Sage Journals: Discover world-class research

Abstract

Introduction:

This study aims to assess the association between mean platelet volume (MPV) and poor outcome in patients with COVID-19.

Methods:

We performed a comprehensive literature search using the PubMed, Embase and Scopus databases with keywords “2019-nCoV” OR “SARS-CoV-2” OR “COVID-19” AND “mean platelet volume” OR “MPV” on 8 July 2021. The primary outcome was composite poor outcome, defined as severe COVID-19 or mortality. The pooled effect estimate was reported as mean differences in terms of MPV between the group with and without outcome.

Results:

There were 17 studies which consist of 4549 patients with COVID-19 were included in this study. The incidence of poor outcome was 25% (20%–30%). Mean MPV was found to be higher in the poor outcome group in compare to no poor outcome group (10.3 ± 1.9 fL vs 9.9 ± 1.7 fL). The mean MPV difference between both group was 0.47 fL [95% CI 0.27, 0.67], p < 0.001; I²: 62.91%, p < 0.001). In the sub-group analysis, patients with severe COVID-19 had higher MPV (mean difference 0.54 fL [95% CI 0.28, 0.80], p < 0.001; I²: 54.84%, p = 0.014). Furthermore, MPV was also higher in the mortality group (mean difference 0.54 fL [95% CI 0.29, 0.80], p = 0.020; I²: 71.11%, p = 0.004). Meta-regression analysis showed that the association between MPV and poor outcome was not affected by age (p = 0.789), gender (p = 0.167), platelets (p = 0.056), white blood cells (p = 0.639), and lymphocytes (p = 0.733).

Conclusion:

This meta-analysis indicated that increased MPV was associated with severity and mortality in patients with COVID-19. Further research is needed to determine the optimum cut-off point.

Keywords

Coronavirus platelet mean platelet volume severity mortality

Introduction

Coronavirus disease 2019 (COVID-19) is currently one of the most prevalent disease in the world.¹ Although most patients have mild symptoms, a significant proportion of patients developed end-organ complications.^2–4 Two of the most common and potentially lethal complications are thrombotic- and coagulopathy-associated with inflammation, activation of coagulation pathway, and endothelial cells.^5–7 Interest is growing toward a cheap, reliable, and readily available laboratory parameter to diagnose and to predict the outcome of COVID-19 infection.

Platelet is crucially involved in the complex interaction of each step in inflammatory cascade.⁸ Platelet activation serves as a response toward pulmonary viral infection and might contribute to detrimental effect of inflammation toward the lung.^9,10 During the course of COVID-19, exaggeration of inflammatory response promotes the release of inflammatory cytokines which influence platelet activation and maturation. One of these cytokines is interleukin-6 (IL-6), which is well known to correlate with the severity of disease.¹¹ IL-6 along with interleukin-1 (IL-1), interleukin-3 (IL-3) and tumor necrosis factor-α (TNF-α) play a significant role in affecting megakaryocyte ploidy through an increased level of thrombopoietin and causing more greater reactive production of megakaryocyte.^8,12–14 Therefore, more immature platelets are released into the circulation which eventually will increase MPV.⁸

Mean platelet volume (MPV) is one of the routinely ordered laboratory examination which reflects the platelet size and its activation, shows a promising result in predicting mortality in COVID-19.^15,16 MPV which reflects platelet size also represent its activation. Platelet size is well known to be influenced by the course and severity of infection therefore it might carry valuable information in determining the prognosis.¹⁷ MPV level is also proven to be a diagnostic and prognostic factor in other settings of infection, such as sepsis, infective endocarditis, pneumonia, brucellosis, cellulitis, and acute pyelonephritis.^18–23 This study aims to assess the association between MPV and poor outcome in patients with COVID-19.

Material and methods

This meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines. This meta-analysis is registered in PROSPERO (Blinded for review).

Search strategy and study selection

We performed a comprehensive literature search using the PubMed, Embase and Scopus databases with keywords “2019-nCoV” OR “SARS-CoV-2” OR “COVID-19” AND “mean platelet volume” OR “MPV” 8 July 2021. The PubMed (MEDLINE) search strategy was ((2019-nCoV) OR (SARS-CoV-2) OR (COVID-19)) AND ((mean platelet volume) OR (MPV)). Two independent authors performed title and abstract screening after removal of duplicates. The articles eligibility was then assessed for their eligibility using the inclusion and exclusion criteria.

Eligibility criteria

Studies were eligible for inclusion if they fulfill all of the following criteria: (1) observational studies reporting patients with confirmed COVID-19 with age ⩾18 years-old, (2) reporting severe COVID-19 or mortality, (3) containing continuous data or dichotomous data on MPV based on the outcome. We did not performed any language restriction due to the possibility of missing a relevant study.

Studies were excluded if they fulfill one of the following criteria: (1) review articles, (2) editorial, (3) non-research letter, (4) case reports, (5) animal studies, (6) and studies in pediatric populations (⩽17 years old).

Outcome

The primary outcome was composite poor outcome, defined as severe COVID-19 or mortality. The pooled effect estimate was reported as mean differences in terms of MPV between the group with and without outcome. Severe COVID-19 was defined as patients that fulfill at least one of the following criteria: (1) respiratory rate ⩾30 breaths per min; (2) oxygen saturation ⩽93% (at rest; (3) ratio of partial pressure of arterial oxygen to fractional concentration of oxygen inspired air (PaO₂:fiO₂ ratio) ⩽300 mmHg; (4) lung infiltrates >50% of the lung field within 24–48 h; or (5) specific complications, such as septic shock, respiratory failure, and or multiple organ dysfunction or required intensive unit care (ICU care).²⁴ Mortality was defined as clinically validated death.

Data extraction

Data extraction was performed independently by two authors by using a standardized extraction form. The variables of interest were author, study design, year of publication, age, gender, MPV, platelet count, white blood cell count, lymphocyte count, and the outcome.

Risk of bias assessment

Two independent authors performed risk of bias assessment using the Newcastle-Ottawa Scale (NOS) Discrepancies that arose were resolved by discussion.

Statistical analysis

Der-Simonian Laird random-effects meta-analysis was used to pool the continuous variable and obtain mean differences along its 95% confidence interval (95% CI) regardless of heterogeneity. p-Values below 0.05 indicates statistical significance. All p-values were two-tailed and statistical significance was set at ⩽0.05. We assessed heterogeneity using the Cochran Q test and I² statistics, and p-values of <0.10 or an I² value of >50% indicates statistically significant heterogeneity. Subgroup analysis were performed for severity and mortality. The potential for publication bias was assessed using funnel-plot analysis and Egger’s test. Non-parametric trim-and-fill analysis using Linear 0 estimator to impute studies on the right side of the plot. Egger’s test was used to assess the potential for small-study effects quantitatively. Restricted-maximum likelihood random effects meta-regression was performed for age, gender, platelets, white blood cells, and lymphocytes. Meta-analysis was performed using STATA software (Version 16; Stata Corporation, College Station, TX).

Results

Baseline characteristics

In total, there were 4549 patients with COVID-19 from 17 studies that were included in this systematic review and meta-analysis (Figure 1).^15,25–40 Baseline characteristics of the included studies are displayed in Table 1. The incidence of poor outcome was 25% (20%–30%). The mean MPV for poor outcome group and without poor outcome group were 10.3 ± 1.9 fL versus 9.9 ± 1.7 fL respectively.

Figure 1.

PRISMA flowchart.

Table 1.

Characteristics of the included studies.

Authors	Study design	Sample size	Severe Case (%)	Age (years)	Male (%)	Platelet (× 10⁹/L)	MPV (fL)	WBC (× 10⁹/L)	Lymphocyte (× 10⁹/L)	Outcome	NOS*
Asan et al.³⁰	Retrospective Cohort	695	3.9	42	48	210	9.6	6.3	NA	Severity	7
Comer et al.³¹	Prospective Cohort	12	50	63	63	313	10.7	7.1	1.1	Severity	7
Ding et al.³²	Retrospective Cohort	72	20.8	49	45.8	180	9.2	4	1	Severity	6
Eraybar S 2021	Retrospective Cohort	938	1.6	41	46.9	241.2	9.7	NA	2.25	Mortality	6
Fois et al.³³	Retrospective Cohort	119	NA	72	64.7	204	8.5	6.74	0.9	Mortality	8
Giusti et al.³⁴	Prospective Cohort	209	24.4	65.9	63.6	187	10.6	NA	NA	Mortality	5
Gowda et al³⁵	Retrospective Cohort	100	NA	47.1	57	228	8.68	NA	NA	Mortality	5
Güçlü et al.¹⁵	Retrospective Cohort	212	62.3	60	55	194	9.6	6.7	1.1	Mortality	8
Lin et al.³⁶	Retrospective Cohort	68	67.6	52.4	58.8	201	10.5	5.7	0.9	Severity	7
Mertoglu et al.³⁷	Retrospective Cohort	555	4.1	49	57.5	233	10.1	7.2	1.88	Severity	8
Pujani et al.³⁸	Prospective Cohort	506	33.3	42	62.3	154	12.2	11.5	2.07	Mortality	6
San I 2021	Retrospective Cohort	388	11.3	45	56.4	209	8.2	5.15	1.25	Severity	7
Taj et al.⁴⁰	Retrospective Cohort	225	54.2	58	65.3	242	10.5	9	1.3	Severity	6
Wang et al.²⁶	Retrospective Cohort	61	39.3	53	50.8	206	10.1	5.4	1.28	Severity	8
Waris et al.²⁷	Cross-sectional	101	24.8	48.9	68.3	209	10	8	1.69	Severity	5*
Xiong et al.²⁸	Retrospective Cohort	57	67	66	61.4	200	10.2	NA	NA	Severity	6
Zhang et al.²⁹	Cross-sectional	241	23.7	48	59.8	174	9.8	NA	NA	Severity	7*

NOS: Newcastle-Ottawa Scale; MPV: mean platelet volume; WBC: white blood cells; NA: not available.

NOS adjusted for cross-sectional study.

Mean platelet volume and poor outcome

COVID-19 patients with poor outcome had higher MPV (mean difference 0.47 fL [95% CI 0.27, 0.67], p < 0.001; I²: 62.91%, p < 0.001) (Figure 2). Patients with severe COVID-19 had higher MPV (mean difference 0.54 fL [95% CI 0.28, 0.80], p < 0.001; I²: 54.84%, p = 0.014). MPV was found to be higher in the mortality group (mean difference 0.54 fL [95% CI 0.29, 0.80], p = 0.020; I²: 71.11%, p = 0.004).

Figure 2.

Mean platelet volume and poor outcome.

Meta-regression

Meta-regression analysis showed that the association between MPV and poor outcome was not affected by age (p = 0.789) (Figure 3(a)), gender (p = 0.167), platelets (p = 0.056) (Figure 3(b)), white blood cells (p = 0.639), and lymphocytes (p = 0.733).

Figure 3.

Meta-regression analysis. Covariates: age (a) and platelets (b).

Publication bias

Egger’s test was not significant for small study effects (p = 0.377). Funnel-plot was asymmetrical (Figure 4(a)) and imputation of three studies to the right side of the plot yielded effect estimate mean difference of 0.557 [95% CI 0.350, 0.763] (Figure 4(b)).

Figure 4.

Publication bias. Funnel-plot analysis (a) and non-parametric trim-and-fill analysis (b).

Discussion

This meta-analysis indicated that increased MPV was associated with severity and mortality in patients with COVID-19.

The mechanisms of platelet alteration during the course of COVID-19 infection are thought to be complex and multifactorial. MPV itself can be considered as a marker of platelet activity as it is associated with aggregation and release of thromboxane A2, platelet factor 4 and b-thromboglobulin.¹³ In patient with community acquired pneumonia, Gorelik et al. observed a raised MPV was associated with higher pneumonia severe scores and lower platelet counts on discharge.⁴¹ Severe inflammation was postulated to be the main potential mechanism of increased MPV and MPV elevation may reflect hypercoagulation which is associated with poor outcome.¹⁴

The role of inflammation and increased MPV in COVID-19 could be proposed as following. Firstly, thrombocytopenia might occur as a consequence of direct involvement of coronavirus in the bone marrow. Secondly, there are increased destruction of platelet related to inflammatory response. Thirdly, platelet consumption as a consequence of platelet aggregation in the lung.⁴² Eventually all of these mechanisms, in addition to the release of proinflammatory cytokines such as IL-6 which directly acts on megakaryocytes, will stimulate megakaryocyte to produce more platelets to counterbalance the accelerated rate of platelet destruction.^8,43 As a result, an increased level of MPV will be found (Figure 5).⁴⁴

Figure 5.

Proposed mechanism of increased MPV in COVID-19 infection.

MPV might show a significant role in the clinical setting as demonstrated in several studies. Güçlü et al. reported that difference between MPV and oxygen saturation during admission, first and third day had showed a significant result in predicting mortality.¹⁵ Another study that was conducted by Gumus et al. also demonstrated the utilization MPV level in predicting COVID-19 in asymptomatic children.¹⁷ Therefore, MPV might serve as an auxiliary test in detecting COVID-19 infection and predicting COVID-19 mortality.⁴⁵

Limitation

Most of the included studies were retrospective studies and did not report the status of their missing data. Most of the studies did not report cut-off points for MPV, thus meta-analysis could not be performed. To be clinically more useful, an optimal cut-off point should be determined in the future studies.

Conclusion

This meta-analysis indicated that increased MPV was associated with severity and mortality in patients with COVID-19. Further research is needed to determine the optimum cut-off point.

Footnotes

Contributorship statement

AFMZZ, CSS, and RP were involved in the conceptualization and design of the manuscript. AFMZZ, CSS, AW, WMR, and RP participated in data curation and investigation. RP performed formal and statistical analysis. AFMZZ, CSS, and WMR drafted the manuscript. AFMZZ and RP review and edited the manuscript.

Data availability

Data are available on reasonable request.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical approval

Not Applicable.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Informed consent

Not Applicable.

Protocol registration number

PROSPERO CRD42021266701

ORCID iDs

Ahmad Fariz Malvi Zamzam Zein

Wilson Matthew Raffaelo

Raymond Pranata

References

WHO. Weekly epidemiological update - 2 March 2021. Geneva: WHO, 2021.

Lim

Pranata

Huang

, et al. Multiorgan failure with emphasis on acute kidney injury and severity of COVID-19: systematic review and meta-analysis. Can J Kidney Health Dis 2020; 7: 2054358120938573.

Pranata

Huang

Raharjo

. Incidence and impact of cardiac arrhythmias in coronavirus disease 2019 (COVID-19): a systematic review and meta-analysis. Indian Pacing Electrophysiol J 2020; 20: 193–198.

Pranata

Huang

Lim

, et al. Delirium and mortality in Coronavirus disease 2019 (COVID-19) - a systematic review and meta-analysis. Arch Gerontol Geriatr 2021; 95: 104388.

Ward

Curley

Lavin

, et al. Von Willebrand factor propeptide in severe coronavirus disease 2019 (COVID-19): evidence of acute and sustained endothelial cell activation. Br J Haematol 2021; 192: 714–719.

Mancini

Baronciani

Artoni

, et al. The ADAMTS13-von Willebrand factor axis in COVID-19 patients. J Thromb Haemost 2021; 19: 513–521.

Wibowo

Pranata

Lim

, et al. Endotheliopathy marked by high von Willebrand factor (vWF) antigen in COVID-19 is associated with poor outcome: a systematic review and meta-analysis. Int J Infect Dis 2022; 117: 267–273.

Zhong

Peng

. Mean platelet volume/platelet count ratio predicts severe pneumonia of COVID-19. J Clin Lab Anal 2021; 35: e23607.

Lê

Schneider

Boergeling

, et al. Platelet activation and aggregation promote lung inflammation and influenza virus pathogenesis. Am J Respir Crit Care Med 2015; 191: 804–819.

10.

Pulavendran

Rudd

Maram

, et al. Combination therapy targeting platelet activation and virus replication protects mice against lethal influenza pneumonia. Am J Respir Cell Mol Biol 2019; 61: 689–701.

11.

Gao

Han

, et al. Diagnostic utility of clinical laboratory data determinations for patients with the severe COVID-19. J Med Virol 2020; 92: 791–796.

12.

Becchi

Al Malyan

Fabbri

, et al. Mean platelet volume trend in sepsis: is it a useful parameter? Minerva Anestesiol 2006; 72: 749–756.

13.

Gasparyan

Ayvazyan

Mikhailidis

, et al. Mean platelet volume: a link between thrombosis and inflammation? Curr Pharm Des 2011; 17: 47–58.

14.

Lee

Park

Han

, et al. An increase in mean platelet volume during admission can predict the prognoses of patients with pneumonia in the intensive care unit: a retrospective study. PLoS One 2018; 13: e0208715.

15.

Güçlü

Kocayiğit

Okan

, et al. Effect of COVID-19 on platelet count and its indices. Rev Assoc Med Bras 2020; 66: 1122–1127.

16.

Martin

Trowbridge

Salmon

, et al. The biological significance of platelet volume: its relationship to bleeding time, platelet thromboxane B2 production and megakaryocyte nuclear DNA concentration. Thromb Res 1983; 32: 443–460.

17.

Gumus

Demir

Yükkaldıran

. Is mean platelet volume a predictive marker for the diagnosis of COVID-19 in children? Int J Clin Pract 2021; 75: e13892.

18.

Catal

Tayman

Tonbul

, et al. Mean platelet volume (MPV) may simply predict the severity of sepsis in preterm infants. Clin Lab 2014; 60: 1193–1200.

19.

Kitazawa

Yoshino

Tatsuno

, et al. Changes in the mean platelet volume levels after bloodstream infection have prognostic value. Intern Med 2013; 52: 1487–1493.

20.

Tok

Canpolat

Tok

, et al. Association of mean platelet volume level with in-hospital major adverse events in infective endocarditis. Wien Klin Wochenschr 2015; 127: 197–202.

21.

Balbaloglu

Korkmaz

Yolcu

, et al. Evaluation of mean platelet volume (MPV) levels in patients with synovitis associated with knee osteoarthritis. Platelets 2014; 25: 81–85.

22.

Erturk

Cure

, et al. The association between serum YKL-40 levels, mean platelet volume, and c-reactive protein in patients with cellulitis. Indian J Med Microbiol 2015; 33: 61–S66.

23.

Tekin

Konca

Gulyuz

, et al. Is the mean platelet volume a predictive marker for the diagnosis of acute pyelonephritis in children? Clin Exp Nephrol 2015; 19: 688–693.

24.

World Health Organization. Report of the WHO-China joint mission on Coronavirus Disease 2019 (COVID-19). Geneva: WHO.

25.

Gharagozloo

Kalantari

Rezaei

, et al. CLINICAL STUDY immune-mediated cochleovestibular disease. Bratisl Lek Listy 2015; 116: 296–301.

26.

Wang

Xing

Yao

, et al. Retrospective study of clinical features of covid-19 in inpatients and their association with disease severity. Med Sci Monit 2020; 26: e927674.

27.

Waris

Din

Khalid

, et al. Evaluation of hematological parameters as an indicator of disease severity in Covid-19 patients: Pakistan’s experience. J Clin Lab Anal 2021; 35: e23809.

28.

Xiong

Liu

Luo

, et al. Prominent hypercoagulability associated with inflammatory state among cancer patients with SARS-CoV-2 infection. Front Oncol 2020; 10: 1345.

29.

Zhang

Liu

Wang

, et al. SARS-CoV-2 binds platelet ACE2 to enhance thrombosis in COVID-19. J Hematol Oncol 2020; 13: 120.

30.

Asan

Üstündağ

Koca

, et al. Do initial hematologic indices predict the severity of covid-19 patients? Turk J Med Sci 2021; 51: 39–44.

31.

Comer

Cullivan

Szklanna

, et al. COVID-19 induces a hyperactive phenotype in circulating platelets. PLoS Biol 2021; 19: e3001109.

32.

Ding

, et al. Dynamic profile and clinical implications of hematological parameters in hospitalized patients with coronavirus disease 2019. Clin Chem Lab Med 2020; 58: 1365–1371.

33.

Fois

Paliogiannis

Scano

, et al. The systemic inflammation index on admission predicts in-hospital mortality in COVID-19 patients. Molecules 2020; 25: 1–13.

34.

Giusti

Gori

Alessi

, et al. Sars-CoV-2 induced coagulopathy and prognosis in hospitalized patients: a snapshot from Italy. Thromb Haemost 2020; 120: 1233–1236.

35.

Bommenahalli Gowda

Gosavi

Ananda Rao

, et al. Prognosis of COVID-19: red cell distribution width, platelet distribution width, and C-reactive protein. Cureus 2021; 13: e13078.

36.

Lin

Mao

Zou

, et al. Associations between hematological parameters and disease severity in patients with SARS-CoV-2 infection. J Clin Lab Anal 2021; 35: e23604.

37.

Mertoglu

Huyut

Arslan

, et al. How do routine laboratory tests change in coronavirus disease 2019? Scand J Clin Lab Invest 2021; 81: 24–33.

38.

Pujani

Raychaudhuri

Verma

, et al. Association of hematologic biomarkers and their combinations with disease severity and mortality in COVID-19- an Indian perspective. Am J Blood Res 2021; 11: 180–190.

39.

Şan

Gemci̇oğlu

Davutoğlu

, et al. Which hematological markers have predictive value as early indicators of severe COVID-19 cases in the emergency department? Turk J Med Sci 2021; 51: 2810–2821.

40.

Taj

Kashif

Arzinda Fatima

, et al. Role of hematological parameters in the stratification of COVID-19 disease severity. Ann Med Surg 2021; 62: 68–72.

41.

Gorelik

Tzur

Barchel

, et al. A rise in mean platelet volume during hospitalization for community-acquired pneumonia predicts poor prognosis: a retrospective observational cohort study. BMC Pulm Med 2017; 17: 137.

42.

Zhou

. Mechanism of thrombocytopenia in COVID-19 patients. Ann Hematol 2020; 99: 1205–1208.

43.

Wool

Miller

. The impact of COVID-19 disease on platelets and coagulation. Pathobiology 2021; 88: 15–27.

44.

Kim

Lee

, et al. An increase in mean platelet volume from baseline is associated with mortality in patients with severe sepsis or septic shock. PLoS One 2015; 10: e0119437.

45.

Sertbaş

Dağcı

Kızılay

, et al. Mean platelet volume as an early predictor for the complication of COVID-19. Haydarpasa Numune Med J 2021; 6: 177–182.

The association between mean platelet volume and poor outcome in patients with COVID-19: Systematic review,meta-analysis,and meta-regression

Abstract

Introduction:

Methods:

Results:

Conclusion:

Keywords

Introduction

Material and methods

Search strategy and study selection

Eligibility criteria

Outcome

Data extraction

Risk of bias assessment

Statistical analysis

Results

Baseline characteristics

Mean platelet volume and poor outcome

Meta-regression

Publication bias

Discussion

Limitation

Conclusion

Footnotes

Contributorship statement

Data availability

Declaration of conflicting interests

Ethical approval

Funding

Informed consent

Protocol registration number

ORCID iDs

References