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\begin{document}

\title{Laboratory markers used to predict mortality in severe COVID-19}



\author[1]{mehmet patmano}%
\author[2]{ahmet bindal}%
\author[3]{funda cansun}%
\affil[1]{Kayseri City Education and Research Hospital}%
\affil[2]{Sanliurfa Egitim ve Arastirma Hastanesi}%
\affil[3]{Şanlıurfa Education and Training Hospital}%


\vspace{-1em}



  
  \date{June 18, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Purpose: In this study, we aimed to evaluate the laboratory markers used
in the diagnosis of COVID-19 and to present the parameters that can be
used to predict mortality. Material and method: The cases followed in
the intensive care unit due to COVID-19 in our clinic between March 2020
and December 2020 were evaluated retrospectively. A total of 374
patients who met the study criteria were included in the study. The
patients were divided into two groups as the patients who were
discharged from the intensive care unit with no mortality and patients
with a mortal course. Patients with no mortality constituted Group-1,
and patients with a mortal course constituted Group-2. Demographic,
clinical, and laboratory characteristics of the patients were compared.
Results: The number of patients in group-1 consisting of patients with
no mortality was 148 (39.5\%), and the number of patients in group-2
consisting of patients with mortality was 226 (60.4\%). In the group of
patients without mortality, 84 (56.8\%) of the patients were male, while
in the mortality group, 127 (56.2\%) were male. In the mortality group,
procalcitonin, CRP, BUN, D-dimer, troponin, LDH, lactate, and INR values
were higher, albumin value was lower, and this difference was
statistically significant (p\textless{}0.001). In the logistic
regression analysis, PLT and D-dimer were found as the independent
variables of mortality. Conclusion: We think that the high procalcitonin
and D-dimer values obtained with routinely examined rapid and easily
accessible blood tests of Covid-19 patients may contribute to mortality
prediction.%
\end{abstract}%



\sloppy


\textbf{Laboratory markers used to predict mortality in severe COVID-19}

\textbf{Purpose:} In this study, we aimed to evaluate the laboratory
markers used in the diagnosis of COVID-19 and to present the parameters
that can be used to predict mortality.

\textbf{Material and method:} The cases followed in the intensive care
unit due to COVID-19 in our clinic between March 2020 and December 2020
were evaluated retrospectively. A total of 374 patients who met the
study criteria were included in the study. The patients were divided
into two groups as the patients who were discharged from the intensive
care unit with no mortality and patients with a mortal course. Patients
with no mortality constituted Group-1, and patients with a mortal course
constituted Group-2. Demographic, clinical, and laboratory
characteristics of the patients were compared.

\textbf{Results:} The number of patients in group-1 consisting of
patients with no mortality was 148 (39.5\%), and the number of patients
in group-2 consisting of patients with mortality was 226 (60.4\%). In
the group of patients without mortality, 84 (56.8\%) of the patients
were male, while in the mortality group, 127 (56.2\%) were male. In the
mortality group, procalcitonin, CRP, BUN, D-dimer, troponin, LDH,
lactate, and INR values were higher, albumin value was lower, and this
difference was statistically significant (p\textless{}0.001). In the
logistic regression analysis, PLT and D-dimer were found as the
independent variables of mortality.

\textbf{Conclusion:} We think that the high procalcitonin and D-dimer
values obtained with routinely examined rapid and easily accessible
blood tests of Covid-19 patients may contribute to mortality prediction.

\textbf{Keywords} ; Covid 19, bilirubin, C-reactive protein, white blood
cell, neutrophil-lymphocyte ratio, predictive factors

The ongoing covid-19 pandemic causes many deaths regardless of age. This
study was conducted with the aim of developing laboratory parameters
that can be used to predict mortality. We aimed to guide our valuable
colleagues with simple blood tests taken during the first application of
the patients. We believe that it is not a subject that has been studied
much before.

\textbf{Introduction}

The coronavirus disease caused by the Coronavirus-2 virus (SARS-CoV-2),
which is especially destructive in the respiratory tract, still impacts
worldwide, although more than one year has passed since the first
appearance of the virus and vaccination studies have been carried out.
Patients show a broad spectrum of clinical manifestations, ranging from
asymptomatic infection to severe respiratory failure resulting in death.
In symptomatic patients, clinical manifestations of the disease begin
less than a week later, consisting of fever (body temperature 37-38-C),
cough, nasal congestion, and fatigue (1). Pneumonia mainly occurs in the
second or third week of symptomatic infection (2,3). Common laboratory
findings include lymphopenia, the elevation of liver enzymes and lactate
dehydrogenase, and inflammation markers such as ferritin, C-reactive
protein, and erythrocyte sedimentation rate. Although white blood cells,
neutrophils, eosinophils, platelets, and other blood cells, including
CD8 cell counts, are partial predictors in distinguishing between mild
and severe COVID-19, their significance is still unclear. Parameters and
rates obtained from tests such as hemogram and biochemistry, which are
fast, easy, and accessible, and routinely studied in the clinic, have
been the subject of many studies to detect biomarkers aimed at
evaluating the course of the disease.

Although the disease course cannot be predicted precisely, biomarkers
that can predict the severity of this disease will make an important
contribution to personalized treatment. Early awareness of the clinical
course will also help reduce mortality. Our aim in this study is to
evaluate the laboratory markers used in patients diagnosed or suspected
of COVID-19 and to develop parameters that can be used to predict
mortality.

\textbf{Material and methods}

\textbf{Study design and patients}

The cases hospitalized with the diagnosis of COVID-19 in the adult
intensive care unit of \selectlanguage{polish}Ş\selectlanguage{english}anl\selectlanguage{polish}ı\selectlanguage{english}urfa Training and Research Hospital between
March 2020 and December 2020 were evaluated retrospectively. Approval
for the study was obtained from the Ethics Committee of Harran
University with the decision no HRU/XXX decree. The study was conducted
per the Declaration of Helsinki. Three hundred seventy-four patients
whose data were fully accessed via the hospital information system were
included in the study. The patients were divided into two groups: those
who were transferred to the pandemic service and those who died. Group-1
consisted of patients without mortality (n=148), and group-2 consisted
of patients who died (n=226).

\textbf{Data}

Patients' age, gender, COVID-19 PCR test result, comorbid diseases,
intubation status, the day of the intensive care hospitalization,
laboratory values during hospitalization in the intensive care unit,
white blood cell (WBC), hematocrit (Hct), thrombocyte (Plt), neutrophil,
lymphocyte, monocytes, red cell distribution of width (RDW), Mean
platelet volume (MPV), c-reactive protein (CRP), total bilirubin,
albumin, aspartate aminotransferase (AST), alanine aminotransferase
(ALT) values, lactate dehydrogenase (LDH), blood groups, procalcitonin,
ferritin, fibrinogen, calcium (Ca), albumin, INR, blood gas parameters
(Ph, PO2, PCO2, lactate) duration of stay in the intensive care unit
were recorded. In addition, neutrophil-lymphocyte ratio (NLR) and
platelet-lymphocyte ratio (PLR) values were calculated according to
laboratory values.

Statistical analysis: Statistical Package for the Social Sciences (SPSS
25 Inc., Chicago, IL, USA) computer software was used for the
biostatistical analyses. The data obtained from the participating
patients were expressed as mean, standard deviation values, and as a
percentage where necessary. The Kolmogorov Smirnov test was used to
examine the normal distribution of the data. Data with normal
distribution were analyzed by student t-test. Group analysis of
non-parametric data was performed with the Mann-Whitney U test. Logistic
regression test was used for univariate analysis and to calculate odds
ratios (OR) with 95\% confidence interval (CI). Categorical groups were
compared with the chi-square test. Cut-off values were obtained by
receiver operating curve (ROC) analysis.

\textbf{Results}

A total of 374 patients who met the study criteria were included in the
study. The number of patients in group-1 consisting of patients with no
mortality was 148 (39.5\%), and the number of patients in group-2
consisting of patients with mortality was 226 (60.4\%). The mean age of
patients without mortality was 70 (min: 20-max: 93) years, and the mean
age of patients who died was 70 (min: 20-max: 92) years, and this
difference was not statistically significant (p=0.001). When we took the
65-age value as the cut-off value and grouped it according to the
cut-off value, it was seen that 82 (55.4\%) of the patients without
mortality and 152 (67.3\%) of the patients who died were over 65 years
old. In the group of patients without mortality, 84 (56.8\%) of the
patients were male, 64 (43.2\%) were female, and in the mortality group,
127 (56.2\%) were male, and 99 (43.8\%) were female. No significant
difference was found between the groups in terms of gender. When we
grouped the patients according to their ethnic origins, in the group
without mortality, 136 (91.9\%) were Turkish, 12 (8.1\%) were Syrian,
and in the mortality group, 199 (88.1\%) were Turkish, and 27 (11.9\%)
were Syrian. Computed tomography (CT) revealed typical involvement in
all patients. When we grouped the patients according to PCR results,
there were 75 PCR Negative patients (50.7\%), PCR Positive 73 patients
(49.3\%) in the group without mortality, 98 PCR Negative patients
(43.4\%), and 128 PCR Positive patients (56.6\%) in the mortality group.
When the patients were evaluated according to their intubation status,
it was seen that 138 patients (93.2\%) in the group without mortality
did not need intubation, and 10 (6.8\%) patients were intubated. In the
mortality group, 18 (8\%) patients did not need intubation, while 208
(92\%) patients were intubated, and the difference was statistically
significant (p\textless{}0.001). In the mortality group, the mean
intubation day was the 2\textsuperscript{nd} day (min:0-max:48), the
mean intubation time was 7 (min:0-max:65) days, the difference was
statistically significant (p\textless{}0.001). When the patients were
examined according to their comorbidities, in Group-1, 61 patients
(41.2\%) had hypertension (HT), 30 patients (20.3\%) diabetes mellitus
(DM), 50 patients (33.8\%) cardiac disease, 44 patients (29.7\%)
pulmonary disease. Twenty-nine patients (19.6\%) had a neurological
disease, and seven patients (4.7\%) had chronic renal failure (CKD). In
Group-2, HT was present in 116 patients (51.3\%), DM in 60 patients
(26.5\%), cardiac disease in 80 patients (35.4\%), pulmonary disease in
62 patients (27.4\%), neurological disease in 43 patients (19\%), and
CRF in 16 patients (7.1\%), and the difference was not statistically
significant.

When the laboratory values of both groups were examined during the
intensive care unit admission, in Group-1, WBC was 11.7 (3.26-34.4),
Rbc4.8 (2.6-5.9), Hb 12.87\selectlanguage{ngerman}±2.06, Htc (\%) 38.82±5.85, Neutrophil 9.9 (
2-27.1), Lymphocyte 1.1 (0.1-12.5), Monocyte 0.5 (0-2.1), Platelet 290
(59-703), RDW 13.8 (11.7-22), MPV 10.6 (8.6-13.7), Procalcitonin 0.2
(0.02- 23), Ferritin 588 (17-16946), NLR 7.42 (0.76-100), PLR 240
(26.82-2500), CRP 94 (2-338), Total Bilirubin 0.5 (0.1-20), Direct
bilirubin 0.1 (0- 19), AST 34 (11-2213), ALT 24 (7-1697), Albumin
3.33±0.44, BUN 47 (18-193), Creatinine 0.95 (0.3-9.4), D-dimer 0.8
(0.15-9.6), Troponin 14 (3-3355), LDH 367 (130-4431), Fibrinogen
491.24±177.15, Na 137 (127-176), K 4.4±0.75, Ca 8.5 (7.3-9.5), Ph 7.43
(6.9-7.51), PO2 was 47 (17-171), PCO2 35 (14-92), Lactate 1.7 (0.83-20),
INR 1.1 (0.8-2.7). In Group-2, WBC was 11.6 (0.02-34.64), Rbc4.4
(2.3-6.5), Hb 12.52±2.12, Htc (\%) 37.97±6.7, Neutrophil 9.6 (0.4-32.2),
Lymphocyte 0.8 (0.2-9.7) , Monocyte 0.45 (0-11), Platelet 219 (20-552),
RDW 14.1 (11.8-20.2), MPV 10.9 (8.4-14.3), Procalcitonin 0.24 (0.04-29),
Ferritin 743 (21-38486), NLR 5 (0.76-18.4), PLR 125.2 (43.8-545), CRP
120 (0.92-435), Total Bilirubin 0.4 (0.1-7.9), Direct bilirubin 0.2
(0-7.4), AST 43 (10-1243), ALT 26 (5-717), Albumin 3.18±0.53, BUN 54
(12-311), Creatinine 1.1 (0.17-6.4), D-dimer 1.8 (0.2-11.1), Troponin 28
(3-1421), LDH 497 (172) -1918), Fibrinogen 487.68±191.27, Na 138
(125-171), K 4.32±0.91, Ca 8.4 (4.5-10.1), Ph 7.38 (6.9-7.56), PO2 39
(11-181), PCO2 36 (20 -95), Lactate 2.3 (0.7-21), INR 1.1 (0.93-9.9). In
the mortality group, procalcitonin, CRP, BUN, D-dimer, troponin, LDH,
lactate and INR values were higher, albumin value was lower, and this
difference was found to be statistically significant
(p\textless{}0.001). When the hospitalization day was evaluated, it was
7 (2-30) days in group-1 and 12 (1-68) days in group-2, the difference
was not statistically significant. The demographic, clinical and
laboratory characteristics of the patients are summarized in Table 1.
Demographic characteristics and laboratory parameters that were
statistically significant between the two groups in the univariant
analysis were included in the multivariate logistic regression analysis.
In the logistic regression analysis, PLT (OR= 0.992, 95\% CI=
0.987-0.996, p=0.000) and D-dimer (OR= 1.230, 95\% CI= 1.015-1.491,
p=0.035) were found to be independent variables for mortality. (Table
2). D-dimer cut-off value in predicting mortality in the ROC curve
analysis of these independent variables: 1.05 (AUC: 0.670, \%95Cl:
0.612-0.728, p: \textless{}0.001), sensitivity was 68.8\%, specificity
was 56.7\%. AUC for platelets: 0.594, \%95Cl: 0.536-0.652, p:0.002
(Figure 1).

\textbf{Discussion}

The COVID-19 disease caused by SARS-CoV-2 continues to show its effects
despite the increased use of antiviral treatments and vaccination. In
this study, we aimed to develop markers that can be used to predict
mortality through laboratory parameters. Studies have reported close to
100\% mortality among patients requiring mechanical ventilation (4). Our
study is even more important since we observed that our mortality rate
was 60.4\% in our 1-year intensive care follow-up. In our study, the
mortality rate was found to be high in intubated patients; the
difference was statistically significant.

If we look at the gender-mortality relationship, the M/F ratio was 1.28
in our study. An in-depth study examining Covid19 effect on Italy, on
123.3 thousand coronavirus deaths, revealed that the fatality rate was
much higher for the male gender. If the mortality rate for female
patients was 2.5 percent, the corresponding figure for male patients was
3.4 percent. However, the COVID-19 case fatality rate is higher in women
than men in a few countries, such as India, one of the worst-affected
countries. As of Sept 30, 2020, India had more than 6·4 million recorded
COVID-19 cases (5). In India, the COVID-19 case fatality rate among men
is 2·9\% and 3·3\% among women (6\emph{).} Both group 1 and group 2
patients had comorbid diseases, but the presence of comorbid disease was
not significant between the two groups.

When laboratory parameters were examined, procalcitonin, CRP, BUN,
D-dimer, troponin, LDH, lactate, high INR, and low albumin were
statistically significant in terms of mortality. As a result of logistic
regression analysis, low PLT and high D-dimer were determined as
independent variables for mortality. In the study conducted by Fan et
al. (7), lymphopenia and increased lactate dehydrogenase (LDH) values
were associated with higher ICU admission rates. Patients transferred to
the ICU had lower lymphocyte counts, lowest monocyte counts, and lowest
hemoglobin and higher peak Neutrophil (NEU) counts, and highest LDH
levels compared to patients who did not require an ICU stay. Similarly,
in SARS disease, high CRP levels, lymphopenia, leukopenia, and elevated
aminotransferase, LDH, and creatine kinase levels have been shown in
most patients (8). Liver dysfunction is observed more frequently in
severe cases of COVID-19 than in those with milder severity, and an
increase in Alanine aminotransferase (ALT), aspartate aminotransferase
(AST), and total bilirubin levels have been observed in many ICU
patients (9). Infection of liver cells with SARS-CoV-2 cannot be
excluded as 2-10\% of patients with COVID-19 have diarrhea, and viral
RNA is detected in both stool and blood samples, suggesting the
possibility of hepatic virus presence (10). High C-reactive protein
(CRP), ferritin and lactate dehydrogenase (LDH) levels were found to be
correlated with the severity of the disease and prognosis (11,12).

In the study of Wang D et al. (13), the ability of procalcitonin
elevation to predict 28-day intensive care mortality was found to be
statistically significant.

In the study of Mardani et al. (14), in which they examined 200 patients
in a single-center, they reported that ALT, CRP, NEU, LDH, and BUN
values had extreme accuracy in predicting PCR positive patients. In the
same study, low WBC and lymphocyte counts were evident in PCR-positive
patients, but we could not find a significant difference between the
patients with a mortal course and the living group in our study.

In the study of Zhao et al. (15), the most common laboratory findings in
COVID-19 patients were decreased lymphocyte count, prolonged APTT,
elevated LDH, CRP, and erythrocyte sedimentation rate (ESR). Erol et al.
(16) examined 101 patients and showed that CRP, LDH, D-Dimer values were
significantly higher, and WBC values were significantly lower in the
patient group who died. In our study, similar to these results, we found
high CRP, D-Dimer LDH values in the patient group who died. Although low
platelet levels did not make a significant difference between the groups
in the study of Erol et al., platelet levels were significantly lower in
the deceased group in our study. In the study of Deng et al. (17),
increased leukocyte count and decreased lymphocyte count in the survival
group were associated with mortality. Our study did not observe
significant differences in leukocyte counts and lymphocyte counts in
both groups.

The predictive value of LDH as an indicator of altered glucose
metabolism in patients with sepsis is known (18). In the study conducted
by Gao et al. (19), laboratory parameters such as ALT, CRP, AST, and LDH
can be used to predict the presence of disease. Tsui et al. (20)
examined 323 cases with severe acute respiratory failure and stated that
high LDH level was an important prognostic factor. Similarly, we found
that LDH levels were significantly higher in the mortality group in our
study.

In our study, we concluded that D-dimer elevation is an independent
variable for mortality. Similarly, according to a report published in
Wuhan, patients with D-dimer levels of 2.0 ng/mL had a higher mortality
rate than patients below 2.0 ng/mL (21).

In a meta-analysis including 25 studies published by Huang et al. and
5350 patients in total, high levels of CRP, procalcitonin, D-dimer, and
ferritin were associated with poor outcomes (22). COVID-19 infection
manifests with a coagulopathy problem characterized by procoagulant
factors such as fibrinogen and predominantly high D-dimer levels,
increasing mortality. In our study, fibrinogen levels were high in both
groups, and the difference was not statistically significant. Likewise,
although ferritin values were higher in the mortality group in our
study, we could not find a significant difference between the two
groups. However, we believe that high ferritin levels are associated
with the hyperinflammatory state.

In a meta-analysis of 11 studies and 910 patients reviewed by Aziz et
al., a correlation was shown between hypoalbuminemia and severe
COVID-19, and it was reported that it would facilitate the early
diagnosis of severe disease and help clinicians (23). In our study, we
found that hypoalbuminemia was statistically significant in the
mortality group.

Similarly, in the study of Erol et al. (16), AST values were higher in
non-survivors compared to survivors, and there was no difference in more
specific ALT values in the liver. In our study, the AST value was higher
in the mortality group, and the difference was not statistically
significant (p=0.001). Zhang et al. (9) showed that 2-11\% of COVID-19
patients have liver comorbidity and 14-53\% of cases have abnormal ALT
and AST levels during COVID-19 disease progression. Thus, liver damage
appears to be more common in severe cases compared to mild cases of
COVID-19. Yang et al. (24) found no difference in the incidence of
abnormal liver function between survivors (30\%) and non-survivors
(28\%).

The limitation of our study is that it is retrospective, the number of
patients is small, and it is single-centered. In conclusion, full
treatment has not been provided yet in the Covid-19 pandemic, which has
left its mark on the last century and has caused many deaths.
Vaccination and vaccine development studies continue. Nevertheless, we
think that the high procalcitonin and D-dimer values obtained with this
study, which was carried out with the markers in the rapid and easily
accessible blood tests routinely examined in Covid-19 patients, may
contribute to the prediction of mortality.

\textbf{Ethics Committee Approval:}

The study was approved by the Harran University Medical Faculty Hospital
Local Ethics Committee \textbf{(} approval no: HRU\textbf{).}

\textbf{Conflict of Interest}

The authors declared no conflict of interest.

\textbf{Financial Disclosure}

The authors declared that this study had received no financial support.

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\textbf{Hosted file}

\verb`Table 1 Patient\selectlanguage{english}'s demographic, clinical and laboratory characteristics.docx`  available at \url{https://authorea.com/users/420493/articles/710805-laboratory-markers-used-to-predict-mortality-in-severe-covid-19}

\textbf{Hosted file}

\verb`table 2 Independent variables for mortality.docx`  available at \url{https://authorea.com/users/420493/articles/710805-laboratory-markers-used-to-predict-mortality-in-severe-covid-19}\selectlanguage{english}
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