Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

An Increase in Mean Platelet Volume from Baseline Is Associated with Mortality in Patients with Severe Sepsis or Septic Shock

  • Chan Ho Kim,

    Affiliations Department of Internal Medicine, International St. Mary’s Hospital, Catholic Kwandong University College of Medicine, Incheon, Korea, Department of Medicine, Graduate School of Medicine, Yonsei University, Seoul, Korea

  • Seung Jun Kim,

    Affiliations Department of Internal Medicine, International St. Mary’s Hospital, Catholic Kwandong University College of Medicine, Incheon, Korea, Department of Medicine, Graduate School of Medicine, Yonsei University, Seoul, Korea

  • Mi Jung Lee,

    Affiliation Department of Internal Medicine, College of Medicine, Yonsei University, Seoul, Korea

  • Young Eun Kwon,

    Affiliation Department of Internal Medicine, College of Medicine, Yonsei University, Seoul, Korea

  • Yung Ly Kim,

    Affiliation Department of Internal Medicine, College of Medicine, Yonsei University, Seoul, Korea

  • Kyoung Sook Park,

    Affiliation Department of Internal Medicine, College of Medicine, Yonsei University, Seoul, Korea

  • Han Jak Ryu,

    Affiliation Department of Internal Medicine, College of Medicine, Yonsei University, Seoul, Korea

  • Jung Tak Park,

    Affiliation Department of Internal Medicine, College of Medicine, Yonsei University, Seoul, Korea

  • Seung Hyeok Han,

    Affiliation Department of Internal Medicine, College of Medicine, Yonsei University, Seoul, Korea

  • Tae-Hyun Yoo,

    Affiliations Department of Internal Medicine, College of Medicine, Yonsei University, Seoul, Korea, Brain Korea 21 PLUS Project for Medical Science, Yonsei University, Seoul, Korea

  • Shin-Wook Kang,

    Affiliations Department of Internal Medicine, College of Medicine, Yonsei University, Seoul, Korea, Brain Korea 21 PLUS Project for Medical Science, Yonsei University, Seoul, Korea

  • Hyung Jung Oh

    ohjmd@naver.com

    Affiliation Department of Internal Medicine, College of Medicine, Yonsei University, Seoul, Korea

An Increase in Mean Platelet Volume from Baseline Is Associated with Mortality in Patients with Severe Sepsis or Septic Shock

  • Chan Ho Kim, 
  • Seung Jun Kim, 
  • Mi Jung Lee, 
  • Young Eun Kwon, 
  • Yung Ly Kim, 
  • Kyoung Sook Park, 
  • Han Jak Ryu, 
  • Jung Tak Park, 
  • Seung Hyeok Han, 
  • Tae-Hyun Yoo
PLOS
x

Abstract

Introduction

Mean platelet volume (MPV) is suggested as an index of inflammation, disease activity, and anti-inflammatory treatment efficacy in chronic inflammatory disorders; however, the effect of MPV on sepsis mortality remains unclear. Therefore, we investigated whether the change in MPV between hospital admission and 72 hours (ΔMPV72h-adm) predicts 28-day mortality in severe sepsis and/or septic shock.

Methods

We prospectively enrolled 345 patients admitted to the emergency department (ED) who received standardized resuscitation (early goal-directed therapy) for severe sepsis and/or septic shock between November 2007 and December 2011. Changes in platelet indices, including ΔMPV72h-adm, were compared between survivors and non-survivors by linear mixed model analysis. The prognostic value of ΔMPV72h-adm for 28-day mortality was ascertained by Cox proportional hazards model analysis.

Results

Thirty-five (10.1%) patients died within 28 days after ED admission. MPV increased significantly during the first 72 hours in non-survivors (P = 0.001) and survivors (P < 0.001); however, the rate of MPV increase was significantly higher in non-survivors (P = 0.003). Nonetheless, the difference in the platelet decline rate over the first 72 hours did not differ significantly between groups (P = 0.360). In multivariate analysis, ΔMPV72h-adm was an independent predictor of 28-day mortality, after adjusting for plausible confounders (hazard ratio, 1.44; 95% confidence interval, 1.01–2.06; P = 0.044).

Conclusions

An increase in MPV during the first 72 hours of hospitalization is an independent risk factor for adverse clinical outcomes. Therefore, continuous monitoring of MPV may be useful to stratify mortality risk in patients with severe sepsis and/or septic shock.

Introduction

The mean platelet volume (MPV) describes the average size of platelets in a blood sample and is routinely measured by automated hematology analyzers using either electrical impedance or optical fluorescence method [1,2]. Increased platelet volume and size reflects the existence of a thrombotic and inflammatory milieu; thus, MPV is suggested as a possible marker of platelet function and activation [35]. Over the past decade, several studies have shown that increased MPV is an independent risk factor for cardio- and cerebrovascular diseases and is associated with poor clinical outcomes of these diseases [610]. Additionally, MPV has been considered an index for inflammation, disease activity, and efficacy of anti-inflammatory treatment in several chronic inflammatory disorders, such as inflammatory bowel disease, rheumatoid arthritis, and ankylosing spondyloarthritis [1115].

The time course of platelet counts and its function in critically ill patients, especially in patients with sepsis, have been elucidated by several previous studies [1619]. Although the underlying mechanism is not yet completely understood, the sophisticated interaction of platelets with pathogens and endothelial cells may culminate in sepsis, a severe pathophysiologic cascade characterized by significant reductions in platelet counts and platelet dysfunction [17,18,20,21]. Only a few studies have revealed the relationship between MPV and prognosis in infectious diseases, including sepsis [2224]. Moreover, little is known about the potential influence of MPV and its change on mortality in a homogenous group of patients with sepsis. Therefore, we investigated whether the change in MPV between baseline and 72 hours after hospital admission has prognostic value for clinical outcomes in severe sepsis and/or septic shock.

Materials and Methods

Patients

Eligible adult patients admitted to the emergency department (ED) with the clinical features of severe sepsis and/or septic shock between November 2007 and December 2011 were assessed for possible enrollment according to inclusion and exclusion criteria. Since November 2007, early goal-directed therapy (EGDT) has been implemented in the intensive care unit and ED of our institution as part of a quality improvement initiative. If a patient presented with two or more systemic inflammatory response syndrome criteria and had a suspicious sign of infection, his or her eligibility for EGDT was assessed. One or both of the following trigger the initiation of the EGDT protocol: (a) initial systolic blood pressure < 90 mmHg despite a 20 mL/kg intravenous crystalloid fluid challenge or (b) initial serum lactate level ≥ 4 mmol/L. Study exclusion criteria were (a) age < 18 years, (b) any contraindication to central venous catheterization, and/or (c) presence of a do-not-resuscitate order [25].

A total of 451 patients who received EGDT in the ED were initially enrolled in the study. Seventy-two were excluded because of uncured malignancy, active gastrointestinal bleeding, and acute coronary syndrome. Seven patients with known platelet disorders, such as idiopathic thrombocytopenic purpura and essential thrombocytosis, were also excluded. The final analysis included 345 patients, excepting 27 patients who died within 72 hours after ED admission (Fig. 1).

thumbnail
Fig 1. Flow diagram of study patients.

From November 2001 to December 2011, 451 patients who received early-goal directed therapy (EGDT) in the emergency department (ED) were assessed for possible enrollment according to inclusion and exclusion criteria, and 345 patients were included in the final analysis.

https://doi.org/10.1371/journal.pone.0119437.g001

The study protocol was approved by the Institutional Review Board of the Yonsei University Health System Clinical Trial Center. All patients provided written informed consent to participate in the study.

Data collection

Baseline characteristics, including demographic information and comorbidities, were collected. The Charlson Comorbidity Index was used to assess the burden of chronic disease [26,27]. For disease severity assessment, both the Acute Physiology and Chronic Health Evaluation II (APACHE II) score and the Sequential Organ Failure Assessment (SOFA) score were determined according to the worst values within the initial 24 hours of ED admission. The SOFA score was calculated from the following parameters: arterial partial pressure of oxygen/fraction of inspired oxygen, platelet count, bilirubin, blood pressure and use of an inotropic agent, Glasgow Coma Scale score, and creatinine level or urine output. In addition, MPV, platelet count, white blood cell (WBC) count, and hemoglobin (Hb) level were measured at initial presentation and at 36 and 72 hours after ED admission. Venous blood samples for laboratory counts were collected from all patients in tubes containing ethylenediamine tetra-acetic acid (EDTA) and analyzed with an Advia 2120 hematology analyzer (Siemens Healthcare Diagnostics, Deerfield, IL) within 30 minutes of sample collection. The normal reference range for MPV in our hospital laboratory is 7.4 to 10.4 fL.

Definitions

Sepsis, severe sepsis, and septic shock were defined based on American College of Chest Physicians/Society of Critical Care Medicine consensus conference definitions [28]. Sepsis was defined by two or more of the following conditions resulting from infection: (i) temperature greater than 38°C or less than 36°C, (ii) heart rate greater than 90 beats/min, (iii) respiratory rate greater than 20 breaths/min or arterial carbon dioxide tension less than 32 mmHg, and (iv) WBC count greater than 12,000 cells/mm3 or less than 4,000 cells/mm3. Severe sepsis was defined as sepsis associated with organ dysfunction, hypoperfusion abnormality, or sepsis-induced hypotension. Hypoperfusion abnormalities included lactic acidosis, oliguria, and acute alteration of mental status. In addition, septic shock was defined as sepsis with hypotension, despite adequate fluid resuscitation. Hypotension was defined as a systolic blood pressure of 90 mmHg or less, or a reduction of greater than 40 mmHg from baseline in the absence of other causes of low blood pressure.

Infection site was categorized as pneumonia, peritonitis, urinary tract infection, exacerbation of chronic obstructive pulmonary disease, catheter-related infection, primary bacteremia (excluding untreated Staphylococcus epidermidis bacteremia), miscellaneous sites (mediastinitis, prostatitis, osteomyelitis, etc.), or multiple sites [29]. Moreover, effectiveness of antibiotics was assessed on the basis of microbial culture results, the known susceptibility of the organism to the antimicrobial agents used, and antimicrobial susceptibility test [29].

Statistical analyses

Continuous variables are expressed as mean ± standard deviation, and categorical variables as numbers with percentages. We evaluated 28-day all-cause mortality as a primary endpoint. Patients who died within 28 days after ED admission were defined as ‘non-survivors.’ Baseline characteristics are presented according to the occurrence of the primary outcome (survivors versus [vs.] non-survivors) and were compared between the 2 groups using Student’s t-test for continuous variables and chi-square test for categorical variables. Changes in platelet indices including MPV and platelet count during the first 72 hours after ED admission were compared between ‘survivors’ and ‘non-survivors’ by linear mixed model analysis. In our implement of the mixed model method, the intercept and the regression coefficient for follow-up time were treated as random effects such that each patient had a unique intercept and regression coefficient. The post-hoc analysis was performed with Bonferroni correction. MPV at 72 hours minus MPV at baseline was considered the change in MPV at 72 hours after ED admission (ΔMPV72h-adm). The relationship between ΔMPV72h-adm and other demographic/biochemical parameters was assessed with Pearson’s correlation analysis. The prognostic value of ΔMPV72h-adm for 28-day mortality was ascertained by Cox proportional hazards model, and the results are presented as hazard ratios (HRs) with 95% confidence intervals (CIs). Covariate selection for the multivariate Cox model was based on P-value < 0.1 in univariate analysis. All tests were two-sided, and a P-value of < 0.05 was considered statistically significant. Statistical analyses were performed with SPSS for Windows version 19.0 (IBM Corporation, Chicago, IL).

Results

Baseline characteristics

The mean age of the patients was 64.2±15.7 years, and 169 (49.0%) were male. The mean APACHE II score was 17.4±7.2, and the mean SOFA score was 8.0±2.8. In addition, MPV ranged from 6.7 to 15.0 fL (median, 8.4 fL; mean, 8.64 fL) at baseline and from 6.6 to 14.7 fL (median, 8.7 fL; mean, 8.96 fL) at 72 hours after ED admission. The main infection sites were urinary tract (25.2%) and lung (24.1%) followed by intra-abdominal cavity (22.0%) (Table 1). The baseline demographic, clinical, and biochemical data of each group stratified by 28-day all-cause mortality are presented in Table 1. As expected, non-survivors exhibited significantly higher APACHE II and SOFA scores and C-reactive protein (CRP) and lactate levels than did survivors, whereas body mass index (BMI); platelet count; estimated glomerular filtration rate (eGFR); and albumin, total cholesterol, and pH levels in non-survivors were significantly lower than those in survivors. The infection sites of survivors were relatively evenly distributed compared with those of non-survivors, whose infection site was mainly the lungs (45.7%). Moreover, the proportions of patients who received renal replacement therapy (RRT) and platelet transfusion were significantly higher in non-survivors. However, there were no significant differences in age, mean arterial pressure, Charlson Comorbidity Index, WBC, Hb, serum creatinine, total bilirubin, bicarbonate, RBC transfusion, and heparin use between the two groups (Table 1).

thumbnail
Table 1. Baseline clinical characteristics and biochemical variables according to the occurrence of 28-day all-cause mortality.

https://doi.org/10.1371/journal.pone.0119437.t001

Trends in the platelet indices during the first 72 hours

The trends in the platelet indices during the first 72 hours after ED admission are shown in Fig. 2 and Table 2. Non-survivors exhibited a significantly higher baseline MPV than survivors (9.54±1.66 vs. 8.54±1.10; P = 0.001). MPV levels increased significantly during the first 72 hours in both non-survivors (P = 0.001) and survivors (P < 0.001). However, the linear mixed model revealed a significantly increased rate of MPV over the first 72 hours in non-survivors than in survivors (P = 0.003) (Fig. 2A). In addition, ΔMPV72h-adm, defined as MPV at 72 hours—MPV at baseline, was significantly greater in non-survivors than in survivors (0.80±1.30 vs. 0.26±0.89 fL; P = 0.021). In contrast, although platelet counts decreased significantly during the first 72 hours in both groups (P < 0.001), a linear mixed model showed no significant difference in the rate of platelet count decline over the first 72 hours between groups (P = 0.360) (Fig. 2B).

thumbnail
Fig 2. Comparison of trends in the platelet indices between survivors and non-survivors during the first 72 hours after emergency department admission.

(A) The rate of mean platelet volume increase over the first 72 hours in non-survivors was significantly different from that observed in survivors (P = 0.003). (B) The rate of platelet count decline over the first 72 hours was comparable between the 2 groups (P = 0.360).

https://doi.org/10.1371/journal.pone.0119437.g002

Correlation between changes in MPV and other parameters

Pearson’s correlation analysis revealed a significant inverse correlation between ΔMPV72h-adm and eGFR (r = -0.128; P = 0.021) (Table 3). In contrast, age, APACHE II score, and CRP showed significant positive associations with ΔMPV72h-adm (r = 0.161; P = 0.003, r = 0.178; P = 0.001, r = 0.131; P = 0.016, respectively) (Table 3).

Increase in MPV and all-cause mortality

Among the 345 patients with severe sepsis and/or septic shock, 35 (10.1%) died within 28 days after ED admission. Univariate Cox regression analysis revealed that ΔMPV72h-adm, male sex, APACHE II and SOFA score, RRT, platelet count, CRP level, lactate level, and infection site were significantly associated with an increased risk of 28-day all-cause mortality (Table 4). On the contrary, higher serum albumin level correlated with a lower risk of 28-day all-cause mortality (Table 4). In multivariate analysis, ΔMPV72h-adm still remained a significant independent risk factor of 28-day all-cause mortality, even after adjusting for age; sex; BMI; SOFA score; RRT; platelet count; and CRP, albumin, and lactate levels (HR, 1.45; 95% CI, 1.02–2.05; P = 0.040 in Model 1) (Table 5). Further adjustment of Model 1 for infection site (Model 2) did not attenuate the significant prognostic value of ΔMPV72h-adm on 28-day mortality risk (HR, 1.44; 95% CI, 1.01–2.06; P = 0.044) (Table 5). In addition, SOFA score (HR, 1.31; 95% CI, 1.08–1.58; P = 0.006) and albumin (HR, 0.45; 95% CI, 0.26–0.80; P = 0.006) were still significantly associated with 28-day all-cause mortality in the final multivariate model (Table 6). Moreover, we repeated the analysis with ΔMPV36h-adm (MPV at 36 hours—MPV at baseline). Twelve patients were alive at 36 hours after hospital admission and data of MPV at 36 hours were available in 8 patients. Therefore, we performed multivariate cox regression analysis in order to investigate the association between 28-day all-cause mortality and ΔMPV36h-adm, including with these patients. Univariate analysis showed that ΔMPV36h-adm was significantly associated with an increased risk of 28-day all-cause mortality (HR, 1.60; 95% CI, 1.13–2.26; P = 0.008). However, its significance was attenuated after adjusting for other covariates in multivariate analysis (data not shown). Receiver operating characteristic (ROC) curves of baseline MPV and ΔMPV72h-adm for 28-day all-cause mortality are shown in Fig. 3. Area under the curve (AUC) of baseline MPV and ΔMPV72h-adm were 0.653 and 0.698, respectively.

thumbnail
Fig 3. Receiver operating characteristic (ROC) curves of baseline MPV and ΔMPV72h-adm for 28-day all-cause mortality.

Area under the curve (AUC) of baseline MPV and ΔMPV72h-adm were 0.653 and 0.698, respectively.

https://doi.org/10.1371/journal.pone.0119437.g003

thumbnail
Table 4. Univariate Cox proportional hazards analysis for 28-day all-cause mortality.

https://doi.org/10.1371/journal.pone.0119437.t004

thumbnail
Table 5. Multivariate Cox proportional hazards analysis for 28-day all-cause mortality.

https://doi.org/10.1371/journal.pone.0119437.t005

thumbnail
Table 6. Multivariate Cox proportional hazards analysis for 28-day all-cause mortality (Model 2).

https://doi.org/10.1371/journal.pone.0119437.t006

Discussion

Severe sepsis and/or septic shock are major healthcare problems affecting millions of people worldwide each year [30,31]. The mortality rates of these conditions are 25% to 80%, depending on illness severity, and the number of occurrences and the severity of organ failure [31,32]. Therefore, a number of initiatives to reduce worldwide sepsis-associated mortality, such as the Surviving Sepsis Campaign that includes the tenets of early hemodynamic optimization (EGDT), have been implemented to overcome this devastating disease [3335]. Moreover, early detection of progressive severe sepsis and/or septic shock would be not only useful for risk stratification in allocating resources, but also helpful in monitoring treatment efficacy and disease progress.

The present study is a prospective clinical investigation into the prognostic value of changes in MPV in patients receiving a standardized resuscitation algorithm (i.e., EGDT) for severe sepsis and/or septic shock. The main findings of this study are as follows. First, we verified the increase in MPV during the first 72 hours after hospital admission and found a steeper MPV increase in non-survivors than in survivors. This result suggests that although the changes in MPV occurred in a similar direction in both groups, the increased width was greater in non-survivors than in survivors. Second, we revealed that ΔMPV72h-adm is an independent risk factor of 28-day all-cause mortality, in patients with severe sepsis and/or septic shock, even after adjusting for plausible confounding variables. To our knowledge, this study is the first to report a significant association between change in MPV and all-cause mortality in a homogenous group of patients with sepsis.

During the clinical course of sepsis, platelets exhibit diverse characteristics on a wide continuum from hyperactivation to exhaustion, and thrombocytopenia occurs frequently, implying that platelets are involved in pathophysiology beyond hemostatic function [17,18,21]. Sepsis-associated thrombocytopenia has been explained by impaired central platelet production and peripheral overconsumption and/or destruction, but the precise mechanism remains undetermined [18,21]. Generally, platelet counts in patients with sepsis markedly decrease during the first 4 days of hospital admission [17], and the inverse relationship between platelet count and MPV has been described frequently in physiologic and some pathologic conditions [3,4]. We also observed this inverse relationship in the present study. Although these changes of platelet indices in critically ill patients have been investigated in previous studies, the mechanism underlying the relationship between these derangements and mortality has not yet been clarified. Thus, we investigated MPV among the various platelet indices in patients with sepsis, focusing particularly on change in MPV. Several reports have demonstrated that MPV increases in septic milieus. Dastugue et al. [36] reported an increase in MPV in patients with shock-related thrombocytopenia. Van der Lelie and Von dem Borne [22] showed a higher MPV in patients with sepsis than in patients with localized infection and suggested that an increase of MPV in patients with bacterial infection could indicate the occurrence of septicemia. Becchi et al. [23] examined the trends of MPV and platelet count during the course of sepsis in a small population and found that the average MPV gradually increased in non-survivors, whereas it decreased in survivors. Furthermore, in studies of septic animal models, MPV increased after the induction of sepsis, whereas platelet count changed inversely [37,38]. These results are consistent with the present study findings, and together, these data suggest that continuous monitoring of changes in MPV may play a role in risk stratification of patients with severe sepsis and/or septic shock.

Although there are several contradictory observations [11,14], the nature of MPV as an inflammatory marker has been suggested by previous studies, which have demonstrated a correlation between a higher MPV and active inflammatory disease as aforementioned [12,13,15]. Furthermore, recent advances in the quantification of laboratory markers may demonstrate more firmly that MPV is a reflection of both proinflammatory and prothrombotic conditions, where thrombopoietin and numerous inflammatory cytokines, such as interleukin (IL) -1, -3, and-6 and tumor necrosis factor-α (TNF-α), regulate thrombopoiesis [3]. Larger platelets, indicating an increased MPV, are functionally, metabolically, and enzymatically more active than smaller ones. Because larger platelets have more intracellular thromboxane A2 and increased levels of procoagulant surface proteins, such as P-selectin and glycoprotein IIIa, they present a greater prothrombotic potential [5]. Moreover, inflammation by itself can induce procoagulant changes and facilitate embolization, which is one of the major causes of death in patients with systemic bacterial infection [39]. Taken together, MPV can be speculated as an integrative measure of the detrimental processes of inflammation and hypercoagulable state in critical illness and thus, the association between increased MPV and mortality in patients with sepsis can be partially explained by this notion.

Several studies reported the relationship between MPV and renal dysfunction, recently [40,41]. In these studies, MPV was significantly increased with progression of chronic kidney disease and independently associated with GFR. These are in line with our result. Actually, Pearson correlation analysis showed a significant association between baseline MPV and creatinine (r = 0.141, P = 0.009). Therefore, an interaction between uremia and MPV may exist. However, we could find that ΔMPV72h-adm was a significantly useful marker for predicting 28-day all-cause mortality even after adjustment for requirement of RRT (Table 5). Moreover, ΔMPV72h-adm was still a significant predictable marker after adjusting AKI episodes (versus non-episode of AKI, HR 1.45; 95% CI 1.010–2.07; P = 0.044). In the future, we need further study for the interaction between uremia and MPV value.

This study has several limitations. First, patients were enrolled from a single medical center in South Korea; thus, it is somewhat difficult to generalize the results. Second, we could not thoroughly investigate the previous use of anti-platelet agents and smoking status, which are known to affect MPV [3]. The record of previous use (within 1 week of enrollment) of anti-platelet agent or NSAIDs could be checked only in 134 of 345 patients (38.8%). Among these patients, there was no significant difference between survivors and non-survivors in the use of medications (16.5% vs. 19.6%, P = 0.785). However, we considered that there is a limitation to interpret these results inductively. Moreover, even after adjusting the modifying factors in the statistical analyses, the risk relationship between MPV and poor clinical outcome may still be subject to residual confounding. Finally, we could not explain the mechanisms triggering changes in MPV, which were different in amount between survivors and non-survivors, exactly. The investigation and application of additional markers, such as IL-1, IL-3, IL-6, TNF-α, and thrombopoietin, may further elucidate this issue regarding a cause-and-effect relationship. Therefore, further research is required to determine the precise mechanisms underlying the association between MPV and mortality in critically ill patients.

Despite these limitations, the main strength of this study is that a relatively large number of patients were included from a single center; therefore, all study patients underwent similar decision-making for critical care with EGDT. Moreover, very few data sets were missing. We also performed a sensitivity analysis because platelet transfusion might influence platelet indices, including MPV, and thus modify the relationship between the changes in MPV and all-cause mortality. When restricting the analysis to patients who did not receive platelet transfusion within 72 hours after ED admission, we also found similar results (HR 2.19; 95% CI 1.28–3.74; P = 0.004 in multivariate analysis with adjusting same variables of Model 2).

Conclusions

We revealed a greater increase of MPV in non-survivors of severe sepsis and/or septic shock compared with survivors during the first 72 hours after hospitalization and found that an increase in MPV from baseline is an independent risk factor for 28-day all-cause mortality. Although further studies are needed to elucidate the role of changes in MPV as a risk factor in patients with severe sepsis and/or septic shock, MPV can be used as an additional and complementary maker with several established measures of illness severity such as SOFA score, APACHE II score, CRP, albumin, and lactate. Moreover, repeating measurement of MPV may be helpful to predict the prognosis of patients with severe sepsis and/or septic shock. Guardedly, we suggest that physicians should be more attentive to septic patients with higher baseline MPV and tendency of increase in MPV.

Author Contributions

Conceived and designed the experiments: CHK HJO. Performed the experiments: SJK MJL YEK YLK KSP HJR. Analyzed the data: JTP SHH THY. Wrote the paper: CHK SWK HJO.

References

  1. 1. Jackson SR, Carter JM. Platelet volume: laboratory measurement and clinical application. Blood Rev. 1993;7: 104–113. pmid:8369659
  2. 2. Briggs C. Quality counts: new parameters in blood cell counting. Int J Lab Hematol. 2009;31: 277–297. pmid:19452619
  3. 3. Gasparyan AY, Ayvazyan L, Mikhailidis DP, Kitas GD. Mean platelet volume: a link between thrombosis and inflammation? Curr Pharm Des. 2011;17: 47–58. pmid:21247392
  4. 4. Leader A, Pereg D, Lishner M. Are platelet volume indices of clinical use? A multidisciplinary review. Ann Med. 2012;44: 805–816. pmid:22413913
  5. 5. Colkesen Y, Muderrisoglu H. The role of mean platelet volume in predicting thrombotic events. Clin Chem Lab Med. 2012;50: 631–634. pmid:22112054
  6. 6. Bath P, Algert C, Chapman N, Neal B. Association of mean platelet volume with risk of stroke among 3134 individuals with history of cerebrovascular disease. Stroke. 2004;35: 622–626. pmid:14976328
  7. 7. Huczek Z, Kochman J, Filipiak KJ, Horszczaruk GJ, Grabowski M, Piatkowski R, et al. Mean platelet volume on admission predicts impaired reperfusion and long-term mortality in acute myocardial infarction treated with primary percutaneous coronary intervention. J Am Coll Cardiol. 2005;46: 284–290. pmid:16022956
  8. 8. Estevez-Loureiro R, Salgado-Fernandez J, Marzoa-Rivas R, Barge-Caballero E, Perez-Perez A, Noriega-Concepcion V, et al. Mean platelet volume predicts patency of the infarct-related artery before mechanical reperfusion and short-term mortality in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention. Thromb Res. 2009;124: 536–540. pmid:19467699
  9. 9. Braekkan SK, Mathiesen EB, Njolstad I, Wilsgaard T, Stormer J, Hansen JB. Mean platelet volume is a risk factor for venous thromboembolism: the Tromso Study, Tromso, Norway. J Thromb Haemost. 2010;8: 157–162. pmid:19496920
  10. 10. Shah B, Oberweis B, Tummala L, Amoroso NS, Lobach I, Sedlis SP, et al. Mean platelet volume and long-term mortality in patients undergoing percutaneous coronary intervention. Am J Cardiol. 2013;111: 185–189. pmid:23102880
  11. 11. Shen J, Ran ZH, Zhang Y, Cai Q, Yin HM, Zhou XT, et al. Biomarkers of altered coagulation and fibrinolysis as measures of disease activity in active inflammatory bowel disease: a gender-stratified, cohort analysis. Thromb Res. 2009;123: 604–611. pmid:18499234
  12. 12. Yazici S, Yazici M, Erer B, Erer B, Calik Y, Ozhan H, et al. The platelet indices in patients with rheumatoid arthritis: mean platelet volume reflects disease activity. Platelets. 2010;21: 122–125. pmid:20050760
  13. 13. Yazici S, Yazici M, Erer B, Erer B, Calik Y, Bulur S, et al. The platelet functions in patients with ankylosing spondylitis: anti-TNF-alpha therapy decreases the mean platelet volume and platelet mass. Platelets. 2010;21: 126–131. pmid:20050759
  14. 14. Kayahan H, Akarsu M, Ozcan MA, Demir S, Ates H, Unsal B, et al. Reticulated platelet levels in patients with ulcerative colitis. Int J Colorectal Dis. 2007;22: 1429–1435. pmid:17549498
  15. 15. Gasparyan AY, Stavropoulos-Kalinoglou A, Toms TE, Douglas KM, Kitas GD. Association of mean platelet volume with hypertension in rheumatoid arthritis. Inflamm Allergy Drug Targets. 2010;9: 45–50. pmid:19906007
  16. 16. Gawaz M, Dickfeld T, Bogner C, Fateh-Moghadam S, Neumann FJ. Platelet function in septic multiple organ dysfunction syndrome. Intensive Care Med. 1997;23: 379–385. pmid:9142575
  17. 17. Akca S, Haji-Michael P, de Mendonca A, Suter P, Levi M, Vincent JL. Time course of platelet counts in critically ill patients. Crit Care Med. 2002;30: 753–756. pmid:11940740
  18. 18. Vincent J-L, Yagushi A, Pradier O. Platelet function in sepsis. Critical care medicine. 2002;30: S313–S317. pmid:12004253
  19. 19. Yaguchi A, Lobo FL, Vincent JL, Pradier O. Platelet function in sepsis. J Thromb Haemost. 2004;2: 2096–2102. pmid:15613012
  20. 20. Akinosoglou K, Alexopoulos D. Use of antiplatelet agents in sepsis: A glimpse into the future. Thromb Res. 2013. 2013/10/10.
  21. 21. Garraud O, Hamzeh-Cognasse H, Pozzetto B, Cavaillon JM, Cognasse F. Bench-to-bedside review: Platelets and active immune functions—new clues for immunopathology? Crit Care. 2013;17: 236. pmid:23998653
  22. 22. Van der Lelie J, Von dem Borne AK. Increased mean platelet volume in septicaemia. J Clin Pathol. 1983;36: 693–696. pmid:6343437
  23. 23. Becchi C, Al Malyan M, Fabbri LP, Marsili M, Boddi V, Boncinelli S. Mean platelet volume trend in sepsis: is it a useful parameter? Minerva Anestesiol. 2006;72: 749–756. pmid:16871155
  24. 24. Kitazawa T, Yoshino Y, Tatsuno K, Ota Y, Yotsuyanagi H. Changes in the mean platelet volume levels after bloodstream infection have prognostic value. Intern Med. 2013;52: 1487–1493. pmid:23812196
  25. 25. Shyamsundar M, Attwood B, Keating L, Walden AP. Clinical review: The role of ultrasound in estimating extra-vascular lung water. Crit Care. 2013;17: 237. pmid:24041261
  26. 26. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40: 373–383. pmid:3558716
  27. 27. Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47: 1245–1251. pmid:7722560
  28. 28. American College of Chest Physicians/Society of Critical Care Medicine Consensus Conference: definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. Crit Care Med. 1992;20: 864–874.
  29. 29. Zahar JR, Timsit JF, Garrouste-Orgeas M, Francais A, Vesin A, Descorps-Declere A, et al. Outcomes in severe sepsis and patients with septic shock: pathogen species and infection sites are not associated with mortality. Crit Care Med. 2011;39: 1886–1895. pmid:21516036
  30. 30. Linde-Zwirble WT, Angus DC. Severe sepsis epidemiology: sampling, selection, and society. Crit Care. 2004;8: 222–226. pmid:15312201
  31. 31. Dombrovskiy VY, Martin AA, Sunderram J, Paz HL. Rapid increase in hospitalization and mortality rates for severe sepsis in the United States: a trend analysis from 1993 to 2003. Crit Care Med. 2007;35: 1244–1250. pmid:17414736
  32. 32. Wang HE, Shapiro NI, Angus DC, Yealy DM. National estimates of severe sepsis in United States emergency departments. Crit Care Med. 2007;35: 1928–1936. pmid:17581480
  33. 33. Rivers E, Nguyen B, Havstad S, Ressler J, Muzzin A, Knoblich B, et al. Early goal-directed therapy in the treatment of severe sepsis and septic shock. N Engl J Med. 2001;345: 1368–1377. pmid:11794169
  34. 34. Jones AE, Focht A, Horton JM, Kline JA. Prospective external validation of the clinical effectiveness of an emergency department-based early goal-directed therapy protocol for severe sepsis and septic shock. Chest. 2007;132: 425–432. pmid:17573521
  35. 35. Dellinger RP, Levy MM, Carlet JM, Bion J, Parker MM, Jaeschke R, et al. Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock: 2008. Crit Care Med. 2008;36: 296–327. pmid:18158437
  36. 36. Dastugue N, Picheloup F, Sie P, Genestal M, Cathala B, Boneu B. [Increase in mean platelet volume in shock-related thrombocytopenia]. Nouv Presse Med. 1982;11: 2899–2901. pmid:7145676
  37. 37. Taniguchi T, Takagi D, Takeyama N, Kitazawa Y, Tanaka T. Platelet size and function in septic rats: changes in the adenylate pool. J Surg Res. 1990;49: 400–407. pmid:2174092
  38. 38. Yilmaz Z, Eralp O, Ilcol YO. Evaluation of platelet count and its association with plateletcrit, mean platelet volume, and platelet size distribution width in a canine model of endotoxemia. Vet Clin Pathol. 2008;37: 159–163. pmid:18533914
  39. 39. Valtonen V, Kuikka A, Syrjanen J. Thrombo-embolic complications in bacteraemic infections. Eur Heart J. 1993;14 Suppl K: 20–23. pmid:8131784
  40. 40. Ju HY, Kim JK, Hur SM, Woo SA, Park KA, Park MY, et al. Could mean platelet volume be a promising biomarker of progression of chronic kidney disease? Platelets. 2014. 2014/04/01.
  41. 41. Ucar H, Gur M, Koyunsever NY, Seker T, Turkoglu C, Kaypakli O, et al. Mean platelet volume is independently associated with renal dysfunction in stable coronary artery disease. Platelets. 2014;25: 274–278. pmid:23772896