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\begin{document}

\title{The Effect of Host Risk Factors in Determining the Mortality in Covid 19
Pneumonia and a Novel Covid-19 Mortality Index: Co-AMSCA}



\author[1]{Mine Gayaf}%
\author[2]{Ceyda Anar}%
\author[1]{Filiz Güldaval}%
\author[1]{Gülistan Karadeniz}%
\author[1]{GÜLRU Polat}%
\author[1]{aysu ayrancı}%
\author[1]{özgür batum}%
\author[3]{Dursun Tatar}%
\author[4]{Ali Kadri Cirak}%
\affil[1]{Affiliation not available}%
\affil[2]{İzmir Katip Çelebi Üniversity}%
\affil[3]{Izmir Chest Diseases and Surgery Training Hospital}%
\affil[4]{University of Health Sciences Dr. Suat Seren Chest Diseases and Surgery Education and Training Hospital, Izmir, Turkey }%


\vspace{-1em}



  
  \date{June 14, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Aim:The purpose of this study was to determine the host risk factors
associated with mortality in COVID-19 patients who are hospitalized for
pneumonia, and also, to find a COVID-19 mortality score based on these.
Methods:All patients diagnosed as confirmed or probable COVID-19
pneumonia whom hospitalised in our Chest Diseases Education and Research
Hospital between March 11, 2020 and October 1,2020 were enrolled. The
optimal cut-off values, sensitivity and specificity values and odds
ratios to be used in mortality prediction of the novel scoring system
created from these parameters were calculated by ROC analysis according
to the area under the curve and Youden index. Results:Over 422 patients
(n: 51 mortal, n: 371 survivors) univariate regression analysis showed
that age, male gender, smoking, comorbidity, and using ACE inhibitor
were prognostic host risk factors for COVID-19-related mortality. Using
this analysis, a novel scoring model Co-AMSCA (Age, Male, Smoking
history, Comorbidity, ACE inh)was established. The cut-off value of this
scoring system (including only host risk factors), which determines the
mortality risk in patients, was 3.5 points with 88.4\% sensitivity and
65.5 \% specificity (AUC = 0.761, 95\% CI 0.697-0.826, P \textless{}
.001) (Figure 1). The mortality risk in patients with a Co-AMSCA
mortality score above 3.5 points was 7.8 times higher than patients with
lower than 3.5 (OR= 7.8; P \textless{} .001).In multivariate logistic
regression analysis, older age and smoking (smoker/ex-smoker) were found
to be important risk factors for mortality (OR = 12.09; 95\% CI
2,564-57,054 P =0.004 and OR = 3.1; 95\% CI 1,381-7,295; P =
0.007,respectively). Counclusion:We created a simple mortality score,
which is easily calculated and does not require laboratory and time.This
study showed that by using Co-AMSCA mortality score that has only host
risk factors achieved a prediction of mortality in COVID-19 patients who
are hospitalized for pneumonia.%
\end{abstract}%



\sloppy


\textbf{The Effect of Host Risk Factors in Determining the Mortality in
Covid 19 Pneumonia and a Novel Covid-19 Mortality Index: Co-AMSCA}

\textbf{Abstract}

Aim:The purpose of this study was to determine the host risk factors
associated with mortality in COVID-19 patients who are hospitalized for
pneumonia, and also, to find a COVID-19 mortality score based on these.

Methods:All patients diagnosed as confirmed or probable COVID-19
pneumonia whom hospitalised in our Chest Diseases Education and Research
Hospital between March 11, 2020 and October 1,2020 were enrolled. The
optimal cut-off values, sensitivity and specificity values and odds
ratios to be used in mortality prediction of the novel scoring system
created from these parameters were calculated by ROC analysis according
to the area under the curve and Youden index.

Results:Over 422 patients (n: 51 mortal, n: 371 survivors) univariate
regression analysis showed that age, male gender, smoking, comorbidity,
and using ACE inhibitor were prognostic host risk factors for
COVID-19-related mortality. Using this analysis, a novel scoring model
Co-AMSCA (Age, Male, Smoking history, Comorbidity, ACE inh)was
established. The cut-off value of this scoring system (including only
host risk factors), which determines the mortality risk in patients, was
3.5 points with 88.4\% sensitivity and 65.5 \% specificity (AUC = 0.761,
95\% CI 0.697-0.826, \emph{P} \textless{} .001) (Figure 1). The
mortality risk in patients with a Co-AMSCA mortality score above 3.5
points was 7.8 times higher than patients with lower than 3.5 (OR=
7.8;\emph{P} \textless{} .001).In multivariate logistic regression
analysis, older age and smoking (smoker/ex-smoker) were found to be
important risk factors for mortality (OR = 12.09; 95\% CI 2,564-57,054 P
=0.004 and OR = 3.1; 95\% CI 1,381-7,295; P = 0.007,respectively).

Counclusion: We created a simple mortality score, which is easily
calculated and does not require laboratory and time.This study showed
that by using Co-AMSCA mortality score that has only host risk factors
achieved a prediction of mortality in COVID-19 patients who are
hospitalized for pneumonia.

\textbf{What's known?}

There are few scoring systems for predicting mortality in COVID-19
infected patients which were clinically impractical.

However, a simple scoring system is still required to predict mortality.
It was reported that the severity of COVID-19 pneumonia is affected by
age and comorbidity

\textbf{What's new?}

We created a simple mortality score, which is easily calculated and does
not require laboratory and time.

\begin{itemize}
\tightlist
\item
  We created a novel mortality model called Co-AMSCA that included five
  parameters, age, male gender, smoking, comorbidity, and using ACE
  inhibitor achieved a prediction of mortality in COVID-19 patients who
  are hospitalized for pneumonia.
\item
  If the Co-AMSCA score is validated with prospective studies, it can be
  used for decreasing mortality and effective utilization of medical
  resources in the COVID-19 pandemic.
\item
  Also, we believe that the host risk score we found will be a useful
  tool for the prevention and treatment of this disease, in its
  detection and more serious follow-up of individuals with high risks.
\end{itemize}

\textbf{Introduction}

The novel coronavirus SARS-19, which started in December 2019, has
spread all over the world from the city of Wuhan, China. It has been
recognized as a pandemic by the World Health Organization (WHO) since
March 11, 2020 (1). The first case that was diagnosed in our country was
announced on March 11, 2020 (2). There have been a great number of cases
infected with COVID-19 and hospitalized because of the severity of the
disease since then. Early detection is essential because thousands of
people die from the COVID-19 outbreak. The clinician needs to predict
which cases possibly have poor progression because of the fact that
patients with a severe course may be mortal. For this reason,
epidemiological, demographic, clinical, laboratory, and radiological
characteristics were examined in some studies to determine the severity
of COVID-19 (3-6).

Advanced age, presence of concomitant cardiovascular and cerebrovascular
disease, lactate dehydrogenase (LDH) and D-dimer levels, and CD3 + CD8 +
T-cell levels were studied in recent studies as predictors of mortality.
However, a simple scoring system is still required to predict mortality.
It was reported that the severity of COVID-19 pneumonia is affected by
age and comorbidity (3-5). A simple scoring system will be useful to the
clinician at the time of patient admission to predict mortality in
hospitalized patients infected with COVID-19, by evaluating personal
risk factors (i.e. age, gender, smoking, Body Mass Index (BMI), ACE
inhibitor use, blood type, and comorbidity) rather than laboratory and
radiological findings, which require time and technique. For this
reason, the purpose of this study was to determine the personal risk
factors associated with mortality in COVID-19 patients who are
hospitalized for pneumonia, and also, to find a COVID-19 mortality score
based on these.

\textbf{Material and Method}

\textbf{Study Population}

This study was approved by both the Scientific Committee of our hospital
and the Ministry of Health COVID-19 Scientific Research Evaluation
Committee date/number 04.05.2020 /3801.

All patients who were hospitalized in our Chest Diseases Training and
Research Hospital between 11 March 2020 and 1 October 2020 diagnosed
with confirmed or probable COVID-19 pneumonia were included in this
retrospective, non-interventional, single-center case-cohort study. All
patients who were hospitalized for COVID-19 pneumonia underwent Reverse
Transcriptase Polymerase Chain Reaction (RT-PCR) nasopharyngeal swab
test for SARS-CoV-2 virus. A positive result in the RT-PCR test of nasal
and pharyngeal swab samples was considered to be a laboratory-confirmed
patient. Also, patients who had negative RT-PCR but positive antibody
tests and clinical and radiological signs compatible with COVID-19
pneumonia were included in the study. The diagnostic criteria, method,
and severity of the disease were evaluated according to the Scientific
Committee Guidelines of the Ministry of Health (7).

\textbf{Data collecting}

The demographic data, comorbidities, and mortality were recorded
retrospectively. All of the data were obtained from the hospital
registration system, patient e-information, and operating system. When
necessary, patient phone contact numbers were used for missing data.
Cases whose data could not be reached were excluded from the study.

\textbf{Statistical Analysis}

Analyses were performed with SPSS software v 25.5 (IBM, NY, USA). To
determine whether continuous data were normally distributed,
Shapiro-Wilk and Kolmogorov-Smirnov normality tests were used.
Mann-Whitney U test and Student T-test were used to compare continuous
variables, and Chi-square and Fisher's exact test were used for
comparison of categorical data. Results were given as mean \selectlanguage{ngerman}± SD, median
(min-max), number, and percentage (\%). P value \textless{}0.05 was
considered statistically significant. The predictive values of the
parameters for mortality were calculated with univariate and
multivariate logistic regression analyzes. The optimal cut-off values,
sensitivity, and specificity values, and probability ratios to be used
in the estimation of mortality of these parameters and the scoring
system formed from these parameters were calculated by ROC analysis
using the area under the curve and Youden index. The results were
presented with 95\% confidence intervals.

\textbf{Results}

\textbf{Demographic characteristics of patients}

A total of 422 patients, who were hospitalized for COVID-19 pneumonia
between 11 March 2020 and 1 October 2020, were included in the study.
The demographic data of all the cases, characteristics of the patients
who died and who survived are shown in Table 1. The diagnosis was made
with 72,5\% PCR positivity and 27,4 \% of the cases were diagnosed with
radiology and rapid antibody test. When the distribution of age groups
between the mortality and survival groups was evaluated, it was found
that there were older patients in the mortality group
(P\textless{}0.001). Male gender, being a cigarette smoker, presence of
comorbidity, and history of using Angiotensin-Converting Enzyme (ACE)
inhibitor drug were statistically more significant in the mortality
group (p values 0.046, 0.001, 0.001, and 0.004, respectively). The blood
groups and Body Mass Index values were not found to be significant
between the mortality and survival groups (p: 0.145 and 0.383,
respectively) (Table 1).

\textbf{Predictor of mortality and severity of pneumonia.}

When the predictive power of host risk factors determined for mortality
was evaluated with univariate analysis, it was found that patients over
65 years of age had a 21.1 times higher mortality risk (OR = 21.1 95\%
CI 4.89-91.5; p\textless{}0,001). Likewise, the mortality risk of male
patients was higher than female patients (Table 2). Ex-smoker and/or
smoker patients had a 4.07 times higher mortality than nonsmoker
patients (OR = 4.07; 95\% CI 2,128-7,796; p\textless{}0,001). And the
mortality risk of patients with mult\selectlanguage{polish}ı\selectlanguage{english}ple additional diseases was 4.9
times higher than those with no additional diseases (OR = 4.07; 95\% CI
2,128-7,796; p\textless{}0,001) (Table 2).

\textbf{A scoring model}

To create a simple score for mortality in COV\selectlanguage{polish}İ\selectlanguage{english}D 19 patients forming only
by host risk factors and facilitate \selectlanguage{polish}ı\selectlanguage{english}ts clinical use, a scoring model
was established \textbf{Co-AMSCA (A} ge, \textbf{M} ale\textbf{, S}
moking history\textbf{, C} omorbidity\textbf{, ACE inh} ) with scores
from 0 to 7 points (Table 3). The cut-off value of this scoring system
(including only host risk factors), which determines the mortality risk
in patients, was 3.5 points with 88.4\% sensitivity and 65.5 \%
specificity (AUC = 0.761, 95\% CI 0.697-0.826, \emph{P} \textless{}
.001) (Figure 1). The mortality risk in patients with a
\textbf{Co-AMSCA} mortality score above 3.5 points was 7.8 times higher
than patients with lower than 3.5 (OR = 7.8; \emph{P} \textless{} .001).

When the predictive power of each risk factors in the Co-AMSCA scoring
model for mortality risk was evaluated by multivariate logistic
regression analysis, older age and smoking (smoker/ex-smoker) were found
to be important risk factors for mortality (OR = 12.09; 95\% CI
2,564-57,054 P =0.004 and OR = 3.1; 95\% CI 1,381-7,295; P = 0.007,
respectively) (Table 4).

\textbf{Discussion}

Using a simple scoring system at the time of admission during the
COVID-19 outbreak can be life-saving in terms of predicting patients who
will have a mortal progression. For this reason, with this study, using
only the host risk factors without the radiology and laboratory findings
of patients at the time of admission, we defined the risk factors of
five hosts consisting of age, male, smoking, comorbidity, using ACE
inhibitor and the risk score called Co-AMSCA to predict the risk of
Covid 19 mortality. It was predicted with this study that the risk of
mortality is 7.8 times higher in patients with a Co-AMSCA score above
3.5 points than in patients with a score below 3.5 points.

The demographic, radiological, laboratory data and treatment modalities
were investigated in many studies to determine the severity and
mortality risk of COVID-19 (3-6, 8, 9). However, in the literature, a
scoring system created with host risk factors was detected only in one
study (10). In this study, which was conducted by Shi et al., three risk
factors and a risk score were defined to detect severe COVID-19 cases.
These risk factors were determined as [?] 50 years of age, male gender,
and hypertension as an additional disease (10). Our study also showed
that advanced age, which is the first risk factor we identified,
increases mortality, which is similar to other studies (11, 12, 13). The
second and third host risk factors, which determine mortality in our
study, were male gender and smoking history. In some previous studies,
male gender and smoking were reported as poor prognostic factors
according to socio-demographic data (8, 9). It was reported in other
studies that having one or more comorbid diseases was a risk factor in
estimating mortality, as in our study (8-10, 14). However, unlike our
study, it was observed in these studies that a mortality risk score was
created by adding laboratory parameters (lymphocyte, d-dimer, CRP, LDH)
to host risk factors (13, 14). Surely a single parameter will not be
enough for predicting severe patients and mortality. Therefore, there
are new scores developed for COVID severity (CALL) and also some
well-known scores which were adapted to COVID-19 (MuLBSTA, qSOFA,
CURB-65, and NEWS2) (13-17). However, these are difficult to apply in
clinical practice, and there are technical and laboratory-requiring
difficulties, and they are time-consuming in terms of scoring. In our
study, another host risk factor in determining mortality was the use of
ACE inhibitors by the patient. ACE2 plays a vital role in RAS. Ang II
promotes atherosclerosis in the cardiovascular system along with
inflammation, oxidative stress, migration of endothelial cells, and
vascular smooth muscle cells (18). ACE2 has a protective effect on many
diseases with reduced expression of ACE2, such as hypertension,
diabetes, and cardiovascular diseases, because it antagonizes the role
of Ang II (19).

Several studies reported that COVID-19 related mortality was higher in
men and the elderly than women and the young, but the level of ACE2 was
shown oppositely (20,21). Moreover, Zhang et al. have reported that
ACEI/ARB was associated with lower mortality in COVID-19 patients with
hypertension (22).

Ji et al. developed a novel scoring model for obtaining the severe
COVID-19 patients called CALL score (14). It was developed for
progressive risk estimation using four parameters; comorbidity, age,
lymphocyte number, and LDH. Using a cut-off value of 6 points, the
positive and negative predictive values were 50.7\% (38.9\%-62.4\%) and
98.5\% (94.7\%-99.8\%), respectively, in this model (14). Zang et al.
developed a scoring model for predicting severity for COVID-19 patients
using age, WBC, neutrophil, GFR, and myoglobin (16). Myrstad et al. in
their study found that A NEWS2 score [?]6 at admission predicted severe
disease with 80.0\% sensitivity and 84.3\% specificity (Area Under the
Curve (AUC) 0.822, 95\% CI 0.690-0.953) and also found that NEWS2 was
superior to qSOFA score [?]2 (AUC 0.624, 95\% CI 0.446-0.810,
\emph{P}\textless{} .05) and other clinical risk scores for this purpose
(17). The mortality score of the Co-AMSCA cut-off value was 3.5 and
above in our study, sensitivity, specificity, NPV, and PPV were 80.4\%,
65.5\%, 96.0\%, 24.3\%, respectively. The reason why PPV was partially
low was that laboratory and radiological findings were included in the
scoring in other studies. Varol et al. obtained the CoLACD mortality
score by adding the age, lymphopenia, and dyspnea parameters as well as
the Carlson Comorbidity Score. They showed that the mortality risk was
11.8 times higher in patients with a CoLACD mortality score that was
higher than 2.5 than patients with a score lower than 2.5 points (4).

Contrary to previous studies, no relations were detected in our study
between BMI, which is one of the host risk factors, and mortality. In
the study of Cai Q et al., it was observed that the risk of developing
severe pneumonia was 86\% higher in overweight patients and 2.42 times
higher in obese patients (23). In the study conducted by Wu J et al.,
BMI values of patients with severe COVID-19 were statistically higher
than those with mild disease (24). Kalligeros M et al. also reported
that patients with BMI [?]35 kg/m\textsuperscript{2} had 5.4 times higher
risk of requiring ICU care (25). Unlike the results of our study, Mehra
MR et al. conducted a study and reported that those who developed
mortality had higher mean BMI scores (26). In another study by Docherty
Annemarie B et al., obesity was found to be associated with increased
hospital mortality (27). Because of the retrospective design of our
study, height and weight measurements were based on patient statements,
and it was considered that this may be the reason for the inability to
detect a relation between BMI and mortality.

No relations were detected between blood groups and mortality in our
study. However, when the incidence of COVID 19 infection in a
meta-analysis was evaluated, it was shown in our study that blood group
A is vulnerable to infections (28). In a donor cohort study with the
primary aim not to determine the relations between the ABO blood group
and COVID-19 infection, the mortality risk in COVID-19 patients with
blood group A was reported to be significantly higher than in those with
blood group O (29).

However, the present study had some limitations. First of all, it was a
single-center, retrospective cohort study; however, all COVID-19
patients, who were admitted to our hospital during the time period we
specified, from the onset of the pandemic, were included. Also, the
hospital where this study was conducted was a specific tertiary chest
diseases training and research hospital in the Aegean region. All of the
components of the Co-AMSCA score were obtained from hospital data.

As a conclusion, we created a simple mortality score, which is easily
calculated and does not require laboratory and time. This study also
showed that a novel model that included five parameters, age, male
gender, smoking, comorbidity, and using ACE inhibitor achieved a
prediction of mortality in COVID-19 patients who are hospitalized for
pneumonia. If the Co-AMSCA score is validated with prospective studies,
it can be used for decreasing mortality and effective utilization of
medical resources in the COVID-19 pandemic. Also, we believe that the
host risk score we found will be a useful tool for the prevention and
treatment of this disease, in its detection and more serious follow-up
of individuals with high risks.

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group to COVID-19 infection. Transfusion and Apheresis Science 2021 May
21;103169.~doi: 10.1016/j.transci.2021.103169.

29. Muniz-Diaz E, Llopis J, Parra R, Roig I, Ferrer G, J Grifols, et al.
Relationship between the ABO blood group and COVID-19 susceptibility,
severity and mortality in two cohorts of patients. Blood Transfus 2021;
19: 54-63 DOI 10.2450/2020.0256-20

Table 1. Comparison of demographic findings of COVID-19 patients who
mortal and non-mortal\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
\textbf{TOTAL} \textbf{N: 422}\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
\textbf{MORTAL} \textbf{N=51}\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
\textbf{NON-MORTAL} \textbf{N=371}\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
\selectlanguage{polish}\textbf{P değeri}\selectlanguage{english}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Age (median) (min-max)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
55,5\selectlanguage{ngerman}±15,4 (18,0-91,0)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
65,5 (36,0-91,0)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
54,0 (18,0-88,0)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{\textless{}0,001}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Age group(n,\%) \textless{}50 age 50-64 age [?] 65 age\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
151 (35,8\%) 151 (35,8\%) 120 (28,4\%)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
2 (\%3,9) 23 (\%45,1) 26 (\%51,0)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
149 (\%40,2) 128 (\%34,5) 94 (\%25,3)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\textbf{\textless{}0,001}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Gender (n,\%) Male Female\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
252 (59,7\%) 170 (40,3\%)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
37 (\%72,5) 14 (\%27,5)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
215 (\%58,0) 156 (\%42,0)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\textbf{0,046}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
BMI (median) (min-max)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
26,3\selectlanguage{ngerman}±4,2 (16,8-46,9)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
24,5 (18,9-39,2)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
25,4 (16,8-46,9)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0,142\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
BMI group (n,\%) Thin Normal Preobese Obese\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
5 (\%1,2) 189 (\%44,8) 156 (\%37,0) 72 (\%17,1)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0 (\%0,0) 26 (\%51,0) 20 (\%39,2) 5 (\%9,8)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
5 (\%1,3) 163 (\%43,9) 136 (\%36,7) 67 (\%18,1)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0,383\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Obesity (n,\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
72 (17,0 \%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5 (\%9,8)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
67 (\%18,1)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0,142\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Overweight (n,\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
51 (12,0\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
25 (\%49,0)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
26 (\%51,0)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0,444\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Cigarette (n,\%) Smoker Exsmoker Nonsmoker\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
64 (\%15,2) 119 (\%28,2) 239 (\%56,6)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
5 (\%9,8) 32 (\%62,7) 14 (\%27,5)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
59 (\%15,9) 87 (\%23,5) 225 (\%60,6)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\textbf{\textless{}0.001}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Cigarette (n,\%) Ever smoker Non smoker\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
183 (43,3\%) 239 (56,6\%)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
37 (\%72,5) 14 (\%27,5)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
146 (\%39,4) 225 (\%60,6)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\textbf{\textless{}0,001}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Blood Group (n,\%) A B AB 0\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
174 (\%41,2) 74 (\%17,5) 52 (\%12,3) 122 (\%28,9)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
16 (\%31,4) 9 (\%17,6) 11 (\%21,6) 15 (\%29,4)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
158 (\%42,6) 65 (\%17,5) 41 (\%11,1) 107 (\%28,8)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0,145\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Pneumonia (n,\%) Mild Middle Severe\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
182 (\%43,1) 153 (\%36,3) 87 (\%20,6)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
8 (\%15,7) 2 (\%3,9) 41 (\%80,4)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
174 (\%46,9) 151 (\%40,7) 46 (\%12,4)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\textbf{\textless{}0.001}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
PCR (n,\%) Positive Negative\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
306 (72,5\%) 116 (27,4\%)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
45 (\%88,2) 6 (\%11,8)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
261 (\%70,4) 110 (\%29,6)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\textbf{0,007}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Comorbidity (n,\%) yes No\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
234 (\%55,5) 188 (\%44,5)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
42 (\%82,4) 9 (\%17,6)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
192 (\%51,8) 179 (\%48,2)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\textbf{\textless{}0.001}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Comorbidity (n,\%) No Single Multiple\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
188 (\%44,5) 118 (\%28,0) 116 (\%27,5)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
9 (\%17,6) 19 (\%37,3) 23 (\%45,1)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
179 (\%48,2) 99 (\%26,7) 93(\%25,1)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\textbf{\textless{}0.001}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
COPD(n,\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
64 (\%15,2)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
14 (\%27,5)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
50 (\%13,5)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{0,009}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Cardiac Diseases (n,\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
70 (\%16,6)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
14 (\%27,5)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
56 (\%15,1)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{0,026}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Hypertansion (n,\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
109 (\%25,8)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
19 (\%37,3)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
90 (\%24,3)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{0,047}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Diabetius mellitus(n,\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
64 (\%15,2)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
11 (\%21,6)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
53 (\%14,3)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0,174\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Asthma(n,\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
19 (\%4,5)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
3 (\%5,9)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
16 (\%4,3)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0,612\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Serebrovasculer disease (n,\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
18 (\%4,3)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
4 (\%7,8)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
14 (\%3,8)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0,178\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Malignancy (n,\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
53 (\%12,6)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
19 (\%37,3)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
34 (\%9,2)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{\textless{}0,001}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Using ACE Inhbitor (n,\%) Present Absent\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
105 (\%24,9) 317 (\%75,1)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
21 (\%41,2) 30 (\%58,8)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
84 (\%22,6) 287 (\%77,4)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\textbf{0,004}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Radyological progression (n,\%) Present Absent\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
68 (16,1\%) 349 (82,7\%)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
35 (\%68,6) 16 (\%31,4)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
33 (\%9,0) 333 (\%91,0)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\textbf{\textless{}0,001}\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

BMI: Body mass Index, COPD: Chronic Obstructive lung disease

Table 2.Univariate analysis of patient host risk factors for mortality
in patients with COVID-19\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
\textbf{Univariate analysis}\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
\textbf{Univariate analysis}\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
\textbf{Univariate analysis}\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
\textbf{Univariate analysis}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Parameters}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\selectlanguage{greek}\textbf{β}\selectlanguage{english}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{OR}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{\%95 GA}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\selectlanguage{polish}\textbf{p değeri}\selectlanguage{english}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Age \textless{}50 age vs \textbf{50-65 age} \textless{}50 age vs
\textbf{\textgreater{}65 age}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2,591 3,052\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
13,343 21,165\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
3,095-57,527 4,895-91,516\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0,001 \textless{}0,001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Gender Female vs \textbf{Male}\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0,651\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
1,918\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
1,002-3,668\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0,049\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Cigarettte Nonsmoker vs \textbf{Ever smoker}\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
1,404\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
4,073\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
2,128-7,796\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\textless{}0,001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Comorbidity Absent vs \textbf{Single} Absent vs \textbf{Multiple}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1,339 1,593\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
3,817 4,919\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1,664-8,756 2,187-11,060\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0,002 \textless{}0,001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Using ACE inhibitor Absent vs \textbf{present}\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0,872\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
2,392\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
1,302-4,394\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0,005\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

ACE: Ang\selectlanguage{polish}ı\selectlanguage{english}otension Converting Enzyme

Table 3. The calculation of AMSCA score for mortality in Covid 19
patients\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{Parameter} & \textbf{Score (point)}\tabularnewline
\midrule
\endhead
Age group \textless{}50 age 50-65 age \textgreater{}65 age & 0 1
2\tabularnewline
Gender Female Male & 0 1\tabularnewline
Cigarette Non-smoker Ever smoker & 0 1\tabularnewline
Comorbidity Absent Single Multiple & 0 1 2\tabularnewline
Using ACE inhibitor Absent Present & 0 1\tabularnewline
Total score (maximum) & 7 point\tabularnewline
\bottomrule
\end{longtable}

ACE: Ang\selectlanguage{polish}ı\selectlanguage{english}otension Converting Enzyme

Table 4. Multivariate logistic regression analysis of mortality risk
factors for patients with COVID-19\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
\textbf{Multivariate analysis}\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
\textbf{Multivariate analysis}\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
\textbf{Multivariate analysis}\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
\textbf{Multivariate analysis}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Parameters}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\selectlanguage{greek}\textbf{β}\selectlanguage{english}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{OR}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{\%95 GA}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\selectlanguage{polish}\textbf{p değeri}\selectlanguage{english}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Age \textless{}50 age vs \textbf{50-65 age} \textless{}50 age vs
\textbf{\textgreater{}65 age}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2,224 2,493\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
9,435 12,095\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2,087-42,645 2,564-57,054\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{0,004} \textbf{0,002}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Gender Female vs \textbf{Male}\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0,008\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
1,009\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0,434-2,342\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0,984\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Cigarettte Nonsmoker vs \textbf{Ever smoker}\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
1,155\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
3,174\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
1,381-7,295\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\textbf{0,007}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Comorbidity Absent vs \textbf{Single} Absent vs \textbf{Multiple}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0,431 0,431\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1,539 1,549\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0,613-3,861 0,594-3,990\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0,359 0,375\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Using ACE inhibitor Absent vs \textbf{present}\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0,263\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
1,301\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0,650-2,604\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0,458\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Legends

Figure 1. ROC curve of AMSCA mortality score. AUC = 0.761; 95\% CI
0.697-0.826; P\textless{}0.001

ACKNOWLEDGEMENT

We would like to thank COVID Study Group of University of Health
Sciences Suat Seren Chest Diseases and Surgery Education and Training
Hospital; Aydan Mertoglu,Bilge Salik, Can Bicmen, Celalettin Yilmaz,
Emel Cireli, Fatma Demirci Ucsular, Fevziye Tuksavul, Gulsum Ari, Gunes
Senol, Hulya Sahin, Mehmet Ufuk Yilmaz, Melih Buyuksirin, Nimet Aksel,
Nisel Y\selectlanguage{polish}ı\selectlanguage{english}lmaz, Ozgur Uslu, Ozlem Ediboglu, Ozlem Yalniz, Pinar Cimen,
Sami Cenk Kirakli, Sami Deniz, Semra Bilaceroglu, Serir Ozkan, Serpil
Tekgul, Zuhre Sarp Taymaz, Yelda Varol , Burcin Hakoglu, Berna
Komurcuoglu, Berrin Akkol, Cagri Atasoy, Eda Bayramic, Gunseli Balci,
Sena Ataman, Sinem Ermin, Enver Yalniz

AUTHOR CONTRIBUTIONS

Conceptualisation: MG, CA; Data curation: MG, CA, FG, GK; Formal
analysis:MG, CA, AS, ; Investigation: \selectlanguage{ngerman}ÖB, DT; Methodology: MG, CA, DT,
AKÇ; Project administration: DT; Resources: MG,CA; Supervision: AKC, ÖB;
Validation: CA; Writing-original draft: CA, MG; Writing-review and
editing: CA, MG, GP

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the
corresponding author, upon reasonable request.\selectlanguage{english}
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