CC BY
29
ADVANCED RESEARCHES
Gender Characteristics of the Novel Coronavir in Middle-Aged Adul
DOI: 10.17691/stm2021.13.4.02 Received April 26, :
E.S. Nekaeva, Head of the Admission and Consultation Department, University Clinic; Specialist in Internal Diseases, University Clinic;
A.E. Bolshakova, PhD, Specialist in Assessing Pharmacological Prescriptions, Admission and Consultation Department, University Clinic; Associate Professor, "epartment of Pharmaceutical Chemistry and Pharmacognosy; .S. Malysheva, MD, PhD, Assistant, Department of Endocrinology and Internal Medicine; Endocrinologist, Admission and Consultation Department, University Clinic;
E.A. Galova, MD, PhD, Deputy Director for Science, University Clinic; Associate Professor, Department of Public Health and Healthcare;
E.V. Makarova, MD, DSc, Associate Professor, Head of the Department of Propedeutics of Internal Diseases; Deputy Director for Academic Affairs, Institute of Internal Diseases;
T.A. Nekrasova, MD, DSc, Associate Professor, Professor, Department of Endocrinology and Internal Medicine; I.V. Polyakova, MD, PhD, Assistant, Vogralik Department of Advanced Internal Medicine and General Medical Practice; Specialist in Internal Diseases, Consultative and Diagnostic Department with Radiation Diagnostics and Day Hospital, University Clinic;
Z.S. Bedretdinova, Specialist in Internal Diseases, Consultative and Diagnostic Department with Radiation Diagnostics and Day Hospital, University Clinic; D.V. Belikina, Assistant, Department of Endocrinology and Internal Medicine; A.A. Lavrenyuk, Student, Medical Faculty;
I.V. Fomin, MD, DSc, Professor, Head of the Vogralik Department of Advanced Internal Medicine and General Medical Practice; Director of the Institute of Internal Diseases
Privolzhsky Research Medical University, 10/1 Minin and Pozharsky Square, Nizhny Novgorod, 603005, Russia
The aim of the study is to assess the gender-related specifics of the COVID-19 course in patients under 55 years of age.
Materials and Methods. This pilot single-center continuous retrospective non-randomized study was carried out in the repurposed infectious diseases hospital of the Privolzhsky Research Medical University (Nizhny Novgorod, Russia). The study inclusion criterion was the age of patients (up to 55 years) and confirmed coronavirus infection. In the groups based on gender differences (25 men, average age 44.0±7.8 years and 32 women, average age 41.9±9.1 years), we monitored complications of COVID-19 such as the transfer of patients to the ICU and the volume of lung damage (determined with CT scans).
Results. The course of COVID-19 in male patients younger than 55 was aggravated by concomitant diseases (y=0.36; p=0.043), among which IHD (y=100; p=0.003) and liver disease (y=0.58; p=0.007) dominated. Frequency analysis confirmed the high prevalence of coronary artery disease in men (p=0.044). Significant differences between the gender-related groups were noted in the volume of lung lesions: at admission (p=0.050), during hospital treatment (p=0.019), and at discharge (p=0.044). Using the logistic regression method, a relationship was found between the transfer of male patients to ICU and the Krebs index [y=-2.033+1.154 male gender+ 1.539 Krebs index (x2=5.68; p=0.059)] and comorbidity [y=-2.836+1.081 male gender+2.052 comorbidity (x2=7.03; p=0.030)]. The influence of the Krebs index and the male gender on the excess volume of lung lesions was shown [y=-1.962+0.575 male gender + 1.915 Krebs index (x2=7.78; p=0.021)].
Conclusion. In individuals under the age of 55 diagnosed with COVID-19, gender is of significant importance: in men, there is a more pronounced lesion of the lung parenchyma and a more significant change in laboratory parameters. Risk factors for a severe course of COVID-19 in men are coronary artery disease and hepatobiliary disorder. Calculating the Krebs index can be used to assess the risk of disease progression.
Key words: COVID-19; SARS-CoV-2; COVID-19 in adults; COVID-19 in men; Krebs index; comorbidity.
How to cite: Nekaeva E.S., Bolshakova A.E., Malysheva E.S., Galova E.A., Makarova E.V., Nekrasova T.A., Polyakova I.V., Bedretdinova Z.S., Belikina D.V., Lavrenyuk A.A., Fomin I.V. Gender characteristics of the novel coronavirus infection (COVID-19) in middle-aged adults. Sovremennye tehnologii v medicine 2021; 13(4): 16, https://doi.org/10.17691/stm2021.13A02
This is an open access article under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/).
Corresponding author: Ekaterina S. Nekaeva, e-mail: katykur@yandex.com
//////////////////////^^^^
16 CTM | 2021 | V0l. 13 | N0.4 E.S. Nekaeva, A.E. Bolshakova, E.S. Malysheva, E.A. Galova, E.V. Makarova, ..., I.V. Fomin
Introduction
The fight against the novel coronavirus infection COVID-19 has been ongoing since December 2019, when the beta coronavirus of severe acute respiratory syndrome 2 (SARS-CoV-2) spread over the world, affecting more than 180 countries with a total of more than 191.773 million confirmed cases, of which 4.128 million were fatal [1]. The main targets of SARS-CoV-2 are pneumocytes, immune cells, and vascular endothelial cells [2]. Clinical manifestations of COVID-19 range from asymptomatic to severe forms, including life-threatening complications [3-6].
The SARS-CoV-2 virus infects people of both sexes, all ages, races, and ethnic groups [7].
Elderly people are more susceptible to SARS-CoV-2 and are more likely to be admitted to the ICU with a high risk of death [8, 9]; therefore, at present, health of patients over 60 years old are of great concern for clinicians. Age-related muscle atrophy and changes in the lung anatomy in the elderly lead to changes in the physiological function of the respiratory system [10, 11]. The progression of mitochondrial dysfunction with age leads to disorders of the immune system (e.g. impaired T cell immunity) and thus contributes to the higher susceptibility to viral infections [12, 13]. The accumulation of abnormal mitochondria may result from metabolic disorders in diabetes mellitus, oncological, neurodegenerative, and other diseases [14].
It was documented that the novel coronavirus infection caused higher mortality in men than in women [15, 16]. This might be due to the higher levels of type 1 interferon produced in the woman's body, which is important for an early response to COVID-19 [17, 18]. The process of SARS-CoV-2 invasion and transmission is mediated by the angiotensin-converting enzyme 2 (ACE2) receptor [19]. This receptor is protected by estradiol, which is the main sex hormone in women [20-22].
In the context of the ongoing pandemic, information on risk factors of life-threatening complications is becoming highly relevant. However, the number of publications on the course of COVID-19 in patients under 55 is significantly less as compared with studies on those over 65. Information about the course of the disease in middle-aged individuals is scarce and, as a rule, limited to the description of the disease in specific regions or in certain ethnic groups [23-25].
The aim of the study is to assess the gender-related specifics of the COVID-19 course in patients under 55 years of age.
Materials and Methods
This pilot single-center continuous retrospective non-randomized study was carried out in the infectious diseases hospital deployed on the basis of the University Clinic of Privolzhsky Research Medical University (Nizhny Novgorod, Russia). The work was performed in
accordance with the Declaration of Helsinki (2013) and approved by the Ethics Committee of the Privolzhsky Research Medical University. All patients signed the informed consent forms to participate in the study and provide biological material.
The criterion for inclusion in the study was the patients' age of 20 to 55 years and the confirmed coronavirus infection (according to the viral RNA amplification test). Normally, this age interval precedes the menopause period in women. The age of the group was determined in accordance with the conditional periods of biological age, based on the anatomical and physiological characteristics of the body of an adult at a mature age (20-55 years).
The study involved 57 people (mean age 42.8±8.5 years), admitted to the hospital 8 [7; 11] days after the onset of the disease. The hospitalization was due to disease exacerbation despite the preceding outpatient treatment. According to CT, the volume of pulmonary parenchyma lesions was 40 [25; 52]% upon admission and 28 [15; 48]% upon discharge. The average bed-day score was 14 [12; 17] days. Transfer to the ICU was needed for 7 patients (12.3%) aged 43.4±7.7 years, who were admitted on day 10 [8; 12] of illness with CT lesion volume of 75 [52; 80]%.
The frequency of the complicated course of COVID-19 (transfer to ICU, excess volume of lung lesions) was determined in gender-specific groups of patients. The group of men included 25 individuals (44%) aged 44.0±7.8 years with a BMI of 28.4 [26.3; 30.9], enrolled on day 8 [8; 11] of illness with an average hospital stay of 16 [12; 18] bed-days. The group of women included 32 individuals (56%) aged 41.9±9.1 years with a BMI of 27.5 [25.2; 32.0], enrolled on day 8.5 [7; 10] of illness with an average hospital stay of 13 [12; 16] bed-days. Both groups were comparable in age (p=0.49), the day of onset of the disease (p=0.81), and BMI (p=0.75). The duration of hospital stay tended to differ between men and women (p=0.096).
Comprehensive diagnosis and treatment procedures in patients with COVID-19 were carried out in accordance with the Interim Methodological Recommendations "Prevention, Diagnosis, and Treatment of Novel Coronavirus Infection (COVID-19)", versions 6 (April 28, 2020) and 7 (June 3, 2020). These recommendations — relevant to the time of hospitalization — were approved by the Ministry of Health of Russia [26, 27] and closely corresponded to the Interim Recommendations of the World Health Organization [28].
In patients with signs of viral pneumonia, the initial assessment included a lung CT scan as the most sensitive method for detecting COVID-19-related changes. In this study, a Toshiba Aquilion 32-slice computer tomograph (Toshiba, Japan) was used.
The general (clinical) blood test (leukocytes, platelets, leukocyte formula, erythrocyte sedimentation rate (ESR)) was carried out using an XT-4000i hematology analyzer (Sysmex, Japan) and the manufacturer's reference
materials and reagents. The Krebs index was calculated as the neutrophil/lymphocyte ratio (NLR).
Biochemical blood tests with determination of creatinine, alanine and aspartate aminotransferases (ALT and AST) were performed according to ReitmanFrankel. Bilirubin, C-reactive protein (CRP) were measured using an Indiko biochemical analyzer (Thermo Fisher Scientific, Finland) and reagents from Randox Laboratories (UK). We also studied the patient's coagulogram because intravascular coagulation is often associated with coronavirus infection. Hemostasis indices were determined by coagulometry and included activated partial thromboplastin time (APTT), prothrombin time (PTT), fibrinogen, antithrombin III (AT III), international normalized ratio (INR), and D-dimer (quantitative method). For the study, we used an ACL TOP 500 CTS coagulometric automatic analyzer (Werfen Instrumentation Laboratory, USA); reagents were from the manufacturer, and reference materials were from RENAM (Russia).
Body mass index was calculated using the conventional Quetelet formula. The glomerular filtration rate (GFR) was estimated according to the Cockcroft-Gault formula. Comorbidity was confirmed by the presence of two or more concomitant diseases in a patient.
Statistical analysis of the data was carried out by methods of descriptive, parametric, and nonparametric statistics using the Statistica 10.0 software (StatSoft, USA). Laboratory and instrumental data not related to normal Gaussian distribution, are presented as a median value with border quartiles [Q1; Q3], age is indicated as a mean and standard deviation M±a. The normality of data distribution was assessed using the Kolmogorov-Smirnov test. The significance of differences in variables other than normal distribution was assessed using the Mann-Whitney test for independent samples of quantitative indicators. The statistical relationship
between the indices was produced using the Spearman (R) and Gamma (y) nonparametric rank correlation method. Statistical testing of the absence/presence of differences in the groups was carried out according to the x2 criterion. The differences between the gender groups were revealed by the frequency analysis. The dependence of the complicated course (transfer to ICU, excess volume of lung damage) on various factors (gender, Krebs index, comorbidity) was assessed by multiple logistic regression. Differences were considered statistically significant at p<0.05.
Results and Discussion
There was only one case of death among patients studied (a 47-year-old woman); therefore, this outcome was not analyzed further.
The majority of patients hospitalized with COVID-19 had comorbidities. Using the rank correlation method, we found that male patients under the age of 55 had two or more concomitant diseases (y=0.36; p=0.043), among which IHD (y=1.00; p=0.003) and liver diseases (y=0.58; p=0.007) prevailed. Frequency analysis confirmed the prevalence of coronary artery disease in this group of men (p=0.044) as a concomitant disease (Figure 1).
Significant inter-gender differences were also noted in the extent of pulmonary parenchyma lesions as revealed by CT examination (Table 1). Upon admission to the hospital, the volume of pulmonary parenchyma lesions was higher in COVID-positive men as compared to women (p=0.050).
Furthermore, the greater increase in the area of lung damage in men (as compared to women) during hospitalization (p=0.019) reflected a faster development of the infectious process; this parameter can serve as a predictor of an unfavorable course of coronavirus infection. The present result necessitates the timely initiation of therapy for coronavirus infection in men
Obesity (BMI >30) Arterial hypertension Liver diseases Diabetes mellitus IHD
Bronchial asthma COPD Blood diseases Kidney diseases Rheumatoid arthritis Gastroduodenitis
"X2=4.05; p=0.004
i x2=3.37; p=0.067
Men 1 Women
0
5.00 10.00 15.00 20.00 25.00
30.00 35.00 40.00 45.00 50.00 Frequency of signs in a group (%)
Figure 1. Frequency analysis of comorbidities in patients with COVID-19 under the age of 55
//////////////////////^^^^
18 CTM I 2021 I V0l. 13 I N0.4 E.S. Nekaeva, A.E. Bolshakova, E.S. Malysheva, E.A. Galova, E.V. Makarova, ..., I.V. Fomin
<55 years old both at the outpatient and early inpatient stage.
We also found gender-related differences in other indicators determined upon admission to the hospital: hyperbilirubinemia (p=0.029), increased hepatic transaminases AST (p=0.043) and ALT (p=0.043); those values were significantly higher in men than in women. The lab findings indicated organ dysfunction, decompensation of concomitant diseases, and the development of complications that were considered when choosing pharmacotherapy and the dosage regimen. The hepatobiliary disorders found in male patients on admission might have resulted from an unhealthy lifestyle or excessive use of medicines at the outpatient period. These disorders should be treated by the timely administration of hepatoprotectors.
The increased transaminase activity in patients with COVID-19 is thought to be a predictor of a poor outcome of the disease [29-33]. The causes of liver dysfunction in COVID-19 may vary: it can be either direct damage to hepatocytes by the SARS-CoV-2 virus or a cytokine storm result. The hepatotoxic effect of medicines used in the treatment of coronavirus infection could not be ruled out.
There was a statistically significant difference in the level of plasma creatinine between men and women throughout the entire hospital stay (p<0.001). The increase is indicative of kidney damage resulted from the cytokine-induced systemic inflammatory response. An additional mechanism might include the virus invasion mediated by the angiotensin-converting enzyme 2 receptor expressed in the kidney [34]. It is known that patients with kidney disease have a significantly higher risk of death in hospital; independently, a high level of serum creatinine is a risk factor for hospital mortality [35].
A relationship was found between the decrease in the absolute value of lymphocytes and the severity of the course of COVID-19 [36]. We have shown the absence of statistically significant differences in the level of leukocytes and lymphocytes in
Ta bl e 1
Clinical laboratory and instrumental indicators in COVID-positive patients under 55 (Me [Q1; Q3])
Indicator Men (n=25) Women (n=32) p (n=57)
CT-evidenced lung lesion volume (%):
on admission 44 [32; 52] 32 [22; 52] 0.050
maximum value 48 [40; 64] 32 [22; 52] 0.019
at discharge 40 [28; 52] 24 [12; 40] 0.044
percent reduction in lung damage 22.2 [12.5; 33.3] 26.8 [15.5; 38.8] 0.350
Leukocytes (*109/L):
on admission 5.9 [5.2; 7.3] 5.3 [3.7; 7.0] 0.100
minimum value 4.4 [3.6; 5.0] 4.8 [3.7; 5.7] 0.420
at discharge 5.8 [5.0; 6.8] 5.7 [4.9; 6.6] 0.490
Lymphocytes (*109/L):
on admission 1.7 [1.1; 2.0] 1.8 [1.2; 2.2] 0.720
minimum value 1.1 [0.9; 1.6] 1.6 [0.8; 2.1] 0.160
at discharge 2.2 [1.7; 2.8] 2.3 [1.8; 2.5] 0.830
Krebs index (%):
on admission 1.9 [1.3; 3.3] 2.0 [1.2; 2.7] 0.580
at discharge 1.3 [0.9; 1.6] 1.1 [0.9; 1.5] 0.670
Monocytes (*109/L):
on admission 1.0 [0.8; 1.4] 0.8 [0.6; 0.9] 0.007
maximum value 1.4 [1.1; 1.9] 0.9 [0.8; 1.1] 0.001
at discharge 0.8 [0.7; 1.2] 0.8 [0.6; 0.9] 0.046
Platelets (*109/L):
on admission 241 [169; 316] 235.5 [167.5; 301.0] 0.850
maximum value 379 [319; 491] 342.5 [282.0; 433.5] 0.150
at discharge 309 [240; 402] 282.5 [254.0; 401.0] 0.890
ESR (mm/h):
on admission 21 [6; 38] 21.0 [11.5; 29.5] 0.770
maximum value 36 [21; 55] 22 [13; 49] 0.180
at discharge 10 [6; 21] 13.5 [9.0; 20.5] 0.610
CRP (mg/L):
on admission 37 [22; 89] 28.5 [9.5; 101.0] 0.390
maximum value 61 [33; 142] 59.0 [14.0; 146.5] 0.590
at discharge 5 [2; 5] 4.5 [2.0; 5.0] 0.760
Total bilirubin (pmol/L):
on admission 12.7 [7.2; 17.6] 8.7 [5.0; 13.6] 0.029
maximum value 14.4 [8.6; 18.7] 16.3 [9.7; 18.1] 0.920
at discharge 9.3 [8.4; 12.7] 9.4 [5.2; 15.8] 0.450
AST (units/L):
on admission 38 [25; 59] 28 [21; 40] 0.043
maximum value 68 [53; 107] 36.5 [25.5; 85.5] 0.014
at discharge 39 [24; 73] 28.5 [24.0; 55.5] 0.390
ALT (units/L):
on admission 34 [28; 63] 29.0 [16.0; 41.5] 0.043
maximum value 107 [73; 190] 55.5 [32.0; 103.5] 0.006
at discharge 66 [35; 151] 38.0 [32.0; 88.5] 0.120
Creatinine (pmol/L):
on admission 100 [91; 119] 73.5 [69.0; 84.5] 0.001
maximum value 120 [110; 147] 88.0 [73.0; 109.0] 0.001
at discharge 96 [88; 110] 73.0 [63.5; 83.5] 0.001
GFR (ml/min):
on admission 119.5 [94.5; 136.8] 116.8 [100.8; 138.1] 0.960
maximum value 94.2 [86.0; 110.4] 98.5 [73.0; 116.5] 0.740
at discharge 122.3 [108.5; 148.1] 117.5 [94.4; 154.6] 0.440
Indicator Men (n=25) Women (n=32) p (n=57)
D-dimer (mg/L):
on admission 0.3 [0.3; 0.5] 0.3 [0.3; 0.4] 0.830
maximum value 0.4 [0.3; 0.9] 0.4 [0.3; 0.9] 0.590
at discharge 0.2 [0.2; 0.3] 0.3 [0.2; 0.3] 0.640
Fibrinogen (g/L):
on admission 5.1 [4.3; 7.2] 4.5 [3.7; 5.4] 0.045
maximum value 6.3 [5.2; 7.2] 4.9 [3.9; 6.4] 0.013
at discharge 3.8 [2.8; 4.3] 3.7 [2.9; 4.3] 0.840
APTT (s):
on admission 32.9 [29.8; 35.0] 31.0 [27.9; 33.4] 0.088
at discharge 31.8 [29.0; 34.2] 33.7 [31.1; 36.7] 0.190
PTT (s):
on admission 12.8 [12.4; 13.7] 12.6 [11.9; 12.9] 0.076
at discharge 12.0 [11.8; 12.5] 12.0 [11.5; 12.8] 0.830
INR (units):
on admission 1.1 [1.1; 1.2] 1.1 [1.0; 1.2] 0.170
at discharge 1.0 [1.0; 1.1] 1.1 [1.0; 1.1] 0.670
AT III activity (%):
on admission 109 [101; 118] 106.5 [98.8; 112.0] 0.800
at discharge 102.0 [95.9; 109.0] 100.5 [92.0; 107.5] 0.830
gender groups, the indicators are reduced in both men and women. Abnormally high presence of monocytes in male patients was noted at admission (p=0.007), at discharge (p=0.046), and during treatment in the hospital (p<0.001). Monocytosis is usually associated with severe disease. We found that the increase in the level of monocytes directly correlated with the male gender (y=0.50, p=0.001; R=0.82, p=0.001) and the volume of pulmonary parenchyma lesions (y=0.39, p=0.003; R=0.32, p=0.014). The increase in the number of monocytes correlated with the severity of the disease course, namely with the transfer to the ICU (y=1.00, p=0.001; R=0.33, p=0.011) and the initiation of biological (y=0.56; p=0.003) and hormonal (y=0.56; p=0.002) therapy.
There is ambiguous information about the effect of monocytosis on the dynamics of coronavirus infection. Thus, Hensel et al. [37] demonstrated a relationship between the cardiological history and mortality of patients with monocytosis (OR=3.91 [1.87; 8.18]; p<0.001); patients with respiratory symptoms (p<0.001) and infection (p<0.001) sought medical attention more often. The statistically significant increase in the monocyte count in COVID-positive men corroborates with the well-known correlation of this indicator with the disease severity.
There were no significant differences in ESR and CRP values between men and women. The high level of acute-phase proteins in both groups was used to monitor the inflammatory process and prescribe appropriate antibacterial, biological, and hormonal therapy [38].
An increased level of D-dimer and fibrinogen on the backdrop of normal PTT and platelet count at the initial
End of the Table 1 stage of the disease is characteristic of the COVID-associated coagulopathy in contrast to coagulopathies caused by bacterial sepsis or disseminated intravascular coagulation [39, 40]. It is, therefore, seen as a poor prognostic factor for developing thrombotic complications [41-45].
Despite the increase in the amount of D-dimer and fibrinogen in most of the patients, a significant difference between the gender groups was found only for fibrinogen at admission (p=0.045) and during treatment (p=0.013). Fibrinogen is a precursor of fibrin and an acute-phase protein. Increased levels of fibrinogen could reflect the stimulation of its biosynthesis in the process of microthrombi formation or, conversely, its suppressed catabolism in the lungs [46, 47].
The values of the chronometric parameters (APTT, PTT) as well as the activity of AT III as criteria of the anticoagulant potential in our patients did not exceed the reference limits. There was a trend towards gender-related differences in the APTT (p=0.088) and PTT (p=0.076), which might be caused by variations of the liver function [48, 49].
The mechanism of coagulopathy in COVID-19 is not fully understood. The one could involve dysregulated immune responses caused by inflammatory cytokines, lymphocyte death, hypoxia, or endothelial damage [50]. On the one hand, an increased thrombus formation may limit the spread of the SARS-CoV-2 virus, on the other hand, endothelial damage inhibits thromboprotection and allows for excessive thrombin production, dysregulation of fibrinolysis, and thrombus formation [5, 51-53].
We considered that the increased values of laboratory indices (monocytes, hepatic transaminases, bilirubin, creatinine, and fibrinogen) might be mainly due to concomitant diseases. The aggravated course and progression of the viral infection in comorbid COVID-positive patients has been documented [54-60]. The duration of a hospital stay and the number of adverse outcomes were higher in patients having two or more comorbidities [61]. It is, therefore, necessary to monitor laboratory parameters reflecting the excretory body functions in order to initiate an adequate early therapy. To reduce the risk of toxic liver damage, the use of hepatoprotective and choleretic agents in patients with COVID-19 may be justified and should be further discussed.
In this study, we also assessed the role of comorbidity and the Krebs index (higher than 2.973) as factors affecting the course of COVID-19 in patients under 55
years of age. The event of patient transfer to the ICU and
//////////////////////^^^^
20 CTM I 2021 I vol. 13 I No.4 E.S. Nekaeva, A.E. Bolshakova, E.S. Malysheva, E.A. Galova, E.V. Makarova, ..., I.V. Fomin
Comorbidity
Krebs index (>2.973)
Increase in lung lesion (percentage by CT)
Transfer to ICU
the increase in the lung damage area were used as markers of the complicated course of the disease. The risks and occurrences of these two events for men and women are shown in Figure 2. There were some differences between the two genders but they did not reach statistical significance.
The present results suggest that gender is an essential factor affecting the course of coronavirus infection in adult patients under 55 years old. A meta-analysis
[62] identified male sex as a risk factor for SARS-CoV-2 infection, transfer to ICU (OR=2.84 [2.06; 3.92]; p<0.001) and likelihood of death (OR=1.39 [1.31; 1.47]; p<0.001). Using the method of multiple logistic regression, we revealed a relationship between the complicated course of COVID-19 and the male gender in combination with the Krebs index and comorbidity (Table 2). The male gender, as well as
comorbidity, increased the likelihood of transfer to the ICU in COVID-19 patients (x2=7.03; p=0.030). It should be noted that both the male gender and the Krebs index value (x2=5.68; p=0.059) contributed almost equally to the frequency of transfer to the ICU.
During the period of hospital treatment, an increase in the lung damage area was noted in 10 patients (17.5%), including 4 women and 6 men; the multivariate analysis showed a correlation between the lung damage and the Krebs index as well as the male gender (x2=7.78; p=0.021).
There was no statistically significant effect of comorbidity on the increase in the volume of lung damage in male patients under study (x2=1.66; p=0.436).
There were no differences in the Krebs index between the gender groups. Its value on admission of COVID-positive patients was used as a prognostic indicator of the disease severity. Thus, NLR >2.973 is regarded as a risk factor for disease progression during hospitalization
[63], and with NLR >6.11, there is a high risk of death
[64]. Therefore, calculating the Krebs index in COVID-positive male patients with comorbidity can be used to assess the risk of disease progression at an earlier stage. To predict the course of the disease it is advisable to introduce the determination of the Krebs index into the standards of patient care in COVID-19.
Limitations of the study. This study has some limitations. The presence/absence of concomitant diseases were based on patient's history either documented or reported verbally. Therefore, we cannot rule out overdiagnosis in these reports. At the same
1.94; p=0.16
■ Men Women
10.00 20.00 30.00 40.00 50.00 60.00 Frequency of signs in a group (%)
Figure 2. Frequency analysis of the complicated course of COVID-19 and factors of its development in patients under 55
Ta bl e 2
Relationship between gender and indicators of the complicated course of COVID-19 in patients under 55
Indicator Regression equation
Transfer to ICU y= -2.836+ 1.081 male gender+ 2.052 comorbidity (x2=7.03; p=0.030)
y= -2.033+ 1.154 male gender+ 1.539 Krebs index (x2=5.68; p=0.059)
Increased percentage y= -1.962+0.575 male gender+ 1.915 Krebs index (x2=7.78; p=0.021)
of lung damage y= -0.913+0.881 male gender+ 0.451 comorbidity (x2=1.66; p=0.436)
time, we were able to confirm the preliminary diagnoses when observing the patients during hospitalization. No population conclusions can be drawn from these data due to the limited number of patients under study.
Conclusion
The course of COVID-19 in men and women under 55 years depends on patient's gender: men have more pronounced damage to the lung parenchyma and a more significant change in laboratory parameters. Risk factors for a severe course of COVID-19 in men are coronary artery disease and disorders of the hepatobiliary system. Calculating the Krebs index can be used to assess the risk of disease progression.
Research funding. The authors declare no special funding for the study.
Conflicts of interest. This work, its theme, subject, and content do not involve conflict of interests.
References
1. WHO. Weekly epidemiological update on COVID-19 — 25 May 2021. URL: https://www.who.int/publications/m/item/ weekly-epidemiological-update-on-covid-19---25-may-2021.
2. Iba T., Connors J.M., Levy J.H. The coagulopathy, endotheliopathy, and vasculitis of COVID-19. Inflamm Res 2020; 69(12): 1181-1189, https://doi.org/10.1007/s00011-020-01401-6.
3. Wang D., Hu B., Hu C., Zhu F., Liu X., Zhang J., Wang B., Xiang H., Cheng Z., Xiong Y., Zhao Y., Li Y., Wang X., Peng Z. Clinical characteristics of 138 hospitalized
0
patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 2020; 323(11): 1061-1069, https://doi. org/10.1001/jama.2020.1585.
4. Wu Z., McGoogan J.M. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72,314 cases from the Chinese Center for Disease Control and Prevention. JAMA 2020; 323(13): 1239-1242, https://doi.org/10.1001/ jama.2020.2648.
5. Tsatsakis A., Calina D., Falzone L., Petrakis D., Mitrut R., Siokas V., Pennisi M., Lanza G., Libra M., Doukas S.G., Doukas P.G., Kavali L., Bukhari A., Gadiparthi C., Vageli D.P., Kofteridis D.P., Spandidos D.A., Paoliello M.M.B., Aschner M., Docea A.O. SARS-CoV-2 pathophysiology and its clinical implications: an integrative overview of the pharmacotherapeutic management of COVID-19. Food Chem Toxicol 2020; 146: 111769, https://doi.org/10.1016/j. fct.2020.111769.
6. Pagliaro P. Is macrophages heterogeneity important in determining COVID-19 lethality? Med Hypotheses 2020; 143: 110073, https://doi.org/10.1016/j.mehy.2020.110073.
7. Sze S., Pan D., Nevill C.R., Gray L.J., Martin C.A., Nazareth J., Minhas J.S., Divall P., Khunti K., Abrams K.R., Nellums L.B., Pareek M. Ethnicity and clinical outcomes in COVID-19: a systematic review and meta-analysis. EClinicalMedicine 2020; 29: 100630, https://doi.org/10.1016/j. eclinm.2020.100630.
8. Zhang J., Wang X., Jia X., Li J., Hu K., Chen G., Wei J., Gong Z., Zhou C., Yu H., Yu M., Lei H., Cheng F., Zhang B., Xu Y., Wang G., Dong W. Risk factors for disease severity, unimprovement, and mortality in COVID-19 patients in Wuhan, China. Clin Microbiol Infect 2020; 26(6): 767-772, https://doi. org/10.1016/j.cmi.2020.04.012.
9. Liu Y., Mao B., Liang S., Yang J.W., Lu H.W., Chai Y.H., Wang L., Zhang L., Li Q.H., Zhao L., He Y., Gu X.L., Ji X.B., Li L., Jie Z.J., Li Q., Li X.Y., Lu H.Z., Zhang W.H., Song Y.L., Qu J.M., Xu J.F.; Shanghai Clinical Treatment Experts Group for COVID-19. Association between age and clinical characteristics and outcomes of COVID-19. Eur Respir J 2020; 55(5): 2001112, https://doi.org/10.1183/13993003.01112-2020.
10. Liu X., Lv J., Gan L., Zhang Y., Sun F., Meng B., Jheon A., Yan F., Li B., Xuan Z., Ma X., Wulasihana M. Comparative analysis of clinical characteristics, imaging and laboratory findings of different age groups with COVID-19. Indian J Med Microbiol 2020; 38(1): 87-93, https://doi. org/10.4103/ijmm.ijmm_20_133.
11. Liu K., Chen Y., Lin R., Han K. Clinical features of COVID-19 in elderly patients: a comparison with young and middle-aged patients. J Infect 2020; 80(6): e14-e18, https:// doi.org/10.1016/j.jinf.2020.03.005.
12. Moreno Fernandez-Ayala D.J., Navas P., Lopez-Lluch G. Age-related mitochondrial dysfunction as a key factor in COVID-19 disease. Exp Gerontol 2020; 142: 111147, https:// doi.org/10.1016/j.exger.2020.111147.
13. McGuire P.J. Mitochondrial dysfunction and the aging immune system. Biology (Basel) 2019; 8(2): 26, https://doi. org/10.3390/biology8020026.
14. Lopez-Lluch G. Mitochondrial activity and dynamics changes regarding metabolism in ageing and obesity. Mech Ageing Dev 2017; 162: 108-121, https://doi.org/10.1016/j. mad.2016.12.005.
15. Meng Y., Wu P., Lu W., Liu K., Ma K., Huang L., Cai J., Zhang H., Qin Y., Sun H., Ding W., Gui L., Wu P. Sex-
specific clinical characteristics and prognosis of Coronavirus Disease-19 infection in Wuhan, China: a retrospective study of 168 severe patients. PLoS Pathog 2020; 16(4): e1008520, https://doi.org/10.1371/journal.ppat.1008520.
16. Haitao T., Vermunt J.V., Abeykoon J., Ghamrawi R., Gunaratne M., Jayachandran M., Narang K., Parashuram S., Suvakov S., Garovic V.D. COVID-19 and sex differences: mechanisms and biomarkers. Mayo Clin Proc 2020; 95(10): 2189-2203, https://doi.org/10.1016/j.mayocp.2020.07.024.
17. Trouillet-Assant S., Viel S., Gaymard A., Pons S., Richard J.C., Perret M., Villard M., Brengel-Pesce K., Lina B., Mezidi M., Bitker L., Belot A.; COVID HCL Study group. Type I IFN immunoprofiling in COVID-19 patients. J Allergy Clin Immunol 2020; 146(1): 206-208.e2, https://doi.org/10.1016/j. jaci.2020.04.029.
18. Webb K., Peckham H., Radziszewska A., Menon M., Oliveri P., Simpson F., Deakin C.T., Lee S., Ciurtin C., Butler G., Wedderburn L.R., loannou Y. Sex and pubertal differences in the type 1 interferon pathway associate with both X chromosome number and serum sex hormone concentration. Front Immunol 2019; 9: 3167, https://doi.org/10.3389/ fimmu.2018.03167.
19. Wan Y., Shang J., Graham R., Baric R.S., Li F. Receptor recognition by the novel coronavirus from Wuhan: an analysis based on decade-long structural studies of SARS coronavirus. J Virol 2020; 94(7): e00127-20, https://doi.org/10.1128/ jvi.00127-20.
20. Zhao Y., Zhao Z., Wang Y., Zhou Y., Ma Y., Zuo W. Single-cell RNA expression profiling of ACE2, the receptor of SARS-CoV-2. Am J Respir Crit Care Med 2020; 202(5): 756759, https://doi.org/10.1164/rccm.202001-0179le. Erratum in: Am J Respir Crit Care Med 2021; 203(6): 782.
21. Culebras E., Hernández F. ACE2 is on the X chromosome: could this explain COVID-19 gender differences? Eur Heart J 2020; 41(32): 3095, https://doi.org/10.1093/ eurheartj/ehaa521.
22. Bukowska A., Spiller L., Wolke C., Lendeckel U., Weinert S., Hoffmann J., Bornfleth P., Kutschka I., Gardemann A., Isermann B., Goette A. Protective regulation of the ACE2/ACE gene expression by estrogen in human atrial tissue from elderly men. Exp Biol Med (Maywood) 2017; 242(14): 1412-1423, https://doi.org/10.1177/ 1535370217718808.
23. Asfahan S., Deokar K., Dutt N., Niwas R., Jain P., Agarwal M. Extrapolation of mortality in COVID-19: exploring the role of age, sex, co-morbidities and health-care related occupation. Monaldi Arch Chest Dis 2020; 90(2), https://doi. org/10.4081/monaldi.2020.1325.
24. Borghesi A., Zigliani A., Masciullo R., Golemi S., Maculotti P., Farina D., Maroldi R. Radiographic severity index in COVID-19 pneumonia: relationship to age and sex in 783 Italian patients. Radiol Med 2020; 125(5): 461-464, https://doi. org/10.1007/s11547-020-01202-1.
25. Bhopal S.S., Bhopal R. Sex differential in COVID-19 mortality varies markedly by age. Lancet 2020; 396(10250): 532-533, https://doi.org/10.1016/s0140-6736(20)31748-7.
26. Ministry of Health of the Russian Federation. Profilaktika, diagnostika i lechenie novoy koronavirusnoy infektsii (COVID-19). Vremennye metodicheskie rekomendatsii. Versiya 6 (28.04.2020) [Prevention, diagnosis and treatment of new coronavirus infection (COVID-19). Temporary guidelines. Version 6 (April 28, 2020)]. URL: https://roszdravnadzor.gov.ru/i/ upload/images/2020/5/28/1590682537.35655-1-117450.pdf.
//////////////////////^^^^
22 CTM I 2021 I V0l. 13 I N0.4 E.S. Nekaeva, A.E. Bolshakova, E.S. Malysheva, E.A. Galova, E.V. Makarova, ..., I.V. Fomin
27. Ministry of Health of the Russian Federation. Profilaktika, diagnostika i lechenie novoy koronavirusnoy infektsii (COVID-19). Vremennye metodicheskie rekomendatsii. Versiya 7 (03.06.2020) [Prevention, diagnosis and treatment of new coronavirus infection (COVID-19). Temporary guidelines. Version 7 (June 3, 2020)]. URL: https://static-0.rosminzdrav.ru/ system/attachments/attaches/000/050/584/original/03062020_ MR_COVID-19_v7.pdf.
28. WHO. Klinicheskoe vedenie tyazheloy ostroy respiratornoy infektsii pri podozrenii na novuyu koronavirusnuyu (2019-nCoV) infektsiyu: vremennye rekomendatsii, 28 yanvarya 2020 g. [Clinical management of severe acute respiratory infection in suspected novel coronavirus (2019-nCoV) infection: temporary guidelines, January 28, 2020]. URL: https://apps. who.int/iris/bitstream/handle/10665/330893/WHO-nCoV-Clinical-2020.3-rus.pdf?sequence=5&isAllowed=y.
29. Zhang C., Shi L., Wang F.S. Liver injury in COVID-19: management and challenges. Lancet Gastroenterol Hepatol 2020; 5(5): 428-430, https://doi.org/10.1016/s2468-1253(20) 30057-1.
30. Ivashkin V.T., Sheptulin A.A., Zolnikova O.Yu., Okhlobystin A.V., Poluektova E.A., Trukhmanov A.S., Shirokova E.N., Gonik M.I., Trofimivskaya N.I. New coronavirus infection (COVID-19) and digestive system. Rossijskij zurnal gastroenterologii, gepatologii, koloproktologii 2020; 30(3): 7-13, https://doi.org/10.22416/1382-4376-2020-30-3-7.
31. Ilchenko L.Yu., Nikitin I.G., Fedorov I.G. COVID-19 and liver damage. Arhiv vnutrennej mediciny 2020; 10(3): 188-197, https://doi.org/10.20514/2226-6704-2020-10-3-188-197.
32. Youssef M., Hussein M.H., Attia A.S., Elshazli R.M., Omar M., Zora G., Farhoud A.S., Elnahla A., Shihabi A., Toraih E.A., Fawzy M.S., Kandil E. COVID-19 and liver dysfunction: a systematic review and meta-analysis of retrospective studies. J Med Virol 2020; 92(10): 1825-1833, https://doi.org/10.1002/jmv.26055.
33. Ye L., Chen B., Wang Y., Yang Y., Zeng J., Deng G., Deng Y., Zeng F. Prognostic value of liver biochemical parameters for COVID-19 mortality. Ann Hepatol 2020; 21: 100279, https://doi.org/10.1016Zj.aohep.2020.10.007.
34. Gabarre P., Dumas G., Dupont T., Darmon M., Azoulay E., Zafrani L. Acute kidney injury in critically ill patients with COVID-19. Intensive Care Med 2020; 46(7): 1339-1348, https://doi.org/10.1007/s00134-020-06153-9.
35. Cheng Y., Luo R., Wang K., Zhang M., Wang Z., Dong L., Li J., Yao Y., Ge S., Xu G. Kidney disease is associated with in-hospital death of patients with COVID-19. Kidney Int 2020; 97(5): 829-838, https://doi.org/10.1016/jkint.2020.03.005.
36. Xu P.P., Tian R.H., Luo S., Zu Z.Y., Fan B., Wang X.M., Xu K., Wang J.T., Zhu J., Shi J.C., Chen F., Wan B., Yan Z.H., Wang R.P., Chen W., Fan W.H., Zhang C., Lu M.J., Sun Z.Y., Zhou C.S., Zhang L.N., Xia F., Qi L., Zhang W., Zhong J., Liu X.X., Zhang Q.R., Lu G.M., Zhang L.J. Risk factors for adverse clinical outcomes with COVID-19 in China: a multicenter, retrospective, observational study. Theranostics 2020; 10(14): 6372-6383, https://doi.org/10.7150/thno.46833.
37. Hensel M., Gradel L., Kutz A., Haubitz S., Huber A., Mueller B., Schuetz P., Hugle T. Peripheral monocytosis as a predictive factor for adverse outcome in the emergency department: survey based on a register study. Medicine (Baltimore) 2017; 96(28): e7404, https://doi.org/10.1097/md. 0000000000007404.
38. Potempa L.A., Rajab I.M., Hart P.C., Bordon J., Fernandez-Botran R. Insights into the use of C-reactive
protein as a diagnostic index of disease severity in COVID-19 infections. Am J Trop Med Hyg 2020; 103(2): 561-563, https:// doi.org/10.4269/ajtmh.20-0473.
39. Iba T., Levy J.H., Connors J.M., Warkentin T.E., Thachil J., Levi M. The unique characteristics of COVID-19 coagulopathy. Crit Care 2020; 24(1): 360, https://doi. org/10.1186/s13054-020-03077-0.
40. Eljilany I., Elzouki A.N. D-dimer, fibrinogen, and IL-6 in COVID-19 patients with suspected venous thromboembolism: a narrative review. Vasc Health Risk Manag 2020; 16: 455462, https://doi.org/10.2147/vhrm.s280962.
41. Asakura H., Ogawa H. COVID-19-associated coagulopathy and disseminated intravascular coagulation. Int J Hematol 2021; 113(1): 45-57, https://doi.org/10.1007/s12185-020-03029-y.
42. Rostami M., Mansouritorghabeh H. D-dimer level in COVID-19 infection: a systematic review. Expert Rev Hematol 2020; 13(11): 1265-1275, https://doi.org/10.1080/17474086. 2020.1831383.
43. Zhang L., Yan X., Fan Q., Liu H., Liu X., Liu Z., Zhang Z. D-dimer levels on admission to predict in-hospital mortality in patients with COVID-19. J Thromb Haemost 2020; 18(6): 1324-1329, https://doi.org/10.1111/jth.14859.
44. Wang P., Sha J., Meng M., Wang C., Yao Q., Zhang Z., Sun W., Wang X., Qie G., Bai X., Liu K., Chu Y. Risk factors for severe COVID-19 in middle-aged patients without comorbidities: a multicentre retrospective study. J Transl Med 2020; 18(1): 461, https://doi.org/10.1186/s12967-020-02655-8.
45. Li Y., Zhao K., Wei H., Chen W., Wang W., Jia L., Liu Q., Zhang J., Shan T., Peng Z., Liu Y., Yan X. Dynamic relationship between D-dimer and COVID-19 severity. Br J Haematol 2020; 190(1): e24-e27, https://doi.org/10.1111/bjh.16811.
46. Hayiroglu M.i., Çinar T., Tekke§in A.i. Fibrinogen and D-dimer variances and anticoagulation recommendations in COVID-19: current literature review. Rev Assoc Med Bras (1992) 2020; 66(6): 842-848, https://doi.org/10.1590/1806-9282.66.6.842.
47. Wool G.D., Miller J.L. The impact of COVID-19 disease on platelets and coagulation. Pathobiology 2021; 88(1): 15-27, https://doi.org/10.1159/000512007.
48. Tripodi A., Caldwell S.H., Hoffman M., Trotter J.F., Sanyal A.J. Review article: the prothrombin time test as a measure of bleeding risk and prognosis in liver disease. Aliment Pharmacol Ther 2007; 26(2): 141-148, https://doi. org/10.1111/j.1365-2036.2007.03369.x.
49. Kaptanoglu L., Kurt N., Sikar H.E. Current approach to liver traumas. Int J Surg 2017; 39: 255-259, https://doi. org/10.1016/j.ijsu.2017.02.015.
50. Iba T., Levy J.H., Levi M., Thachil J. Coagulopathy in COVID-19. J Thromb Haemost 2020; 18(9): 2103-2109, https://doi.org/10.1111/jth.14975.
51. Colling M.E., Kanthi Y. COVID-19-associated coagulopathy: an exploration of mechanisms. Vasc Med 2020; 25(5): 471-478, https://doi.org/10.1177/1358863x20932640.
52. Perico L., Benigni A., Casiraghi F., Ng L.F.P., Renia L., Remuzzi G. Immunity, endothelial injury and complement-induced coagulopathy in COVID-19. Nat Rev Nephrol 2021; 17(1): 46-64, https://doi.org/10.1038/s41581-020-00357-4.
53. Stenmark K.R., Frid M.G., Gerasimovskaya E., Zhang H., McCarthy M.K., Thurman J.M., Morrison T.E. Mechanisms of SARS-CoV-2-induced lung vascular disease: potential role of complement. Pulm Circ 2021; 11(2): 20458940211015799, https://doi.org/10.1177/20458940211015799.
54. Emami A., Javanmardi F., Pirbonyeh N., Akbari A. Prevalence of underlying diseases in hospitalized patients with COVID-19: a systematic review and meta-analysis. Arch Acad Emerg Med 2020; 8(1): e35.
55. Fang X., Li S., Yu H., Wang P., Zhang Y., Chen Z., Li Y., Cheng L., Li W., Jia H., Ma X. Epidemiological, comorbidity factors with severity and prognosis of COVID-19: a systematic review and meta-analysis. Aging (Albany NY) 2020; 12(13): 12493-12503, https://doi.org/10.18632/ aging.103579.
56. Ejaz H., Alsrhani A., Zafar A., Javed H., Junaid K., Abdalla A.E., Abosalif K.O.A., Ahmed Z., Younas S. COVID-19 and comorbidities: deleterious impact on infected patients. J Infect Public Health 2020; 13(12): 1833-1839, https://doi. org/10.1016/j.jiph.2020.07.014.
57. Sanyaolu A., Okorie C., Marinkovic A., Patidar R., Younis K., Desai P., Hosein Z., Padda I., Mangat J., Altaf M. Comorbidity and its impact on patients with COVID-19. SN Compr Clin Med 2020; 2: 1069-1076, https://doi.org/10.1007/ s42399-020-00363-4.
58. Yang J., Zheng Y., Gou X., Pu K., Chen Z., Guo Q., Ji R., Wang H., Wang Y., Zhou Y. Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis. Int J Infect Dis 2020; 94: 91-95, https://doi.org/10.1016Zj.ijid.2020.03.017.
59. Ebrahimi M., Saki Malehi A., Rahim F. COVID-19 patients: a systematic review and meta-analysis of laboratory findings, comorbidities, and clinical outcomes comparing medical staff versus the general population. Osong Public Health Res Perspect 2020; 11(5): 269-279, https://doi. org/10.24171/j.phrp.2020.11.5.02.
60. Zaki N., Alashwal H., Ibrahim S. Association of hypertension, diabetes, stroke, cancer, kidney disease, and high-cholesterol with COVID-19 disease severity and fatality: a
systematic review. Diabetes Metab Syndr 2020; 14(5): 11331142, https://doi.org/10.1016/j.dsx.2020.07.005.
61. Guan W.J., Liang W.H., Zhao Y., Liang H.R. Chen Z.S., Li Y.M., Liu X.Q., Chen R.C., Tang C.L., Wang T. Ou C.Q., Li L., Chen P.Y., Sang L., Wang W., Li J.F., Li C.C. Ou L.M., Cheng B., Xiong S., Ni Z.Y., Xiang J., Hu Y. Liu L., Shan H., Lei C.L., Peng Y.X., Wei L., Liu Y., Hu Y.H. Peng P., Wang J.M., Liu J.Y., Chen Z., Li G., Zheng Z.J. Qiu S.Q., Luo J., Ye C.J., Zhu S.Y., Cheng L.L., Ye F. Li S.Y., Zheng J.P., Zhang N.F., Zhong N.S., He J.X.; China Medical Treatment Expert Group for COVID-19. Comorbidity and its impact on 1590 patients with COVID-19 in China: a nationwide analysis. Eur Respir J 2020; 55(5): 2000547, https://doi.org/10.1183/13993003.00547-2020.
62. Peckham H., de Gruijter N.M., Raine C., Radziszewska A., Ciurtin C., Wedderburn L.R., Rosser E.C., Webb K., Deakin C.T. Male sex identified by global COVID-19 meta-analysis as a risk factor for death and ITU admission. Nat Commun 2020; 11(1): 6317, https://doi.org/10.1038/s41467-020-19741-6.
63. Long L., Zeng X., Zhang X., Xiao W., Guo E., Zhan W., Yang X., Li C., Wu C., Xu T., Zhan C., Chen Y., Jiang M., Zhong N., Lai K. Short-term outcomes of COVID-19 and risk factors for progression. Eur Respir J 2020; 55(5): 2000990, https://doi.org/10.1183/13993003.00990-2020.
64. Cai J., Li H., Zhang C., Chen Z., Liu H., Lei F., Qin J.J., Liu Y.M., Zhou F., Song X., Zhou J., Zhao Y.C., Wu B., He M., Yang H., Zhu L., Zhang P., Ji Y.X., Zhao G.N., Lu Z., Liu L., Mao W., Liao X., Lu H., Wang D., Xia X., Huang X., Wei X., Xia J., Zhang B.H., Yuan Y., She Z.G., Xu Q., Ma X., Wang Y., Yang J., Zhang X., Zhang X.J., Li H. The neutrophil-to-lymphocyte ratio determines clinical efficacy of corticosteroid therapy in patients with COVID-19. Cell Metab 2021; 3(2): 258-269.e3, https://doi.org/10.1016/j cmet.2021.01.002.
¿mmmmmmfmmmmmmmmimmmmmmmmimmim
