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Table of Contents
Year : 2021  |  Volume : 12  |  Issue : 3  |  Page : 147-150

Clinico-radiological progression in COVID-19 patients: Study in a tertiary care hospital

1 Department of General Medicine, Smt. NHLMMC and SVPIMSR, Ahmedabad, Gujarat, India
2 Department of Radiodiagnosis, Smt. NHLMMC and SVPIMSR, Ahmedabad, Gujarat, India

Date of Submission18-Mar-2021
Date of Decision10-Apr-2021
Date of Acceptance11-Apr-2021
Date of Web Publication09-Jul-2021

Correspondence Address:
Dr. Akash G Dholakia
Department of General Medicine, Smt. NHLMMC and SVPIMSR, Ahmedabad, Gujarat
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/injms.injms_39_21

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Background: Coronavirus disease 19 (COVID-19) caused by a severe acute respiratory syndrome coronavirus-2 belonging to the Coronaviridae family has caused a global pandemic. As it has emerged as a newer disease, there is a lack of information in many aspects of it. Materials and Methods: We tried to study the progression in the COVID-19 patients in terms of their clinical, laboratory, and X-rays as a radiological modality, for that, we did a single-centered retrospective observational study in 159 laboratory-confirmed COVID-19 positive patients with sample size being duration dependent. Results: We found statistically significant correlation between clinical parameters and lung involvement based on chest X-ray (CXR) scoring and also temporal variation as the disease progression occurs. Conclusion: Along with clinical and laboratory parameters, CXRs can be used as a useful bedside, inexpensive, and easily available radiological tool for assessment in resource-poor settings where high-resolution computed tomography scans are not feasible.

Keywords: Chest X-ray scoring, clinical modalities, COVID-19, laboratory parameters

How to cite this article:
Dholakia AG, Patel MN, Dave RB, Shah DS, Poddar SO, Trada PK. Clinico-radiological progression in COVID-19 patients: Study in a tertiary care hospital. Indian J Med Spec 2021;12:147-50

How to cite this URL:
Dholakia AG, Patel MN, Dave RB, Shah DS, Poddar SO, Trada PK. Clinico-radiological progression in COVID-19 patients: Study in a tertiary care hospital. Indian J Med Spec [serial online] 2021 [cited 2023 Mar 31];12:147-50. Available from: http://www.ijms.in/text.asp?2021/12/3/147/321050

  Introduction Top

Coronavirus disease 19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 belonging to the Coronaviridae family has caused a global pandemic. Due to its recent origin, there is a dearth of information on various aspects.[1] COVID-19 pneumonia often show an increase in inflammatory and/or thromboembolic markers.[2] High-resolution computed tomography (HRCT) is currently the most sensitive radiological investigation in suspected COVID-19 pneumonia.[3] It is also useful in assessing the extent of lung involvement, disease severity, and prognostication.[4],[5],[6] Considering these factors, we decided to study the correlation between the extent of chest X-ray (CXR) infiltration, clinical backgrounds of patients, and laboratory parameters which may aid the physician in assessment and prognostication.

  Materials and Methods Top

Study design

Ours is a single-center retrospective study of COVID-19 patients admitted to a tertiary care center during the month of May, 2020. All patients had tested positive by real time polymerase chain reaction at a government certified laboratory.

The study was approved by the institutional review board (IRB no. NHLIRB-07/07/2020) and consent was waived looking at the nature of the study.

Eligibility criteria

Nonpregnant patients aged 18 years and greater were considered for the study. Intubated patients, patients with preexisting interstitial lung disease, lung carcinomas, active tuberculosis, and evident lung lesions were excluded so as to avoid the confounding CXR findings.

Data collection

Clinical and laboratory data of all patients from the day of admission to the day 10 were extracted, compiled, and analyzed.

All the laboratory tests were carried out at the institutional laboratory and were done by approved and trained personnel. The laboratory parameters evaluated were C-reactive protein (CRP) (negative: <0.6 mg/dl, positive: ≥0.6 mg/dl) lactate dehydrogenase (LDH) (100–250 U/L), Ferritin (10–282 mg/dl), D-Dimer (negative: <0.5 mcg/ml, positive: >0.5 mcg/ml).

Image acquisition and interpretation [Figure 1]
Figure 1: Division of the lungs into six zones on frontal chest radiograph. Line A is drawn at the level of the inferior wall of the aortic arch. Line B is drawn at the level of the inferior wall of the right inferior pulmonary vein. A and D upper zones; B and E middle zones; C and F lower zones[7]

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  • Score 0: No lung abnormalities
  • Score 1: Interstitial infiltrates
  • Score 2: Interstitial and alveolar infiltrates (interstitial predominance)
  • Score 3: Interstitial and alveolar infiltrates (alveolar predominance).

The scores of the six lung zones are then added to obtain an overall “CXR SCORE” ranging from 0 to 18.[7]

Statistical analysis

The quantitative data were described as the mean ± standard deviation or as the median (interquartile range [IQR]). The qualitative data were described by number of cases (proportion, %). Patient characteristics were compared using the Fisher's exact test for categorical data, and the ANOVA for continuous data. Pearson's r was used for correlation between two variables. All analysis was done using Microsoft Excel 2017® and IBM SPSS® Statistics for Windows, Version 25.0. Armonk, NY:IBM Corp. P <0.05 was considered as statistically significant.

  Results Top

We analyzed the data of 159 patients [Table 1]. The mean age was 47.44 ± 15.72 years with 103 (64.78%) of them being male. All were symptomatic on admission with the most common symptom being fever in 119 patients (70.41%) followed by shortness of breath in 48 (28.40%). About 76 (47.80%) of them had comorbidities, the most common being hypertension in 55 (32.54%) and diabetes in 43 (25.44%) patients, respectively.
Table 1: Clinical characteristics of patients with coronavirus disease 19

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The median CXR score on admission was 3 (IQR = 0–7), on day 3–5 was 4 (IQR = 0–8) and on day 7–10 was 4 (IQR = 2–8) with the most common lobes being involved were bilateral lower lobes. A significant correlation was observed between the CXR score and age (r = 0.441, P < 0.01). Patients with comorbidities had worse CXR scores with a median CXR score of 5 (2–9) compared to the group having no comorbidities having a median score of 0.5 (0–5) P < 0.01. There was no statistically significant difference between CXR scores for male and female on admission (female: 2 [0–7.75] vs. male 3 [0–7], P = 0.07).

The laboratory parameter which best correlates with the CXR score on admission was [Table 2] LDH r (159) =0.618, P ≤ 0.01 followed by Ferritin r (159) =0.448, P ≤ 0.01.
Table 2: Chest X-ray and laboratory correlation on day 0

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The laboratory parameter which best correlates with the CXR score on day 3–5 [Table 3] is D-dimer levels r (159) =0.467, P ≤ 0.01 followed by serum ferritin levels r (159) =0.444, P ≤ 0.01.
Table 3: Chest X-ray and laboratory correlation on day 3-5

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The laboratory parameter which best correlates with the CXR score on day 7–10 [Table 4] is Ferritin r (159) =0.542, P ≤ 0.01 followed by CRP levels r (159) =0.533, P ≤ 0.01.
Table 4: Chest X-ray and laboratory correlation on day 7-10

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There was also a strong negative correlation between saturation of oxygen (SpO2) and CXR score (day 0: r = −0.577, P < 0.01; day 3–5: r = −0.206, P < 0.01; day 7–10: r = −0.493, P < 0.01).

There is a strong positive correlation noted between CXR severity score and mild, moderate and moderately severe COVID-19 clinical categories[8] [Table 5]. (day 0: P <0.01; day 3–5: P <0.01; day 7–10: P <0.01).
Table 5: Correlation of chest X-ray severity score in mild, moderate, and severe coronavirus disease 19 categories

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  Discussion Top

This study provides evidence and quantifies the correlation between CXR, clinical and inflammatory biomarkers in COVID-19 pneumonia. There was a statistically significant correlation between age and worsening CXR scores (r = 0.441, P < 0.01) which corroborates with studies documenting worsening severity with increase in age.[9],[10] Patients with comorbidities had significantly greater lung involvement and elevated inflammatory markers. Many studies suggest a link between comorbidities and increased mortality/worsening in COVID-19.[11],[12]

Relevant inflammatory and thrombotic biomarkers evaluated; namely, CRP, LDH, Ferritin, and D-Dimer levels correlated significantly with CXR scores throughout the 10-day study period but each had a different level of correlation which varied temporally. LDH correlated best with lung involvement on the day of admission but it gradually weakened. Ferritin correlated at nearly the same level throughout the 10-day period whereas CRP had a relatively weak correlation early on which improved by the day 10.

The CXR score showed a significant inverse correlation, with SpO2 confirming that this score represents a good indicator of respiratory function. Direct correlation between COVID-19 pneumonia severity, blood values, and SpO2 has been previously reported for chest CT.[13],[14]

A statistically significant correlation is noted between CXR severity scores and COVID 19 clinical categories.[8]


Our study has certain limitations such as the small sample size and the lack of ethnic diversity among the population studied which may not be enough to generalize the findings.

Apart from that, we only studied, moderate, and moderately severe patients. We did not include critically ill patients.

  Conclusion Top

Our study demonstrates the correlation between clinical parameters and lung involvement based on X-ray scoring demonstrating a strong positive correlation with age and comorbidities, no relation with sex and a strong inverse correlation with SpO2 levels. It also describes the correlation between the inflammatory biomarkers and lung involvement and also the temporal variation in it as the disease progresses. Compared to HRCT thorax, CXRs have the advantages in terms of being inexpensive, can be done bedside, having lower radiation exposure and easy availability. This may serve as a useful tool in the armamentarium against COVID-19 to better model, predict and understand the disease activity and progression, especially in resource-poor settings where CT scans may not be feasible.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

  References Top

Coronaviridae Study Group of the International Committee on Taxonomy of Viruses. The species severe acute respiratory syndrome-related coronavirus: Classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol 2020;5:536-44.  Back to cited text no. 1
Zeng F, Huang Y, Guo Y, Yin M, Chen X, Xiao L, et al. Association of inflammatory markers with the severity of COVID-19: A meta-analysis. Int J Infect Dis 2020;96:467-74.  Back to cited text no. 2
Chung M, Bernheim A, Mei X, Zhang N, Huang M, Zeng X, et al. CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology 2020;295:202-7.  Back to cited text no. 3
Colombi D, Bodini FC, Petrini M, Maffi G, Morelli N, Milanese G, et al. Well-aerated lung on admitting chest CT to predict adverse outcome in COVID-19 pneumonia. Radiology 2020;296:E86-96.  Back to cited text no. 4
Yang R, Li X, Liu H, Zhen Y, Zhang X, Xiong Q, et al. Chest CT severity score: An imaging tool for assessing severe COVID-19. Radiol Cardiothorac Imaging 2020;2:e200047.  Back to cited text no. 5
Choi H, Qi X, Yoon SH, Park SJ, Lee KH, Kim JY, et al. Extension of coronavirus disease 2019 on chest CT and implications for chest radiographic interpretation. Radiol Cardiothorac Imaging 2020;2:e200107.  Back to cited text no. 6
Borghesi A, Maroldi R. COVID-19 outbreak in Italy: Experimental chest X-ray scoring system for quantifying and monitoring disease progression. Radiol med 2020;125:509-13. doi: 10.1007/ s11547-020-01200-3.  Back to cited text no. 7
COVID-19 Treatment Guidelines Panel. Coronavirus Disease 2019 (COVID-19) Treatment Guidelines. National Institutes of Health. Available from: https://www.covid19treatmentguidelines.nih.gov/. [Last accessed on 2021 Apr 06].  Back to cited text no. 8
Liu Y, Mao B, Liang S, Yang JW, Lu HW, Chai YH, et al. Association between age and clinical characteristics and outcomes of COVID-19. Eur Respir J 2020;55:2001112. doi: 10.1183/13993003.01112-2020.  Back to cited text no. 9
Goldstein JR, Lee RD. Demographic perspectives on the mortality of COVID-19 and other epidemics. Proc Natl Acad Sci U S A 2020;117:22035-41.  Back to cited text no. 10
Toussie D, Voutsinas N, Finkelstein M, Cedillo MA, Manna S, Maron SZ, et al. Clinical and chest radiography features determine patient outcomes in young and middle-aged adults with COVID-19. Radiology 2020;297:E197-206.  Back to cited text no. 11
Guan WJ, Liang WH, Zhao Y, Liang HR, Chen ZS, Li YM, et al. Comorbidity and its impact on 1590 patients with COVID-19 in China: A nationwide analysis. Eur Respir J 2020;55:2000547. doi: 10.1183/13993003.00547-2020.  Back to cited text no. 12
Wang K, Kang S, Tian R, Zhang X, Zhang X, Wang Y. Imaging manifestations and diagnostic value of chest CT of coronavirus disease 2019 (COVID-19) in the Xiaogan area. Clin Radiol 2020;75:341-7.  Back to cited text no. 13
Xiong Y, Sun D, Liu Y, Fan Y, Zhao L, Li X, et al. Clinical and high-resolution CT features of the COVID-19 infection: Comparison of the initial and follow-up changes. Invest Radiol 2020;55:332-9.  Back to cited text no. 14


  [Figure 1]

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]


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