Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome.

To perform a multicenter assessment of the CT Pneumonia Analysis prototype for predicting disease severity and patient outcome in COVID-19 pneumonia both without and with integration of clinical information. Our IRB-approved observational study included consecutive 241 adult patients (> 18 years; 105 females; 136 males) with RT-PCR-positive COVID-19 pneumonia who underwent non-contrast chest CT at one of the two tertiary care hospitals (site A: Massachusetts General Hospital, USA; site B: Firoozgar Hospital Iran). We recorded patient age, gender, comorbid conditions, laboratory values, intensive care unit (ICU) admission, mechanical ventilation, and final outcome (recovery or death). Two thoracic radiologists reviewed all chest CTs to record type, extent of pulmonary opacities based on the percentage of lobe involved, and severity of respiratory motion artifacts. Thin-section CT images were processed with the prototype (Siemens Healthineers) to obtain quantitative features including lung volumes, volume and percentage of all-type and high-attenuation opacities (≥ -200 HU), and mean HU and standard deviation of opacities within a given lung region. These values are estimated for the total combined lung volume, and separately for each lung and each lung lobe. Multivariable analyses of variance (MANOVA) and multiple logistic regression were performed for data analyses. About 26% of chest CTs (62/241) had moderate to severe motion artifacts. There were no significant differences in the AUCs of quantitative features for predicting disease severity with and without motion artifacts (AUC 0.94-0.97) as well as for predicting patient outcome (AUC 0.7-0.77) (p > 0.5). Combination of the volume of all-attenuation opacities and the percentage of high-attenuation opacities (AUC 0.76-0.82, 95% confidence interval (CI) 0.73-0.82) had higher AUC for predicting ICU admission than the subjective severity scores (AUC 0.69-0.77, 95% CI 0.69-0.81). Despite a high frequency of motion artifacts, quantitative features of pulmonary opacities from chest CT can help differentiate patients with favorable and adverse outcomes.

Clinical and imaging features predict mortality in COVID-19 infection in Iran.

The new coronavirus disease 2019 (COVID-19) pandemic has challenged many healthcare systems around the world. While most of the current understanding of the clinical features of COVID-19 is derived from Chinese studies, there is a relative paucity of reports from the remaining global health community. In this study, we analyze the clinical and radiologic factors that correlate with mortality odds in COVID-19 positive patients from a tertiary care center in Tehran, Iran. A retrospective cohort study of 90 patients with reverse transcriptase-polymerase chain reaction (RT-PCR) positive COVID-19 infection was conducted, analyzing demographics, co-morbidities, presenting symptoms, vital signs, laboratory values, chest radiograph findings, and chest CT features based on mortality. Chest radiograph was assessed using the Radiographic Assessment of Lung Edema (RALE) scoring system. Chest CTs were assessed according to the opacification pattern, distribution, and standardized severity score. Initial and follow-up Chest CTs were compared if available. Multiple logistic regression was used to generate a prediction model for mortality. The 90 patients included 59 men and 31 women (59.4 ± 16.6 years), including 21 deceased and 69 surviving patients. Among clinical features, advanced age (p = 0.02), low oxygenation saturation (p<0.001), leukocytosis (p = 0.02), low lymphocyte fraction (p = 0.03), and low platelet count (p = 0.048) were associated with increased mortality. High RALE score on initial chest radiograph (p = 0.002), presence of pleural effusions on initial CT chest (p = 0.005), development of pleural effusions on follow-up CT chest (p = 0.04), and worsening lung severity score on follow-up CT Chest (p = 0.03) were associated with mortality. A two-factor logistic model using patient age and oxygen saturation was created, which demonstrates 89% accuracy and area under the ROC curve of 0.86 (p<0.0001). Specific demographic, clinical, and imaging features are associated with increased mortality in COVID-19 infections. Attention to these features can help optimize patient management.

Computed Tomography Radiomics Can Predict Disease Severity and Outcome in Coronavirus Disease 2019 Pneumonia.

PURPOSE: This study aimed to assess if computed tomography (CT) radiomics can predict the severity and outcome of patients with coronavirus disease 2019 (COVID-19) pneumonia. METHODS: This institutional ethical board-approved study included 92 patients (mean age, 59 ± 17 years; 57 men, 35 women) with positive reverse transcription polymerase chain reaction assay for COVID-19 infection who underwent noncontrast chest CT. Two radiologists evaluated all chest CT examinations and recorded opacity type, distribution, and extent of lobar involvement. Information on symptom duration before hospital admission, the period of hospital admission, presence of comorbid conditions, laboratory data, and outcomes (recovery or death) was obtained from the medical records. The entire lung volume was segmented on thin-section Digital Imaging and Communication in Medicine images to derive whole-lung radiomics. Data were analyzed using multiple logistic regression with receiver operator characteristic area under the curve (AUC) as the output. RESULTS: Computed tomography radiomics (AUC, 0.99) outperformed clinical variables (AUC, 0.89) for prediction of the extent of pulmonary opacities related to COVID-19 pneumonia. Type of pulmonary opacities could be predicted with CT radiomics (AUC, 0.77) but not with clinical or laboratory data (AUC, <0.56; P > 0.05). Prediction of patient outcome with radiomics (AUC, 0.85) improved to an AUC of 0.90 with the addition of clinical variables (patient age and duration of presenting symptoms before admission). Among clinical variables, the combination of peripheral capillary oxygen saturation on hospital admission, duration of symptoms, platelet counts, and patient age provided an AUC of 0.81 for predicting patient outcomes. CONCLUSIONS: Radiomics from noncontrast CT reliably predict disease severity (AUC, 0.99) and outcome (AUC, 0.85) in patients with COVID-19 pneumonia.