was the best predictor of severe outcome at 1 month. A score based solely on clinical variables predicted a severe outcome with an area under the curve of 0.64 (95% CI: 0.62, 0.66), improving to 0.69 (95% CI: 0.6, 0.71) when it also included the extent of pneumonia and coronary calcium score at CT. Conclusion Using predefined criteria, CT reading is not influenced by reader's experience and helps predict the outcome at 1 month. ClinicalTrials.gov identifier: NCT04355507 Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Rubin in this issue.

AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia.

Coronavirus disease 2019 (COVID-19) emerged in 2019 and disseminated around the world rapidly. Computed tomography (CT) imaging has been proven to be an important tool for screening, disease quantification and staging. The latter is of extreme importance for organizational anticipation (availability of intensive care unit beds, patient management planning) as well as to accelerate drug development through rapid, reproducible and quantified assessment of treatment response. Even if currently there are no specific guidelines for the staging of the patients, CT together with some clinical and biological biomarkers are used. In this study, we collected a multi-center cohort and we investigated the use of medical imaging and artificial intelligence for disease quantification, staging and outcome prediction. Our approach relies on automatic deep learning-based disease quantification using an ensemble of architectures, and a data-driven consensus for the staging and outcome prediction of the patients fusing imaging biomarkers with clinical and biological attributes. Highly promising results on multiple external/independent evaluation cohorts as well as comparisons with expert human readers demonstrate the potentials of our approach.