key: cord-0883327-etis11i8 authors: Yang, Ran; Li, Xiang; Liu, Huan; Zhen, Yanling; Zhang, Xianxiang; Xiong, Qiuxia; Luo, Yong; Gao, Cailiang; Zeng, Wenbing title: Chest CT Severity Score: An Imaging Tool for Assessing Severe COVID-19 date: 2020-03-30 journal: Radiol Cardiothorac Imaging DOI: 10.1148/ryct.2020200047 sha: 651da6e18f10bdc794295a31b5b8438553bc48e6 doc_id: 883327 cord_uid: etis11i8 BACKGROUND: Quantitative and semi-quantitative indicators to evaluate the severity of lung inflammation in Coronavirus Disease 2019 (COVID-19) could provide an objective approach to rapidly identify patients in need of hospital admission. PURPOSE: To evaluate the value of chest computed tomography severity score (CT-SS) in differentiating clinical forms of COVID-19. MATERIALS AND METHODS: Inclusion of 102 patients with COVID-19 confirmed by positive real-time reverse transcriptase polymerase chain reaction on throat swabs underwent chest CT (53 men and 49 women, 15-79 years old, 84 cases with mild and 18 cases with severe disease). The CT-SS was defined by summing up individual scores from 20 lung regions; scores of 0, 1, and 2 were respectively assigned for each region if parenchymal opacification involved 0%, less than 50%, or equal or more than 50% of each region (theoretical range of CT-SS from 0 to 40). The clinical and laboratory data were collected, and patients were clinically subdivided according to disease severity by the Chinese National Health Commission guidelines. RESULTS: The posterior segment of upper lobe (left, 68/102; right, 68/102), superior segment of lower lobe (left, 79/102; right, 79/102), lateral basal segment (left, 79/102; right, 70/102) and posterior basal segment of lower lobe (left, 81/102; right, 83/102) were the most frequently involved sites in COVID-19. Lung opacification mainly involved the lower lobes, in comparison with middle-upper lobes. No significant differences in distribution of the disease were seen between right and left lungs. The individual scores of in each lung, as well as the total CT-SS were higher in severe COVID-19 when compared with mild cases (P<0.05. The optimal CT-SS threshold for identifying severe COVID-19 was 19.5 (area under curve, 0.892), with 83.3% sensitivity and 94% specificity. CONCLUSION: CT-SS could be used to quickly and objectively evaluate the severity of pulmonary involvement in COVID-19 patients. Since December 2019, a cluster of cases with unknown pneumonia with similar clinical manifestations suggesting viral pneumonia appeared in Wuhan City, Hubei Province, China. A new type of coronavirus was isolated from the lower respiratory tract samples, named Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) by the International Virus Classification Commission [1] . The disease it causes was named Coronavirus Disease 2019 (COVID-19) by WHO in February 11, 2020 [2] . SARS-CoV-2 belong to β-coronavirus, which is a typical RNA virus. It is generally round or oval shaped, with a diameter of 60 to 140 nm under the electron microscope. Its outer membrane had unique spikes, about 9 to 12 nm, similar to the solar corona [3] . The study found that the SARS-CoV-2 shares 92% homology with the bat coronavirus sequence RaTG3, which suggests a zoonotic origin for this outbreak [4] . SARS-CoV-2 can spread from person-to-person [5] and has been declared a pandemic disease. The common clinical symptoms of patients with COVID-19 are fever, cough, dyspnea and fatigue, which are similar to those of severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) [6, 7] . Severe cases can lead to acute respiratory distress syndrome, or even death. According to the severity of the patient's condition, the treatment is different. Mild patients receive antiviral, symptomatic support and oxygen therapy. However, severe cases need to be admitted to intensive care unit as soon as possible. At present, the diagnosis of COVID-19 depends on real-time reverse transcriptase polymerase chain reaction (RT RT-PCT) or next-generation sequencing [8] . On imaging, computed tomography (CT) manifestations resemble those seen in viral pneumonias [9] , with multifocal ground-glass opacities and consolidation in a peripheral distribution being the most common findings [10, 11] . Although these findings lack specificity for COVID-19 diagnosis on imaging grounds, we hypothesize that CT could be used to provide objective assessment about the extension of the lung opacities, which could be used as an imaging surrogate for disease burden. The main purpose of our study was to evaluate the performance and inter-reader concordance of a semi-quantitative CT severity score designed to identify severity of COVID-19. If feasible, such approach could expedite the identification and management of severe patients in specific instances where a fast triage method is needed. The study was approved by the Ethics Committee of Chongqing Three Gorges Central Hospital. We retrospectively studied the patients who were diagnosed with COVID-19 from January 21, 2020 to February 5, 2020 in our hospital. According to our hospital protocol, all patients with suspected COVID-19 routinely underwent non-contrast CT examinations and were admitted in hospital for isolation and observation. CT was chosen over chest radiographs based on the assumption that the former is more sensitive to detect lung opacities. A total of 102 patients with COVID-19 were confirmed by RT RT-PCR throat swab [12] . Patients with lung malignancy, a history of lobectomy, tuberculosis, or atelectasis were excluded from this study. According to the "Diagnosis and Treatment Program of Pneumonia of New Coronavirus Infection (Trial Fifth Edition)" [13] (1) respiratory distress, RR ≥ 30 beats / min; (2) resting blood oxygen saturation ≤ 93%; or For the purposes of this study, common cases were included in the Mild Disease group, while severe and critical cases were merged into the Severe Disease group, because of the small number of cases in the latter category (n = 3). We developed a chest CT severity score (CT-SS) for assessing COVID-19 burden on the initial scan obtained at admission. This score uses lung opacification as a surrogate for extension of the disease in the lungs. The CT-SS is an adaptation of a method previously used to describe ground-glass opacity, interstitial opacity, and air trapping, which was correlated regions were subjectively evaluated on chest CT using a system attributing scores of 0, 1, and 2 if parenchymal opacification involved 0%, less than 50%, or equal or more than 50% of each region. The CT-SS was defined as the sum of the individual scored in the 20 lung segment regions, which may range from 0 to 40 points. All CT images were independently reviewed by two chest radiologists with more than 10 years of experience, blinded to the clinical data and laboratory indicators, in a standard clinical Picture Archiving and Diagnostic System workstation. All thin-section CT scans were reviewed at a window width and level of 1000 to 2000 HU and -700 to -500 HU, respectively, for lung parenchyma. Chest CT imaging was performed on a 16-detector CT scanner (Emotion; SIEMENS). All patients were examined in supine position. CT images were then acquired during a single inspiratory breath-hold. The scanning range was from the apex of lung to costophrenic angle. CT scan parameters: X-ray tube parameters -120KVp, 350mAs; rotation time -0.5 second; pitch -1.0; section thickness -5 mm; intersection space -5 mm; additional reconstruction with sharp convolution kernel and a slice thickness of 1.5 mm. Statistical analysis was performed using R (version 3.5.1). P＜0.05 was regarded to demonstrate statistical significance. Quantitative data were expressed as mean ± standard deviation or median and interquartile range. The Weighted Kappa coefficient was used to compare the consistency of two observers in each lung segment. Interrater reliability was evaluated using intraclass correlation coefficient (ICCs) for continuous variables (ICCs was classified as follows: no agreement, 0-0.2; weak agreement, 0.21-0.4; moderate agreement, 0.41-0.60; good agreement, 0.61-0.80; and excellent agreement, 0.81-1.0). All measurements were assessed with normality tests. A Chi square or Fisher exact test was used to compare the scores of each lung segment between the mild and severe groups. A Wilcoxon rank sum test was used to compare the difference of left lung, right lung and total score between the mild group and the severe group, and the Wilcoxon matched-pairs signed-rank test was used to compare the difference of scores between lower lung and middle-upper lung, left lung and right lung. Receiver operator characteristic (ROC) curve analysis was performed to calculate the threshold, specificity, sensitivity and accuracy for discriminating the Mild from the Severe COVID-19 group. The inter-reader ICCs for CT-SS was excellent (n=102, ICCmedian=0.925, ICCmean=0.936). The scores provided by one of the two readers was randomly chosen for further analyses. (Table 2, Figure 2 and Figure 3) . The lower lobe scores were higher than the middle-upper lobe scores in each group. However, there were no significant differences between left and right lung scores ( Table 3) . Pleural effusions were found in 7 cases and lymphadenopathy in 2 cases in the Severe group, while pleural effusion and lymphadenopathy were not found in the Mild group ( Table 4) . The ROC curve analysis for CT-SS is shown in On January 30th, 2020, the World Health Organization declared COVID-19 as the sixth public health emergency deserving international attention. COVID-19 is highly contagious and has spread worldwide. Strategies for disease containment and patient management heavily rely on disease diagnosis [3, 15, 16] . However, COVID-19 testing has been challenged by limited laboratory facilities and inadequate supply of nucleic acid kits [17] . Moreover, the lack of early abnormalities on chest radiographs can lead to a large number of false negatives [18] . Thin-section chest CT is more sensitive than chest radiography, showing abnormal changes in the lung parenchyma in early stages of disease [19, 20] . For these reasons, chest CT has become a forefront diagnostic method during the outbreak of COVID-19 in China [18] . The typical imaging manifestations of early COVID-19 are patchy, rounded, segmental or subsegmental ground-glass opacities, with or without consolidation [20] . Lesions are multiple and asymmetrically distributed and are more common in the peripheral areas [11, 21, 22] . Some imaging findings of COVID-19 overlap with those of other RNA viral infection, such as respiratory syncytial virus (RSV) and human parainfluenza virus (HPIV) [9, 23] . Song et al [24] retrospectively analysed the chest CT images of 51 patients with COVID-19; the results showed that 82% of the patients had posterior lung involvement, which are closely matched with our observations. Noticeably, the dominant posterior distribution is similar to reports on SARS-CoV and MERS-CoV infection [25, 26] . In our study, we also found a slight predominance of opacities in the lower lobes when compared with the middle and upper lobes, without significant differences between the right and left lung, which was also consistent with previous analyses [27] . In this study, we devised a semi-quantitative scoring method using the amount of lung opacification involving 20 lung regions as a surrogate for COVID-19 burden. We found that the CT-SS was higher in severe when compared to mild cases. Most importantly, we determined that a CT-SS threshold of 19.5 could identify severe COVID-19, with a sensitivity of 83.3% and a specificity of 94%, resulting in an NPV of 96.3%. Moreover, inter-reader agreement was excellent between our two radiologists. We envision that this relatively straightforward method could provide objective means to expedite the identification of patients with severe disease, especially in situations of limited availability of healthcare resources. This retrospective study has several limitations. First, the CT-SS assumes that the amount of lung opacification is a surrogate for COVID-19 burden; however, there was no histologic confirmation of the findings. Second, we chose to analyze the first chest CT obtained on admission; therefore, the studies were not controlled by the number of days since the start of symptoms, which could potential implications for interpretation of the CT-SS. In conclusion, this study provides a straightforward semi-quantitative method for assessing severity of COVID-19 in the initial chest CT. A CT-SS score less than 19.5 could rule out severe or critical forms of disease with a high NPV of 96.3% in our cohort. 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Radiological Diagnosis of New Coronavirus Infected Pneumonitis: Expert Recommendation from the Chinese Society of Radiology Radiologic Pattern of Disease in Patients with Severe Acute Respiratory Syndrome: The Toronto Experience1 Imaging Profile of the COVID-19 Infection: Radiologic Findings and Literature Review Novel Coronavirus (2019-nCoV) Pneumonia CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV) Imaging of Pulmonary Viral Pneumonia Emerging Coronavirus 2019-nCoV Pneumonia Severe acute respiratory syndrome: radiographic appearances and pattern of progression in 138 patients Middle East respiratory syndrome coronavirus (MERS-CoV) infection: chest CT findings Chest CT Findings in Patients with Corona Virus Disease 2019 and its Relationship with Clinical Features The authors thank Xinghua Liu, Yun Wen, Jingxian Xiong, Department of Radiology, Chongqing Three Gorges Central Hospital, for assisting with CT imaging data collection, and Xu Wang, GE Healthcare, for assisting with data statistical analysis.