A comparison of radiographic features between non-survivors and survivors from ICU

  • Gang Wu
    Affiliations
    Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
    Search for articles by this author
  • Shuchang Zhou
    Correspondence
    Corresponding author at: Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, China.
    Affiliations
    Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
    Search for articles by this author
Open AccessPublished:April 13, 2021DOI:https://doi.org/10.1016/j.ejro.2021.100338

      Abstract

      The clinical and imaging data of 121 ICU patients with SARS-CoV-2 infection (63 survivors and 58 non-survivors) were retrospectively reviewed. The clinical results and radiographic features were compared between survivors and non-survivors. Compared with survivors, non-survivors were more likely to develop ARDS (53 [91 %] vs. 22 [35 %], P < 0.0001), shock (6 [10 %] vs. 0, P = 0.009), cardiac injury(18 [31 %] vs. 6 [10 %], P = 0.003), acute kidney injury(21 [36 %] vs. 10 [16 %], P = 0.01), and pneumothorax(5 [9%] vs. 0, P = 0.017). There were typical radiographic features for ICU patients with SARS-CoV-2 pneumonia. Extensive air-space opacities could be seen in all patients. Middle and lower lung involvement was significantly more serious than upper lung (score 6.8 ± 1.9, 7.2 ± 2.1, and 5.7 ± 1.7, respectively, P < 0.0001). Based on X-ray involvement score, non-survivors were in a more critical condition than survivors (20.3 ± 4.6 vs. 19.1 ± 3.1, P = 0.038).

      Keywords

      1. Introduction

      Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia is clinically divided into mild, common, severe and critically ill types [

      World Health Organization. Corona-virus disease (COVID-19) outbreak (https://www.who.int). In.

      , ,

      Chinese National Health Committee. Diagnosis and treatment of COVID-19 pneumonia (trial seventh edition) (2020-03-04). http://www.nhc.gov.cn/yzygj/s7653p/202003/46c9294a7dfe4cef80dc7f5912eb1989.shtml. In.

      ]. Critical ill patients need to be treated in intensive care unit (ICU), which has a high mortality rate. High resolution CT could identify typical ground-glass opacities (GGO), multifocal patchy consolidation, and crazy-paving sign in the peripheral area of the lungs of patients with SARS-CoV-2 pneumonia [
      • Pan F.
      • Ye T.
      • Sun P.
      • et al.
      Time course of lung changes on chest ct during recovery from 2019 novel coronavirus (COVID-19) pneumonia.
      ,
      • Shi H.
      • Han X.
      • Jiang N.
      • et al.
      Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study.
      ,
      • Zhou S.
      • Wang Y.
      • Zhu T.
      • Xia L.
      CT features of coronavirus disease 2019 (COVID-19) pneumonia in 62 patients in Wuhan, China.
      ]. Follow-up CT can be used to determine whether pulmonary infection improves or progresses [
      • Zu Z.Y.
      • Jiang M.D.
      • Xu P.P.
      • et al.
      Coronavirus disease 2019 (COVID-19): a perspective from China.
      ]. However, CT scan is difficult to be performed for patients in ICU, where mobile X-ray system serves as alternative in monitoring SARS-CoV-2 pneumonia. Many previous studies reported the clinical and imaging features [
      • Pan F.
      • Ye T.
      • Sun P.
      • et al.
      Time course of lung changes on chest ct during recovery from 2019 novel coronavirus (COVID-19) pneumonia.
      ,
      • Zu Z.Y.
      • Jiang M.D.
      • Xu P.P.
      • et al.
      Coronavirus disease 2019 (COVID-19): a perspective from China.
      ,
      • Bernheim A.
      • Mei X.
      • Huang M.
      • et al.
      Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection.
      ,
      • Duan Y.N.
      • Qin J.
      Pre- and posttreatment chest CT findings: 2019 novel coronavirus (2019-nCoV) pneumonia.
      ,
      • Guan W.J.
      • Ni Z.Y.
      • Hu Y.
      • et al.
      Clinical characteristics of coronavirus disease 2019 in China.
      ,
      • Li Q.
      • Guan X.
      • Wu P.
      • et al.
      Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia.
      ,
      • Zhou F.
      • Yu T.
      • Du R.
      • et al.
      Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study.
      ] of SARS-CoV-2 pneumonia. However, there are few studies comparing radiographic manifestations between survivors and non-survivors from ICU. We retrospectively investigated 121 ICU patients with SARS-CoV-2 infection (including 63 survivors and 58 non-survivors), and aimed to clarify the difference in X-ray manifestations between the two groups.

      2. Materials and methods

      This retrospective study was approved by our ethics committee, and written informed consent was waived.
      The clinical data of 121 critically ill SARS-CoV-2 patients in ICU were collected and retrieved using the Radiologists Information System (RIS).The diagnosis of SARS-CoV-2 infection was confirmed by positive RT-PCR results, and the critically ill patients were defined as those admitted to ICU who required mechanical ventilation or those had shock [

      Chinese National Health Committee. Diagnosis and treatment of COVID-19 pneumonia (trial seventh edition) (2020-03-04). http://www.nhc.gov.cn/yzygj/s7653p/202003/46c9294a7dfe4cef80dc7f5912eb1989.shtml. In.

      ] or those had a fraction of inspired oxygen (FiO2) of at least 60 % or more [
      • Dominguez-Cherit G.
      • Lapinsky S.E.
      • Macias A.E.
      • et al.
      Critically Ill patients with 2009 influenza A(H1N1) in Mexico.
      ,
      • Fowler R.A.
      • Lapinsky S.E.
      • Hallett D.
      • et al.
      Critically ill patients with severe acute respiratory syndrome.
      ]. The recorded information included: age, gender, underlying diseases, initial symptoms, X-ray imaging signs and scores.

      3. Acquisition of chest X-ray radiographs

      Chest radiographs were all taken with a mobile X-ray scanner (uDR370i; United Imaging, Shanghai, China) in ICU. Scanning protocol was as follows: 80 kV, 3.2 mAs, 100-cm film-to-focus distance, with a broad tube focus. The radiograph images were reviewed on a picture archiving and communication system (Synapse; Fujifilm).

      4. Image analysis

      Two experienced thoracic radiologists (with 8 and 10 years of thoracic diagnostic experience, respectively) without knowing the patient’s clinical data assessed the X-rays. The radiograph signs and involvement scores were determined on consensus.
      The involvement of lung on X-ray was assessed using a visual scoring method according to previous studies [
      • Antonio G.E.
      • Ooi C.G.
      • Wong K.T.
      • et al.
      Radiographic-clinical correlation in severe acute respiratory syndrome: study of 1373 patients in Hong Kong.
      ,
      • Ko S.F.
      • Lee T.Y.
      • Huang C.C.
      • et al.
      Severe acute respiratory syndrome: prognostic implications of chest radiographic findings in 52 patients.
      ] as follows: each lung was divided into three equidistant zones (upper, middle, and lower) from the apex to the bottom (diaphragm), adding up to six zones together. For each zone: score 0, no involvement; score 1, 1%–25 % involved; score 2, 26 %–50 % involved; score 3, 51 %–75 % involved; score 4, 76 %–100 % involved. The total score was acquired by summing the scores of all the six zones, with the maximum value of 24. If a patient had multiple X-ray examinations, all X-rays were assessed, and the highest score was recorded.

      5. Statistical analysis

      Continuous variables were expressed as mean ± SD or median (IQR), and categorical variables as number (%). All data statistical analysis was performed with SPSS (22.0, IBM, USA). We assessed differences between survivors and non-survivors using two-sample t-test for normal distribution data or Mann-Whitney U test for non-normal continuous variables, and Chi-square test or Fisher’s exact-test for categorical variables. The Kruskal-Wallis test was used to compare involvement score among upper, middle and lower lung. P value less than 0.05 was considered significant difference.

      6. Results

      121 ICU patients with SARS-CoV-2 pneumonia were included in this study. The median age was 63 years. 78 (64 %) patients were men. 74 (61 %) patients had underlying diseases. Hypertension (26 %) and chronic cardiac disease (15 %) were most common underlying diseases, followed by malignancy (12 %) and chronic pulmonary disease (12 %). The most common initial symptoms were fever (92 %), cough (68 %), and dyspnea (49 %). 75 (62 %) patients developed acute respiratory distress syndrome (ARDS), 6 (5%) with shock, 31 (26 %) with acute kidney injury, 24 (20 %) with cardiac injury, and 5 (4%) with pneumothorax. 38 (31 %) patients were treated with invasive mechanical ventilation, 4 (3 %) with extracorporeal membrane oxygenation (ECMO), and 12 (10 %) with renal replacement therapy. All patients received antiviral therapy and antibacterial agents, and 48 (40 %) patients received corticosteroids (Table 1). The patients underwent one or more mobile X-rays. 119 (98 %) patients had bilateral infiltrates on chest x-ray. Extensive air-space opacities (Fig. 1, Fig. 2) could be seen in all patients. 24 (20 %) patients had pleural effusion, and 5 (4%) had pneumothorax (Fig. 3). Middle and lower lung involvement was significantly more serious than upper lung (score 6.8 ± 1.9, 7.2 ± 2.1, and 5.7 ± 1.7, respectively, P < 0.01).
      Table 1Main clinical information of 121 critically ill patients with SARS-CoV-2 infection. Comparisons of clinical features were performed between non-survivors and survivors using two-sample t-test for normal distribution data, and Chi-square test or Fisher’s exact-test for categorical variables.
      Clinical featuresall patients (n = 121)non-survivors (n = 58)survivors (n = 63)P value
      Age
      mean age (IQR)63 (50, 74)68 (54,80)58 (45,73)<0.0001
      Gender
      male78 (64 %)39 (67 %)39 (62 %)0.540
      female43 (36 %)19 (33 %)24 (38 %)
      Initial symptoms
      fever111(92 %)56 (97 %)55(87 %)0.065
      cough82(68 %)42 (72 %)40(63 %)0.294
      dyspnea59(49 %)31 (53 %)28(44 %)0.322
      malaise33(27 %)12 (21 %)11(17 %)0.651
      diarrhea8(7 %)5 (9 %)3(5 %)0.393
      myalgia4(3 %)2 (3 %)2(3 %)0.933
      hemoptysis3(2 %)2 (3 %)1(2 %)0.511
      headache1(1 %)1(2 %)00.295
      Underlying diseases
      tuberculosis8(7 %)5 (9 %)3(5 %)0.393
      diabetes13(11 %)8 (14 %)5(8 %)0.299
      hypertension32(26 %)20 (34 %)12(19 %)0.054
      cerebrovascular disease9(7 %)5 (9 %)4(6 %)0.634
      chronic pulmonary

      disease
      15(12 %)8 (14 %)7(11 %)0.655
      chronic cardiac disease18(15 %)10 (17 %)8(13 %)0.483
      malignancy14(12 %)8 (14 %)6(10 %)0.463
      Hepatitis C3(2%)2 (3%)1(2%)0.511
      goiter2(2%)1 (2%)1(2%)0.953
      Complications
      acute respiratory distress

      syndrome
      75(62 %)53(91 %)22(35 %)<0.0001
      shock6(5 %)6(10 %)00.009
      acute kidney injury31(26 %)21(36 %)10(16 %)0.01
      renal insufficiency6(5 %)6(10 %)00.009
      cardiac injury24(20 %)18(31 %)6(10 %)0.003
      gastrointestinal

      haemorrhage
      3(2%)3(5%)00.068
      pneumothorax5(4%)5(9%)00.017
      pulmonary embolism2(2%)2(3%)00.137
      hemolytic anemia1(1 %)1(2 %)00.295
      cellulitis1(1 %)1(2 %)00.295
      Treatment
      non-invasive mechanical ventilation86(71 %)49(84 %)37(59 %)0.002
      invasive mechanical ventilation38(31 %)31(53 %)7(11 %)<0.0001
      renal replacement

      therapy
      12(10 %)9(16 %)3(5%)0.048
      corticosteroids48(40 %)21(36 %)27(43 %)0.455
      SARS-CoV-2=severe acute respiratory syndrome coronavirus 2; COPD = chronic obstructive pulmonary disease.
      Fig. 1
      Fig. 1A “white lung” at a 69-year-old male with laboratory-confirmed SARS-CoV-2 pneumonia. He died of respiratory failure two days after this scan.
      Fig. 2
      Fig. 2A 71-year-old male non-survivor with hypertension and coronary heart disease. His initial symptoms were fever and diarrhea. RT-PCR for SARS-CoV-2 was positive. Multiple air space opacities could be seen on first chest X-ray. Significant progress could be seen on second X-ray 4 days later (arrows). Involvement was more serious for the lower lung compared with upper lung.
      Fig. 3
      Fig. 3A 57-year-old female non-survivor with fever and diarrhea as the initial symptom. RT-PCR for SARS-CoV-2 was positive. Typical imaging features of SARS-CoV-2 pneumonia could be seen on first chest CT. Pneumothorax could be easily seen (arrows) on chest X-ray 17 days later, as well as extensive air space opacities and pleural effusion.
      For 58 non-survivors, the median duration from onset of symptoms to death was 30 days, and the median duration from ICU admission to death was 9 days. The median time from last mobile X-ray to death was 2 days.
      Compared with survivors, non-survivors were more likely to develop ARDS (53 [91 %] vs. 22 [35 %], P < 0.01), shock (6 [10 %] vs. 0, P = 0.009), acute kidney injury(21 [36 %] vs. 10 [16 %], P = 0.01), cardiac injury (18 [31 %] vs. 6 [10 %], P = 0.003), pneumothorax(5 [9%] vs. 0, P = 0.017) (Table 1).
      Based on X-ray involvement score, non-survivors were in a more critical condition than survivors (20.3 ± 4.6 vs. 19.1 ± 3.1, P = 0.038). As summarized in Table 2, survivors and non-survivors did not differ in involvement score on zone level, but differed significantly in score of whole lung. Non-survivors were more likely to have pneumothorax than survivors (5 [9%] vs. 0, P = 0.017).
      Table 2Main radiographic features of 121 critically ill patients with SARS-CoV-2 infection. Comparisons of radiographic features were performed between non-survivors and survivors using Mann-Whitney U test for involvement score, and Chi-square test or Fisher’s exact test for categorical variables. The Kruskal-Wallis test was used to compare involvement score among upper, middle and lower lung.
      Radiographic featuresCritical ill(n = 121)Non-survivors (n = 58)Survivors (n = 63)P value
      bilateral pneumonia119(98 %)57(98 %)61(97 %)0.608
      unilateral pneumonia2(2 %)1(2 %)2(3 %)
      mean involvement score (standard deviation)19.7 ± 3.820.3 ± 4.619.1 ± 3.10.038
      pleural effusion24(20 %)15 (26 %)9(14 %)0.111
      pleural thickening8(7 %)4 (7 %)4(6 %)0.904
      pneumothorax5(4 %)5 (9 %)00.017
      Involvement score
      Six zones
      right upper2.4 ± 0.72.5 ± 0.82.3 ± 0.70.24
      right middle3.4 ± 0.73.5 ± 0.93.3 ± 0.80.28
      right lower3.6 ± 1.03.7 ± 1.13.5 ± 1.00.25
      left upper2.3 ± 0.92.4 ± 0.92.2 ± 0.80.19
      left middle3.4 ± 0.93.5 ± 1.03.3 ± 0.90.12
      left lower3.6 ± 1.23.7 ± 1.23.5 ± 1.10.17
      Three regions
      upper lung5.7 ± 1.75.9 ± 1.85.5 ± 1.70.09
      middle lung6.8 ± 1.97.0 ± 1.96.6 ± 1.80.105
      lower lung7.2 ± 2.17.4 ± 2.17.0 ± 2.00.08
      P value<0.0001<0.0001<0.0001
      SARS-CoV-2=severe acute respiratory syndrome coronavirus 2. ARDS = acute respiratory distress syndrome.
      As summarized in Table 3, survivors and non-survivors differed significantly in multiple laboratory findings. Fig. 1 shows a “white lung” of a non-survivor with ARDS. Fig. 2 shows the rapid progress of SARS-CoV-2 pneumonia for a non-survivor. Fig. 3 shows pneumothorax in SARS-CoV-2 pneumonia. Fig. 4 shows the evolution of involvement score (with time) in part of patients.
      Table 3Main laboratory findings of 121 critically ill patients with SARS-CoV-2 infection. Comparisons of laboratory findings were performed between non-survivors and survivors using two-sample t-test for normal distribution data, or Mann-Whitney U test for non-normal data. Medians and IQR were provided in the table.
      All patient (n = 121)Non-survivors (n = 58)Survivors(n = 63)P value
      Leucocyte (109/L)9.5 [7.1, 13.61]11.64 [9.37, 15.61]7.22 [6.1, 8.79]<0.0001
      Platelet (109/L)130.4 [83, 188]118 [63, 179]144.5 [113.5, 227.75]0.004
      Erythrocyte (1012/L)3.61 [3.01, 4.06]3.48 [2.71, 3.89]3.76 [3.59, 4.17]0.001
      Neutrophils (109/L)8.72 [6.5, 11.12]9.44 [7.36, 12.71]7.8 [5.45, 9.37]<0.0001
      Lymphocyte (109/L)0.6 [0.41, 0.81]0.5 [0.32, 0.74]0.70 [0.47, 0.93]<0.0001
      Hemoglobin (g/L)117 [102.2, 128]115.5 [91, 127]120 [112.5, 129]0.01
      Glucose (mmol/L)8.33 [7.24, 9.76]9.11 [7.62, 13.66]7.44 [6.46, 9.48]0.019
      Total protein (g/L)60.6 [58.9, 65.8]59.9 [56.8, 65.4]61.9 [59.8, 67.9]0.18
      Globulin (g/L)35.1 [31.25, 38.7]37.9 [33.3, 40.7]32.2 [29.25, 33.85]<0.0001
      Albumin (g/L)30.1 [27.28, 33.5]28 [25.08, 30.95]32.3 [28.2, 37.2]0.588
      Creatinine (μmol/L)84 [63, 109.5]86 [66, 179.5]82 [59, 103.5]0.008
      Uric acid (μmol/L)189 [136.4, 330.9]190.5 [114.5, 309]188 [148, 362.4]0.537
      Total bilirubin (μmol/L)14.7 [10.4, 18.5]17.4 [11.5, 20.4]11.4 [8.6, 13.4]<0.0001
      Direct bilirubin (μmol/L)8.35 [6.15, 11.4]9.75 [6.4, 14.4]6.8 [5.35, 9.93]<0.0001
      Indirect bilirubin (μmol/L)8.24 [5.72, 9.65]9.15 [5.45, 11]7.6 [5.8, 9.35]0.083
      Urea (mmol/L)11.35 [7.28, 15.68]14.55 [10.23, 20.08]7.85 [6.98, 10.17]<0.0001
      Estimated glomerular filtration rate (ml/min/1.73m2)75.7 [62.35, 89.2]69.3 [41.35, 89.4]82 [73.4,88.6]0.008
      Lactate (mmol/L)1.98[1.09, 2.95]2.7 [1.94, 3.48]1.15 [0.95, 1.33]<0.0001
      Alanine aminotransferase (U/L)40 [23, 68]43 [24, 104]37 [21, 57]0.054
      Aspartate aminotransferase (U/L)38.4 [22.75, 67.15]39 [17, 77.25]38 [24.25, 61.75]0.700
      Myoglobin (ng/mL)214.35 [127.45, 336.6]280.6 [152.15, 736.8]147 [84.45, 229.55]0.002
      High sensitive cardiac troponin I (pg/mL)127.53 [48.68, 290.5]202.2 [68.95, 460.18]50.55 [44.5, 77.6]<0.0001
      MB isoenzyme of creatine kinase (ng/mL)3.57 [1.1, 6.45]5.85 [2.63, 11.93]1.2 [0.7, 1.9]0.014
      Lactate dehydrogenase (U/L)398.75 [351.4, 504.4]490 [358.5, 591]306.5 [281.5, 368.25]<0.0001
      Creatine kinase (U/L)179 [82, 303]180 [43, 503]178 [84, 230.5]0.241
      Prothrombin time (second)15.7 [13.79, 17.23]16.5 [15.3, 19.4]14.3 [13.25, 15.83]<0.0001
      Fibrinogen (g/L)5.87 [4.69, 6.13]5.92 [4.82, 6.3]5.22 [4.59, 5.78]0.069
      Activated partial thromboplastin time (second)45.3 [41.6, 48.45]46.4 [42.5, 56]43.5 [40.65, 46.8]0.002
      Thrombin time (second)16.1 [14.3, 17.8]17.5 [15.7, 20.6]14.55 [13.3, 15.6]0.446
      D-dimer (μg/mL)3.99 [2.16, 5.97]5.47 [2.73, 12.52]2.22 [1.82, 2.92]<0.0001
      Prothrombin activity72 % [62 %, 82 %]68 % [55 %, 75 %]79 % [64 %, 91 %]<0.0001
      International normalized ratio1.19 [1.03, 1.42]1.3 [1.22, 1.56]1.08 [0.99, 1.21]<0.0001
      Fibrinogen degradation products (μg/mL)19.55 [9.6, 35.75]29.65 [17.13, 62.65]9.9 [9, 11.5]<0.0001
      Procalcitonin (ng/mL)0.52 [0.24, 1.25]0.97 [0.27, 2.58]0.27 [0.13, 0.41]<0.0001
      N-terminal pro-brain natriuretic peptide (pg/mL)2425.6 [1145.25, 3489.5]3375.5 [1491.75, 8102.75]1263 [522, 1483.5]<0.0001
      Ferritin (μg/L)945.45 [803.5, 1632.56]1064.5 [814.25, 2658.5]826.8 [616.75, 1481.5]0.137
      Hypersensitive C-reactive protein (mg/L)105.8 [68.94, 155.43]142.7 [80.9, 209]73.1 [42.3, 127.1]<0.0001
      SARS-CoV-2=severe acute respiratory syndrome coronavirus 2.
      Fig. 4
      Fig. 4Seven survivors and nine non-survivors from ICU exactly underwent three X-rays. X-ray images were given a involvement score according to the following rules: each lung was divided into upper, middle, and lower zones, adding up to six zones together; for each zone, score 0 = no involvement, 1 = 1 %–25 % involved, 2 = 26 %–50 % involved, 3 = 51 %–75 % involved, 4 = 76 %–100 % involved; the total score was acquired by summing the scores of six zones. The involvement score varied during disease course. Improvement could be seen in follow-up X-rays of survivors, In contrast, progress could be seen for non-survivors.

      7. Discussions

      We retrospectively reviewed clinical data and imaging data of 121 ICU patients with confirmed SARS-CoV-2 infection. Compared with survivors, non-survivors were more likely to develop ARDS, to have underlying diseases, and had higher X-ray involvement score, higher incidence of pneumothorax.
      Refractory hypoxemia occurred one week after the onset of COVID and then deteriorated into ARDS in part of cases. ARDS is the fundamental pathophysiology of severe viral pneumonia [
      • Xu Z.
      • Shi L.
      • Wang Y.
      • et al.
      Pathological findings of COVID-19 associated with acute respiratory distress syndrome.
      ,
      • Alsaad K.O.
      • Hajeer A.H.
      • Al B.M.
      • et al.
      Histopathology of Middle East respiratory syndrome coronovirus (MERS-CoV) infection - clinicopathological and ultrastructural study.
      ], with a mortality at 28 days near 50 % [
      • Bellani G.
      • Laffey J.G.
      • Pham T.
      • et al.
      Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries.
      ]. ARDS was pathologically related to diffuse alveolar damage with cellular fibromyxoid exudates [
      • Xu Z.
      • Shi L.
      • Wang Y.
      • et al.
      Pathological findings of COVID-19 associated with acute respiratory distress syndrome.
      ,
      • Ding Y.
      • Wang H.
      • Shen H.
      • et al.
      The clinical pathology of severe acute respiratory syndrome (SARS): a report from China.
      ,
      • Ng D.L.
      • Al H.F.
      • Keating M.K.
      • et al.
      Clinicopathologic, immunohistochemical, and ultrastructural H.F.iNdings of a fatal case of Middle East respiratory syndrome coronavirus infection in the United Arab Emirates, April 2014.
      ,
      • Russell C.D.
      • Millar J.E.
      • Baillie J.K.
      Clinical evidence does not support corticosteroid treatment for 2019-nCoV lung injury.
      ,
      • Shang L.
      • Zhao J.
      • Hu Y.
      • Du R.
      • Cao B.
      On the use of corticosteroids for 2019-nCoV pneumonia.
      ]. Previous studies reported that ACE2, the receptor for SARS-CoV-2, was expressed on myocytes and vascular endothelial cells, as well as on tubular cells, glomerular epithelial cells [
      • Mendoza-Torres E.
      • Oyarzun A.
      • Mondaca-Ruff D.
      • et al.
      ACE2 and vasoactive peptides: novel players in cardiovascular/renal remodeling and hypertension.
      ], resulting in potential direct cardiac and renal attack. Acute kidney or cardiac injury was observed in our cohort, which could have been related to direct effects of the virus, hypoxia, or shock. Non-survivors were more likely to develop acute kidney injury and cardiac injury.
      The diagnostic value of chest CT for COVID-19 has been validated by many studies [
      • Zhao W.
      • Zhong Z.
      • Xie X.
      • Yu Q.
      • Liu J.
      Relation between chest CT findings and clinical conditions of coronavirus disease (COVID-19) pneumonia: a multicenter study.
      ,
      • Li K.
      • Wu J.
      • Wu F.
      • et al.
      The clinical and chest CT features associated with severe and critical COVID-19 pneumonia.
      ,
      • Xu X.
      • Yu C.
      • Qu J.
      • et al.
      Imaging and clinical features of patients with 2019 novel coronavirus SARS-CoV-2.
      ]. However, CT is not easily performed for ICU patients, especially when ICU is far from CT rooms. This study found mobile X-ray provided adequate image quality. Follow-up X-ray could also be used to determine whether pneumonia improves or progresses. Extensive lung involvement (or “white lung”) is the key radiographic feature of SARS-CoV-2 ARDS. The middle and lower lung involvement was more serious than upper lung. Lung involvement was more serious in non-survivors versus survivors. For survivors, significant improvement generally occurred in the follow-up X-ray. In contrast, progress of pneumonia occurred in most of non-survivors.
      As for laboratory tests, lymphocytes were less in non-survivor compared to survivor, indicating that excessive immune response played an important role on pathogenesis of fatal SARS-CoV-2, and that the degree of lymphocytopenia was related to the severity of the disease. In our study, leukocytes and neutrophils were both high in non-survivors. It suggested that in critically ill patients, perhaps neutrophils were activated to induce the immune response, causing cytokine storms. LDH was a predictor in many diseases related to inflammatory reaction and tissue damage. CRP was a widely used biochemical indicator for inflammation, such as microbial invasion or tissue damage. We found that LDH and CRP were also significantly higher in non-survivors. We speculated that the virus may trigger a series of immune responses and induces cytokine storm in vivo. The level of inflammation indicators may correlate with the severity of the disease and prognosis.
      This study has some limitations. First, this study was conducted at a single-center for severe SARS-CoV-2 patients; therefore, there may be selection bias. Second, most patients didn’t have CT images at ICU, which was a more accurate imaging method in monitoring the disease course. A larger cohort study of SARS-CoV-2 pneumonia from multiple centers would help further explore the disease.
      In conclusion, there were typical clinical and radiographic features of ICU patients with SARS-CoV-2 pneumonia. Extensive air space opacities or “white lung” was the key radiographic sign for critical ill patients. Compared to survivors, non-survivors had more serious lung involvement, as well as a higher incidence of pneumothorax.

      Ethical statement

      This study was approved by the ethics committee of Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology (approval number: HUST-TJ-20200168). Written informed consent was waived.

      Funding

      This study was funded by NSFC 81801663 .

      CRediT authorship contribution statement

      Gang Wu: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing. Shuchang Zhou: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing.

      Declaration of Competing Interest

      The authors declare that they have no conflict of interest.

      References

      1. World Health Organization. Corona-virus disease (COVID-19) outbreak (https://www.who.int). In.

      2. World Health Organization.https://www.who.int/emergencies/diseases/novel-coronavirus-2019. In.

      3. Chinese National Health Committee. Diagnosis and treatment of COVID-19 pneumonia (trial seventh edition) (2020-03-04). http://www.nhc.gov.cn/yzygj/s7653p/202003/46c9294a7dfe4cef80dc7f5912eb1989.shtml. In.

        • Pan F.
        • Ye T.
        • Sun P.
        • et al.
        Time course of lung changes on chest ct during recovery from 2019 novel coronavirus (COVID-19) pneumonia.
        Radiology. 2020; : 200370
        • Shi H.
        • Han X.
        • Jiang N.
        • et al.
        Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study.
        Lancet Infect. Dis. 2020;
        • Zhou S.
        • Wang Y.
        • Zhu T.
        • Xia L.
        CT features of coronavirus disease 2019 (COVID-19) pneumonia in 62 patients in Wuhan, China.
        AJR Am. J. Roentgenol. 2020; : 1-8
        • Zu Z.Y.
        • Jiang M.D.
        • Xu P.P.
        • et al.
        Coronavirus disease 2019 (COVID-19): a perspective from China.
        Radiology. 2020; : 200490
        • Bernheim A.
        • Mei X.
        • Huang M.
        • et al.
        Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection.
        Radiology. 2020; : 200463
        • Duan Y.N.
        • Qin J.
        Pre- and posttreatment chest CT findings: 2019 novel coronavirus (2019-nCoV) pneumonia.
        Radiology. 2020; : 200323
        • Guan W.J.
        • Ni Z.Y.
        • Hu Y.
        • et al.
        Clinical characteristics of coronavirus disease 2019 in China.
        N. Engl. J. Med. 2020;
        • Li Q.
        • Guan X.
        • Wu P.
        • et al.
        Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia.
        N. Engl. J. Med. 2020;
        • Zhou F.
        • Yu T.
        • Du R.
        • et al.
        Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study.
        Lancet. 2020;
        • Dominguez-Cherit G.
        • Lapinsky S.E.
        • Macias A.E.
        • et al.
        Critically Ill patients with 2009 influenza A(H1N1) in Mexico.
        JAMA. 2009; : 1880-1887
        • Fowler R.A.
        • Lapinsky S.E.
        • Hallett D.
        • et al.
        Critically ill patients with severe acute respiratory syndrome.
        JAMA. 2003; 290: 367-373
        • Antonio G.E.
        • Ooi C.G.
        • Wong K.T.
        • et al.
        Radiographic-clinical correlation in severe acute respiratory syndrome: study of 1373 patients in Hong Kong.
        Radiology. 2005; : 1081-1090
        • Ko S.F.
        • Lee T.Y.
        • Huang C.C.
        • et al.
        Severe acute respiratory syndrome: prognostic implications of chest radiographic findings in 52 patients.
        Radiology. 2004; 233: 173-181
        • Xu Z.
        • Shi L.
        • Wang Y.
        • et al.
        Pathological findings of COVID-19 associated with acute respiratory distress syndrome.
        Lancet Respir. Med. 2020;
        • Alsaad K.O.
        • Hajeer A.H.
        • Al B.M.
        • et al.
        Histopathology of Middle East respiratory syndrome coronovirus (MERS-CoV) infection - clinicopathological and ultrastructural study.
        Histopathology. 2018; 72: 516-524
        • Bellani G.
        • Laffey J.G.
        • Pham T.
        • et al.
        Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries.
        JAMA. 2016; 315: 788-800
        • Xu Z.
        • Shi L.
        • Wang Y.
        • et al.
        Pathological findings of COVID-19 associated with acute respiratory distress syndrome.
        Lancet Respir. Med. 2020;
        • Ding Y.
        • Wang H.
        • Shen H.
        • et al.
        The clinical pathology of severe acute respiratory syndrome (SARS): a report from China.
        J. Pathol. 2003; 200: 282-289
        • Ng D.L.
        • Al H.F.
        • Keating M.K.
        • et al.
        Clinicopathologic, immunohistochemical, and ultrastructural H.F.iNdings of a fatal case of Middle East respiratory syndrome coronavirus infection in the United Arab Emirates, April 2014.
        Am. J. Pathol. 2016; : 652-658
        • Russell C.D.
        • Millar J.E.
        • Baillie J.K.
        Clinical evidence does not support corticosteroid treatment for 2019-nCoV lung injury.
        Lancet. 2020; 395: 473-475
        • Shang L.
        • Zhao J.
        • Hu Y.
        • Du R.
        • Cao B.
        On the use of corticosteroids for 2019-nCoV pneumonia.
        Lancet. 2020; 395: 683-684
        • Mendoza-Torres E.
        • Oyarzun A.
        • Mondaca-Ruff D.
        • et al.
        ACE2 and vasoactive peptides: novel players in cardiovascular/renal remodeling and hypertension.
        Ther. Adv. Cardiovasc. Dis. 2015; 9: 217-237
        • Zhao W.
        • Zhong Z.
        • Xie X.
        • Yu Q.
        • Liu J.
        Relation between chest CT findings and clinical conditions of coronavirus disease (COVID-19) pneumonia: a multicenter study.
        Am. J. Roentgenol. 2020; : 1-6
        • Li K.
        • Wu J.
        • Wu F.
        • et al.
        The clinical and chest CT features associated with severe and critical COVID-19 pneumonia.
        Invest. Radiol. 2020;
        • Xu X.
        • Yu C.
        • Qu J.
        • et al.
        Imaging and clinical features of patients with 2019 novel coronavirus SARS-CoV-2.
        Eur. J. Nucl. Med. Mol. Imaging. 2020;