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\begin{document}

\title{The Correlations Between Epidemiological and Clinical Characteristics,
laboratory tests and CT Scan reports in the diagnosis of cases 2019
novel coronavirus pneumonia. A Diagnostic Accuracy Study}



\author[1]{Bobby Branson}%
\author[2]{Ramin Tavakoli}%
\author[3]{Mansoor  Khaledi}%
\author[4]{Seyed Mohammad  Shafiee}%
\author[3]{Hamed  Afkham}%
\author[2]{Shahdokht Rastegar}%
\affil[1]{Comenius University in Bratislava Jessenius Faculty of Medicine in Martin}%
\affil[2]{Ahvaz Jondishapour University of Medical Sciences}%
\affil[3]{Shahed University}%
\affil[4]{Shiraz University of Medical Sciences}%


\vspace{-1em}



  
  \date{May 07, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Background: The role of laboratory parameters and the relationship of
them with radiology reports, CT scan and clinical outcomes in screening
of COVID-19 patients not been definitely established, but this disease
presented a major challenge in the field of clinical tests, radiology
reports, clinical outcomes that help to monitoring and treatment
COVID-19 disease. Methods: This study was performed on 340 suspected
COVID-19 patients, who presented to Chamran Hospital, Shiraz University
of Medical Sciences Shiraz, Iran from 20 February to 31 August, 2020.
Information each patient's will be completed using a data collection
forms based on records. The evaluation of lungs involvement in CT scan
and their relationship with laboratory indicator including biochemical
and hematological factors, is the best scale for the severity and
prognosis of Covid 19 patients. Results and Conclusion: The findings of
this study indicated ALT, AST, CRP, NEU, LDH, and Urea have very good
accuracy in predicting cases with positive RT-PCR for COVID-19,
respectively. In this study we shown the correlation of clinical and
laboratory findings with CT-based quantitative score of pulmonary
involvement in COVID-19 pneumonia and attempted that our findings could
be usable to development future clinical research associated with
COVID-19 infection and show the relationship of reports CT scan and
clinical outcomes in the diagnosis and severity of patients with
COVID-19.%
\end{abstract}%



\sloppy


\textbf{The Correlations Between Epidemiological and Clinical
Characteristics, laboratory tests and CT Scan reports in the diagnosis
of cases 2019 novel coronavirus pneumonia. A Diagnostic Accuracy Study}

\textbf{Running Title:} The correlations between Clinical Findings,
laboratory tests and CT reports in the diagnosis of with COVID-19
patients.

Bobby Branson\textsuperscript{1}, Ramin Tavakoli\textsuperscript{2},
Mansoor Khaledi\textsuperscript{3} , Seyed Mohammad
Shafiee\textsuperscript{2} , Hamed Afkham\textsuperscript{3} , Shahdokht
Rastegar\textsuperscript{4}*

1. Biomedical Centre Martin, Department of surgery, Jessenius Faculty of
Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia.

2. Department of Biochemistry, Faculty of Medicine, Student Research
Committee, Shiraz University of Medical Sciences, Shiraz, Iran.

3. Department of Microbiology, Faculty of Medicine, Shahed University,
Tehran, Iran

4. Toxicology Research Center, Department of Biochemistry, School of
Medicine, Student Research Committee, Ahvaz Jundishapur University of
Medical Sciences, Ahvaz, Iran.

\textbf{Corresponding author:} Shahdokht Rastegar*. Department of
Biochemistry, Toxicology Research Center, School of Medicine, Student
Research Committee, Ahvaz Jundishapur University of Medical Sciences,
Ahvaz, Iran. Tel: +989389237286 , E-mails: h.a.university.ac@gmail.com,
Shahdokht63rastegar@gmail.com

\textbf{Address:} Department of Biochemistry, Toxicology Research
Center, School of Medicine, Student Research Committee, Ahvaz
Jundishapur University of Medical Sciences, Ahvaz, Iran.

\textbf{Abstract}

\textbf{Background:} The role of laboratory parameters and the
relationship of them with radiology reports, CT scan and clinical
outcomes in screening of COVID-19 patients not been definitely
established, but this disease presented a major challenge in the field
of clinical tests, radiology reports, clinical outcomes that help to
monitoring and treatment COVID-19 disease.

\textbf{Methods:} This study was performed on 340 suspected COVID-19
patients, who presented to Chamran Hospital, Shiraz University of
Medical Sciences Shiraz, Iran from 20 February to 31 August, 2020.
Information each patient's will be completed using a data collection
forms based on records. The evaluation of lungs involvement in CT scan
and their relationship with laboratory indicator including biochemical
and hematological factors, is the best scale for the severity and
prognosis of Covid 19 patients.

\textbf{Results and Conclusion:} The findings of this study indicated
ALT, AST, CRP, NEU, LDH, and Urea have very good accuracy in predicting
cases with positive RT-PCR for COVID-19, respectively. In this study we
shown the correlation of clinical and laboratory findings with CT-based
quantitative score of pulmonary involvement in COVID-19 pneumonia and
attempted that our findings could be usable to development future
clinical research associated with COVID-19 infection and show the
relationship of reports CT scan and clinical outcomes in the diagnosis
and severity of patients with COVID-19.

\textbf{Keywords:} Clinical Outcomes, laboratory findings,
coronaviruses, COVID- 19 infection, CT scan reports.

\textbf{What is already known about this topic?}

1- The investigation of laboratory parameters and CT scan reports have
an important role and very good accuracy in predicting cases with
positive RT-PCR for COVID-19 clinical outcomes and screening of COVID-19
patients.

\textbf{What does this article add?}

1- In this study we investigated the correlation of clinical and
laboratory findings with CT-based quantitative score of pulmonary
involvement in COVID-19 pneumonia, that we realized radiology reports
and CT scan findings may be predictive of patient's outcome and had a
correct correlate with laboratory findings and disease severity, the use
of laboratory findings in besides of CT scan reports as a first line
test can be beneficial to speed up diagnostic and therapeutic workflow.

\textbf{1. INTRODUCTION}

The new covid-19 coronavirus, a severe infectious disease with rapid
transmission to humans, was discovered in December 2019, and its
outbreak was declared by the World Health Organization as a public
health emergency and has now claimed the lives of millions
worldwide\textsuperscript{1}. Diagnosis of Covid - 19 disease is
performed using polymerase chain reaction tests, chest CT scans, and
ELISA test kits\textsuperscript{2,3}. Research published since 2019
claims that chest CT scans, along with polymerase chain reaction tests
and other laboratory parameters, have the highest efficiency in
detecting Covid 19\textsuperscript{4,5}. Therefore, chest CT scan and
radiological reports have been proposed as an adjunct to polymerase
chain reaction tests as a complementary method \textsuperscript{6}. No
findings can completely confirmed or rule out the possibility of
COVID-19 \textsuperscript{7}. Some observational studies have been
partially characterized Clinical, laboratory, and imaging features of
Covid-19, but until now few systematic study on relate these features to
each other have been published to date \textsuperscript{8}. Laboratory
tests scales in the most patients with COVID-19 showed a significant
changes in serum pro-inflammatory cytokine levels and blood biochemical
markers such as interleukins, TNF-a, LDH, AST, ALT BUN, Creatinine,
D-dimers, platelet count, cardiac troponin, renal markers WBC, HB and
Lymphocytes (according to figure. 1) \textsuperscript{9,10}. Evaluation
of clinical features and radiological reports in severe cases of
COVID-19 will help in the initial prognosis, accurate diagnosis and
treatment of patients with Covid-19 \textsuperscript{11-13}. It was
detecting that there are differences in the abundance of metabolic
markers and biochemistry indicators in COVID-19 patients than healthy,
but there is a question about the subject whether change in blood
biochemistry markers are associated with CT-scan findings and the
severity of COVID-19 disease? Most of the articles published in the
field have reported the epidemiological and clinical characteristics of
patients with COVID-19 disease, but data regarding the relationship of
laboratory characteristics with CT-scan findings of infected individuals
are limited \textsuperscript{14-16}. However, the evaluation of lungs
involvement in CT scan and their relationship with laboratory indicator
including biochemical and hematological factors, is the best scale for
the severity and prognosis of Covid 19 patients \textsuperscript{17}.
Various studies described the epidemiological and clinical
characteristics of hospitalized patients \textsuperscript{10,18-20}.
However, there is a limited number of detailed analysis on blood
chemicals in discharged patients. In this study, we attempted that our
findings could be usable to development future clinical research
associated with COVID-19 infection. In concluded, this study aimed to
evaluate laboratory parameters and the relationship them with CT scan
reports and clinical outcomes in the diagnosis of patients with
COVID-19.

\textbf{2. METHODS}

This study is a cross sectional study conducted of collected data on
patients of all age groups, this study was performed on 340 suspected
COVID-19 patients, who presented to Chamran Hospital, Shiraz University
of Medical Sciences Shiraz, Iran from 20 February to 31 August 2020 and
in this study after obtaining permission from the ethics committee and
approving the research plan, the clinical and laboratory findings of
covid-19 patients were extracted from the electronic medical records of
the patients. Information each patient including the evaluation of lungs
and kidney involvement in CT scan and sonography and their relationship
with laboratory indicator including biochemical and hematological
factors for the severity and prognosis of Covid 19 disease was
investigated by a trained team of physicians. The data about clinical
symptoms, medicine treatment and laboratory parameters were collected by
patient record, and based on the doctor's diagnosis and CT scan reports
and PCR test of throat Secretions. Nasal and pharyngeal swab samples and
sputum were obtained from patients on admission. We retrospectively
analyzed and evaluated the epidemiological history, comorbidity, vital
signs, and symptoms obtained from electronic medical records. The data
collection forms were reviewed independently by two experienced
physicians, and the severity of the disease is determined by the need
for ICU, sepsis, involvement of the lungs and other organs, intubation,
ventilation, death. The information of each case was completed using a
data collection form based on the records recorded in the patient file
and the examination of radiological photographs, and a sample of the
data collection form was attached. Other variables studied include the
need for ICU hospitalization and the time of hospitalization from the
onset of symptoms to hospitalization and the need for intubation and
ventilation. Problems include ARDS, Renal Injury, Septic Shock and
Ventilator Associated Injury. In this study, CT scan reports are
examined and the consistency of patients' deterioration based on these
reports with the patient's fate in the case is examined. Inclusion
criteria included, all positive coronavirus patients referred to chamran
Hospital, who need to be hospitalized. Exclusion criteria included,
deaths before admission to the hospital and positive coronavirus
patients or those who for some reason refused to visit the hospital.
This project has been approved by the research ethics committee with the
code of~IR.SUMS.REC. 1399.S605 and project number of 99-10-24-13415
by~Shiraz University of Medical Sciences in Shiraz, Iran.

\textbf{2.1 Statistical analyses}

The collected data was entered and analyzed through the data collection
form in SPSS software version 20. For descriptive analysis, mean and
standard deviation will be used for quantitative variables and frequency
and frequency percentage will be used for qualitative variables. To
evaluate the effect of each variable as a univariate test, independent
two-sample t-test, one-way ANOVA and chi-square will be performed for
quantitative and qualitative factors, respectively. Also, the study of
the effect of variables is multivariate modeled using logistic
regression model and significance is considered at the level of 0.05.
The Mann--Whitney U test was utilized to compare significant differences
among continuous data. All statistical tests were two tailed, and the p
value less than 0.05 was considered statistically significant.

\textbf{3. RESULTS}

Demographic characteristics of all patients was listed in Table 1. The
mean age of patients was 41.7, the median age 63 years (range 32--69)
for critical patients which 65 \% were male and 34 \% were female
(one-way ANOVA, p \textless{} 0.0001). Almost 198 patients (58\%) were
aged [?] 38~years. Generally, 183 (53\%) patients had one or two types of
chronic diseases, such as cancer, immune system defects, coronary heart
disease (CHD), hypertension (HBP) and diabetes (DM). Hypertension 154
(45.29 \%), Cardiovascular Disease 90 (26.47\%) and Diabetes 178
(52.35\%) were the most common Coexisting Conditions. According to the
results of this study, 28 patients (9\%) of patients had familial
infection or familial clusters. There was significant difference among
patients by their epidemiology history. The patients indeed presented
significantly different on the most common symptoms like Fever, Dyspnea,
Muscular pain, Fatigue, Shortness of Breath, Chill, Dry Cough and
Diarrhea (Table~1) (one-way ANOVA, p = 0.001). Furthermore, the mean
length of hospital stay in these patients were 14.12 days that in the
critically ill patients took average 2.08 days longer than total average
levels. From the 340 cases with the suspicion of COVID-19 infection, 230
patients (164 males, 76 females with a positive RT-PCR test for
covid-19). The most common clinical manifestations were fever, coughing,
Muscular pain and dyspnea. In this study change CRP levels were found in
patients and increased D-dimer levels were found in patients and
decreased lymphocyte count was observed in 98 patients according to
Table 2 Summary of Changes in Biomarkers Seen in Severe COVID-19
Infection. Change clinical in biochemistry parameters patients are
summarized in Table 2. The results of this study show that the white
blood cell (WBC) and neutrophil cell numbers were significantly higher
and platelets and HB severely lower and a quite distinguishable
difference on the blood biochemical test at different patients. Among
patients in people with underlying diseases and high-risk groups showed
significantly higher levels of serum ALT, AST, LDH, than other healthy.
According to Table 2 ALT, CRP, LDH, and Urea had very good precision in
predicting cases with positive RT-PCR for COVID19. The result of this
study RT-PCR for COVID-19 patients was positive in 252 (74\%) cases and
negative in 88(25\%). Based on the CDC clinical scoring for covid-19
infection (10), 150 (44.11\%) were classified as mild, 118 (34.70\%) as
severe, and 72 patients (21.17\%) as critical.

The results of this study ( Table 3) in CT features, scoring, screening
patients show the most common patterns of disease included GGO, observed
in 152 patients (44.7\%), followed by followed by crazy-paving pattern
68 (20 \%) and parenchymal consolidations in 78 (22.9\%) (Fig. 2).
Features and characteristics related to CT were found as follows:
fibrosis (n = 57; 16.76\%), sub pleural lines (n= 43; 12.46\%), pleural
effusion (n = 28; 8.23\%), precordial effusion (n = 8; 2.35\%), and
mediastinal lymphadenopathy (n = 27; 7.94 \%), COPD thinking of ILD, ILS
(n = 30; 8.82 \%). Findings of this study also shown lobar involvement,
lesion distribution, and localization in pulmonary parenchyma.
Pathological involvement in the left lower lobe (LLL) in 157 patients
(46.17\%) was most common and right lower lobe (RLL) in 143 patients
(42.05 \%). The mean of involvement lung lobes and CT scores were show
in (Fig. 3) (Table.4).

According to the average global CT score was 12.3\selectlanguage{ngerman}±11.1. All patient did
show parenchymal involvement at CT reports and there are not any patient
therefor scored as 0. In comparisons between lung lobes, the mean CT
score was significantly higher in RLL than in ML and RUL
(p\textless{}0.0001) and the mean CT score was significantly higher in
LLL than in LUL (p\textless{}0.0001) (Fig. 3), also the distribution of
parenchymal abnormalities in pathological findings were posterior in 118
patients (34.70\%) and anterior in 68 patients (20 \%). In the 50
patients (16.8\%), there was involvement of both anterior and posterior
areas. Regarding the results of this study and investigation of CT
features, demonstrated the GGO pattern was most prevalent in early-phase
disease and late-phase disease, while crazy-paving and consolidation
patterns were most common in late-phase, also fibrosis were
significantly common in late-phase. The pleural effusion and
lymphadenopathy in patients were rarely observed in late-phase. CT score
in late-phase was significantly higher than in early-phase patients
(p\textless{}0.0001). CT score between age range groups statistically
significant difference was found (p=0.0018), in this way CT score was
significantly higher in age range \textgreater{}75 than in other age
groups (p=0.001).

Furthermore, the results of this study indicated statistically
significant correlations between CT score with SAA (p\textless{}0.0001,
r= 0.4314), LDH (p\textless{}0.0001, r= 0.3214), Cardiac troponin
(p\textless{}0.0001, r= 0.6714), Renal biomarkers Urea \& creatinine
(p\textless{}0.0001, r= 0.3314), CRP (p\textless{}0.0001, r= 0.6314),
D-dimer (p\textless{}0.0001, r=0.6427), lymphocyte count (p=0.0001, r
=0.1630) levels. Univariate and multivariate analyses of 340 patients in
this study show that 48 patients (11.17 \%) died during a mean follow-up
of 14.1±4.8 days (range 1--26 days), all of which indicated at least one
or more of the previously mentioned underlying diseases. The mortality
rate was significantly higher in patients \selectlanguage{english}[?]75 years old (n=39; 11.47\%)
and among critical patients (12/12; 100\%). The univariate analysis CT
score in this patients indicated a higher risk of death in patients with
a CT score \selectlanguage{english}[?]18 (HR, 8.23; 95\% CI, 2.17--25.63; p\textless{}0.0001), and
significantly correlated with increase of age (HR,1.02;95\%
CI,1.01--1.21; p=0.001), HDL (HR, 1.01; 95\% CI, 1.03--1.07;
p\textless{}0.001), Cardiac troponin (HR, 1.003; 95\% CI, 1.10--1.00;
p\textless{}0.001), Urea (HR, 1.06; 95\% CI, 1.03--1.07;
p\textless{}0.001), creatinine (HR, 1.01; 95\% CI, 1.00 --1.01;
p\textless{}0.001) CRP (HR, 1.06; 95\% CI, 1.03--1.07;
p\textless{}0.001) and D-dimer levels (HR,1.011; 95\% CI,1--1.08.001;
p=0.0001). Table 5 show the correlations between Clinical Findings,
laboratory tests and CT reports.

In this study we investigated the correlation of clinical and laboratory
findings with CT-based quantitative score of pulmonary involvement in
COVID-19 pneumonia, that we realized CT scan findings may be predictive
of patients `outcome and had a correct correlate with laboratory
findings and disease severity.

\textbf{Table 1 Demographic and clinical characteristics.}\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{Demographic characteristics} & \textbf{Covid -- 19 patients (N
\%)}\tabularnewline
\midrule
\endhead
\textbf{Sex: (n \%)} \textbf{Male} \textbf{Female} & \textbf{223 (65.48
\%)} \textbf{117 (34.52 \%)}\tabularnewline
\textbf{Age: (n \%)} \textbf{0-25} \textbf{26-50} \textbf{51-75}
\textbf{\textgreater{}75} & \textbf{25 (7. 36 \%)} \textbf{77 (22.46
\%)} \textbf{183 (54.11 \%)} \textbf{55 (16.07 \%)}\tabularnewline
\textbf{Symptoms:} \textbf{Fever} \textbf{Coughing} \textbf{Dyspnea}
\textbf{Diarrhea} \textbf{Headache} \textbf{Muscular pain} & \textbf{211
(62.03 \%)} \textbf{157 (46.17 \%)} \textbf{204 (60 \%)} \textbf{98
(28.82 \%)} \textbf{142 (41.79 \%)} \textbf{201 (59.11
\%)}\tabularnewline
\textbf{Underlying disease} \textbf{Hypertension} \textbf{Cardiovascular
Disease} \textbf{Obesity or Hyperlipidemia} \textbf{Diabetes}
\textbf{Chronic obstructive pulmonary disease} \textbf{Neoplasm}
\textbf{Chronic kidney disease} \textbf{Cancer}
\textbf{Immunocompromised} & \textbf{154 (45.29 \%)} \textbf{90
(26.47\%)} \textbf{98 (28.78\%)} \textbf{178 (52.35\%)} \textbf{85 (25
\%)} \textbf{77 (22.64 \%)} \textbf{44 (12.94 \%)} \textbf{68 (20 \%)}
\textbf{55(16.17 \%)} \textbf{48(14.11\%)}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2} \textbf{Comparing the laboratory findings of COVID-19
patients between Positive and Negative cases.}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Parameters} & \textbf{Total (n=340)} &
\textbf{RT-PCRforCOVID-19} & \textbf{RT-PCRforCOVID-19} & \textbf{P-
value}\tabularnewline
\midrule
\endhead
& & \textbf{Positive (n=252)} & \textbf{Negative (n=88(}
&\tabularnewline
\textbf{WBC} & \selectlanguage{ngerman}\textbf{6962.8±2127} & \selectlanguage{ngerman}\textbf{4343±1002} &
\selectlanguage{ngerman}\textbf{6994±1982} & \textbf{\textless{}0.0001}\tabularnewline
\textbf{NEU} & \textbf{48.1} & \textbf{62.2} & \textbf{45.7} &
\textbf{\textless{}0.0001}\tabularnewline
\textbf{LYM} & \textbf{45.8} & \textbf{39.1} & \textbf{52.1} &
\textbf{\textless{}0.0001}\tabularnewline
\textbf{POSITIVE CRP a} & \textbf{39} & \textbf{57} & \textbf{29} &
\textbf{\textless{}0.0001}\tabularnewline
\textbf{ESR} & \selectlanguage{ngerman}\textbf{27.2±8.6} & \textbf{32} & \textbf{20} &
\textbf{0.486}\tabularnewline
\textbf{AST} & \selectlanguage{ngerman}\textbf{28.6±8.6} & \selectlanguage{ngerman}\textbf{32.1±8.01} & \selectlanguage{ngerman}\textbf{21.24±
8.1} & \textbf{0.533}\tabularnewline
\textbf{ALT} & \selectlanguage{ngerman}\textbf{32±7.1} & \selectlanguage{ngerman}\textbf{38.8±2.1} & \selectlanguage{ngerman}\textbf{25.2±5.9} &
\textbf{0.926}\tabularnewline
\textbf{LDH} & \selectlanguage{ngerman}\textbf{385.6±125} & \selectlanguage{ngerman}\textbf{471.2±110.2} &
\selectlanguage{ngerman}\textbf{361.6±94.1} & \textbf{0.0001}\tabularnewline
\textbf{SAA} & \selectlanguage{ngerman}\textbf{28.2±8.01} & \selectlanguage{ngerman}\textbf{36.1±8.5} &
\selectlanguage{ngerman}\textbf{24.8±4.9} & \textbf{0.0001}\tabularnewline
\textbf{Urea} & \selectlanguage{ngerman}\textbf{43.12±32.41} & \selectlanguage{ngerman}\textbf{66.81±53.83} &
\selectlanguage{ngerman}\textbf{42.78±33.42} & \textbf{0.001}\tabularnewline
\textbf{Creatinine} & \selectlanguage{ngerman}\textbf{1.39±0.95} & \selectlanguage{ngerman}\textbf{1.69±0.95} &
\selectlanguage{ngerman}\textbf{1.97±9.32} & \textbf{0.877}\tabularnewline
\textbf{CK-MB} & \selectlanguage{ngerman}\textbf{899.35± 255.21} & \selectlanguage{ngerman}\textbf{1033.45±1754.19} &
\selectlanguage{ngerman}\textbf{216.38±474.51} & \textbf{0.0001}\tabularnewline
\textbf{D-dimer} & \selectlanguage{ngerman}\textbf{0.4 ±1.5} & \selectlanguage{ngerman}\textbf{0.5±2.9} &
\selectlanguage{ngerman}\textbf{0.3±0.7} & \textbf{0.0001}\tabularnewline
\textbf{Blood urea Nitrogen} & \textbf{35} & \textbf{58-70} &
\textbf{8-24} & \textbf{0.001}\tabularnewline
\bottomrule
\end{longtable}

\textbf{WBC: white blood cell; NEU: Neutrophil; LYM Lymphocyte AST:
aspartate aminotransferase; ALT: alanine aminotransferase; LDH lactate
dehydrogenase; ESR: erythrocyte sedimentation rate; CRP: C reactive
protein; CK-MB, creatine kinase-muscle and brain type; SAA; serum
amyloid A}

\textbf{Table 3 Frequency of main patterns and features in covid-19
patients.}\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\textbf{CT features in COVID -19 patients} & \textbf{N\%} & \textbf{p
value}\tabularnewline
\midrule
\endhead
\textbf{Main CT pattern} \textbf{Ground glass opacity} \textbf{Crazy
paving} \textbf{Consolidation} \textbf{Mixed GGO and consolidation} &
\textbf{152/340 (44.7\%)} \textbf{68/340 (20 \%)} \textbf{78/340
(22.9\%)} \textbf{94/340 (27.64\%)} & \textbf{\textless{} 0.0001}
\textbf{0.0003} \textbf{0.0021} \textbf{0.0001}\tabularnewline
\textbf{Related features} \textbf{Fibrosis} \textbf{Sub pleural lines}
\textbf{Pleural effusion} \textbf{precordial effusion}
\textbf{mediastinal Lymphadenopathy} \textbf{COPD thinking of ILD, ILS}
& \textbf{57/340 (16.76\%)} \textbf{43/340 (12.46\%)} \textbf{28/340
(8.23\%)} \textbf{8/340 (2.35\%)} \textbf{27/340 (7.94 \%)}
\textbf{30/340 (8.82 \%)} & \textbf{\textless{} 0.0001} \textbf{0.0024}
\textbf{0.422} \textbf{0.210} \textbf{0.321}
\textbf{0.541}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 4 Frequency of involvement of each lobe with related CT
score.}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{Categories} & \textbf{covid-19 patient n\%} & \selectlanguage{ngerman}\textbf{CT mean
score ± SD} & \textbf{p value}\tabularnewline
\midrule
\endhead
\textbf{Lung Lobe} \textbf{Right upper lobe (RUL)} \textbf{Middle lobe
(ML)} \textbf{Right lower lobe (RLL)} \textbf{Left upper lobe (LUL)}
\textbf{Left lower lobe (LLL)} & \textbf{272(80\%)} \textbf{258 (76\%)}
\textbf{143 (42.05 \%)} \textbf{299 (88\%)} \textbf{157 (46.17\%)} &
\selectlanguage{ngerman}\textbf{2.4 ± 1.8} \textbf{1.7 ± 1.4} \textbf{3.4 ± 1.3} \textbf{2.4 ±
1. 8} \textbf{3.9 ± 1.8} & \textbf{0.0001} \textbf{0.0001}
\textbf{0.0001} \textbf{0.0001} \textbf{0.0001}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 5 The correlations between Clinical Findings, laboratory
tests and CT reports.}\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\textbf{laboratory tests} & \textbf{CT SCORE} & \textbf{CT
SCORE}\tabularnewline
\midrule
\endhead
& \textbf{r P- value} & \textbf{r P- value}\tabularnewline
\textbf{WBC} \textbf{NEU} \textbf{LYM} & \textbf{r =0.1520} \textbf{r
=0.2630} \textbf{r =0.1630} & \textbf{0.0001} \textbf{0.0001}
\textbf{0.0001}\tabularnewline
\textbf{POSITIVE CRP \textsuperscript{a}} \textbf{ESR} & \textbf{r=
0.6314} \textbf{r= 0.6314} & \textbf{\textless{}0.0001}
\textbf{\textless{}0.0001}\tabularnewline
\textbf{AST} \textbf{ALT} \textbf{LDH} \textbf{SAA} \textbf{Urea \&
Creatinine} \textbf{TROPONIN} \textbf{CK-MB} \textbf{D-dimer}
\textbf{Blood urea Nitrogen} & \textbf{r= 0.3517} \textbf{r= 0.5310}
\textbf{r= 0.3211} \textbf{r= 0.4319} \textbf{r= 0.3314} \textbf{r=
0.6714} \textbf{r= 0.3214} \textbf{r= 0.4214} \textbf{r= 0.6427} &
\textbf{\textless{}0.0001} \textbf{\textless{}0.0001}
\textbf{\textless{}0.0001} \textbf{\textless{}0.0001}
\textbf{\textless{}0.0001} \textbf{\textless{}0.0001}
\textbf{\textless{}0.0001} \textbf{\textless{}0.0001}
\textbf{\textless{}0.0001}\tabularnewline
\bottomrule
\end{longtable}

\textbf{4. DISCUSSION}

According the findings of the present study, it seems that there is a
significant relationship between chest CT scan characteristics and change
clinical parameters with outcomes of COVID-19 cases. Based on this
results ground Glass Opacity (GGO) and consolidation were the most
common chest CT scan findings, which was match to other
researches\textsuperscript{21,22}. The most common location of
abnormalities was lower zone involvement that observed more than upper
zone, which was consistent with findings of other studies
\textsuperscript{19,23}. An investigation carried out on other patients
show that CT reports will be more obvious, when revealed longer the
onset of symptoms\textsuperscript{24}. In this study radiological
patterns have correlation with the stages of the development disease,
which was in match to the studies \textsuperscript{24,25}. The findings
of this study indicated ALT, AST, CRP, NEU, LDH, and Urea have very good
accuracy in predicting cases with positive RT-PC R for COVID-19,
respectively. Also, liver injury is more prevalent in severe cases
compared to mild cases of COVID-19. This study was similar to study Chen
et al. Another study indicated that 2--11\%of patients with COVID-19 had
ALT and AST levels during progression of COVID-19 disease
\textsuperscript{19}. In comparison to the normal range, in patients
with positive RT-PCR COVID19, the rate of WBC and LYM counts was
decreased and NEU counts increase, which is in line with another study
\textsuperscript{26}. The COVID -19 Virus extensive through the
respiratory system and infected immune cells and causing changes in
number of peripheral white blood cells such as lymphocytes
\textsuperscript{27}. Various studies proposed the substantial decrease
in the number of lymphocytes following infection with the coronavirus
that influence immune cells and prevent immune action
\textsuperscript{28}. Also, others report show that high neutrophil
count and LDH level in COVID-19 patients were autonomous predictors of
an adverse clinical outcome \textsuperscript{29}. The results of
laboratory parameters in this study, such as ALT, CRP, AST, LDH, and NEU
indicated that there could be used to foretell the presence of COVID-19
disease, while WBC were poor predicted of the disease. This information
is match to findings reported by Wang et al.\textsuperscript{30} and Gao
et al. \textsuperscript{31}. Thus, some laboratory parameters can use to
screening cases with positive RT-PCR forCOVID-19. Overall, Clinical
course of the COVID-19 disease is unforeseeable, due to the
heterogeneity of its manifestations and multi organ failure. At present,
there are no prognostic biological markers to identify Covid 19 patients
and estimate their associated mortality. It seems that predicting CT
scan reports of disease progression and its relationship with
laboratory-clinical findings may be useful in patient triage and
symptomatic treatment of patients \textsuperscript{32}. In this study we
used a previously CT score for confirming this presumption, that it was
based on the lobar Involvement expanse of as reported by Pan et al
\textsuperscript{33}. The results of the study shown correlation CT
scoring with laboratory parameters, age, dyspnea at admission and the
presence of pre-existing comorbidities like coronary arteries and
diabetes. Information of mortality rate in this study have approved the
outstanding importance of age and its rate in patients older than 75
years. our finding in the study substantially assessment CT parenchymal
reports and may reflect short and high-term outcome through direct
visualization of anatomic damages than with non-specific laboratory
parameters and inflammatory biomarkers. Based on clinical criteria
provided by the WHO \textsuperscript{34} and as expected, finding of
this study indicated the correlation of disease severity with CT reports
and laboratory parameters, so CT scores were significantly confirmed
high correlation between imaging findings and clinical stages, however,
the diagnostic role of CT scan and its correlation to laboratory
parameters is controversial and debated. Although, the use of CT is as a
first line test, but highly sensitive imaging method like CT associated
with specific clinical parameters, might be beneficial to speed up
diagnostic and therapeutic workflow.

\textbf{5. Conclusion}

Considering the significant difference in CT scoring and laboratory
parameters, can hope to model or predict the results of coronavirus
testing based on routine laboratory tests. In conclusion, finding of
this study shown the correlation of clinical and laboratory findings
with CT-based quantitative score of pulmonary involvement in COVID-19
pneumonia and our findings could be usable to development future
clinical research associated with COVID-19 infection and show
relationship of CT scan reports and clinical outcomes in the diagnosis
and severity of patients with COVID-19.Thus, expected future studies can
indicated better clarify impact on clinical decision-making and larger
clinical trials.

\textbf{Limitation}

Differences between Iran's National Guideline for COVID-19 and
guidelines of other countries, in addition to usual limitations of cross
sectional studies, were among the most important limitations of this
study. We performed a retrospective analysis evaluation in a relatively
limited cohort of patients, but the severity of the current health care
emergency implies that a prospective evaluation would have been
extremely complex and longer to complete.

\textbf{ACKNOWLEDGEMENTS}

The authors would like to thank Shiraz University of Medical Sciences,
Shiraz, Iran, Clinical Biochemistry Research Center of Shiraz University
of Medical Sciences, Shiraz, Iran, Department of Laboratory Sciences,
School of Paramedical Sciences, Ahvaz Jundishapur University of Medical
Sciences, Ahvaz, Iran for the Specialized Commissioning in this paper.

\textbf{DISCLOSURE}

The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to
influence the work reported in this paper.

\textbf{AUTHORCONTRIBUTIONS}

BB, RT, MK, SR: design of study. BB, MK, HA, SMS: acquisition of data.
BB, SR, RT, MK, : evaluation of data, preparation of the manuscript. BB,
RT, MK, SR : assessment of data. All authors read and approved the final
manuscript.

\textbf{DATA AVAILABILITY STATEMENT}

The data that supports the findings of this study are available in the
article Further enquiries can be directed to the corresponding authors.

\textbf{ORCID}

Bobby Branson https://orcid.org/0000-0002-0844-156X

Ramin Tavakoli https://orcid.org/0000-0002-6030-2217

Mansoor Khaledi https://orcid.org/0000-0002-1576-5968

Hamed Afkham https://orcid.org/0000-0002-1110-6447

Shahdokht Rastegar https://orcid.org/0000-0001-9947-5900

\textbf{Funding}

This research did not receive any specific grant from funding agencies
in the public, commercial, or not-for-profit sectors.

\textbf{Abbreviations}

\textbf{ARDS} Acute respiratory distress syndrome \textbf{CDC} Center of
Disease Control \textbf{COVID-19} Coronavirus disease 2019

\textbf{CRP} C-reactive protein \textbf{GGO} Ground glass
opacity\textbf{LLL} Left lower lobe \textbf{LUL} Left upper lobe
\textbf{ML}Middle lobe

\textbf{RLL} Right lower lobe \textbf{RT-PCR} Real-time polymerase chain
reaction \textbf{RUL} Right upper lobe

\textbf{TAT} Turnaround time \textbf{WHO} World Health Organization

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\textbf{Figure legends~}

Fig. 1 Different CT score of RLL involvement in COVID-19 pneumonia on
axial, sagittal, and coronal images. 0\% of RLL lobe involvement (a);
\textless{}5\% of RLL involvement (b); 20\% of RLL involvement (c); 40\%
of RLL lobe involvement (d); 70\% of RLL involvement (e);
\textgreater{}75\% of RLL involvement (f) \textsuperscript{35}.

Fig. 2 Chest CT findings of COVID-19 pneumonia on axial images. GGO (A);
crazy-paving pattern (GGO with superimposed inter- and interlobular
septal thickening) (B); consolidation (C); GGO, crazy-paving pattern and
consolidation (D).

Fig. 3 Lobar CT scores of patients Covid-19, data are expressed as mean
value \selectlanguage{ngerman}± SD (\% of occurrences of involvement for each lobe) (****p
\textless{} 0.0001). RUL, right upper lobe; ML, middle lobe; RLL, right
lower lobe; LUL, left upper lobe; LLL, left lower lobe.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{CT Score 0. 0\% of RLL}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{CT Score 1. \textless{} 5 \% of RLL}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{CT Score 2. 5-25 \% of RLL}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{CT Score 3. 26-50 \% of RLL}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{CT Score 4. 51-75 \% of RLL}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{CT Score 5. 76-100 \% of RLL}

\textbf{Fig. 1 Different CT score of RLL involvement in COVID-19
pneumonia on axial, sagittal, and coronal images. 0\% of RLL lobe
involvement (a); \textless{}5\% of RLL involvement (b); 20\% of RLL
involvement (c); 40\% of RLL lobe involvement (d); 70\% of RLL
involvement (e); \textgreater{}75\% of RLL involvement (f) {[}21{]}.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\textbf{A}

\textbf{B}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}

\textbf{C}

\textbf{D}

\textbf{Fig. 2 Chest CT findings of COVID-19 pneumonia on axial images.
GGO (A); crazy-paving pattern (GGO with superimposed inter- and
interlobular septal thickening) (B); consolidation (C); GGO,
crazy-paving pattern and consolidation (D).}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image11/image11}

\end{center}
\end{figure}

\selectlanguage{ngerman}\textbf{Fig. 3 Lobar CT scores of patients Covid-19, data are expressed
as mean value ± SD (\% of occurrences of involvement for each lobe)
(****p \textless{} 0.0001). RUL, right upper lobe; ML, middle lobe; RLL,
right lower lobe; LUL, left upper lobe; LLL, left lower lobe.}

\selectlanguage{english}
\FloatBarrier
\end{document}

