Prognostic Value of Chest-Computed Tomography in Patients with COVID-19
1
Department of Chest Disease, Kars Harakani State Hospital, Kars 36200, Turkey
2
Department of Radiology, Kars Harakani State Hospital, Kars 36200, Turkey
3
Department of Cardiology, Faculty of Medicine, Kafkas University, Kars 36000, Turkey
*
Author to whom correspondence should be addressed.
Adv. Respir. Med. 2022, 90(4), 312-322; https://doi.org/10.3390/arm90040041
Submission received: 1 July 2022
/
Revised: 7 July 2022
/
Accepted: 22 July 2022
/
Published: 9 August 2022
Abstract
:Highlights
- Our study verifies that chest CT is one of the most helpful tools for diagnosing COVID-19 pneumonia.
- The chest CT scoring system shown in the current study provides important prognostic value in patients with COVID-19.
- The current study showed a correlation between chest CT score and inflammation biomarkers in COVID-19 pneumonia.
Abstract
Background: The diagnostic value for chest CT has been widely established in patients with COVID-19. However, there is a lack of satisfactory data about the prognostic value of chest CTs. This study investigated the prognostic value of chest CTs in COVID-19 patients. Materials and Methods: A total of 521 symptomatic patients hospitalized with COVID-19 were included retrospectively. Clinical, laboratory, and chest CT characteristics were compared between survivors and non-survivors. Concerning chest CT, for each subject, a semi-quantitative CT severity scoring system was applied. Results: Most patients showed typical CT features based on the likelihood of COVID-19. The global CT score was significantly higher in non-survivors (median (IQR), 1 (0–6) vs. 10 (5–13), p < 0.001). A cut-off value of 5.5 for the global CT score predicted in-hospital mortality with 74% sensitivity and 73% specificity. Global CT score, age, C-reactive protein, and diabetes were independent predictors of in-hospital mortality. The global CT score was significantly correlated with the C-reactive protein, D-dimer, pro-brain natriuretic peptide, and procalcitonin levels. Conclusion: The global CT score could provide valuable prognostic data in symptomatic patients with COVID-19.
1. Introduction
Since 2020, the coronavirus disease2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been a significant global threat, with millions of victims [1]. COVID-19 occurs mainly in the form of an acute respiratory infection (pneumonia). The reverse-transcription polymerase chain reaction (RT-PCR) nucleic acid assay is the gold standard for clinical diagnosis [2]. However, the sensitivity of RT-PCR screening is relatively low under certain circumstances. The test may produce false negatives if the volume of the viral genome is insufficient or if the same temporal window of viral replication is missed [3].
Moreover, the RT-PCR process takes much time, and shortages of test kit supplies are common in some nations [4]. Chest-computed tomography (CT) might play a pivotal role in diagnosing and predicting the disease’s severity [5,6,7,8]. According to a growing number of reports, combined RT-PCR and CT imaging might assist healthcare professionals in diagnosing COVID-19 [8,9].
Countless reports have addressed the role of chest CT in COVID-19 disease. However, most of these studies included a smaller population or superficially rendered chest CTs [10,11,12]. Furthermore, there is a lack of satisfying data regarding the prognostic values of chest CTs in patients with COVID-19 [13]. Therefore, we investigated the prognostic values of chest CTs in hospitalized patients with symptomatic COVID-19.
2. Materials and Methods
2.1. Study Population
This single-center retrospective study reviewed the records of hospitalized patients with symptomatic COVID-19 from 12 April to 6 October 2020. We analyzed the demographics, comorbidities, laboratory results, and detailed chest CT findings. We included 521 adult patients who presented to our center within 96 h of the onset of symptoms. All patients tested positive for SARS-CoV-2 with confirmed RT-PCR. A 5-day favipiravir medication was administered for each subject. The study population was classified into two groups according to survival, and the findings were compared between survivors and non-survivors. Exclusion criteria were as follows: (1) Asymptomatic patients, (2) lack of chest CT, and (3) patients with erroneous or artifact chest CTs, which were not suitable for analysis.
2.2. Clinical and Laboratory Parameters
We collected data, including respiratory rate, heart rate, blood pressure, oxygen saturation, and laboratory findings at admission from hospital records. We analyzed the following biochemical and hematological parameters: albumin, total protein, C-reactive protein (CRP), D-dimer, ferritin, procalcitonin, and B-type natriuretic peptide (pro-BNP), white blood cell, lymphocyte, neutrophil, hemoglobin, platelet, and creatinine. Comorbidities of hypertension, diabetes, chronic obstructive pulmonary disease, coronary artery disease, cerebrovascular disease, chronic kidney disease, and chronic heart failure were interrogated. Smoking status was also checked. Chronic heart failure was defined as a left ventricle ejection fraction of <50%, and an estimated glomerular filtration rate (eGFR) of less than 60 mL/min was considered chronic kidney disease. The history of hemorrhagic or an ischemic cerebral attack was described as having a cerebrovascular disease.
2.3. CT Protocol
Chest CT images were obtained at the end-inspiration level with patients in supine positions with their arms elevated. Chest CT images of the patients were acquired using a 16-slice Toshiba Alexion CT scanner (Toshiba Medical Systems, Nasu, Japan). The scanning parameters were as follows: tube voltage = 120 kVP, automatic tube current modulation (30–80 mAs), pitch = 0.93–1.43 mm, matrix = 512 × 512 slice thickness = 2 mm, and field of view = 370 mm × 370 mm. All axial images with slice thicknesses of 2 mm were acquired with the fast scan method. All chest CTs were performed within 96 h of the onset of symptoms.
2.4. Chest CT Evaluation
A 7-year experienced radiologist (CÖ) retrospectively evaluated the CT images, blinded to the final diagnosis. Radiological terms, such as ground–glass opacity (GGO), crazy-paving pattern, and pulmonary consolidation, were based on the standard glossary for thoracic imaging reported by the Fleischner Society [14]. CT features were classified into three groups based on the likelihood of COVID-19 pneumonia according to the Radiological Society of North America Expert Consensus Statement [15] (1) typical, (2) indeterminate, and (3) atypical. The typical group included “bilateral, peripheral and basal GGO with or without consolidation”, “bilateral, peripheral and basal consolidation”, “multifocal and rounded GGO with or without consolidation”, “peribronchialenlargement”, “halosign”, “reversed halo sign”, “vascular enlargement” and “crazy-paving pattern”. The indeterminate group covered “unilateral GGO with or without consolidation”, “perihilar GGO with or without consolidation”, “diffuse GGO with or without consolidation”, “few and small non-peripheric GGO” and “perihilar consolidation”. The atypical group included “lobar pneumonia”, “bronchopneumonia”, “pleural effusion”, “interlobular septal thickening”, “pulmonary fibrosis” and “lymphadenopathy”.
Furthermore, a semi-quantitative CT severity scoring system proposed by Pan et al. [11] was applied to each subject. By this system, each of the five lung lobes was assessed and given a score from 0 to 5, according to the extent of lobar involvement (0: no involvement, 1: involvement less than 5%, 2: 5–25% involvement, 3: 26–49% involvement, 4: 50–75% involvement, 5: involvement more than 75%). Each subject’s final global CT score was the sum of each lobar score, from 0 to 25 (Figure 1).
2.5. Statistical Analysis
Data obtained from this study were evaluated by using the SPSS 20 program. The normality test was maintained by using the Kolmogorov–Smirnov test. All continuous data in the study were not normally distributed, expressed as the median with the interquartile range. Categorical data were expressed as percentages and analyzed using the chi-square test. The Mann–Whitney U-test was used to analyze continuous variables. The statistical significance level was accepted as p < 0.05. A multivariate logistic regression analysis was performed to identify the independent predictors of in-hospital mortality, using variables showing significant associations in the univariate analysis. The receiver operating characteristic (ROC) curve analysis was also used to detect the optimum cut-off value of the global CT score for predicting in-hospital mortality. Additionally, a correlation between blood markers and the global CT score was assessed using Spearman’s correlation test.
Ethics Approval
The study protocol was approved by the Ethical Committee of Kafkas University and the health ministry (reference number 80576354-050-99/276; date of approval: 4 November 2020).
3. Results
3.1. Patient Characteristics
Of 521 patients (median age (IQR), 66 years (51–78)), 267 (51.2%) were male, 309 (59.3%) were discharged with recovery, and 212 (40.7%) expired throughout the hospitalization. The mortality rate was higher in males compared to females (63.7% vs. 36.3%, p < 0.001). Table 1 compares the demographic and clinical characteristics between survivors and non-survivors. Non-survivors were older than survivors [51 years (31–66) vs. 78 years (66–87.5), p < 0.001]. The most prevalent symptoms at presentation were dyspnea (55.5%), cough (45.5), fatigue (42%), arthralgia/myalgia (36.1%), and fever (27.1%). Fever, cough, dyspnea, fatigue, nausea, anosmia, anorexia, ageusia, sore throat, abdominal pain, headache, and arthralgia/myalgia were more common in non-survivors than survivors (all p values < 0.05). The most frequently recorded comorbidities were hypertension (46.1%), chronic obstructive pulmonary disease (COPD) (29%), and diabetes (16.9%). Frequencies of comorbidities (hypertension, diabetes, coronary artery disease, chronic heart failure, COPD, chronic kidney disease (CKD), cerebrovascular disease (CVD)), and smoking were higher in non-survivors (all p values < 0.05).
3.2. Results of Laboratory Characteristics
Considering laboratory findings, pro-BNP, CRP, procalcitonin, and D-dimer levels were significantly higher in non-survivors than survivors (median (IQR), 37.5 (13–137) vs. 555 (90–1936), 4.2 (2–10.5) vs. 79.7 (35.6–150.3), 0.052 (0.037–0.073) vs. 0.233 (0.117–0.589), and 343 (224–802) vs. 924 (324–157), all p values < 0.001, respectively). Ferritin was significantly lower in non-survivors than survivors (128 (42–254) vs. 439 (186–1053), p < 0.001). Despite being in normal ranges, WBC, creatinine, and neutrophil levels were higher in non-survivors than survivors. However, hemoglobin, lymphocyte, platelets (PLT), total protein, and albumin showed significantly lower medians(all p values < 0.001) (Table 1).
Results of Chest CT
According to chest CT, 411 (78.9%) patients had lung involvement. A total of 316 (60.7%) patients showed typical CT features based on the likelihood of COVID-19, while indeterminate and atypical features were found in 61 (11.7%) and 34 (6.5%) patients, respectively (Figure 2). The comparisons of the CT features between survivors and non-survivors are presented in Table 2. In the typical group, the most common disease pattern included bilateral, peripheral, and basal GGO with or without consolidation, and it was significantly higher in non-survivors than survivors (45% vs. 76.4%, p < 0.001). The second common pattern was bilateral, peripheral, and basal consolidation (11.1%), also significantly higher in non-survivors than survivors (8.7% vs. 14.6%, p = 0.036). However, multifocal rounded GGO with or without consolidation (4.8%) and the crazy paving pattern(1.9%) showed significantly lower proportions in non-survivors (p values, 0.01 and <0.001, respectively). Peribronchial enlargement and the reversed halo sign presented similar rates between the two groups (p values, 0.108 and 0.209, respectively). No halo sign and vascular enlargement were exhibited as CT findings. The proportion of patients who showed indeterminate features was similar between survivors and non-survivors (p = 0.058). In the atypical group, the most commonly found disorder, effusion (3.1%), was higher in non-survivors than survivors (0% vs. 7.5%, p < 0.001).
For features based on lesion localization and lobar involvement, the pathologies were as follows: LUL in 305 patients (58.5%), LLL in 299 patients (57.4%), RUL in 297 patients (57%), RLL in 250 patients (47.9%), and RML in 242 patients (46.4%). All were higher in non-survivors (all p values < 0.001). The global CT score median was 5 (IQR, 0–10), and it was significantly higher in non-survivors than survivors (median (IQR), 1 (0–6) vs. 10 (5–13), p < 0.001).
According to the univariate analysis, variables, including “age”, “gender”, “bilateral, peripheral and basal GGO with or without consolidation”, “bilateral, peripheral and basal consolidation”, “multifocal rounded GGO with or without consolidation”, “crazy paving pattern”, “typical CT involvement”, “unilateral GGO with or without consolidation”, “atypical CT involvement”, “effusion”, “total CT score”, “smoking”, “COPD”, “hypertension”, “coronary artery disease”, “chronic heart failure”, “diabetes”, “CVD”, “CRP”, “D-dimer”, and “pro-NBP” were significantly associated with in-hospital mortality. Thus, we put these variables in the multivariate analysis. The global CT score, age, CRP, and diabetes were independent predictors of in-hospital mortality (Table 3).
The optimal cut-off value of the global CT score for in-hospital mortality was 5.5 (with a sensitivity of 74% and specificity of 73% (area under the curve (AUC): 0.794; p < 0.001)) (Figure 3). Concerning correlation with blood markers, the global CT score was significantly associated with CRP (p < 0.0001, r = 0.566), D-dimer (p < 0.0001, r = 0.252), pro-BNP (p < 0.0001, r = 0.329), and procalcitonin (p < 0.0001, r = 0.359) levels (Figure 4).
4. Discussion
We investigated the prognostic values of chest CT features in hospitalized symptomatic COVID-19 patients. Our study showed that the global CT score could provide valuable predictive data for these patients. The global CT score, age, CRP, and diabetes were independent predictors of in-hospital mortality.
Consistent with prior reports [16,17,18], the most common CT findings in our study involved typical involvements, including bilateral, peripheral, and basal GGO with or without consolidation, bilateral, peripheral, and basal consolidation, multifocal rounded GGO with or without consolidation, peribronchial enlargement, and a crazy paving pattern.
Francone M et al. [19] studied a CT scoring methodology in patients with COVID-19. The CT score was significantly higher in critically ill patients than in mild disease patients. It was also significantly higher among late-phase than in early-phase patients. They found a significant correlation between CT score and CRP and D-Dimer. Similarly, our study showed that the median global CT score was significantly higher in non-survivors compared with survivors and that the global CT score was positively correlated with blood markers of CRP, D-dimer, pro-BNP, and procalcitonin.
Moreover, levels of these markers were increased in COVID-19 patients and strongly associated with outcome, as revealed in priorly published reports by Lippi G et al. [20], Lui F et al. [21], and Marin BG et al. [5]. In another study, Yuan et al. [22] investigated the characteristics of chest CT in COVID-19 patients. They also determined a CT score similar to ours. Non-survivors showed a higher CT score when compared to survivors. However, this study had a limited number of participants (only 27).
In the current study, pulmonary fibrosis and interlobular thickening were found in a few patients. Presumably, it was due to imaging times performed in the early stage of the disease. The longer the time between the onset of symptoms and the acquisition of the CT scan, the higher the probability of detecting fibrotic alterations indicative of late-stage pneumonia [23].
Our mortality data verified the prominent prognostic significance of age and the male gender [5]. Moreover, elevated heart rate and respiratory rate, low saturation and blood pressure levels, higher frequencies of coronary artery disease, chronic heart failure, chronic kidney disease, diabetes, COPD, and CVD were significantly correlated with mortality, as established by previous reports [2,24,25].
5. Limitations
Marked limitations of our study were:(1) We excluded asymptomatic patients, so we could not link our findings to all COVID-19 patients. (2) We lacked knowledge regarding discharged patients’ outcomes. (3) We only assessed chest CTs on admission; herewith, we could not provide data about the progression of the disease. (4) Since we lacked detailed data on pharmacological therapy for comorbidities, we could not calculate how treatment affected survival in-depth. (5) Although the radiologist was blinded to the final diagnosis, he was aware that the chest CTs he interpreted were suspects of COVID-19. Future prospective studies with large populations are expected to clarify the prognostic value of chest CT features in this field.
6. Conclusions
Our data show that as non-invasive and rapid imaging modalities, chest CTs might play a significant role in recognizing high-risk COVID-19 patients. Moreover, the global CT score could provide valuable prognostic data in symptomatic patients with COVID-19. Our study verifies prior reports concerning the diagnostic role of chest CTs in patients with COVID-19.
Author Contributions
Conceptualization, G.P. and T.O.; methodology, G.P.; software, T.O.; validation, G.P., C.Ö. and T.O.; formal analysis, T.O.; investigation, G.P.; resources, C.Ö.; data curation, G.P.; writing—original draft preparation, G.P.; writing—review and editing, T.O.; visualization, G.P.; supervision, C.Ö.; project, G.P. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Ethical Committee of Kafkas University, reference No. 80576354-050-99/276, Approval date 4 November 2020.
Informed Consent Statement
Since this was a retrospective study Ethical Committee of Kafkas University waived the need to obtain consent from subjects for the collection, analysis, and publication of the retrospectively obtained and anonymized data for this non-interventional study.
Data Availability Statement
Data supporting the reported results are available to the authors.
Conflicts of Interest
The authors declare no conflict of interest.
References
- World Health Organisation. Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019 (accessed on 22 March 2021).
- Chen, N.; Zhou, M.; Dong, X.; Qu, J.; Gong, F.; Han, Y.; Qiu, Y.; Wang, J.; Liu, Y.; Wei, Y.; et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. Lancet 2020, 395, 507–513. [Google Scholar] [CrossRef] [Green Version]
- Zou, L.; Ruan, F.; Huang, M.; Liang, L.; Huang, H.; Hong, Z.; Yu, J.; Kang, M.; Song, Y.; Xia, J.; et al. SARS-CoV-2 Viral Load in Upper Respiratory Specimens of Infected Patients. N. Engl. J. Med. 2020, 382, 1177–1179. [Google Scholar] [CrossRef] [PubMed]
- Lauer, S.A.; Grantz, K.H.; Bi, Q.; Jones, F.K.; Zheng, Q.; Meredith, H.R.; Azman, A.S.; Reich, N.G.; Lessler, J. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Ann. Intern. Med. 2020, 172, 577–582. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gallo Marin, B.; Aghagoli, G.; Lavine, K.; Yang, L.; Siff, E.J.; Chiang, S.S.; Salazar-Mather, T.P.; Dumenco, L.; Savaria, M.C.; Aung, S.N.; et al. Predictors of COVID-19 severity: A literature review. Rev. Med. Virol. 2021, 31, 1–10. [Google Scholar] [CrossRef] [PubMed]
- Zheng, Y.; Wang, L.; Ben, S. Meta-analysis of chest CT features of patients with COVID-19 pneumonia. J. Med. Virol. 2021, 93, 241–249. [Google Scholar] [CrossRef]
- Ye, Z.; Zhang, Y.; Wang, Y.; Huang, Z.; Song, B. Chest CT manifestations of new coronavirus disease 2019 (COVID-19): A pictorial review. Eur. Radiol. 2020, 30, 4381–4389. [Google Scholar] [CrossRef] [Green Version]
- Dong, D.; Tang, Z.; Wang, S.; Hui, H.; Gong, L.; Lu, Y.; Xue, Z.; Liao, H.; Chen, F.; Yang, F.; et al. The Role of Imaging in the Detection and Management of COVID-19: A Review. IEEE Rev. Biomed. Eng. 2021, 14, 16–29. [Google Scholar] [CrossRef]
- Böger, B.; Fachi, M.M.; Vilhena, R.O.; Cobre, A.F.; Tonin, F.S.; Pontarolo, R. Systematic review with meta-analysis of the accuracy of diagnostic tests for COVID-19. Am. J. Infect. Control 2021, 49, 21–29. [Google Scholar] [CrossRef]
- Sun, Q.; Li, Q.; Gao, F.; Li, J.; Xu, X.; Huang, X. Evolution of computed tomography manifestations of eleven patients with severe coronavirus disease 2019 (COVID-19) pneumonia. Int. J. Clin. Pract. 2021, 75, e13654. [Google Scholar] [CrossRef]
- Pan, F.; Ye, T.; Sun, P.; Gui, S.; Liang, B.; Li, L.; Zheng, D.; Wang, J.; Hesketh, R.L.; Yang, L.; et al. Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19). Radiology 2020, 295, 715–721. [Google Scholar] [CrossRef] [Green Version]
- Herpe, G.; Lederlin, M.; Naudin, M.; Ohana, M.; Chaumoitre, K.; Gregory, J.; Vilgrain, V.; Freitag, C.A.; De Margerie-Mellon, C.; Flory, V.; et al. Efficacy of Chest CT for COVID-19 Pneumonia Diagnosis in France. Radiology 2021, 298, E81–E87. [Google Scholar] [CrossRef]
- Yan, L.; Zhang, H.-T.; Goncalves, J.; Xiao, Y.; Wang, M.; Guo, Y.; Sun, C.; Tang, X.; Jing, L.; Zhang, M.; et al. An interpretable mortality prediction model for COVID-19 patients. Nat. Mach. Intell. 2020, 2, 283–288. [Google Scholar] [CrossRef]
- Hansell, D.M.; Bankier, A.A.; MacMahon, H.; McLoud, T.C.; Müller, N.L.; Remy, J. Fleischner Society: Glossary of Terms for Thoracic Imaging. Radiology 2008, 246, 697–722. [Google Scholar] [CrossRef] [Green Version]
- Simpson, S.; Kay, F.U.; Abbara, S.; Bhalla, S.; Chung, J.H.; Chung, M.; Henry, T.S.; Kanne, J.P.; Kligerman, S.; Ko, J.P.; et al. Radiological Society of North America Expert Consensus Statement on Reporting Chest CT Findings Related to COVID-19. Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA-Secondary Publication. J. Thorac. Imaging 2020, 35, 219–227. [Google Scholar] [CrossRef]
- Brouqui, P.; Amrane, S.; Million, M.; Cortaredona, S.; Parola, P.; Lagier, J.-C.; Raoult, D. Asymptomatic hypoxia in COVID-19 is associated with poor outcome. Int. J. Infect. Dis. 2021, 102, 233–238. [Google Scholar] [CrossRef]
- Wong HY, F.; Lam HY, S.; Fong, A.H.-T.; Leung, S.T.; Chin, T.W.-Y.; Lo CS, Y.; Lui, M.M.-S.; Lee JC, Y.; Chiu, K.W.-H.; Chung, T.W.-H.; et al. Frequency and Distribution of Chest Radiographic Findings in Patients Positive for COVID-19. Radiology 2020, 296, E72–E78. [Google Scholar] [CrossRef] [Green Version]
- Chung, M.; Bernheim, A.; Mei, X.; Zhang, N.; Huang, M.; Zeng, X.; Cui, J.; Xu, W.; Yang, Y.; Fayad, Z.A.; et al. CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV). Radiology 2020, 295, 202–207. [Google Scholar] [CrossRef] [Green Version]
- Francone, M.; Iafrate, F.; Masci, G.M.; Coco, S.; Cilia, F.; Manganaro, L.; Panebianco, V.; Andreoli, C.; Colaiacomo, M.C.; Zingaropoli, M.A.; et al. Chest CT score in COVID-19 patients: Correlation with disease severity and short-term prognosis. Eur. Radiol. 2020, 30, 6808–6817. [Google Scholar] [CrossRef]
- Lippi, G.; Favaloro, E.J. D-dimer is Associated with Severity of Coronavirus Disease 2019: A Pooled Analysis. Thromb. Haemost. 2020, 120, 876–878. [Google Scholar] [CrossRef] [Green Version]
- Liu, F.; Li, L.; Xu, M.; Wu, J.; Luo, D.; Zhu, Y.; Li, B.; Song, X.; Zhou, X. Prognostic value of interleukin-6, C-reactive protein, and procalcitonin in patients with COVID-19. J. Clin. Virol. 2020, 127, 104370. [Google Scholar] [CrossRef]
- Yuan, M.; Yin, W.; Tao, Z.; Tan, W.; Hu, Y. Association of radiologic findings with mortality of patients infected with 2019 novel coronavirus in Wuhan, China ed O Schildgen. PLoS ONE 2020, 15, e0230548. [Google Scholar] [CrossRef] [Green Version]
- Caruso, D.; Polici, M.; Zerunian, M.; Pucciarelli, F.; Polidori, T.; Guido, G.; Rucci, C.; Bracci, B.; Muscogiuri, E.; De Dominicis, C.; et al. Quantitative Chest CT analysis in discriminating COVID-19 from non-COVID-19 patients. Radiol. Med. 2021, 126, 243–249. [Google Scholar] [CrossRef]
- Wang, D.; Hu, B.; Hu, C.; Zhu, F.; Liu, X.; Zhang, J.; Wang, B.; Xiang, H.; Cheng, Z.; Xiong, Y.; et al. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA 2020, 323, 1061–1069. [Google Scholar] [CrossRef]
- Guan, W.; Ni, Z.; Hu, Y.; Liang, W.; Ou, C.; He, J.; Liu, L.; Shan, H.; Lei, C.; Hui, D.S.C.; et al. Clinical Characteristics of Coronavirus Disease 2019 in China. N. Engl. J. Med. 2020, 382, 1708–1720. [Google Scholar] [CrossRef]
Figure 1.
Chest CT findings of COVID-19 showthe CT score of right lower lobe involvement on axial, sagittal, and coronal images. (A) Score 0, 0% involvement; (B) Score 1, <5% involvement; (C) Score 2, 5–25% involvement; (D) Score 3, 26–49% involvement; (E) Score 4, 50–75% involvement; (F) Score 5, >75% involvement. CT: Computed tomography.
Figure 1.
Chest CT findings of COVID-19 showthe CT score of right lower lobe involvement on axial, sagittal, and coronal images. (A) Score 0, 0% involvement; (B) Score 1, <5% involvement; (C) Score 2, 5–25% involvement; (D) Score 3, 26–49% involvement; (E) Score 4, 50–75% involvement; (F) Score 5, >75% involvement. CT: Computed tomography.
Figure 2.
Chest CT findings of COVID-19. Transaxial images show (A) Bilateral, peripheral GGO; (B) Bilateral consolidation; (C) Multifocal rounded GGO. CT: computed tomography, GGO: ground-glass opacity.
Figure 2.
Chest CT findings of COVID-19. Transaxial images show (A) Bilateral, peripheral GGO; (B) Bilateral consolidation; (C) Multifocal rounded GGO. CT: computed tomography, GGO: ground-glass opacity.
Figure 3.
Diagnostic accuracy of the global CT score on in-hospital mortality in symptomatic patients with COVID-19 by the ROC curve.
Figure 3.
Diagnostic accuracy of the global CT score on in-hospital mortality in symptomatic patients with COVID-19 by the ROC curve.
Figure 4.
Correlation graphics between global CT score and C-reactive protein (A), global CT score and D-dimer (B), global CT score and pro-BNP (C), and global CT score and procalcitonin (D).
Figure 4.
Correlation graphics between global CT score and C-reactive protein (A), global CT score and D-dimer (B), global CT score and pro-BNP (C), and global CT score and procalcitonin (D).
Table 1.
Demographic, clinical, and laboratory characteristics.
| Overall (n = 521) | Survivors (n = 309) | Non-Survivors (n = 212) | p-Value | |
|---|---|---|---|---|
| Male, n (%) | 267 (51.2) | 132 (42.7) | 135 (63.7) | <0.001 |
| Female, n (%) | 254 (48.8) | 177 (57.3) | 77 (36.3) | <0.001 |
| Age (years), median (IQR) | 66 (51–78) | 51 (31–66) | 78 (66–87.5) | <0.001 |
| Initial Vital Signs | ||||
| SBP (mmHg), median (IQR) | 110 (110–120) | 120 (110–130) | 110 (95–120) | <0.001 |
| DBP (mmHg), median (IQR) | 70 (60–70) | 70 (70–80) | 60 (60–70) | <0.001 |
| Heart Rate, median (IQR) | 96 (82–122) | 82 (78–92) | 126 (122–130) | <0.001 |
| Saturation (%), median (IQR) | 90 (80–94) | 94 (93–96) | 76 (72–80) | <0.001 |
| RR/minute, median (IQR) | 22 (20–30) | 20 (18–20) | 31 (28–32) | <0.001 |
| Symptoms at arrival | ||||
| Fever, n (%) | 141 (27.1) | 54 (17.5) | 87 (41) | <0.001 |
| Cough, n (%) | 237 (45.5) | 109 (35.3) | 128 (60.4) | <0.001 |
| Dyspnea, n (%) | 289 (55.5) | 97 (31.4) | 192 (90.6) | <0.001 |
| Fatigue, n (%) | 224 (43) | 76 (24.6) | 148 (69.8) | <0.001 |
| Nausea, n (%) | 26 (5) | 24 (7.8) | 2 (0.9) | <0.001 |
| Diarrhea, n (%) | 9 (1.7) | 8 (2.6) | 1 (0.5) | 0.068 |
| Anosmia, n (%) | 15 (2.9) | 13 (4.2) | 2 (0.9) | 0.029 |
| Anorexia, n (%) | 87 (16.8) | 15 (4.9) | 72 (34) | <0.001 |
| Ageusia, n (%) | 17 (3.3) | 16 (5.2) | 1 (0.5) | 0.003 |
| Sore throat, n (%) | 59 (11.3) | 52 (16.8) | 7 (3.3) | <0.001 |
| Abdominal pain, n (%) | 10 (1.9) | 9 (2.9) | 1 (0.5) | 0.046 |
| Headache, n (%) | 85 (16.3) | 66 (21.4) | 19 (9) | <0.001 |
| arthralgia/Myalgia, n (%) | 188 (36.1) | 49 (15.9) | 139 (65.6) | <0.001 |
| Laboratory findings at admission | ||||
| Hgb (g/dL), median (IQR) | 13.8 (12.4–15.3) | 14.1 (12.8–15.7) | 13.8 (10.2–14.7) | <0.001 |
| WBC (×103/μL), median (IQR) | 6.9 (5.2–10.6) | 5.7 (4.08–7.2) | 7.35 (4.84–11.2) | <0.001 |
| Lymphocyte (×103/μL), median (IQR) | 1.23 (0.64–1.71) | 1.53 (1.11–2) | 0.73 (0.44–1.39) | <0.001 |
| Neutrophil (×103/μL), median (IQR) | 4.92 (3.01–8.37) | 3.17 (2–4.92) | 5.58 (3.96 -9.49) | <0.001 |
| PLT (×103/μL), median (IQR) | 191 (147–254) | 198 (174–248) | 183 (98–232) | 0.037 |
| Total protein (g/L), median (IQR) | 68.8 (63–73) | 72 (68–74) | 62 (53–67) | <0.001 |
| Albumin (g/L), median (IQR) | 37.8 (29.7–42.8) | 43 (39–45) | 32 (25–37) | <0.001 |
| ProBNP (pg/mL), median (IQR) | 134 (21–1102) | 37.5 (13–137) | 555 (90–1936) | <0.001 |
| CRP (mg/L), median (IQR) | 25.1 (4.5–111.9) | 4.2 (2–10.5) | 79.7 (35.6–150.3) | <0.001 |
| Procalcitonin (ug/L), median (IQR) | 0.061 (0.039–0.155) | 0.052 (0.037–0.073) | 0.233 (0.117–0.589) | <0.001 |
| Ferritin (ug/L), median (IQR) | 233 (68–505) | 128 (42–254) | 439 (186–1053) | <0.001 |
| Creatinine (mg/dL), median (IQR) | 0.94 (0.75–1.44) | 0.78 (0.69–1) | 0.95 (0.82–1.59) | <0.001 |
| D-Dimer (μg/mL), median (IQR) | 531 (295–1543) | 343 (224–802) | 924 (324–1570) | <0.001 |
| Comorbidities | ||||
| Hypertension, n (%) | 240 (46.1) | 96 (31.1) | 144 (67.9) | <0.001 |
| Diabetes, n (%) | 88 (16.9) | 27 (8.7) | 61 (28.8) | <0.001 |
| Cigarette smoking, n (%) | 187 (35.9) | 98 (31.7) | 89 (42) | 0.016 |
| Coronary artery disease, n (%) | 54 (10.4) | 16 (5.2) | 38 (17.9) | <0.001 |
| chronic heart failure, n (%) | 24 (4.6) | 4 (1.3) | 20 (9.4) | <0.001 |
| COPD, n (%) | 151 (29) | 63 (20.4) | 88 (41.5) | <0.001 |
| CKD (eGFR < 60 mL/min/m2), n (%) | 66 (12.7) | 22 (7.1) | 44 (20.8) | <0.001 |
| CVD, n (%) | 39 (7.5) | 7 (2.3) | 32 (15.1) | <0.001 |
SBP, systolic blood pressure; DBP, diastolic blood pressure; RR, respiratory rate; Hgb, hemoglobin; WBC, white bloodcount; PLT, platelet; BNP, B type natriuretic peptide; CRP, C-reactive protein; COPD, chronic obstructive pulmonary disease; CKD, chronic kidney disease; CVD, cerebrovascular disease.
Table 2.
Chest CT characteristics.
| Overall (n = 521) | Survivors (n = 309) | Non-Survivors (n = 212) | p-Value | ||
|---|---|---|---|---|---|
| Typical, n (%) | 316 (60.7) | 150 (48.5) | 166 (78.3) | <0.001 | |
| Bilateral, peripheral, and basal GGO with or without consolidation, n (%) | 301 (57.8) | 139 (45) | 162 (76.4) | <0.001 | |
| Bilateral, peripheral, and basalconsolidation, n (%) | 58 (11.1) | 27 (8.7) | 31 (14.6) | 0.036 | |
| Multifocal rounded GGO with or without consolidation, n (%) | 25 (4.8) | 21 (6.8) | 4 (1.9) | 0.01 | |
| Peribronchial enlargement, n (%) | 42 (8.1) | 20 (6.5) | 22 (10.4) | 0.108 | |
| Reversed halo sign, n (%) | 10 (1.9) | 4 (1.3) | 6 (2.8) | 0.209 | |
| Crazy paving pattern, n (%) | 10 (1.9) | 0 (0) | 10 (4.7) | <0.001 | |
| Indeterminate, n (%) | 61 (11.7) | 43 (13.9) | 18 (8.5) | 0.058 | |
| Unilateral GGO with or without consolidation, n (%) | 40 (7.7) | 34 (11) | 6 (2.8) | 0.001 | |
| Perihilar GGO with or without consolidation, n (%) | 4 (0.8) | 0 (0) | 4 (1.9) | 0.015 | |
| Diffuse GGO, n (%) | 5 (1) | 5 (1.6) | 0 (0) | 0.063 | |
| Few and small non-peripheric GGO, n (%) | 3 (0.6) | 3 (1) | 0 (0) | 0.15 | |
| Atypical, n (%) | 34 (6.5) | 10 (3.2) | 24 (11.3) | <0.001 | |
| Lobar pneumonia, n (%) | 4 (0.8) | 2 (0.6) | 2 (0.9) | 0.704 | |
| Effusion, n (%) | 16 (3.1) | 0 (0) | 16 (7.5) | <0.001 | |
| Interlobular septal thickening, n (%) | 6 (1.2) | 2 (0.6) | 4 (1.9) | 0.193 | |
| Pulmonary fibrosis, n (%) | 6 (1.2) | 2 (0.6) | 4 (1.9) | 0.193 | |
| Lymphadenopathy, n (%) | 2 (0.4) | 2 (0.6) | 0 (0) | 0.241 | |
| Involved Lobes and global Score | |||||
| RUL, n (%) | 297 (57) | 132 (42.7) | 165 (77.8) | <0.001 | |
| RML, n (%) | 242 (46.4) | 98 (31.7) | 144 (67.9) | <0.001 | |
| RLL, n (%) | 250 (47.9) | 134 (43.3) | 170 (80) | <0.001 | |
| LUL, n (%) | 305 (58.5) | 129 (41.7) | 176 (83) | <0.001 | |
| LLL, n (%) | 299 (57.4) | 123 (39.8) | 176 (83) | <0.001 | |
| Global score, median (IQR) | 5 (0–10) | 1 (0–6) | 10 (5–13) | <0.001 | |
CT, computed tomography.
Table 3.
Univariable and multivariable predictors of in-hospital mortality.
| Univariate Analysis | Multivariate Analysis | |||
|---|---|---|---|---|
| OR (95% CI) | p-Value | OR (95% CI) | p-Value | |
| Age | 1.12 (1.10–1.15) | <0.001 | 1.10 (1.02–1.18) | 0.008 |
| CRP | 1.018 (1.015–1.02) | <0.001 | 1.013 (1.002–1.023) | 0.015 |
| Diabetes | 4.21 (2.54–6.91) | <0.001 | 5.51 (1.77–17.2) | 0.003 |
| Global CT score | 1.25 (1.19–1.30) | <0.001 | 1.73 (1.27–2.34) | <0.001 |
CRP, C-reactive protein; CT, computed tomography.
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Perincek, G.; Önal, C.; Omar, T. Prognostic Value of Chest-Computed Tomography in Patients with COVID-19. Adv. Respir. Med. 2022, 90, 312-322. https://doi.org/10.3390/arm90040041
AMA Style
Perincek G, Önal C, Omar T. Prognostic Value of Chest-Computed Tomography in Patients with COVID-19. Advances in Respiratory Medicine. 2022; 90(4):312-322. https://doi.org/10.3390/arm90040041
Chicago/Turabian StylePerincek, Gökhan, Canver Önal, and Timor Omar. 2022. "Prognostic Value of Chest-Computed Tomography in Patients with COVID-19" Advances in Respiratory Medicine 90, no. 4: 312-322. https://doi.org/10.3390/arm90040041
APA StylePerincek, G., Önal, C., & Omar, T. (2022). Prognostic Value of Chest-Computed Tomography in Patients with COVID-19. Advances in Respiratory Medicine, 90(4), 312-322. https://doi.org/10.3390/arm90040041
