Content uploaded by Henrique Gobbi Totola
Author content
All content in this area was uploaded by Henrique Gobbi Totola on May 07, 2023
Content may be subject to copyright.
*Corresponding author: Dominik Lenz
Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0.
Predictive factors of mortality in COVID-19 patients: Analysis of an intensive care
unit in Espirito Santo, Brazil
Henrique Gobbi Tótola, Tadeu Uggere de Andrade and Dominik Lenz *
Pharmaceutical sciences, University Vila Velha, Espirito Santo, Brazil.
International Journal of Science and Technology Research Archive, 2023, 04(01), 050–064
Publication history: Received on 18 November 2022; revised on 01 January 2023; accepted on 03 January 2023
Article DOI: https://doi.org/10.53771/ijstra.2023.4.1.0178
Abstract
Background: Coronavirus disease (COVID-19) is a global health issue that keeps testing academic efforts to cease it.
Many factors are involved in progression to severe disease including metabolic and inflammatory disorders. The aim of
this study is to analyze the factors associated with mortality in those admitted for COVID-19 in the Intensive Care Unit
(ICU) of a hospital in Espirito Santo, Brazil.
Methods: This is a retrospective, cross-sectional study, with data collection from medical records. The primary outcome
studied is mortality, categorized by length of stay in the ICU. A total of 163 patients were included in this study. The
data of these patients where then separated in two groups, first with 93 discharges and second with 64 deaths. The
parametric Student’s T-test and the nonparametric Mann–Whitney U test for continuous data and the χ2 and Fisher’s
exact test for categorical data were used to compare the variables between both groups. Variables with a p-value < 0.05
(in the bivariate analysis) were submitted to the Cox Survival Hazard multivariate survival model.
Results: Bivariate analysis identified as factors on admission associated with mortality: age > 60 years, high blood
pressure, diabetes mellitus, heart disease, cerebrovascular disease thrombocytopenia, lactate dehydrogenase elevation,
D-dimer elevation and use of supplemental oxygen. Complications while on ICU associated with higher mortality are:
mechanical ventilation, mechanical ventilation> 14 days, acute renal failure, bacterial pneumonia, post COVID-19 acute
arrhythmia, bloodstream infection, acute renal injury and anemia. To exclude possible interference, a multifactorial
analysis was applied with the Cox Survival Hazard proportional risk model, showing that mechanical ventilation (OR:
17.254, 95%CI 4.35-68.43, p <0.0001) and anemia (OR: 2.17, 95%CI 1.15-4.09, p<0.016) are independent variables
related to mortality.
Conclusion: This study identified that anemia on admission and the need to provide mechanical ventilation during ICU
stay are independent factors for predicting mortality.
Keywords: Coronavirus; SARS-CoV-2; Inflammatory markers; Severe disease; Comorbidities; Chest tomography
1 Introduction
The COVID-19 pandemic has transformed the habits of the population on a global scale. In December 2019 there were
reports of pneumonia of unknown origin, with an unusual pattern, rapidly evolving with Severe Acute Respiratory
Syndrome (SARS), Wuhan City, Hubei Province, China. The virus was identified as a coronavirus, SARS-CoV-2, and the
disease named Coronavirus Disease 2019 (COVID-19).1,2
International Journal of Science and Technology Research Archive, 2023, 04(01), 050–064
51
Initially the virus spread to other provinces in China and in the coming weeks there was an accelerated spread of the
virus to other countries, most notably in Europe and North America, with the declaration of a COVID -19 pandemic in
March 2020 by the World Health Organization3, 9, the same month when the first COVID-19 related death occurred in
Brazil.
SARS-CoV-2 is an RNA virus of the order Nidovirales, family Coronaviridae, subfamily Orthocoronavirinae, genus
Betacoronavirus, subgenus Sarbecovirus.4
Coronaviruses infect many animals and SARS-CoV-2 appears to be a mutation from coronaviruses present in bats
and/or pangolins from China due to the genetic Yesilarity found between coronaviruses that infect these animals and
SARS-CoV-2. 1
COVID-19 is a disease that affects several organs, leading to temporary and permanent injuries, with varying degrees of
tissue fibrosis, depending on the evolution of the disease. The most affected organ are lungs followed by the kidneys,
heart and brain, but not limited to these. The involvement of several systems occurs both by changes related to
circulation and by the expression of binding proteins in cell membranes, especially the Angiotensin Converting Enzyme
2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) receptors, which are present in greater quantity in the
organs of the systems mentioned above, in addition to being present in large quantities in endothelial cells. 1,5
The SARS-CoV-2 coronavirus, upon entering the respiratory tract through the nasopharynx, tends to replicate and
migrate to the lower respiratory tract. Viral replication, and especially the exacerbated inflammatory response, causes
a cytokine storm, leading to direct and indirect damage to lung tissue, which can lead to dyspneic symptoms in patients
with severe acute respiratory syndrome. Histopathological analysis of the lungs of coronavirus-infected patients who
progressed to a severe condition showed diffuse alveolar damage, hyaline membrane formation with pneumocyte
desquamation and fibrin deposits. Immunohistochemical tests showed antigen for SARS-CoV-2 present in both the
upper and lower respiratory tract, as well as type I pneumocytes, type II pneumocytes and alveolar macrophages. 1,6
Disseminated vascular coagulation in the most severe cases, associated with small vessel thrombosis, corroborates the
symptoms of the respiratory, cardiovascular and urinary systems.6
In the cardiovascular system, the disease can lead to myocarditis, acute myocardial infarction, cerebrovascular accident,
and the lesions may be caused by viral invasion into the cell or even as a result of metabolic changes related to more
severe conditions with systemic hypoxia. 6,7,8
Regarding urinary system, it can cause acute tubular necrosis by direct viral injury and by rhabdomyolysis or even
cortical necrosis, leading to acute and/or chronic renal failure. 6
This is a virus with a high transmissibility rate, with very aggressive strains, which has already caused more than
600,000 deaths in Brazil alone and 5 million worldwide.9 Considering the global involvement and the emergence of new
variants of viral strains, there is a need for an adequate understanding of the disease as well as the preparation of the
health system to support this and eventual new pandemics. This research aims to identify the factors associated with
mortality of patients hospitalized with COVID-19 in a Hospital in Espirito Santo, Brazil.
2 Methods
2.1 Study design
This is a retrospective, cross-sectional study, carried out by analyzing data from medical records of patients who were
admitted to Intensive Care Units (ICU) at Hospital Meridional in Cariacica, Espirito Santo, Brasil. The Hospital is private
and is part of an aggregated network of 06 hospitals. It attends the spontaneous demand of the emergency room and is
a reference for the most complex cases. The criteria for admission of patients to the ICU at this service include at least
one of the following: chest computed tomography with > 50% Ground Glass Opacity (GGO), use of oxygen therapy with
a flow greater than 04 liters per minute, clear signs of acute respiratory failure.
2.2 Ethical issues
The study was approved by the Research Ethics Committee of Universidade Vila Velha (CAAE: 49128821.4.0000.5064),
which is subject to the norms of the National Health Council – Ministry of Health of Brazil. The informed consents were
International Journal of Science and Technology Research Archive, 2023, 04(01), 050–064
52
presented to the patient or their legal representative, online, due to the context of the pandemic, participating in the
study only those in which there was agreement.
2.3 Patient selection
Data collection was carried out in the medical records of patients hospitalized from May to December 2020 in intensive
care units. Inclusion criteria were patients in a cohort bed in an intensity unit with positive RT-PCT test for SARS-CoV-
2 or with immunochromatographic positive SARS-CoV-2 test plus chest computed tomography with ground glass
opacity. Exclusion criteria were: Terminal illness with a proposal for palliative care prior to admission, primary hospital
admission due to other diseases, incomplete medical records for analysis due to hospital transfer, refusal of consent
form, telephone contact was not possible or did not respond to the consent form.
Initially, the medical records of 287 patients were selected from the management software database called Epimed®.
Of these, 124 patients were excluded by the exclusion criteria: 13 had a negative laboratory test for SARS-CoV-2, 02
were terminally ill patients with a proposal for palliative treatment prior to admission, 41 were hospitalized for other
reasons, prior to infection, 07 were transferred to another service, 04 refused to participate. In addition, 57 patients
who did not respond to telephone contacts or who did not respond to the consent form were excluded.
Clinical data were collected through individual review of electronic medical records on the MVPep® Platform. Both
qualitative and quantitative data were collected, encompassing clinical, laboratory and imaging data.
2.4 Data classification
Acute kidney injury (AKI) was categorized as a serum creatinine value > 1.3 mg/dL, anemia was defined as a serum
hemoglobin < 12 g/dL, leukocytosis as a total WBC count > 10,500/µL, thrombocytopenia if a platelet count < 150,000/
µL, lymphopenia <1,500/ µL, hypercapnia when Partial Pressure of Carbon dioxide (pCO2) >45 mmHg on arterial blood
gases and hypoxemia when Blood Pressure of Oxygen (PaO2) < 80 mmHg on arterial blood gases and coagulopathy was
defined as partial thromboplastin time (APTT) > 42 seconds or INR (international normalized ratio) >1.3 or
prothrombin activation time (TAP) <70%. The results of D-dimer, ferritin, lactate dehydrogenase (LDH) and C-reactive
protein (CRP) tests were collected according to their quantitative numerical value.
Pulmonary involvement on chest tomography was recorded according to the amount of ground-glass opacity in the
pulmonary area and its total percentage of involvement, according to the radiologist's report. A classification of up to
25%, >25-50%, >50-75% and >75% of total lung area affected was used.
The P/F ratio (Blood Pressure of Oxygen over the Fraction of Inspired Oxygen) was calculated for all patients, and its
numerical value was recorded. The fraction of inspired oxygen (FiO2) was determined according to the oxygen delivery
device (nasal catheter, high-flow mask or orotracheal intubation) and the volume of oxygen delivered on each device.
2.5 Statistical Analysis
To evaluate a sample of n=163 patients (64 deaths and 99 discharges) in order to identify clinical and laboratory
parameters that are associated with patient survival, considering length of stay in the ICU, descriptive and inferential
statistical methods were applied.
Qualitative variables were presented by distribution of absolute and relative frequencies. Quantitative variables were
presented by measures of central tendency and variation and had their normality assessed by the D'Agostino-Pearson
test.
In the inferential part, the following methods were applied: (a) To assess the association between the qualitative
variables and the Death outcome, the chi-square test was applied and when the restriction npq < 5 occurred, the Fisher's
Exact test was applied; (b) To compare the quantitative variables, according to the Discharge and Death groups, the
Student's t test was preferably applied and when any variable did not present a Gaussian distribution, the Mann-
Whitney U test was applied; (c) Variables with a p-value < 0.05 (in the bivariate analysis) were submitted to the Cox
Survival Hazard multivariate survival model.
An alpha error was previously set at 5% for rejection of the null hypothesis and the statistical processing was performed
in BioEstat® version 5.3 and SPSS® Version 27.
International Journal of Science and Technology Research Archive, 2023, 04(01), 050–064
53
The clinical variables studied were sex, age, pre-existing comorbidities at ICU admission: High Blood Pressure (HBP),
Diabetes Mellitus (DM), Obesity, Chronic Obstructive Pulmonary Disease (COPD), Cancer, Solid Organ Transplantation
(SOT), Smoking, Cerebrovascular Diseases (Stroke, Encephalopathy), Smoking, Asthma, Chronic Kidney Disease (CKD),
Dyslipidemia (DLP), and Heart Diseases (Arrhythmias, Heart Failure or Ischemic Heart Disease), days of onset of
symptoms and admission on oxygen use.
To evaluate the impact of complications, the following variables have been studied: respiratory failure with need of
mechanical ventilation (MV), Acute Kidney Failure (AKF) dialysis-requiring, deep vein thrombosis (DVT) and/or or
Pulmonary Thromboembolism (PTE), Bloodstream Infections (BSI), Pneumothorax, Bacterial Pneumonia, Acute
Arrhythmia and Cerebral Ischemia).
The laboratory variables were the Partial Pressure of Carbon dioxide, Inspired Oxygen Fraction (FiO2), Blood Pressure
of Oxygen, as well as the Blood Pressure Oxygen / Inspired Oxygen Fraction (P/F Ratio), hemoglobin count, leukocytes,
lymphocytes, platelets, INR (international normalized ratio) value, Prothrombin Activity Time, Activated Partial
Thromboplastin Time, C-Reactive Protein, D-dimer, Ferritin, Lactate Dehydrogenase and initial impairment on Chest
Tomography (assessed by percentage of peripheral ground glass opacity in the total lung area).
3 Results
The present study analyzed data from n=163 patients (64 deaths and 99 discharges) in order to identify clinical and
laboratory parameters that are associated with patient survival, considering the length of stay in the ICU.
Initially, tables 1, 2 and 3 present the bivariate analysis to identify the qualitative factors that are significantly associated
(p-value <0.05) with the death of patients.
Table 1 lists the variables related to the patient's profile and previous comorbidities. In this table, the following
qualitative variables had p-value <0.05: HBP, DM, COPD, Heart Disease, Cerebrovascular Disease.
Table 1 Comorbidities associated with mortality of patients hospitalized in the ICU, with COVID-19, at Hospital
Meridional de Cariacica-ES, year 2020
Total (n=163)
%
Death (n=64)
%
Discharge (n=99)
%
p-value
Sex
0.1697
Masculino
101
62.0
35
34,7
66
65,3
Feminino
62
38.0
29
46,8
33
53,2
HBP
0.0060*
Yes
97
59.5
47
48,5
50
51,5
No
66
40.5
17
25,8
49
74,2
DM
0.0071*
Yes
48
29.4
27
56.3
21
43.8
No
115
70.6
37
32.2
78
67.8
Smoking
0.9723
Yes
24
14.7
10
41.7
14
58.3
No
139
85.3
54
38.8
85
61.2
COPD
0.0010*
Yes
18
11.0
14
77.8
4
22.2
No
145
89.0
50
34.5
95
65.5
Asthma
0.2929
Yes
10
6.1
6
60.0
4
40.0
International Journal of Science and Technology Research Archive, 2023, 04(01), 050–064
54
No
153
93.9
58
37.9
95
62.1
CKD
0.2929
Yes
10
6.1
6
60.0
4
40.0
No
153
93.9
58
37.9
95
62.1
Heart Disease
0.0225*
Yes
22
13.5
14
63.6
8
36.4
No
141
86.5
50
35.5
91
64.5
Cancer
0.6666
Yes
5
3.1
1
20.0
4
80.0
No
158
96.9
63
39.9
95
60.1
SOT
0.7007
Yes
3
1.8
2
66.7
1
33.3
No
160
98.2
62
38.8
98
61.3
Obesity
0.4955
Yes
39
23.9
13
33,3
26
66,7
No
124
76.1
51
41,1
73
58,9
DLP
0.6733
Yes
16
9.8
5
31,3
11
68,8
No
147
90.2
59
40,1
88
59,9
Cerebrovascular Disease
0.0022*
Yes
10
6,1
9
90,0
1
10,0
No
153
93,9
55
35,9
98
64,1
*Chi-square test. High Blood Pressure (HBP), Diabetes Mellitus (DM), Chronic Obstructive Pulmonary Disease (COPD), Cancer, Solid Organ
Transplantation (SOT), Chronic Kidney Disease (CKD), Dyslipidemia (DLP).
In Table 2, which lists laboratory and imaging findings, the following qualitative variables had p-values < 0.05: AKI,
anemia and thrombocytopenia.
Table 2 Laboratory and imaging associated with mortality of patients hospitalized in the ICU, with COVID-19, at
Hospital Meridional de Cariacica-ES, year 2020
Total (n=163)
%
Death (n=64)
%
Discharge (n=99)
%
p-value
% GGO
0.4984
<25%
31
19.0
11
35.5
20
64.5
>25 a 50%
46
28.2
13
28.3
33
71.7
>50 a 75%
63
38.7
29
46.0
34
54.0
>75%
23
14.1
11
47.8
12
52.2
AKI
0.0001*
Yes
27
16.6
20
74.1
7
25.9
No
136
83.4
44
32.4
92
67.6
Anemia
<0.0001*
International Journal of Science and Technology Research Archive, 2023, 04(01), 050–064
55
Yes
55
33.7
36
65.5
19
34.5
No
108
66.3
28
25.9
80
74.1
Leukocytosis
0.6098
Yes
51
31.3
22
43.1
29
56.9
No
112
68.7
42
37.5
70
62.5
Lymphopenia
0.9079
Yes
152
93.3
59
38.8
93
61.2
No
11
6.7
5
45.5
6
54.5
Thrombocytopenia
0.0029*
Yes
27
16.6
18
66.7
9
33.3
No
136
83.4
46
33.8
90
66.2
Coagulopathy
0.0216
Yes
20
12.3
8
40.0
12
60.0
No
143
87.7
56
39.2
87
60.8
Hypercapnia
0.3715
Yes
15
9.2
8
53.3
7
46.7
No
148
90.8
56
37.8
92
62.2
Hypoxemia
0.8772
Yes
84
51.5
32
38.1
52
61.9
No
79
48.5
32
40.5
47
59.5
*Chi-square test. Acute Kidney Injury (AKI), Ground Glass Opacity (GGO).
Table 3 lists complications and the need for supportive measures with oxygen therapy. In this table, the following
qualitative variables had p-value < 0.05: mechanical ventilation, bloodstream infection, acute renal failure, bacterial
pneumonia, post-covid arrhythmia and use of supplemental oxygen at admission.
Table 3 Complications associated with mortality of patients hospitalized in the ICU, with COVID-19, at Hospital
Meridional de Cariacica-ES, year 2020
Total (n=163)
%
Death (n=64)
%
Discharge(n=99)
%
p-value
MV
<0.0001*
Yes
80
49.1
60
75.0
20
25.0
No
83
50.9
4
4.8
79
95.2
MV>14 days
<0.0001*
Yes
45
27.6
34
75.6
11
24.4
No
118
72.4
30
25.4
88
74.6
BSI
0.0444*
Yes
13
8.0
9
69.2
4
30.8
No
150
92.0
55
36.7
95
63.3
AKF - dialysis
<0.0001*
Yes
32
19.6
27
84.4
5
15.6
International Journal of Science and Technology Research Archive, 2023, 04(01), 050–064
56
No
131
80.4
37
28.2
94
71.8
DVT/PTE
0.7757
Yes
10
6.1
3
30.0
7
70.0
No
153
93.9
61
39.9
92
60.1
Pneumothorax
0.1658
Yes
7
4.3
5
71.4
2
28.6
No
156
95.7
59
37.8
97
62.2
Bacterial Pneumonia
<0.0001*
Yes
119
73.0
61
51.3
58
48.7
No
44
27.0
3
6.8
41
93.2
Post COVID-19 Arrhythmia
0.0372*
Yes
9
5.5
7
77.8
2
22.2
No
154
94.5
57
37.0
97
63.0
Oxygen intake
0.0044*
Yes
145
89.0
63
43.4
82
56.6
No
18
11.0
1
5.6
17
94.4
*Chi-square test. Mechanical Ventilation (MV), Bloodstream infection (BSI), AKF (Acute Kidney Failure), Deep Venous Thrombosis (DVT),
Pulmonary Thromboembolism (PTE).
Table 4 Summary of the risk of mortality of patients hospitalized in the ICU, with COVID-19, at Hospital Meridional de
Cariacica-ES, year 2020
Odds Ratio
CI95%
p-value
Mechanical Ventilation
59.2500
19.2
182.4
<0.0001*
MV>14 days
9.0667
4.08
20.1
<0.0001*
High Blood Pressure
2.7094
1.37
5.35
0.0060*
Diabetes Mellitus
2.7104
1.35
5.41
0.0071*
COPD
6.6500
2.07
21.2
0.0010*
Heart Disease
3.1850
1.25
8.11
0.0225*
Cerebrovascular Disease
16.0364
1.97
129.9
0.0022*
Oxygen intake
13.0610
1.69
100.7
0.0044*
AKF - dyalisis
13.7189
4.91
38.3
<0.0001*
Bacterial Pneumonia
14.3736
4.21
48.9
<0.0001*
Post COVID-19 Arrhythmia
5.9561
1.19
29.6
0.0372*
BSI
3.8864
1.14
13.2
0.0444*
AKI
5.9740
2.35
15.1
0.0001*
Anemia
5.4135
2.68
10.9
<0.0001*
Plaquetopenia
3.9130
1.63
9.39
0.0029*
* Chi-square test. Mechanical Ventilation (MV), Chronic Obstructive Pulmonary Disease (COPD), AKF (Acute Kidney Failure), AKI (Acute Kidney
Injury), Bloodstream Infection (BSI).
International Journal of Science and Technology Research Archive, 2023, 04(01), 050–064
57
For the assessment of the Odds Ratio and Confidence Interval in Tables 1, 2 and 3, the Table 4 shows the Odds Ratio and
Confidence Interval. The variables with perceived p-values, in the previous tables, present Odds Ratio >1, which denotes
a risk factor for mortality in the bivariate analysis.
The values in table 4 show that the variables associated with the death outcome were mechanical ventilation (OR: 59.25,
95%CI: 19.2-182.4, p<0.0001), MV > 14 days (OR: 9.06, 95%CI: 4.08- 20.1, p<0.0001), high blood pressure (OR: 2.70,
95% CI: 1.37-5.35, p=0.0060), diabetes mellitus (OR: 2.71, 95% CI: 1.35-5.35, p=0.0071), heart disease ( OR: 3.18,
95%CI: 1.25-8.11, p=0.0225), cerebrovascular disease (OR: 16.03, 95%CI: 1.97-129.9, p=0.0022), use of supplemental
oxygen (OR: 13.06, 95%CI: 1.69-100 , p=0.0044), acute renal failure dialysis-requiring (OR: 13.71, 95% CI: 4.91-38.3,
p<0.0001), secondary pneumonia (OR: 13.37, 95% CI: 4.21-48.9, p<0.0001), post-COVID-19 acute arrhythmia (OR: 5.95,
95% CI: 1.19-29.6, p=0.0372), bloodstream infection (OR: 3.88, 95% CI: 1.14-13.2, p=0.0444), acute kidney injury (OR:
: 5.97, IC95%: 2.35-15.1, p=0.0001), anemia (OR: 5.41, IC95%: 2.68-10.9, p<0.0001) and thrombocytopenia (OR: 3.91,
IC95%: 1.63-9.39, p= 0.0029).
3.1 Multivariate Analysis – Cox Survival Hazard
The variables that obtained p-value <0.05, in the bivariate analysis, were submitted to the multivariate model of survival
Cox Survival Hazard. The Proportional Risk Survival Model was performed with n=163 patients, with 64 deaths and 99
survivors. The multivariate model had x2= 66.1 with GL = 21 (degrees of freedom), p-value <0.0001* (highly significant)
and -2 Log Likelihood = 475.6.
Figure 1 presents the evaluation according to the analysis model described above, distinguishing deaths from discharges
in patients with Anemia admitted to the ICU, using the Kaplan-Meyer diagram.
Figure 1 Kaplan-Meyer Diagram of Survival Function of n= 163 patients, being Deaths (n=64) and Survivors (n=99),
based on ICU Time (days) according to the presence of Anemia
Figure 2 presents the assessment according to the analysis model described above, distinguishing deaths from
discharges in patients undergoing mechanical ventilation admitted to the ICU, using the Kaplan-Meyer diagram.
International Journal of Science and Technology Research Archive, 2023, 04(01), 050–064
58
Figure 2 Kaplan-Meyer Diagram of Survival Function of n= 163 patients, being Deaths (n=64) and Survivors (n=99),
based on ICU Time (days) according to the performance of Mechanical Ventilation
4 Discussion
4.1 Anemia
This research identified that Anemia on admission is an independent risk factor for mortality in patients hospitalized
with COVID-19 in an intensive care setting. In the multivariate analysis, there was statistical significance with p=0.016
(OR=2.17, 95% CI 1.15- 4.09). In a recent meta-analysis, it was identified that the pooled analysis of data showed a
significant association of severe disease with anemia.10 Another study comparing patients with and without anemia
showed that anemia on admission is an independent variable for greater severity of COVID-19, in agreement with the
result of this study.11
4.2 Ferritin
Ferritin is a well-established inflammatory marker in COVID-19, but because it is elevated by several other factors and
comorbidities, it cannot be used specifically for patients with COVID-19. In a recent meta-analysis, the importance of
ferritin in determining the severity of hospitalized patients was studied, with higher rates being identified in patients
with positive COVID-19 when compared to those with a negative test for COVID, there are higher rates of ferritin in
more severe patients compared to those with mild to moderate diseases, there are higher ferritin rates in patients who
required ICU and in those undergoing orotracheal intubation, and there was also a positive correlation of higher ferritin
rates in patients who died compared to those who survived. 12
Despite being considered an independent marker of severity for COVID-19, in this research there was no statistical
significance, with p=0.7759. Only 28 patients (17%) of those studied did not have ferritin data in the electronic chart.
4.3 C-reactive protein
The increase in C-reactive protein, similar to ferritin, can be caused by several factors that lead to the activation of the
inflammatory response. C-reactive protein is the most predominant inflammatory acute phase protein, being widely
used in the diagnosis of inflammation. In COVID-19, the increase in this inflammatory marker is usually relevant,
especially in more severe cases.13,14 Despite these data, in this work there was no statistical significance in this variable,
with p=0.1966.
4.4 Coagulopathies and D-dimer
The prothrombotic state in patients with systemic inflammation causes a greater possibility of developing
coagulopathies. COVID-19 coagulopathy is characterized by mild thrombocytopenia, prolonged prothrombin time, and
high levels of D-dimer.15 This research identified that high D-dimer is a risk factor for death, with p=0.0187 and an
International Journal of Science and Technology Research Archive, 2023, 04(01), 050–064
59
estimated cut-off point of 1176, considering that for values above this number there is a sensitivity of 55% and
specificity of 58% for identification of patient who died. However, it was not possible to identify by statistical methods
the relationship between coagulopathy (assessed by laboratory methods) and increased risk of mortality. Only 13
patients (8%) had no D-dimer laboratory data and only 10 (6%) had no clotting factor data.
Elevated D-dimer was associated with a higher risk of poor outcome in previous researches,5,16 corroborated by the
findings of a meta-analysis that identified that hospital admission with higher D-dimer values is correlated with greater
disease severity and higher mortality.17
4.5 Lymphopenia and Thrombocytopenia
Marked lymphopenia is a common feature of patients with COVID-19 infection, similar to SARS-CoV-1 and Mers-Cov
infection. It was evidenced in a meta-analysis that lymphopenia is an independent variable for the risk of unfavorable
evolution.16 In this research, there was no statistical difference between the patients who were discharged and those
who died, with p=0.9079.
In this study it was identified that thrombocytopenia is a risk factor for mortality, with p=0.0029, but in the multivariate
analysis it was not possible to identify it as an independent factor. A study carried out at the beginning of the pandemic
did not identify thrombocytopenia as a risk factor,18 while a recent study states that patients with COVID-19 have mild
thrombocytopenia and appear to have increased platelet consumption.5 Despite thrombocytopenia, patients with
COVID-19 usually present a hypercoagulative state, which predominates clinically, with thrombosis being much more
common than bleeding in these patients.19
4.6 Lactate Dehydrogenase
Lactate dehydrogenase is a protein that increases in whole blood as inflammation occurs leading to erythrocyte
hemolysis. The increase in lactic dehydrogenase was used to classify severity in some studies, with higher values
indicating greater severity of clinical conditions, with greater probability of ICU admission.18,20,21 In this study, in the
bivariate analysis, the LDH dosage was also shown to be a risk factor for increased mortality from COVID-19, with a
median of 364.5 IU/L and a cutoff point for deaths of 277 IU/L. However, it is worth mentioning that only 66% of the
patients had this exam in their medical records.
4.7 High Blood Pressure and Diabetes Mellitus
There is a positive correlation between hypertension and more severe cases of COVID, perhaps due to direct changes
related to affinity with ACE2 or due to cardiovascular changes caused by high blood pressure.22 Previously published
review article shows that arterial hypertension is correlated with more severe cases with an unfavorable outcome for
rates of mechanical ventilation and death.2 Another study shows that both high blood pressure and diabetes were risk
factors for ICU admission.13 Research carried out in the United States identified that Diabetes Mellitus was an
independent risk factor for ICU admission and mortality.23
This study identified that Hypertension and Diabetes Mellitus are risk factors with p=0.006 and 0.0071 respectively, but
in the multivariate analysis it was not possible to establish them as independent risk factors.
4.8 Age and Sex
There is a positive correlation between age and COVID-19 mortality. Increasing age is a risk factor for the disease, with
more severe diseases occurring in patients of older age groups. 24,25
This study identified that age is a risk factor for mortality, with p=0.0027, with a cut-off point of 60 years, with a
sensitivity of 58% and specificity of 56% at this cut-off point, coinciding with a study carried out in Brazil.26
A recent study indicates that male gender is a risk factor for ICU admission, but it was not associated with mortality.23
similarly, this work failed to identify a relationship between sex and mortality, with p=0.1697 and confidence interval
crossing zero.
4.9 Obesity, Dyslipidemia and Heart Disease
Among the risk factors for serious disease, obesity stands out for having increased its frequency in recent decades,
especially in younger patients, exposing an earlier age group to the risks of the disease. It is characterized by being a
International Journal of Science and Technology Research Archive, 2023, 04(01), 050–064
60
disease that indicates unbalanced metabolic health leads to reduced immune system response, chronic inflammation
and is related to greater severity of viral disease in patients with COVID-19.16,27
Heart disease, which in this study encompasses heart failure, ischemic heart disease and arrhythmias, was identified as
a risk factor, with OR: 3.18, 95% CI: 1.25-8.11, p=0.0225. A study carried out in Italy identified similar findings, with
higher mortality in this group of patients.28
Patients with cardiovascular disease tend to be older, with prevalence increasing with increasing age.29 The immune
system of these patients is more fragile, therefore, they become a population at increased risk for diseases in general,
including infectious diseases. 30
4.10 Complications on ICU
Complications of patients admitted to the ICU tend to occur with the most severe patients, regardless of the reason that
led them to be admitted to the intensive care unit. The analysis of these complications can help to identify cases with
greater association with mortality and lead to an early approach, enabling better patient care.
4.11 Mechanical Ventilation
Mechanical ventilation is the factor most related to mortality. Obviously, the use of mechanical ventilation is not the
reason for the death of these patients. But it is observed that patients who need ventilation have much higher mortality
than those who do not. A meta-analysis evaluating the mortality rate in patients on mechanical ventilation identified
that approximately half of patients on mechanical ventilation died, ranging from 47.9% (95% CI 46.4-49.4) in patients
aged ≤40 years to 84.4% (95% CI 83.3 -85.4%) in patients aged >80 years.31
In this study, mechanical ventilation was considered an independent risk factor for death after multiparametric analysis
in ICU patients, with OR:17.25 (95%CI: 4.35-68.4 and p-value:<0.0001).
4.12 Deep venous thrombosis and pulmonary thromboembolism
It is known that systemic inflammation, abnormal coagulation status and multiple organ dysfunction are all contributing
factors to venous thromboembolism. 32
However, this study did not identify a higher risk of death related to DVT and PTE, probably due to the low rate of
patients in the sample with a radiological diagnosis of thrombosis (n=11). Some patients were treated with full
anticoagulation in clinical suspicion, due to the severity and urgency of the condition, not having clinical conditions for
transport for angiotomography. Therefore, it was decided not to include these patients in the study, which could be a
confounding factor.
4.13 Bacterial Pneumonia
In this study, there was a correlation between secondary pneumonia and mortality with OR: 13.37, 95%CI: 4.21-48.9,
p<0.0001. Bacterial pneumonia can occur with the initial phase of viral pneumonia, but more commonly it starts after a
few days, and may even occur in the recovery phase. The recognition of secondary pneumonia depends on a high rate
of suspicion, since the symptoms of viral and bacterial pneumonia can be very similar and the limitation of repetitive
collection of more invasive exams in the respiratory tree, in order to preserve the health of health professionals, makes
it difficult further accurate diagnosis of infection overlap. 33,34
4.14 Acute Renal Failure dialysis-requiring
Dialysis-requiring ARF is a frequent treatment therapy in hospitalized patients, who mainly present with septic shock.
However, the virus can cause direct kidney damage, in addition to the problems associated with low blood volume to
kidneys. This study identified a higher risk of death in patients who had dialysis-requiring ARF with OR 13.71, CI95%
4.91-38.3 and p<0.0001.
A study carried out to identify survival and chronic kidney disease after COVID, identified that in hospitalized patients
requiring dialysis, there was an increase in the risk ratio by 11.3 [CI95%: 9.6-13.1]. 35
International Journal of Science and Technology Research Archive, 2023, 04(01), 050–064
61
4.15 Post COVID-19 Arrhythmia
Arrhythmia can occur in the context of the most critically ill patients admitted to the ICU, but it appears to be even more
common in critically ill patients with COVID-19. Conduction alterations can occur due to hypoxia, inflammatory stress
and abnormal metabolism. 36
This study identified that post COVID-19 arrhythmias were a factor associated with death, with OR: 5.95, 95%CI: 1.19-
29.6, p=0.0372. These data are in agreement with findings in other studies, which identified higher mortality in patients
who developed this complication. Cardiac arrhythmias can be a direct consequence of the effects of COVID-19 but also
secondary to the systemic deleterious effects and interaction with drugs used to treat the disease. 37
4.16 Study limitations
As this is a retrospective study, there may be flaws both due to lack of data and incorrect recording of data in medical
records, especially in the context of pandemic peak. In addition, the retrospective study has a high probability of
incurring a selection bias, especially when trying to correlate the causes with the outcome, since it is not possible to say
with absolute certainty that the patient's other comorbidities were not responsible for the death, regardless of the
COVID-19 infection.
In this study, there was a failure in the collection of a few laboratory tests in some patients. These flaws were identified
and described in the body of this work. In addition, the increase in the flow of admissions of patients with respiratory
syndrome also generated a high demand for admissions to the ward, another factor that prolonged the length of stay in
the ICU of some patients, because there was simply no room/ward space available.
Another important factor to be reported is the variability of drug and non-drug treatment that occurred over the months
with the evolution of understanding and care with the disease, the rational use of mechanical ventilation, better
adjustments of ventilation parameters, among others, likely to affect mortality.
5 Conclusion
The identification of predictive factors of mortality in patients with COVID-19 makes it possible to prioritize patients at
higher risk of mortality from the disease, instituting earlier treatment to avoid complications that lead to death. This
study identified that anemia on admission and the need for mechanical ventilation during ICU admission are
independent variables for predicting mortality.
Compliance with ethical standards
Acknowledgments
The participation of Robert Russ for editing the English language of this manuscript is acknowledged
Disclosure of conflict of interest
The authors state there is no conflict of interest.
Statement of ethical approval
The study was approved by the Research Ethics Committee of Universidade Vila Velha (CAAE: 49128821.4.0000.5064),
which is subject to the norms of the National Health Council – Ministry of Health of Brazil.
Statement of informed consent
The informed consents were presented to the patient or their legal representative, online, due to the context of the
pandemic, participating in the study only those in which there was agreement.
International Journal of Science and Technology Research Archive, 2023, 04(01), 050–064
62
References
[1] Hu B, Guo H, Zhou P, Shi ZL. Characteristics of Sars-CoV-2 and COVID-19. Nat Rev Microbiol. 2021 Mar;19(3):141-
154. doi: 10.1038/s41579-020-00459-7. Epub 2020 Oct 6. PMID: 33024307; PMCID: PMC7537588
[2] Tadic M, Cuspidi C, Grassi G, Mancia G. COVID-19 and arterial hypertension: Hypothesis or evidence? J Clin
Hypertens (Greenwich). 2020 Jul;22(7):1120-1126. doi: 10.1111/jch.13925. Epub 2020 Jul 6. PMID: 32627330;
PMCID: PMC7362072
[3] World Health Organization. WHO Director-General’s – Briefing on COVID-19 11 March, 2020 [Internet]. Geneva:
World Health Organization; Available on: https://www.who.int/director-general/speeches/detail/who-
director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020.
[4] V'kovski P, Kratzel A, Steiner S, Stalder H, Thiel V. Coronavirus biology and replication: implications for Sars-CoV-
2. Nat Rev Microbiol. 2021 Mar;19(3):155-170. doi: 10.1038/s41579-020-00468-6. Epub 2020 Oct 28. PMID:
33116300; PMCID: PMC7592455.
[5] Wool GD, Miller JL. The Impact of COVID-19 Disease on Platelets and Coagulation. Pathobiology. 2021;88(1):15-
27. doi: 10.1159/000512007. Epub 2020 Oct 13. PMID: 33049751; PMCID: PMC7649697.
[6] Batlle D, Soler MJ, Sparks MA, Hiremath S, South AM, Welling PA, Swaminathan S; COVID-19 and ACE2 in
Cardiovascular, Lung, and Kidney Working Group. Acute Kidney Injury in COVID-19: Emerging Evidence of a
Distinct Pathophysiology. J Am Soc Nephrol. 2020 Jul;31(7):1380-1383.
[7] Abboud H, Abboud FZ, Kharbouch H, Arkha Y, El Abbadi N, El Ouahabi A. COVID-19 and SARS-Cov-2 Infection:
Pathophysiology and Clinical Effects on the Nervous System. World Neurosurg. 2020 Aug;140:49-53. doi:
10.1016/j.wneu.2020.05.193. Epub 2020 May 28. PMID: 32474093; PMCID: PMC7255736.
[8] Bansal M. Cardiovascular disease and COVID-19. Diabetes Metab Syndr. 2020 May-Jun;14(3):247-250. doi:
10.1016/j.dsx.2020.03.013. Epub 2020 Mar 25. PMID: 32247212; PMCID: PMC7102662.
[9] World Health Organization. WHO Director-General’s – Briefing on COVID-19 11 March, 2020 [Internet]. Geneva:
World Health Organization; Available on: https://covid19.who.int/
[10] Hariyanto TI, Kurniawan A. Anemia is associated with severe coronavirus disease 2019 (COVID-19) infection.
Transfus Apher Sci. 2020 Dec;59(6):102926. doi: 10.1016/j.transci.2020.102926. Epub 2020 Aug 28. PMID:
32893135; PMCID: PMC7452827.
[11] Tao Z, Xu J, Chen W, Yang Z, Xu X, Liu L, Chen R, Xie J, Liu M, Wu J, Wang H, Liu J. Anemia is associated with severe
illness in COVID-19: A retrospective cohort study. J Med Virol. 2021 Mar;93(3):1478-1488. doi:
10.1002/jmv.26444. Epub 2020 Nov 10. PMID: 32813298; PMCID: PMC7461220.
[12] Kaushal K, Kaur H, Sarma P, Bhattacharyya A, Sharma DJ, Prajapat M, Pathak M, Kothari A, Kumar S, Rana S, Kaur
M, Prakash A, Mirza AA, Panda PK, Vivekanandan S, Omar BJ, Medhi B, Naithani M. Serum ferritin as a predictive
biomarker in COVID-19. A systematic review, meta-analysis and meta-regression analysis. J Crit Care. 2021 Nov
19;67:172-181. doi: 10.1016/j.jcrc.2021.09.023. Epub ahead of print. PMID: 34808527; PMCID: PMC8604557.
[13] Cattelan AM, Di Meco E, Trevenzoli M, Frater A, Ferrari A, Villano M, Gomiero F, Carretta G, Sasset L. Clinical
characteristics and laboratory biomarkers changes in COVID-19 patients requiring or not intensive or sub-
intensive care: a comparative study. BMC Infect Dis. 2020 Dec 9;20(1):934. doi: 10.1186/s12879-020-05647-7.
PMID: 33297986; PMCID: PMC7724444.
[14] Marcolino MS, Ziegelmann PK, Souza-Silva MVR, Nascimento IJB, Oliveira LM, Monteiro LS, Sales TLS, Ruschel KB,
Martins KPMP, Etges APBS, Molina I, Polanczyk CA; Brazilian COVID-19 Registry Investigators. Clinical
characteristics and outcomes of patients hospitalized with COVID-19 in Brazil: Results from the Brazilian COVID-
19 registry. Int J Infect Dis. 2021 Jun;107:300-310. doi: 10.1016/j.ijid.2021.01.019. Epub 2021 Jan 12. PMID:
33444752; PMCID: PMC7801187.
[15] Chan NC, Weitz JI. COVID-19 coagulopathy, thrombosis, and bleeding. Blood. 2020 Jul 23;136(4):381-383. doi:
10.1182/blood.2020007335. PMID: 32702124; PMCID: PMC7378461.
[16] Malik P, Patel U, Mehta D, Patel N, Kelkar R, Akrmah M, Gabrilove JL, Sacks H. Biomarkers and outcomes of COVID-
19 hospitalisations: systematic review and meta-analysis. BMJ Evid Based Med. 2021 Jun;26(3):107-108. doi:
10.1136/bmjebm-2020-111536. Epub 2020 Sep 15. PMID: 32934000; PMCID: PMC7493072.
International Journal of Science and Technology Research Archive, 2023, 04(01), 050–064
63
[17] Nugroho J, Wardhana A, Maghfirah I, Mulia EPB, Rachmi DA, A'yun MQ, Septianda I. Relationship of D-dimer with
severity and mortality in SARS-CoV-2 patients : A meta-analysis. Int J Lab Hematol. 2021 Feb;43(1):110-115. doi:
10.1111/ijlh.13336. Epub 2020 Sep 15. PMID: 32931146.
[18] Fan BE, Chong VCL, Chan SSW, Lim GH, Lim KGE, Tan GB, Mucheli SS, Kuperan P, Ong KH. Hematologic parameters
in patients with COVID-19 infection. Am J Hematol. 2020 Jun;95(6):E131-E134. doi: 10.1002/ajh.25774. Epub
2020 Mar 19. Erratum in: Am J Hematol. 2020 Nov;95(11):1442. PMID: 32129508.
[19] Mei H, Luo L, Hu Y. Thrombocytopenia and thrombosis in hospitalized patients with COVID-19. J Hematol Oncol.
2020 Dec 1;13(1):161. doi: 10.1186/s13045-020-01003-z. PMID: 33261634; PMCID: PMC7705847.
[20] Tseng L, Hittesdorf E, Berman MF, Jordan DA, Yoh N, Elisman K, Eiseman KA, Miao Y, Wang S, Wagener G.
Predicting Poor Outcome of COVID-19 Patients on the Day of Admission with the COVID-19 Score. Crit Care Res
Pract. 2021 May 31;2021:5585291. doi: 10.1155/2021/5585291. PMID: 34123422; PMCID: PMC8189812.
[21] Cai YQ, Zhang XB, Zeng HQ, Wei XJ, Hu L, Zhang ZY, Ming Q, Peng QP, Chen LD. Prognostic value of neutrophil-to-
lymphocyte ratio, lactate dehydrogenase, D-dimer, and computed tomography score in patients with coronavirus
disease 2019. Aging (Albany NY). 2021 Sep 8;13(17):20896-20905. doi: 10.18632/aging.203501. Epub 2021 Sep
8. PMID: 34495869; PMCID: PMC8457612.
[22] Salazar M, Barochiner J, Espeche W, Ennis I. COVID-19, hipertensión y enfermedad cardiovascular [COVID-19 and
its relationship with hypertension and cardiovascular disease]. Hipertens Riesgo Vasc. 2020 Oct-Dec;37(4):176-
180. Spanish. doi: 10.1016/j.hipert.2020.06.003. Epub 2020 Jun 18. PMID: 32591283; PMCID: PMC7301092.
[23] Kim L, Garg S, O'Halloran A, Whitaker M, Pham H, Anderson EJ, Armistead I, Bennett NM, Billing L, Como-Sabetti
K, Hill M, Kim S, Monroe ML, Muse A, Reingold AL, Schaffner W, Sutton M, Talbot HK, Torres SM, Yousey-Hindes
K, Holstein R, Cummings C, Brammer L, Hall AJ, Fry AM, Langley GE. Risk Factors for Intensive Care Unit
Admission and In-hospital Mortality Among Hospitalized Adults Identified through the US Coronavirus Disease
2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET). Clin Infect Dis. 2021 May
4;72(9):e206-e214. doi: 10.1093/cid/ciaa1012. PMID: 32674114; PMCID: PMC7454425.
[24] Centers for Disease Control and Prevention. CDC updates, expands list of people at risk of severe COVID-19 illness
(Published 25 jun 2020). Available on: https://www.cdc.gov/media/releases/2020.
[25] Chen T, Wu D, Chen H, Yan W, Yang D, Chen G, Ma K, Xu D, Yu H, Wang H, Wang T, Guo W, Chen J, Ding C, Zhang X,
Huang J, Han M, Li S, Luo X, Zhao J, Ning Q. Clinical characteristics of 113 deceased patients with coronavirus
disease 2019: retrospective study. BMJ. 2020 Mar 26;368:m1091. doi: 10.1136/bmj.m1091. Erratum in: BMJ.
2020 Mar 31;368:m1295. PMID: 32217556; PMCID: PMC7190011.
[26] Mascarello KC, Vieira ACBC, Souza ASS, Marcarini WD, Barauna VG, Maciel ELN. COVID-19 hospitalization and
death and relationship with social determinants of health and morbidities in Espírito Santo State, Brazil: a cross-
sectional study. Epidemiol Serv Saude. 2021 Jul 9;30(3):e2020919. English, Portuguese. doi: 10.1590/S1679-
49742021000300004. PMID: 34287551.
[27] Petrakis D, Margină D, Tsarouhas K, Tekos F, Stan M, Nikitovic D, Kouretas D, Spandidos DA, Tsatsakis A.
Obesity - a risk factor for increased COVID-19 prevalence, severity and lethality (Review). Mol Med Rep. 2020
Jul;22(1):9-19. doi: 10.3892/mmr.2020.11127. Epub 2020 May 5. PMID: 32377709; PMCID: PMC7248467.
[28] Inciardi RM, Adamo M, Lupi L, Cani DS, Di Pasquale M, Tomasoni D, Italia L, Zaccone G, Tedino C, Fabbricatore D,
Curnis A, Faggiano P, Gorga E, Lombardi CM, Milesi G, Vizzardi E, Volpini M, Nodari S, Specchia C, Maroldi R, Bezzi
M, Metra M. Characteristics and outcomes of patients hospitalized for COVID-19 and cardiac disease in Northern
Italy. Eur Heart J. 2020 May 14;41(19):1821-1829. doi: 10.1093/eurheartj/ehaa388. Erratum in: Eur Heart J.
2020 Dec 21;41(48):4591. PMID: 32383763; PMCID: PMC7239204.
[29] Amir Tajbakhsh, Seyed Mohammad Gheibi Hayat, Hajar Taghizadeh, Ali Akbari, Masoumeh inabadi, Amir
Savardashtaki, Thomas P. Johnston & Amirhossein Sahebkar (2021) COVID-19 and cardiac injury: clinical
manifestations, biomarkers, mechanisms, diagnosis, treatment, and follow up, Expert Review of Anti-infective
Therapy, 19:3, 345-357, DOI: 10.1080/14787210.2020.1822737
[30] Kovaiou RD, Herndler-Brandstetter D, Grubeck-Loebenstein B. Age-related changes in immunity: implications
for vaccination in the elderly. Expert Rev Mol Med. 2007 Feb 1;9(3):1-17. doi: 10.1017/S1462399407000221.
PMID: 17266794.
[31] Lim ZJ, Subramaniam A, Ponnapa Reddy M, Blecher G, Kadam U, Afroz A, Billah B, Ashwin S, Kubicki M, Bilotta F,
Curtis JR, Rubulotta F. Case Fatality Rates for Patients with COVID-19 Requiring Invasive Mechanical Ventilation.
International Journal of Science and Technology Research Archive, 2023, 04(01), 050–064
64
A Meta-analysis. Am J Respir Crit Care Med. 2021 Jan 1;203(1):54-66. doi: 10.1164/rccm.202006-2405OC. PMID:
33119402; PMCID: PMC7781141.
[32] Long B, Brady WJ, Koyfman A, Gottlieb M. Cardiovascular complications in COVID-19. Am J Emerg Med. 2020
Jul;38(7):1504-1507. doi: 10.1016/j.ajem.2020.04.048. Epub 2020 Apr 18. PMID: 32317203; PMCID:
PMC7165109.
[33] Wu CP, Adhi F, Highland K. Recognition and management of respiratory co-infection and secondary bacterial
pneumonia in patients with COVID-19. Cleve Clin J Med. 2020 Nov 2;87(11):659-663. doi:
10.3949/ccjm.87a.ccc015. PMID: 32393593.
[34] Ginsburg AS, Klugman KP. COVID-19 pneumonia and the appropriate use of antibiotics. Lancet Glob Health. 2020
Dec;8(12):e1453-e1454. doi: 10.1016/S2214-109X(20)30444-7. Epub 2020 Nov 11. PMID: 33188730; PMCID:
PMC7833845.
[35] Ng JH, Hirsch JS, Hazzan A, Wanchoo R, Shah HH, Malieckal DA, Ross DW, Sharma P, Sakhiya V, Fishbane S, Jhaveri
KD; Northwell Nephrology COVID-19 Research Consortium. Outcomes Among Patients Hospitalized With COVID-
19 and Acute Kidney Injury. Am J Kidney Dis. 2021 Feb;77(2):204-215.e1. doi: 10.1053/j.ajkd.2020.09.002. Epub
2020 Sep 19. PMID: 32961245; PMCID: PMC7833189.
[36] Long B, Brady WJ, Koyfman A, Gottlieb M. Cardiovascular complications in COVID-19. Am J Emerg Med. 2020
Jul;38(7):1504-1507. doi: 10.1016/j.ajem.2020.04.048. Epub 2020 Apr 18. PMID: 32317203; PMCID:
PMC7165109.
[37] Manolis AS, Manolis AA, Manolis TA, Apostolopoulos EJ, Papatheou D, Melita H. COVID-19 infection and cardiac
arrhythmias. Trends Cardiovasc Med. 2020 Nov;30(8):451-460. doi: 10.1016/j.tcm.2020.08.002. Epub 2020 Aug
16. PMID: 32814095; PMCID: PMC7429078
