|Year : 2022 | Volume
| Issue : 2 | Page : 115-118
Neutrophil and Platelet Count Upon Hospital Admission as Predictors of Severe COVID-19 Infection: An Observational Study
I Made Bagiada, I Gde Raka Widiana
Department of Internal Medicine, Faculty of Medicine, Udayana University, Bali, Indonesia
|Date of Submission||07-Feb-2022|
|Date of Decision||26-Mar-2022|
|Date of Acceptance||05-Apr-2022|
|Date of Web Publication||09-May-2022|
I Gde Raka Widiana
Department of Internal Medicine, Faculty of Medicine, Udayana University, Jl. PB Sudirman Denpasar 80232, Bali
Source of Support: None, Conflict of Interest: None
Background: The severity of COVID-19 infection may depend on severe inflammation and hypercoagulability mechanisms. These processes may be rapidly identified in peripheral blood tests. This study aims to determine whether components of complete blood counts are able to predict the severity of COVID-19 infection. Materials and Methods: This is a prospective, observational analytical study carried out in the Indonesian population. We included all patients admitted to our hospital for COVID-19 during a 3-month period. We obtained blood samples for complete blood count examinations upon hospital admission. Confirmation of COVID-19 infection was based on a polymerase chain reaction test. Severe COVID-19 infection was determined if the patients had a Severe Community-Acquired Pneumonia (SCAP) score of >10. We collected blood samples upon hospital admission for leukocyte, neutrophil, lymphocyte, platelet, and monocyte counts. Results: We included 131 patients consisting of 77 (58.8%) males and 54 (41.2%) females. There were significant associations between neutrophil count and SCAP score (r = 0.28; P = 0.001) and platelet count (r = 0.023; P = 0.007). Upon regression analysis, we found that every 1,000 declines in platelet count was associated with increased risk (0.8%) of severe COVID-19, whereas every 1,000 declines in the neutrophil count was associated with decreased risk (18%) of severe COVID-19. Conclusion: There is a significant, weak positive correlation between neutrophil and platelet counts and the severity of COVID-19 infection as expressed by the SCAP score.
Keywords: COVID-19, human, Indonesia, inflammation, polymerase chain reaction
|How to cite this article:|
Bagiada I M, Widiana I G. Neutrophil and Platelet Count Upon Hospital Admission as Predictors of Severe COVID-19 Infection: An Observational Study. Bali J Anaesthesiol 2022;6:115-8
|How to cite this URL:|
Bagiada I M, Widiana I G. Neutrophil and Platelet Count Upon Hospital Admission as Predictors of Severe COVID-19 Infection: An Observational Study. Bali J Anaesthesiol [serial online] 2022 [cited 2022 Jun 27];6:115-8. Available from: https://www.bjoaonline.com/text.asp?2022/6/2/115/344886
| Introduction|| |
Coronavirus disease-19 (COVID-19) is the disease caused by a new coronavirus, SARS-CoV-2, which emerged in late 2019. In March 2020, the World Health Organization (WHO) declared the worldwide spread of the infectious disease COVID-19 a pandemic. COVID-19’s worldwide death toll reached three million in April 2021. The causes of death in COVID-19 are secondary to multi-organ failure, septic shock, and thromboembolic. Mortality is significantly related to older age, comorbidities, and high D-dimer.
COVID-19 symptoms are caused by inflammatory response of innate immune cells. About 10–20% asymptomatic and mild cases of COVID-19 develop interstitial pneumonia and acute respiratory distress syndrome (ARDS), especially in elderly and those with comorbidities. This subgroup of patients is well-known for high serum ferritin and D-dimer levels, as well as liver dysfunction and thrombotic tendencies that are implicated in the development of macrophage activated syndrome, also known as secondary hemophagocytic lymphohistiocytosis. In susceptible individuals, COVID-19 causes severe inflammatory response known as cytokine storm.
The question arises whether simple parameters that reflect immune and coagulation responses can be used to predict the severity of COVID-19 infection. Fast information is needed using laboratory parameters, especially routine blood laboratory parameters in predicting the occurrence of severe infections in patients with COVID-19. This study aims to find laboratory parameters, especially routine blood components to predict severe COVID-19.
| Materials and Methods|| |
This study is an analytical observational study using initial laboratory parameters upon hospital admission. The study was carried out from August to October 2021. The study protocol was approved by the Institutional Review Board (registry number 18l7/UN14.2.2.VII.14/LT/2021 dated on July 8, 2021). All participating subjects and/or their legal guardian provided written informed consent to be included in this study.
All COVID-19 patients who were confirmed by polymerase chain reaction aged 18–80 years who were admitted to state-owned Sanglah General Hospital during the mentioned period were initially included in this study. Pregnancy, hematological disorders, history of blood coagulation disorder, diabetes, asthma and COPD, liver diseases, and those who refused to participate in the study were then subsequentially excluded.
We collected blood sample upon hospital admission for leukocyte, neutrophil, lymphocyte, platelet, monocyte counts. We followed the patients for 14 consecutive days, and we classified them as severe COVID-19 if at any time they developed signs of severe COVID-19. COVID-19 severity was determined using Severe Community-Acquired Pneumonia (SCAP) score, where score >10 was categorized as severe COVID-19 patient.
The characteristics of the subjects and the overall research variables were presented descriptively. Variables that scale numerical data, if normally distributed, are displayed using the mean and standard deviation, and median was used otherwise. We used logistic regression multivariate analysis to determine the relationship between risk factors and outcome. Thereafter, independent variables from blood parameters will be determined with their respective odds ratio (OR) values. The resulting logistic regression equation will be used to determine the probability equation (risk) that can be used to generate a risk score. A P-value of less than 0.05 was considered significant. All statistical analysis was carried out using SPSS 25.0 (IBM Corp., Released 2017, IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY, USA).
| Results|| |
During the study, 131 patients were admitted to the study consisting of 77 (58.8%) men and 54 (41.2%) women [Table 1]. A total of 65 (49.6%) of the subjects developed severe infections (SCAP score ≥10) with a median SCAP score of 10. The distribution of SCAP score components and distribution of SCAP score categories can be seen in [Table 2].
We found a significant relationship between neutrophil and platelet counts with severe COVID-19 infection as expressed by the SCAP score [Table 3]. We conducted the backward stepwise multiple logistic regression analysis to determine which blood test parameters were significantly related to severe COVID-19, as shown in [Table 4]. We found that every 1,000 decline in platelet count was associated with increased risk (0.8%) of severe COVID-19, whereas every 1,000 decline in neutrophil count was associated with decreased risk (18%) of severe COVID-19.
|Table 3: Pearson’s correlation coefficient between blood-count parameters and SCAP score|
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|Table 4: Multiple logistic regression analysis for variables that are suspected to be severe COVID-19 predictors|
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| Discussion|| |
Severe viral inflammation caused by cytokine storm primarily activates lymphocytes and suppresses neutrophils. Cytokine storm is initiated by activation of antigen-presenting cells, macrophages by viral infection, and release of interleukin (IL)-6 cytokines., COVID-19 patients with cytokine storm had elevated levels of inflammatory cytokines, including IL-1β, IL-6, IL-10, tumor necrosis factor, interferon-γ, macrophage inflammatory protein 1α and 1β, and vascular endothelial growth factor. Higher levels of IL-6 are strongly associated with higher mortality rates. In addition to cytokine abnormalities, cytokine storms also occur in laboratory biomarker abnormalities, including C-reactive protein levels, D-dimer, hypoalbuminemia, and kidney dysfunction.
It is not known whether immune hyperactivity or failure to resolve the inflammatory response is due to ongoing viral replication or is essentially immune dysregulation. Cytokine storms in COVID-19 often lead to thromboembolic events, as clotting problems can occur in all cytokine storm disorders. IL-6 is thought to play a major role in this cytokine storm. Several studies have revealed a strong correlation between serum IL-6 levels and impending respiratory failure.
Low neutrophils and increased lymphocytes are indicated by a low neutrophil-to-lymphocyte ratio (NLR). Several studies have shown a negative relationship between NLR and the degree of COVID-19. A meta-analysis of 30 articles that assessed the accuracy of the NLR for diagnosing severe COVID-19 reported that the sensitivity and specificity of the NLR in predicting severe COVID-19 were 0.82 and 0.77, respectively, with an area under the curve of 0.87. This shows that the NLR can accurately predict severe COVID-19. In this study, the lymphocyte count was not associated with the degree of severe COVID-19 infection. Thus, we stipulated that the neutrophil count is more dominant than the lymphocyte count in predicting severe COVID-19 infection.
Coagulation and microthrombosis are the main mechanisms in the process of organ failure. Several reports indicate a thrombotic process at necropsy of COVID-19 patients who experienced cardiac death and stroke. High D-dimer levels in COVID-19 are associated with thrombotic events. Boonyawat et al. traced 36 studies and found that 28% of patients admitted to the intensive care unit (ICU) experienced venous thromboembolic event (VTE) and 3% arterial thromboembolic event, whereas in non-ICU patients the incidence of VTE was 10%. Other investigators found the incidence of deep vein thrombosis (DVT) admitted to the ICU is 17% and pulmonary embolism 40.2%.
The incidence of DVT is associated with D-dimer levels >5 mg/L upon admission to the hospital. Platelets and coagulation factors circulate in the blood and become activated at the site of vascular damage. Microvascular thrombosis is a frequent complication of critical illness conditions, such as sepsis, trauma, and malignancy. It has been reported that about 50% of patients have elevated D-dimer levels, and abnormal D-dimer levels are associated with a poor prognosis.,
Severity assessment and place of care decisions for patients with pneumonia are critical to patient safety and adequate allocation of resources. Late admission to the ICU has been associated with increased mortality. Since 1993, efforts have been made to identify SCAP requiring ICU care because ICU is an expensive and scarce resource. The SCAP score derives and validates a clinical prediction system to identify SCAP patients in the emergency department based on eight criterion weights. The eight combined components were clinical factors (age, consciousness, systolic blood pressure, respiratory rate), laboratory biomarkers (arterial blood pH, PO2, BUN), and radiographs (bilateral multilobe pneumonia). The severity of pneumonia is predicted by dividing into two major criteria pH and systolic pressure and minor criteria. The combined performance of these systems is used for predicting the composite definition of SCAP (hospital death, mechanical ventilation, or shock), and results are 92% (0.83–0.97) for sensitivity and 64% (0.5–0.76) for specificity. Two other investigators performed a cohort on the combined performance of SCAP scores to predict ICU admission with similar results in sensitivity (94%) but lower in specificity (46%). A study validated this SCAP score system and compared it with the PSI, CURB-65, and SMART systems in COVID-19 patients with the results of all systems studied good for COVID-19 pneumonia and the best sensitivity and specificity is the SCAP score.
By providing a risk score from the regression analysis, then the prediction of severe COVID-19 infection can quickly be done using simple complete blood laboratory results. Complete blood counts are available in all clinics and hospitals, thus anticipating the patient’s prognosis and planning for more appropriate therapy and action.
| Conclusion|| |
There is a weak positive correlation between neutrophil and platelet counts and the severity of COVID-19 infection as expressed by the SCAP score. This study shows that a high neutrophil count is associated with an increased risk of severe COVID-19. In addition, a low platelet count is also associated with increased risk of severe COVID-19.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Ryalino C. Covid-19: What we know so far. Bali J Anaesthesiol 2020;4:1-2. [Full text]
Rieg S, von Cube M, Kalbhenn J, Utzolino S, Pernice K, Bechet L, et al
; COVID UKF Study Group. COVID-19 in-hospital mortality and mode of death in a dynamic and non-restricted tertiary care model in Germany. PLoS One 2020;15:e0242127.
Auld SC, Caridi-Scheible M, Blum JM, Robichaux C, Kraft C, Jacob JT, et al
; and the Emory COVID-19 Quality and Clinical Research Collaborative. ICU and ventilator mortality among critically ill adults with coronavirus disease 2019. Crit Care Med 2020;48:e799-804.
Jahani M, Dokaneheifard S, Mansouri K. Hypoxia: A key feature of COVID-19 launching activation of HIF-1 and cytokine storm. J Inflamm (Lond) 2020;17:33.
Crayne CB, Albeituni S, Nichols KE, Cron RQ. The immunology of macrophage activation syndrome. Front Immunol 2019;10:119.
Chang FY, Chen HC, Chen PJ, Ho MS, Hsieh SL, Lin JC, et al
. Immunologic aspects of characteristics, diagnosis, and treatment of coronavirus disease 2019 (COVID-19). J Biomed Sci 2020;27:72.
Zitvogel L, Kepp O, Kroemer G. Decoding cell death signals in inflammation and immunity. Cell 2010;140:798-804.
Ahmed-Hassan H, Sisson B, Shukla RK, Wijewantha Y, Funderburg NT, Li Z, et al
. Innate immune responses to highly pathogenic coronaviruses and other significant respiratory viral infections. Front Immunol 2020;11:1979.
Fajgenbaum DC, June CH. Cytokine storm. N Engl J Med 2020;383:2255-73.
Wang Y, Zhao J, Yang L, Hu J, Yao Y. Value of the neutrophil–lymphocyte ratio in predicting COVID-19 severity: A meta-analysis. Dis Markers 2021;2021:2571912.
Boonyawat K, Chantrathammachart P, Numthavaj P, Nanthatanti N, Phusanti S, Phuphuakrat A, et al
. Incidence of thromboembolism in patients with COVID-19: A systematic review and meta-analysis. Thromb J 2020;18:34.
Baccellieri D, Bertoglio L, Apruzzi L, Ardita V, D’Angelo A, Bossi M, et al
. Incidence of deep venous thrombosis in COVID-19 hospitalized patients during the first peak of the Italian outbreak. Phlebology 2021;36:375-83.
Jackson SP, Nesbitt WS, Kulkarni S. Signaling events underlying thrombus formation. J Thromb Haemost 2003;1:1602-12.
Levi M, Ten Cate H. Disseminated intravascular coagulation. N Engl J Med 1999;341:586-92.
Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, et al
; China Medical Treatment Expert Group for Covid-19. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med 2020;382:1708-20.
Tang N, Li D, Wang X, Sun Z. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J Thromb Haemost 2020;18:844-7.
Marti C, Garin N, Grosgurin O, Poncet A, Combescure C, Carballo S, et al
. Prediction of severe community-acquired pneumonia: A systematic review and meta-analysis. Crit Care 2012;16:R141.
España PP, Capelastegui A, Gorordo I, Esteban C, Oribe M, Ortega M, et al
. Development and validation of a clinical prediction rule for severe community-acquired pneumonia. Am J Respir Crit Care Med 2006;174:1249-56.
Anurag A, Preetam M. Validation of PSI/PORT, CURB-65 and SCAP scoring system in COVID-19 pneumonia for prediction of disease severity and 14-day mortality. Clin Respir J 2021;15:467-71.
[Table 1], [Table 2], [Table 3], [Table 4]