Abstract
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.
Highlights
The rapid spread of the coronavirus disease 2019 (COVID-19) caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has exerted unprecedented strain on the global healthcare system [1]
The American College of Radiology and the Society of Thoracic Radiology do not recommend chest computed tomography (CT) for screening or diagnosis of COVID-19 [11]. These recommendations are echoed by the World Health Organization (WHO) consensus guidelines, which recommend the use of reverse-transcription polymerase chain reaction (RT-PCR) over chest imaging for the diagnosis of COVID-19 [12]
We found younger age, higher oxygen saturation, lower WBC count, increased lymphocyte fraction, presence of myalgia, lack of sore throat, lack of loss of consciousness, a higher number of symptoms, lower Radiographic Assessment of Lung Edema (RALE) score, lower CT severity score on follow-up CT, and absence of pleural effusion correlated with survival
Summary
The rapid spread of the coronavirus disease 2019 (COVID-19) caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has exerted unprecedented strain on the global healthcare system [1]. Use of imaging in diagnosis and evaluation of suspected or known COVID-19 infection is variable among different countries. Given its low specificity and overall predictive value [10], imaging features are currently not considered helpful for diagnosis of COVID-19 infection by most clinicians. The American College of Radiology and the Society of Thoracic Radiology do not recommend chest computed tomography (CT) for screening or diagnosis of COVID-19 [11]. These recommendations are echoed by the World Health Organization (WHO) consensus guidelines, which recommend the use of reverse-transcription polymerase chain reaction (RT-PCR) over chest imaging for the diagnosis of COVID-19 [12]. Common findings on chest CT range from normal to peripheral ground-glass opacities to more diffuse parenchymal opacities [14]
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