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Am J Hematol ; 92(1): 62-67, 2017 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-27737505

RESUMO

Plasma volume and red cell mass are key health markers used to monitor numerous disease states, such as heart failure, kidney disease, or sepsis. Nevertheless, there is currently no practically applicable method to easily measure absolute plasma or red cell volumes in a clinical setting. Here, a novel marker for plasma volume and red cell mass was developed through analysis of the observed variability caused by plasma volume shifts in common biochemical measures, selected based on their propensity to present with low variations over time. Once a month for 6 months, serum and whole blood samples were collected from 33 active males. Concurrently, the CO-rebreathing method was applied to determine target levels of hemoglobin mass (HbM) and blood volumes. The variability of 18 common chemistry markers and 27 Full Blood Count variables was investigated and matched to the observed plasma volume variation. After the removal of between-subject variations using a Bayesian model, multivariate analysis identified two sets of 8 and 15 biomarkers explaining 68% and 69% of plasma volume variance, respectively. The final multiparametric model contains a weighting function to allow for isolated abnormalities in single biomarkers. This proof-of-concept investigation describes a novel approach to estimate absolute vascular volumes, with a simple blood test. Despite the physiological instability of critically ill patients, it is hypothesized the model, with its multiparametric approach and weighting function, maintains the capacity to describe vascular volumes. This model has potential to transform volume management in clinical settings. Am. J. Hematol. 92:62-67, 2017. © 2016 Wiley Periodicals, Inc.


Assuntos
Biomarcadores/sangue , Determinação do Volume Sanguíneo/métodos , Volume de Eritrócitos , Volume Plasmático , Adulto , Teorema de Bayes , Contagem de Células Sanguíneas , Volume Sanguíneo , Hemoglobinas/análise , Humanos , Masculino , Pessoa de Meia-Idade , Análise Multivariada
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