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

Paolo Zamboni, Anna Maria Malagoni, Erica Menegatti, Riccardo Ragazzi, Valentina Tavoni, Mirko Tessari and Clive B Beggs

PLOS ONE, 2020, vol. 15, issue 10, 1-18

Abstract: 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 (r2 = 0.612), with a mean-absolute-error of 1.455 cmH2O, which rose to 2.436 cmH2O 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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0240057

DOI: 10.1371/journal.pone.0240057

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