Skip to main content
Erschienen in: BMC Infectious Diseases 1/2022

Open Access 05.01.2022 | COVID-19 | Research

Monocyte distribution width as a novel sepsis indicator in COVID-19 patients

verfasst von: Laila Alsuwaidi, Saba Al Heialy, Nahid Shaikh, Firas Al Najjar, Rania Seliem, Aaron Han, Mahmood Hachim

Erschienen in: BMC Infectious Diseases | Ausgabe 1/2022

Abstract

Background

The severe acute respiratory syndrome coronavirus (SARS-CoV-2) is a highly transmittable virus which causes the novel coronavirus disease (COVID-19). Monocyte distribution width (MDW) is an in-vitro hematological parameter which describes the changes in monocyte size distribution and can indicate progression from localized infection to systemic infection. In this study we evaluated the correlation between the laboratory parameters and available clinical data in different quartiles of MDW to predict the progression and severity of COVID-19 infection.

Methods

A retrospective analysis of clinical data collected in the Emergency Department of Rashid Hospital Trauma Center-DHA from adult individuals tested for SARS-CoV-2 between January and June 2020. The patients (n = 2454) were assigned into quartiles based on their MDW value on admission. The four groups were analyzed to determine if MDW was an indicator to identify patients who are at increased risk for progression to sepsis.

Results

Our data showed a significant positive correlation between MDW and various laboratory parameters associated with SARS-CoV-2 infection. The study also revealed that MDW ≥ 24.685 has a strong correlation with poor prognosis of COVID-19.

Conclusions

Monitoring of monocytes provides a window into the systemic inflammation caused by infection and can aid in evaluating the progression and severity of COVID-19 infection.
Hinweise

Supplementary Information

The online version contains supplementary material available at https://​doi.​org/​10.​1186/​s12879-021-07016-4.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Abkürzungen
MBRU
Mohammed Bin Rashid University of Medicine and Health Sciences
DHA
Dubai Health Authority
DSREC
Dubai Scientific Research Ethics Committee

Introduction

The severe acute respiratory syndrome coronavirus (SARS-CoV-2) is a highly transmittable virus which causes the novel coronavirus disease (COVID-19) that has affected over 131 million people worldwide and has caused 2.85 million deaths globally as of April 5th, 2021. The most common clinical presentation of this disease includes fever, dry cough and fatigue. However, in a subset of COVID-19 patients, severe outcomes such as viral sepsis are seen. Sepsis is a life-threatening systemic illness which can result in dysregulated immune responses leading to organ dysfunction and a leading cause of mortality [1].
To date, several biomarkers have been identified as early markers to evaluate inflammation and disease outcomes such as C-reactive protein, creatinine and D-dimer [2]. In response to infection, the first immune cells to be recruited are neutrophils and monocytes. In fact, monocyte distribution width (MDW) is used as a biomarker for sepsis where levels > 20 are indicative of sepsis [3]. MDW is an in-vitro hematological parameter which describes the changes in monocyte size distribution and can indicate progression from localized infection to systemic infection [4]. This parameter can be performed along with other routine parameters on several Beckman Coulter DxH analyzers. MDW alone or in combination with white blood count (WBC) can be used to detect early sepsis in the emergency department [5]. A recent study showed that combining MDW ≥ 20 and Neutrophil-to-lymphocyte ratio (NLR) < 3.2 is more efficient in identifying COVID-19 and can be actually used to distinguish SARS-CoV-2 infection from influenza infection and other upper respiratory tract infections [6]. Monitoring of monocytes provides a window into the systemic inflammation caused by infection and can aid in evaluating the progression and severity of the infection.
In this study, we retrospectively analyzed the clinical and biological characteristics of the COVID-19 infected patients and investigated the ability of MDW to predict at an earlier time the disease severity, in comparison with other biomarkers. We also investigated the correlation between routine laboratory parameters in different quartiles of MDW values to evaluate the usefulness of this value in predicting disease outcomes.

Materials and methods

Study population and design

This is a retrospective cohort study, which includes all adult individuals (≥ 18 years old) tested for SARS-CoV-2 in the Emergency Department—Rashid Hospital Trauma Center of DHA between January and June 2020. We included only the laboratory-confirmed cases, as the diagnosis was performed by real-time reverse transcriptase-polymerase chain reaction (RT-PCR) conducted on a nasopharyngeal swab of the patient according to the World Health Organization (WHO) guidance.
Epidemiological characteristics including demographics, recent exposure history, clinical symptoms and signs, and laboratory findings, were obtained from the patients’ electronic medical records in DHA unified electronic system Salama using a standardized data collection form, which is a modified version of the WHO/International Severe Acute Respiratory and Emerging Infection Consortium case record form for severe acute respiratory infections (Additional file 1: Appendix 1).

Clinical and laboratory data

In terms of epidemiological information, we considered patient demographic characteristics including age and gender; clinical symptoms including fever, cough, respiratory symptom, ear, nose and throat symptom; comorbidities including hypertension, diabetes, cardiovascular disease, respiratory disease, and other disease.
Venous blood samples and nasal-pharyngeal swabs were collected and examined by the Emergency Department Laboratory of Rashid Hospital Trauma Center of DHA. Initial investigations included hematological analysis (complete blood count and coagulation profile), serum biochemical test (renal and liver function, creatine kinase, lactate dehydrogenase, electrolytes, and serum ferritin) in addition to some inflammatory markers (procalcitonin and C-Reactive Protein). Frequency of examinations was determined according to the disease progress. For hospitalized patients, nasopharyngeal swab specimens were obtained for SARS-CoV-2 RT-PCR re-examination every other day after clinical remission of symptoms, including fever, cough, and dyspnea. Repeat RT-PCR tests were performed for SARS-CoV-2 done in patients confirmed to have COVID-19 infection to show viral clearance before hospital discharge or discontinuation of isolation as per national guidelines at the time of this study.
The MDW, which was measured in this study using Beckman Coulter DxH 900 analyzer, is an additional parameter that was recorded in the data collection form. MDW values were compared among the studied groups to determine its usefulness as an indicator to identify patients who are at increased risk for progression to sepsis.

Statistical analysis

Data were presented as mean and standard deviation for continuous variables and frequency (number and percentage; %) for categorical variables. For all statistical analyses and tests, SPSS was used (Released 2019. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp). The normality test for all groups was done by Shapiro–Wilk tests using SPSS, and sig. of all independent variables > 0.05 means that all groups were normally distributed. To assess the differences between COVID-19 patients different groups based on MDW, ANOVA: analysis of variance used to identify and compare variances among groups for the continuous variables and Chi-square test was used for the categorical variables. P value < 0.05 had been considered significant.

Results

From January to June 2020, 2899 patients were tested positive for SARS-CoV-2 in the Emergency Department of Rashid Hospital Trauma Center of DHA. Only positive COVID-19 patients who had no comorbidities were selected for further analysis (n = 2454) as demonstrated in Fig. 1. The age range was 72 (18–90) years, and 78.7% were men. Further characteristics of the studied population are summarized in Table 1.
Table 1
Characteristics of the study population
 
No
Mean
Std. error of mean
Std. deviation
Skewness
Std. error of skewness
Range
Minimum
Maximum
Demographics
Age (years)
2454
41.54
0.282
13.994
0.777
0.049
72
18
90
Hematology markers
White blood cell (× 103 per μL)
2454
8.082
0.0836
4.1409
2.186
0.049
37.9
1.2
39.1
Platelet (× 103 per μL)
2454
227.71
1.863
92.27
2.095
0.049
1008
7
1015
Neutrophil %
2454
70.059
0.2629
13.0235
− 0.722
0.049
85.7
10.6
96.3
Lymphocyte %
2454
18.919
0.2144
10.6225
1.125
0.049
86
1.1
87.1
Monocyte %
2454
9.764
0.0872
4.3198
0.958
0.049
42.5
1.4
43.9
Neutrophil absolute (× 103 per μL)
2454
5.911
0.0765
3.7892
2.251
0.049
35.2
0.5
35.7
Lymphocyte absolute (× 103 per μL)
2454
1.345
0.0204
1.01
13.219
0.049
31.9
0.1
32
Monocyte absolute (× 103 per μL)
2454
0.728
0.0079
0.3923
2.01
0.049
4.7
0
4.7
Monocyte distribution Width (U)
2454
23.5053
0.07008
3.47177
2.594
0.049
33.48
20
53.48
Coagulation markers
Prothrombin time (s)
1518
14.31
0.05
1.931
6.718
0.063
37
11
48
APTT (s)
1499
38.97
0.161
6.252
3.633
0.063
99
13
112
D-dimer (μg/mL)
729
1.24
0.069
1.857
5.102
0.091
18
0
18
Fibrinogen (mg/dL)
16
559.88
32.539
130.158
− 0.158
0.564
433
357
790
Troponin (pg/mL)
447
79.77
37.2
786.492
19.126
0.115
16,048
3
16,051
COVID-19 inflammation markers
C-reactive protein (mg/L)
2276
69.1298
1.74835
83.40931
1.999
0.051
569.1
0.4
569.5
LDH (U/L)
1287
303.47
4.427
158.832
3.68
0.068
2492
6
2498
Ferritin (ng/mL)
1047
849.17
30.249
978.782
4.326
0.076
13,951
9
13,960
Procalcitonin (ng/mL)
1887
1.91744
0.512958
22.28269
29.416
0.056
831.38
0.02
831.4
Liver enzymes
Albumin (g/dL)
1851
3.8925
0.01282
0.55173
− 0.945
0.057
4.8
0.6
5.4
ALT (U/L)
1855
44.178
1.8189
78.3383
22.22
0.057
2662.8
3.2
2666
AST (U/L)
350
76.12
18.165
339.827
14.614
0.13
5808
0
5808
Bilirubin, total (mg/dL)
1856
0.67
0.022
0.963
18.824
0.057
31
0
31
Creatinine (mg/dL)
2276
1.079
0.0795
3.7931
26.99
0.051
125.8
0.1
125.9
Death
43
43,954.11
3.031601
19.87954
0.379
0.361
68.2875
43,924.94
43,993.23
APTT activated partial thromboplastin time, LDH lactate dehydrogenase, ALT alanine aminotransferase, AST aspartate aminotransferase
As presented in Table 2, the correlation between MDW and major hematology laboratory markers used routinely in assessing cases of COVID-19 in an emergency department setting. Pearson Correlation between MDW and all blood results for all patients included in the study (n = 2454) showed that MDW was positively correlated with WBC (r = 0.101, p < 0.001), neutrophils percentage (NE%) (r = 0.250, p < 0.001), neutrophils count (NE#) (r = 0.162, p < 0.001). Nevertheless, significant negative correlation was observed between MDW and total platelet (PLT) (r = − 0.140, p < 0.001), lymphocytes percentage (LY%) (r = − 0.168, p < 0.001), and monocytes percentage (MO%) (r = − 0.262, p < 0.001).
Table 2
Correlation between MDW and major laboratory markers used routinely in assessing cases of COVID-19 in an emergency department setting
Correlations
 
MDW
Age (years)
Pearson correlation
0.065
 
Sig. (2-tailed)
0.001
 
N
2454
White blood cell (× 103 per μL)
Pearson correlation
0.101
 
Sig. (2-tailed)
 < 0.001
 
N
2454
Neutrophil %
Pearson correlation
0.250
 
Sig. (2-tailed)
 < 0.001
 
N
2454
Lymphocyte %
Pearson correlation
− .168
 
Sig. (2-tailed)
 < 0.001
 
N
2454
Monocyte %
Pearson correlation
− .262
 
Sig. (2-tailed)
 < 0.001
 
N
2454
Neutrophil absolute (× 103 per μL)
Pearson correlation
0.162
 
Sig. (2-tailed)
 < 0.001
 
N
2454
Lymphocyte absolute (× 103 per μL)
Pearson correlation
− .104
 
Sig. (2-tailed)
 < 0.001
 
N
2454
Monocyte absolute (× 103 per μL)
Pearson correlation
− .175
 
Sig. (2-tailed)
 < 0.001
 
N
2454
Platelet (× 103 per μL)
Pearson correlation
− 0.140
 
Sig. (2-tailed)
 < 0.001
 
N
2454
MDW was positively correlated with total WBC and neutrophils and negatively correlated with total platelet, lymphocytes, monocytes
The results of the current study indicated significant positive correlation between MDW and COVID inflammation markers including C-reactive protein (CRP) (r = 0.401, p < 0.001), lactate dehydrogenase (LDH) (r = 0.381, p < 0.001), Ferritin (r = 0.305, p < 0.001), and Procalcitonin (r = 0.133, p < 0.001) as shown in Table 3. Interestingly, MDW was significantly correlated with the prothrombin time (PT) (r = 0.174, p < 0.001), activated partial thromboplastin time (APTT) (r = 0.204, p < 0.001), and D-Dimer (r = − 0.218, p < 0.001) but there was no correlation between MDW and fibrinogen level and Troponin (Table 4). Additionally, MDW was positively correlated with liver enzymes, alanine aminotransferase (ALT) (r = 0.091, p < 0.001), aspartate aminotransferase (AST) (r = 0.115, p < 0.001), and Total Bilirubin (r = 0. 109, p < 0.001). The only negative correlation was between MDW and Serum albumin r = − 0. 322, p < 0.001) (Table 5).
Table 3
Correlation between MDW and COVID-19 inflammation markers
Correlations
 
MDW
C-reactive protein (mg/L)
Pearson correlation
0.401
 
Sig. (2-tailed)
 < 0.001
 
N
2276
LDH (U/L)
Pearson correlation
0.381
 
Sig. (2-tailed)
 < 0.001
 
N
1287
Ferritin (ng/mL)
Pearson correlation
0.305
 
Sig. (2-tailed)
 < 0.001
 
N
1047
Procalcitonin (ng/mL)
Pearson correlation
0.133
 
Sig. (2-tailed)
 < 0.001
 
N
1887
LDH lactate dehydrogenase
Table 4
Correlation between MDW and coagulation markers
Correlations
 
MDW
Prothrombin time (s)
Pearson correlation
0.174
 
Sig. (2-tailed)
 < 0.001
 
N
1518
APTT (s)
Pearson correlation
0.204
 
Sig. (2-tailed)
 < 0.001
 
N
1499
D-dimer (μg/mL)
Pearson correlation
0.218
 
Sig. (2-tailed)
 < 0.001
 
N
729
Fibrinogen (mg/dL)
Pearson correlation
0.237
 
Sig. (2-tailed)
0.377
 
N
16
Troponin (pg/mL)
Pearson correlation
− 0.016
 
Sig. (2-tailed)
0.732
 
N
447
PT prothrombin time; APTT activated partial thromboplastin time
Table 5
Correlation between MDW and liver enzymes
Correlations
 
MDW
Albumin (g/dL)
Pearson correlation
− 0.322
 
Sig. (2-tailed)
 < 0.001
 
N
1851
ALT (U/L)
Pearson correlation
0.091
 
Sig. (2-tailed)
 < 0.001
 
N
1855
AST (U/L)
Pearson correlation
0.115
 
Sig. (2-tailed)
0.031
 
N
350
Bilirubin, total (mg/dL)
Pearson correlation
0.109
 
Sig. (2-tailed)
 < 0.001
 
N
1856
Creatinine (mg/dL)
Pearson correlation
0.023
 
Sig. (2-tailed)
0.273
 
N
2276
ALB albumin; ALT alanine aminotransferase, AST aspartate aminotransferase
Based on the MDW value, the patients were divided into quartiles with approximately equal numbers of patients assigned to each of the four groups as follows: Q1 (MDW < 21.215, n = 614), Q2 (MDW = 21.215–22.535, n = 614), Q3 (MDW = 22.535–24.685, n = 614) and Q4(MDW ≥ 24.685, n = 614) (Fig. 1). Comparing the different blood biomarkers in each MDW quartile showed that patients with MDW ≥ 24.685 (Q4) demonstrated a strong correlation with poor prognosis COVID-19 related biomarkers. Such patients showed significantly lower platelet counts (Q1 = 240.65 ± 101.408, Q2 = 236.4 ± 96.429, Q3 = 223.53 ± 82.662 and Q4 = 210.24 ± 84.356, p < 0.001) and higher neutrophils percentage (Q1 = 66.449 ± 12.8279, Q2 = 67.864 ± 12.6981, Q3 = 70.98 ± 11.8736 and Q4 = 74.946 ± 13.0348, p < 0.001). Likewise, Q4 patients showed lower lymphocytes percentage (Q1 = 21.301 ± 10.9329, Q2 = 19.717 ± 10.5829, Q3 = 18.373 ± 10.0544 and Q4 = 16.284 ± 10.2825, p < 0.001) and monocytes percentage (Q1 = 10.489 ± 4.0981, Q2 = 10.815 ± 4.2217, Q3 = 9.732 ± 4.1094 and Q4 = 8.019 ± 4.307, p < 0.001). Apparently, the results revealed that all inflammatory markers and risk to develop coagulations markers were significantly higher in Q4 patients compared to the rest of patients in different quartiles (Table 6).
Table 6
Comparing the different blood biomarkers of COVID-19 patients in each MDW quartile
Parameter
Quartile
N
Mean
Std. deviation
Std. error
Minimum
Maximum
ANOVA
Hematology markers
White blood cell (× 103 per μL)
1
613
8.056
3.999
0.1615
2.2
33.5
0.403
 
2
614
7.845
3.6683
0.148
2.1
27.2
 
 
3
614
7.883
3.7089
0.1497
2.2
36.8
 
 
4
613
8.544
5.0169
0.2026
1.2
39.1
 
 
Total
2454
8.082
4.1409
0.0836
1.2
39.1
 
Platelet (× 103 per μL)
1
613
240.65
101.408
4.096
77
1015
 < 0.001
 
2
614
236.4
96.429
3.892
34
980
 
 
3
614
223.53
82.662
3.336
10
650
 
 
4
613
210.24
84.356
3.407
7
638
 
 
Total
2454
227.71
92.27
1.863
7
1015
 
Neutrophil %
1
613
66.449
12.8279
0.5181
22.5
96
 < 0.001
 
2
614
67.864
12.6981
0.5125
19.1
94.8
 
 
3
614
70.98
11.8736
0.4792
10.6
94
 
 
4
613
74.946
13.0348
0.5265
18.4
96.3
 
 
Total
2454
70.059
13.0235
0.2629
10.6
96.3
 
Lymphocyte %
1
613
21.301
10.9329
0.4416
2
57.8
 < 0.001
 
2
614
19.717
10.5829
0.4271
1.6
62.9
 
 
3
614
18.373
10.0544
0.4058
2
87.1
 
 
4
613
16.284
10.2825
0.4153
1.1
65.7
 
 
Total
2454
18.919
10.6225
0.2144
1.1
87.1
 
Monocyte %
1
613
10.489
4.0981
0.1655
1.6
26.7
 < 0.001
 
2
614
10.815
4.2217
0.1704
2.1
43.9
 
 
3
614
9.732
4.1094
0.1658
1.6
40.5
 
 
4
613
8.019
4.307
0.174
1.4
32.1
 
 
Total
2454
9.764
4.3198
0.0872
1.4
43.9
 
Neutrophil absolute (× 103 per μL)
1
613
5.628
3.6845
0.1488
0.7
31.4
 < 0.001
 
2
614
5.569
3.3693
0.136
0.6
25.8
 
 
3
614
5.751
3.1023
0.1252
0.6
24.3
 
 
4
613
6.697
4.7034
0.19
0.5
35.7
 
 
Total
2454
5.911
3.7892
0.0765
0.5
35.7
 
Lymphocyte absolute (× 103 per μL)
1
613
1.517
0.8252
0.0333
0.2
6.8
 < 0.001
 
2
614
1.362
0.6802
0.0275
0.2
4.3
 
 
3
614
1.336
1.4619
0.059
0.2
32
 
 
4
613
1.164
0.8607
0.0348
0.1
11.7
 
 
Total
2454
1.345
1.01
0.0204
0.1
32
 
Monocyte absolute (× 103 per μL)
1
613
0.779
0.3538
0.0143
0.2
2.4
 < 0.001
 
2
614
0.789
0.3908
0.0158
0.2
4.7
 
 
3
614
0.723
0.3839
0.0155
0.1
3.7
 
 
4
613
0.621
0.4162
0.0168
0
4.4
 
 
Total
2454
0.728
0.3923
0.0079
0
4.7
 
Coagulation markers
Prothrombin time (s)
1
331
14.13
1.456
0.08
11
27
0.403
 
2
356
14.22
1.461
0.077
12
28
 
 
3
397
14.16
2.153
0.108
12
48
 
 
4
434
14.64
2.3
0.11
12
32
 
 
Total
1518
14.31
1.931
0.05
11
48
 
APTT (s)
1
327
37.68
4.29
0.237
27
54
 < 0.001
 
2
348
38.8
6.171
0.331
26
81
 
 
3
394
38.6
5.844
0.294
13
107
 
 
4
430
40.42
7.542
0.364
27
112
 
 
Total
1499
38.97
6.252
0.161
13
112
 
D-dimer (μg/mL)
1
146
1.1
1.863
0.154
0
14
 < 0.001
 
2
148
1.14
2.148
0.177
0
18
 
 
3
197
0.99
1.146
0.082
0
11
 
 
4
238
1.59
2.081
0.135
0
18
 
 
Total
729
1.24
1.857
0.069
0
18
 
Troponin (pg/mL)
1
112
176.49
1515.662
143.217
3
16,051
0.403
 
2
84
66.21
420.444
45.874
3
3843
 
 
3
114
25.1
89.552
8.387
3
928
 
 
4
137
54.5
167.589
14.318
3
1408
 
 
Total
447
79.77
786.492
37.2
3
16,051
 
COVID-19 inflammation markers
Ferritin (ng/mL)
1
215
466.13
477.287
32.551
9
2835
 < 0.001
 
2
234
616.69
773.147
50.542
9
8018
 
 
3
280
865.08
793.872
47.443
9
5222
 
 
4
318
1265.2
1303.864
73.117
41
13,960
 
 
Total
1047
849.17
978.782
30.249
9
13,960
 
LDH (U/L)
1
267
232.35
88.064
5.389
6
748
 < 0.001
 
2
300
250.94
100.639
5.81
109
682
 
 
3
348
307.09
132.538
7.105
119
1115
 
 
4
372
393.51
208.041
10.786
104
2498
 
 
Total
1287
303.47
158.832
4.427
6
2498
 
C-reactive protein (mg/L)
1
557
38.1835
54.85315
2.3242
0.4
384.7
 < 0.001
 
2
564
48.38
67.75575
2.85303
0.4
509.3
 
 
3
578
69.8327
74.31234
3.09099
0.4
418.6
 
 
4
577
118.5815
103.7148
4.3177
0.6
569.5
 
 
Total
2276
69.1298
83.40931
1.74835
0.4
569.5
 
Procalcitonin (ng/mL)
1
437
0.31069
2.794236
0.133666
0.02
57.94
0.018
 
2
456
0.44145
2.449261
0.114697
0.02
32.54
 
 
3
487
2.28523
37.79447
1.712631
0.02
831.4
 
 
4
507
4.27661
21.36995
0.949073
0.03
256.24
 
 
Total
1887
1.91744
22.28269
0.512958
0.02
831.4
 
Liver enzymes
ALT (U/L)
1
422
37.334
34.9598
1.7018
5
273
 < 0.001
 
2
454
36.455
30.2239
1.4185
4.7
222
 
 
3
471
44.571
66.076
3.0446
3.5
1091
 
 
4
508
56.4
127.7525
5.6681
3.2
2666
 
 
Total
1855
44.178
78.3383
1.8189
3.2
2666
 
AST (U/L)
1
75
40.69
48.437
5.593
0
341
 < 0.001
 
2
86
37.28
40.325
4.348
0
303
 
 
3
92
46.93
64.371
6.711
12
592
 
 
4
97
165.62
633.57
64.329
1
5808
 
 
Total
350
76.12
339.827
18.165
0
5808
 
Albumin (g/dL)
1
421
4.0435
0.53899
0.02627
1.8
5.4
 < 0.001
 
2
454
4.0132
0.51224
0.02404
0.8
5
 
 
3
469
3.9004
0.52955
0.02445
0.6
5
 
 
4
507
3.6516
0.53603
0.02381
1.7
4.8
 
 
Total
1851
3.8925
0.55173
0.01282
0.6
5.4
 
Bilirubin, total (mg/dL)
1
421
0.57
0.434
0.021
0
4
0.403
 
2
457
0.62
0.631
0.03
0
8
 
 
3
471
0.71
1.548
0.071
0
31
 
 
4
507
0.77
0.795
0.035
0
8
 
 
Total
1856
0.67
0.963
0.022
0
31
 
Creatinine (mg/dL)
1
569
1.026
3.8608
0.1619
0.2
92.2
 < 0.001
 
2
558
0.907
1.0408
0.0441
0.1
24.1
 
 
3
571
1.284
6.3533
0.2659
0.1
125.9
 
 
4
578
1.095
1.0308
0.0429
0.2
10.8
 
 
Total
2276
1.079
3.7931
0.0795
0.1
125.9
 
APTT activated partial thromboplastin time; LDH lactate dehydrogenase; ALT alanine aminotransferase; AST aspartate aminotransferase

Discussion

In contrast to the delayed neutrophil response specially in viral infections, circulating monocytes are first responders in a proportional magnitude that match to the intensity of microbial exposure [3]. Blood monocytes are transient stage between site of production and site of action during infection, therefore, assessing monocyte activation by MDW can be a direct measure of the level and stage of infection [7]. MDW is a morphometric biomarker in the course of sepsis development and can be an early indicator of sepsis. Recent studies showed that adding MDW to WBC can enhance medical decision making during early sepsis management especially in neonates patients and whenever monitoring sepsis biomarkers is not accessible due to various reasons such as high coast or testing cannot be done for every suspected cases as in pandemics [5, 8]. Our data showed a significant positive correlation between MDW and various laboratory parameters linked with poor prognosis of COVID-19 including total WBC, neutrophils, liver enzymes and inflammatory markers such as CRP. Furthermore, our data revealed that MDW ≥ 24.685 has a strong correlation with poor prognosis of COVID-19.
A negative correlation between MDW and lymphocytes was noted in the current study which is consistent with several studies’ observations that severe illness is associated with lower lymphocyte counts and may predict poor outcomes and higher rate of mortality in patients with COVID‐19 [911]. Studies on SARS suggested that SARS-CoV-2 exhaust and eliminate natural killer cells and T cells leading to lymphopenia, making lymphopenia a useful predictor for prognosis in the patients as Intensive Care Unit (ICU) admitted patients show a dramatic decrease in T cells, especially CD8-T cell counts [12, 13]. Lymphocyte/monocyte count was found to be the main markers discriminating high- and low-risk groups in COVID-19 patients [14]. We found that peripheral blood from deceased patients with COVID-19 frequently showed neutrophilic leukocytosis and lymphopenia that makes serial white blood cell count and lymphocyte count a useful predictors of progression towards a more severe form of COVID-19 as documented by other studies [15, 16]. Additionally, elevated neutrophil counts were significantly correlated to the mortality of COVID‐19 patients, so combined admission lymphopenia and neutrophilia are associated with poor outcomes in patients with COVID-19 [17, 18].
In all cases, the demonstrated correlation between MDW and poor prognostic WBC, neutrophils and lymphocytes is not surprising as previous studies suggested that circulating monocytes and tissue macrophages participate in all stages of SARS COVID-19 [7]. SARS-CoV-2 can infect monocytes through angiotensin-converting enzyme 2(ACE2)-dependent and independent pathways and shifts in monocyte subpopulations in mediating severity of the disease has been proposed [19, 20]. Certain subsets were disturbed and cells co-expressing markers of M1 and M2 monocytes were found in intermediate and non-classical subsets [21]. Those overactivated monocytes play a role in the cytokine storm that leads to the acute pulmonary injury and acute respiratory distress syndrome (ARDS) in COVID‐19 patients [22]. Initially in COVID-19 patients there may be monocytopaenia that is corrected on the 5th day onwards with abnormal activated monocytes characterized by marked anisocytosis, cytoplasmic vacuolisation and paucity of granules [23]. Monocytes in COVID-19 patients have increased lipid droplets accumulation leading to changes in MDW and making this a clinically attractive biomarker for macrophage abnormalities, and structural functional correlation [24].
In our study, MDW was significantly positively correlated with COVID-19 inflammatory markers including CRP, LDH, Ferritin, and Procalcitonin. The level of plasma CRP is known to positively correlate with the severity of COVID-19 pneumonia and can serve as an earlier indicator for severe illness and provides easy guidance to primary care enabling effective intervention measures ahead of time to reduce the rates of severe illness and mortality [2527]. It is well known that systemic inflammation associated with elevated plasma CRP conferred a phenotype on Peripheral Blood Mononuclear Cells (PBMC), specifically through monocyte tissue factor (TF) expression by monocytes/macrophages leads to thrombin generation linked to sepsis [28, 29]. Moreover, it was reported that monocytes can transport CRP in blood flow through monocyte-derived exosomes to maintain chronic inflammation [30].
The findings of the current study presented significant negative correlation between MDW and total platelet (r = − 0.140, p < 0.001). These findings are concurrent with the fact that COVID-19 is associated with mild thrombocytopenia that is linked with more severe disease and mortality as SARS-CoV-2 can alter platelet number, form, and function [31, 32]. Also, MDW was significantly correlated with the prothrombin time (PT) (r = 0.174, p < 0.001), activated partial thromboplastin time (APTT) (r = 0.204, p < 0.001), and D-Dimer (r = − 0.218, p < 0.001). Studies have reported disturbed coagulation in COVID-19 patients, including decreased antithrombin, prolonged prothrombin time, and increased fibrin degradation products such as D-dimer [33, 34]. This implies increased risk of bleeding, as well as thromboembolic disease that could dispose to the most serious cases including the development of disseminated intravascular coagulation (DIC) [35]. Additionally, D-dimer level at presentation with COVID-19 was shown to predict ICU admission [36].
This study has limitation for being a single-institution study and focused on adults COVID-19 patients. Nevertheless, the interesting about the study is the investigation, for the first time, the correlation between routine laboratory parameters in different quartiles of MDW values and the use of large sample size to support the findings precision. The MDW correlation with different inflammation markers involved in the cytokine storm induced by SARS-CoV-2, such as Interlukin-6 (IL6) and granulocyte colony-stimulating factor (GCSF), is a focal point for future research to increase our understanding of the MDW as a novel sepsis indicator in COVID-19 patients. Further study to investigate the MDW relationship with the clinical evolution of the patients is suggested to make the prognostic value of MDW in disease progress.

Conclusions

To conclude, MDW can be predictor of poor outcome in patients presenting to the emergency setting with COVID-19. Interventions and specific therapeutics to target macrophage activation may be useful in mitigating adverse outcomes in these populations and manage the inflammatory response in COVID-19, preventing progressing to sepsis and multiorgan failure.

Acknowledgements

The Authors wish to thank all members of the DHA unified electronic system Salama for helping in data collection from the electronic patient medical record.

Declarations

The Dubai Scientific Research Ethics Committee (DSREC) of the Dubai Health Authority (DHA) reviewed and approved the present study (DSREC-06/2020-55). Further clarification can be obtained from the DSREC at DSREC@dha.gov.ae. This study was initiated in the DHA all methods were performed in accordance with the relevant guidelines and regulations (Declaration of Helsinki). No patients were enrolled for this study hence informed consent was waived off by the Dubai Scientific Research Ethics Committee (DSREC). No questionnaire or survey was separately created or designed for this study. This was indicated in the IRB application that was submitted to DHA-DSREC which approved the waiver.
Not applicable.

Competing interests

The authors declare that they have no competing interests.
Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://​creativecommons.​org/​licenses/​by/​4.​0/​. The Creative Commons Public Domain Dedication waiver (http://​creativecommons.​org/​publicdomain/​zero/​1.​0/​) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Literatur
2.
Zurück zum Zitat Malik P, et al. Biomarkers and outcomes of COVID-19 hospitalisations: systematic review and meta-analysis. BMJ Evid Based Med. 2020;26(3):107–8.CrossRefPubMed Malik P, et al. Biomarkers and outcomes of COVID-19 hospitalisations: systematic review and meta-analysis. BMJ Evid Based Med. 2020;26(3):107–8.CrossRefPubMed
3.
4.
Zurück zum Zitat Crouser ED, et al. Monocyte distribution width enhances early sepsis detection in the emergency department beyond SIRS and qSOFA. J Intensive Care. 2020;8:33.CrossRefPubMedPubMedCentral Crouser ED, et al. Monocyte distribution width enhances early sepsis detection in the emergency department beyond SIRS and qSOFA. J Intensive Care. 2020;8:33.CrossRefPubMedPubMedCentral
5.
Zurück zum Zitat Crouser ED, et al. Monocyte distribution width: a novel indicator of sepsis-2 and sepsis-3 in high-risk emergency department patients. Crit Care Med. 2019;47(8):1018–25.CrossRefPubMedPubMedCentral Crouser ED, et al. Monocyte distribution width: a novel indicator of sepsis-2 and sepsis-3 in high-risk emergency department patients. Crit Care Med. 2019;47(8):1018–25.CrossRefPubMedPubMedCentral
6.
Zurück zum Zitat Lin HA, et al. Clinical impact of monocyte distribution width and neutrophil-to-lymphocyte ratio for distinguishing COVID-19 and influenza from other upper respiratory tract infections: a pilot study. PLoS ONE. 2020;15(11):e0241262.CrossRefPubMedPubMedCentral Lin HA, et al. Clinical impact of monocyte distribution width and neutrophil-to-lymphocyte ratio for distinguishing COVID-19 and influenza from other upper respiratory tract infections: a pilot study. PLoS ONE. 2020;15(11):e0241262.CrossRefPubMedPubMedCentral
8.
Zurück zum Zitat Polilli E, et al. Comparison of monocyte distribution width (MDW) and procalcitonin for early recognition of sepsis. PLoS ONE. 2020;15(1):e0227300.CrossRefPubMedPubMedCentral Polilli E, et al. Comparison of monocyte distribution width (MDW) and procalcitonin for early recognition of sepsis. PLoS ONE. 2020;15(1):e0227300.CrossRefPubMedPubMedCentral
9.
Zurück zum Zitat Huang G, Kovalic AJ, Graber CJ. Prognostic value of leukocytosis and lymphopenia for coronavirus disease severity. Emerg Infect Dis. 2020;26(8):1839–41.CrossRefPubMedPubMedCentral Huang G, Kovalic AJ, Graber CJ. Prognostic value of leukocytosis and lymphopenia for coronavirus disease severity. Emerg Infect Dis. 2020;26(8):1839–41.CrossRefPubMedPubMedCentral
10.
Zurück zum Zitat Yamada T, et al. Value of leukocytosis and elevated C-reactive protein in predicting severe coronavirus 2019 (COVID-19): a systematic review and meta-analysis. Clin Chim Acta. 2020;509:235–43.CrossRefPubMedPubMedCentral Yamada T, et al. Value of leukocytosis and elevated C-reactive protein in predicting severe coronavirus 2019 (COVID-19): a systematic review and meta-analysis. Clin Chim Acta. 2020;509:235–43.CrossRefPubMedPubMedCentral
11.
Zurück zum Zitat Sayad B, et al. Leukocytosis and alteration of hemoglobin level in patients with severe COVID-19: association of leukocytosis with mortality. Health Sci Rep. 2020;3(4):e194–e194.CrossRefPubMedPubMedCentral Sayad B, et al. Leukocytosis and alteration of hemoglobin level in patients with severe COVID-19: association of leukocytosis with mortality. Health Sci Rep. 2020;3(4):e194–e194.CrossRefPubMedPubMedCentral
12.
Zurück zum Zitat Fathi N, Rezaei N. Lymphopenia in COVID-19: therapeutic opportunities. Cell Biol Int. 2020;44(9):1792–7.CrossRefPubMed Fathi N, Rezaei N. Lymphopenia in COVID-19: therapeutic opportunities. Cell Biol Int. 2020;44(9):1792–7.CrossRefPubMed
13.
Zurück zum Zitat Yan S, Wu G. Is lymphopenia different between SARS and COVID-19 patients? FASEB J. 2021;35(2):e21245.CrossRefPubMed Yan S, Wu G. Is lymphopenia different between SARS and COVID-19 patients? FASEB J. 2021;35(2):e21245.CrossRefPubMed
14.
Zurück zum Zitat Biamonte F, et al. Combined lymphocyte/monocyte count, D-dimer and iron status predict COVID-19 course and outcome in a long-term care facility. J Transl Med. 2021;19(1):79.CrossRefPubMedPubMedCentral Biamonte F, et al. Combined lymphocyte/monocyte count, D-dimer and iron status predict COVID-19 course and outcome in a long-term care facility. J Transl Med. 2021;19(1):79.CrossRefPubMedPubMedCentral
16.
Zurück zum Zitat Huang Y, Zhang Y, Ma L. Meta-analysis of laboratory results in patients with severe coronavirus disease 2019. Exp Ther Med. 2021;21(5):449–449.CrossRefPubMedPubMedCentral Huang Y, Zhang Y, Ma L. Meta-analysis of laboratory results in patients with severe coronavirus disease 2019. Exp Ther Med. 2021;21(5):449–449.CrossRefPubMedPubMedCentral
17.
Zurück zum Zitat Shi L, et al. Is neutrophilia associated with mortality in COVID-19 patients? A meta-analysis and meta-regression. Int J Lab Hematol. 2020;42(6):e244–7.CrossRefPubMed Shi L, et al. Is neutrophilia associated with mortality in COVID-19 patients? A meta-analysis and meta-regression. Int J Lab Hematol. 2020;42(6):e244–7.CrossRefPubMed
18.
Zurück zum Zitat Henry B, et al. Lymphopenia and neutrophilia at admission predicts severity and mortality in patients with COVID-19: a meta-analysis. Acta Biomed. 2020;91(3):e2020008.PubMedPubMedCentral Henry B, et al. Lymphopenia and neutrophilia at admission predicts severity and mortality in patients with COVID-19: a meta-analysis. Acta Biomed. 2020;91(3):e2020008.PubMedPubMedCentral
20.
Zurück zum Zitat Pence BD. Atypical monocytes in COVID-19: lighting the fire of cytokine storm? J Leukoc Biol. 2021;109(1):7–8.CrossRefPubMed Pence BD. Atypical monocytes in COVID-19: lighting the fire of cytokine storm? J Leukoc Biol. 2021;109(1):7–8.CrossRefPubMed
21.
Zurück zum Zitat Matic S, et al. SARS-CoV-2 infection induces mixed M1/M2 phenotype in circulating monocytes and alterations in both dendritic cell and monocyte subsets. PLoS ONE. 2020;15(12):e0241097.CrossRefPubMedPubMedCentral Matic S, et al. SARS-CoV-2 infection induces mixed M1/M2 phenotype in circulating monocytes and alterations in both dendritic cell and monocyte subsets. PLoS ONE. 2020;15(12):e0241097.CrossRefPubMedPubMedCentral
22.
Zurück zum Zitat Karimi Shahri M, Niazkar HR, Rad F. COVID-19 and hematology findings based on the current evidences: a puzzle with many missing pieces. Int J Lab Hematol. 2021;43(2):160–8.CrossRefPubMed Karimi Shahri M, Niazkar HR, Rad F. COVID-19 and hematology findings based on the current evidences: a puzzle with many missing pieces. Int J Lab Hematol. 2021;43(2):160–8.CrossRefPubMed
23.
Zurück zum Zitat Singh A, et al. Morphology of COVID-19—affected cells in peripheral blood film. BMJ Case Rep. 2020;13(5):e236117.CrossRefPubMed Singh A, et al. Morphology of COVID-19—affected cells in peripheral blood film. BMJ Case Rep. 2020;13(5):e236117.CrossRefPubMed
25.
27.
Zurück zum Zitat Liu SL, et al. Expressions of SAA, CRP, and FERR in different severities of COVID-19. Eur Rev Med Pharmacol Sci. 2020;24(21):11386–94.PubMed Liu SL, et al. Expressions of SAA, CRP, and FERR in different severities of COVID-19. Eur Rev Med Pharmacol Sci. 2020;24(21):11386–94.PubMed
28.
Zurück zum Zitat Cai H, et al. Importance of C-reactive protein in regulating monocyte tissue factor expression in patients with inflammatory rheumatic diseases. J Rheumatol. 2005;32(7):1224–31.PubMed Cai H, et al. Importance of C-reactive protein in regulating monocyte tissue factor expression in patients with inflammatory rheumatic diseases. J Rheumatol. 2005;32(7):1224–31.PubMed
29.
Zurück zum Zitat Osterud B. Tissue factor expression by monocytes: regulation and pathophysiological roles. Blood Coagul Fibrinolysis. 1998;9(Suppl 1):S9-14.PubMed Osterud B. Tissue factor expression by monocytes: regulation and pathophysiological roles. Blood Coagul Fibrinolysis. 1998;9(Suppl 1):S9-14.PubMed
30.
Zurück zum Zitat Melnikov I, et al. CRP is transported by monocytes and monocyte-derived exosomes in the blood of patients with coronary artery disease. Biomedicines. 2020;8(10):435.CrossRefPubMedCentral Melnikov I, et al. CRP is transported by monocytes and monocyte-derived exosomes in the blood of patients with coronary artery disease. Biomedicines. 2020;8(10):435.CrossRefPubMedCentral
32.
Zurück zum Zitat Parra-Izquierdo I, Aslan JE. Perspectives on platelet heterogeneity and host immune response in coronavirus disease 2019 (COVID-19). Semin Thromb Hemost. 2020;46(7):826–30.CrossRefPubMedPubMedCentral Parra-Izquierdo I, Aslan JE. Perspectives on platelet heterogeneity and host immune response in coronavirus disease 2019 (COVID-19). Semin Thromb Hemost. 2020;46(7):826–30.CrossRefPubMedPubMedCentral
35.
Zurück zum Zitat 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(04):844–7.CrossRefPubMedPubMedCentral 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(04):844–7.CrossRefPubMedPubMedCentral
Metadaten
Titel
Monocyte distribution width as a novel sepsis indicator in COVID-19 patients
verfasst von
Laila Alsuwaidi
Saba Al Heialy
Nahid Shaikh
Firas Al Najjar
Rania Seliem
Aaron Han
Mahmood Hachim
Publikationsdatum
05.01.2022
Verlag
BioMed Central
Schlagwort
COVID-19
Erschienen in
BMC Infectious Diseases / Ausgabe 1/2022
Elektronische ISSN: 1471-2334
DOI
https://doi.org/10.1186/s12879-021-07016-4

Weitere Artikel der Ausgabe 1/2022

BMC Infectious Diseases 1/2022 Zur Ausgabe

Leitlinien kompakt für die Innere Medizin

Mit medbee Pocketcards sicher entscheiden.

Seit 2022 gehört die medbee GmbH zum Springer Medizin Verlag

Update Innere Medizin

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.