Prognostic value of initial chest CT findings for clinical outcomes in patients with COVID-19.

Rationale: To identify whether the initial chest computed tomography (CT) findings of patients with coronavirus disease 2019 (COVID-19) are helpful for predicting the clinical outcome. Methods: A total of 224 patients with laboratory-confirmed COVID-19 who underwent chest CT examination within the first day of admission were enrolled. CT findings, including the pattern and distribution of opacities, the number of lung lobes involved and the chest CT scores of lung involvement, were assessed. Independent predictors of adverse clinical outcomes were determined by multivariate regression analysis. Adverse outcome were defined as the need for mechanical ventilation or death. Results: Of 224 patients, 74 (33%) had adverse outcomes and 150 (67%) had good outcomes. There were higher frequencies of more than four lung zones involved (73% vs 32%), both central and peripheral distribution (57% vs 42%), consolidation (27% vs 17%), and air bronchogram (24% vs 13%) and higher initial chest CT scores (8.6±3.4 vs 5.4±2.1) (P < 0.05 for all) in the patients with poor outcomes. Multivariate analysis demonstrated that more than four lung zones (odds ratio [OR] 3.93; 95% confidence interval [CI]: 1.44 to 12.89), age above 65 (OR 3.65; 95% CI: 1.11 to 10.59), the presence of comorbidity (OR 5.21; 95% CI: 1.64 to 19.22) and dyspnea on admission (OR 3.19; 95% CI: 1.35 to 8.46) were independent predictors of poor outcome. Conclusions: Involvement of more than four lung zones and a higher CT score on the initial chest CT were significantly associated with adverse clinical outcome. Initial chest CT findings may be helpful for predicting clinical outcome in patients with COVID-19.

Comparison of clinical characteristics between coronavirus disease 2019 pneumonia and community-acquired pneumonia.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) has high morbidity and mortality, and spreads rapidly in the community to result in a large number of infection cases. This study aimed to compare clinical features in adult patients with coronavirus disease 2019 (COVID-19) pneumonia to those in adult patients with community-acquired pneumonia (CAP). METHODS: Clinical presentations, laboratory findings, imaging features, complications, treatment and outcomes were compared between patients with COVID-19 pneumonia and patients with CAP. The study group of patients with COVID-19 pneumonia consisted of 120 patients. One hundred and thirty-four patients with CAP were enrolled for comparison. RESULTS: Patients with COVID-19 pneumonia had lower levels of abnormal laboratory parameters (white blood cell count, lymphocyte count, procalcitonin level, erythrocyte sedimentation rate and C-reactive protein level) and more extensive radiographic involvement. More severe respiratory compromise resulted in a higher rate of intensive care unit admission, acute respiratory distress syndrome (ARDS) and mechanical ventilation (36% vs 15%, 34% vs 15% and 32% vs 12%, respectively; all p < .05). The 30 day mortality was more than twice as high in patients with COVID-19 pneumonia (12% versus 5%; p = .063), despite not reaching a statistically significant difference. CONCLUSIONS: Lower levels of abnormal laboratory parameters, more extensive radiographic involvement, more severe respiratory compromise, and higher rates of ICU admission, ARDS and mechanical ventilation are key characteristics that distinguish patients with COVID-19-associated pneumonia from patients with CAP.

Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy.

Background Coronavirus disease 2019 (COVID-19) has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. Purpose To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performance. Materials and Methods In this retrospective and multicenter study, a deep learning model, the COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT scans for the detection of COVID-19. CT scans of community-acquired pneumonia (CAP) and other non-pneumonia abnormalities were included to test the robustness of the model. The datasets were collected from six hospitals between August 2016 and February 2020. Diagnostic performance was assessed with the area under the receiver operating characteristic curve, sensitivity, and specificity. Results The collected dataset consisted of 4352 chest CT scans from 3322 patients. The average patient age (±standard deviation) was 49 years ± 15, and there were slightly more men than women (1838 vs 1484, respectively; P = .29). The per-scan sensitivity and specificity for detecting COVID-19 in the independent test set was 90% (95% confidence interval [CI]: 83%, 94%; 114 of 127 scans) and 96% (95% CI: 93%, 98%; 294 of 307 scans), respectively, with an area under the receiver operating characteristic curve of 0.96 (P < .001). The per-scan sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175 scans) and 92% (239 of 259 scans), respectively, with an area under the receiver operating characteristic curve of 0.95 (95% CI: 0.93, 0.97). Conclusion A deep learning model can accurately detect coronavirus 2019 and differentiate it from community-acquired pneumonia and other lung conditions. © RSNA, 2020 Online supplemental material is available for this article.