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Association between mean platelet volume and major adverse cardiac events in percutaneous coronary interventions: a systematic review and meta-analysis

Chen, Zhongxiua; Li, Nanb; Wang, Jingc; Li, Chena; He, Sena; Zhou, Xiaorongd; He, Yonga

Author Information
doi: 10.1097/MCA.0000000000000885

Abstract

Introduction

Although the introduction of percutaneous coronary intervention (PCI) coupled with additional treatments has been tremendously helpful in the field of cardiology, incidences of restenosis and long-term major adverse cardiac events (MACE) remain high [1]. Several clinical biomarkers have been established to assess the post-PCI prognosis with little effect on the prediction of MACE [2]. Therefore, there is a pressing need for biomarkers that could offer insight into the long-term prognosis of PCI patients. Platelets play an important role in the pathophysiology of coronary artery disease (CAD) [3]. Mean platelet volume (MPV), a routinely reported parameter of the complete blood count analysis, is an accurate measure reflecting platelet size. Previous studies have proposed platelet volume as a potential indicator of platelet reactivity and long-term MACE in PCI patients [4–7]. However, their assessment was disputed due to a lack of direct correlation between MPV and MACE [8–10]. This might be partially due to differences in thresholds among studies. On account of the existing controversial conclusions and aiming to produce more powerful evidence on cardiovascular risk stratification following PCI, this meta-analysis freshly evaluates the relationship between MPV and MACE in PCI patients.

Methods

Search strategy

This meta-analysis was performed in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) guidelines [11]. We systematically searched Pubmed, Embase, Medline, BIOSIS Previews, and Cochrane Library databases for relevant studies from their respective inception until 30 June 2019. The following medical subject headings terms and keywords were used to identify relevant articles: coronary stenting or percutaneous coronary intervention or PCI or angioplasty or coronary angioplasty or stents, and platelet volume. Studies references have also been checked for suitable articles. No language restriction was applied.

Study selection

Several assessments were performed, followed by duplicate articles removal after the initial screening. Relevant publications titles and abstracts were further screened for suitability before full article retrieval. Additionally, meeting abstracts, editorials, and reviews were also checked and excluded from the analysis. The inclusion criteria were as follows: (1) articles with MPV as one of their study factors; (2) studies published in peer-reviewed journals with available full-texts; (3) studies with MACE (including either death/cardiovascular death, myocardial infarction (MI), repeat revascularization, hospitalization for heart failure, ischemic stroke, stent thrombosis, or stent restenosis) as the outcome of interest; (4) studies with sufficient data for pooling (number of subjects, mean MPV and SD, frequencies of subjects in a contingency table of high/low MPV, and MACE groups for categorical data). Studies with insufficient pooling data were excluded after several attempts and failure to reach their authors. Three investigators (Z.X.C., N.L., and X.R.Z.) independently reviewed all retrieved studies, and differences were resolved via consensus.

Data extraction and quality assessment

Each study data on the followings: first author, study design, location of study, sample size, clinical baseline characteristics, methods used for MPV measurement, types of MACE, frequency of patients in high and low MPV groups for dichotomous outcome, mean MPV in patients with MACE and those without, and incidence of mortality for continuous outcome, were independently extracted by three investigators (Z.X.C., C.L., and J.W.). The study quality was evaluated according to the Newcastle–Ottawa Quality scale [12]. High-quality studies were defined as studies with a modified Newcastle–Ottawa score of ≥5 (maximum, 9).

Statistical analysis

The mean difference in MPV between research groups was estimated for each study and pooled using an unstandardized mean difference (USMD) for continuous outcomes. MPV was classified as high or low according to the original study data. Hazard ratios or odds ratios of having MACE or death among high and low MPV groups were estimated for each study given that different studies used different thresholds to define high and low MPV. In both cases, heterogeneity of the effect measure was assessed by the Q statistic and I2. A random-effect model (Dersimonian and Laird method) was applied if heterogeneity was detected (P value <0.10 or I2 ≥ 25%); otherwise, a fixed-effect model (inverse-variance method) was used. Sources of heterogeneity were explored by fitting each of the covariables (mean age, study setting, percentage of males, diabetes, hypertension, the timing of MPV test, type of cases) in a meta-regression model. A P value < 0.05 was considered statistically significant. RevMan 5.3 software was used for the statistical analysis.

Results

Study selection

We identified 370 publications in Pubmed, 392 publications in Cochrane Library, and a total of 534 publications in EMBASE, Ovid MEDLINE, and BIOSIS combined. Of these 1296 studies, 511 were found to be duplicates. Thirty-three of the remaining studies met the inclusion criteria [4,6,8–10,13–41]. A detailed search strategy is shown in Fig. 1.

Fig. 1
Fig. 1:
PRISMA flow diagram of the study selection. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement.

Study characteristics and quality assessment

Of the 33 included studies, 14 (42%) reported a mean difference in MPV between the outcomes of MACE groups [6,9,14,15,17,18,23,25–27,31,32,36,40], while 10 (30%) reported either a hazard ratio or OR of high MPV [4,6,10,19,23,27,30,37,38,41], the remaining 3 (9%) reported both [6,23,27]. Thirteen of the included studies were prospective studies and 15 were retrospective studies. The mean Newcastle and Ottawa risk of bias criteria score was 6.6, and 31 of the 33 studies had a quality score of more than 5. The mean age of the study participants ranged from 55 to 68.9 years. Percentages of male, hypertension, and patients with diabetes ranged from 51 to 99%, 41.5 to 85.6%, and 11.3 to 100%, respectively. General characteristics of the included studies are summarized in Table 1. The MACE definition of the included studies is summarized in Table 2.

Table 1 - Baseline characteristics of the included studies
Author/Year Study type* Quality score* Setting Study design* Number Type of CAD Type of PCI Age (years) Male Hypertension Diabetes Outcome measurement Anticoagulants Testing time
Abdullah Dogan/2012 1 5 Turkey Cannot assess 344 Cannot assess Cannot assess 61.9 69.50% 49.2% 34.01% 1 year K2-EDTA Within 30 min
Ae Ran Moon/2015 2 8 Korea R 372 Cannot assess Cannot assess 65.2 63.20% 57.3% 33.3% 25.8 months K2-EDTA Within 2 h
Akgul, Ozgur/2013 2 7 Turkey P 495 STEMI PPCI 55.57 80.40% 39.9% 20.2% 6 months EDTA Cannot assess
Alon Eisen/2013 2 4 Israel R 7585 Cannot assess Cannot assess 67.7 76.00% 73.8% 41.3% 4 years K2-EDTA Cannot assess
Andrzej Lekston/2014 2 8 Poland Cannot assess 1557 STEMI PPCI 62 68.00% 63.84% 34.62% 12 months K2-EDTA within 30 min
Binita Shah/2013 2 7 USA R 1512 Cannot assess Cannot assess 66 99.00% 77% 40% 8.7 years Cannot assess Cannot assess
Dong-Hyun Choi/2018 1 5 Korea R 364 Cannot assess Cannot assess 65 62.90% 57.42% 32.97% 29.3 months K2- EDTA Within 2 h
Elisabetta Ricottini/2018 1 5 Italy R 502 Cannot assess Cannot assess 67 78.00% 81% 41% Peri-procedural Tripotassium EDTA Within 2 h
Eyup Avci/2018 1 7 Turkey R 480 STEMI PPCI 60 76.70% 44.58% 23.96% 65.9 months K2-EDTA Within 1 h
Hideki Wada/2018 2 5 Japan P 2872 Stable CAD EPCI 67 83.10% 74.3% 46.2% 5.6 years K2-EDTA Cannot assess
Hong-Joo Seo/2015 2 7 Korea R 372 Cannot assess Cannot assess 65.2 63.20% 57.3% 33.3% 25.8 months K2-EDTA Within 2 h
Hong-Mei Lai/2015 2 6 China P 453 STEMI PPCI 56.2 84.40% 41.5% 25.17% 30 days K2-EDTA Within 30 min
Ismail Bolat/2016 2 6 Turkey P 462 STEMI PPCI 55 80.30% Cannot assess Cannot assess 1 year K2-EDTA After 120 min
Karolina Supel/2013 1 7 Poland P 106 ACS Cannot assess 68.9 51.00% 61.3% 24.5% 3 days EDTA Immediately
Kurtulus Karauzum/2017 1 8 Turkey R 118 Cannot assess Cannot assess 63.5 82.20% 76.27% 47.46% 23 months EDTA Within 30 min
Matthias K. Freynhofer/2017 3 6 Austria P 486 Cannot assess Cannot assess 64 68.30% 85.60% 28.81% 6 months Sodium citrate Cannot assess
Mir Hossein Seyyed-Mohammadzad/2015 2 5 Iran R 181 Cannot assess EPCI 58.2 68.50% 54.70% 16.57% 12 months EDTA Cannot assess
Mislav Vrsalovic/2012 1 6 Croatia P 543 STEMI PPCI 67 60.00% 58% 27% 30 days Cannot assess Within 1 h
Monica Verdoia/2013 1 6 Italy P 1056 Cannot assess Cannot assess 67.4 73.20% 72.8% Cannot assess Cannot assess Tripotassium EDTA Within 2 h
Ping Jiang/2018 2 7 China Cannot assess 1389 Cannot assess EPCI 59 76.00% 76.17% 100% 2 years K2- EDTA Within 2 h
Qin Wenpei/2015 2 8 China P 102 STEMI PPCI 59.8 76.50% 54.9% 25.49% 6 months EDTA-K2 Within 2 h
Rodrigo Estévez-Loureiro/2009 1 8 Spain P 617 STEMI PPCI 63 81.00% 37.5% 17.5% 30 days Cannot assess Within 1 h
Sandro Cadaval Goncalves/2011 2 9 Austria P 1432 Cannot assess Cannot assess 62.8 72.80% 60.2% 27.9% 1 year K2-EDTA Cannot assess
Seo-Won Choi/2014 2 8 Korea R 208 Cannot assess Cannot assess 65.4 66.80% 54.8% 32.7% 228 days K2-EDTA Cannot assess
Tomasz Rechciński/2013 2 9 Poland P 538 STEMI PPCI 58 69.00% 32.5% 11.3% 790 days K2-EDTA Approximately 1 min
Tongtong Yu/2017 2 8 china R 887 NSTEMI Cannot assess 62 68.07% 60.54% 35.85% 28 months EDTA within 2 h
Turgay Celik/2015 2 5 Turkey Cannot assess 306 STEMI PPCI 59.4 77.78% 42.48% 24.18% Cannot assess Tripotassium EDTA Within 10 min
Younes Nozari/2014 2 6 Iran R 2539 Cannot assess EPCI 58.02 70.00% 50.1% 27.5% 1 year EDTA Within 2 h
Young-Jae Ki/2014 2 8 Korea R 372 Cannot assess Cannot assess 65.2 63.00% 57.3% 57.3% 25.8 months K2-EDTA Within 2 h
Zenon Huczek/2005 2 9 Poland Cannot assess 398 STEMI PPCI 60 72.00% 61.06% 16.08% 6 months K2-EDTA 30 min
Tian, C/2018 2 4 China R 962 STEMI PPCI Cannot assess Cannot assess 48.64% 32.26% 30 months K2-EDTA Within 2 h
Machado/2018 2 6 Brazil P 625 STEMI PPCI 60.7 67.50% 59% 23.7% 1 year Cannot assess Cannot assess
Younes Nozari/2019 2 6 Iran R 4199 Unstable angina or NSTEMI EPCI 59.9 65.7% 59.80% 32.6% 1 year Cannot assess Cannot assess
Study type* (‘case-control study’ = 1; ‘cohort study’ = 2; ‘cross-sectional study’ = 3). Quality score* (the Newcastle–Ottawa Scale). Study design* (‘R’ = Retrospective; ‘P’ = Prospective).
ACS, acute coronary syndrome; CAD, coronary artery disease; EPCI, elective PCI; NSTEMI, non-ST segment elevation myocardial infarction; PCI, percutaneous coronary intervention; PPCI, primary PCI; STEMI, ST-segment elevation myocardial infarction.

Table 2 - Major adverse cardiac event definition and the value of mean platelet volume e of the included studies
Author/Year MACE definition Using continuous MPV (1) or high vs. low MPV (2) Cutoff value of high vs. low MPV, fl
Abdullah Dogan/2012 Cardiac death, nonfatal MI with and without ST elevation, recurrent angina with revascularization or hospitalization, or hospitalization for heart failure (1) and (2) 9.9
Ae Ran Moon/2015 Cardiac death, MI, TVR, ischemic stroke and stent thrombosis (2) 8.0
Akgul, Ozgur/2013 Cardiovascular mortality, reinfarction, or repeat TVR (2) 8.9
Alon Eisen/2013 Death, MI and TVR (1) /
Andrzej Lekston/2014 Death, rehospitalization for acute coronary syndromes and stroke (1) /
Binita Shah/2013 Mortality (2) 8.8
Dong-Hyun Choi/2018 Cardiac death, nonfatal MI, and definite/probable stent thrombosis (1) /
Elisabetta Ricottini/2018 Periprocedural MI (1) /
Eyup Avci/2018 Mortality (1) /
Hideki Wada/2018 All-cause death and non-fatal MI (2) 9.8
Hong-Joo Seo/2015 Cardiac death, MI, TVR, ischemic stroke, and stent thrombosis (2) 8.2
Hong-Mei Lai/2015 Mortality (2) 9.85
Ismail Bolat/2016 Mortality (1) /
Karolina Supel/2013 Cardiogenic shock (1) /
Kurtulus Karauzum/2017 Restenosis (1) /
Matthias K. Freynhofer/2017 Cardiovascular death, nonfatal MI and any unplanned revascularization. (1) /
Mir Hossein Seyyed-Mohammadzad/2015 Long-standing CCU hospitalization postoperatively, TVR, MI, stroke and death (2) 9.6
Mislav Vrsalovic/2012 Mortality (2) 8.5
Monica Verdoia/2013 Periprocedural MI (1) /
Ping Jiang/2018 Cardiac mortality (2) 10.9
Qin Wenpei/2015 Recurrent MI, TVR, TLR, in-stent restenosis, stent thrombosis, stroke, and death (1) /
Rodrigo Estévez-Loureiro/2009 Mortality (2) 8.95
Sandro Cadaval Goncalves/2011 Mortality and MI (1) and (2) 9.1
Seo-Won Choi/2014 Cardiac death, MI, TVR, and stent thrombosis (2) 8.55
Tomasz Rechciński/2013 Cardiac death, non-fatal reinfarction, and repeat revascularization (2) 11.7
Tongtong Yu/2017 All-cause mortality and nonfatal reinfarction (1) /
Turgay Celik/2015 In-stent thrombosis, nonfatal MI, and in-hospital mortality (1) /
Younes Nozari/2014 Death, MI, TVR, and TLR (1) and (2) 9.1
Young-Jae Ki/2014 Cardiac death, MI, TVR, ischemic stroke and stent thrombosis (2) 8.2
Zenon Huczek/2005 Mortality (1) and (2) 10.3
Tian, C/2018 Cardiac mortality and nonfatal reinfarction (1) /
Machado/2018 Any cause death, new MI, stent thrombosis and stroke (1) /
Younes Nozari/2019 In-hospital mortality, cardiac death, nonfatal MI, TLR, or TVR (1) and (2) 10.1
CCU, cardiac care unit; MI, myocardial infarction; TVR, target vessel revascularization; TLR, target lesion revascularization.

Pooling major adverse cardiac event as the outcome

Pooling mean differences of mean platelet volume

An overall pooling comparing MPVs between PCI patients with MACE (including death, MI, stent thrombosis, repeat target vessel revascularization, or composite endpoints that contained either death or MI) and those without was performed on 12 studies [6,9,14,18,23,25–27,31,32,36,40]. The mean difference in MPV between PCI patients with MACE and those without was estimated for each study, and the estimated USMD was 0.29 fL (95% CI, 0.04–0.54), Fig. 2. This indicated that the mean MPV of PCI patients with MACE was 0.29 fL larger than that of patients without MACE. High heterogeneity was observed (Chi-square = 276.35, P value < 0.001, I2 = 96%). Therefore, a sensitivity analysis was further conducted to evaluate the credibility of the outcomes by eliminating studies one by one and changing the analysis model to the fixed-effect model (Supplemental Fig. 1, Supplemental digital content 1, http://links.lww.com/MCA/A335). However, the results of the analysis were essentially unchanged. We performed subgroup analysis according to case types. Neither coefficient was significant nor was reduction of I2 observed in the ST-segment elevation myocardial infarction (STEMI) subgroup (Supplemental Fig. 2, Supplemental digital content 2, http://links.lww.com/MCA/A336). Sources of heterogeneity were also explored by fitting age, study setting, percentage of males, diabetes, and hypertension, and the timing of MPV test one by one in meta-regression. None of these variables were found to be the sources of heterogeneity. Thus, we suggested that the difference in MACE definition was more likely due to heterogeneity.

Fig. 2
Fig. 2:
Pooling of weighted mean differences of studies comparing MPV between patients with and without MACE. MACE, major adverse cardiac event; MPV, mean platelet volume.

Risk of major adverse cardiac event in patients with high vs. low mean platelet volume

Fifteen of the studies [8–10,14,16,21,22,24,28,29,32–35,39] having MACE as the outcome provided numbers of patients with high and low MPV. The effects of MPV were heterogeneous among those studies (Chi-square = 137.20, df = 14, P value < 0.001, I2 = 90%) with a pooled risk ratio of 1.81 (95% CI, 1.29–2.55), Fig. 3. This suggested that patients with higher MPV had an additional 81% risk of dying, developing an MI, having repeated revascularization, being hospitalized for heart failure, having an ischemic stroke, stent thrombosis, or stent restenosis compared to those with low MPV. Significant I2 reduction (I2 = 10%) and similar results (risk ratio of 2.57, 95% CI, 1.29–2.55) were observed in the subgroup analysis of STEMI patients, see Supplemental Fig. 3, Supplemental digital content 3, http://links.lww.com/MCA/A337 indicating that CAD types may represent the source of heterogeneity for MACE risk in PCI patients with high vs. low MPV.

Fig. 3
Fig. 3:
Pooling relative risks of MACE in patients with high vs. low MPV. MACE, major adverse cardiac event; MPV, mean platelet volume.

Analysis for the pooling hazard ratio of major adverse cardiac event

The MPV effect analysis of additional studies [4,23,37,38,41] reporting a hazard ratio in the pooling frequency data of MACE yielded similar results (pooled hazard ratio of 1.25, 95% CI, 1.07–1.46; I2 = 79%), Fig. 4. All pooled studies assessed the outcome using continuous MPV, meaning that the risk of MACE increased with the increase of MPV. Those results were not affected by the gradual removal of pooling literature or model change (fixed-effect model) (Supplemental Fig. 4, Supplemental digital content 4, http://links.lww.com/MCA/A338). A similar trend was observed in the subgroup analysis of STEMI patients (Supplemental Fig. 5, Supplemental digital content 5, http://links.lww.com/MCA/A339).

Fig. 4
Fig. 4:
Pooled hazard ratio of MPV and MACE. MACE, major adverse cardiac event; MPV, mean platelet volume.
Fig. 5
Fig. 5:
Pooling of mean differences of MPV in deceased and surviving PCI patients. MPV, mean platelet volume.

Pooling death as the outcome

Pooling mean differences of mean platelet volume

Four of the studies reported MPV as a mortality predictor [6,15,17,23]. Mean differences of MPV were moderately heterogeneous across studies (Chi-squared test = 14.26, P value <0.01, I2 = 79%) with a USMD of 0.39 fL (95% CI, 0.09–0.68). This is indicative of a higher MPV among deceased patients, Fig. 5. These results remained unchanged even after a gradual reduction in pooling literature and model change (fixed-effect model) (Supplemental Fig. 6, Supplemental digital content 6, http://links.lww.com/MCA/A340). A higher MPV (USMD of 0.66 fL, 95% CI, 0.21–1.11) was observed in the STEMI subgroup, see Supplemental Fig. 7, Supplemental digital content 7, http://links.lww.com/MCA/A341.

Fig. 6
Fig. 6:
Pooling relative risks of death in patients with high vs. low MPV. MPV, mean platelet volume.
Fig. 7
Fig. 7:
Pooled hazard ratio of MPV and death. MPV, mean platelet volume.

Risk of death in patients with high vs. low mean platelet volume

Fifteen studies [8–10,14–16,21,22,24,28,29,32,34,35,39] (n = 8769) were included in the high MPV and death risk ratio poolings. Cutoff levels used for defining high MPV varied across studies ranging from ≥8.0 fL to ≥11.7 fL (Table 2). The results indicated heterogeneous high MPV effects (Chi-square = 104.79, df = 14, P value <0.001, I2 = 87%) with a pooled risk ratio of 2.34 (95% CI, 1.52–3.60). This indicated that patients with higher MPV were about 134% more likely to die than patients with lower MPV (Fig. 6). Similar results (risk ratio of 3.38, 95% CI, 2.47–4.61) with significant I2 reduction (I2 = 32%) were observed in the subgroup analysis of STEMI patients, see Supplemental Fig. 8, Supplemental digital content 8, http://links.lww.com/MCA/A342. Moreover, a subgroup analysis was further performed according to MPV cutoff points (≥8.0 fL for two studies, and >9.4 fL for three studies) in STEMI cases. The analysis yielded pooled risk ratios of 4.17 (95% CI, 1.77–9.81), and 2.82 (95% CI, 1.86–4.28), see Supplementary Fig. 9, Supplemental digital content 9, http://links.lww.com/MCA/A343. These suggested that the risk of death between the two cutoff groups was not much different. However, the degree of heterogeneity was increased in the cutoff ≥8.0 fL (I2 = 68%) but decreased in the cutoff >9.4 fL (I2 = 0%), indicating that a higher MPV (>9.4 fL) was more highly associated with mortality in the STEMI subgroup than in the non-STEMI patients.

Analysis for the pooling hazard ratio of death

Eight studies [6,19,23,27,30,37,38,41] reported the hazard ratio of MPV for mortality. One study [19] assessed the outcome using high vs. low MPV, while others used continuous MPV. Analysis performed on pooling frequency of death data from eight additional studies also yielded similar MPV effect-related results (pooled hazard ratio of 1.33, 95% CI, 1.16–1.53; I2 = 84%), Fig. 7. Furthermore, these results were also impervious to a gradual pooling literature reduction or model change (fixed-effect model) (Supplemental Fig. 10, Supplemental digital content 10, http://links.lww.com/MCA/A344). However, similar results (hazard ratio of 1.45, 95% CI, 1.28–1.64) with significant I2 reduction (I2 = 37%) were observed in the subgroup analysis of STEMI patients, see Supplemental Fig. 11, Supplemental digital content 11, http://links.lww.com/MCA/A345.

Discussion

This systematic review and meta-analysis examined the association between MPV and long-term MACE following PCI. To the best of our knowledge, this is the first meta-analysis assessing such connection in PCI patients. Our findings indicated significantly larger (approximately 0.29 fL) MPV in PCI patients with MACE than their counterparts. Additionally, deceased patients had a significantly larger mean MPV (0.39 fL) as opposed to their surviving counterparts. The MPV was significantly higher in the STEMI patients with MACE and the deceased ones, 0.49 fL and 0.66 fL, respectively. Intriguingly, our meta-analysis also found that MPV is a good predictor of MACE and death in PCI patients. Compared to those with low MPV, patients with higher MPV had a 76% higher risk of long-term MACE and approximately twice the incidence of mortality. In the STEMI subgroup, the risk ratio further increased to 2.57 for MACE and 3.34 for mortality. The cutoff point for MPV >9.4 fL was highly associated with mortality in the STEMI subgroup than in the non-STEMI patients.

Platelets play a pivotal role in the progression of atherosclerotic lesions, plaque destabilization, and thrombosis [42,43]. They secrete several substances that are crucial mediators of coagulation, inflammation, and atherosclerosis. Larger platelets are younger and are shown to contain more alpha granules, have more expression of adhesion receptors, be more metabolically and enzymatically active, and possess increased thrombogenic properties [44–46]. Moreover, high remaining platelet reactivity can limit the antiplatelet therapy and increase cardiovascular events during PCI procedure and long-term follow-up [47]. MPV is a precise indicator of platelet size and activity. Unlike platelet function testing, which is time-consuming, costly, and technically complicated [48], MPV is routinely reported during complete blood count analysis. No matter the used measurement method (aggregation, thromboxane synthesis, β-thromboglobulin release, procoagulant function, or adhesion molecule expression), MPV elevation represents the first indication of platelet activation [49]. Thus, MPV was proposed as a potential biomarker for risk assessment and prognosis of heart disease [50].

Mean platelet volume as a prognostic indicator in percutaneous coronary interventions patients

Our findings indicated that MPV was significantly larger in PCI patients with MACE (including the deceased ones). Additionally, the results have shown that deceased patients had a significantly larger MPV as compared to their alive counterparts, especially in the STEMI subgroup. Another meta-analysis on CAD and MPV performed by Sansanayudh et al. [51] reported similar results indicating that CAD patients with MACE also had a higher MPV as compared to those without MACE. The same study also found that deceased patients had a higher MPV than their surviving counterparts. Additionally, pooled results reported by Chu et al. [52] and obtained from five cohort studies with a total sample size of 430 patients also found that coronary angioplasty patients who developed restenosis had a significantly higher MPV than those without restenosis. The same study also indicated that patients with acute MI had an increased risk of mortality and elevated MPV (two-folds) compared to their counterparts. Furthermore, it also found that patients with higher MPV were more prone to die or have cardiovascular events than those with lower MPV.

However, the mentioned meta-analysis did not examine the influence of MPV on MACE recurrence or patients’ mortality. Despite prior controversial conclusions [8–10], our meta-analysis combined all cohort studies aiming to fill the gap in knowledge regarding the effects of MPV on the recurrence of MACE or patients’ mortality and demonstrated that not only there is a correlation between MPV and patients outcome but that high MPV may also be considered as MACE prognostic biomarker in those patients. Therefore, these present findings support the role of MPV as a prognostic biomarker for the prediction of cardiovascular events in PCI patients, including MI and death.

The studies included in our meta-analysis contained substantial variation in percentages of diabetes. This may result in high heterogeneity among the obtained data. One study performed by Jiang et al. [34] investigated the association between MPV and PCI in diabetic patients. It showed that an elevated MPV represented a significant risk factor for 2-years cardiac mortality (approximately twice as likely) in diabetic patients following PCI. Interestingly, another study carried out by Moon et al. [21] indicated considerably higher effects of MPV on patients’ outcomes as compared to other similar studies. In our subanalysis, we observed significant heterogeneity in MACE reduction and death prediction between STEMI patients with high and low MPV, indicating that CAD types might be the source of the heterogeneity. However, we unsuccessfully attempted to analyze the causes of MPV differences between deceased PCI patients or PCI patients with MACE and their counterparts. We suspect that it might be the result of multiple factors rather than a single one. In our meta-analysis, all the results were not affected by a gradual reduction of pooling literature and a switch to a fixed-effect model, further confirming the statement that high MPV might be a potentially useful prognostic biomarker for PCI patients.

Mean platelet volume as a potential biomarker for other thrombotic diseases and a tool for antithrombotic therapy selection

Prior studies have not only reported a correlation between an increase in MPV and MACE in patients with CAD [53] but also proposed MPV as a potential biomarker for other thrombotic diseases. Indeed, MPV was put forward as a biomarker for stroke in atrial fibrillation [54,55], and as a predictor of venous thromboembolism [56] as well as restenosis after carotid angioplasty and stenting [57]. Recently, the association between platelet activation and the treatment of AF received more attention. In patients with atrial fibrillation who underwent electrical cardioversion or cryoballoon ablation, MPV significantly decreased 4-weeks after cardioversion and 6-months post-ablation as compared to the preprocedural values [58,59]. This scenario may be attributed to changes in platelet reactivity to thrombin after the restoration of sinus rhythm. This proves that arrhythmia intrinsically leads to increased reactivity of platelets. However, controversial results have been reported. Aksoy [60] demonstrated that compared with baseline, MPV values are higher after radiofrequency ablation. In another study, Bin Waleed et al. [59] showed that there were no changes in MPV after radiofrequency ablation. Thus, the role of MPV in thromboembolism risk prediction for different therapeutic methods in patients with atrial fibrillation needs a combination of several platelet indices and more related studies.

Anticoagulant therapy is an important step for stroke prevention in patients with high-risk atrial fibrillation. The effect of anticoagulant drugs on MPV has been noticed recently. Duzen et al. [61] revealed that MPV is not affected by new-generation oral anticoagulants used in patients with nonvalvular atrial fibrillation. High residual platelet reactivity can increase the frequency of cardiovascular events in patients with CAD and limit the effectiveness of antiplatelet therapy. MPV can be readily measured before PCI using automated hematology analyzers, subsequently providing valuable information for antiplatelet therapy. Kim et al. [62] reported an association between higher MPV and reduced response to aspirin and clopidogrel. Some investigators have also suggested that a gradual increase in MPV following PCI is associated with a high on-treatment platelet reactivity [63]. Moreover, Choi et al. [35] suggested that MPV is more accurate than a platelet function test in terms of cardiac death or cardiovascular events prediction in PCI patients, particularly those in the acute coronary syndrome subgroup.

Challenges for mean platelet volume implementation in clinical settings

Despite numerous studies reporting encouraging results in patients with PCI, there are still challenges to be addressed before a comprehensive clinical application of MPV. First, several supportive studies, such as the present, are based on pooled effects which, are prone to high heterogeneity, and heterogeneity sources are not always easily identifiable (in this current study for example). This is partially due to the disparity among included studies (study population, outcomes of interest, anticoagulants and testing time of MPV, types of PCI and CAD, the timing of outcome measurement). Additionally, many of the pooled studies did not adjust for confounding variables leading to a possible overestimation of the MPV effect. Second, standard operating procedures, including the use of anticoagulants in the collection tube, the type of analyzer, the delay in sampling and analysis time, and the temperature of sample storage, have not yet been established [64]. Additionally, the calcium bind in anticoagulants such as (EDTA) and sodium citrate had been reported to induced platelet swelling, structural and morphological changes in platelets, leading to an increase in volume. Various automated cell counters, different detection technologies, and varying upper thresholds among hematology laboratories can also lead to significant variations in MPV normal range. Moreover, the recommended time interval of MPV measurement and its storage temperature are within an hour and in-room temperature (37°C), respectively [65,66]. However, these are not unified norms in clinical and investigational settings. This might explain why many laboratories do not report MPV for diagnostic purposes. Third, there is the possibility of platelet volume being affected by confounding factors. For example, nonprolonged venipuncture, careful filing, and gentle mixing of blood samples are required for MPV analysis. An inaccurate sample collection or handling may result in platelet activation yielding erroneous results. Additionally, the use of platelet inhibitors such as clopidogrel [62], the duration of antithrombotic treatment, and the disease itself might affect platelet size. Lastly, the thresholds used to classify patients into high and low MPV groups varied dramatically among studies with no exact cutoff values for risk prediction, emphasizing the importance of workflow automatization and the need for large sample studies with unified procedures. Reliable isolation methods, cross-platform accuracy, and standardization are needed to generate robust and reproducible results and to pave the way for MPV implementation as a prognostic biomarker for thrombotic risk in clinical practice.

Conclusion

These findings indicate a significant correlation between elevated MPV and MACE but also mortality among PCI patients. Additionally, we also show that higher MPV amongst post-PCI patients are associated with higher risks of MACE. Therefore, these results suggest that MPV might be an important prognostic biomarker for patients undergoing PCI. However, additional large sample studies and standardized testing processes are needed to solve existing challenges for a more comprehensive application of MPV in clinical settings, especially for PCI patients.

Acknowledgements

This study was funded by grants from The Science & Technology Pillar Program of Sichuan Province (grant number 2017FZ0077). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conflicts of interest

There are no conflicts of interest.

N.L., C.L., S.H., and X.R.Z. independently performed the searches, and titles and abstracts screening. The statistical analysis was done by Z.X.C. and J.W.. Z.X.C. was responsible for manuscript writing. Y.H. conceived, instructed, reviewed, and revised the manuscript. All authors read and approved the final version.

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Keywords:

mean platelet volume; mortality; percutaneous coronary intervention

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