Background: Chest radiography (CXR) has not been validated for its prognostic utility in evaluating patients with coronavirus disease 2019 .Purpose: The purpose of this study was to analyze the prognostic value of a CXR severity scoring system for younger (non-elderly) patients with COVID-19 upon initial presentation to the emergency department (ED). Outcomes of interest included hospitalization, intubation, prolonged stay, sepsis, and death. Materials & Methods:In this retrospective study, patients between the ages of 21 and 50 years who presented to EDs of an urban multicenter health system from March 10 -26, 2020 with COVID-19 confirmation on real-time reverse transcriptase polymerase chain reaction (RT-PCR) were identified.Each patient's ED CXR was divided into 6 zones and examined for opacities by two cardiothoracic radiologists with scores collated into a total concordant lung zone severity score. Clinical and laboratory variables were collected. Multivariable logistic regression was utilized to evaluate the relationship between clinical parameters, CXR scores, and patient outcomes. Results:The study included 338 patients: 210 males (62%), median age 39 [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]. After adjustment for demographics and co-morbidities, independent predictors of hospital admission (n=145, 43%) were CXR severity score ≥ 2 (OR: 6.2, 95% CI 3.5-11, p<0.001) and obesity (OR 2.4 (1.1-5.4) or morbid obesity. Of patients who were admitted, a CXR score ≥3 was an independent predictor of intubation (n=28) (OR: 4.7, 95% CI 1.8-13, p=0.002) as was hospital site. We found no significant difference in primary outcomes across race/ethnicity, those with a history of tobacco use, asthma or diabetes mellitus type II. Conclusion:For patients aged 21-50 with COVID-19 presenting to the emergency department, a chest xray severity score was predictive of risk for hospital admission and intubation.

Purpose We describe the presenting characteristics and hospital course of 11 novel coronavirus (COVID-19) patients who developed spontaneous subcutaneous emphysema (SE) with or without pneumomediastinum (SPM) in the absence of prior mechanical ventilation. Materials and methods A total of 11 non-intubated COVID-19 patients (8 male and 3 female, median age 61 years) developed SE and SPM between March 15 and April 30, 2020 at a multi-center urban health system in New York City. Demographics (age, gender, smoking status, comorbid conditions, and body-mass index), clinical variables (temperature, oxygen saturation, and symptoms), and laboratory values (white blood cell count, C-reactive protein, D-dimer, and peak interleukin-6) were collected. Chest radiography (CXR) and computed tomography (CT) were analyzed for SE, SPM, and pneumothorax by a board-certified cardiothoracic-fellowship trained radiologist. Results Eleven non-intubated patients developed SE, 36% (4/11) of whom had SE on their initial CXR. Concomitant SPM was apparent in 91% (10/11) of patients, and 45% (5/11) also developed pneumothorax. Patients developed SE on average 13.3 days (SD: 6.3) following symptom onset. No patients reported a history of smoking. The most common comorbidities included hypertension (6/11), diabetes mellitus (5/11), asthma (3/11), dyslipidemia (3/11), and renal disease (2/11). Four (36%) patients expired during hospitalization. Conclusion SE and SPM were observed in a cohort of 11 non-intubated COVID-19 patients without any known cause or history of invasive ventilation. Further investigation is required to elucidate the underlying mechanism in this patient population.

“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence . This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To train a deep learning classification algorithm to predict chest radiography severity scores and clinical outcomes in patients with coronavirus disease 2019 (COVID-19). Materials and Methods In this retrospective cohort study, we identified patients of ages of 21 to 50 who presented to the emergency department (ED) of a multicenter urban health system from March 10–26, 2020 with COVID-19 confirmation on real-time reverse transcription polymerase chain reaction. We collected the initial chest radiographs (CXRs), clinical variables, and outcomes including admission, intubation, and survival within 30 days ( n = 338; median age 39; 210 men). Two fellowship-trained cardiothoracic radiologists examined CXRs for opacities and assigned a clinically validated severity score. We trained a deep learning algorithm to predict outcomes on a holdout test set composed of confirmed COVID-19 patients who presented from March 27–29, 2020 ( n = 161; median age 60; 98 men) for both younger (ages 21–50; n = 51) and older (ages > 50; n = 110) populations. Bootstrapping methods computed confidence intervals. Results The model trained on the CXR severity score produced the following areas under the receiver operating characteristic (AUCs): 0.80 (0.73,0.88) for the CXR severity score, 0.76 (0.68,0.84) for admission, 0.66 (0.56,0.75) for intubation, and 0.59 (0.49,0.69) for death. The model trained on clinical variables produced the following AUCs 0.64 (0.55,0.73) for intubation and 0.59 (0.50,0.68) for death. Combining CXR and clinical variables increased AUC of intubation and death to 0.86 (0.79,0.96) and 0.82 (0.72,0.91), respectively. Conclusion The combination of imaging and clinical information improves outcome predictions.