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Accuracy of right atrial pressure estimation using a multi-parameter approach derived from inferior vena cava semi-automated edge-tracking echocardiography: a pilot study in patients with cardiovascular disorders

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Abstract

The echocardiographic estimation of right atrial pressure (RAP) is based on the size and inspiratory collapse of the inferior vena cava (IVC). However, this method has proven to have limits of reliability. The aim of this study is to assess feasibility and accuracy of a new semi-automated approach to estimate RAP. Standard acquired echocardiographic images were processed with a semi-automated technique. Indexes related to the collapsibility of the vessel during inspiration (Caval Index, CI) and new indexes of pulsatility, obtained considering only the stimulation due to either respiration (Respiratory Caval Index, RCI) or heartbeats (Cardiac Caval Index, CCI) were derived. Binary Tree Models (BTM) were then developed to estimate either 3 or 5 RAP classes (BTM3 and BTM5) using indexes estimated by the semi-automated technique. These BTMs were compared with two standard estimation (SE) echocardiographic methods, indicated as A and B, distinguishing among 3 and 5 RAP classes, respectively. Direct RAP measurements obtained during a right heart catheterization (RHC) were used as reference. 62 consecutive ‘all-comers’ patients that had a RHC were enrolled; 13 patients were excluded for technical reasons. Therefore 49 patients were included in this study (mean age 62.2 ± 15.2 years, 75.5% pulmonary hypertension, 34.7% severe left ventricular dysfunction and 51% right ventricular dysfunction). The SE methods showed poor accuracy for RAP estimation (method A: misclassification error, ME = 51%, R2 = 0.22; method B: ME = 69%, R2 = 0.26). Instead, the new semi-automated methods BTM3 and BTM5 have higher accuracy (ME = 14%, R2 = 0.47 and ME = 22%, R2 = 0.61, respectively). In conclusion, a multi-parametric approach using IVC indexes extracted by the semi-automated approach is a promising tool for a more accurate estimation of RAP.

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Notes

  1. Secondary indexes of elevated RAP are considered right ventricular restrictive filling, tricuspid E wave deceleration time < 120 ms or tricuspid E/E′ > 6.

Abbreviations

BTM:

Binary Tree Model

CCI:

Cardiac Caval Index

CI:

Caval Index

IVC:

Inferior Vena Cava

IVCd:

Inferior Vena Cava expiratory diameter

ME:

Misclassification Error

PH:

Pulmonary Hypertension

RAP:

Right Atrial Pressure

RCI:

Respiratory Caval Index

RHC:

Right Heart Catheterization

SE:

Standard Estimation

US:

Ultrasound

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Author information

Authors and Affiliations

  1. Cardiothoracic Department, Division of Cardiology and Postgraduate School in Cardiovascular Sciences, University of Trieste, Trieste, Italy

    Stefano Albani, Bruno Pinamonti, Enrico Fabris, Davide Stolfo, Andrea Perkan & Gianfranco Sinagra

  2. Mathematical Biology and Physiology, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy

    Tatiana Giovinazzo & Luca Mesin

  3. Department of Medical Science, Surgery and Health (DCSMCS), University of Trieste, Trieste, Italy

    Marco de Scordilli

  4. Department of Medical Sciences, Biostatistics Unit, University of Trieste, Trieste, Italy

    Caterina Gregorio & Giulia Barbati

  5. Department of Statistical Sciences, University of Padova, Padua, Italy

    Caterina Gregorio

  6. Cardiothoracic Department, Division of Pneumology and Respiratory Intermediate Care Unit, ASUITS, Trieste, Italy

    Pietro Geri & Marco Confalonieri

  7. Pulmonary Hypertension National Service, Hammersmith Hospital Imperial College NHS Trust, London, UK

    Francesco Lo Giudice

  8. Fondazione Toscana G. Monasterio, Pisa, Italy

    Giovanni D. Aquaro

  9. Department of Internal Medicine, Internal Medicine 1, University of Turin, Turin, Italy

    Paolo Pasquero & Massimo Porta

Authors
  1. Stefano Albani
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  2. Bruno Pinamonti
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  16. Gianfranco Sinagra
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  17. Luca Mesin
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Corresponding author

Correspondence to Stefano Albani.

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Online Table 1

Misclassification error of all the edge tracking methods tested. There is no statistical difference between method A when using either one diameter (B mode estimation) or the mean diameter (using IVC semi-automated tracking system) (p=0.69) and method B using one diameter and method B using mean diameter (p=1). (BTM: Binary Tree Model). (DOCX 11 kb)

Online Table 2

Misclassification error of the all edge tracking method tested against patients with atrial fibrillation (AF). (BTM: Binary Tree Model). (DOCX 12 kb)

Online Table 3

Distribution of RCI (left) and CCI (right) in all patients or in those with either low (i.e., trivial or mild) or moderate/high severity of tricuspid regurgitation (the medians are lower in the latter case, but Wilcoxon rank sum test indicates that those differences are not significant). (RCI: Respiratory Caval Index; CCI: Cardiac Caval Index). (DOCX 29 kb)

Online Table 4

The plot provides the misclassification error of pulmonary hypertension class assessment using standard echocardiographic method (method A was tested) compared to BTM3 and BTM5. In our cohort of patients tricuspid regurgitation velocity was available in 36 out 49 patients. (RAP: Right Atrial Pressure; BTM: Binary Tree Model). (DOCX 200 kb)

Online Table 5

Main characteristics of the patients according to invasive RAP. (RAP: Right Atrial Pressure; NYHA FC: New York Heart Association Functional Class). (DOCX 13 kb)

Table 2 Panel B

The accuracy of the main studies available in literature (see text for further details; BTM binary tree model) (DOCX 26 kb)

Appendix

Appendix

The parameters were selected automatically by the routine implementing the training of the binary tree models (BTM). Specifically, all possible input features were considered. Given the input set of a specific BTM to be trained, the development of the BTM requires choosing the specific features for each binary separation (thus, which feature and in which order), selecting the threshold value for each splitting and how many divisions to consider. The BTM was implemented in MATLAB R2019a (The Mathworks, Natick, Massachusetts, USA), using the Gini’s diversity index as splitting criterion. The best categorical predictor split was chosen from all possible combinations of choices. The models were cross-validated considering 30 folds. The one providing the best generalization to the validation sets (i.e., minimum number of misclassified observations in the validation sets) was then selected.

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Albani, S., Pinamonti, B., Giovinazzo, T. et al. Accuracy of right atrial pressure estimation using a multi-parameter approach derived from inferior vena cava semi-automated edge-tracking echocardiography: a pilot study in patients with cardiovascular disorders. Int J Cardiovasc Imaging 36, 1213–1225 (2020). https://doi.org/10.1007/s10554-020-01814-8

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