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Table of Content - Volume 20 Issue 2 - November 2021


 

Role of serum ferritin, IL-6 and D-Dimer, in assessment and prediction of severity in COVID-19 patients - Retrospective study at tertiary care centre

 

Shivanee Sharma1, Shweta Joshi2, Syed Sarfaraz Ali3*, Vandana Agrawal4

 

1Tutor (PG resident), 3Assistant Professor, 4Professor & HOD, Department of Pathology, LN Medical College and JK Hospital, Bhopal, Madhya Pradesh, INDIA.

2Senior Resident, Department of Pathology, Doon Medical College, Dehradun, Uttarakhand, INDIA.

Email: sarfarazhayaz@gmail.com

 

Abstract              Background: In December 2019, an outbreak caused by virus appeared in China and quickly turned into a pandemic. Within three months of onset of disease it rapidly spread across the world. Clinical spectrum of the disease varies from asymptomatic cases/mild infection to severe respiratory distress with respiratory failure and even death. The outbreak of novel corona virus put health systems on high alert in India and across the world. Aims and Objectives- The study aimed to assess inflammatory markers (S. Ferritin and IL-6) and fibrin degradation products (D-Dimer) in the assessment of disease severity and prognosis for COVID-19 patients. Patients, Materials and Method - A retrospective single center study of 150 confirmed cases of COVID-19, divided into non-severe and severe group by using guidelines provided by MOHFW. Laboratory tests included are complete blood count, D-Dimer, S.Ferritin and IL-6. Results: Median age of the patients was 50.00 years (IQR, 23). Seventy percent of the population were males. Comparison showed significant difference in D-Dimer levels. S.Ferritin and IL-6 were also significantly raised in severe group. Conclusion: Inflammatory markers and fibrin degradation products could help assess the severity of patients with COVID-19.

Key word: COVID-19, Inflammatory markers, Laboratory parameter

 

INTRODUCTION

In December 2019, an outbreak caused by virus appeared in China and quickly turned into a pandemic (Lagadinou, 2020). The emerging virus was rapidly characterized as a novel member of the coronavirus family (Raju et al., 2020). The disease was named as Coronavirus Disease-2019 (COVID-19) by the World Health Organization (WHO) on 12 February 2020. Within three months of onset of disease it rapidly spread across the world. On 11 March 2020 COVID-19 was declared a pandemic by the World Health Organization (Cucinotta et al.,2020). SARS-Co V-2 belongs to genus beta coronavirus of the coronavirus family. It has genetic homology of 79 % to SARS-Co V and 51.8% to MERS-Co V (Yuan et al., 2020). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a positive sense single-stranded enveloped virus which is responsible for the COVID-19 pandemic has become a significant health problem all over the world (Abdullah et al., 2021). Clinical spectrum of the disease varies from asymptomatic cases/mild infection to severe respiratory distress with respiratory failure and even death (Lagadinou, 2020). Diagnosis of COVID-19 is typically performed using polymerase chain reaction testing via nasopharyngeal swab. However, because of the false-negative test result rates of Sars-CoV-2 PCR testing by nasopharyngeal swabs, several clinical, laboratory, and imaging findings have been useful to make a presumptive diagnosis (Biamonte et al., 2021). India reported first three cases of COVID-19 in Kerala on January 30,2020 and Madhya Pradesh reported its first confirmed case (4 cases) at Jabalpur in the month of March 2020. COVID-19 is still spreading at an unprecedented rate and cases are increasing. The outbreak of novel corona virus put health systems on high alert in India and across the world (Lagadinou, 2020). Laboratory biomarkers to forecast the severity of COVID-19 are essential in a pandemic, because resource allocation must be carefully planned, especially in the context of respiratory support readiness (I. Huang et al., 2020) .Various published studies comment on epidemiology and clinical characteristics of COVID-19. Few studies explored comparison of laboratory results in between survivors and non survivors. Some studies have compared laboratory test and radiological findings between mild and severe cases.(F. Wang et al., 2020) Our study aimed to assess the role of inflammatory markers (S. Ferritin and IL-6) and fibrin degradation product(D-Dimer) in the assessment of disease severity in COVID-19 patients and the use of these investigations as prognostic markers early after admission.

 

MATERIALS AND METHOD

Patients: It was a retrospective single center study carried out at LNMC and associated JK Hospital, Bhopal which was a designated COVID-19 care center. Patients who were admitted between 1st August 2020 and 30 September 2020 were included in this study.

Inclusion criteria: 1) Age >18 years 2) Confirmed cases (tested positive on reverse transcriptase polymerase chain reaction assay) of COVID-19 3) Investigated for D-Dimer, Serum ferritin and IL-6 early after admission. Patients <18 years and those who missed this investigation were excluded.

A total of 150 patients were included and classified in two groups non-severe and severe on the basis of guidelines provided by MOHFW.Demographic data, clinical information, laboratory results and HRCT findings of COVID-19 patients were retrieved from electronic medical records.

Laboratory tests: Laboratory tests included complete blood count performed in EDTA sample by automated hematology analyzer (Mindray 5 part), D-dimer performed in Sodium citrate sample by immunoturbidity method, Ferritin level and IL-6 both assessed in serum sample by using ECLIA (Enzyme Chemiluminescence immunoassay)

Changes in laboratory test results were analyzed and compared across the two groups (non-severe and severe group) of patients. The study was approved by the IEC. Need for informed consent was waived off in light of it being an anonymous observational and retrospective study.

 

STATISTICAL ANALYSIS

SPSS-20 statistical software was used for data analysis. Socio-demographic and clinical variables were tabulated and central tendencies were computed. The median of age and the comparison of laboratory parameters between the non-severe and severe patients were determined by using the Mann-Whitney U test, as the data were non- normally distributed. Sex was compared using the χ2 test. All continuous data has been shown as median with interquartile range. The area under the curve (AUC) and the 95% confidence interval (CI) of the receiver operator characteristic (ROC) curve and logistic regression analysis was computed using the predicted probability of the severe COVID-19. The optimal cutoff points to predict the severity of COVID-19 were determined by Youden's index. P value less than .05 was considered significant.

 

RESULTS

1. Socio-demographic data

The study was done on 150 patients. The median age of the patients was 50.00 years (IQR, 23). Seventy percent of the population were males. Out of 150 patients, those with the severe form of the illness (n=73) had a median age of 56.00 years (IQR, 20). The median age of patients categorised as non-severe was 44.00 years (IQR, 21). The difference in distribution of age among the two groups was statistically significant. There were 71.4% males and 28.6 % females in the non-severe group whereas 68.50 % males and 31.5% females in the severe group. There was no statistically significant difference between the two groups based on gender. (Table 1)


 

 

Table 1: Demography of the population

Variables

Total Patients n=150

Non-severe group n=77

Severe group n=73

P-value

Age (in years)

Median (IQR)

50.00(23.00)

44.00(21.00)

56.00(20.00)

<0.0001

Sex Distribution: n (%)

Male

105(70)

55(71.4)

50(68.5)

0.69

Female

45(30)

22(28.6)

23(31.5)

 

Table 2

Laboratory parameters of 150 patients with COVID-19

Group

N

D-Dimer

IL-6

S.Ferritin

Hb

TLC

N%

L%

M%

E%

Platelets

Sex

Male

105

550.00(878.94)

9.20

(13.35)

266.00(360.25)

14.00(2.15)

6500.00(3550.00)

68.00(18.00)

28.00(17.00)

2.00(1.00)

2.00(1.00)

2.33(1.10)

Female

45

630.00(984.33)

6.10(12.35)

125.60(376.50)

12.40(1.75)

6400.00(5800.00)

72.00(26.00)

23.00(26.00)

2.00(1.00)

2.00(1.00)

2.77(1.13)

P-value

0.89

0.00

0.35

<0.0001

0.68

0.89

0.81

0.99

0.20

0.00

Age (years)

≤60

 

115

507.00(645.00)

 

6.10(11.20)

 

177.48(296.00)

 

13.70(2.00)

 

6400.00(3700.00)

 

67.00(20.00)

 

29.00(19.00)

 

2.00(1.00)

 

2.00(1.00)

 

2.40(1.12)

 

>60

35

1198.00(2946.59)

14.00(29.20)

464.10(604.00)

12.40(2.50)

 

7400.00(5400.00)

 

73.00(21.00)

 

22.00(20.00)

 

2.00(1.00)

 

2.00(1.00)

 

2.64(1.30)

 

P-value

<0.0001

<0.0001

0.001

0.009

0.05

0.008

0.01

0.10

0.01

0.47

Severity

Non-Severe

77

336.05(244.56)

5.40(7.50)

153.00(237.60)

13.50(2.05)

6400.00(3150.00)

66.00(21.50)

30.00(21.00)

2.00(1.00)

2.00(0.00)

2.50(0.96)

Severe

73

1163.00(2045.26)

11.20(20.35)

390.90(520.95)

13.10(2.65)

6900.00(5250.00)

72.00(23.00)

24.00(21.00)

2.00(1.00)

2.00(2.00)

2.58(1.46)

P-value

<0.0001

<0.0001

<0.0001

0.30

0.07

0.01

0.01

0.96

0.67

0.43

 


2.Laboratory parameters:

2.1 Stratified by Sex(Table 2)

Haemtological Parameters

The median concentration of Haemoglobin in males was significantly higher 14.00gm%(IQR,2.15) compared to females 12.40gm% (IQR,1.75). The males had statistically significant lower median platelet counts of 2.33 (IQR,1.10) compared to females with median platelet count of 2.77(IQR,1.13). Rest of the haematological parameters were statistically insignificant among the two genders.

Inflammatory markers

The median S.IL-6 levels in males was found to be 9.20(IQR,13.35) pg/ml whereas it was 6.10(12.35) pg/ml in females. The difference was statistically significant. The levels of D-Dimer or S. Ferritin did not appear to be significantly different between the two genders.

2.2 Stratified by Age(Table 2)

The study population was divided into two subgroups, first group having all patients with age 60years or less whereas the rest were categorised into the second group.

Haematological Parameters

There were statistically significant differences found between the two age groups for Hb, N%, L%, M% and E%. The TLC and Platelet counts did not differ.

Inflammatory markers

The D-Dimer, IL-6 and S. Ferritin were found to have significant statistical differences when compared between those aged 60 years and below vs patients above 60 years.

 

2.3 Stratified by severity(Table 2)

Haematological parameters

The levels of Haemoglobin, Total Leucocyte Count, Monocytes, Eosinophils and Platelets were not significantly different between the two groups. The level of Neutrophils in the severe group was significantly higher than in the non-severe group The level of Lymphocytes was significantly lower in the severe group than in the non-severe group

Inflammatory markers

The level of IL-6 was significantly higher in the severe group than in the non-severe group (median: 5.4 pg/mL; IQR 7.5 pg/ml). S. Ferritin level was significantly higher in the severe group (median:390.90 ng/mL; IQR: 520.95 ng/mL) than in the non-severe group (median:153.00 mg/l; IQR: 237.60 mg/l). D-Dimer level was significantly higher in the severe group (median:1163.00 ng/ml; IQR: 2045.26 ng/ml) than in the non-severe group (median:336.05 ng/ml; IQR: 244.56 ng/ml).

Analysis by ROC

The ROC curve was used to analyze the role of D-Dimer, IL-6 and S. Ferritin in predicting the severity of COVID-19 infection. The AUC used by D-Dimer to predict the severity of pneumonia was 0.934 (p = <.001) indicating excellent results. The optimum critical point was 552.50 ng/mL, with a 91.8% sensitivity and 84.4% specificity. The AUC of IL-6, S. Ferritin were 0.708 and 0.709 indicating poor prognostic utility. The prediction efficiency is shown in Figure 1 and Table 3.


 

 

Figure 1

Figure 1: Receiver operator characteristic curves comparing the potential of different variables to predict the severity of COVID-19

A- The prediction of the severe COVID-19 variables for Individual indicators.

B- The prediction of the severe COVID-19 variables for D-Dimer, combined with Interleukin-6 and Serum Ferritin.

 


Table 3: ROC curve analysis of clinical laboratory data

Variables

AUC

95% CI

P-value

D-Dimer

0.93

0.89-0.97

<0.001

IL-6

0.70

0.62-0.79

<0.001

S.Ferritin

0.70

0.62-0.79

<0.001

Haemoglobin

0.45

0.35-0.54

0.30

TLC

0.58

0.49-0.67

0.07

Neutrophils

0.61

0.52-0.70

0.01

Lymphocytes

0.38

0.29-0.47

0.01

Eosinophils

0.50

0.40-0.59

0.96

Monocytes

0.48

0.38-0.57

0.70

Platelets

0.53

0.44-0.63

0.43

D-Dimer, IL-6, S.Ferritin

0.93

0.89-0.97

<0.001

 

DISCUSSION

This study reports a cohort of 150 patients who were admitted with laboratory confirmed COVID-19. The patients were symptomatic and were categorised into having non-severe and severe illnesses as per the guidelines provided by the MOHFW. The existing facilities have been focused on early identification/diagnosis and prompt supportive treatment initiation. Much emphasis has been laid on devising methods that may help in predicting who may go on to develop severe form of illness. Our study reported that the severe group was significantly older and had a higher proportion of males compared to females. This has also been reflected in a meta-analysis that studied the thrombo-inflammatory markers in patients severely affected with COVID-19 (Chaudhary et al., 2021). This study also compared the results of complete blood counts and infection related biomarkers of adult patients diagnosed with COVID-19. It was found in our study that the Neutrophil % was lower in the non -severe group compared to severe group (P = 0.01) whereas the Lymphocyte % was higher in the non-severe group compared to severe group (P = 0.01). These results are consistent with previous studies showing lymphopenia and increased neutrophils in those with severe illness (Song et al., 2020). The infection related biomarkers for COVID-19 were studied and our results showed significant differences between the non-severe and severe group. The thrombo-inflammatory biomarkers continue to hold their importance in predicting poor prognosis and severity of COVID-19 infection, especially D-Dimer. Several prior studies have reported the association of elevated D-Dimer levels with poor prognosis of patients, which need to be interpreted cautiously considering its poor specificity and multiple possible reason for its elevated levels eg .advanced age, female sex, immobility, and prior thrombo-embolic disease (Shah et al., 2020). Of note, should also be the fact that it is released at a much later stage in the haemostatic process when a clot is degraded by the fibrinolytic processes. These levels do not capture the dynamic effects of functional interactions among platelets, endothelium, and fibrinolytic processes (Rahul Chaudhary et al., 2020). The D-Dimer levels in our study were more than two times increased in the severe group compared to the non-severe (P = <0.0001). Similar results were observed by other researchers where a study of 183 patients with COVID-19 found that D-Dimer values were nearly 3.5-fold higher in those with severe disease (median: 2.12 mg/L; IQR: 0.77–5.27 mg/L) than in those without (median: 0.61 mg/L; IQR: 0.35–1.29 mg/L; p  < 0.001) (Tang et al., 2020). Another study found D-dimer values were nearly 2.5-fold higher in patients with severe disease (median: 4.14 mg/L; IQR: 1.91–13.2 mg/L) than in those without (median: 1.66 mg/L; IQR: 1.01–2.85 mg/L; p  < 0.001) in 138 patients (D. Wang et al., 2020). Serum ferritin levels have aided in monitoring prognosis in COVID-19 patients. Raised values have been associated with “cytokine storm,” anticipating development of ARDS and tissue damage progressing into multiorgan failure (MOF). Raised levels have been studied to progress into severe forms of illness (Wu et al., 2020). A study from eastern India reported an elevated level (Mean 683.89 ± S.D 503.21 ng/ml) of S. Ferritin in 50 moderate-severe hospitalised COVID-19 patients (Pal et al., 2021). Our study showed a median of 153.00(IQR, 237.60) ng/ml in non-severe patients compared with 390.90(IQR, 520.95) ng/ml in severe patients.

SARS-CoV-2 causes activation of different immune cells like macrophages, monocytes, and dendritic cells. This phenomenon helps to secrete proinflammatory cytokine IL-6 and other inflammatory cytokines which then give rise to the theory of ‘Cytokine Storm’ leading to increased levels of circulating interleukins, most notable being IL-6 (Moore and June, 2020). Studies have shown elevated levels of IL-6 upto 24.11pg/ml, correlating with disease severity (Zhu et al., 2020). The circulating levels of IL-6 have also been reported to be associated with the levels of circulating viral RNA and disease progression (L. Huang et al., 2020). Studying the role of IL-6 in viral diseases, it has been noted that IL-6 together with transforming growth factorbeta induces the differentiation of naïve CD4 into Th17 cells. These cells are important for the defense against viruses and other pathogens at mucosal sites. In addition, there is synergic interaction between IL-6 and IL-7 and IL-15 to induce the differentiation and catalytic ability of CD8 T cells which is important in the response against viral infections (Velazquez-Salinas et al., 2019). Our study reported a significant difference in levels of circulating IL-6 between the non-severe and severe group with median and inter-quartile range of 5.40(7.50) and 11.20(20.35) respectively. This displays the increase in levels with increasing severity. Among these risk factors, the ROC curve was used to analyze the specificity and sensitivity of different variables in severe COVID-19 patients. The AUC of D-Dimer, IL-6 and S. Ferritin were 0.93, 0.70 and 0.70, respectively while those of Hb, TLC, N%, L%, M%, E% and Platelets were below 0.750, thus leading to poor predictive value. When all the three biomarkers were jointly predicted, the ROC curve integral of severe COVID-19 was 0.93 (P < .01) and the combined detection effect was no better than D-Dimer alone. Other studies have shown a synergistic effect when combining biomarkers in predicting the severity (Gao et al., 2020).

In conclusion, our findings suggest that D-Dimer levels, IL-6 and S. Ferritin can be used to estimate the severity of COVID-19. This study has several limitations. Firstly, our study has a relatively small sample size, which limits the general disability of these results and due to the large scale outbreak of the epidemic restricting the flow of people, data on healthy patients are lacking as blank controls. Secondly, since this study was a retrospective study, not all patients were continuously monitored for all indicators in the blood including D-dimer, IL-6 and S. Ferritin levels. In future, studies with larger sample size are needed to study the role of these biomarkers and data should be collected from healthy patients as blank controls to further explore the predictive value of D-dimer, IL-6 and S. Ferritin for patients with SARS-COV-2 infection, this will help in reducing the lag before initiation of appropriate treatment protocol.

 

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