Hematological Markers and Coagulation Profiles in COVID-19 Patients: A Retrospective Cohort in Jeddah, Saudi Arabia

INTRODUCTION

China experienced an epidemic of pneumonia in December 2019 that was brought on by the SARS-CoV-2 virus [1]. The World Health Organization (WHO) officially designated the illness as coronavirus disease 2019 (COVID-19) in February [2]. In a short amount of time, the overall number of cases increased dramatically, reaching thousands. The latest coronavirus epidemic was deemed a worldwide pandemic by the WHO on March 11, 2020 [2]. The number of cases in Saudi Arabia reached 101,914 on June 7, 2020, an average of 1,039 new cases every day, after the first case was discovered on March 2, 2020 (trend test, p < 0.000) [3]. The COVID-19 daily infection rate peaked on June 17, 2020, the day when the nation had the highest infection rate of 4,919, at 14 infections per 100,000 people. By November 22, 2021, when the overall number of infected cases reached 549,518 (1,556 per 100,000 individuals), this proportion had drastically decreased and was practically nonexistent. However, nearly all of the recovered cases, 538,640 (1,526 per 100,000 persons), were still present [4].

SARS-CoV-2 infection results in multi-organ affection through many mechanisms. First, there is direct cellular invasion and toxicity [5]. Second, endothelial cell injury by direct infection results in release of thrombotic factors, inhibition of fibrinolysis, activation of complement factors and coagulation pathways. The net result is imbalance between pro and anti-coagulation and microthrombi deposition and microvascular dysfunction. Also, hypoxia induced hyper-viscosity and upregulation of hypoxia induced factor add to hypercoagulable state [6]. The third is resulting from the cytokine and chemokine release. The inflammatory cytokines, notably interleukin (IL)-1β, IL-6, IL-12, IL-18, IL-33, and TNFα, which are released in an uncontrollably high amount during SARS-CoV-2-induced pneumonia, are described as a "cytokine storm" [7]. Elevations of C-reactive protein, erythrocyte sedimentation rate, ferritin, D-dimer, fibrinogen, and lactate dehydrogenase have all been linked to severe COVID-19 disease [7].

People of all ages are vulnerable to contracting SARS-CoV-2. Clinical manifestations might range from moderate non-specific symptoms, such as fever, dry cough, and exhaustion, to invasive mechanical ventilation needed for acute respiratory distress syndrome (ARDS) [1, 8]. Acute heart damage, thrombotic episodes, diarrhea, and neurological disorders are examples of non-respiratory affection that have been documented [1, 9]. Mild to moderate symptoms account for 80% of cases [10]. Patients who come with severe and critical conditions are often elderly and have concomitant conditions such as diabetes, hypertension, cardiovascular disease (CVD), chronic lung illnesses, and chronic kidney disease (CKD) [1, 10]. Male gender, associated diabetes, hypertension, COPD increase risk of death two times [11]. Also, obesity, low socio-economic state, immune dysfunction are predictors of severe lethal disease [8].

Reverse transcription polymerase chain reaction (RT-PCR) is the gold standard method for detecting viral RNA to diagnose SARS-CoV2 infection; however, a large degree of variation in RNA viruses, improper sampling, and insufficient viral material in the specimen can result in reduced detection and false negative results [12]. As a result, SARS-Cov2 may be detected by RT-PCR, which has a high specificity but a sensitivity of 59%–71%) [13].

A number of blood indicators, including ferritin, D-dimer, IL-6, C-reactive protein (CRP), and lactate dehydrogenase (LDH), have increased after COVID-19 infection. Changes in laboratory values and hematologic abnormalities can be utilized for early diagnosis, and they were connected with the prognosis and severity of the disease, even if none were specifically for COVID-19 diagnosis [14, 15] and can help in risk stratification and early intervention [16]. 

LDH present in liver, muscles, lung, heart, and brain tissue. It is a non-specific marker of tissue damage and cellular death in COVID-19 infected patients [17]. Erythrocyte sedimentation rate (ESR) is also a non-specific inflammatory biomarker that correlate with the change in the size, shape of RBCs and concentration of plasma. CRP levels increased due to overproduction of inflammatory cytokines and by tissue destruction. CRP levels correlated with severity of infection and high immune response [18]. Increased liver enzyme levels in COVID-19 patients could result from direct hepatocellular injury, drug induced hepatotoxicity, or hepatic ischemia [19]. Hypoalbuminemia and high bilirubin was reported in 55% and 18% of hospitalized patients with COVID-19 infection [20, 21]. Acute kidney injury (AKI) was noted as one of the important complications of COVID-19 as a result from direct infection, indirect injury secondary to immune response or drug induced tubular dysfunction [22].

Complete blood count (CBC) is an easy, inexpensive routinely performed tool which can help in early diagnosis of the infection. While lymphopenia is the most common laboratory finding in COVID-19, it is also common to see other hematological abnormalities such as anemia, leucocytosis, neutrophilia, low eosinophil count or eosinophilia, thrombocytopenia, and infrequently thrombocytosis. Lymphopenia is found in 25–80% of patients at the time of presentation [23]. Anemia can result direct viral injury resulting in hemolysis, dysregulated iron metabolism, blood loss during different critical care interventions and excessive use of anticoagulation [24]. Lymphopeia can be caused by direct SARS-CoV-2 infection of lymphocytes because of the expression of ACE2 receptors on their surface. This can lead to cell lysis, lymphocyte apoptosis as a result of the cytokine storm, or decreased proliferation because of concurrent lactic acidosis [25]. Several studies have employed high neutrophil-to-lymphocyte ratios (NLR), neutrophil to CD4+ lymphocyte ratios (NCD4LR), and neutrophil count to albumin ratios (NAR) to predict COVID-19 infection [24]. Zhang et al. showed that almost half of the patients hospitalized with COVID-19 had eosinopenia (absolute counts <0.02 × 109 cells/L) [26]. Between 5% and 50% of individuals in various groups experienced thrombocytopenia. Theories include decreased bone marrow output, decreased megakaryocyte fragmentation and platelet generation as a result of COVID-19-induced lung and pulmonary capillary bed damage, or platelet consumption as a result of endothelial cell damage [14]. Coagulopathy associated with COVID-19 infection carries high risk of morbidity and mortality [27]. Disseminated intravascular coagulation (DIC) can be caused by fibrin deposition, systemic coagulation cascade activation, microvascular thrombi in different organs, and the consumption of clotting components and platelets, which can end in potentially fatal bleeding. Thirty-five percent of patients with COVID-19 infection had DIC, and ninety-five percent had thrombotic and bleeding problems, respectively. Prothrombin time (PT) activated partial thromboplastin time (APTT), D-dimer, and fibrin degradation products (FDP) are all extended in COVID-19-infected patients [26, 27]. The purpose of this study was to look at how the SARS-CoV2 infection affected several haematological markers and how that relationship related to the severity and pace of progression in hospitalized patients in the Saudi Arabian city of Jeddah.

MATERIALS AND METHODS

Study Design and Participants

This study was a retrospective cohort that comprised all patients hospitalized between July 2020 and July 2022 at the Armed Forces Hospital in Jeddah, Saudi Arabia (all ages and both genders participating). Based on a positive PCR test result, the COVID-19 infection diagnosis.

Relevant variables were collected form medical records including:

1. Demographic and Clinical data: age, sex, nationality, weight, height, body mass index, symptoms and signs of the disease and duration of time from admission to discharge either living or dead.
2. Medical history of hypertension or diabetes.
3. Laboratory parameters: blood type; complete blood count (CBC), which counts red blood cells and their indices as mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), red cell distribution width (RDW), count of red blood cells (RBC), count of reticulocytes; count of white blood cells with differential count; count of platelets; and count of other platelets as mean platelet volume (MPV).
4. Hemostatic parameters, which are prothrombin time (PT), activated partial thromboplastin time (APTT), fibrinogen, and D -dimers.
5. Biochemical and inflammatory markers which are serum creatinine, LDH, liver enzymes, C-reactive protein and ferritin.

Outcomes

1. Compare the difference in clinical characteristics and the mean of hematological and biochemical parameters among patients infected with COVID-19 and COVID-19 negative patients including:

• Blood cells count and morphology.
• Coagulation profile
• Iron stores

2. Correlate the hematological parameters with markers of disease severity in patients infected with COVID-19.

Statistical Analysis

Version 20.0 of the IBM SPSS software program was used to feed data into the computer and analyze it (Armonk, NY: IBM Corp.). When describing quantitative data that was not regularly distributed, the terms median and range were used, whereas mean ± SD was used for properly distributed data. The Kolmogorov-Smirnov test was employed to confirm that the distribution was normal. Descriptive statistics were used to analyze baseline attributes. Depending on how the data were distributed, the Student t-test, Mann-Whitney U test, or Kruskal-Wallis test were used to compare the groups' quantitative data. The relative risk ratio and the chi-square test were applied to the qualitative data. Correlation with Pearson was used to test for any association between variables.   

RESULTS AND DISCUSSION

Data of all patients admitted to Armed Forces Hospital in Jedda city, Saudi Arabia during the period from July 2020 to July 2022 (n=586) is represented in Table 1. Mean age was 48.12 ± 23 years. Males constituted 57.3% of the cohort. 513 patients (96.8.5%) were Saudi citizens and the remaining 19 (3.2%) were from other nationalities. Mean duration of admission was 10.5± 25.8 days. Mean Hb was 11.98 ± 2.41 gm/dl. Of all patients, 387 patients (66.1%) tested PCR- positive for SARS-CoV2 infection and 199 patients tested negative (33.9%).

Table 2 shows the variations in clinical features between COVD-19 individuals and non-COVID patients. Males made up 113 (56.8%) of the patients who were not infected with COVID-19, compared to 223 (56.86%) who were. 96.6% of non-COVID-19 patients and 97.0% of COVID-19 patients were Saudi nationals. When comparing COVID-19 patients (10.1%) to non-COVID-19 patients (31.7%), hypertension was more common. Compared to 35.4% of non-COVID-19 patients, 33.3% of COVID-19 patients had diabetic millets. The non-COVID-19 group had a greater mortality rate (14.1 vs. 7.5%, respectively) than the COVID-19 group.

Laboratory parameters were examined between COVID-19 patients and non-COVID-19 patients. The mean RBC count was substantially greater in COVID-19-positive patients compared to the negative group, although the mean indices of MCV, MCH, and RDW were significantly lower. The mean Hb level, on the other hand, did not alter much. The overall leucocytic count was much lower in the COVID-19 group. The average indices showed a substantial decrease in eosinophil and basophil counts and an increase in monocyte counts; however, there was no significant difference in the mean counts of neutrophils and lymphocytes. the mean platelet count showed a non-significant difference. Regarding coagulation profile, COVID-19 patients had significantly higher fibrinogen levels and lower prothrombin time than non-COVID-19 patients. Tests for liver enzymes and renal function did not significantly differ between the two groups. Patients with COVID-19 have much reduced levels of the enzyme lactate dehydrogenase (LDH). The levels of acute-phase reactants, such as ferritin and C-reactive protein (CRP), did not change significantly. The length of hospital stay was not significantly different for either group (Table 3).

D-dimer level had a positive correlation with red cell distribution width (RDW) and prothrombin time (PT) and was negatively correlated with red blood cell count (RBC). C-reactive protein (CRP) level showed a positive correlation with RDW, WBC and PT and was negatively correlated with RBC (Table 4).

*. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).