Visual lung damage CT score at hospital admission of COVID-19 patients and 30-day mortality.

OBJECTIVES: Chest CT has been widely used to screen and to evaluate the severity of COVID-19 disease in the early stages of infection without severe acute respiratory syndrome, but no prospective data are available to study the relationship between extent of lung damage and short-term mortality. The objective was to evaluate association between standardized simple visual lung damage CT score (vldCTs) at admission, which does not require any software, and 30-day mortality. METHODS: In a single-center prospective cohort of COVID-19 patients included during 4 weeks, the presence and extent of ground glass opacities(GGO), consolidation opacities, or both of them were visually assessed in each of the 5 lung lobes (score from 0 to 4 per lobe depending on the percentage and out of 20 per patient = vldCTs) after the first chest CT performed to detect COVID-19 pneumonia. RESULTS: Among 210 confirmed COVID-19 patients, the number of survivors and non-survivors was 162 (77%) and 48 (23%), respectively at 30 days. vldCTs was significantly higher in non-survivors, and the AUC of vldCTs to distinguish survivors and non-survivors was 0.72 (95%CI 0.628-0.807, p < 0.001); the best cut-off vldCTs value was 7. During follow-up, significant differences in discharges and 30-day mortality were observed between patients with vldCTs ≥ 7 versus vldCTs < 7: (98 [85.2%] vs 49 [51.6%]; p < 0.001 and 36 [37.9%] vs 12 [12.4%]; p < 0.001, respectively. The 30-day mortality increased if vldCTs ≥ 7 (HR, 3.16 (1.50-6.43); p = 0.001), independent of age, respiratory rate and oxygen saturation levels, and comorbidities at admission. CONCLUSIONS: By using chest CT in COVID-19 patients, extensive lung damage can be visually assessed with a score related to 30-day mortality independent of conventional risk factors of the disease. KEY POINTS: • In non-selected COVID-19 patients included prospectively during 4 weeks, the extent of ground glass opacities(GGO) and consolidation opacities evaluated by a simple visual score was related to 30-day mortality independent of age, respiratory rate, oxygen saturation levels, comorbidities, and hs-troponin I level at admission. • This severity score should be incorporated into risk stratification algorithms and in structured chest CT reports requiring a standardized reading by radiologists in case of COVID-19.

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.