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\begin{document}

\title{Investigation of Biochemical Blood Parameters of COVID-19 Patients Short
(running) title: Biochemical Blood Parameters of COVID-19 Patients}



\author[1]{Semih ERİTEN}%
\affil[1]{Istanbul Sultanbeyli Devlet Hastanesi}%


\vspace{-1em}



  
  \date{June 13, 2024}


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\selectlanguage{english}
\begin{abstract}
Objectives: In this study, it was aimed to investigate the effect of
biochemical blood values on the course of the disease. Material and
Methods: This is a retrospective study. For the treatment of COVID-19,
clinical features and biochemical test parameters of 243 patients who
applied to Malatya Training and Research Hospital in Malatya province of
Turkey between 03 March and 31 December 2021 were used. Results: In the
regression analysis performed in patients with Covid, glucose, urea,
ALP, LDH, albumin, calcium and potassium levels were found to be
effective on the severity of the disease. Conclusion: Biochemical
parameters can be used as clinical findings to predict the diagnosis and
course of COVID-19.%
\end{abstract}%



\sloppy


Title: Investigation of Biochemical Blood Parameters of COVID-19
Patients

Short (running) title: Biochemical Blood Parameters of COVID-19 Patients

Author name: Semih ERITEN, Sultanbeyli State Hospital Department Of
Emergency, Battalgazi, Pa\selectlanguage{polish}ş\selectlanguage{english}ak\selectlanguage{ngerman}öy st. 34935~\selectlanguage{polish}İ\selectlanguage{english}stanbul, Turkey,
semiheriten@hotmail.com

There is no conflict of interest in the study

No financial support was received in the study

ABSTRACT

Objectives: In this study, it was aimed to investigate the effect of
biochemical blood values on the course of the disease.

Material and Methods: This is a retrospective study. For the treatment
of COVID-19, clinical features and biochemical test parameters of 243
patients who applied to Malatya Training and Research Hospital in
Malatya province of Turkey between 03 March and 31 December 2021 were
used.

Results: In the regression analysis performed in patients with Covid,
glucose, urea, ALP, LDH, albumin, calcium and potassium levels were
found to be effective on the severity of the disease.

Conclusion: Biochemical parameters can be used as clinical findings to
predict the diagnosis and course of COVID-19.

Keywords: Covid-19, Biochemical, Biomarker, Pandemic.

INTRODUCTION

The global epidemic known as the coronavirus disease (COVID-19) was
first seen in 2019 in Wuhan, China (1). This recently discovered
epidemic still continues to spread worldwide as a severe and contagious
disease (2). Coronavirus Disease 2019 (COVID-19) has a variable clinical
presentation from asymptomatic to severe illness leading to death (3).

Studies are continuing to develop effective treatments in the fight
against COVID-19 (4-10). Although there are many new treatment options
available to combat COVID-19, the success rate varies. For this reason,
laboratory tests; It plays an important role in predicting the severity
and course of the disease and in determining the appropriate treatment
(11).

Although studies in the literature have broadly defined the clinical
features of COVID-19, the evaluation of changes in the most common
biochemical parameters reported in patients with COVID-19 is still not
fully determined (2). At the same time, it is seen that there are
certain differences in the results due to the different designs of the
studies and insufficient sample sizes (11).

Since the biochemical changes of the blood play an important role in
predicting the condition and prognosis of the patients, guiding the
treatment and even evaluating the curative effect, there is a need to
obtain more convincing results about the biochemical (11-14). In this
study, it was aimed to evaluate the changes in biochemical parameters of
COVID-19 patients according to clinical features.

METHOD

Data Collecting

This is a retrospective study. For the treatment of COVID-19, clinical
features and biochemical test parameters of 243 patients who applied to
Malatya Training and Research Hospital in Malatya province of Turkey
between 03 March and 31 December 2021 were used. Data were obtained from
Malatya Training and Research Hospital patient registration automation.
The study was approved by the Malatya Training and Research Hospital
Ethics Review Board (Approval Number: 23536505-000-13874).

Statistical Analysis

In the study, descriptive statistics and categorical variables were
shown as numbers and percentages, and continuous variables as median
(minimum-maximum). The chi-square test was used to examine the
relationship between two independent categorical variables. The
Mann-Whitney U test was used to test the significance of the difference
between the means of two non-parametric variables. Diagnostic test
performances of biochemical blood parameters were tested by ROC
analysis. Multivariate logistic regression analysis was performed to
evaluate the independent factors that were statistically significant.
Statistical analyzes were performed using ``IBM SPSS Statistics (Version
25.0) for Windows and Jamovi (Version 2.3) for Windows software''.

FINDINGS

In the study, there is a significant relationship between the
discharge/ex statues of the patients and their
age\({(X}^{2}=\)25,09,\emph{p} \textless{}0.05), chronic disease
(\(X^{2}=\)32,84\emph{p} \textless{}0.05) and duration of
hospitalization (\(X^{2}=\)23,41 \emph{p} \textless{}0.05).
There is no significant relationship between gender and discharge/ex
(\(X^{2}=\)2,37,\emph{p} \textgreater{}0.05) (Table 1).

Table 1. Comparison of Ex and Discharge Status of Patients According to
Demographic Variables\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Categories & Discharge & Ex & \(X^{2}\) &
\emph{p}\tabularnewline
\midrule
\endhead
Age & & & &\tabularnewline
\textless{}65 & 100 (93,7) & 7 (6,5) & 25,09 & 0,00*\tabularnewline
[?]~65 & 91 (66,9) & 45 (33,0) & &\tabularnewline
Gender & & & &\tabularnewline
Female & 80 (74,0) & 28 (29,5) & 2,37 & 0,12\tabularnewline
Male & 111 (82,2) & 24 (17,7) & &\tabularnewline
Chronic Disease & & & &\tabularnewline
Yes & 37 (54,4) & 31 (45,5) & 32,84 & 0,00*\tabularnewline
No & 154 (88) & 21 (12) & &\tabularnewline
Duration of Hospitalization & & & &\tabularnewline
\textless{}10 & 134 (87,5) & 19(12,4) & 23,41 & 0,00*\tabularnewline
[?]~10 & 50 (60,2) & 33(39,7) & &\tabularnewline
\bottomrule
\end{longtable}

*\emph{p} \textless{}0.05 significant diffrence, Chi Square.

The significance of the difference between ex and discharge was tested
with the Mann-Whitney U test. As a result of the analysis, statistically
significant differences were found between ex and discharge. According
to the results, Glucose, Urea, Creatinine, AST, ALP, LDH, CK, T.
Bilirubin, D. Bilirubin, Sodium, Potassium and CRP values, mean age and
hospital stay were higher in patients with ex. Protein, Albumin and
Calcium values were found to be lower (Table 2).

Table 2. Comparison of Measurements by Discharge and Ex Condition\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
& Discharge & Ex & U & \emph{p}\tabularnewline
\midrule
\endhead
& M(Min-Max) & M(Min-Max) & &\tabularnewline
Age & 64(21-93) & 79(55-93) & 2514,0 & 0,00*\tabularnewline
Duration of Hospitalization & 7(0-52) & 12(1-65) & 3155,0 &
0,00*\tabularnewline
GLUCOSE & 118,0(28,0-54,0) & 156,8(16,0-476,0) & 2928,0 &
0,00*\tabularnewline
UREA & 40,(0,57-338,0) & 109,3(11,0-405,0) & 1310,5 &
0,00*\tabularnewline
CREATININE & 0,81(0,37-107,0) & 1,4(0,3-7,5) & 2666,5 &
0,00*\tabularnewline
AST & 29,0(12,0-122,0) & 43,0(11,0-2898,0) & 3064,5 &
0,00*\tabularnewline
ALT & 29,9(6,0-264,0) & 24,5(3,0-942,0) & 4455,5 & 0,26\tabularnewline
ALP & 78,0(32,0-338,0) & 99,0(35,0-514,0) & 3321,0 &
0,00*\tabularnewline
GGT & 41,0(5,0-408,0) & 55,5(12,0-669,0) & 4428,5 & 0,23\tabularnewline
LDH & 293,0(26,0-1087,0) & 555,0(234,0-4151,0) & 1503,0 &
0,00*\tabularnewline
CK & 69,9(0,5-88,0) & 143,0(25,0-4002,1) & 3116,0 & 0,00*\tabularnewline
CK-MB & 17,8(0,5-88,0) & 17,8(0,9-103,0) & 4167,5 & 0,07\tabularnewline
T.BILIRUBIN & 0,57(0,12-15,89) & 0,8(0,2-15,0) & 2804,5 &
0,00*\tabularnewline
D.BILIRUBIN & 0,13(0,02-8,67) & 0,3(0,06-9,1) & 2001,5 &
0,00*\tabularnewline
PROTEIN & 6,2(4,1-8,13) & 5,4(3,4-7,3) & 2357,5 & 0,00*\tabularnewline
ALBUMIN & 3,3(1,9-4,7) & 2,3(0,9-4,2) & 1356,0 & 0,00*\tabularnewline
CALCIUM & 8,7(2,5-11,0) & 7,2(1,7-15,2) & 2660,0 & 0,00*\tabularnewline
PHOSPHORUS & 3,21(1,4-10,3) & 3,2(1,5-15,6) & 4616,0 &
0,43\tabularnewline
SODIUM & 139,0(124,0-188,0) & 141,0(3,0-164,0) & 3538,0 &
0,00*\tabularnewline
POTASIUM & 4,45(3,2-515,0) & 4,9(2,2-8,2) & 3950,5 &
0,02*\tabularnewline
CHLORINE & 103,8(91,0-148,0) & 103,8(94,0-121,0) & 4794,0 &
0,70\tabularnewline
CRP & 1,5(0,0-247,0) & 12,5(0,1-37,7) & 1248,0 & 0,00*\tabularnewline
\bottomrule
\end{longtable}

*\emph{p} \textless{}0.05 significant diffrence, Mann-Whitney U., M:
Median

The COVID-19 diagnostic test performances of the biochemical blood
values that were significant in the comparison analyzes of the discharge
and Ex group were tested with ROC analysis. In the literature, an AUC of
0.5 suggests no discrimination (i.e., ability to diagnose patients with
and without the disease or condition based on the test), 0.7 to 0.8 is
considered acceptable, 0.8 to 0.9 is considered excellent, and more than
0.9 is considered outstanding (15, 16). Accordingly, among the ``area
under the ROC Curve (AUC)'' values, Urea (AUC=0.868), LDH (AUC=0.849),
Albumin (AUC=0.863) and CRP(AUC=0.874) values can be considered
excellent. The highest significant AUC value was obtained for CRP
(AUC=0.874) (Table 3).

Table 3. Diagnostic Performance of Biochemical Blood Parameters\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
n=243 & AUC (95\% CI) & \emph{p} & Cut-off & Sensitivity (\%) &
Specificity (\%) & PPV(\%) & NPV (\%)\tabularnewline
\midrule
\endhead
GLUCOSE & 0,705(0,62-0,78) & 0,00 & [?] 128 & 80,77 & 59,69 & 35,29 &
91,94\tabularnewline
UREA & 0,868(0,80-0,93) & 0,00 & [?] 52 & 86,54 & 69,11 & 43,27 &
94,96\tabularnewline
CREATININE & 0,732(0,63-0,82) & 0,00 & [?] 1,03 & 69,23 & 74,35 & 42,35 &
89,87\tabularnewline
AST & 0,691(0,60-0,77) & 0,00 & [?] 40 & 55,77 & 73,82 & 36,71 &
85,98\tabularnewline
ALP & 0,666(0,57-0,75) & 0,00 & [?] 82 & 73,08 & 57,07 & 31,67 &
88,62\tabularnewline
LDH & 0,849(0,79-0,90) & 0,00 & [?] 344 & 86,54 & 64,92 & 40,18 &
94,66\tabularnewline
CK & 0,686(0,60-0,76) & 0,00 & [?] 161,45 & 42,31 & 87,43 & 47,83 &
84,77\tabularnewline
T.BILIRUBIN & 0,718(0,63-0,80) & 0,00 & [?] 0,69 & 61,54 & 71,73 & 37,21 &
87,26\tabularnewline
D.BILIRUBIN & 0,798(0,73-0,86) & 0,00 & [?] 0,2 & 69,23 & 76,44 & 44,44 &
90,12\tabularnewline
PROTEIN & 0,763(0,68-0,84) & 0,00 & [?] 5,1 & 44,23 & 95,29 & 71,88 &
86,26\tabularnewline
ALBUMIN & 0,863(0,80-0,91) & 0,00 & [?] 2,7 & 69,23 & 82,72 & 52,17 &
90,8\tabularnewline
CALCIUM & 0,732(0,64-0,82) & 0,00 & [?] 6,7 & 48,08 & 90,58 & 58,14 &
86,5\tabularnewline
SODIUM & 0,644(0,54-0,74) & 0,00 & [?] 141 & 55,77 & 70,68 & 34,12 &
85,44\tabularnewline
POTASIUM & 0,602(0,49-0,70) & 0,02 & [?] 4,84 & 53,85 & 73,82 & 35,9 &
85,45\tabularnewline
CRP & 0,874(0,81-0,93) & 0,00 & [?] 4,28 & 88,46 & 71,2 & 45,54 &
95,77\tabularnewline
\bottomrule
\end{longtable}

PPV: ``Positive predictive value''; NPV: ``Negative predictive value''\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1. Curves of biochemical blood parameter of COVID-19 patients who
were died\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2. Curves of biochemical blood parameter of COVID-19 patients who
were died

Biochemical blood parameters found to be significant as a result of
univariate analyzes were included in the multivariate logistic
regression model. First, VIF (Variance Inflation Factor) analysis was
performed to detect multiple linear correlation. VIF is calculated to
determine the degree of relationship of an independent variable with
other independent variables (17). If VIF is greater than or equal to 10,
there is a multicollinearity problem (18-20). In the study, the VIF
value of T. Bilirubin and D. Bilirubin parameters was found to be above
10. T. Bilirubin and D. Bilirubin were removed from the model and the
model was re-established. The result of the established multivariate
logistic regression analysis model, the mean increases in Glucose
(OR:1.01, \emph{p} \textless{}0.05), Urea (OR:1.03,\emph{p}
\textless{}0.05), ALP (OR:1.01, \emph{p} \textless{}0,05), LDH (OR:
1.01, \emph{p} \textless{}0.05) parameters were found to increase the
risk of death. On the other hand the mean increases in Albumin (OR:0,22,
\emph{p} \textless{}0,05), Calcium (OR:0,73,\emph{p} \textless{}0,05)
and Potasium (OR:0,38, \emph{p} \textless{}0,05) parameters were found
to decrease the risk of death (Table 4).

Table 4. Evaluation of Risk Factors Affecting Ex with Multivariate
Logistic Regression Analysis\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
& B & B & OR(95\% CI) & \emph{p}\tabularnewline
\midrule
\endhead
GLUCOSE & 0,01 & 1,01(1,00-1,01) & 1,01(1,00-1,01) &
0,01*\tabularnewline
UREA & 0,03 & 1,03(1,01-1,05) & 1,03(1,01-1,05) & 0,00*\tabularnewline
CREATININE & -0,01 & 1,00(0,90-1,10) & 1,00(0,90-1,10) &
0,93\tabularnewline
AST & 0,00 & 1,00(0,99-1,00) & 1,00(0,99-1,00) & 0,56\tabularnewline
ALP & 0,01 & 1,01(1,00-1,01) & 1,01(1,00-1,01) & 0,01*\tabularnewline
LDH & 0,01 & 1,01(1,00-1,01) & 1,01(1,00-1,01) & 0,00*\tabularnewline
CK & 0,00 & 1,00(1,00-1,00) & 1,00(1,00-1,00) & 0,80\tabularnewline
PROTEIN & 0,06 & 1,06(0,46-2,44) & 1,06(0,46-2,44) & 0,89\tabularnewline
ALBUMIN & -1,51 & 0,22(0,06-0,89) & 0,22(0,06-0,89) &
0,03*\tabularnewline
CALCIUM & -0,31 & 0,73(0,57-0,93) & 0,73(0,57-0,93) &
0,01*\tabularnewline
SODIUM & -0,02 & 0,98(0,91-1,06) & 0,98(0,91-1,06) & 0,61\tabularnewline
POTASIUM & -0,98 & 0,38(0,16-0,86) & 0,38(0,16-0,86) &
0,02*\tabularnewline
CRP & 0,00 & 1,00(0,98-1,02) & 1,00(0,98-1,02) & 0,78\tabularnewline
Observed Overall Percentage: 92,6 & Observed Overall Percentage: 92,6 &
Observed Overall Percentage: 92,6 & Observed Overall Percentage: 92,6 &
Observed Overall Percentage: 92,6\tabularnewline
\bottomrule
\end{longtable}

DISCUSSION

In the study, age, chronic illness, length of stay and biochemical blood
parameters of the patients were evaluated separately and their effects
on the risk of death were investigated. In our study, the mortality rate
of patients [?]65 was found to be higher. Similarly, Ergenc et al. (21)
found that patients with [?]65 deaths had a higher mortality rate. In the
study of Teker et al. (22), found that the course of COVID-19 disease
worsened with age and deaths increased. . Unver-Ulusoy et al. (23) found
that 68.8\% of the patients hospitalized in the intensive care unit were
65 years and older. According to the information reported by the US
Centers for Disease Control and Prevention (CDC), the mortality rate
observed in [?]65 patients is higher (24). It is thought that weakened
immune functions in elderly patients predispose to infection (25).
Therefore, it is thought that the mortality rate may be higher in
patients over 65.

In our study, it is seen that the mortality rate is higher in patients
with chronic diseases. Wang et al. (26) reported that elderly people are
at higher risk for chronic diseases and infections, and that mortality
due to COVID-19 increases in those with hypertension and coronary heart
disease. Reyes-S\selectlanguage{ngerman}ánchez et al. (27) in his study on adults who tested
positive for COVID-19 in Mexico, revealed that chronic diseases may be
associated with the severity of COVID-19 and deaths. Considering the
information in the literature, it is seen that the state of chronic
illness can be used as a leading parameter in estimating the course of
the disease.

In our study, it was concluded that the risk of death from COVID-19 did
not differ significantly by gender. However, it is seen that there are
different results in the literature. The effect of gender on the risk of
COVID-19 and death was investigated by Doerre and Doblhammer (28). In
the study, it was determined that the mortality rate in men was 2 times
higher than that in women. Unver-Ulusoy et al. (23) found that men have
a higher risk of death. Peckham et al. (29) found that male patients
were almost three times more likely to be admitted to the intensive care
unit than females and had higher mortality rates. Considering the
results in the literature, it can be said that men are in the
disadvantaged group in deaths caused by COVID-19.

The hospitalization time of the patients who died was found to be
higher. According to Rozman et al. (30), the probability of being
discharged within the first 7 days after hospitalization was 36.5\%,
while the probability of dying in the same period was 7.9\%. On the 21st
day, the probability of being discharged increased to 72.9\%, while the
probability of death increased to 14.4\%. da Costa Sousa et al. (31)
revealed that a longer hospital stay may be related to age. More
comprehensive studies are needed to explain the relationship between
length of stay and death.

In our study, the diagnostic power of biochemical blood parameters of
COVID-19 patients was investigated. Among the biochemical parameters
included in the study, the AUC values of Urea, LDH, Albumin and CRP were
found to be high (0.8-0.9). Among these parameters, the highest AUC
value belongs to CRP. Logistic regression analysis was performed to
understand the effect of biochemical parameters on the risk of death.
The increase in Glucose, Urea, ALP and LDH values increased the risk of
death; It was observed that the increase in Albumin, Calcium and
Potassium values decreased the risk of death. In the study of Ergenç et
al. (21), WBC, procalcitonin, troponin, ferritin, monocytes, INR, LDH,
AST, ALT, MVC and PLT values were found to be higher in the old ex.
Ferrari et al. (32) stated that simple hematological tests can be used
in the diagnosis of COVID-19 in developing countries where RT-PCR
testing is limited.

Strength and Limitation of the Study

The biggest strength of this study is the high sample size and the use
of various biochemical blood values.

The major limitation of this study is that it is a retrospective study
with its own limitations.

Conflict of Interest

There is no conflict of interest

Financial Support

No financial support

Patient Consent

It is a study that does not require patient consent.

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