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| ORIGINAL ARTICLE |
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| Year : 2022 | Volume
: 13
| Issue : 2 | Page : 57-63 |
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Predicting clinical outcome with computed tomography severity score in COVID-19
T Preeth Pany, Nithin Theckumparampil, Aravintho Natarajan
Department of Radiology, Pondicherry Institute of Medical Sciences, Puducherry, India
| Date of Submission | 06-Aug-2022 |
| Date of Acceptance | 23-Sep-2022 |
| Date of Web Publication | 10-Jan-2023 |
Correspondence Address:
Dr. Aravintho Natarajan
Department of Radiology, Pondicherry Institute of Medical Sciences, Kalapet, Puducherry - 605 014
India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/mjmsr.mjmsr_43_22
Introduction: COVID-19 is a major public health burden in the world, and chest computed tomography (CT) is the ideal imaging modality to diagnose and monitor disease progression. Objectives: The objective was to review the common and uncommon chest CT findings of patients with COVID-19 and to correlate the CT findings with short-term prognosis. Methods: All patients who had laboratory-confirmed COVID-19 infection and underwent CT scan were reviewed. The imaging features and the distribution of abnormality were evaluated. A CT severity score (CT-SS) system out of 40 was used. Mann–Whitney U-test and Chi-square or Fisher's exact test were used for analysis. Two-sided P < 0.05 was considered statistically significant. A receiver operating characteristic curve analysis was performed to calculate the CT-SS cutoff for hospital admission and intensive care unit (ICU) admission. Results: A total of 149 individuals were eligible. The most common imaging features were ground-glass opacities (GGO) (88%), septal thickening (70%), and reticulations (50%). The least common imaging features were pneumothorax (1%) and vascular enlargement (1%). The most common distributions of GGO were bilateral (92%) and multifocal (95%), with peripheral (100%) and lower lobe predominance (77%). A higher CT-SS had a significant association with longer hospital stay and ICU admission, with CT-SS of 18 and 10 being optimal cutoff, respectively. Conclusion: Our study is one of the few studies to correlate the imaging finding with clinical outcomes in the south Indian population. The common findings in our study were consistent with the literature. CT-SS plays an important role in predicting prognosis.
Keywords: Chest computed tomography, computed tomography severity score, COVID-19, prognosis
How to cite this article:
Pany T P, Theckumparampil N, Natarajan A. Predicting clinical outcome with computed tomography severity score in COVID-19. Muller J Med Sci Res 2022;13:57-63 |
How to cite this URL:
Pany T P, Theckumparampil N, Natarajan A. Predicting clinical outcome with computed tomography severity score in COVID-19. Muller J Med Sci Res [serial online] 2022 [cited 2023 Jun 2];13:57-63. Available from: https://www.mjmsr.net/text.asp?2022/13/2/57/367408 |
| Introduction | |  |
A cluster of 41 cases of pneumonia of unknown etiology was reported in Wuhan City, Hubei Province, China, in December 2019. The causative pathogen was discovered to be a novel coronavirus, later named as severe acute respiratory syndrome corona virus-2 (SARS-CoV2), and the disease was referred to as COVID-19.[1] Since then, COVID-19 has spread unabated and has become a major public health and economic burden of international concern. Although reverse transcription polymerase chain reaction (RT-PCR) is the gold standard for diagnosis, it has its own pitfall and plays a very little role in prognosis. This virus primarily affects the respiratory system; hence, chest imaging plays a vital role in the diagnosis and management of COVID-19. Chest computed tomography (CT) has proved to be the imaging modality of choice for diagnosis and monitoring of the disease course.[2] Common imaging findings of COVID-19 reported by various studies are bilateral, multilobar ground-glass opacities (GGO) with or without consolidation in peripheral distribution. Other findings include interlobular, intralobular septal thickening, and band-like opacities.[3],[4] Apart from diagnosis, CT also plays an important role in prognosticating the patient by assessing the extent of lung involvement.[5]
In this retrospective study, we aimed to review the common and uncommon chest CT findings of patients with COVID-19, and to correlate the CT findings with short-term prognosis of the patients hospitalized in our hospital, which is a tertiary care center in south India.
| Methods | | |
General
This study received the Ethical Committee Approval, and the requirement for patient informed consent was waived in accordance with the Council for International Organizations of Medical Sciences guidelines.
Patients
We retrospectively identified individuals who have both a laboratory-confirmed COVID-19 infection and a subsequent CT scan done at our tertiary care. Clinical data of these individuals were collected from our hospital information management system. The diagnosis of COVID-19 was determined by isolation of SARS-CoV2 in reverse transcription polymerase chain reaction (RT-PCR) assay from nasal swab samples. All the individuals who participated in the study underwent scanning with the 128-slice CT scanner (Ingenuity CT core 128, Philips, Best, the Netherlands). CT images were acquired with the patient in the supine position and at full inspiration.
Image interpretation
All images were viewed on a display system using IntelliSpace Portal software (version 9.0, Philips Medical Systems, Best, the Netherlands) by a single experienced radiologist. The images were viewed with both lung (window width: 1600 HU; window level: −600 HU) and mediastinal (window width: 360 HU; window level: 60 HU) window settings. A total of 14 imaging features were evaluated: GGO, consolidation, mixed GGO and consolidation, centrilobular nodules, architectural distortion, septal thickening, bronchial wall thickening, reticulation, subpleural bands, traction bronchiectasis, intrathoracic lymph node enlargement, vascular enlargement in the lesion, pleural effusions, reverse halo, and pneumothorax. We specifically described the distribution of the GGO in 4 ways: scatter predominance (multifocal/focal), laterality (bilateral/unilateral), transverse predominance (peripheral/central), and craniocaudal predominance (upper/lower lobe). A CT severity score (CT-SS) system out of 40 was used to evaluate the extent of disease.[5]
Statistical analysis
Continuous variables were presented as medians and compared by Mann–Whitney U-test. Categorical variables were presented as numbers and percentages and were compared by Chi-square or Fisher's exact test. Two-sided P < 0.05 was considered statistically significant. A receiver operating characteristic (ROC) curve analysis was performed to calculate the CT-SS cutoff for hospital admission and intensive care unit (ICU) admission. All statistical analyses were performed with SPSS software (version 26, IBM, Armonk, NY, USA).
| Results | | |
Demographics
A total of 154 individuals with a positive RT-PCR test and subsequent CT scan of the chest (done at our hospital) were identified for 6 months (first wave) between August 2020 and January 2021. Five individuals were excluded due to suboptimal image quality and overlapping features. Our final cohort had 149 adults, of which 39 were women and 110 were men [Table 1].
Symptoms
Ninety-seven percentage (145 out of 149) of individuals had symptoms. The most common symptoms were found to be fever (74%) and cough (54%). The least common symptoms were vomiting (9%), diarrhea (9%), and rhinorrhea (6%) [Table 2]. The median number of symptoms per individual was two, with an interquartile range (IQR) of 2–3. The median duration of symptoms before presentation to the hospital was 2 days (IQR: 2–3 days). The median duration of symptoms before imaging was 7 days (IQR: 5–7 days).
Imaging
The median interval between onset of symptoms and CT examination was 7 days (IQR, 5–10 days). The most common imaging features were GGO (88%), septal thickening (70%), and reticulations (50%). The least common imaging features were pneumothorax (1%) and vascular enlargement (1%) [Table 3]. The median number of imaging features in this cohort was 4 (IQR: 2–6). Ten (7%) individuals had no abnormal CT findings. The most common distributions of GGO were bilateral (92%) and multifocal (95%), with peripheral (100%) and lower lobe predominance (77%) [Table 4].
Clinical course
The median hospital stay for our cohort was 6 days (IQR: 2–10 days). Twenty-seven (18%) individuals were admitted to the ICU and 9 (6%) succumbed to the illness [Table 5].
Regression analysis
A higher CT-SS had a significant association (P < 0.5) with the male gender, certain symptoms (dyspnea and cough), longer hospital stay, and ICU admission [Table 6]. | Table 6: Multiple linear regression of computed tomography severity score versus clinical variables
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Receiver operating characteristic curve
There was a statistically significant correlation between CT-SS and "ICU admission." When a CT-SS of 18 (out of 40) was considered the cutoff, there was a sensitivity of 52% and specificity of 60%, with the area under the curve of 0.74 [Figure 1]. This suggests that there is a 74% of chance that the CT-SS of 18 (out of 40) could distinguish individuals who required ICU admissions from those who did not. | Figure 1: ROC curve of CT-SS for ICU admission. ROC = Receiver operating characteristic, CT-SS = Computed tomography severity score, ICU = Intensive care unit
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Significant positive correlation of CT-SS with increased hospital stay was noted. Optimal CT-SS cutoff for hospital admission was 10 (out of 40) with the sensitivity of 87.5%, specificity of 66.6%, and AUC of 0.74 [Figure 2]. | Figure 2: ROC curve of CT-SS for hospital admission. ROC = Receiver operating characteristic, CT-SS = Computed tomography severity score
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| Discussion | | |
COVID-19 is a major public health burden in the world, with approximately 4 crore infections and 5 lakh deaths recorded in India as of May 2022.[6] Chest CT is the ideal imaging modality to diagnose and monitor disease progression considering its high sensitivity and easy availability.[2] In this study, we have analyzed the common and uncommon CT findings of COVID-19 in the south Indian population and proposed a CT-SS cutoff which could predict hospital admission and ICU admission.
Majority of the patients in our population were symptomatic (97%). The most common symptoms were fever (74%), cough (54%), sore throat (30%), dyspnea (23%), and myalgia (21%). Only 10 (7%) patients had a normal report of chest CT. The main reason for this high CT positivity rate in our study was because majority of the patients were symptomatic, and multiple studies have reported similar high CT positivity rates in their population.[2],[7],[8]
Among the patients with abnormality in chest CT, multifocal (95%) and bilateral (92%) lung involvement with a peripheral distribution (100%) of pulmonary opacities were commonly observed. Lower lobes were frequently involved compared to the upper lobes. This pattern of distribution in our study is consistent with those reported in the literature.[3],[8] The common parenchymal abnormalities in our study were ground-glass densities (88%), interlobular septal thickening (70%), subpleural bands (40%), and consolidation with (34%) or without ground-glass densities (36%). Bronchial thickening (17%), nodules (17%), lymphadenopathy (16%), pleural effusion (9%), traction bronchiectasis (7%), reverse halo (2%), pneumothorax (1%), and vascular enlargement (1%) were less frequently encountered in our population [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9], [Figure 10]. A meta-analysis of 9907 patients with COVID-19 conducted by Zarifian et al. reported similar results, in which GGO with/without consolidation (77.18%), reticulations (46.24%), and air bronchogram (41.61%) were the common chest CT findings. These CT findings were concluded to be common abnormalities in multiple meta-analyses and systematic reviews.[8],[9] However, vascular enlargement, reported in various trials as a common finding in COVID-19, was seen in only 1% of our study population.[10],[11],[12] Possible reasons could be differences in ethnicity of the study population and differences in the timing at which CT chest was performed. | Figure 3: Axial nonenhanced CT image (lung window) showing left-sided focal ground-glass attenuation (arrow head). CT = Computed tomography
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 | Figure 4: Axial nonenhanced CT image (lung window) showing bilateral ground-glass attenuation with superimposed septal thickening resulting in a crazy paving appearance. CT = Computed tomography
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 | Figure 5: Axial nonenhanced CT image (lung window) showing diffuse areas of septal thickening (arrow heads) in the bilateral lower lobes. CT = Computed tomography
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 | Figure 6: Axial nonenhanced CT image (lung window) showing focal consolidation with air bronchogram within (arrow head). CT = Computed tomography
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 | Figure 7: Axial nonenhanced CT image (lung window) showing two focal areas of central ground-glass opacity with a surrounding halo of consolidation resulting in a reverse halo appearance (arrow heads). CT = Computed tomography
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 | Figure 8: Axial nonenhanced CT image (lung window) showing vascular enlargement/thickening (arrow heads) bilaterally. CT = Computed tomography
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 | Figure 9: Axial nonenhanced CT image (lung window) showing subpleural bands (arrow heads) bilaterally. CT = Computed tomography
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 | Figure 10: Axial nonenhanced CT image (lung window) showing typical COVID-19 features (bilateral crazy paving) and pneumopericardium (arrow head). CT = Computed tomography
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Apart from clinical and laboratory parameters, chest CT plays an important role in disease management as an aid in prognosis. A consensus statement from the Fleischner Society has recommended the use of chest CT in many clinical scenarios.[13],[14] Various semi-quantitative scoring systems based on the extent of lung involvement in chest CT have been reported in the literature.[15] We have used a scoring system that was developed by Yang et al. and is widely used in research and clinical practice.[5]
There was a significant association of CT-SS with hospital stay (days) and ICU admissions (P < 0.05) in our study. CT-SS of 18 (18/40 [~45%]) was found to be the optimal cutoff for ICU admission with the sensitivity of 52%, specificity of 60%, and AUC of 0.74. The optimal CT-SS cutoff for hospital admission was 10 (10/40 (~25%) with the sensitivity of 87.5%, specificity of 66.6%, and AUC of 0.74. Our results are comparable to the result obtained by Yang et al., wherein 19.5 (19.5/40 [~48%]) was optimal cutoff to identify severe disease (5). Similarly, the optimal CT-SS cutoff for hospital admission was 10 (10/40 (~25%) with the sensitivity of 87.5%, specificity of 66.6%, and AUC of 0.74. This is comparable to a prospective study of 741 COVID-19 patients, which concluded that CT-SS of 9 (9/25 (~36%) and CT-SS of 13 (13/25 (~52%) to be optimal cutoff for hospital admission and ICU admission, respectively.[16] Our findings also corroborated with the results of a multicenter trial of 496 COVID-19 patients conducted by Luo et al., wherein they found the CT pulmonary opacity score cutoff as 41% to be independent predictor of disease severity, ICU admission, and long hospital stay.[15] Jain et al., in their retrospective study conducted in India, found the optimal CT-SS threshold to be 15 (15/25 [~60%]) in ROC analysis; however, their endpoint was mortality which could explain the higher threshold.[17]
The main limitation of our study is that it was a retrospective study. Various clinical and laboratory parameters were not analyzed in this study. Another important limitation was a relatively small number of patients in the study compared to the prevalence of COVID-19.
To conclude, our study is one of the few studies to correlate imaging finding with clinical outcomes in the south Indian population. The common findings in our study were consistent with the literature. CT-SS plays an important role in predicting prognosis, and we found CT-SS of 10 (10/40 [~25%] and 18 (18/40 [~45%]) to be optimal cutoff for hospital admission and ICU admission, respectively.
Acknowledgment
The authors would like to thank Dr. Manikandan, Community Medicine Department, Pondicherry Institute of Medical Sciences, Puducherry, India, for providing us with useful inputs during the statistical analysis of the study.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9], [Figure 10]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]
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