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Central venous pressure estimation from ultrasound assessment of the jugular venous pulse

OBJECTIVES: Acquiring central venous pressure (CVP), an important clinical parameter, requires an invasive procedure, which poses risk to patients. The aim of the study was to develop a non-invasive methodology for determining mean-CVP from ultrasound assessment of the jugular venous pulse. METHODS:...

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Detalles Bibliográficos
Autores principales: Zamboni, Paolo, Malagoni, Anna Maria, Menegatti, Erica, Ragazzi, Riccardo, Tavoni, Valentina, Tessari, Mirko, Beggs, Clive B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7592775/
https://www.ncbi.nlm.nih.gov/pubmed/33112871
http://dx.doi.org/10.1371/journal.pone.0240057
Descripción
Sumario:OBJECTIVES: Acquiring central venous pressure (CVP), an important clinical parameter, requires an invasive procedure, which poses risk to patients. The aim of the study was to develop a non-invasive methodology for determining mean-CVP from ultrasound assessment of the jugular venous pulse. METHODS: In thirty-four adult patients (age = 60 ± 12 years; 10 males), CVP was measured using a central venous catheter, with internal jugular vein (IJV) cross-sectional area (CSA) variation along the cardiac beat acquired using ultrasound. The resultant CVP and IJV-CSA signals were synchronized with electrocardiogram (ECG) signals acquired from the patients. Autocorrelation signals were derived from the IJV-CSA signals using algorithms in R (open-source statistical software). The correlation r-values for successive lag intervals were extracted and used to build a linear regression model in which mean-CVP was the response variable and the lagging autocorrelation r-values and mean IJV-CSA, were the predictor variables. The optimum model was identified using the minimum AIC value and validated using 10-fold cross-validation. RESULTS: While the CVP and IJV-CSA signals were poorly correlated (mean r = -0.018, SD = 0.357) due to the IJV-CSA signal lagging behind the CVP signal, their autocorrelation counterparts were highly positively correlated (mean r = 0.725, SD = 0.215). Using the lagging autocorrelation r-values as predictors, mean-CVP was predicted with reasonable accuracy (r(2) = 0.612), with a mean-absolute-error of 1.455 cmH(2)O, which rose to 2.436 cmH(2)O when cross-validation was performed. CONCLUSIONS: Mean-CVP can be estimated non-invasively by using the lagged autocorrelation r-values of the IJV-CSA signal. This new methodology may have considerable potential as a clinical monitoring and diagnostic tool.