***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: Background Platelet activation and consumption are common in critically ill patients and are associated with poorer prognosis . Mean platelet volume is a simple surrogate for platelet activation , with higher MPV being associated with worse clinical condition on a large array of clinical diagnoses . We therefore aim ed to investigate associations between changes in platelet count and mean platelet volume ( MPV ) with prognosis and inflammatory cytokine values in critically ill patients . Methods This study prospect ively included 84 critically ill patients . Patients were stratified into four groups according to proportional changes in MPV ( ΔMPV24h ) and platelet count ( ΔPlat24h ) in the first 24 hours after admission . Mortality between groups was compared using the χ2 test . Logistic regression was performed using hospital mortality as outcome and Simplified Acute Physiology Score ( SAPS 3 ) , ΔPlat24h and ΔMPV24h as covariates . Concentrations of the following inflammatory mediators were measured using Miliplex ® technology : IL1β , IL6 , IL8 , IL10 , epidermal growth factor , vascular endothelial growth factor , TNFα and IFNα . Cytokine concentrations were compared between groups using the Kruskal-Wallis test with Bonferroni correction . Results Patients in whom MPV increased and platelet count decreased had higher mortality rates ( 46 % ) . According to logistic regression , ΔMPV24h was independently associated with increased mortality ( OR 1.28 per 1 % increase ; 95 % CI 1.08 to 1.48 ) . No strong associations between inflammatory mediators and changes in MPV and platelet count were found . Conclusion An increase in MPV after admission to an ICU is independently associated with higher hospital mortality 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 prospect ively enrolled 345 patients admitted to the emergency department ( ED ) who received st and ardized 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 AIM Sepsis , as an uncontrolled generalized inflammatory response , involves also the haemostatic mechanisms . Mean platelet volume ( MPV ) measurement has been available since 1970s , but neither its relationship with platelet count nor the clinical meaning of this relation has been understood in sepsis yet . This study aim ed to evaluate both the trend and the relationship between  platelet count ( PC ) and their MPV and to explore their significance in the course of sepsis . METHODS Seventy septic patients , were recruited in this prospect i ve study . The  PC and MPV  were measured repeatedly at the enrollment and , thereafter , whenever patient 's sequential organ failure assessment ( SOFA ) score changed by + /-8 % up to 5 sample s. St and ard correlation test , ANOVA for repeated measures , logistic regression and Wilcoxon test were used . RESULTS The relationship between PC and MPV , expressed as means and frequency distributions , showed a negative correlation ( 95 % CI ; r -0.34 ; P<0.0001 ) with an inverse trend during sepsis course . The logistic regression showed a three times increase in death probability ( 95 % CI ; OR=3.04 ; P<0.05 ) of patients with a MPV<9.7 fL at the recruitment time . CONCLUSIONS The behaviour of platelets and their respective MPV could be an indirect sign of disturbance in platelet production and activity , and bone marrow response in sepsis . Our results could suggest that routine concomitant measurement of PC and MPV trend could be considered as a quick and reliable guide in the assessment of bone marrow response to sepsis evolution Rheumatoid arthritis ( RA ) is one of the most common chronic inflammatory disorders associated with enhanced cardiovascular morbidity and mortality . Established high prevalence of classical cardiovascular risk factors may only partly explain cardiovascular phenomenon in this disease . Emerging risk factors , markers of inflammation and prothrombotic state such as platelet size are believed to reflect activity of RA . We aim ed to study mean platelet volume ( MPV ) in a cohort of patients with RA and to clarify possible effects of classical cardiovascular and RA-associated risk factors on MPV . Demographic , clinical and a wide range of laboratory parameters , including  MPV and platelet count  , were obtained for 400 RA patients .  Platelet size and count  were also assessed in 360 non-RA controls from the local population . We found significantly increased MPV in RA patients compared with controls ( P=0.001 ) . The difference retained significant after adjustment for age and sex . High values of MPV ( > or=10.7 femtoliter [ fL ] ) were more frequent in RA patients than in controls ( 21 % vs. 9.2 % ; P<0.0001 ) . In RA patients , blood pressure greater than 140/90 mmHg was associated with high levels of MPV ( Odds Ratio [ OR ] 2.2 , 95 % Confidence Interval [ CI ] 1.3 - 3.7 ; P=0.003 ) . It is possible that MPV as a surrogate marker of platelet function reflects enhanced vascular risk . To further explore the role of MPV as a marker for cardiovascular risk in RA , prospect i ve studies are warranted INTRODUCTION The aim of this study was to show variability in the measurement of the mean platelet volume ( MPV ) depending on the instrument used . METHODS This prospect i ve analysis was carried out to measure MPV with three instruments , in 30 healthy controls and 113 hospital patients . RESULTS Firstly , for values in the normal range , the values obtained with the Siemens Advia ( ® ) 2120 are lower than those given by the Beckman Coulter LH750 ( ® ) ( -0.89 ) , which are in turn lower than those obtained with the Sysmex XE-2100D ( ® ) ( -1.11 ) , which represents a 20 - 25 % variation in the measurement . These results emphasize the lack of universal external calibration for MPV analysis and thus make any intercentre comparison of MPV impossible unless the automated haematology analyser used is indicated . Secondly , we stress the differences in behaviour of the instruments in the presence of abnormally large platelets , i.e. an underestimate of the platelet count and the MPV may be provided because instruments using impedance technology may fail to take into account these platelets , but they rightly flag them . CONCLUSION To harmonize our procedures , we propose definitions of platelet size ( normal size , macroplatelets and giant platelets ) based on the coordinated interpretation of the MPV , the distribution of platelet volume and the morphological appearance The present study was design ed to investigate the interaction between platelet indices ( mean platelet volume ( MPV ) , platelet count ( PLC ) and platelet mass ( PLM ) ) , inflammatory markers and disease activity in ankylosing spondylitis ( AS ) subjects . The effects of anti-TNF-α therapy and conventional treatment on platelet indices were also compared . We studied 68 patients with AS ( group I , 46 men , age : 36.4 ± 6.9 years ) and as control group 34 age and sex-matched healty subjects . All patients received conventional therapy ( CT ) at the beginning ( Group I ) . The patients were reevaluated after 3 months according to Bath Ankylosing Spondylitis Disease Activity Index ( BASDAI ) score . Group II consisted of 35 subjects who responded to the CT and continued to take the same therapy for 3 months additionally . Group III consisted of 33 subjects who had a high disease activity score ( BASDAI > 4 ) after 3 months and were accepted refractory to the CT therapy . In Group III the treatment was switched to infliximab and continued for 3 months at the st and ard intravenous dose . Significantly higher baseline MPV , PLC and PLM was reported as compared to controls decreased by therapy ( 9.12 ± 1.20 vs. 8.35 ± 0.94 fl , p < 0.001 , 340 ± 69 vs. 251 ± 56 ( ×103/ µL ) p < 0.0001 , 3096 ± 736 vs. 2110 ± 384 ; p < 0.0001 , respectively ) . In the same way , they were substantially lowered by both treatments in group II and group III . PLC and PLM were positively correlated with WBC and ESR ( r : 0.44 ; p < 0.0001 , r : 0.41 ; p = 0.001 , r : 0.52 ; p < 0.0001 , r : 0.41 ; p = 0.001 ) , respectively ) in AS patients . Additionally , MPV and PLM were positively correlated with BASDAI score ( r : 0.41 ; p < 0.001 , r = 0.29 ; p < 0.001 respectively ) . We have found that increased platelet activity reduced by therapy in AS patients . Additionally , it was correlated with inflammatory markers and disease activity . According to these results , it can be suggested that both anti-TNF-α and conventional therapy might contribute to a decrease in the risk of cardiovascular morbidity and mortality in AS patients A quantitative systematic review , or meta- analysis , uses statistical methods to combine the results of multiple studies . Meta-analyses have been done for systematic review s of therapeutic trials , diagnostic test evaluations , and epidemiologic studies . Although the statistical methods involved may at first appear to be mathematically complex , their purpose is simple : They are trying to answer four basic questions . Are the results of the different studies similar ? To the extent that they are similar , what is the best overall estimate ? How precise and robust is this estimate ? Finally , can dissimilarities be explained ? This article provides some guidance in underst and ing the key technical aspects of the quantitative approach to these questions . We have avoided using equations and statistical notations ; interested readers will find implementations of the described methods in the listed references . We focus here on the quantitative synthesis of reports of r and omized , controlled , therapeutic trials because far more meta-analyses on therapeutic studies than on other types of studies have been published . For practical reasons , we present a stepwise description of the tasks that are performed when statistical methods are used to combine data . These tasks are 1 ) deciding whether to combine data and defining what to combine , 2 ) evaluating the statistical heterogeneity of the data , 3 ) estimating a common effect , 4 ) exploring and explaining heterogeneity , 5 ) assessing the potential for bias , and 6 ) presenting the results . Deciding Whether To Combine Data and Defining What To Combine By the time one performs a quantitative synthesis , certain decisions should already have been made about the formulation of the question and the selection of included studies . These topics were discussed in two previous articles in this series [ 1 , 2 ] . Statistical tests can not compensate for lack of common sense , clinical acumen , and biological plausibility in the design of the protocol of a meta- analysis . Thus , a reader of a systematic review should always address these issues before evaluating the statistical methods that have been used and the results that have been generated . Combining poor- quality data , overly biased data , or data that do not make sense can easily produce unreliable results . The data to be combined in a meta- analysis are usually either binary or continuous . Binary data involve a yes/no categorization ( for example , death or survival ) . Continuous data take a range of values ( for example , change in diastolic blood pressure after antihypertensive treatment , measured in mm Hg ) . When one is comparing groups of patients , binary data can be summarized by using several measures of treatment effect that were discussed earlier in this series [ 3 ] . These measures include the risk ratio ; the odds ratio ; the risk difference ; and , when study duration is important , the incidence rate . Another useful clinical measure , the number needed to treat ( NNT ) , is derived from the inverse of the risk difference [ 3 ] . Treatment effect measures , such as the risk ratio and the odds ratio , provide an estimate of the relative efficacy of an intervention , whereas the risk difference describes the intervention 's absolute benefit . The various measures of treatment 
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Initial values of MPV might not be used as a prognostic marker of mortality in critically ill patients .
Subsequent values of MPV after the 3rd day and the lower mortality rate unit might be useful .