|Year : 2022 | Volume
| Issue : 4 | Page : 119-125
Clinical and baseline computed tomography features of patients infected with the B.1.617.2 (Delta) variant of severe acute respiratory syndrome coronavirus 2
Haixia Mao1, Jixiong Xu1, Shengbing Gong2, Hongwei Chen1, Xiangming Fang1
1 Department of Radiology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, Jiangsu Province, China
2 Department of Radiology, Yangzhou Third People's Hospital, District Branch of North Jiangsu People's Hospital, Yangzhou, Jiangsu Province, China
|Date of Submission||27-Sep-2022|
|Date of Decision||20-Nov-2022|
|Date of Acceptance||20-Dec-2022|
|Date of Web Publication||21-Mar-2023|
Department of Radiology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi 214023, Jiangsu Province
Source of Support: None, Conflict of Interest: None
PURPOSE: The purpose of this study was to investigate the clinical and baseline computed tomography (CT) features and their correlation in patients infected with the B.1.617.2 (Delta) variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
MATERIALS AND METHODS: Clinical and chest baseline CT data of patients infected with the Delta variant of SARS-CoV-2 from July to August 2021 were collected. First, the correlation between the clinical data and baseline CT results was analyzed according to CT positivity or negativity. Then, subgroup analysis was performed between different age distributions and clinical characteristics. Next, the CT characteristics and clinical data of all baseline CT-positive patients were collected, and the correlations between CT characteristics and age, vaccination status, and chronic disease were analyzed. Lesions in patients with baseline CT positivity were evaluated by semi-quantitative scoring to analyze the correlations between the semi-quantitative scores and vaccination status and age distribution.
RESULTS: A total of 221 nucleic acid-positive patients with the SARS-CoV-2 Delta variant were included, of whom 107 patients were baseline CT positive and 114 were baseline CT negative. Baseline CT positivity was associated with age distribution, and baseline CT positivity was most common in patients aged >60 years (P < 0.001), but not with vaccination status or gender. The results of the subgroup analysis according to age distribution indicated that different age distribution subgroups had different vaccination statuses, and the majority of patients aged <18 years and >60 years were unvaccinated (90.5%, 19/21, and 57.3%, 63/110, respectively). In contrast, most patients aged 18–60 years had received two doses of the vaccine (61.1%, 55/90) (P < 0.001). Different age distribution subgroups had different clinical infection types. Asymptomatic and mild cases were most common in patients aged ≤60 years, and moderate and severe or critical cases were most common in patients aged >60 years. For baseline CT-positive patients, the extent of lung involvement was associated with age, vaccination status, and chronic disease. The number of involved lobes was higher in patients who were unvaccinated or who had received one injection, who were aged >60 years or had chronic disease. There was a statistical difference in CT semi-quantitative scores between the different age subgroups. Compared with patients aged < 60 years, patients aged >60 years had higher semi-quantitative scores (P < 0.001). However, there was no statistical difference between the different vaccination groups.
CONCLUSIONS: Age had a large effect on baseline CT positivity, CT characteristics, and semi-quantitative CT scores in patients infected with the Delta variant.
Keywords: B.1.617.2 (Delta), computed tomography, severe acute respiratory syndrome coronavirus 2, vaccination
|How to cite this article:|
Mao H, Xu J, Gong S, Chen H, Fang X. Clinical and baseline computed tomography features of patients infected with the B.1.617.2 (Delta) variant of severe acute respiratory syndrome coronavirus 2. Radiol Infect Dis 2022;9:119-25
|How to cite this URL:|
Mao H, Xu J, Gong S, Chen H, Fang X. Clinical and baseline computed tomography features of patients infected with the B.1.617.2 (Delta) variant of severe acute respiratory syndrome coronavirus 2. Radiol Infect Dis [serial online] 2022 [cited 2023 Jun 3];9:119-25. Available from: http://www.ridiseases.org/text.asp?2022/9/4/119/372193
| Introduction|| |
The B.1.617.2 (Delta) variant is a variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The Delta variant has a short incubation period, rapid transmission, and high infectivity. From July to August 2021, the Delta variant of SARS-CoV-2 resulted in short-term human infection in our city. Currently, most of the vaccines administered in China are produced from inactivated viruses and differ from mRNA vaccines used in other countries. There are only a few reports in China on the correlation between vaccination and the baseline computed tomography (CT) characteristics and clinical data of patients infected with the Delta variant of SARS-CoV-2. In this study, the clinical data and baseline CT characteristics of patients infected with the Delta variant were collected and analyzed.
| Materials and Methods|| |
The clinical data and initial CT data of patients with SARS-CoV-2, Delta variant, treated in our hospital from July to August 2021 were collected. Patients with positive nucleic acid test results were evaluated using high-throughput whole-genome sequencing. Patients whose vaccination history was unknown or who were vaccinated <14 days before enrollment; those who had complications, such as interstitial pneumonia, pulmonary tuberculosis, or other pulmonary infectious diseases; or whose images had severe artifacts were excluded from the study. The patient selection flowchart is shown in [Figure 1].
The vaccines were generated from inactivated viruses and were produced by Sinopharm China Biologics Beijing Institute of Biological Products Co., Ltd. (Beijing Institute) or Beijing Kexing Zhongwei Biopharm Co., Ltd. (Kexing Zhongwei).
A total of 221 patients were enrolled, of whom 96 were male and 125 were female (median age: 36 years, interquartile range: 24–66 years). Nineteen cases had diabetes mellitus, 62 had hypertension, 12 had coronary heart disease, 7 had cerebral infarction, 3 had hyperlipidemia, 5 had asthma, 2 had dementia, 1 had hepatitis, 2 had a history of right kidney tumor resection, and 13 had a history of a malignant tumor (1 case of bowel cancer, 2 cases of lung cancer, 1 case of cervical cancer, 2 cases of thyroid carcinoma, 1 case of prostate cancer, 3 cases of breast cancer, 2 cases of esophageal cancer, and 1 case of gastric cancer).
The protocol for the study was approved by the Ethics Committee of our hospital (Institutional Review Board Approval Number: KS202003). The need to obtain informed consent for this retrospective study was waived.
Computed tomography protocols
All CT examinations were performed on a 32-row multidetector scanner (SOMATOM go.Up; Siemens, Erlangen, Germany). The scanning range was from the tip of the lungs to the top of the diaphragm with the following scanning parameters: 450 mm field of view, 120 kV tube voltage, automatic tube current, 1° pitch, 5 mm slice thickness, lung window with a width of 1500 HU and a level of −600 HU, mediastinal window with a width of 400 HU and a level of 40 HU, and reconstruction of a 1 mm thick lung window with a bone reconstruction algorithm.
Chest CT images of all patients were anonymized after admission and sent to a picture archiving and communication system and postprocessing workstation. The CT images were evaluated double-blind by two radiologists with more than 5 years of chest imaging diagnostic experience. Images with inconsistent evaluation between the radiologists were re-evaluated after consultation and discussion until consensus was reached. Chest CT images were evaluated as negative or positive, first, then the CT signs and semi-quantitative scores of the CT-positive group were evaluated.
CT signs comprised the number of lobes involved (≤3 lobes or 4–5 lobes), density (ground-glass opacity, solid, mixed ground-glass opacity), shape (nodular or irregular), edge (clear or fuzzy), distribution (peripheral, central, or irregular), vascular enlargement, crazy-paving pattern, air bronchogram, cavitation, and other extrapulmonary manifestations (mediastinal lymph node enlargement, pleural thickening, pleural effusion, and pericardial effusion) and were defined using the Fleischner society glossary of terms for thoracic imaging.
The lungs were divided into upper, middle, and lower regions with the tracheal carina and lower pulmonary vein as the boundary; i.e., six regions were defined in both lungs. A semi-quantitative CT score was calculated on the basis of the extent of lobar involvement, as follows: 0: 0%; 1: <25%; 2: 25%–50%; 3: 51%–75%; and 4: >75%. The maximum CT score for both lungs was 24 points. The total scores for both lungs were determined by adding the score of each lung region. The final result was determined by averaging the two radiologists' semi-quantitative scores.
SPSS 26.0 was used for data analysis (IBM Corp., Armonk, NY, USA). The Chi-square test or Fisher's exact test was used to determine the correlation between the baseline CT findings and clinical data, differences in the clinical characteristics of the population in the different age distribution subgroups, the correlation between the CT characteristics of patients with baseline CT positivity and the clinical data, and the correlation between CT signs and the semi-quantitative score and clinical data. Normally distributed measurement data were presented as mean ± standard deviation, and skewed measurement data were presented as median (interquartile range). P < 0.05 (Bonferroni correction) was considered statistically significant.
| Results|| |
A total of 221 patients with nucleic acid-positive test results for the Delta variant were included in this study, of whom 102 were unvaccinated, 46 had received one dose, and 73 had received two doses. There were 107 cases who were CT positive and 114 cases who were CT negative. There were 46 asymptomatic cases, 88 mild cases, 81 moderate cases, and 6 severe or critical cases. The clinical types of the Delta variant infections were in accordance with the diagnosis and treatment for novel coronavirus pneumonia (Trial Eighth Edition).
Correlations between the baseline computed tomography findings and the clinical data
The rate of baseline CT positivity was statistically significantly different between the different age subgroups (P < 0.001). Patients aged <18 years and 18–60 years were mostly baseline CT negative (85.7%, 18/21, and 64.4%, 58/90, respectively), and patients aged >60 years were mostly baseline CT positive (65.5%, 72/110) (P < 0.05). Baseline CT positivity was not related to gender or vaccination status [Table 1].
|Table 1: Correlations between the baseline computed tomography results and the clinical data|
Click here to view
Clinical characteristics of the populations in the different age distribution subgroups
Baseline CT positivity was related to age distribution, and the subgroup analysis of the patients' clinical characteristics was performed on the basis of the different age distributions. The results indicated that the different age distribution subgroups had different vaccination statuses. The majority of patients aged <18 years and >60 years were unvaccinated (90.5%, 19/21, and 57.3%, 63/110, respectively), while most patients aged 18–60 years had received two doses of the vaccine (61.1%, 55/90) (P < 0.001). Different age distribution subgroups had different clinical types of SARS-CoV-2 infection. Asymptomatic and mild cases were more common in patients aged ≤60 years versus >60 years, and moderate and severe or critical cases were more common in patients >60 years versus ≤60 years [Table 2].
|Table 2: Baseline clinical characteristics of the population according to different age distributions|
Click here to view
Factors affecting the computed tomography findings in baseline computed tomography-positive patients
Among patients who were baseline CT positive, there was a statistically significant difference in the number of involved lung lobes according to vaccination status, age, and the presence of chronic disease (P = 0.017, <0.001, and 0.024, respectively). The number of involved lung lobes was higher in patients who were unvaccinated or who had received one dose, those who were >60 years of age, or those with chronic disease. The number of involved lung lobes was lower in patients who had received two vaccination doses, those aged ≤60 years, or those without chronic disease [Figure 2]. The density of the lesions was correlated with vaccination status (P = 0.044), and the majority of the patients with solid-density lesions had received two doses of the vaccine [Figure 3]. Vascular enlargement and crazy-paving pattern were correlated with age (P = 0.014 and 0.019, respectively), and the rates of vascular enlargement and crazy-paving pattern were highest in patients aged >60 years [Figure 4]. Other signs, such as lesion morphology, margins, air bronchograms, cavitation, mediastinal lymph node enlargement, pleural thickening, pleural effusion, and pericardial effusion, were not associated with vaccination status, age, or chronic disease [Table 3].
|Figure 2: (a) CT image of an unvaccinated 80-year-old female with ground-glass lesions in both lungs (red arrow). (b) CT image of a 68-year-old female who received two doses of vaccine, with patchy solid-density shadows visible only in the left superior lung lobe red arrow. CT = Computed tomography|
Click here to view
|Figure 3: (a) CT image of a 58-year-old female who received two doses of vaccine, showing solid patchy shadows (red arrow) under the pleura in the left inferior lung lobe. (b) CT image of a 62-year-old male who received one dose of vaccine, showing ground-glass densities (red arrow) under the pleura in the right middle and inferior lung lobes. Thickened interlobular septa and vessels are visible in the right lung, with a “crazy-paving” pattern. CT = Computed tomography|
Click here to view
|Figure 4: (a) CT image of a 78-year-old female who was unvaccinated for severe acute respiratory syndrome coronavirus 2, showing ground-glass densities scattered under the pleura of both lungs, with thick interlobular septa and vessels showing a “crazy-paving” pattern (red arrow). (b) CT image of a 6-year-old female who was unvaccinated, showing solid nodular shadows (red arrow) in the left inferior lung lobe. CT = Computed tomography|
Click here to view
|Table 3: Factors affecting the computed tomography findings in baseline computed tomography-positive patients|
Click here to view
Correlations of computed tomography semi- quantitative scores with vaccination status and age
Because baseline CT positivity was related to age distribution, and different age distribution subgroups had different vaccination statuses, the CT semi-quantitative scores were calculated for the different age distribution and vaccination status subgroups. The results showed that there was no statistically significant difference in CT semi-quantitative scores between the different vaccination status subgroups; however, the difference was statistically significant for the different age groups (P < 0.001); the older group had the highest semi-quantitative scores [Table 4].
|Table 4: Correlations of computed tomography semi-quantitative scores with vaccination status and age|
Click here to view
| Discussion|| |
In this study, the clinical data and baseline CT characteristics of patients infected with the Delta variant of SARS-CoV-2 were statistically analyzed. The results showed that the baseline CT positivity rate and CT semi-quantitative scores for patients infected with the Delta variant were correlated with age but not with vaccination status. Additionally, different age distribution subgroups had different vaccination statuses and clinical types of coronavirus 2019 (COVID-19). There was a correlation between baseline CT features and clinical data, namely age, vaccination status, and chronic disease.
To explore the relevant influencing factors of baseline CT positivity, the correlations between the baseline CT results and the clinical data were analyzed. The results revealed statistically significant differences in the baseline CT positivity rate for the different age distribution subgroups. The baseline CT status in patients aged <18 years and 18–60 years was mostly negative, while that of patients aged >60 years was mostly positive. The results from this study suggest that age is a greater risk factor for baseline CT positivity compared with vaccination status. This may be related to different levels of resistance to infection in patients in different age groups. Older patients are less resistant to infection compared with younger patients, which results in a higher probability of baseline CT positivity.
The results of the subgroup analysis of the patients' clinical characteristics according to the different age distributions showed that the different age distribution subgroups had different vaccination statuses and clinical infection types. This study found that the majority of patients aged <18 years and >60 years were unvaccinated, while most patients aged 18–60 years had received two doses of the vaccine. Given China's national circumstances and different stages of COVID-19 development compared with other countries, vaccinations in China were mainly administered to individuals aged 18–60 years before July 2021. The vaccination of patients included in this study conformed to this distribution, with different vaccination statuses for the three different age subgroups: <18 years, 18–60 years, and >60 years. The results of this study also indicated that asymptomatic and mild cases were most common in patients aged ≤60 years, and moderate and severe or critical cases were most common in patients aged >60 years. This is consistent with the results of other relevant studies. Studies have shown that children have lower rates of infection, severity, and mortality associated with COVID-19 compared with adults., Some studies have suggested that maternal respiratory inflammation trains the child's immune system to develop long-term immune memory against respiratory viruses. This may partially explain the mild disease observed in children infected with SARS-CoV-2., Additionally, studies have suggested that frequent contact with respiratory viruses in school may have a partial protective effect against SARS-COV-2 in children. Another explanation for the differences in severity between children and adults could be that high angiotensin-converting enzyme 2 receptor levels in children may be the reason why children are less susceptible to SARS-COV-2 infections. Furthermore, studies have shown that older age is a risk factor for severe and critical COVID-19 infections. Age is the most important factor influencing the severity of COVID-19, and the risk is greater in people aged > 65 years. These findings are consistent with those in the present study.
In this study, we found a correlation between CT signs and clinical findings in patients with baseline CT positivity; CT findings in CT-positive patients were associated with vaccination status, age, and chronic disease. The results showed that patients who had received fewer vaccine doses, who were older, or who had concurrent chronic disease had more involved lung lobes compared with patients without these characteristics. These results may be related to increased immunity after vaccination, and the lower immunity in older patients or patients with chronic diseases compared with unvaccinated or younger patients and those without chronic disease. Studies have shown that diabetes, hypertension, cardiovascular and cerebrovascular diseases, male gender, and obesity are risk factors for severe COVID-19 infection., These findings were consistent with our observations in this study. The current study also found that the majority of patients with solid-density lesions had received two doses of the vaccine. The authors speculated that vaccination might change the density of the lesion from ground glass to solid; however, dynamic studies are needed for confirmation. The results also showed that the positivity rate of vascular enlargement and crazy-paving pattern in the lesions was highest in patients aged >60 years, indicating that lesions were more likely to involve small vessels and interlobular septa with age.
In this study, a semi-quantitative score for the lesions was calculated and was associated with age but not with vaccination status. Currently, the analysis of COVID-19 CT images is based mainly on subjective evaluation and semi-quantitative methods.,
The results of this study indicated that older age was associated with severe degree of lung infiltration, consistent with findings in previous studies, and showing that COVID-19 infections are more severe with age. We hypothesized that viral resistance in elderly patients was lower compared with younger patients. Older patients have low viral resistance, which resulted in a high number of involved lung lobes, in this study. Studies have shown that older individuals have been most severely affected by COVID-19. The results of the present study also showed that vaccination affected the number of involved lung lobes, in the baseline CT images; however, there was no significant correlation between vaccination and semi-quantitative scores. This may be because semi-quantitative scores are dependent on the number of involved lung lobes as well as on the number of lesions in the affected lung area.
| Conclusions|| |
Age had a large effect on baseline CT positivity and semi-quantitative CT scores of patients infected with the Delta variant of SARS-CoV-2. In future medical treatment for COVID-19, specific attention is needed for the elderly, especially those with chronic diseases. Factors affecting immunity, such as age, vaccination, and chronic diseases, could affect the baseline CT characteristics of patients with COVID-19. Therefore, we should aim for increased vaccination of older people, which may reduce the degree of pulmonary invasion on baseline CT.
This study had some limitations. Owing to geographical factors and epidemiology, most of the cases included in this study comprised elderly people with a specific age distribution. Additionally, the correlation between vaccination time and clinical and imaging manifestations was not statistically analyzed. Therefore, an influence of vaccination time on clinical and imaging manifestations of patients with the Delta variant of SARS-CoV-2 could not be ruled out. More detailed and in-depth multi-center studies are needed to validate our findings.
We thank Jane Charbonneau, DVM, from Liwen Bianji (Edanz) (www.liwenbianji.cn) for editing the English text of a draft of this manuscript.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Dougherty K, Mannell M, Naqvi O, Matson D, Stone J. SARS-CoV-2 B.1.617.2 (Delta) variant COVID-19 outbreak associated with a gymnastics facility – Oklahoma, April-May 2021. MMWR Morb Mortal Wkly Rep 2021;70:1004-7.
Pormohammad A, Zarei M, Ghorbani S, Mohammadi M, Aghayari Sheikh Neshin S, Khatami A, et al.
Effectiveness of COVID-19 vaccines against delta (B.1.617.2) variant: A systematic review and meta-analysis of clinical studies. Vaccines (Basel) 2021;10:23.
Hansell DM, Bankier AA, MacMahon H, McLoud TC, Müller NL, Remy J. Fleischner society: Glossary of terms for thoracic imaging. Radiology 2008;246:697-722.
Ooi GC, Khong PL, Müller NL, Yiu WC, Zhou LJ, Ho JC, et al.
Severe acute respiratory syndrome: Temporal lung changes at thin-section CT in 30 patients. Radiology 2004;230:836-44.
Xu Y, Gao ZC. Interpretation of diagnosis and treatment for novel coronavirus pneumonia (Trial Eighth Edition). Zhonghua Jie He He Hu Xi Za Zhi 2021;44:11-3.
Cheng VC, Wong SC, Chen JH, Yip CC, Chuang VW, Tsang OT, et al.
Escalating infection control response to the rapidly evolving epidemiology of the coronavirus disease 2019 (COVID-19) due to SARS-CoV-2 in Hong Kong. Infect Control Hosp Epidemiol 2020;41:493-8.
Kloc M, Ghobrial RM, Kuchar E, Lewicki S, Kubiak JZ. Development of child immunity in the context of COVID-19 pandemic. Clin Immunol 2020;217:108510.
Cristiani L, Mancino E, Matera L, Nenna R, Pierangeli A, Scagnolari C, et al.
Will children reveal their secret? The coronavirus dilemma. Eur Respir J 2020;55:2000749.
Dong Y, Mo X, Hu Y, Qi X, Jiang F, Jiang Z, et al.
Epidemiology of COVID-19 among children in China. Pediatrics 2020;145:e20200702.
Gao YD, Ding M, Dong X, Zhang JJ, Kursat Azkur A, Azkur D, et al.
Risk factors for severe and critically ill COVID-19 patients: A review. Allergy 2021;76:428-55.
Brodin P. Immune determinants of COVID-19 disease presentation and severity. Nat Med 2021;27:28-33.
Hayden MR. An immediate and long-term complication of COVID-19 may be type 2 diabetes mellitus: The central role of β-cell dysfunction, apoptosis and exploration of possible mechanisms. Cells 2020;9:2475.
Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al.
Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet 2020;395:1054-62.
Francone M, Iafrate F, Masci GM, Coco S, Cilia F, Manganaro L, et al.
Chest CT score in COVID-19 patients: Correlation with disease severity and short-term prognosis. Eur Radiol 2020;30:6808-17.
Leger T, Jacquier A, Barral PA, Castelli M, Finance J, Lagier JC, et al.
Low-dose chest CT for diagnosing and assessing the extent of lung involvement of SARS-CoV-2 pneumonia using a semi quantitative score. PLoS One 2020;15:e0241407.
Munayco C, Chowell G, Tariq A, Undurraga EA, Mizumoto K. Risk of death by age and gender from CoVID-19 in Peru, March-May, 2020. Aging (Albany NY) 2020;12:13869-81.
[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2], [Table 3], [Table 4]