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ORIGINAL ARTICLE
Year : 2021 | Volume
: 13
| Issue : 3 | Page : 136-141
Can the immature granulocyte count have a role in the diagnosis of coronavirus 2019 disease?
Fatih Selvi, Cihan Bedel, Mustafa Korkut
Department of Emergency Medicine, Health Science University Antalya Training and Research Hospital, Antalya, Turkey
Correspondence Address:
Dr. Cihan Bedel
Department of Emergency Medicine, Health Science University Antalya Training and Research Hospital, Kazım Karabekir Street, 07100, Muratpaşa, Antalya
Turkey
Source of Support: None, Conflict of Interest: None
CheckDOI: 10.4103/ijmbs.ijmbs_43_21
Background: The pathophysiology of COVID-19 disease is not clearly understood; inflammation has been shown to play a major role. The immature granulocytes count (IGC) can be an indicator of inflammation. To the best of our knowledge, there is no data on the usability of IGC for the diagnosis of COVID-19. Objectives: We aim to investigate the usability of the inflammatory marker IGC in the diagnosis of COVID-19. Patients and Methods: COVID-19 patients admitted to a tertiary university hospital were included in this study, and hemogram parameters, white blood cells, hemoglobin, neutrophils, lymphocytes, and IGC were investigated. According to the real-time reverse transcriptional polymerase chain reaction, patients were categorized into two groups as COVID-19 positive and COVID-19 negative. Results: The mean value of IGC was 0.02 (0.02) for the COVID-19-positive group and 0.11 (0.04) for the COVID-19-negative group. Patients with COVID-19 positive were found to have an IGC value that is significantly lower than the other group (P < 0.001). For IGC, it was calculated at a cut-off value of 0.03 (area under the curve: 0.718; sensitivity: 66.7%; specificity: 72.3%; P < 0.001). Conclusions: The results of our study have shown that on-admission IGC level is a novel, cost-effective, and readily available biomarker with a promising predictive marker for COVID-19 patients.
Keywords: COVID-19, diagnosis, emergency service, immature granulocytes, inflammation
Coronavirus disease 2019 (COVID-2019), which emerged in Wuhan, China, in 2019, has caused acute respiratory syndrome (SARS-CoV-2) and resulted in a pandemic that has caused significant mortality and morbidity all over the world.[1] According to the World Health Organization (WHO), more than 155 million cases were detected, and more than 3 million died as of May.[2] While weakness, fever, myalgia, and cough are common symptoms of COVID-19 disease, atypical symptoms such as diarrhea, headache, vomiting, and hemoptysis can also be seen.[3]
Even though the respiratory symptoms of COVID-19 are mild, significant hypoxia can develop rapidly to respiratory failure and cause mortality.[4] Although the pathophysiology of COVID-19 disease is not clearly understood, inflammation has been shown to play a significant role.[5] There is good evidence that inflammation plays an essential role in COVID-19 disease and that pro-inflammatory cytokines increase. The disease progresses to viral pneumonia in severe cases, and hyper inflammation may result in host response, leading to acute respiratory syndrome and multiorgan failure.[6],[7],[8]
Although it has been shown in daily practice that the increase in C-reactive protein (CRP), D-dimer, albumin, ferritin, and LDH levels with COVID-19 can predict the disease and its severity is no specific biomarker that is accepted in the literature.[9] Studies in the literature have demonstrated that the immature granulocytes count (IGC) can be an indicator of inflammation and its usability in the diagnosis and prognosis of many diseases.[10],[11] To the best of our knowledge, there are no data regarding the usability of IGC in the diagnosis of COVID-19.
Patients and MethodsStudy design and settings
COVID-19 patients admitted to a tertiary university hospital between April 01, 2020 and June 01, 2020, were included in this retrospective cohort study. COVID-19 was diagnosed based on WHO guidelines.[12] Eight hundred and twenty-eight patients with clinical data were observed, and the patient flow chart is shown in [Figure 1].
Study participants
Patients without laboratory data or chest tomography and those whose chest tomography was compatible with viral pneumonia but negative for quantitative reverse transcriptional polymerase chain reaction (RT-PCR) were excluded from the study. Two hundred and fifty patients were included in the final analysis.
Methodology
Patient demographic data, laboratory results, and chest tomographies were analyzed and recorded using the hospital electronic file system. Peripheral venous blood samples obtained on admission were analyzed using the Sysmex XN-1000 modular system device (Sysmex Corp., Kobe, Japan). Of the hemogram parameters, white blood cells (WBCs), hemoglobin, neutrophils, lymphocytes, and IGC were investigated. D-dimer and fibrinogen were recorded from the biochemical parameters and blood coagulation factors.
To identify SARS-CoV-2, real-time RT-PCR was performed on samples taken from the upper respiratory tract (nasopharyngeal and oropharyngeal secretions). Following the recommendations of the Turkish Ministry of Health's diagnostic treatment guide, Coronex COVID-19 QPCR (DS BIO and NANO Tech. Ltd., Ankara, Turkey) kit was used as the RT-PCR standard method. According to the results of RT-PCR, patients were categorized into two groups as COVID-19 positive and COVID-19 negative.
Statistical analysis
Standard deviation and mean values were calculated for continuous variables; median and interquartile range were calculated for nonparametric data. Each of the independent variables was compared by applying the Chi-square test, and if suitable, independent t-test.
Descriptive statistical analysis of all variables was evaluated using SPSS 21.0. Logistic regression analysis was performed to examine the factors associated with the diagnosis of COVID-19 and the IGC value. The optimum cut-off value of IGC that shows the diagnostic relationship in COVID-19 patients was examined by receiver operating characteristic (ROC) analysis.
ResultsDemographics of the patients
Two hundred and fifty patients who met the inclusion criteria were analyzed. Patients were categorized into two groups as COVID-19 positive and COVID-19 negative. COVID-19 positive was detected in 40 (16%) of these patients. 24 (60%) of the male patients were in the COVID-19-positive group, and 125 (59%) were in the COVID-19-negative group. There was no significant difference between the groups in terms of gender (P = 0.779). The mean age of the patients was significantly higher in the COVID-19-positive group than in the COVID-19-negative group (57.0 ± 18.3 vs. 48.9 ± 15.2 years; P = 0.003). The most common comorbid diseases were hypertension, diabetes mellitus, coronary heart disease, hyperlipidemia, and cerebrovascular disease; however, there was no significant difference between the two groups.
Hematological characteristics
The mean WBC, neutrophil, and lymphocyte counts were found to be lower in the COVID-19-positive group compared to the COVID-19-negative group (P < 0.001, P < 0.001, P = 0.019, respectively). The mean value of IGC was 0.02 (0.02) for the COVID-19-positive group and 0.11 (0.04) for the COVID-19-negative group. Patients with COVID-19 positive were found to have an IGC value that is significantly lower than the other group (P < 0.001). Blood glucose was found to be lower in the COVID-19-positive group compared to the COVID-19-negative group (102 [29%] vs. 116 [55%], P = 0.005). There was no significant difference between groups for other parameters [Table 1].
Table 1: Clinical and laboratory characteristics of patients with and without coronavirus disease 2019Predictive analysis
In multivariate logistic regression analysis, age (odds ratio [OR]: 0.250, 95% confidence interval [CI]: 0.090–0.280, P = 0.008), WBC (OR: 0.038, 95% CI: 0.05–0.305, P = 0.002), and IGC (OR: 0.470, 95% CI: 0.218–1.010, P < 0.001) were defined as independent predictors that could predict diagnostic associations in COVID-19 patients [Table 2]. The efficiency of IGC, WBC, neutrophil, and glucose values in predicting the diagnosis of COVID-19 was calculated by drawing ROC curves. For IGC, it was calculated at a cut-off value of 0.03 (area under the curve [AUC]: 0.718; sensitivity: 66.7%; specificity: 72.3%; P < 0.001) [Figure 2] and [Table 3].
Table 2: Logistic regression analysis of the independent predictors of in coronavirus disease 2019-positive and coronavirus disease 2019-negative patients
Table 3: The receiver operating characteristic curves for prediction coronavirus disease 2019
Figure 2: Receiver operating characteristic curve analysis of the parameters in the diagnosis of COVID-19 Discussion COVID-19 is spreading rapidly around the world, and it causes serious mortality and morbidity. Although studies have been conducted on changes in laboratory parameters, a specific routine laboratory biomarker for COVID-19 is not yet known.[13] The relationships among COVID-19 and parameters such as CRP, D-dimer, albumin, and ferritin disease severity were examined in daily practice.[9] To the best of our knowledge, no studies in the literature show the usability of IGC in the diagnosis of COVID-19. Our study has demonstrated the usability of IGC in the diagnosis of COVID-19 patients with a 66.7% sensitivity and 72.3% specificity.
Patients infected with SARS-CoV-2 have a wide clinical spectrum that ranges from mild symptoms to severe respiratory failure.[4] Epithelial cells, dendritic cells, and alveolar macrophages found in the airway are the main components of innate immunity and play a role in the host's immune response until adaptive immunity is established.[4],[14] When excessive viral load is reached following COVID-19 infection, these two immune mechanisms begin to react simultaneously as a host response to organ damage. Thus, the activated immune system creates a cytokine release storm and a dysfunctional immune response against the virus antigen.[8],[15],[16],[17] Studies in severe COVID-19 patients have shown that lymphopenia occurs and plasma concentrations of proinflammatory cytokines increase due to a decrease in peripheral T cell leukocytes.[18],[19]
Recent studies have also shown that immature granulocytes can be an indicator of inflammation. It has been used effectively with its easy and fast obtainability from the hemogram test.[20] Recently, the relationship of IGC with many diseases has been investigated. In one of these studies, the efficiency of IGC in the diagnosis of acute appendicitis and in showing complications was reported. This study showed that IGC could detect acute appendicitis at a cut-off value of 0.06, and it could also detect acute appendicitis complications at a cut-off value of 0.1.[21] Another study reported the effectiveness of IGC in demonstrating the severity of the disease in patients with acute pancreatitis.[11] In a study conducted by Jayasekara et al., it was emphasized that IGC could show sepsis in neonatal patients with high sensitivity and specificity at the cut-off value of 0.03. Wu et al. showed in their study on patients with spinal cord injury that increased IGC values within 24 h after surgery can predict secondary infection in patients with high sensitivity and specificity.[22] Another study showed that an IGC value of 1.05 predicts sepsis and mortality due to peritonitis after operations.[23] In the present study, we investigated the diagnostic usability of IGC in COVID-19 patients and found significantly lower IGC values in patients with COVID-19 positive compared to those without COVID-19. In our study, the mean of IGC was calculated as 0.02 in the COVID-19-positive group. A statistically significant difference was found when compared with the COVID-19-negative group. Pozdnyakova et al. showed in an intensive care unit setting that while IGC values were significantly higher in patients with COVID-19 positive compared to those without COVID-19 (2.46 vs. 0.64, P = 0.012) when the mortality of patients admitted to intensive care was compared, it was reported that IGC was not a significant parameter (0.20 vs. 0.26, P = 0.447).[24] In another study investigating the relationships between prognostic risk factors in COVID-19 patients, the relationship between IGC and hypoxemia was demonstrated; an increase in the value of IGC for the need for mechanical ventilation was found (OR = 16.41, P = 0.00623).[25] In our study, the cut-off value of 0.03 for IGC was found to be significant, with low sensitivity and specificity, in the predictivity of COVID-19 (AUC: 0.718; sensitivity: 66.7%; specificity: 72.3%; P < 0.001).
Our study has some limitations. First, it is designed as a single-center, retrospective study. Another limitation of ours is that the prognostic process of the patients is not followed. Our most important limitation is that serial IGC values could not be measured as the parameters analyzed were only from samples taken at the time of admission of our patients. We think that a better analysis of IGC values with more beneficial results can be obtained by conducting multicenter prospective studies, including large populations.
ConclusionsThe results of our study have shown that on-admission IGC level is a novel, cost-effective, a promising predictive marker for COVID-19 patients. It is also a promising marker that can show the clinically poor prognosis of the diseases.
Authors contribution
All authors contributed to the ents, conception of the study, data collection, data analysis, and drafting and final revision and approval of the manuscript.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
Compliance with ethical principles
This study was approved by Health Science University Antalya Training and Research Hospital Ethics Committee.
References
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