key: cord-1048397-r9t8jnbs authors: Lyu, Peijie; Liu, Xing; Zhang, Rui; Shi, Lei; Gao, Jianbo title: The performance of chest CT in evaluating the clinical severity of COVID-19 pneumonia: identifying critical cases based on CT characteristics date: 2020-04-17 journal: Invest Radiol DOI: 10.1097/rli.0000000000000689 sha: d9684de2d9f394c567ef850f4af0996cca6c148e doc_id: 1048397 cord_uid: r9t8jnbs OBJECTIVES: To assess the clinical severity of COVID-19 pneumonia using qualitative and/or quantitative chest CT indicators and identify the CT characteristics of critical cases. MATERIALS AND METHODS: Fifty-one patients with COVID-19 pneumonia including ordinary cases (group A, n=12), severe cases(group B, n=15) and critical cases (group C, n=24) were retrospectively enrolled. The qualitative and quantitative indicators from chest CT were recorded and compared using Fisher's exact test, one-way ANOVA, Kruskal-Wallis H test and receiver operating characteristic analysis. RESULTS: Depending on the severity of the disease, the number of involved lung segments and lobes, the frequencies of consolidation, crazy-paving pattern and air bronchogram increased in more severe cases. Qualitative indicators including total severity score for the whole lung and total score for crazy-paving and consolidation could distinguish groups B and C from A(69% sensitivity, 83% specificity and 73% accuracy) but were similar between group B and group C. Combined qualitative and quantitative indicators could distinguish these three groups with high sensitivity(B+C vs. A, 90%; C vs. B, 92%), specificity(100%, 87%) and accuracy(92%, 90%). Critical cases had higher total severity score(>10) and higher total score for crazy-paving and consolidation(>4) than ordinary cases, and had higher mean lung density(>-779HU) and full width at half maximum(>128HU) but lower relative volume of normal lung density(≦50%) than ordinary/severe cases. In our critical cases, eight patients with relative volume of normal lung density smaller than 40% received mechanical ventilation for supportive treatment, and two of them had died. CONCLUSION: A rapid, accurate severity assessment of COVID-19 pneumonia based on chest CT would be feasible and could provide help for making management decisions, especially for the critical cases. The outbreak of the coronavirus disease 2019(COVID-19) has spread rapidly throughout Wuhan (Hubei province) to other provinces in China and other more than 75 countries around the world [1] [2] [3] [4] , representing a significant and urgent threat to the global health. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a new virus responsible for the outbreak of respiratory illness known as COVID-19, has sickened more than 95,000 people and killed more than 3,200, most in China, as of Mar 5th, 2020. The clinical spectrum of COVID-19 pneumonia ranges from mild to critical cases, among which the diagnosis of ordinary, severe and critical cases were all correlated with chest CT findings 5, 6 . Previously published studies have described the general typical and atypical CT image manifestations 6, 7 , the time-course evolution of CT findings 8, 9 , the correlation between CT features and clinical features 1, 10 , and evaluated the CT severity of patients with COVID pneumonia 8, [11] [12] [13] [14] [15] [16] [17] . In order to reduce or eliminate the subjectivity in the qualitative and semi-quantitative visual evaluation of CT severity scores 8, 15, 17 , quantitative approaches for assessing lung opacification percentage of the whole lung have developed, such as deep learning method 18 , computer tool 16 or the calculation method of combing mean attenuation values and opacity volumes 14 . However, these quantitative analysis methods did not fully specify information characterizing and quantifying different clinical stages with CT features, especially for critical cases. 7 patients were included with demographics and clinical characteristics recorded. The flowchart of patient selection is shown in Figure 1 . All examinations represented the initial CT scans for every individual patient. All CT images were acquired at the end of inhalation using a 256-row CT scanner (Revolution CT, GE Healthcare, Waukesha, Wisconsin, USA) with detector configuration of 256 ×0.625mm or using a 192-slice CT scanner (Somatom Force, Siemens Healthineers, Forchheim, Germany) with detector collimation of 192 ×0.6 mm. Other acquisition parameters for these two scanners were set as follows: tube voltage of 120 kV, automatic tube current modulation of 100-300 mA(AutomA, GE Healthcare; CareDose 4D, Siemens Healthineers), pitch of 0.99-1.375 and matrix of 512×512. Images were reconstructed at slice thickness/interval of 1-1.25 mm with a hybrid adaptive statistical iterative reconstruction (40% level) using stand (mediastinal) and bone plus (lung) kernels (GE Healthcare) or with an advanced modeled iterative reconstruction(strength 3) using Br40 (mediastinal) and BI57(lung) kernels (Siemens Healthineers). The mediastinal and lung window width and level were set as 350/40HU and 1500/-700HU (GE Healthcare ) or 400/40HU and 1500/-500(Siemens Healthineers) respectively to evaluate the abnormalities in the mediastinum and lung parenchyma. years' experience]) without access to clinical or laboratory findings. According to previously published papers for COVID-19 [6] [7] [8] 23 , the CT image findings of ground-glass opacity (GGO), consolidation, crazy-paving pattern, septal thickening and pulmonary fibrosis were included in calculating the severity score of each lobe, which was classified from score 0 to score 4 with an increment of 1, representing a degree of involvement of 0 to ≧75% with an increment of 25%, respectively 24 . Total severity scores for the whole lung was the sum of five lung lobe scores (0-20 scores). Since previous reports 24, 25 showed that the main CT manifestations of COVID-19 pneumonia at baseline were bilateral, peripheral and basal GGO and consolidation, and developed into crazy-paving and consolidation with multi-lobar involvement at the peak of lung involvement, we took the sum extent of crazy-paving and consolidation involving the lung as an index to evaluate the progression of pneumonia. Crazy-paving pattern is defined as consisting of scattered or diffuse ground-glass attenuation with superimposed interlobular septal thickening and intralobular lines 26 while consolidation is defined as a uniform increase of lung parenchyma with obscuration of the underlying vessels 5 . The sum involvement of crazy-paving and consolidation of each lobe was scored using the above-mentioned scoring criteria, and the sum of the five lobes was taken as the total lung scores(0-20 scores) 24 . ensure accurate lung segmentation. For the segmented lung, the volume(ml), relative volume(%), mean lung density(MLD)(HU) and full width at half maximum(FWHM)(HU) were measured within the preset threshold range of -950HU and -200HU. The setting of the threshold range is based on the findings that CT values of normal parenchyma range from -950HU to-750HU while those in vessels or pneumonia are ≧ -200HU, from the instructions of the manufacturer, previous studies [27] [28] [29] , and our practical experience. The evaluation index method was displayed by quantifying the percentage of the voxel below the low attenuation value (LAV) (threshold of -950HU) and above the high attenuation value (HAV) (threshold of -200 HU). The FWHM parameter marks the width of frequency distribution at half of the maximum CT value, representing the heterogeneity of lung tissue density 30 . A subrange analysis method was used to display the relative volume of the segmented lung within a predetermined HU range, which was -1000 to -200HU (in 8 colors representing 8 subranges). Percentile analysis was used to calculate and display relative volume (HU) within predefined percentage values of the lung segmentation(0-100%), representing the cumulative number of voxels. Considering that the threshold of GGO has been reported to range from -800 to -500 HU 31 , the threshold range of normal CT values in our study was finally set at between -950HU and -800HU instead of -950 to -750 HU to assess the relative volume of residual normal lung density of COVID-19 pneumonia. In order to facilitate readers to better understand the performance of lung quantitative analysis methods on pneumonia, we included normal lung CT images from another 10 cases collected retrospectively for the comparison. Copyright © 2020 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. This paper can be cited using the date of access and the unique DOI number which can be found in the footnotes. Analyses were done with SPSS software version 16 The demographics and clinical characteristics of all patients are summarised in the Comparisons of the qualitative image findings among the three groups are shown in Pulmonary fibrosis, as an uncommon CT finding, accounts for similar frequencies in the three groups. From group A to groups B and C, in more severe cases, the number of involved lung segments and lobes, the total severity score for the whole lung and total score for crazy-paving and consolidation all increased, significantly higher in groups B and C(All p<0.05). And the frequencies of these image patterns were similar between group B and group C. The time interval between the initial CT scan and the symptom onset were longer in groups B and C (8 days[IQR,4,13], 10 days [6, 14] ) than that in group A(4 days [1, 7] )(both p<0.05), but was similar between group B and group C. Copyright © 2020 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. This paper can be cited using the date of access and the unique DOI number which can be found in the footnotes. Table 3 and Figure 2 . A normal lung CT group (n = 10) was included for the quantitative comparison with the other three COVID-19 pneumonia groups. Patients in group C had significantly lower total lung volumes, higher MLD, higher FWHM and higher HAV than the other three groups (All p<0.001), but showed similar LAV values to them. No statistical differences in the quantitative indicators were found between groups A and B except MLD, which was higher in group B than group A (P=.038). The percentile analysis showed that relative volume of normal lung density (from -950HU to -800HU ) within the total segmented lung was 43.01%(SD,13.42) in group C, which was significantly lower than those in the other three groups (group A 87.83%[SD,6.73]; group B 62.25%[SD,14.80]; normal group 88.91%[SD, 3.35])(All p<0.001) (Fig 3) . Compared with the normal group, the relative volume of normal lung density was lower in group B(P<.001) but was similar to group A, with the latter two groups significantly different from each other (p=0.03). By using the receiver operating characteristic curves, the threshold values of statistically significant parameters were determined to optimize both the sensitivity and the specificity for differentiating each group from the other two groups ( Table 4 ) For example, patients in groups C were significantly different from groups A and B with a higher number of involved lung segments(>8, sensitivity and specificity of 100% and 37%), higher total severity score(>10, 67% and 74%), higher total score for crazy-paving and consolidation (>4, 87% and 44%), higher MLD(>-779HU,100%, and 85%), higher Copyright © 2020 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. This paper can be cited using the date of access and the unique DOI number which can be found in the footnotes. FWHM(>116HU,83% and 81%), and lower relative volume of normal lung density (≦50%, 83% and 92%). As the intermediate stage between group A and group C, group B was similar to these two groups in qualitative indicators except for the total score for crazy-paving and consolidation which is significantly different from group A(threshold value of 8, sensitivity and specificity of 92% and 40%). Compared with group B, group C showed higher MLD(>-779HU, sensitivity and specificity of 100% and 73%)and FWHM(>128HU,75% and 80%) but lower relative volume of normal lung density(≦50%, 83% and 80%), while group A showed lower MLD(≦-816HU,92% and 80%) and FWHM (≦102HU, 92% and 67%) but higher relative volume of normal lung density (>80%, 92% and 100%)( Table 5 ). In short, using qualitative indicators could not differentiate group C from group B, but quantitative indicators could distinguish them. Based on the results of qualitative and quantitative indicators to distinguish the three groups, a summary diagram was drawn with the illustrations attached for each group (Figure 4 ). Combined use of the qualitative and quantitative indicators showed higher sensitivity(90%), specificity(100%) and accuracy (92%) in distinguishing groups B and C from group A than qualitative indicators alone (sensitivity, specificity and accuracy: 69%, 83% and 73%, p<0.001) ( Table 6 ). Based on the qualitative results of distinguishing groups B and C from group A, we further achieved sensitivity of 92% , specificity of 87% , and accuracy of 90% to distinguish group C from group B using the quantitative indicators( Figure 5 ). Copyright © 2020 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. This paper can be cited using the date of access and the unique DOI number which can be found in the footnotes. The novel coronavirus SARS-CoV-2, the seventh member of the coronaviridae family, leads to a very high case-fatality rate of COVID-19, varying by country, age and the presence of underlying disease [2] [3] [4] . It's difficult to obtain the exact mortality at present as the COVID-19 is still spreading across the world and posing a significant global health threat because of its high infectiousness and lack of specialized treatments. Since the mainstay of treatment for COVID -19 pneumonia has been supportive care, early identification of clinical stages is essential for initial management, especially for critical patients, who are related to high mortality 4 and need aggressive treatments and intensive care treatment. Similar to previous studies 1,4 , the predisposing conditions for COVID-19 pneumonia in the critical cases tended to be old age(>55 years old) and original existing disease(such as chronic pulmonary disease, cardiovascular disease and cerebrovascular disease), perhaps due to their poor immunity. The predominant abnormal chest CT pattern observed was bilateral and peripheral GGO and consolidation 6, 23 , the frequency of the former was not specific in identifying the cases in different clinical stages. This can be explained by the pathological findings that early alveolar damage caused by virus invasion into pulmonary interstitium includes alveolar edema, protein exudate and thickening of the interlobular interstitium 32,33 which will evolve to diffuse alveolar damage with cellular fibromyxoid exudate as the disease progresses to the critical stage 34 , both manifesting as GGO. From the ordinary stage to the severe/critical stage, in more severe cases, the number of involved lung segments and lobes, the frequencies of consolidation, crazy-paving pattern and air bronchogram all increased, making the total severity score for the whole lung and total score for crazy-paving and consolidation significantly higher in the severe/critical cases compared to the ordinary cases. These findings were consistent with Copyright © 2020 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. This paper can be cited using the date of access and the unique DOI number which can be found in the footnotes. previous studies 16, 23 showing that the progression of septal thickening, crazy-paving and lung consolidation were noted in the progression or peak period of pneumonia(1-3 weeks). Progression of consolidation and crazy-paving might represent further infiltration of the lung parenchyma and lung interstitium 5, 35 ，indicating that the virus has invaded the respiratory epithelium which is characterized by diffuse alveolar damage and necrotizing bronchitis, leading to alveoli completely filled by inflammatory exudation. Some of the severe(2[13%]) and critical cases (8[33%] ) in our study presented with pleural effusion on CT, the presence of which has been shown as a poor prognostic indicator in patients with Middle East respiratory syndrome coronavirus 36 . One of our critical cases with bilateral pleural effusion was found dead during our later follow-up. The time interval between the initial CT scan and the symptom onset in the severe/critical cases were longer than that in the ordinary cases, partly might be due to the late initial CT scan for the transferred patients(33%[13/39]) from the county or township hospitals with limited medical equipment and ability, and partly due to the fact that some cases were not hospitalized until their clinical symptoms progressed. The COVID-19 viral disease is now officially a pandemic, the World Health Organization announced Mar 10th, 2020. Chest CT has been widely used as an effective tool for diagnosing patients with COVID-19 pneumonia. However, the diversified CT patterns of COVID-19 pneumonia made it difficult to accurately and quickly assess the clinical severity. Our study demonstrated that severe/critical cases could be distinguished from ordinary cases using the combined qualitative indicators including total severity score for the whole lung and total score for crazy-paving and consolidation (sensitivity, specificity and accuracy: 69%, 83% and 73%). However, the diversity of virus manifestations and small imaging differences between Copyright © 2020 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. This paper can be cited using the date of access and the unique DOI number which can be found in the footnotes. the critical cases and severe cases make the qualitative indicators insufficient to distinguish them. This shortcoming might be compensated by the quantitative indicators. Compared with severe cases, critical cases showed higher MLD (>-779HU, sensitivity and specificity of 100% and 73%) and FWHM (>128HU,75% and 80%) but lower relative volume of normal lung density (≦50%, 83% and 80%). The combined quantitative indicators could achieve high sensitivity(92%) , specificity (87%) and accuracy (90%) in distinguishing critical cases from severe cases, based on the qualitative results of distinguishing severe/critical cases from ordinary cases. Lung density on CT, positively correlated with the proportion of consolidation 16 , might mirror an inflammatory response in the lung 28 . .FWHM represents the heterogeneity and density distribution of the lung parenchyma, the higher values of which might indicate mixed and diverse inflammatory components. The residual relative volume of normal lung density might be related to the lung function 37 . In our critical cases, eight patients with residual normal lung density smaller than 40% received mechanical ventilation for supportive treatment, two of them had died. The substantial difference in the relative volume of residual normal lung density among the three groups, indicating the value is associated with the severity of illness and thus prognosis. The similar LAV values of the three COVID-19 pneumonia groups to the normal CT groups indicated that no obvious sign of emphysema observed in pneumonia at the initial CT scan, as the setting of the LAV threshold for emphysema was -950 HU 30 . The HAV values increased in more severe cases, indicating an increase in high-density lesions and providing evidence that the total score for crazy-paving and consolidation could be as a qualitative indicator for evaluating disease progression. The higher HAV values (above than -200 HU) in the critical cases also helped explain why the total lung volume within the preset threshold range of -950HU and -200HU lower than the ordinary/severe cases. Copyright © 2020 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. This paper can be cited using the date of access and the unique DOI number which can be found in the footnotes. It should be noted that the time interval between the initial CT scan and the symptom onset ranged from 0 to 20 days in our study, and 63%(32/51) of CT scans weren't obtained at an early stage(0-5 days) 8, 9 . The evolution of diverse CT imaging findings of COVID-19 pneumonia with time 8 and the interobserver variability of imaging assessment would make the visually accurate evaluation or staging of the disease difficult. However, the method of quantitative analysis of pneumonia based on the lung density and volume changes was standard, except for the manual adjustment if necessary to ensure the accuracy of automatic lung segmentation using the software, which would make it easier and objective for radiologists to evaluate the extent of disease. Different from previous quantitative studies 14,16,18 which evaluated the extent of the disease by quantifying the CT lung opacification percentage using a deep-learning, computer or computation-based method, our study assessed the extent of pulmonary changes and the severity of COVID-19 by quantifying the relative volume of normal lung density using a commercial CT Pulmo 3D software, which would provide valuable knowledge and a feasible clinical tool for the management of these patients and broaden the technical spectrum of lung quantitative analysis. Our study had several limitations. First, only 51 patients were included in our study. We hope that the significant findings presented here will encourage a larger cohort study in the future. Second, the application of CT quantification using specific software limits its widespread clinical application. However, the use of qualitative indicators in distinguishing severe/critical cases from ordinary cases would also provide help for initial management for clinical care. Third, only the initial CT scan was included for analysis, more follow-up time points would be assessed in our next research. Fourth, the correlation of clinical features and outcome with the CT features, especially for the quantitative indicators, has not been assessed in our study, this work is currently in progress. Copyright © 2020 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. This paper can be cited using the date of access and the unique DOI number which can be found in the footnotes. In conclusion, depending on the severity of the disease, the number of involved lung segments and lobes, the frequencies of consolidation, crazy-paving pattern and air bronchogram increased in more severe cases. Using qualitative indicators alone could distinguish severe/critical cases from ordinary cases, but provide little help to differentiate severe cases from critical cases. The combined use of qualitative and quantitative indicators could distinguish cases at different clinical stages, might provide help to facilitate the fast identification and management of critical cases, thus reducing the mortality rate. Critical cases had higher total severity score(>10) and total score for crazy-paving and consolidation(>4) than ordinary cases, and had higher mean lung density(>-779HU) and full width at half maximum(>128HU) but lower relative volume of normal lung density(≦50%) than ordinary/severe cases. CT imaging findings could help to continuously monitor the treatment effects objectively in the follow-up as well as provide guidance for clinical management and treatment. Copyright © 2020 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. This paper can be cited using the date of access and the unique DOI number which can be found in the footnotes. (1, 7, 19) , the total severity score (2, 7, 16) and the total score for crazypaving and consolidation increased in more severe cases (1, 5, 12) . Three-dimensional volumerendering(VR) images showed the distribution of lesions in the lung clearly while the subrange images displayed the distribution of lesions by using different colors representing different subranges of HU ranges, such as red color representing higher lung density of consolidation. The percentile curve images manifested that the relative volume area of normal CT density (-950HU to -800HU) under the curve gradually decreased from the normal group, group A to group C. Copyright © 2020 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. This paper can be cited using the date of access and the unique DOI number which can be found in the footnotes. Copyright © 2020 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. This paper can be cited using the date of access and the unique DOI number which can be found in the footnotes. Copyright © 2020 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. This paper can be cited using the date of access and the unique DOI number which can be found in the footnotes. Copyright © 2020 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. This paper can be cited using the date of access and the unique DOI number which can be found in the footnotes. Copyright © 2020 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. This paper can be cited using the date of access and the unique DOI number which can be found in the footnotes. Copyright © 2020 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. This paper can be cited using the date of access and the unique DOI number which can be found in the footnotes. Copyright © 2020 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. This paper can be cited using the date of access and the unique DOI number which can be found in the footnotes. Note: Data are the numbers with the percentage in parentheses except where specified.* Data are mean± standard deviation. †Difference among groups A-C. Exposure history indicates the history of cases exposed to infected individuals or epidemic areas. Copyright © 2020 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. This paper can be cited using the date of access and the unique DOI number which can be found in the footnotes. Copyright © 2020 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. This paper can be cited using the date of access and the unique DOI number which can be found in the footnotes. Copyright © 2020 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. This paper can be cited using the date of access and the unique DOI number which can be found in the footnotes. Copyright © 2020 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. This paper can be cited using the date of access and the unique DOI number which can be found in the footnotes. Copyright © 2020 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. This paper can be cited using the date of access and the unique DOI number which can be found in the footnotes. 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