564 Acta Chim. Slov. 2022, 69, 564–570
Osredkar et al.:  The Predictive Value of Oxidative Stress Index in Patients    ...
DOI: 10.17344/acsi.2022.7432
Scientific paper
The Predictive Value of Oxidative Stress Index in Patients 
with Confirmed SARS-COV-2 Infection
Joško Osredkar,1,3,* Sara Pucko,1,3 Milica Lukić,2 Teja Fabjan,1,3  
Elizabeta Božnar Alič,1 Kristina Kumer,1,3 Maria Martin Rodriguez4  
and Matjaž Jereb 2,5
1 University Medical Centre Ljubljana, Clinical Institute of Clinical Chemistry and Biochemistry, Zaloška cesta 2,  
1000 Ljubljana, Slovenia
2 University Medical Centre Ljubljana, Infectious Diseases Department, Zaloška cesta 2, 1000 Ljubljana, Slovenia
3 University Ljubljana, Faculty of Pharmacy, Aškerčeva 7, 1000 Ljubljana, Slovenia
4 University of Alcala, Faculty of Pharmacy, Carretera Madrid-Barcelona, Km.33,600  
28871 Alcala de Henares (Madrid), Spain
5 University Ljubljana, Medical Faculty, Vrazov trg 1, 1000 Ljubljana, Slovenia
* Corresponding author: E-mail: josko.osredkar@kclj.si
Received: 05-27-2022
Abstract
Disbalance balance between oxidants and antioxidants is called oxidative stress and could be presented as oxidative stress 
index (OSI). OSI is determined by the reactive oxygen metabolites test (d-ROM test) to assess oxidants and the plasma 
antioxidant capacity test (PAT test) to measure antioxidants. The aim of the study was to evaluate the predictive value 
of OSI in the disease COVID-19. d-ROMs results were the highest in the SARS-CoV-2 POSITIVE group (365+/-112), 
lower in the SARS-CoV-2 NEGATIVE group (314+/-72.4), and the lowest in an INTENSIVE CARE UNIT group (ICU) 
(277+/-142) U.Carr. PAT test values were the lowest in the SARS-CoV-2 POSITIVE group (2762+/-387), higher in the 
ICU group (2772 +/-786), and the highest in the SARS-CoV-2 NEGATIVE group (2808+/-470), and are not statistically 
significantly different (P > 0.05), while OSI was: healthy with average value of 49 and the critical ill with average value 
of 109 (P = 0.016). Cut-offs for predicting ICUs admission was at OSI 62, with 80.0% sensitivity and 68.2% specificity.
Keywords: Oxidative stress; SARS-CoV-2; OSI Index
1. Introduction
Oxidative stress in cells and tissues is caused by an 
imbalance between the formation of reactive oxygen spe-
cies (ROS) and the ability to detoxify ROS with the anti-
oxidant system. The balance of ROS and antioxidants may 
be disturbed by the increased formation of ROS and/or de-
creased antioxidant activity. This imbalance leads to many 
spontaneous oxidations in the cell and, because ROS can 
be reducers of almost all cellular components, oxidation 
of biological macromolecules such as lipids, proteins, and 
nucleotides. Oxidation of macromolecules leads to their 
denaturation and, consequently, changes in their physio-
logical functions. The chronic production of ROS causes 
toxic effects that lead to cell damage, long-term oxidative 
stress, accelerated aging, and many diseases including de-
mentia, inflammation, cancer, diabetes, and cardiovascu-
lar disease. In contrast to these diseases, short-term acute 
oxidative stress does not yield typical clinical signs to di-
vulge its presence. Symptoms of oxidative stress usually 
come to the fore only when chronic diseases develop.1,2
Although abnormal levels of ROS are detrimental, 
small amounts of ROS production occur normally and 
have physiological roles. In a homeostatic state, slightly 
more oxidants are present than antioxidants because small 
amounts of ROS are formed as by-products of oxygen 
metabolism. Normal levels of ROS can assist with cellular 
signaling by changing gene and protein expression, syn-
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Osredkar et al.:  The Predictive Value of Oxidative Stress Index in Patients    ...
thesizing certain hormones, and defending against infec-
tions.3
Inflammation is the body’s normal response to inju-
ries, pathogens, irritants, and other toxins. The cells of the 
immune system that are involved in this process include 
neutrophils and monocytes during acute inflammation, 
and macrophages, especially in chronic inflammation. 
These phagocytes use very strong oxidants from ROS and 
reactive nitrogen species (RNS) groups when microbes in-
vade.1 The sudden production of large amounts of reactive 
species produced by phagocytes is called an oxidative erup-
tion. This process is usually limited to the acute response 
to a pathogen, but if chronic inflammation occurs, it can 
cause chronic oxidative stress. In chronic inflammation, the 
increasing amounts of ROS and RNS lead to the oxidation 
of cellular components and, thus, damage and apoptosis.
The entry of a virus into the cell first triggers the acti-
vation of innate immune cells (macrophages, neutrophils) 
that arrive at the site of infection and trigger an inflamma-
tory response. Macrophages secrete cytokines and produce 
several oxidants that they use to defend themselves against 
the virus. Production of oxidants by macrophages depends 
on NADPH oxidase, which leads to the formation of O2, 
and on myeloperoxidase, which catalyzes the formation of 
hypochlorous acid. ROS can activate epithelial cells and 
alveolar macrophages to generate chemotactic molecules 
that further attract neutrophils and, especially, monocytes 
and lymphocytes into the lungs, providing an ideal envi-
ronment for the development of chronic inflammation. 
Inflammation is key in the progression of COVID-19 
pathology. Presentations of SARS-CoV-2 infection have 
ranged from asymptomatic or mildly symptomatic to se-
vere disease and death. Common symptoms include fever, 
headache, cough, and shortness of breath. Other symp-
toms, such as malaise and acute respiratory distress syn-
drome (ARDS), have also been described.4
Particular laboratory features have been associated 
with a more severe course of the disease and worse out-
comes. A progressive decline in the lymphocyte count and 
rise in the D-dimer concentration was observed in those 
who succumbed to the disease, compared with survivors 
who exhibited more stable levels of the D-dimer.5
In severe cases of COVID-19, it is common to ob-
serve prolonged prothrombin time, elevated levels of lac-
tate dehydrogenase, deficient cellular immune response, 
activation of coagulation, and damage to the heart, liver, 
and kidneys.6,7
The immune response plays a key role in controlling 
the SARS-CoV-2 infection, but excessive and uncontrolled 
activation of the immune response can contribute to a 
more severe course of the disease.8
Preclinical studies suggest that increased ROS pro-
duction and decreased antioxidant responses play an 
important role in the pathogenesis of viral infection and 
also in disease progression and severity. The severe course 
of COVID-19 disease involves the connection of sever-
al pathophysiological processes such as cytokine storm, 
inflammation, cellular apoptosis, and redox imbalance, 
which contribute to the poor outcomes of COVID-19.9
Lymphocyte infiltration into the lungs may explain 
the lymphopenia and elevated neutrophil to lymphocyte 
ratio observed in critically ill COVID-19 patients. The ele-
vated neutrophil to lymphocyte ratio is also used to predict 
the death of critically ill COVID-19 patients. The conse-
quence of increased ROS secreted by neutrophils, mac-
rophages, and other immune cells has so far had two out-
comes: 1) ROS damages erythrocytes, which release heme 
into the bloodstream, which is broken down by heme oxy-
genase, which releases free iron; and 2) an oxidative erup-
tion occurs, leading to the formation of a superoxide rad-
ical and hydrogen peroxide. Furthermore, oxidative stress 
and free iron convert fibrinogen into abnormal fibrin clots, 
leading to the formation of micro thromboses in the vas-
cular system and pulmonary microcirculation.7,10
Increased ROS production also directly or indirectly 
triggers the NF-κB signaling pathway, and studies suggest 
that its activation is responsible for the more severe course 
of COVID-19 disease. NF-κB is one of the major media-
tors of cytokine and chemokine induction and is a central 
coordinator of the innate and adaptive immune respons-
es.7,11,12
If over-activation of all these pathways occurs, like-
ly depending on the amount of virus present, a cytokine 
storm can develop, leading to ARDS. The cytokine storm 
is triggered via these oxidative stress-signaling pathways 
by activated leukocytes, including B and T cells, mac-
rophages, monocytes, neutrophils, dendritic cells, as well 
as epithelial and endothelial cells.13–15
Hydroperoxides are formed by the oxidation of 
various biological molecules such as amino acids, pep-
tides, proteins, nucleotides, and, to the greatest extent, 
by the oxidation of lipids. Peroxides are only one of the 
groups of reactive oxygen species, but they are an ear-
ly marker of lipid oxidation as they are formed in the 
initial stages of oxidative stress unlike other markers 
(malondialdehyde, isoprostane). Therefore, peroxides 
are early indicators of oxidative stress.16–18In this study, 
we wanted to investigate how the OSI index may be a 
good predictor of the severity of COVID-19 disease. 
2. Materials and Methods
2. 1. Patients
Measurements of oxidants and antioxidants were 
performed on 171 (M/F = 42/129) samples taken at Uni-
versity Medical Centre Ljubljana (UMCL). Subjects were 
divided into 3 groups according to the course of the dis-
ease.
Group 1 (SARS-CoV-2 NEGATIVE): employees of 
UMCL who had a negative PCR test for SARS-CoV-2 in-
fection (79).
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Osredkar et al.:  The Predictive Value of Oxidative Stress Index in Patients    ...
Group 2 (SARS-CoV-2 POSITIVE): employees of 
UMCL who had a positive PCR test for SARS-CoV-2 in-
fection without symptoms (51).
Group 3 (INTENSIVE CARE): A group of people 
who were hospitalized in the intensive care unit (ICU) of 
UMCL due to a severe course of COVID-19 (41).
2. 2. Methods
We used d-ROMs to measure oxidants and a PAT 
test to measure serum antioxidants. From the values of 
both tests, we then calculated the values of the oxidative 
stress index (OSI index) according to the FRAS5 analyzer 
algorithm, which summarizes the values of d-ROMs and 
PAT tests into one value to facilitate the evaluation of ox-
idative stress.
d-ROMs fast is a photometric test that gives us the 
status of oxidants in a biological sample by measuring hy-
droperoxides (ROOH).
The d-ROMs fast test is based on the Fenton reaction. 
Measurement with a FRAS5 photometer was performed at 
505-546 nm. The color intensity was directly proportional 
to the ROS concentration in the sample.
The PAT test is a method that evaluates the antioxi-
dant power of a biological sample. Measuring the antiox-
idant power of a sample is important as antioxidants are 
the first line of defense in the fight against oxidative dam-
age.16,18
The PAT test is used to quantify water-soluble anti-
oxidants in a biological sample by measuring its ability to 
reduce ferric ions (Fe3+) to Fe2+ ions. The measured an-
tioxidants represent the main components of plasma in 
defense against oxidation: vitamin C, vitamin E, uric acid, 
and bilirubin.
The values of the OSI index are obtained by a certain 
arithmetic transformation and enable easier interpretation 
of oxidative stress for an individual sample. The OSI index 
does not have to replace the results of d-ROMs and PAT 
test, but complements them and presents the state of oxi-
dative stress in the body.19
2. 3. Statistics
Statistical analyses were performed with IBM SPSS 
(version 22). We first established whether our data sets 
were normally distributed with the Shapiro-Wilk test 
for normality and established that the distribution of 
the oxidative stress index was nonparametric. The data 
were logarithmically transformed and a follow-up Shap-
iro-Wilk test determined that the logarithmically-trans-
formed data were normally distributed. We used the 
one-factor ANOVA parametric test and the post hoc 
Bonferroni test and Dunn’s Method test for further anal-
ysis of statistical significance. For descriptive statistics, 
we used mean and standard deviation (SD) to summa-
rize our data.
3. Results and Discussion
Table 1: Basic statistics of d-ROMs, PAT, and OSI tests.
  N d-ROMs PAT OSI
  [U. Carr] [U. Cor] Median (IRQ)
SARS-CoV-2 79   
NEGATIVE Mean 314 2808 46
 SD 72.4 470 (28–61)
SARS-CoV-2 51   
POSITIVE Mean 365 2762 56
 SD 112 387 (31–84)
INTENSIVE 41   
CARE Mean 277 2772 109
 SD 142 786 (60–134)
* d-ROMs - ROS concentration assay
PAT- antioxidant concentration assay
OSI - index of oxidative stress
SARS-CoV-2 - SARS-associated coronavirus
3. 1.  Comparison of Groups in the Coordinate 
System
We used a coordinate system to show where certain 
groups of patients are concentrated based on their OSI 
(Figure 1). The purpose of the OSI index is to integrate a 
single value based on d-ROMs and PAT test results despite 
different units of measure and different value ranges. With 
the OSI index, we can show exactly what the disease state 
of each group is, for example, the levels of d-ROMs = 500 
U.Carr and PAT = 1800 U.Cor can show us the same OSI 
value (142) as the result d-ROMs = 95 U.Carr and PAT = 
3900 U.Cor, although these are completely different con-
ditions. Namely, the OSI value serves as a rough picture of 
oxidative stress; if the values are normal (below 40) we can 
ascertain that the patient’s redox ratio is balanced, other-
wise, when the values are higher (above 40) it is necessary 
to investigate the cause and look at the values of oxidants 
and antioxidants. We can most reliably interpret the pa-
tient’s condition based on the results of all 3 parameters, 
clinical laboratory tests, and when the sample was taken 
during the patient’s illness.
We entered d-ROMs test values on the y-axis and 
PAT test values on the x-axis. Based on these two tests, 
with the help of OB Manager Online copyright © H&D 
S.r.l. In: 2.0.16 calculated oxidative stress index values.
3. 2.  The Interpretation of the Results in 
Specific Quadrants
The first quadrant includes individuals with normal 
or high values of d-ROMs test and normal or high values 
of PAT test:
–  High values of d-ROMs and normal PAT values indicate 
567Acta Chim. Slov. 2022, 69, 564–570
Osredkar et al.:  The Predictive Value of Oxidative Stress Index in Patients    ...
an initial oxidative outbreak due to an innate immune 
response, but the patient still maintains a good antiox-
idant defense.
–  High values of d-ROMs and high values of PAT are the 
result of an increase in oxidant species and an anoma-
lous increase of the antioxidant reserve that might reflect 
a state of cellular destruction and release in circulation.
  The second quadrant includes individuals with normal 
or high values for d-ROMs test and normal or low values 
for the PAT test:
–  High values of d-ROMs and low values of PAT indicate 
the increase of the antioxidant species and the decrease 
of the antioxidant response, signs of possible inflamma-
tion onset, and hospitalization.
–  The interpretation of high values of d-ROMs and normal 
PAT values remain the same as for the first quadrant.
  The third quadrant includes individuals with normal or 
low values of d-ROMs and normal or low values of PAT:
–  Low values of d-ROMs and normal PAT values indicate 
a long-term infection and exhaustion of the body. Thus, 
the body is unable to form normal ROS, the efficacy of 
the innate immune response declines, and antioxidant 
defenses decline due to pre-existing damage indicating a 
loss of redox signaling power.
–  Low values of d-ROMs and low PAT values show the ex-
haustion of the ROS system and the antioxidant network.
  The fourth quadrant is comprised of individuals with 
normal or low d-ROMs and normal or high PAT test 
values.
–  Low values of d-ROMs and normal PAT values in the 
fourth quadrant indicate the same conditions as for the 
third quadrant.
–  Low values of d-ROMs and high values of PAT indicate a 
long-term infection, which involves extensive inflamma-
tion and tissue damage.
For the statistical comparison of groups, we used 
the parametric test one-factor ANOVA and Bonferroni 
post hoc test. We first performed a test of homogeneity of 
variances and found that there was no statistically signif-
icant difference between all three groups; variances were 
homogeneous (P = 0.395). This indicated that we could go 
forward with the one-factor ANOVA and Bonferroni test. 
The ANOVA result showed that there was a statistically 
significant difference (P = 0.016) between the individual 
groups, as shown in Table 2.
Table 2: Calculated differences between groups.
                     Group comparison for OSI P
SARS-CoV-2 POSITIVE SARS-Cov-2 NEGATIVE 0.272
SARS-CoV-2 POSITIVE INTENSIVE CARE 0.471
SARS-Cov-2 NEGATIVE INTENSIVE CARE 0.024
 *OSI - index of oxidative stress
SARS-CoV-2 - SARS-associated coronavirus
Figure 1: Coordinate system representing OSI and four different quadrants for interpretation. OSI values less than 40 represent normal levels. Values 
between 41-65 are borderline alert and normal. Values ranging from 66-120 signify a concerning imbalance between oxidants and antioxidants. 
Values above 121 signify a critical imbalance of oxidants and antioxidants.
* OSI - index of oxidative stress
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Osredkar et al.:  The Predictive Value of Oxidative Stress Index in Patients    ...
We did not prove a statistically significant difference 
between the SARS-Cov-2 positive and negative groups (P 
= 0.272). The average oxidative stress index of the posi-
tive group was 17 units higher than the negative group. 
According to the reference table, a value of 17 is a con-
cerning value, whereas the control group is in the range of 
the oxidative stress borderline state. Due to the less stress-
ful course of the disease (from asymptomatic patients to 
patients with mild symptoms, which did not require hos-
pitalization of patients), there was no critically impaired 
state of oxidants/antioxidants.
We demonstrated a statistically significant difference 
between the ICU COVID-19 patients and the SARS-Cov-2 
negative group (P = 0.024). This difference was expected as 
the redox ratio of hospitalized persons in ICU was severely 
disrupted. Some patients had a very high amount of oxidants 
present, yet others had a very low amount of oxidants, both 
indicative of oxidative stress. Normal amounts of oxidants 
are necessary for the normal functioning of the patient.
We did not prove statistically significant differenc-
es between the SARS-Cov-2 positive group and the ICU 
group (P = 0.471). The SARS-CoV-2 positive group with-
out symptoms had more oxidative stress than the SARS-
CoV-2 negative group, but much less than the patients 
hospitalized in ICU.
3. 2.  Interpretation of OSI Values for SARS-
Cov-2 NEGATIVE Group:
The vast majority of patients had normal values of 
d-ROMs and PAT, and, consequently, the largest share of 
them (43.7%) had OSI values below 40, while only 2.3% 
had OSI above 121. These slight deviations were likely 
caused by other underlying conditions (such as obesity 
and differences in physical activity).
3. 3.  Interpretation of OSI Values for SARS-
CoV-2 POSITIVE Group:
Individuals from this group were concentrated in ap-
proximately the same part of the coordinate system, name-
ly in quadrants I and II. Normal or high values of d-ROMs 
and normal or high values of PAT were measured. Most in-
dividuals (41.5%) of this group had an oxidative stress in-
dex below 40, i.e. they had normal levels without oxidative 
stress. Furthermore, 26.8% of them had values between 
66-120 (alert state) and 22% of individuals had values be-
tween 41-65 (borderline). The last group of 66-120, which 
is already considered a warning state, included the fewest 
individuals (9.7%). The results, which were slightly above 
normal but not critical, were in agreement with the symp-
toms of the participants, which were mild although they 
tested positive for SARS-CoV-2. We hypothesize that the 
cause of high values of d-ROMs is an oxidative outbreak 
due to the innate immune response, while the antioxidant 
system also functions to fight high amounts of ROS.
3. 4.  Interpretation of OSI Values for 
INTENSIVE CARE Group:
We observed the most diverse patient conditions in 
this group. The majority of individuals had OSI values be-
low 40 (33.3%) and above 121 (26.7%). Based on the results 
of d-ROMs, PAT tests, and OSI values, this large variation 
in patient condition was expected in the ICU group. We 
observed very diverse values in intensive care patients and 
most (66.7%) completely disturbed the balance of oxidants/
antioxidants. In quadrant II were individuals who had most-
ly elevated values from the d-ROMs test and normal or de-
creased values from the PAT test. Based on these two results, 
we concluded that these patients were in the initial stage of 
COVID-19 disease and had just been admitted to ICU.
Individuals in quadrants I and II were patients with 
very high values of d-ROMs and normal or elevated PAT 
values. The first quadrant includes patients who were in 
the initial stage of the disease. In those who had elevated 
levels of d-ROMs and PAT, there was an extensive immune 
response that triggered an oxidative outburst and conse-
quently an increased response of antioxidants.
Individuals in quadrants III and IV +- were patients 
with very low d-ROMs scores and normal PAT scores, 
and patients with very low d-ROMs scores and high PAT 
scores. In both cases, these are samples taken during hos-
pitalization in the ICU after the COVID-19 infection had 
been going on for some time. At this point during infec-
tion, the body is already exhausted and unable to form 
ROS, nor is there an effective innate immune response. 
The antioxidant system is also active, trying to remove 
the damage. In the group with low d-ROMs and high PAT 
scores, high PAT values indicated inadequate redox signa-
ling and increasingly severe tissue damage.
Individuals in quadrant IV were critically ill patients 
with low d-ROMs and high PAT values. As in the above 
example, there was an increasing amount of tissue damage 
and slow organ failure.
The frequency of the OSI index is shown in Figure 2.
A receiver operating characteristics (ROC) curve 
was constructed and Youden Index was used to determine 
the optimal cut-off for predicting intensive care unit (ICU) 
admission. The ROC curve is presented in Figure 3.
4. Conclusions
The oxidative stress index serves as a predictor for 
the course of COVID-19 disease. Based on our data, it is 
reasonable to think that OSI is a good predictive index for 
ICU admission where a cut-off of 62 was identified.
The very low d-ROMs level observed in patients 1 
and 2 can be explained by the pathological status of the 
subjects. In contrast, high PAT levels can be explained 
by hemolysis processes, through which a high amount of 
glutathione (GSH) is released from red blood cells. Simul-
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Osredkar et al.:  The Predictive Value of Oxidative Stress Index in Patients    ...
taneously, the antioxidants are not used by the individual 
due to the lack of ROS species, which could contribute to 
why PAT levels are quite high in some cases. Moreover, 
this could be the reason why statistically significant differ-
ences in d-ROMs and PAT were not identified in the pa-
tients analyzed since the levels strongly depend upon the 
time course of the disease, and pathology can occur with 
low or high ratios of d-ROMs/PAT.
With the help of the coordinate system, we evaluated 
the disease status of the patients. We found that the PCR 
negative group was concentrated approximately in the 
middle of the coordinate system, signifying that most of 
the values of the measured parameters were within nor-
mal reference limits. The PCR positive group was primar-
ily found in quadrants I and II, and the values of oxida-
tive stress parameters indicated a shift from the normal 
reference limits. A wide variety of disease conditions were 
present in the ICU group, some of which were in the initial 
stage of the disease and had just been admitted to the ICU, 
and the results of d-ROMs, PAT, OSI were not as severe as 
in individuals with long-term hospitalization.
A comparison of the oxidative stress index in two 
ICU patients with biochemical and hematological param-
eters (supplemental data) showed that the values of the 
OSI index correlated very well with the patients’ disease 
state and the inflammatory parameters. We compared 
CRP, lymphocyte and neutrophil count, IL-6, and oxida-
tive stress index. The latter varied with a lag compared to 
the others, but this is consistent with studies by test man-
ufacturers d-ROMs and PAT, where we found that oxidant 
levels rise when there is actual oxidative damage and thus 
reflect the current state of the body.
Doğan et al. published a study in which they cal-
culated the OSI index from the parameters total oxidant 
status (TOS) and total antioxidant status (TAS). The calcu-
lated OSI levels were significantly different between severe 
moderate and mild groups of patients infected with SARS-
CoV-2.20
The same method for calculating OSI was also used 
by Çakırca et al. Their results revealed that the increase in 
oxidative stress and decrease in antioxidant levels in COV-
ID-19-infected patients were associated with worsening of 
disease.21 The results of our study, in which we calculated 
OSI based on d-ROM and PAT determinations, are com-
parable to the results of both of these studies, which used 
TOS and TAS to calculate OSI.
In our study of oxidative stress, our results suggest 
that OSI could be a predictor of the course of SARS-CoV-2 
infection and warrants further investigation. Our studies 
were conducted on a small number of samples so further 
research with more samples is necessary.
Acknowledgments
Funding
The study was funded by the research program of the 
Research Agency of the Republic of Slovenia (P3-0124).
Author Contributions
Conceptualization JO; Writing – Original draft 
Preparation JO; Clinical data of the Patients ML, MJ; Lab-
oratory Analysis SP, TF, EBA; Statistics TF; Writing – Re-
view & Editing MJ.
Figure 2: Calculated OSI of the whole study group.
* OSI - index of oxidative stress
Figure 3: ROC curve for predicting ICU admission for COVID-19 
positive patients.
* OSI - index of oxidative stress      AUC - area under the ROC curve
570 Acta Chim. Slov. 2022, 69, 564–570
Osredkar et al.:  The Predictive Value of Oxidative Stress Index in Patients    ...
Institutional Review Board Statement
The study was conducted according to the guidelines 
of the Declaration of Helsinki, and approved by the Nation-
al Ethics Committee; protocol number – 012-60/2021/5.
Conflicts of interest
The authors declare no conflict of interest.
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Povzetek
Neravnovesje med oksidanti in antioksidanti imenujemo oksidativni stres in ga lahko prikažemo kot indeks oksidativne-
ga stresa (OSI). OSI določimo s testom reaktivnih presnovkov kisika (d-ROM) za oceno oksidantov in testom plazemske 
antioksidativne kapacitete (PAT) za merjenje antioksidantov. Namen študije je bil oceniti napovedno vrednost OSI pri 
bolezni COVID-19. Rezultati tasta d-ROM so bili najvišji v skupini SARS-CoV-2 pozitivni (365+/-112), nižji v skupini 
SARS-CoV-2 negativni (314+/-72,4) in najnižji v skupini kritično bolnih v enoti za intenzivno nego (ICU) (277+/-142) 
U.Carr. Vrednosti testa PAT so bile najnižje v skupini SARS-CoV-2 pozitivni (2762+/-387), višje v skupini kritično bol-
nih (2772 +/-786) in najvišje v skupini SARS-CoV-2 negativni (2808 +/-470). Skupine se med sabo statistično značilno 
ne razlikujejo (P>0,05). OSI se statistično značilno razlikuje med zdravimi s povprečno vrednostjo 49 in kritično bolnimi 
s povprečno vrednostjo 109 (P = 0,016). Določili smo mejno vrednost za napovedovanje sprejema pacienta v enoto in-
tenzivne nege na osnovi analize OSI, in sicer 62, z 80,0 % občutljivostjo in 68,2 % specifičnostjo.