1. Introduction
The SARS-CoV-2 pandemic posed a significant challenge to the medical community. Although the infection typically leads to mild to moderate disease, with viral replication generally confined to the upper respiratory tract, severe to critical illness can sometimes develop. These clinical forms of disease often manifest as pneumonia, respiratory failure, and multiple organ failure. These forms typically develop about a week after the initial symptoms, with dyspnea being one of the most observed clinical manifestations, resulting from hypoxemia [
1,
2], followed by the progression to respiratory failure. Numerous studies have demonstrated the significance of dysregulated immune processes due to SARS-CoV-2 infection in the developing severe and critical COVID-19. The hyperactivation of immune cells (macrophages, NK cells, B and T lymphocytes) leads to abnormal systemic inflammation, with cytokine release syndrome (CRS), which has been identified as a pathogenic mechanism in the progression of severe COVID-19 [
3,
4,
5,
6].
Additionally, macrophage activation syndrome (MAS) has been observed, commonly seen in autoimmune and malignant diseases [
7,
8,
9,
10,
11], as well as in sepsis. This syndrome is characterized by the hyperactivation of tissue macrophages and the overproduction of inflammatory cytokines, such as IL-1, IL-6, and IL-8, and elevated levels of markers of inflammation, C-reactive protein (CRP), ferritin, and coagulation disorders involving D-dimer [
12,
13,
14,
15,
16,
17,
18,
19,
20,
21]. Dysregulation in immune function is also reflected in the leucocyte number, showing leukocytosis, lymphopenia, and an increased neutrophil-to-lymphocyte ratio (NLR) [
22,
23], with a correlation established between lymphopenia and an elevated NLR on one side, and the severity of COVID-19 on the other. Numerous studies have been conducted on the dynamics of various biomarkers throughout SARS-CoV-2 infection. Changes in specific biomarkers have been identified as strong indicators of the degree of immune dysfunction, and the severity of infection, and have high prognostic value for the outcome of the disease. These include elevated ferritin levels (a result of macrophage activation), increased NLR, elevated levels of aspartate aminotransferase (ASAT) and lactate dehydrogenase (LDH), indicative of liver involvement, increased D-dimer due to coagulation abnormalities, and elevated CRP and IL-6, indicative of hyperinflammation [
24,
25,
26,
27,
28].
The dysregulation of immune function in severe and critical clinical forms of COVID-19 provides a rationale for including medications in the treatment regimen that modulate the immune response through various mechanisms.
In 2021, the European Medicines Agency (EMA), and in 2022, the U.S. Food and Drug Administration (FDA) approved the use of Anakinra for COVID-19 [
29,
30,
31]. Anakinra is a recombinant human IL-1 receptor antagonist that binds to IL-1α and IL-1β receptors. Studies have demonstrated a connection between the early initiation of Anakinra therapy in hospitalized COVID-19 patients and the positive dynamics of examined biomarkers, including the reduction in CRP, ferritin, D-dimer, and NLR [
32,
33,
34,
35,
36,
37].
Considering the above, a study was conducted to evaluate the dynamics of biomarkers in the treatment of hospitalized patients with moderate to severe COVID-19 using Anakinra, compared to those receiving standard care.
2. Materials and Methods
2.1. Study Population
A retrospective clinical–epidemiological study was conducted, involving 65 patients with moderate to severe clinical forms of SARS-CoV-2 infection (according to the National Institute of Health NIH severity scale) who were hospitalized at the Specialized Hospital for Active Treatment of Infectious and Parasitic Diseases “Prof. Ivan Kirov”, Sofia with COVID-19 during the period from November 2022 to November 2023.
To enhance the internal validity of the study and improve the statistical power of detected associations within this selected patient cohort, the study carefully defines the cohort using precise inclusion and exclusion criteria. This approach reduces confounding factors and variability in the sample, thereby allowing for more robust results.
The following inclusion criteria were used: positive PCR test for SARS-CoV-2, age over 18 years, hospital admission between the 5th and 10th day from disease onset, radiological evidence of pneumonia, presence of at least 5 of the following laboratory criteria: WBC > 10.5 × 109/L, NLR > 5.0, CRP > 60 mg/L, ASAT > 38 U/L, LDH > 250 U/L, D-dimer > 0.5 mg/L, ferritin > 250 ng/mL, IL-6 > 25 pg/mL.
The exclusion criteria were as follows: age under 18 years, pregnant women, patients with COVID-19 who are hospitalized for reasons other than COVID-19, patients with CLcr < 50 mL/min, patients who refuse to sign informed consent on the day of admission, patients transferred to ICU, patients who could not complete 7 days of Anakinra treatment or whose hospital stay was less than 7 days.
All data used in the study were collected from the medical records of patients who signed an informed consent form for the use of their data for scientific purposes on the day of hospital admission. The study was approved by the ethics committee at Specialized Hospital for Active Treatment of Infectious and Parasitic Diseases “Prof. Ivan Kirov”, Sofia (protocol number 14/2022).
2.2. Specimen Sampling and Biomarker Measurement
The diagnosis of COVID-19 was etiologically confirmed by qualitatively detecting the genetic material of SARS-CoV-2 in samples taken from epithelial cells of the upper respiratory tract (nasopharyngeal swabs). A multiplex qualitative RT–PCR (Real-Time Polymerase Chain Reaction) analysis was used to detect the viral RNA (SARS-CoV-2 RNA).
The PCR diagnostic system for SARS-CoV-2 contains specific oligonucleotide primers, fluorescently labeled probes targeting specific regions of the viral genes, and the matrix RNA (mRNA) of human ribonuclease P. The system also included the reverse transcriptase enzyme (DNA polymerase), a human ribonuclease inhibitor, free deoxynucleotides (dNTPs), and a positive control. The reaction proceeded through the following steps: 1. Extraction of viral RNA; 2. Reverse transcription of complementary DNA; 3. Amplification of specific sections of complementary DNA using specific oligonucleotide primers, the reverse transcriptase enzyme, and free deoxynucleotides as building blocks of complementary DNA; and 4. Visualization of the PCR products (amplicons).
The PCR diagnostic system simultaneously detected the following three different regions of the SARS-CoV-2 viral genome: the Nucleocapsid gene (N), the Envelope gene (E), and the RNA-dependent RNA polymerase gene (RdRP), as well as the matrix RNA (mRNA) of human ribonuclease P (RNase P). The detection of RNase P mRNA was an internal control to monitor the RNA extraction and amplification processes, reducing false-negative results.
In addition to the internal control, the RT–PCR reaction included positive and negative controls. For each target gene, the reaction was considered positive if the fluorescence curve for the corresponding gene crossed the threshold line by the 40
th cycle (Ct < 40). The result was considered positive if at least two of the three SARS-CoV-2 genes tested positive. If only one of the three target genes was positive, the result was considered indeterminate, and a new test was required (
Table 1).
The analysis was conducted using the Tianlong
® Real-Time PCR System (Tianlong Technology Co., Xi’an, China). Reagents from the SOLIScript
® SARS-CoV-2 RT-qPCR Multiplex Assay by Solis BioDyne (Tartu, Estonia) were used for the RT–PCR analysis of SARS-CoV-2 RNA. These reagents comply with ISO 9001 [
38] and ISO 13485 [
39] standards.
For the patients included in the study, hematological (WBC, NLR), biochemical (CRP, ASAT, LDH, ferritin), coagulation (D-dimer), and immunological (IL-6) parameters were measured on days 1, 3, and 7. The tests were conducted in the Clinical Laboratory of Specialized Hospital for Active Treatment of Infectious and Parasitic Diseases “Prof. Ivan Kirov”, Sofia, which is accredited according to ISO 9001 [
38] and SGS.
Blood samples for testing the parameters in blood and serum were collected using vacutainer tubes—closed vacuum tubes. Hematological parameters (WBC, neutrophils, lymphocytes) were determined using the automated hematology analyzer Arcus 380 (Diatron Group, Budapest, Hungary). Whole blood collected with EDTA anticoagulant was used for this analysis. The analysis is based on the impedance method. For biochemical (CRP, ASAT, LDH, ferritin) and immunological (IL-6) parameter measurements, vacutainer tubes with a clot activator, which accelerates the blood clotting process and separates serum needed for the analysis, were used. The analyses were conducted on the Selectra pro S automated biochemical analyzer (ELITechGroup, Puteaux, France), while IL-6 was measured using the Cobas e 411 automated analyzer (Roche Diagnostics, Basel, Switzerland). Plasma was used as the sample material for D-dimer testing. Vacutainer tubes containing sodium citrate were used, and the testing was performed on the Finecare Fia Meter Wondfo (Guangzhou Wondfo Biotech Co., Guangzhou, China).
The analysis of CRP is based on the immunofluorescent method, and ASAT is analyzed using the method recommended by the International Federation of Clinical Chemistry (IFCC). The LDH is analyzed through a chemical reaction involving the oxidation of lactate to pyruvate. Ferritin is measured using an immunoturbidimetric assay, D-dimer by fluorescent immunoassay, and IL-6 by chemiluminescence immunoassay.
The measurements of hematological parameters (WBC, neutrophils, lymphocytes) and biochemical parameters (CRP, ASAT, LDH, ferritin, and D-dimer) were conducted immediately after sample collection. Following centrifugation of the blood samples, the separated sera for the IL-6 measurement were stored at −80 °C until analysis.
2.3. Statistical Methods
Data were entered and processed using the statistical software packages IBM SPSS Statistics 25.0 (IBM Corp., Armonk, NY, USA), MedCalc Version 19.6.3 (
www.medcalc.org; accessed 22 November 2024), and Excel Office 2021 (Microsoft Corp., Redmond, WA, USA). Differences with
p-values less than 0.05 were considered statistically significant.
The following statistical methods were applied: descriptive analysis–frequency distribution of the observed variables was presented in tabular form; graphical analysis was used for visualizing the obtained results; Comparison of Relative Shares; Fisher’s Exact Test, Fisher–Freeman–Halton Exact Test, and Chi-square χ2 for testing the hypothesis for dependency between categorical variables; Kolmogorov–Smirnov and Shapiro–Wilk Nonparametric Tests were used to test the normality of distributions. Student’s T-Test was used to test hypotheses regarding the differences between the means of two independent samples; the Mann–Whitney Nonparametric Test was used to test hypotheses regarding the differences between two independent samples; repeated measures ANOVA–analysis of variance–was used to compare arithmetic averages in multiple comparisons; Mauchly’s Test was performed to check the assumptions of sphericity in repeated measures ANOVA; the Friedman Nonparametric Test was used to test hypotheses regarding differences between multiple dependent samples; and the Wilcoxon Nonparametric Test was used to test hypotheses regarding differences between two dependent samples.
4. Discussion
The relationship between the severity of COVID-19, patient outcomes, and the values and dynamics of biomarkers such as leukocytes, neutrophil-to-lymphocyte ratio (NLR), CRP, ASAT, LDH, D-dimers, ferritin, and IL-6 has been demonstrated in numerous studies.
In their study, Chaudhary et al. (2021) found significantly higher levels of CRP, D-dimers, ferritin, and IL-6 in patients with severe COVID-19 and those with fatal outcomes [
14]. These results are consistent with those reported by Ullah et al. (2022), who, in a study of 500 COVID-19 patients, demonstrated a correlation between elevated levels of biomarkers, including CRP, LDH, D-dimers, and IL-6 with the severity of COVID-19 and the likelihood of a fatal outcome [
40]. Similar conclusions were drawn by other authors as follows: Yun et al. (2020) observed significantly higher levels of neutrophils, CRP, ASAT, LDH, D-dimers, and lower levels of lymphocytes in patients with severe and critical forms of COVID-19 [
17]. Elshazli et al. (2020), in a meta-analysis, also identified similar correlations between elevated biomarker levels, including increased leukocyte counts, and the progression to critical COVID-19 [
18]. Wu et al. (2020) highlighted the importance of elevated neutrophil, LDH, and D-dimer levels as risk factors for the development of ARDS and death [
16,
41].
The hyperactivation of the immune system, resulting in abnormal inflammation and cytokine release syndrome, has been established as a pathogenic mechanism in the development of severe and critical COVID-19. The effect of medications that modulate the immune response on the above-mentioned biomarkers has been examined in multiple studies [
5,
33,
42]. In this context, the role of Anakinra, an interleukin-1 receptor antagonist, has been explored in numerous studies [
32,
33,
34,
35,
37,
43].
In the present study, the values and dynamics of the indicated biomarkers were monitored in patients treated with Anakinra, compared to those who received standard treatment.
The analysis from
Table 22 reveals the following findings regarding the dynamics of the studied blood parameters (WBC, NLR, and CRP):
WBC–The dynamics of WBC significantly depend on the administration of Anakinra. In the Anakinra group, there was a statistically significant decrease in WBC values, whereas no dynamic changes were observed in the control group. Additionally, the Anakinra group showed significantly lower average WBC values during the control checks on Day 3 and Day 7.
NLR (Neutrophil-to-Lymphocyte Ratio)–Average NLR values are significantly lower in the Anakinra group on Days 3 and 7 compared to the control group.
CRP (C-reactive Protein)–The dynamics of CRP (specifically the downward trend) were not statistically significantly dependent on Anakinra administration. However, significantly lower average CRP values were observed in the Anakinra group during the control check on Day 7 compared to the control group.
Table 22.
Dynamics of Blood Parameters (WBC, NLR, and CRP) by Group, Gender, and Age.
Table 22.
Dynamics of Blood Parameters (WBC, NLR, and CRP) by Group, Gender, and Age.
Indicator | Group | n | Day of the Examination |
---|
1 | 3 | 7 |
---|
| SD | | SD | | SD |
---|
WBC | Control Group | 24 | 7.92 a | 4.23 | 8.26 a | 3.69 | 8.60 a | 4.34 |
Anakinra | 41 | 7.23 ac | 3.29 | 5.69 b | 2.80 | 6.02 bc | 2.35 |
p = | | 0.639 | | 0.002 | | 0.012 | |
NLR | Control Group | 24 | 8.27 ad | 7.04 | 6.70 bd | 5.82 | 5.49 c | 4.39 |
Anakinra | 41 | 8.97 a | 7.71 | 5.01 b | 5.91 | 4.26 b | 4.50 |
p = | | 0.755 | | 0.033 | | 0.075 | |
CRP | Control Group | 24 | 161.04 a | 103.51 | 87.75 b | 76.56 | 43.69 c | 42.28 |
Anakinra | 41 | 146.95 a | 54.34 | 68.12 b | 62.17 | 17.28 c | 24.96 |
p = | | 0.978 | | 0.206 | | <0.001 | |
From
Table 23, the following observations are made regarding the dependence of the dynamics of the studied blood parameters (ASAT, LDH, D-dimer, ferritin, and IL-6) and oxygen saturation (Sat.O
2):
ASAT–the dynamics of ASAT are significantly influenced by Anakinra treatment. In the Anakinra group, there is a consistent and statistically significant decrease in ASAT values throughout the study, while in the control group, a statistically significant drop was only observed during the control check on Day 7.
LDH–The dynamics of LDH also show a significant dependence on Anakinra treatment. In the Anakinra group, a statistically significant decrease occurred as early as Day 3. In the control group, a significant decrease was only observed on Day 7, and this was only when compared to Day 3, not the baseline values.
D-dimer–The dynamics of D-dimer levels are significantly dependent on Anakinra treatment. The Anakinra group showed a consistent and statistically significant reduction in D-dimer levels, whereas no dynamics were observed in the control group. Furthermore, significantly (or borderline significantly, p < 0.1) lower mean D-dimer values were recorded in the Anakinra group on Days 3 and 7.
Ferritin–There is a significant dependence of the downward trend in ferritin levels on Anakinra treatment. The Anakinra group experienced a consistent and statistically significant decrease in ferritin levels, while no significant changes were observed in the control group. Additionally, borderline significantly lower mean ferritin values were noted in the Anakinra group during the control checks on Days 3 and 7.
IL-6–The downward trend in IL-6 levels is significantly influenced by Anakinra treatment. In the Anakinra group, a consistent and statistically significant decrease in IL-6 was observed throughout the study, while no significant changes were detected in the control group. Furthermore, significantly lower IL-6 values were recorded in the Anakinra group during the control checks on Days 3 and 7.
Oxygen saturation–The upward trend of this indicator is statistically significantly influenced only by the administration of Anakinra and by age, as observed at the control check on Day 7. In the Anakinra group, there is a consistent statistically significant increase, whereas in the control group, the increase appears only on Day 7. In younger patients, the increase in the indicator’s values is consistent, while in older patients, the increase on Day 7 is statistically similar to that on Day 3. Additionally, a significantly higher mean value can be noted among patients receiving Anakinra at the control check on Day 7.
Table 23.
Dynamics of Blood Parameters (ASAT, LDH, D-dimer, Ferritin, and IL-6) and oxygen saturation (Sat.O2).
Table 23.
Dynamics of Blood Parameters (ASAT, LDH, D-dimer, Ferritin, and IL-6) and oxygen saturation (Sat.O2).
Indicator | Group | n | Day of the Examination |
---|
1 | 3 | 7 |
---|
| SD | | SD | | SD |
---|
ASAT | Control Group | 24 | 44.08 a | 32.62 | 38.04 a | 31.12 | 31.83 b | 17.84 |
Anakinra | 41 | 45.85 a | 26.71 | 41.07 b | 24.93 | 32.20 c | 16.42 |
p = | | 0.514 | | 0.395 | | 0.828 | |
LDH | Control Group | 24 | 337.00 ac | 115.27 | 317.63 a | 122.88 | 284.46 bc | 137.08 |
Anakinra | 41 | 343.59 a | 139.06 | 302.93 b | 173.60 | 286.83 b | 209.91 |
p = | | 0.930 | | 0.135 | | 0.324 | |
D-dimer | Control Group | 24 | 1.76 a | 1.59 | 1.87 a | 1.71 | 1.69 a | 1.80 |
Anakinra | 41 | 1.57 a | 1.58 | 1.34 b | 1.91 | 0.96 c | 1.87 |
p = | | 0.693 | | 0.064 | | 0.013 | |
Ferritin | Control Group | 24 | 878.18 a | 749.52 | 832.09 a | 711.07 | 848.72 a | 883.93 |
Anakinra | 41 | 783.42 a | 740.26 | 597.65 b | 569.24 | 480.10 c | 422.31 |
p = | | 0.532 | | 0.097 | | 0.054 | |
IL-6 | Control Group | 24 | 122.65 a | 303.71 | 62.99 a | 84.18 | 44.67 a | 37.06 |
Anakinra | 41 | 120.33 a | 184.10 | 34.90 b | 66.79 | 12.37 c | 22.64 |
p = | | 0.876 | | <0.001 | | <0.001 | |
| Control Group | 24 | 91.33 a | 3.62 | 91.71 a | 4.45 | 93.50 b | 5.38 |
Sat.O2 | Anakinra | 41 | 91.02 a | 4.13 | 92.80 b | 4.05 | 94.56 c | 5.20 |
| p = | | 0.784 | | 0.204 | | 0.034 | |
The results showed a significant relationship between the dynamics of leukocytes, ASAT, LDH, D-dimer, ferritin, and IL-6, and the administration of Anakinra. A statistically significant decrease in ASAT and LDH values in the control group was noted only on Day 7. In the Anakinra group, significantly (or with borderline statistical significance, p < 0.1) lower mean values were observed for leukocytes, NLR, D-dimer, ferritin, and IL-6 on Days 3 and 7 compared to the control group. Additionally, significantly lower CRP values were detected in the Anakinra group on Day 7 compared to the control group. In our study, we observed that patients receiving Anakinra showed not only changes in the levels of laboratory indicators but also improvement regarding the oxygenation status. No statistically significant differences were found regarding the duration of hospitalization. Mortality rates were higher in the control group (12.50% versus 7.32% in the Anakinra group); however, due to the small sample size, no statistical difference was observed between the two groups. Further investigations with larger patient populations are needed to evaluate the potential benefits of Anakinra treatment on mortality rates.
The study has the following limitations: First, there is a small sample size and an imbalance between the control and Anakinra groups, as well as a gender imbalance. While uneven group sizes can introduce potential bias and reduce the robustness of statistical comparisons, we applied rigorous criteria consistently across both groups to minimize such effects. Additionally, we used statistical methods that are appropriate for smaller and unevenly distributed samples, reducing the risk of bias in dependency testing. Second, as a single-center study, the generalizability of our findings to broader populations may be limited. Third, the lack of long-term follow-up, partly due to poor patient compliance, restricts our ability to assess longer-term outcomes.