Keywords
Covid-19 - HRCT chest - oxygen requirement
Introduction
SARS CoV 2 infection, which initially began in Wuhan, China in December 2019 has been
declared as a pandemic by WHO.[[1]] The main concern of a pandemic is the overwhelming of medical facilities due to
the sheer number of patients presenting at the same time.[[2]] This may also trigger panic in the general population leading to societal issues.
Lockdowns are primarily to slow the number of patients presenting at the same time,
allowing medical facilities to try and cope. An important component of alleviating
the pressure on medical establishments is to triage patients.[[3]] COVID-19 pneumonia has a wide spectrum of presentations and outcomes, ranging from
asymptomatic to severe hypoxia which may result in death.[[4]] Triage is needed to decide the order of treatment when the number of patients is
large, outweighing the capacity of the available facilities. As the clinical presentation,
management, and outcomes of COVID-19 is so varied, triaging patients into those who
require management at home, covid care facilities or those who require hospitalization
is essential. Additionally, for the patients in hospital, whether they will require
admission in a ward or ICU, whether the oxygen requirement would be low flow, high
flow oxygen, or whether they would require non-invasive ventilation or mechanical
ventilation via intubation is also important. The role of CT for triaging patients
has been emerging.
COVID-19 pneumonia pathologically is a diffuse alveolar damage, which progresses in
a temporal manner.[[5]] Initially patients may be mildly symptomatic but can rapidly progress to severe
hypoxia requiring oxygen support and hospitilisation. The extent of lung involvement
in the second phase of diffuse alveolar damage approximately between day 7 and 10
may be a good surrogate to decide disease burden.[[6]] A low percentage of aerated lung tissue corelates with a poor prognosis. For this
purpose, a CT severity score has been proposed to determine the extent of lung involvement
based on extent of involvement on CT scan.[[7]]
To test this hypothesis, we undertook a study to determine whether extent of lung
involvement on a CT scan can help triage. The main components of which include the
requirement for oxygen, type of oxygen support required as well as location of admission
in the health care facility.
Subjects and Methods
This was a prospective observational study conducted at Breach Candy Hospital Trust,
which is a tertiary care hospital at Mumbai. Laboratory, clinical, and radiological
data of all patients who underwent Ct scan for covid between April and September 2020
was collected. Ethical committee waived the need for ethical clearance and informed
consent for this non interventional study.
All patients with RT PCR positive for SARS COV2 who underwent Ct scan in our hospital
were included. All CT scans were done within one week of disease onset. We excluded
patients with 2 consecutive negative RT PCR, patients lost to follow up, and patients
with underlying chronic lung disease like ILD, organising pneumonias, carcinoma lung
and Tb sequelae.
We collected the baseline demographics – age, gender and comorbidities. Laboratory
data collected included IL6, D-dimer, CRP, procalcitonin and ferritin levels. The
blood tests were done within 48 hours of CT scan. We also noted the need for hospitalisation,
need for ICU admission, medical therapy administered and mortality. Of note, we differentiated
between the oxygen requirement of patients as those on room air, with low oxygen requirement
(up to 6 L/min), high oxygen requirement (7-15 L/min), NIV or HFNC, and need for intubation
and mechanical ventilation. All patients undergoing CT scan from August 2020 to October
2020 were included by complete enumeration method and were followed up for as long
as they were admitted in the hospital, resulting either in death or discharge.
We evaluated all the patients based on 3 criteria- 20 point CT score (OS1), 25 point
CT score (OS2) and opacity percentage (OP). For each of the 3 criteria, patients were
divided into mild moderate and severe group based on the cut offs as shown in [[Table 1]].
Table 1
Group cut-offs
|
0S1 (0-20)
|
0S2 (0-25)
|
0P
|
|
Mild
|
0-4
|
0 to 7
|
0-10%
|
|
Moderate
|
5-10
|
8-14
|
10-30%
|
|
Severe
|
10 and above
|
15 and above
|
30% and above
|
CT analysis
All chest CT scans were performed using a 16 slice CT scanner (Siemens Biograph Horizon)
with the following parameters: 130 kV, tube voltage 100-200 mAs, rotation time 0·6
s, pitch 1·35. 1 mm slice thickness, sharp convolution kernel reconstructions with
a window width of 1200 HU and a window length -600 HU was performed. The scanning
range was from the apex of the lung to the costophrenic angle. No IV contrast was
administered. The scan was captured in the end-inspiratory phase, whenever it was
possible for the patient to hold the breath adequately. Using CT pneumonia analysis
prototype software (Siemens Healthcare version 2·5·2, Erlangen, Germany), an AI algorithm
based on three-dimensional segmentation automatically. The software automatically
detected and quantified abnormal tomographic patterns (ground-glass opacities and
consolidations) in each and both lung parenchyma based on deep learning and deep reinforcement
learning.
The CT was assessed for features of COVID-19 infection, like ground-glass opacities,
consolidation, crazy paving pattern, etc., by 2 radiologists with more than 10 years
of experience. All CT scans were evaluated by CT pneumonia analysis software. The
result provided the percentage of opacity in the lungs and a severity score based
on the percentage of opacities in individual lung lobes. The AI software, automatically
processed CT-SS, volume and percentage of opacity. Opacity score was calculated by
dividing the lung parenchyma into five anatomical lobes and assigning scores by adding
percentage of opacity within the lobes. If the parenchymal involvement was 0, <25%,
25-50%, 50-75% and >75% they were assigned a score of 0, 1, 2, 3 and 4 respectively
for OS1 and involvement was 0-5%, 5-25%, 25-50%, 50-75% and >75% they were assigned
a score of 1, 2, 3, 4 and 5 respectively for OS2. The scores for each lobe were added
to provide the final CT severity score. The software also provided a total percentage
of lung involvement representing opacity percentage of lung involvement.
Based on this result patients were divided into three groups – mild, moderate and
severe disease.
Statistical analysis
All clinical and imaging data items were entered into Microsoft Office Excel and all
statistical analyses were performed using STATA statistical software. Categorical
variables are presented as frequency and percentages, and continuous variables as
the median. ROC curve and Youden’s index were used to determine the cut off points
for OS1, OS2 and OP. Multinomial logistic regression was used to study the relations
of OS1, OS2 and OP with oxygen requirement and place of admission. Hosmer-Lemeshow
test was done to test the goodness of fit of our models. A two-sided P value of less than 0.05 was considered to be statistically significant. Details of
variables considered are mentioned in [[Table 2]].
Table 2
Variable Used
|
Name
|
Details
|
|
Oxygen
|
0 - Room air
|
|
Requirement
|
1 - Low
|
|
Oxygen
|
|
2 - High
|
|
Oxygen
|
|
3 - HVNC/NIV
|
|
4 - Intubated
|
|
Place of
|
0 - Opd
|
|
Admission
|
1 - Ward
|
|
2 - ICU
|
The patient enrolment flowchart As shown in [[Figure 1]].
Figure 1: Flowchart
Results
After exclusion, 740 patients were included in our study. Majority of them belonged
to the mild group (64%, 69% and 73% based on OS1, OS2 and OP, respectively). This
was followed by moderate group and the least number of patients were in the severe
group (based on OS1 27% and 9%, OS2 22% and 9% and on OP 16% and 11% belonged to moderate
and severe group respectively). Thus, the distribution of the patients was not equal
between the 3 groups and the patient characteristics between the 3 categories also
varied.
The mean age of the patients was 59 years and there were 65% males (n = 483). The mean age was lesser in the mild group as compared to moderate and severe.
The mean duration between swab positivity and CT was 2 days. 34·18% of the patients
had no comorbidities. Of the patients with comorbidities, the most common ones were
diabetes mellitus in 41·58%, hypertension in 44·6% and ischemic heart disease in 15·4%.
The remaining comorbidities along with their distribution between the 3 groups and
the treatment given are as shown in [[Table 3]].
Table 3
Baseline Demographics and Comorbidities
|
Total
|
0S1
|
0P
|
0S2
|
|
|
Mild
|
Moderate
|
Severe
|
Mild
|
Moderate
|
Severe
|
Mild
|
Moderate
|
Severe
|
|
Total number
|
740
|
473
|
201
|
66
|
541
|
120
|
79
|
511
|
165
|
64
|
|
Age (in years)
|
59
|
55
|
61
|
62
|
56
|
61
|
62
|
55
|
63
|
63
|
|
Male
|
483 (65%)
|
292 (62%)
|
144 (72%)
|
47 (71%)
|
338 (62%)
|
91 (76%)
|
54 (68%)
|
312 (61%)
|
124 (75%)
|
47 (73%)
|
|
Comorbidities- Diabetes Mellitus
|
308 (42%)
|
189 (40%)
|
86 (43%)
|
33 (50%)
|
216 (40%)
|
54 (45%)
|
38 (48%)
|
201 (40%)
|
76 (46%)
|
31 (48%)
|
|
Hypertension
|
331 (45%)
|
194 (41%)
|
100 (50%)
|
37 (56%)
|
222 (41%)
|
65 (54%)
|
44 (55%)
|
208 (41%)
|
87 (53%)
|
36 (56%)
|
|
Ischemic Heart Disease
|
113 (15%)
|
66 (14%)
|
34 (17%)
|
13 (20%)
|
75 (14%)
|
23 (19%)
|
15 (19%)
|
69 (13%)
|
31 (19%)
|
13 (20%)
|
|
Obstructive airway disease
|
32 (4%)
|
18 (4%)
|
10 (5%)
|
4 (6%)
|
24 (4%)
|
4 (3%)
|
4 (5%)
|
21 (4%)
|
7 (4%)
|
4 (6%)
|
|
Hypothyroid
|
48 (6%)
|
34 (7%)
|
10 (0.5%)
|
4 (6%)
|
37 (7%)
|
6 (5%)
|
5 (6%)
|
33 (6%)
|
12 (7%)
|
3 (5%)
|
|
Chronic kidney disease
|
21 (3%)
|
7 (1-5%)
|
10 (5%)
|
4 (6%)
|
14 (3%)
|
4 (3%)
|
3 (4%)
|
10 (2%)
|
7 (4%)
|
4 (6%)
|
|
Parkinson’s disease
|
6 (1%)
|
6 (1.3%)
|
0
|
0
|
4 (1%)
|
2 (2%)
|
0
|
5 (1%)
|
1
|
0
|
|
Liver cirrhosis
|
2 (0.27%)
|
2 (0.4%)
|
0
|
0
|
2 (0.4%)
|
0
|
0
|
2 (0-4%)
|
0
|
0
|
|
Others
|
7 (9%)
|
4 (8%)
|
1 (0-5%)
|
2 (3%)
|
5 (9%)
|
0
|
2 (2-5%)
|
4 (0.8%)
|
0
|
3 (5%)
|
|
Time between swab and CT (in days)
|
3
|
3
|
3
|
4
|
3
|
4
|
5
|
3
|
3
|
4
|
|
Treatment- Antiviral (Remdesivir, Favipiravir)
|
479 (65%)
|
295 (62%)
|
120 (60%)
|
64 (97%)
|
337 (62%)
|
70 (59%)
|
72 (91%)
|
317 (62%)
|
102 (62%)
|
60 (94%)
|
|
Steroid (Dexamethasone, Methylprednisolone)
|
556 (75%)
|
320 (68%)
|
170 (85%)
|
66 (100%)
|
387 (71%)
|
90 (75%)
|
79 (100%)
|
373 (73%)
|
119 (72%)
|
64 (100%)
|
|
Plasma therapy
|
17 (2%)
|
0
|
2 (0.1%)
|
15 (23%)
|
0
|
0
|
17 (26%)
|
0
|
2 (1%)
|
15 (23%)
|
|
Hydrochloroquine
|
517 (70%)
|
392 (83%)
|
100 (50%)
|
25 (38%)
|
447 (82%)
|
40 (33%)
|
30 (38%)
|
395 (77%)
|
97 (59%)
|
25 (39%)
|
|
Others (Doxycycline, Azithromycin)
|
590 (80%)
|
365 (77%)
|
176 (87%)
|
49 (74%)
|
429 (80%)
|
103 (86%)
|
58 (73%)
|
406 (80%)
|
139 (84%)
|
45 (70%)
|
|
Tocilizumab/Itolizumab
|
84 (11%)
|
0
|
50 (25%)
|
34 (51%)
|
0
|
32 (26%)
|
52 (66%)
|
0
|
42 (25%)
|
42 (65%)
|
Relation with oxygen requirement
80·75% of the patients were on room air. Majority of them belonged to the mild group
(77%, 81% and 85% based on OS1, OS2 and OP respectively). Only 0·67% from OS, 0·67%
from OS2 and 1·5% from OP patients from severe group were on room air. 10% of the
patients were on low oxygen (oxygen requirement 1-6 L/min). Majority of them belonged
to mild group followed by moderate group. Only 22% (based on OS1), 21% (based on OS2)
and 29% (based on OP) of the patients were from severe group. A clear majority of
patients who were on high oxygen, HFNC/NIV and intubated belonged to the severe group
as shown in [[Table 4]].
Table 4
Relation with Oxygen Requirement
|
Oxygen Requirement
|
0S1
|
0P
|
0S2
|
|
Mild
|
Moderate
|
Severe
|
Mild
|
Moderate
|
Severe
|
Mild
|
Moderate
|
Severe
|
|
RA
|
|
|
|
|
|
|
|
|
|
|
Total Number
|
458
|
132
|
4
|
509
|
76
|
9
|
486
|
104
|
4
|
|
% within category
|
96-82%
|
65-67%
|
6-06%
|
94-08%
|
63-33%
|
11-39%
|
95-10%
|
63-03%
|
6-25%
|
|
% of total
|
77-10%
|
22-22%
|
0-67%
|
85-69%
|
12-79%
|
1-51%
|
81-81%
|
17-63%
|
0-67%
|
|
Low oxygen
|
|
|
|
|
|
|
|
|
|
|
Total Number
|
9
|
49
|
17
|
21
|
32
|
22
|
17
|
42
|
16
|
|
% within category
|
4-47%
|
24-37%
|
25-75%
|
3-88%
|
26-66%
|
27-84%
|
3-32%
|
25-45%
|
25%
|
|
% of total
|
12%
|
65-33%
|
22-66%
|
28%
|
42-66%
|
29-33%
|
22-66%
|
56%
|
21-33%
|
|
High oxygen
|
|
|
|
|
|
|
|
|
|
|
Total Number
|
2
|
12
|
14
|
7
|
5
|
16
|
6
|
8
|
14
|
|
% within category
|
0-42%
|
5-97%
|
21-21%
|
1-29%
|
4-16%
|
20-25%
|
1-17%
|
4-84%
|
21-87%
|
|
% of total
|
7.14%
|
42.85%
|
50%
|
25%
|
17-85%
|
57-14%
|
21-42%
|
28-57%
|
50%
|
|
HFNC/NIV
|
|
|
|
|
|
|
|
|
|
|
Total Number
|
2
|
3
|
12
|
1
|
4
|
12
|
1
|
5
|
11
|
|
% within category
|
0-42%
|
1-49%
|
18-18%
|
0-18%
|
3-33%
|
15-18%
|
0-19%
|
3-03%
|
17-18%
|
|
% of total
|
11-76%
|
17-64%
|
70-58%
|
5-88%
|
23-52%
|
70-58%
|
5-88%
|
29-41%
|
64-70%
|
|
Intubated
|
|
|
|
|
|
|
|
|
|
|
Total number
|
2
|
5
|
19
|
3
|
3
|
20
|
1
|
6
|
19
|
|
% within category
|
0-42%
|
2-48%
|
28-78%
|
0-55%
|
2-5%
|
25-31%
|
0-19%
|
3-63%
|
29-68%
|
|
% of total
|
7-69%
|
19-23%
|
73-07%
|
11-53%
|
11-53%
|
76-92%
|
3-84%
|
23-07%
|
73-07%
|
We ran a multinomial logistic regression to study the relation of OS1, OS2 and OP
with oxygen requirement. The regression shows a significant positive relation between
the scores and oxygen requirement for all 3 categories (p-value = 0·000). The models
show that OS1 and OS2 were slightly better than OP to predict the oxygen requirement.
The adjusted prediction graphs, [[Figures 2], [3], [4]], show how the probability of a patient in room air (Outcome = 0) decreases as OS1,
OS2 and OP increase, while the probability of a patient being intubated (Outcome =
4) increases as OS1, OS2 and OP increase.
Figure 2: Prediction of oxygen requirement by OS1
Figure 3: Prediction of oxygen requirement by OS2
Figure 4: Prediction of oxygen requirement by OP
ROC curve analysis showed a score of 4 for OS1 (AUC-0·9230 Youden Index – 0·668304
Sensitivity – 61% specificity – 98%), 9 for OS2 (AUC-0·9197 Youden index-0·6711637
sensitivity- 58% specificity – 97%) and 12·7% according to OP (AUC- 0·9135 Youden
Index- 0·6644 sensitivity – 58% specificity – 97%) as the cut off for oxygen requirement.
Relation with place of admission
Majority of the patients were admitted in ward (51%) followed by OPD (44%). Almost
all the patients treated on OPD basis belonged to the mild group. Only 4%, 2% and
05% patients from moderate group in OS1 OS2 and OP category were treated as OPD patients.
Majority of the patients from severe category were admitted in the ICU (74% in OS1,
76% in OS2 and 83% in OP group) and the remaining were admitted in ward. Majority
of patients with moderate disease were admitted in ward (73% in OS1, 72% in OS2 and
60% in OP group). The distribution of patients is as shown in [[Table 5]].
Table 5
Relation with Place of admission
|
Place of treatment
|
Total
|
0S1
|
0P
|
0S2
|
|
Mild
|
Moderate
|
Severe
|
Mild
|
Moderate
|
Severe
|
Mild
|
Moderate
|
Severe
|
|
OPD
|
251 (44%)
|
239 (95-21%)
|
12 (4-78%)
|
0
|
245 (97-62%)
|
5 (1-99%)
|
1 (0-39%)
|
244 (97-21%)
|
7 (2-79%)
|
0
|
|
Hospital ward
|
381 (51%)
|
217 (56-85%)
|
147 (38-58%)
|
17 (4-46%)
|
270 (70-86%)
|
88 (23-09%)
|
23 (6-03%)
|
246 (64-56%)
|
120 (31-49%)
|
15 (3-83%)
|
|
ICU
|
108 (15%)
|
17 (15-74%)
|
42 (38-88%)
|
49 (45-37%)
|
26 (24-07%)
|
27 (25%)
|
55 (50-92%)
|
21 (19-44%)
|
38 (35-18%)
|
49 (45-37%)
|
We ran a multinomial logistic regression to study the relation of OS1, OS2 and OP
with place of admission. The regression showed that the odds of a patient being admitted
to Ward and ICU significantly increases with an increase in OS1, OS2 and OP (p value
= 0·000). Based on the pseudo r-squared values, model using OS1 performed marginally
better than the models using OS2 and OP. The adjusted prediction graphs, [[Figures 5], [6], [7]], show the probability of a patient being admitted to OPD decreases and ICU increases
as the scores increase.
Figure 5: Prediction of place of admission by OS1
Figure 6: Prediction of place of admission by OS2
Figure 7: Prediction of place of admission by OP
Relation with laboratory parameters
The laboratory parameters assessed included D-dimer, Ferritin, Interleukin 6, Procalcitonin
and CRP. The values were available for only 390, 285, 268, 54 and 188 patients respectively
as the lab tests were done depending upon the clinical condition of the patient and
on discretion of the treating consultant. The median values of the lab parameters
for each group are mentioned in [[Table 6]]. Even though the values are higher as the severity increases, no statistically
significant correlation could be found between lab values and severity scores. The
correlation matrix between laboratory values and Ct scores is shown in [[Figure 8]].
Figure 8: Scatterplot matrix of laboratory parameters and scores
Table 6
Relation with laboratory parameters
|
LAB parameters
|
0S1
|
0P
|
0S2
|
|
Mild
|
Moderate
|
Severe
|
Mild
|
Moderate
|
Severe
|
Mild
|
Moderate
|
Severe
|
|
D-dimer
|
344
|
499
|
1135
|
360
|
547-5
|
1092
|
344
|
539
|
1185
|
|
CRP
|
11
|
43
|
53
|
18
|
57
|
57-85
|
12-8
|
57
|
53
|
|
Ferritin
|
124
|
316
|
670
|
156
|
408
|
512
|
151
|
337
|
700
|
|
Procalcitonin
|
0-08
|
0-09
|
0-545
|
0-08
|
0-09
|
0-52
|
0-073
|
0-09
|
0-54
|
|
IL6
|
32
|
46
|
81
|
32-28
|
50
|
76
|
29
|
49
|
84
|
Relation with mortality
There were 23 deaths in our study group. The distribution is as shown in the bar graph
[[Figure 9]]. Majority of deaths were in the severe group. The relationship between severity
score and mortality was found to be statistically significant in all the 3 groups
(P value- 0·000). The correlation between mortality and severity scores is as shown
in [[Figures 10], [11], [12]].
Figure 9: Deaths by group
Figure 10: Probability of death & OS1
Figure 11: Probability of death & OS2
Figure 12: Probability of death & OP
Discussion
A number of CT severity scoring systems have been proposed. There are 20, 25, 40 and
72 point scales which evaluate extent of lung involvement depending upon the involvement
of each lobe, expressed as percentage, which is finally summed up to provide the final
score.[[8], [9], [10], [11]] This may be done subjectively with visual interpretation of the scans or by an
automated deep learning software programme.[[12], [13]] The subjective visual method is fraught with numerous inter and intra observer
errors as it is a visual assumption, resulting in significant under and over estimation.
This process is manual; thus, it is extremely time consuming which does not help in
a pandemic where accurate and timely information is required for triage. In view of
this we utilized an automated deep learning software - Siemens Healthcare version
2·5·2, Erlangen, Germany.
The extent of lung involvement was correlated with oxygen requirement, laboratory
parameters, place of admission and mortality.
We found a statistically significant relation between the severity scores and oxygen
requirement, need for hospital and ICU admission and mortality. There was no significant
relation between the scores and laboratory parameters, which could probably be because
laboratory parameters were available for a limited number of patients in our study.
Since only the early CT scans, done within one week of disease, were taken into consideration,
these findings can help in early triaging of the patients who are likely to deteriorate
and administer early medical therapy even if the patients clinically have mild symptom.
Since a number of scoring systems are available, we compared the 20, 25 point scales
and percentage of lung involvement to determine whether one predictor is better than
other. Our study found that all three scoring systems related well with the oxygen
requirement, though the performance of 0-20 scale model was slightly better. Thus,
any of the scoring systems are equally predictive of patient outcome and can be used
for assessing the prognosis of patients. On ROC curve analysis we found a cut off
of 4 for OS1, 9 for OS2 and 12·5% for OP to be a predictor of oxygen requirement.
Majority of the patients in the mild group were on room air and were treated on an
OPD basis whereas majority of the patients in severe group were admitted in ICU and
were either on NIV/HFNC or were intubated. This finding was consistent for all the
3 scoring systems. Mortality was highest in the severe group. Unlike the findings
of Zhang et al., we could not find a correlation between the scoring system and the laboratory parameters.[[14]]
The review of relevant literature is as shown in [[Table 7]].
Table 7
Literature review
|
Author
|
SCORING SYSTEM USED
|
No of patients
|
Result
|
|
Lanza et al.[[15]]
|
Percentage of compromised lung
|
222
|
Compromised lung volume was the most accurate outcome predictor (logistic regression,
P<0-001)
Compromised lung volume values in the 6-23% range increased risk of oxygenation support;
values above 23% were at risk for intubation.
|
|
Colombi et al.[[16]]
|
Percentage of well aerated lung
|
236
|
A percentage of well aerated lung less than 73% was a predictor of ICU admission or
death
|
|
Leonardi et al.[[17]]
|
Percentage of compromised lung
|
189
|
A cut-off of 23% of lung involvement showed distinguished critically ill patients
from patients with less severe disease.
|
|
Sandoval et al.[[18]]
|
AI based software-percentage involvement of lung
|
166
|
Threshold for 51% for mortality and 25% for mechanical ventilation
|
|
Jiayi Liu et al.[[18]]
|
0-20
|
24
|
As the severity increased, the number of lobe involved and CT severity score increased
from 4 to 5 and 6 to 12 respectively. A cut off of 5 helped to identify cases with
severe pneumonia (i.e. SpO2 less than 93% on room air and P/F<300)
|
|
Tabatabei et al.[[19]]
|
0-20
|
90 non-elderly patients. 30 who expired were in case group and 60 who were discharged
were in control group
|
CT severity score is the only statistically significant CT predictor of mortality.
A score of 7.5 was cut-off point of CT severity score with the highest sensitivity
(0-83) and specificity for predicting mortality.
|
|
Lyu et al.[[20]]
|
0-20. used both qualitative and quantitative indicators
|
51
|
Cut off >10 to differentiate severe cases from mild and moderate.
|
|
Li et al.[[9]]
|
0-20
|
78
|
Cut off of 7.5 to diagnose severe- critical cases (SpO2 less than 93% on room air)
|
|
Fancone et a/.191
|
0-25
|
130
|
CT score was significantly higher in critical and severe than in mild stage. A CT
score of >18 was associated with an increased mortality risk and was found to be predictive
of death.
|
|
Saeed et al.[[21]]
|
0-25
|
902
|
The 25-point CT severity score correlates well with the Covid-19 clinical severity.
|
|
Mahdjoub et al.[[22]]
|
0-25
|
142
|
CT score >13 was related to poor 5-day outcome
|
|
Zhou et al.[[23]]
|
0-25
|
134
|
The cut-off value of total CT scores was determined to be 16-5 for predicting poor
prognosis in patients with Covid-19.
|
|
Feng et al.[[24]]
|
0-25
|
298
|
CT severity score is an independent predictor for progression to severe Covid-19 pneumonia
|
|
Abbasi et al.[[25]]
|
0-24
|
262
|
Optimal CT severity score threshold for identifying deceased patients was 10. The
mean score of survivors was 7 and deceased patients was 14.
|
This was a prospective study conducted with an appropriate sample size. This study
demonstrated a direct relationship between the CT severity scores and oxygen requirement
and need for intubation, independent of the laboratory values and comorbidities of
the patients. To the best of our knowledge, no study has provided a comparison between
the different scoring systems for CT severity. This study showed that all 3 scoring
systems were predictive and none was significantly superior to the other. While most
of the studies provided a cut off for predicting mechanical ventilation and oxygen
requirement based on opacity scores, this study provides a cut off for predicting
oxygen requirement.
However, our study has certain limitations. Studies were not controlled by number
of days since start of symptoms, which could have potential limitations for interpretation
of CT severity score. The first scan performed was utilized, it is known severe disease
may have a longer interval between beginning of symptoms and height of disease. Laboratory
values were available only for a limited number of patients in our study and hence
we could not find any correlation. Factors like age, comorbidities, inflammatory markers
can also affect the outcome and oxygen requirement. We did not independently analyse
the effect of these parameters in our study. It was a single centre study and hence
representative of data in a particular community. The results may vary for different
communities. Even with these limitations there was an excellent correlation over a
large cohort of patients.
In conclusion, an early CT scan in patients affected with Covid-19 is predictive of
the oxygen requirement of the patient. As severity scores increase the chances of
requirement of higher oxygen and intubation increase. The severity scoring system
can be based on lobar involvement and scored 0-20 or 0-25 or based on percentage of
lung involved, as they are all predictive of oxygen requirement. CT severity scoring
using an automated deep learning software programme is a great boon for determining
oxygen requirement and triage.[[25]]
