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Radiology Business
Original Research
2022
:12;
17
doi:
10.25259/JCIS_11_2022

A retrospective study of association of CT severity with clinical profile and outcomes of patients with COVID-19 in the second wave

Department of Radio Diagnosis, Government Institute of Medical Sciences, Kasna, Greater Noida, Uttar Pradesh, India
Department of Medicine, Government Institute of Medical Sciences, Kasna, Greater Noida, Uttar Pradesh, India
Corresponding author: Neema Agarwal, Department of Radio Diagnosis, Government Institute of Medical Sciences, Kasna, Greater Noida, Uttar Pradesh, India. neemaagarwal@yahoo.com
Licence
This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-Share Alike 4.0 License, which allows others to remix, transform, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.

How to cite this article: Agarwal N, Jain P, Khan TN, Raja A. A retrospective study of association of CT severity with clinical profile and outcomes of patients with COVID-19 in the second wave. J Clin Imaging Sci 2022;12:17.

Abstract

Objectives

This study aimed to find out the association of CT severity score with demographic and clinical characteristics as well as mortality in the patients who were confirmed to have COVID-19 disease in the second wave.

Material and Methods

This retrospective study included collection and assessment of the demographic, clinical, laboratory data, and mortality of the patients, suspected with COVID-19 infection who underwent chest HRCT scan during March to April 2021. The findings of the chest HRCT were retrieved manually from the Medical Records section. Determination of the severity was done by the scoring system that involved the visual evaluation of the affected lobes.

Results

CT severity score was mild, moderate, and severe in 21.94%, 41.60%, and 30.48% patients, respectively. Mortality rate was 5.70%. Age of the patients with mild, moderate, and severe CT severity score was significantly more than those with normal severity score (50 vs. 50 vs. 50 vs. 31, P=0.0009). When compared to patients with normal score, those with mild, moderate, and severe CT severity score had significantly higher dyspnoea (10.39% vs. 67.81% vs. 97.20% vs. 0%), significantly more cases with diabetes mellitus (16.88% vs. 25.34% vs. 31.78% vs. 9.52%, P=0.044), hypertension (27.27% vs. 21.23% vs. 32.71% vs. 4.76%, P=0.026), and obesity (6.49% vs. 8.90% vs. 23.36% vs. 0%, P=0.0005). Total leucocyte counts, absolute neutrophil counts, creatinine, serum glutamic pyruvic transaminase (SGPT), lactate dehydrogenase (LDH), ferritin, and D-dimer were deranged in significantly more patients of severe score (53.27%, 62.62%, 60.75%, 85.05%, 90.65%, 97.20%, and 95.35%, respectively). Interleukin-6 (IL-6) and C-reactive protein were deranged in significantly more patients with moderate disease (98.18% and 98.63%, respectively). Increasing severity scores were associated with increased mortality (mild vs. moderate vs. severe: 1.30% vs. 1.37 vs. 15.89%, P<.0001). Oxygen saturation (SpO2) was significantly lowest in severe score followed by moderate, mild and normal scores (87 vs. 90 vs. 96 vs. 97, P<.0001). Duration of non-rebreather mask (NRBM), noninvasive ventilation (NIV), high-flow nasal cannula (HFNC), Venture/face mask, and intubation was also associated with increasing severity scores (P<0.0001).

Conclusion

CT scans play an important role in guiding physicians with their management plans and can serve as a predictor of disease severity and outcomes.

Keywords

COVID-19
CT severity score
HRCT
Ground-glass opacities

INTRODUCTION

Coronavirus disease 2019 (COVID-19) was originally identified in a group of 41 patients in Wuhan, China, in December 2019, who all had unexplained cases of pneumonia. COVID-19 was declared to be public health emergency by the World Health Organization on January 30, 2020. COVID-19 is an infection that has spread widely and quickly worldwide, resulting in a pandemic with serious consequences for the economy and healthcare delivery systems.[1]

It was a new and unforeseen challenge because the clinical presentations ranged from asymptomatic carriers to patients requiring assisted ventilatory support, and ICU stays with higher mortality. The diagnostic test utilized as the standard of reference for confirmation of the disease has been the nasopharyngeal swab reverse transcription-polymerase chain reaction (RT-PCR) test.[2] The test has proven to be an effective tool, but it is reported to be associated with a fewer but significant number of false-negative findings.[3]

There is a significant role of the non-contrast high-resolution CT (HRCT) chest in detecting the disease in the early stage, specifically among patients who have false-negative reports of RT-PCR. It also helps in observing the disease progression.[4] The typical findings of chest CT in the case of COVID-19 pneumonia include “bilateral, peripheral, and basal predominant ground-glass opacities with or without consolidation and broncho-vascular thickening.”[5] In addition, atypical findings are “cavitations, central upper lobe predominance, nodules, masses, tree-inbud sign, lymphadenopathy, and pleural effusion.”

For the diagnosis of COVID-19 pneumonia after a mean duration of 5 days, the HRCT chest is reported to show a sensitivity of 97%.[6] Chest computed tomography severity score was developed to assist evaluation of the effect of COVID-19 on the scan performed at the time of admission.[7] This severity scoring system gives an approach for recognizing the requirement of hospital admission in patients. In addition, the severity of the disease can be determined through findings of CT scan, which supports the clinicians in clinical evaluation and taking decisions regarding appropriate management.[8]

During the acute phase of the disease, a CT severity score system allows patients’ outcomes to be recognized. In critically-ill patients, the severity of the disease can also affect the prognosis, allowing for proper early involvement of intensive care.[9,10]

Many previous researches used visual as well as software quantitative assessments for examining the involvement of lungs on the chest CT images.[11,12] There is a scarcity of studies that evaluated the association of chest CT severity scores and the outcomes in COVID-19 patients, especially in India.[13-19] Thus, we conducted this study to find out the association of CT severity score with demographic and clinical characteristics as well as mortality in the patients who were confirmed to have COVID-19 disease in the second wave.

MATERIALS AND METHODS

Patient Selection and Data Collection

A retrospective observational study was done in a tertiary care hospital, Government Institute of Medical Sciences in Greater Noida, India. The institute caters to a population of western Uttar Pradesh and NCR collectively, resulting in a generous influx of patients. Collection of data of patients admitted over three months, i.e., March, April, and May 2021; of the second wave of COVID-19 in India was done.

A total number of COVID-19 patients that visited the hospital in the duration of the study were 465. Among them, the asymptomatic and mildly symptomatic patients, turning up in OPDs, were sent for home quarantine as per government advisory guidelines while only moderate to severely affected patients were admitted and included in the study.

The demographic (age, gender), clinical symptoms, comorbidities, and laboratory data [total leukocyte count (TLC), absolute neutrophil count, absolute lymphocyte count, creatinine, alanine aminotransferase (ALT), lactate dehydrogenase (LDH), C-reactive protein (CRP), ferritin, IL-6, D-dimer] related to the patients, suspected with COVID-19 infection who underwent chest HRCT scan during March to April 2021, were collected retrospectively from an electronic medical record system. The data relating to mortality was also collected. The findings of the chest HRCT were retrieved manually from the medical records section. Exclusion criteria included patients who had lung malignancy, tuberculosis, or atelectasis and those who underwent lobectomy. Since it was a retrospective study, ethical clearance and informed consent were waived off.

Sample Size Calculation

The study of Saeed GF et al.[19] observed a mortality rate of 3.7% in COVID-19 pneumonia patients. Taking this value as reference, the minimum required sample size with a 2% margin of error and 5% level of significance is 343 patients. To reduce the margin of error, the total sample size taken is 351.

The formula used was:

N ≥ (p(1 -p))/(ME/zα)2

Where Zα is the value of Z at two-sided alpha error of 5%, ME is the margin of error and p is the mortality rate.

Calculations:

n>=((.037*(1-.037))/(.02/1.96)2 =342.20=343(approx.)

CT Images Assessment

When patients presented to the hospital, they underwent an initial chest HRCT scan through the “128 slice GE Revolution EVO CT Scanner.” When laid down in a supine position, patients were asked to be in single breath-hold. The CT scan parameters included: “scan direction (craniocaudally), tube voltage (120 kV), tube current (100–600 mA)—smart mA dose modulation, slice collimation (64 × 0.625 mm), width (0.625 × 0.625 mm), pitch,[13] rotation time (0.5 seconds), and scan length (60.00–1300.00s).”

CT Images Analysis

For finding out the disease severity score in each patient, the evaluation of the images was done by two radiologists who had >5 years of experience. The evaluation was done according to the “Radiological Society of North America (RSNA) Consensus statement.” The CT scan images were first evaluated whether negative or positive for typical findings of COVID-19 pneumonia, including “bilateral, multilobe, posterior peripheral ground-glass opacities.”[14] Then, determination of the severity was done by the scoring system that involved the visual evaluation of the affected lobes [Tables 1 and 2].[15]

Table 1:: Individual lobar scores on the basis of % of involvement.[15]
Lobar involvement Score
≤5% 1
5–25% 2
26–49% 3
50–75% 4
>75% 5
Table 2:: The severity of the five lobes is indicated by sum of the individual lobar scores.[15]
Total score Category of severity
≤7 Mild
8–17 Moderate
≥18 Severe

The outcome measures were mortality and duration of non-rebreather mask (NRBM), noninvasive ventilation (NIV), a high-flow nasal cannula (HFNC), venture/face mask, and hospital stay. The patients were followed till discharge or death.

Statistical analysis: The presentation of the categorical variables was done in the form of number and percentage (%). The quantitative data with non-normal distribution were represented as median with 25th and 75th percentiles (interquartile range). The data normality was checked by using the Kolmogorov-Smirnov test. The cases in which the data was not normal, nonparametric tests were used.

  1. The association of age, oxygen saturation (SpO2) on admission (room air), duration of NRBM, NIV, HFNC, venture/face mask, intubation with CT severity score was done using Kruskal-Wallis test. Mann-Whitney U test was used for the association of age, SpO2 on admission (room air), duration of NRBM, NIV, HFNC, venture/face mask, intubation with mortality.

  2. The association of gender, dyspnea, diabetes mellitus, hypertension, TLC, Absolute neutrophil count, creatinine, ALT, LDH, ferritin with CT severity score was analyzed using the Chi-square test. The association of gender, cough, diabetes mellitus, hypertension, obesity with mortality was also done using the Chi-square test.

  3. The association of fever, cough, diarrhea, headache, obesity, chronic kidney disease (CKD), asthma/chronic obstructive pulmonary disease (COPD), absolute lymphocyte count, CRP, IL-6, D-dimer, mortality with CT severity score was done using Fisher’s Exact test. The association of fever, dyspnea, diarrhea, headache, CKD, asthma/COPD, TLC, absolute neutrophil count, absolute lymphocyte count, creatinine, CRP, IL-6, D-dimer, ALT, LDH, ferritin with mortality was also analyzed using Fisher’s Exact test.

  4. Multivariate logistic regression was used to find out significant risk factors of mortality.

The data entry was done in the Microsoft Excel spreadsheet and the final analysis was done with the use of Statistical Package for Social Sciences (SPSS) software, IBM manufacturer, Chicago, USA, ver 21.0.

For statistical significance, P-value of less than 0.05 was considered statistically significant.

RESULTS

The mean age of the patients in the present study was 49.62 ± 15.3 years with 69.52% of the patients being males. The chief symptoms were fever (80.63%), cough (68.95%), and dyspnea (60.11%). Hypertension diabetes mellitus and obesity were present in 25.07%, 24.50%, and 12.25%, respectively [Table 3].

Table 3:: Distribution of demographic and clinical characteristics of study subjects.
Demographic and clinical characteristics Frequency Percentage
Gender
Female 107 30.48%
Male 244 69.52%
Symptoms
Fever 283 80.63%
Cough 242 68.95%
Dyspnoea 211 60.11%
Diarrhea 33 9.40%
Headache 62 17.66%
Diabetes mellitus 86 24.50%
Hypertension 88 25.07%
Obesity 43 12.25%
CKD 5 1.42%
Asthma/COPD 18 5.13%
Age (years)
Mean ± SD 49.62 ± 15.3
Median (25th–75th percentile) 50 (38–61)
Range 14–90

Regarding respiratory support parameters, the mean values of SpO2 on admission were 90.49 ± 6.07. The median duration of NRBM, NIV, HFNC, venture/face mask, and intubation were 0, 0, 0, 1, and 0 respectively [Table 4].

Table 4:: Descriptive statistics of respiratory support of study subjects.
Respiratory support Mean ± SD Median (25th–75th percentile) Range
SpO2 on admission (room air) 90.49 ± 6.07 90 (87–96) 70–99
NRBM 1.05 ± 2.17 0 (0–1) 0–10
NIV 0.32 ± 1.14 0 (0–0) 0–10
HFNC 1.05 ± 2.51 0 (0–1) 0–18
Venture/face mask 2.75 ± 3.54 1 (0–5) 0–20
Intubation 0.11 ± 0.54 0 (0–0) 0–5

TLC, absolute neutrophils, absolute lymphocyte, Creatinine, ALT, CRP, LDH, Ferritin, IL-6, and D-dimer were deranged in 39.03%, 38.18%,85.19%,45.87%, 58.69%, 95.16%, 66.38%, 64.96%, 96.40%, and 82.72%, respectively [Table 5].

Table 5:: Distribution of investigations of study subjects.
Investigations Normal Deranged
TLC (106/L) {Normal {4000–10000}} 214 (60.97%) 137 (39.03%)
Abs Neutrophil count (109/L) {Normal {2–7}) 217 (61.82%) 134 (38.18%)
Abs Lymphcyte count (109/L) {Normal {1–3}} 52 (14.81%) 299 (85.19%)
Creatinine (mg/dL) {Normal {0.2–1.2}} 190 (54.13%) 161 (45.87%)
ALT (U/L) {Normal {<45}} 145 (41.31%) 206 (58.69%)
CRP Day 1 (U/L) {Normal {<5}} 17 (4.84%) 334 (95.16%)
LDH (U/L) {Normal {240–480}} 118 (33.62%) 233 (66.38%)
Ferritin (ng/mL) {Normal {10–290}} 123 (35.04%) 228 (64.96%)
IL-6(pg/mL) {Normal {0–17}} 5 (3.60%) 134 (96.40%)
D-dimer (mcg/mL) {Normal {0–0.5}} 33 (17.28%) 158 (82.72%)

CT severity score was normal in 5.98% of patients, mild in 21.94%, moderate in 41.60%, and severe in 30.48% of patients [Figure 1]. Mortality rate was 5.70% in the study [Table 6]. The representative case images with varying severity is shown in Figures 2-4.

Table 6:: Distribution of mortality of study subjects.
Mortality Frequency Percentage
Survivors 331 94.30%
Died 20 5.70%
Total 351 100.00%
Figure 1:: Distribution of CTSS of study subjects.
Figure 2:: A 54-year-old female admitted with complaints of shortness of breath and fever for 1 week. HRCT coronal section revealed ground glass density opacities (black arrow) diffusely scattered in bilateral lungs, along with subpleural fibrotic bands (white arrow) and traction bronchiectasis. There is more than 75% involvement of all five lobes of both the lungs, with severity score of 25/25, which is severe involvement associated with poor prognosis.
Figure 3:: A 50-year-old male who presented with dyspnea and cough. HRCT coronal section revealed patchy areas of consolidation (black arrow) and subpleural ground glass opacities (white arrow) scattered in bilateral lungs. The CT severity score is 13/25 suggestive of moderate lung involvement.
Figure 4:: A 69-year-old female who presented with complaints of fever and mild cough. Coronal section of HRCT thorax revealed patchy subpleural ground glass opacities (white arrow) and patchy consolidation (black arrow). The score of involvement is 6/25 which is mild involvement. The occasional subpleural fibrosis is suggestive of concomitant resolution of inflammation and is of prognostic value.

Age of the patients with mild, moderate, and severe CT severity scores was significantly more than those with normal scores (50 vs. 50 vs. 50 vs. 31, P = 0.0009). Gender was not associated with CT severity score (P = 0.195).

Regarding symptoms, when compared to patients with a normal score, those with mild, moderate, and severe CT severity scores had significantly higher dyspnea (10.39% vs. 67.81% vs. 97.20% vs. 0%). Other symptoms were comparable. When compared to patients with normal score, those with mild, moderate, and severe CT severity score had significantly more cases with diabetes mellitus (16.88% vs. 25.34% vs. 31.78% vs. 9.52%, P = 0.044), hypertension (27.27% vs. 21.23% vs. 32.71% vs. 4.76%, P = 0.026), and obesity (6.49% vs. 8.90% vs. 23.36% vs. 0%, P = 0.0005).

Derangement of blood parameters increased with increasing severity of CT score, as TLC, absolute neutrophil counts, creatinine, ALT, LDH, ferritin, and D-dimer were deranged in significantly more patients with severe scores (53.27%, 62.62%, 60.75%, 85.05%, 90.65%, 97.20%, and 95.35%, respectively), IL-6 and CRP were deranged in significantly more patients with moderate disease (98.18% and 98.63%, respectively).

Increasing severity scores were associated with increased mortality (mild vs. moderate vs. severe: 1.30% vs. 1.37 vs. 15.89%, P < 0.0001). SpO2 was significantly lowest in severe scores followed by moderate, mild, and normal scores (87 vs. 90 vs. 96 vs. 97, P < 0.0001). Duration of NRBM, NIV, HFNC, Venture/face mask, and intubation was also associated with increasing severity scores (P < 0.0001) [Table 7].

Table 7:: Association of parameters with CT severity score.
Parameters Normal Mild Moderate Severe Total -valueP
Age (years) 31 (26–45) 50 (36–59) 50 (38–62) 50 (40–61) 50 (38–61) 0.0009
Gender
Female 10 (47.62%) 26 (33.77%) 44 (30.14%) 27 (25.23%) 107 (30.48%) 0.195
Male 11 (52.38%) 51 (66.23%) 102 (69.86%) 80 (74.77%) 244 (69.52%)
Symptoms
Fever 17 (80.95%) 67 (87.01%) 113 (77.40%) 86 (80.37%) 283 (80.63%) 0.393*
Cough 10 (47.62%) 50 (64.94%) 102 (69.86%) 80 (74.77%) 242 (68.95%) 0.079
Dyspnea 0 (0%) 8 (10.39%) 99 (67.81%) 104 (97.20%) 211 (60.11%) <0.0001
Diarrhea 2 (9.52%) 10 (12.99%) 12 (8.22%) 9 (8.41%) 33 (9.40%) 0.664*
Headache 2 (9.52%) 11 (14.29%) 28 (19.18%) 21 (19.63%) 62 (17.66%) 0.619*
Diabetes mellitus 2 (9.52%) 13 (16.88%) 37 (25.34%) 34 (31.78%) 86 (24.50%) 0.044
Hypertension 1 (4.76%) 21 (27.27%) 31 (21.23%) 35 (32.71%) 88 (25.07%) 0.026
Obesity 0 (0%) 5 (6.49%) 13 (8.90%) 25 (23.36%) 43 (12.25%) 0.0005*
CKD 0 (0%) 0 (0%) 3 (2.05%) 2 (1.87%) 5 (1.42%) 0.719*
Asthma/COPD 1 (4.76%) 3 (3.90%) 6 (4.11%) 8 (7.48%) 18 (5.13%) 0.595*
Total leucocyte count (106/L)
Normal {4000–10000} 13 (61.90%) 56 (72.73%) 95 (65.07%) 50 (46.73%) 214 (60.97%) 0.002
Deranged 8 (38.10%) 21 (27.27%) 51 (34.93%) 57 (53.27%) 137 (39.03%)
Absolute Neutrophil count (109/L)
Normal {2–7} 18 (85.71%) 65 (84.42%) 92 (63.01%) 42 (39.25%) 217 (61.82%) <0.0001
Deranged 3 (14.29%) 12 (15.58%) 54 (36.99%) 65 (60.75%) 134 (38.18%)
Absolute Lymphocyte count (109/L)
Normal {1–3} 4 (19.05%) 6 (7.79%) 29 (19.86%) 13 (12.15%) 52 (14.81%) 0.066*
Deranged 17 (80.95%) 71 (92.21%) 117 (80.14%) 94 (87.85%) 299 (85.19%)
Creatinine (mg/dL)
Normal {0.2–1.2} 20 (95.24%) 49 (63.64%) 81 (55.48%) 40 (37.38%) 190 (54.13%) <0.0001
Deranged 1 (4.76%) 28 (36.36%) 65 (44.52%) 67 (62.62%) 161 (45.87%)
ALT (U/L)
Normal {<45} 20 (95.24%) 55 (71.43%) 54 (36.99%) 16 (14.95%) 145 (41.31%) <0.0001
Deranged 1 (4.76%) 22 (28.57%) 92 (63.01%) 91 (85.05%) 206 (58.69%)
CRP Day 1 (U/L)
Normal {<5} 6 (28.57%) 9 (11.69%) 2 (1.37%) 0 (0%) 17 (4.84%) <0.0001*
Deranged 15 (71.43%) 68 (88.31%) 144 (98.63%) 107 (100%) 334 (95.16%)
LDH (U/L)
Normal {240–480} 9 (42.86%) 45 (58.44%) 54 (36.99%) 10 (9.35%) 118 (33.62%) <0.0001
Deranged 12 (57.14%) 32 (41.56%) 92 (63.01%) 97 (90.65%) 233 (66.38%)
Ferritin (ng/mL)
Normal {10–290} 20 (95.24%) 61 (79.22%) 39 (26.71%) 3 (2.80%) 123 (35.04%) <0.0001
Deranged 1 (4.76%) 16 (20.78%) 107 (73.29%) 104 (97.20%) 228 (64.96%)
IL-6 (pg/mL)
Normal {0–17} 1 (100%) 1 (11.11%) 1 (1.82%) 2 (2.70%) 5 (3.60%) 0.016*
Deranged 0 (0%) 8 (88.89%) 54 (98.18%) 72 (97.30%) 134 (96.40%)
D-dimer (mcg/mL)
Normal {0–0.5} 4 (100%) 13 (68.42%) 12 (14.63%) 4 (4.65%) 33 (17.28%) <0.0001*
Deranged 0 (0%) 6 (31.58%) 70 (85.37%) 82 (95.35%) 158 (82.72%)
Mortality
No 21 (100%) 76 (98.70%) 144 (98.63%) 90 (84.11%) 331 (94.30%) <0.0001*
Yes 0 (0%) 1 (1.30%) 2 (1.37%) 17 (15.89%) 20 (5.70%)
SpO2 on admission (room air) 97 (97–98) 96 (95–98) 90 (88.25–94) 87 (83–88.5) 90 (87–96) <0.0001
Duration of NRBM (days) 0 (0–0) 0 (0–0) 0 (0–1) 1 (0–4) 0 (0–1) <0.0001
Duration of NIV (days) 0 (0–0) 0 (0–0) 0 (0–0) 0 (0–1) 0 (0–0) <0.0001
Duration of HFNC (days) 0 (0–0) 0 (0–0) 0 (0–0) 1 (0–3) 0 (0–1) <0.0001
Duration of Venture/face mask (days) 0 (0–0) 0 (0–0) 2 (0–5) 4 (0–6) 1 (0–5) <0.0001
Duration of intubation (days) 0 (0–0) 0 (0–0) 0 (0–0) 0 (0–0) 0 (0–0) <0.0001
Fisher’s exact test,
Chi-square test,
Kruskal-Wallis test

On performing multivariate regression, age(years), SpO2 on admission (room air), intubation, obesity were significant independent risk factors of mortality after adjusting for confounding factors. With the increase in SpO2 on admission (room air), risk of mortality significantly decreases with an adjusted odds ratio of 0.848 (0.727–0.99). With the increase in age (years) risk of mortality significantly increases with an adjusted odds ratio of 1.075 (1.006–1.148). Patients with intubation, obesity had a higher risk of mortality with an adjusted odds ratio of 4.049 (1.654–9.914), 6.643 (1.052–41.955) respectively [Table 8].

Table 8:: Multivariate logistic regression to find out significant risk factors of mortality.
Variable Beta coefficient Standard error P-value Odds ratio Odds ratio Lower bound (95%) Odds ratio Upper bound (95%)
Age (years) 0.072 0.033 0.0316 1.075 1.006 1.148
SpO2 on admission (room air) –0.164 0.079 0.037 0.848 0.727 0.990
NRBM -0.237 0.209 0.257 0.789 0.524 1.189
NIV 0.001 0.205 0.997 1.001 0.670 1.495
Venture/face mask -0.320 0.165 0.053 0.726 0.525 1.004
Intubation 1.399 0.457 0.002 4.049 1.654 9.914
Dyspnea 1.440 1.658 0.385 4.222 0.164 108.741
Diabetes mellitus 0.241 0.811 0.766 1.273 0.260 6.237
Hypertension 0.773 0.917 0.399 2.166 0.359 13.056
Obesity 1.894 0.940 0.044 6.643 1.052 41.955
CKD 1.435 1.661 0.388 4.200 0.162 108.917
TLC (106/L)
Normal {4000–10000} 1.000
Deranged 1.736 1.154 0.133 5.673 0.591 54.496
Abs Neutrophil count (109/L)
Normal {2–7} 1.000
Deranged -0.569 1.162 0.624 0.566 0.058 5.514
Creatinine (mg/dL)
Normal {0.2–1.2} 1.000
Deranged 0.254 0.920 0.782 1.290 0.212 7.832
ALT (U/L)
Normal {<45} 1.000
Deranged 0.125 1.030 0.904 1.133 0.151 8.524
LDH (U/L)
Normal {240–480}
Deranged 0.054 1.082 0.960 1.056 0.127 8.802
Ferritin (ng/mL)
Normal {10–290}
Deranged 0.637 1.217 0.601 1.890 0.174 20.553

DISCUSSION

COVID-19 is a highly contagious disease that has spread around the world. Disease diagnosis is crucial to disease containment and patient management strategies. However, restricted laboratory facilities and insufficient availability of nucleic acid kits have hampered COVID-19 testing. The WHO suggested that chest imaging is advised for diagnosis of COVID-19 disease on non-availability of the RT-PCR testing, delay in test findings, and in the clinically suspected cases of COVID-19 with negative RT-PCR findings. Clinicians are suggested to work in collaboration with the radiologists for taking appropriate decisions regarding the choice of imaging modality.[16,17]

We found a significant association of CT severity score with age, dyspnea, diabetes mellitus, hypertension, obesity, TLC, absolute neutrophil count, creatinine, ALT, CRP, LDH, ferritin, IL-6, D-dimer, SpO2 on admission, and duration of NRBM, NIV, HFNC, venture/face mask, and intubation.

Since COVID-19 remains a serious disease with adverse outcomes, the prediction holds the key in terms of CT severity score and other indexes like age, gender, symptoms, and investigative findings.

In the present study, increased age was associated with more severity of disease as mild, moderate, and severe scores had more age than those with normal scores (50 vs. 31 years, P = 0.0009).

This is in trend with the findings by Yang et al.[18] who found that those with severe scores had significantly more age than patients with mild scores (52.83 vs. 43.70, P = 0.004).

Saeed et al.[19] also found that normal scans and mild severity were present mostly in patients of 30–39 years. Moderate severity was present in the majority of patients with 40–49 years of age (31.7%) and severe score in the 50–59 years of age group (34.4%).

The elderly patients are more affected by COVID-19 because of several factors such as the stage of the pandemic during the conduction of the study, presence of comorbidities, and availability of healthcare services.

We found that CT severity scores were not associated with the gender of the patients. Similar findings were reported by Yang et al.[18] who found no significant association between CT severity and gender of the patients. However, Saeed et al.[19] found that males were more affected by the severe disease (93.4%). Factors behind this were the protective effect of estrogen in females.

In the present study, dyspnea increased with increasing severity of CT scores; however, other symptoms such as fever, cough, and diarrhea were not significantly different in patients with mild, moderate, and severe disease.

This is in accordance with the findings by Yang et al.[18] as they also found fever (84.52% vs. 83.33%, P = 0.817), cough (67.86% vs. 83.33%, P = 0.191), and expectoration (5.95% vs. 16.67%, P = 0.293) to be similar in patients with mild and severe disease. Xiao et al.[20] found that chest tightness was the only symptom present in significantly more patients with severe scores than moderate scores (42.5% vs. 23.5, P = 0.002). Other symptoms of COVID-19 were similar.

We found that hypertension, diabetes mellitus, and obesity were significantly associated with severity scores.

This is supported by previous studies that reported risk factors, such as hypertension, diabetes, lung diseases, and coronary artery diseases, is associated with poor prognosis, and worse outcome in the presence of multiple risk factors.[21,22] However, Saeed et al.[19] reported that there was no significant correlation of risk factors such as hypertension and diabetes with CT severity scores.

In the present study, blood investigations such as TLC, absolute neutrophil counts, creatinine, ALT, CRP, LDH, ferritin, IL-6, and D-dimer were more deranged in severe cases followed by moderate, mild, and normal severity scores.

As reported by Saeed et al.,[19] there was a correlation between lymphopenia and increasing CT severity scores. Lymphopenia indicates an inflammatory cytokine storm. Reduced T-cell counts were also found more among patients with severe scores. The serum CRP level was also significantly correlated with CT severity scores.

Yang et al.[18] reported that the severe cases had significantly lower mean lymphocytes (109/L) (0.68 vs. 1.21) and significantly higher HSCRP (107.91 vs. 11.29) and PCT (0.09 vs. 0.4) (P < 0.001).

Treatment of COVID-19 at an early stage can be considered using CRP as a predictive marker for the likelihood of disease progression.[23] Similarly, serum ferritin is a vital mediator of immune dysregulation, and its level was closely linked to the severity of the disease. D-dimer likewise can be used as a prognostic indicator, where higher levels are seen in more critical conditions. However, there is a lack of evidence regarding the causal effect. It is not yet clear whether this increase is related to the direct effect of the virus or the systemic inflammatory response

The mortality rate in the present study was 5.70%. Increasing severity scores were associated with increased mortality, with the highest mortality seen in patients with severe scores, followed by moderate and mild scores (P < 0.0001). Those with severe scores had the lowest SpO2 followed by moderate, mild, and normal scores (87 vs. 90 vs. 96 vs. 97, P < 0.0001). Duration of NRBM, NIV, HFNC, venture/face mask, and intubation was also associated with increasing severity scores (P < 0.0001).

In the study by Saeed et al.,[19] mortality rate was 3.7%. The death rate was increased in patients with severe severity scores. Other outcomes were similar to the present study as among severe patients, only two patients were not on oxygen support, the nasal cannula was needed in 13.1% patients, face mask in 13.1%, nonrebreather mask in 8.2%, bi-level positive airway pressure (BiPAP)/HFNC in 6.6%, and intubation in 55.7% patients. Oxygen requirement was significantly correlated with CT severity scores (P < 0.0001, r = 0.529). Overall, the need of oxygen increases with the increasing CT severity scores.[24,25]

The present study found age, SpO2 on admission (room air), intubation, obesity as the significant independent risk factors of mortality. Among other studies, Iftimie et al.[26] found that in the first wave, the independent risk factors of mortality were old age and the presence of fever, dyspnea, acute respiratory distress syndrome, diabetes, and cancer; whereas, the risk factors associated with second wave were age, gender, and the presence of acute respiratory distress syndrome and chronic neurological diseases.

In another study, Carbonell et al.[27] reported that there was no significant difference in ICU mortality between first and second waves according to age subsets except for the subgroup of 61–75 years of age, as decreased unadjusted ICU mortality was observed in the second/third waves (first vs. second/third: 38.7% vs. 34%, P = 0.048).

Limitations

One of the limitations of the study was that as it is a single-center study, its findings cannot be generalized. Thus, further multicenter studies should be conducted for increasing the accuracy of the findings. Also, evaluation of the severity of disease on CT scans can be subjective. We have not considered parameters such as lifestyle factors that might play a role in the outcomes. Also, self-reporting of the comorbidities by the patients’ needs to take into account. Lastly, the treatment given for COVID-19 in the study was not compiled and that can have a bearing on mortality and length of hospital stay.

CONCLUSION

To conclude, CT scans can play an important role in guiding physicians with their management plans and can serve as a predictor of disease severity and outcomes. In patients with COVID-19 infection, the CT severity score is linked to age, diabetes, hypertension, obesity, inflammatory laboratory markers, and oxygen consumption. Higher CT scores showed a significant association with increased mortality, oxygen requirement, and duration of intubation.

Declaration of patient consent

Patient’s consent is not required as patient’s identity is not disclosed or compromised.

Financial support and sponsorship

The authors have no funding source to declare.

Conflicts of interest

The authors have no conflicts of interest to declare.

Statement of ethics

The research complies with the guidelines for human studies and was conducted ethically in accordance with the World Medical Association Declaration of Helsinki.

Data availability statement

The data of all the patients in the excel form is available on request from which the results have been derived.

References

  1. , , , , , , et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395:497-506.
    [CrossRef] [PubMed] [Google Scholar]
  2. , , , , , , et al. Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Euro Surveill. 2020;25:2000045.
    [CrossRef] [PubMed] [Google Scholar]
  3. , . Pitfalls of quantitative real-time reverse-transcription polymerase chain reaction. J Biomol Tech. 2004;15:155-66.
    [PubMed] [Google Scholar]
  4. , , . The indispensable role of chest CT in the detection of coronavirus disease 2019 (COVID-19) Eur J Nucl Med Mol Imaging. 2020;47:1638-9.
    [CrossRef] [PubMed] [Google Scholar]
  5. , , , , , , et al. Temporal changes of CT findings in 90 patients with COVID-19 pneumonia: A longitudinal study. Radiol. 2020;296:1-9.
    [CrossRef] [PubMed] [Google Scholar]
  6. , , , , , , et al. COVID-19 pneumonia: A review of typical CT findings and differential diagnosis. Diagn Interv Imaging. 2020;101:263-8.
    [CrossRef] [PubMed] [Google Scholar]
  7. , , , , , , et al. Time course of lung changes at chest CT during recovery from coronavirus disease 2019 (COVID-19) Radiology. 2020;295:715-21.
    [CrossRef] [PubMed] [Google Scholar]
  8. , , , , , , et al. Chest CT findings in coronavirus disease-19 (COVID-19): Relationship to duration of infection. Radiology. 2020;295:200463.
    [CrossRef] [PubMed] [Google Scholar]
  9. , , , , , , et al. Role of computed tomography in predicting critical disease in patients with COVID-19 pneumonia: A retrospective study using a semiautomatic quantitative method. Eur J Radiol. 2020;130:109202.
    [CrossRef] [PubMed] [Google Scholar]
  10. , , , , , , et al. Intubation and ventilation amid the COVID-19 outbreak: Wuhan’s experience. Anesthesiology. 2020;132:1317-32.
    [CrossRef] [PubMed] [Google Scholar]
  11. , , , , , , et al. CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19) Eur Radiol. 2020;30:4407-16.
    [CrossRef] [PubMed] [Google Scholar]
  12. , , , , , , et al. Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software. Eur J Nucl Med Mol Imaging. 2020;47:2525-32.
    [CrossRef] [PubMed] [Google Scholar]
  13. World Health Organization. Coronavirus Disease (COVID-19). Available from http://www.who.int.https://www. who.int/emergencies/diseases/novel-coronavirus-2019?gclid=AIaIQobChMImt2m3afS 6wIVvSB7Ch3xdQDoEAAYASAAEgLb-_D_BwE—[Accessed November 2021]
  14. , , , , , , et al. Radiological Society of North America expert consensus document on reporting chest CT findings related to COVID-19: Endorsed by The Society of Thoracic Radiology, the American College of Radiology, and RSNA. Radiol Cardiothoracic Imaging. 2020;2:e200152..
    [CrossRef] [PubMed] [Google Scholar]
  15. , , , , , , et al. Pulmonary sequelae in convalescent patients after severe acute respiratory syndrome: Evaluation with thin-section CT. Radiology. 2005;236:1067-75.
    [CrossRef] [PubMed] [Google Scholar]
  16. World Health Organization Use of Chest Imaging in COVID-19. Available from https://www.who.int/publications/i/item/use-of-chest-imaging-in-covid-19 [Accessed November 2021]
  17. , , , , , , et al. Chest computed tomography severity score to predict adverse outcomes of patients with COVID-19. Infect Chemother. 2021;53:308-18.
    [CrossRef] [PubMed] [Google Scholar]
  18. , , , , , , et al. Chest CT severity score: An imaging tool for assessing severe COVID-19. Radiol Cardiothoracic Imaging. 2020;2:e200047..
    [CrossRef] [PubMed] [Google Scholar]
  19. , , , , , , et al. Correlation between Chest CT severity scores and the clinical parameters of adult patients with COVID-19 pneumonia. Radiol Res Pract. 2021;2021:6697677.
    [CrossRef] [PubMed] [Google Scholar]
  20. , , , , , , et al. Maximum chest CT score is associated with progression to severe illness in patients with COVID-19: A retrospective study from Wuhan, China. BMC Infect Dis. 2020;20:953.
    [CrossRef] [PubMed] [Google Scholar]
  21. , , , , , , et al. Clinico-radiological evaluation and correlation of CT chest images with progress of disease in COVID-19 patients. J Assoc Physicians India. 2020;68:34-42.
    [PubMed] [Google Scholar]
  22. , , , , , , et al. Comorbidity and its impact on 1590 patients with COVID-19 in China: A nationwide analysis. Eur Respir J. 2020;55:2000547..
    [CrossRef] [PubMed] [Google Scholar]
  23. , , , , , , et al. D-dimer as a biomarker for disease severity and mortality in COVID-19 patients: A case control study. J Intens Care. 2020;8:49.
    [CrossRef] [Google Scholar]
  24. , . Acute lung injury and acute respiratory distress syndrome: mechanisms and potential new therapies. Drug Disco Today Dis Mechan. 2004;1:123-8.
    [CrossRef] [Google Scholar]
  25. , , , , , , et al. Pulmonary vascular endothelialitis, thrombosis, and angiogenesis in Covid-19. N Engl J Med. 2020;383:120-8.
    [CrossRef] [PubMed] [Google Scholar]
  26. , , , , , , et al. First and second waves of coronavirus disease-19: A comparative study in hospitalized patients in Reus, Spain. PLoS One. 2021;16:e0248029.
    [CrossRef] [Google Scholar]
  27. , , , , , , et al. Mortality comparison between the first and second/third waves among 3,795 critical COVID-19 patients with pneumonia admitted to the ICU: A multicentre retrospective cohort study. Lancet Reg Health Eur. 2021;11:100243.
    [CrossRef] [Google Scholar]
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