Unveiling Immunological and Hematological Markers in COVID-19: Insights from a Clinical Study

Abstract

The COVID-19 pandemic has affected the global health system and economies largely. Therefore, knowledge about the clinical and laboratory profiles of patients with COVID-19 would help in the management and prognosis of the disease. The immunological and hematological indices have emerged as critical determinants for the severity of the disease and the prognosis; however, association with COVID-19 is clouded. The present study is aimed to characterize the immunological and hematological profiles of patients with COVID-19 in correlation with the disease severity. The study included 1,019 polymerase chain reaction (PCR)–confirmed patients with COVID-19 who were classified into serious and nonserious groups, considering severity criteria. Clinical laboratory investigations included hematological, biochemical, and immunological parameters regarding leukocyte counts, hemoglobin levels, and inflammatory markers. Our analysis of immunological and hematological differences between serious and nonserious patients with COVID-19 indicates that serious cases reflected elevated levels of pro-inflammatory markers such as lactate dehydrogenase, C-reactive protein (CRP), D-dimer, and ferritin, representing immune system dysregulation and systemic inflammation. Furthermore, in serious cases, discrepancies had also been noticed for many hematological parameters than nonserious ones, which also contained leukocyte count and hemoglobin level. Additionally, the CRP, D-dimer, blood urea nitrogen, alanine transaminase, and albumin levels could be independent predictors of COVID-19 severity by multivariate logistic regression analysis. Cutoff values for these biomarkers were defined by receiver operating characteristic curve analysis defining optimal parameters for the risk stratification and prognostication. The current investigation provides a comprehensive understanding of immunological and hematological correlation with COVID-19 severity, refining clinical decision-making and therapeutic interventions to improve patient outcomes.

Introduction

The pandemic of coronavirus disease 2019 (COVID-19) due to the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) brought with it some of the most overwhelming challenges in health systems and economies across the world. The virus, since its appearance in December 2019 in Wuhan, China, has now affected most of the countries across the world, causing millions of infections (Huang et al., 2020).

Knowledge of clinical and laboratory characteristics of patients with COVID-19 is vital for appropriate management and prognostication of the disease. A variety of clinical presentations of COVID-19 has been reported in many studies, from being asymptomatic or with mild respiratory symptoms to severe pneumonia, acute respiratory distress syndrome, and multi-organ dysfunction (Zhou et al., 2020).

The host response to SARS-CoV-2 infection is majorly mediated through immunological indices of cytokine profiles, lymphocyte subsets, and inflammatory markers. A dysregulated immune system, with cytokine storm and lymphopenia, has been postulated to be in the pathogenesis of severe COVID-19 and associated complications (Mehta et al., 2020; Siddiqi and Mehra, 2020). Moreover, similar changes in hematological parameters, including leukocyte counts, hemoglobin (Hb) levels, and coagulation markers, have also been observed in COVID-19 patients and based on these changes, may also prognosticate disease severity and clinical outcomes (Lippi et al., 2020; Zhang et al., 2020).

Despite the increasing amount of research on the clinical and laboratory characteristics of COVID-19, there’s still a significant gap in understanding how immunological and hematological markers relate to the severity of the disease. Addressing this gap with detailed studies could shed light on the underlying mechanisms of COVID-19, leading to better prognostic tools and treatments. This knowledge is crucial for tailoring interventions and improving patient outcomes in the face of this global health challenge.

In this study, we aimed to characterize the immunological and hematological profiles of COVID-19 patients and investigate their association with disease severity. We analyzed a cohort of polymerase chain reaction (PCR)–confirmed patients with COVID-19 to identify potential prognostic indicators and elucidate the underlying mechanisms contributing to COVID-19 pathogenesis. Our findings have the potential to improve clinical decision-making, risk stratification, and therapeutic interventions for patients with COVID-19, ultimately contributing to improved outcomes and patient care in the ongoing battle against the pandemic.

Materials and Methods

Study population

In this research, 1,019 PCR-confirmed patients with COVID-19 were included. These participants were divided into serious and nonserious categories reflecting the severity of their condition. Patients admitted to the intensive care unit (ICU) or exhibiting arterial oxygen partial pressure ≤300 mmHg and a respiratory rate ≥30 breaths per minute were classified as serious cases. Patients receiving any treatment or with concurrent serious morbidities such as malignancy, acquired immunodeficiency syndrome, hepatitis, or renal failure were excluded from the study.

Data collection procedure

Real-time data was collected from tertiary care hospitals with informed approvals and consent from patients and hospital administration. The samples were collected during the COVID-19 mass vaccination time. Ten milliliters of blood were collected from each participant using the venipuncture technique. Blood samples were collected in vacutainer tubes containing ethylenediaminetetraacetic acid (EDTA) and serum-separating gel. These tubes were immediately transported to the laboratory in a dry ice container for further processing.

Hematological parameters, including red blood cell (RBC) and white blood cell (WBC) indices, were assessed using the Sysmex XP-100 analyzer. Immunological biomarkers, specifically D-dimer, ferritin, and lactate dehydrogenase (LDH), were measured using solid-phase sandwich enzyme-linked immunosorbent assay, Hitachi 912, and Cobas-111 analyzers.

Renal and liver function tests were conducted using the Roche Diagnostic Cobas-311 analyzer. Liver function tests included alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), bilirubin, gamma-glutamyl transferase (GGT), total protein, albumin (ALB), globulin, and albumin-to-globulin ratio (A/G ratio). Renal function tests measured urea and creatinine levels.

Statistical analysis

Statistical Package for the Social Sciences (SPSS) version 25 (IBM, USA) was used for statistical interpretation of the data. The data distribution was analyzed by the Shapiro–Wilk test. Descriptive statistics were employed to summarize the data, with continuous variables presented as median and interquartile range and categorical variables as frequencies and/or percentages. Given the skewed distributions of continuous data, the Mann–Whitney U test was used; for categorical variables, the chi-square or Fisher’s exact tests were employed. This strategy ensured robust analysis, even those with non-normal data. To evaluate the relationship between the severity of the disease and its components, Spearman’s coefficient was utilized. Predictive indicators were identified while taking potential confounders into account using stepwise logistic regression. The regression model’s fit was assessed using goodness-of-fit tests, such as the Hosmer–Lemeshow test for logistic regression models. The forecast accuracy was determined with the help of the area under the curve. As necessary, adjustments were also made for repeated comparisons.

Results

Demographics of the study participants

A total of 1,019 patients with COVID-19 were included in the study, comprising 573 (56.23%) males and 446 (43.77%) females. The prevalence of COVID-19 was higher among male patients than their female counterparts.

The age spectrum of patients with COVID-19 ranged from 5 to 98 years, with a mean age of 44.7 ± 17.6 years. To analyze age-related patterns, patients were categorized into five distinct age brackets: age group I (≤20 years), age group II (21–40 years), age group III (41–60 years), age group IV (61–80 years), and age group V (>80 years). Remarkably, the highest prevalence of COVID-19 was noted in age group II, while the lowest incidence was observed in age group I. Nevertheless, no statistically significant difference in COVID-19 prevalence was detected among the various age groups (Table 1).

Table 1.

Demographic and Clinical Features of COVID-19 Patients. [Median (Interquartile Range)]

Variables	Reference range	Total cohort (n = 1019)	Nonserious (n = 575)	Serious (n = 444)	p-Value
Age (years)	—	42 (30–58)	40 (29–60)	42 (30–58)	0.620
Age groups n (%)
≤20	—	31 (3.04)	19 (3.03)	12 (2.70)	0.089
21–40	—	475 (46.61)	270 (46.95)	205 (46.17)
41–60	—	325 (31.89)	172 (29.91)	153 (34.45)
61–80	—	153 (15.01)	87 (15.13)	66 (14.86)
>80	—	35 (3.43)	27 (4.69)	08 (1.80)
Gender n (%)
Male	—	573 (56.23)	313 (54.6)	260 (45.4)	0.188
Female	—	446 (43.77)	262 (58.7)	184 (41.3)	0.188
Inflammatory markers
LDH	230–460 µL	567 (520–767)	532 (457–555)	780 (743–865)	<0.001
Ferritin	25–350 ng/dL	465 (400–634)	409 (373–447)	650 (568–755)	<0.001
CRP	0–10 mg/L	23.8 (14.9–32.1)	21.35 (13.5–27.6)	29.8 (18.1–42.2)	<0.001
D-dimer	<0.5 mg/mL	0.76 (0.59–1.1)	0.61 (0.53–0.66)	1.16 (1.0–1.3)	<0.001
RBC indices
HB	13–17 g/dL	14.5 (13.9–14.5)	14.5 (141–4.5)	14.2 (13.7–14.5)	<0.001
RBCs	4–6 × 10¹²/L	5.5 (4.7–6.2)	5.6 (4.7–6.2)	5.3 (4.7–6.2)	0.505
HCT	40–50%	44.9 (41.6–48.5)	45.1 (42–48)	44.7 (41–48)	0.141
MCV	80–95 fL	88.12 (84.0–94.8)	88.1 (83.5–91.9)	87.9 (84.3–91.6)	0.955
MCH	26–32 pg/dL	40 (38–56)	43 (38–56)	40 (38–56)	0.026
MCHC	26–32 g/dL	32.5 (31.2–33.7	32.5 (31.2–33.7)	32.7 (31.3–33.8)	0.325
Platelets	150–450 × 10³/µL	321 (242.9–409.4)	319.9 (343–403)	322.2 (241–416)	0.451
WBC indices
Eosinophil	1–6%	1 (1–3)	2 (1–3)	1 (1–3)	<0.001
TLC	4–11 × 10³/µL	7.6 (6.2–9.2)	8.9 (7.9–9.9)	5.9 (4.8–6.9)	<0.001
Neutrophil	50–70%	74 (65–83)	75 (66–81)	74 (65–90)	0.019
Lymphocytes	20–45%	35.5 (31.7–39.6)	37.19 (33.72–41.16)	33.2 (28.3–37.0)	<0.001
Monocytes	2–10%	6 (4.8–7.4)	7 (5.5–8.4)	5.1 (4.1–6.0)	<0.001
RFTs
Creatinine	0.5–1.2 mg/dL	0.77 (0.65–0.88)	0.7 (0.6–0.8)	0.9 (0.75–1.0)	<0.001
BUN	6–24	15.6 (11.1–18.8)	12.5 (9.0–14.9)	13.2 (9.7–16.5)	<0.001
LFTs
Total Bilirubin	0.2–1.1 mg/dL	0.36 (0.3–0.96)	0.35 (0.3–0.62)	0.5 (0.3–1.2)	0.751
ALT	5–42 µ/L	15 (7–25)	9 (5–12)	30 (23–34)	<0.001
AST	5–45 µ/L	15 (7–25)	9 (5–12)	30 (23–34)	<0.001
ALP	90–306 µ/L	123 (96–236)	121.5 (96–256)	123 (98–210)	0.787
TP	6.4–8.3 g/dL	7.3 (6.9–7.7)	7.1 (6.7–7.5)	7.5 (7.1–7.8)	<0.001
ALB	3.4–5.4 g/dL	4.5 (4.1–4.9)	4.7 (4.4–5.1)	4.2 (3.8–4.6)	<0.001
Globulin	2.5–3.5 g/dL	2.9 (2.7–3.1)	2.9 (2.6–3.0)	3 (2.7–3.2)	<0.001

Representation of changes in hematological factor.

ALB, albumin; ALT, alanine transaminase; ALP, alkaline phosphatase; AST, aspartate transaminase; BUN, blood urea nitrogen; CRP, C-reactive protein; HB, hemoglobin; HCT, hematocrit; LDH, lactate dehydrogenase; MCHC, mean cell hemoglobin concentration; MCV, mean cell volume; RBC, red blood cell; TLC, total leukocyte count; TP, total protein; WBC, white blood cell; RFTs, renal function tests; LFTs, liver function tests.

Further analysis revealed that the median age of patients classified as serious cases was marginally higher than that of nonserious cases, although this dissimilarity did not reach statistical significance. Upon examining severity with gender and age, it became evident that the incidence of severe infection was notably higher among males and individuals aged between 21 and 40 years (Table 1).

Clinical characteristics of study participants

Elevated levels of key immunological biomarkers (LDH, ferritin, CRP, and D-dimer) were notably observed in patients with severe COVID-19 compared with milder cases (Table 1). Importantly, these immunological markers exhibited a significant upward trend with increasing disease severity.

Furthermore, analysis of RBC and WBC indices revealed lower levels not only of hemoglobin (Hb), RBC count, hematocrit, mean corpuscular volume (MCV), and mean corpuscular hemoglobin (MCH) in serious patients compared with nonserious patients but also of eosinophils, neutrophils, lymphocytes, and monocytes. These differences were statistically significant, highlighting their relevance in assessing disease severity.

While renal function parameters, specifically creatinine and blood urea nitrogen (BUN), remained within normal ranges for serious and nonserious patients, the serious group exhibited significantly higher levels of these parameters than the nonserious group.

Moreover, liver function tests, including total bilirubin, ALT, AST, ALP, total protein, and globulin, demonstrated elevated levels in serious patients with COVID-19 compared with their nonserious counterparts. Statistical analysis indicated significant differences between serious and nonserious patients’ AST, ALP, total protein, and globulin levels.

These findings underscore the importance of considering a range of clinical parameters in evaluating the severity of COVID-19 cases and guiding appropriate clinical management strategies.

Correlations of immunological, biochemical, and hematological indices with COVID-19 severity

Analysis of the correlations between immunological, biochemical, and hematological indices and the severity of COVID-19 revealed insightful patterns. Within the hematological profile, Hb and RBCs displayed a robust negative association with disease severity. Conversely, total WBCs and absolute lymphocyte count exhibited a weaker correlation. Notably, absolute neutrophil count demonstrated a significant positive correlation with disease severity (Table 2).

Table 2.

Spearman’s Correlation of Immunological and Hematological Parameters with the Disease Severity

Variables	ρ	p-Value
LDH	0.856	<0.001
Ferritin	0.859	<0.001
D-dimer	0.857	<0.001
CRP	0.812	<0.001
HB	−0.283	<0.001
RBC	−0.021	<0.001
HCT	−0.046	0.144
MCV	−0.003	0.932
MHC	−0.038	0.222
MCHC	0.032	0.302
Platelets	0.024	0.439
TLC	−0.804	<0.001
Neutrophils	0.085	0.007
Lymphocytes	−0.391	<0.001
Monocytes	−0.538	<0.001
Eosinophils	−0.157	<0.001
Creatinine	0.542	<0.001
BUN	0.799	<0.001
Total Bilirubin	0.067	0.033
ALT	0.824	<0.001
AST	0.824	<0.001
ALP	0.039	0.208
Total protein	0.357	<0.001
ALB	−0.509	<0.001
Globulin	0.254	<0.001
CRP	0.860	<0.001
Age	0.001	0.969

Similarly, immunological markers such as CRP, ferritin, D-dimer, and LDH exhibited a robust positive association with disease severity (Table 2).

Regarding biochemical markers reflecting liver functionality, ALT and AST showed significant positive correlations, while ALP and globulin also displayed positive correlations. Conversely, ALB exhibited a significant negative correlation with disease severity (Table 2).

Renal functional parameters, namely creatinine and BUN, demonstrated notable correlations with disease severity (Table 2).

Risk factors associated with disease severity

The parameters that showed significant correlations with the disease severity were included in the logistic regression analysis. Univariate and multivariate binary logistic regression analyses were applied to assess the predictors of COVID-19 severity. Univariate analysis revealed that LDH, CRP, D-dimer, Hb, creatinine, BUN, ALT, AST, total protein, ALB, and globulin were associated significantly with the severity of COVID-19. In multivariate analysis, CRP, D-dimer, BUN, ALT, and ALB appeared as independent predictors of disease severity (Table 3).

Table 3.

Univariate and Multivariate Regression Analysis to Predict the Disease Severity

Parameters	Univariate model		Multivariate model
Parameters	OR (95% CI)	p-Value	OR (95% CI)	p-Value
LDH	1.079 (1.062–1.096)	<0.001	1.005 (0.998–1.011)	0.156
CRP	2.670 (1.851–3.421)	<0.001	2.982 (1.352–3.689)	0.002
D-dimer	1.143 (0.923–1.121)	<0.001	2.998 (1.079–3.920)	0.001
HB	0.546 (0.469–0.635)	<0.001	0.657 (0.303–1.426)	0.288
RBCs	0.952 (823–1.100)	0.504
Platelets	1.000 (0.999–1.002)	0.451
Creatinine	0.956 (0.294–0.310)	<0.001	5.471 (0.213–7.971)	0.671
BUN	2.896 (2.473–3.392)	<0.001	3.219 (1.584–6.542)	0.001
ALT	1.676 (1.557–1.805)	<0.001	1.402 (1.168–1.684)	<0.001
AST	1.018 (1.006–1.030)	0.002	0.984 (0.896–1.082)	0.745
TP	5.428 (4.053–7.268)	<0.001	2.276 (0.244–21.245)	0.471
ALB	0.065 (0.045–0.093)	<0.001	0.007 (0.000–0.232)	0.006
Globulin	1.141 (0.649–1.478)	<0.001	3.197 (0.238–4.304)	0.166

Receiver operating characteristic analysis

The current investigation analyzed receiver operator characteristics (ROC) for critical markers, including CRP, D-dimer, BUN, ALT, and ALB. The curves generated for CRP, D-dimer, BUN, and ALT lie above the reference line, indicating a positive correlation with disease severity. Meanwhile, the ALB curve was below the reference line, demonstrating an inverse correlation with the severity of COVID-19 (Fig. 1). This graphical depiction emphasizes the utility of these markers in clinical assessments and decision-making processes by providing essential insights into their efficacy in predicting the severity of COVID-19 processes.

FIG. 1.

Receiver operative characteristics curves (ROC) of CRP, D-dimer, BUN, ALT, and ALB to predict disease severity in patients with COVID-19. ALB, albumin; ALT, alanine transaminase; BUN, blood urea nitrogen; CRP, C-reactive protein.

Youden’s index was utilized to determine the optimal cutoff values for CRP, D-dimer, BUN, ALT, and ALB (Table 4). The significant area under curve (AUC) values for CRP, D-dimer, BUN, and ALT underscore their substantial predictive power regarding the severity of the disease. This evidence suggests that these biomarkers are highly effective at distinguishing between varying severities of COVID-19 cases, rendering them indispensable for clinical evaluation and management.

Table 4.

Optimum Cutoff Values for CRP, D-dimer, BUN, ALT, and ALB

Variables	AUC	Optimum cutoff value	Sensitivity	Specificity
CRP	0.688	34.96	0.38	1
D-dimer	0.927	0.69	0.75	1
BUN	0.965	15.37	0.95	0.82
ALT	0.979	20.5	0.97	1
ALB	0.204	4.069	0.57	0.03

AUC, area under the curve.

Discussion

The last two decades have seen three types of coronaviruses: severe acute respiratory syndrome, identified in 2003; the Middle East respiratory syndrome coronavirus; and SARS-CoV-2. Though 80% of patients who are positive for SARS-CoV-2 are clinically asymptomatic or have a mild infection, 13.8% of patients progress to severe disease, and 6.1% of them develop acute, life-threatening disease that requires hospitalization with intensive care support (Chang, 2020). A few researchers have proposed potential hematological predictors of outcomes; these include lymphocyte count, neutrophil–lymphocyte ratio, CRP, LDH, cardiac troponin-I, and low-density lipoproteins (Khandait et al., 2020; Lim et al., 2021). Differences in hematological manifestations were detected between severe and nonsevere patients. The severity of COVID-19 is defined according to the clinical management of severe acute respiratory infection when COVID-19 disease is suspected by the World Health Organization (WHO). Hematological, biochemical, and immunological parameters of patients with COVID-19 classified as serious and nonserious were assessed in the current study in order to determine if anomalies in these parameters may be related to the severity of the illness. These deviations from normal hematological and immunological markers might be important prognostic indicators of the disease’s severity (Rahman et al., 2021). Timely intervention may be facilitated, and the risk of death may be decreased if anomalies in these crucial laboratory markers are identified early (Gajendra, 2022).

Male patients and those between the ages of 21 and 40 were found to be more susceptible to COVID-19 infection. This observation may be explained by the fact that men and people in this age range engage in more social and physical activities and are exposed to more of the outside world, which increases their risk of contracting infections (Imran et al., 2023; Mahat et al., 2017). The greater amount of immunosuppressive testosterone and the abundance of angiotensin-converting enzyme 2 (ACE 2) receptors may lead to a higher prevalence of COVID-19 infection in men (Ejaz et al., 2021). Laboratory hematological and biochemical markers may help to predict COVID-19 prognosis (Liu et al., 2020). Many studies have pinpointed various prognostic markers, including D-dimer, CRP, LDH, and high-sensitivity cardiac troponin, in the serum of patients with COVID-19 with poor outcomes (Cecconi et al., 2020). Deep analysis of abnormal levels of such factors and the interface between their functions in body organs and mechanisms of viral infection can provide the basis for first-line diagnosis as an efficient screening tool.

Elevated levels of pro-inflammatory markers, including CRP, ferritin, D-dimer, and lactate LDH, were consistently observed in serious patients with COVID-19 in our study, corroborating previous research (Ali et al., 2022; Hadi et al., 2022; Tufa et al., 2022). These markers reflect an exaggerated immune response and may indicate disease progression and organ damage. Furthermore, considering laboratory parameters could be a cost-efficient and rapid diagnostic strategy in the later stages of an outbreak (Nahari et al., 2022). Among laboratory diagnostic tests, real-time reverse transcription-polymerase chain reaction (rRT-PCR) is currently considered the most popular test for diagnosing patients with COVID-19. Nevertheless, due to its unsatisfactory test sensitivity, rRT-PCR is prone to false negative results in low viral load samples, primarily in patients representing mild disease manifestations (Meng et al., 2021; Teymoori-Rad et al., 2020). D-dimer elevation and prolonged prothrombin time were observed in severe COVID-19 cases (Wang et al., 2020).

In the present study increased levels of ferritin, D-dimer, LDH, and CRP were observed in serious patients with COVID-19 as compared to the nonserious parents. This aligns with the previous studies affirming a significant positive correlation of these inflammatory markers with serious patients (Devang et al., 2022; Sidhwani et al., 2023). Increased values of D-dimer reflect an increased breakdown of fibrin which may increase the risk of coagulation abnormalities associated with COVID-19 (Maldonado-Cabrera et al., 2021), whereas increased CRP levels indicate hyperactivity of the immune system leading to organ damage (Ali, 2020).

Furthermore, variations in the hematological parameters, particularly the WBC profile, may also serve as an early indicator for the progression of the disease severity (Taj et al., 2021). Based on the previously reported clinical diagnostic data neutrophilia and lymphopenia, as observed in the current study also, are among the commonly observed hematological abnormalities during severe viral infections including COVID-19 (Elkhalifa et al., 2022). Neutrophilia predicts the severity of the inflammatory response, whereas lymphopenia indicates the magnitude of immune cell damage (Latifi-Pupovci et al., 2022). The significant increase in neutrophils and decrease in the lymphocytes reported in the present study in serious patients may be justified, keeping in view the findings of the previous studies, which highlight the importance of these immune cells as the body’s natural defense against infection and their contribution in eradicating the pathogen-infected cells (Sana and Avneesh, 2022; Waris et al., 2021). Decreased levels of Hb, RBCs, HCT, MCV, and MCH were observed in the serious group as compared to the patients in the non-serious group. The coronavirus directly damages the red blood cells due to the availability of angiotensin and ACE 2 interacting proteins on the RBCs (Gajendra, 2022), which might be one of the reasons for reduced levels of hemoglobin in serious patients with COVID-19.

In the present study, the total bilirubin levels, ALT, AST, ALP, creatinine, and BUN were in the normal range. Still, in comparison with the nonserious patients, the serious patients demonstrated higher levels of these liver and renal parameters. The increase in the level of these biomarkers was comparable with the previous studies, which also reported an increase in these biomarkers and highlighted the potential of these parameters as predictive determinants of disease severity (Fan et al., 2020; Tummala et al., 2022).

Keeping in view the previous studies, univariate and multivariate binary logistic regression analyses were applied to determine the possible predictive factors associated with the progression of disease toward severity (Yu et al., 2020). Based on the results of the multivariate regression analysis Leulseged et al. reported WBC count as a potential determinant of severity of illness (Leulseged et al., 2021). In the current study, the results of multivariate regression analysis revealed CRP, D-dimer, BUN, ALT, and ALB as independent predictors of disease severity. These results are somewhat similar to the study conducted by Bennouar et al., which highlighted CRP, BUN, LDH, serum ALB, and sodium levels as predictive determinants of the advancement of disease (Bennouar et al., 2020).

ROC is an important statistical tool commonly used to estimate the cut-off values of various hematological, biochemical, and immunological parameters and to measure the accuracy of these diagnostic biomarkers (Geraili et al., 2022; Gong et al., 2020). Moreover, Zakariya Al Aamir et al. reported LDH, CRP, D-dimer, ferritin, lymphocyte count, and serum ALB as predictors for disease severity (Al Aamri et al., 2022), whereas the results of our study revealed CRP, D-dimer, BUN, ALT, and ALB as potential biomarkers to predict the progression of disease severity. The cutoff value of D-dimer reported in the previous study was 0.69 (Al Aamri et al., 2022), which is similar to the value estimated in the current study. In contrast to a previous study where the CRP cutoff value was estimated at 41.4, the current study identified a CRP cutoff value of 34.96 (Luo et al., 2020). In the current study, the values of AUC for CRP, D-dimer, BUN, ALT, and ALB levels were 0.688, 0.927, 0.965, 0.979, and 0.204, respectively. In contrast, according to a previously reported study, the AUC for CRP and D-dimer was 0.737 and 0.758, respectively. The variations in AUC and cutoff values may reflect differences in patient populations and COVID-19 variants (Yousaf et al., 2022).

Conclusion

In conclusion, there is a higher risk in men and those between the ages of 21 and 40. Additionally, there are changes in blood parameters and heightened pro-inflammatory markers. These results emphasize the complexity of COVID-19’s effects and the significance of considering a wide variety of parameters when assessing the disease’s severity. Moreover, our investigation revealed elevated liver and kidney markers in more severe instances, corroborating the predictive potential of many biomarkers. These insights are helpful in clinical settings because they give healthcare professionals the essential tools to enhance patient care and successfully control risk. These studies will improve our capacity to classify risks more accurately and customize treatment plans, which will benefit patients dealing with this global health emergency. By expanding on the knowledge acquired from research such as ours, we may further enhance our comprehension of COVID-19 and enhance patient outcomes globally.

Footnotes

Acknowledgments

The authors are grateful to all the technicians at the microbiology and pathology laboratory of the department for their technical assistance during the project. The authors are also thankful to all the patients with COVID-19 for their participation in the study.

Authors’ Contributions

Z.K. conceived and designed the work, provided logistic support, and critically reviewed the article for intellectual content and statistical analysis. M.B.A. was involved in statistical analysis and writing the original draft. H.I. acquired data, conducted the lab work, and revised, and approved the final article. S.G. and F.S. analyzed and interpreted data and revised and approved the final article.

Ethics Approval and Consent to Participate

The study protocol was approved by the Institutional Review Board under approval no. IRB-UG-23446. Moreover, written consent was obtained from all the participants.

Human and Animal Rights

All research studies on humans (individuals, samples, or data) have been performed in accordance with the principles stated in the Declaration of Helsinki.

Author Disclosure Statement

No conflict of interest has been declared by the authors.

Funding Information

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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