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

The International Journal of Cardiovascular Imaging volume 36, pages1213–1225(2020)Cite this article

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. 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

References

  1. 1.

    Rudski LG, Lai WW, Afilalo J et al (2010) Guidelines for the echocardiographic assessment of the right heart in adults: a report from the American Society of Echocardiography. Endorsed by the European Association of Echocardiography, a registered branch of the European Society of Cardiology, and the Canadian Society of Echocardiography. J Am Soc Echocardiogr 23:685–713

    Article  Google Scholar 

  2. 2.

    Brennan JM, Blair JE, Goonewardena S et al (2007) Reappraisal of the use of inferior vena cava for estimating right atrial pressure. J Am Soc Echocardiogr 20:857–861

    Article  Google Scholar 

  3. 3.

    Beigel R, Cercek B, Luo H, Siegel RJ (2013) Noninvasive evaluation of right atrial pressure. J Am Soc Echocardiogr 26:1033–1042

    Article  Google Scholar 

  4. 4.

    Lang RM, Badano LP, Mor-Avi V et al (2015) Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. Eur Hear J Cardiovasc Imaging 16:233–271

    Article  Google Scholar 

  5. 5.

    Mesin L, Giovinazzo T, D'Alessandro S et al (2019) Improved repeatability of the estimation of pulsatility of inferior vena cava. Ultrasound Med Biol 45(10):2830–2843

    Article  Google Scholar 

  6. 6.

    Fisher MR, Forfia PR, Chamera E et al (2009) Accuracy of doppler echocardiography in the hemodynamic assessment of pulmonary hypertension. Am J Respir Crit Care Med 179:615–621

    Article  Google Scholar 

  7. 7.

    Magnino C, Omedè P, Avenatti E et al (2017) Inaccuracy of right atrial pressure estimates through inferior vena cava indices. Am J Cardiol 120:1667–1673

    Article  Google Scholar 

  8. 8.

    Tsutsui RS, Borowski A, Tang WHW et al (2014) Precision of echocardiographic estimates of right atrial pressure in patients with acute decompensated heart failure. J Am Soc Echocardiogr 27:1072–1078.e2

    Article  Google Scholar 

  9. 9.

    Mesin L, Pasquero P, Albani S et al (2015) Semi-automated tracking and continuous monitoring of inferior vena cava diameter in simulated and experimental ultrasound imaging. Ultrasound Med Biol 41:845–857

    Article  Google Scholar 

  10. 10.

    Mesin L, Pasquero P, Roatta S (2019) Tracking and monitoring pulsatility of a portion of inferior vena cava from ultrasound imaging in long axis. Ultrasound Med Biol 45:1338–1343

    Article  Google Scholar 

  11. 11.

    Mesin L, Albani S, Sinagra G (2019) Non-invasive estimation of right atrial pressure using inferior vena cava echography. Ultrasound Med Biol 45(5):1331–1337

    Article  Google Scholar 

  12. 12.

    Otto C (2004) Textbook of Clinical Echocardiography, 5th edn. W.B. Saunders, Philadelphia

    Google Scholar 

  13. 13.

    Milan A, Magnino C, Veglio F (2010) Echocardiographic indexes for the non-invasive evaluation of pulmonary hemodynamics. J Am Soc Echocardiogr 23:225–239

    Article  Google Scholar 

  14. 14.

    Galiè N, Humbert M, Vachiery J-L et al (2016) 2015 ESC/ERS guidelines for the diagnosis and treatment of pulmonary hypertension. Eur Heart J 37:67–119

    Article  Google Scholar 

  15. 15.

    Mintz GS, Kotler MN, Parry WR et al (1981) Reat-time inferior vena caval ultrasonography: normal and abnormal findings and its use in assessing right-heart function. Circulation 64:1018–1025

    CAS  Article  Google Scholar 

  16. 16.

    Nakao S, Come PC, McKay RG, Ransil BJ (1987) Effects of positional changes on inferior vena caval size and dynamics and correlations with right-sided cardiac pressure. Am J Cardiol 59:125–132

    CAS  Article  Google Scholar 

  17. 17.

    Jue J, Chung W, Schiller NB (1992) Does inferior vena cava size predict right atrial pressures in patients receiving mechanical ventilation? J Am Soc Echocardiogr 5:613–619

    CAS  Article  Google Scholar 

  18. 18.

    Simonson JS, Schiller NB (1988) Sonospirometry: a new method for noninvasive estimation of mean right atrial pressure based on two-dimensional echographic measurements of the inferior vena cava during measured inspiration. J Am Coll Cardiol 11:557–564

    CAS  Article  Google Scholar 

  19. 19.

    Moreno FL, Hagan AD, Holmen JR et al (1984) Evaluation of size and dynamics of the inferior vena cava as an index of right-sided cardiac function. Am J Cardiol 53:579–585

    CAS  Article  Google Scholar 

  20. 20.

    Kircher BJ, Himelman RB, Schiller NB (1990) Noninvasive estimation of right atrial pressure from the inspiratory collapse of the inferior vena cava. Am J Cardiol 66:493–496

    CAS  Article  Google Scholar 

  21. 21.

    Ommen SR, Nishimura RA, Hurrell DG, Klarich KW (2000) Assessment of right atrial pressure with 2-dimensional and doppler echocardiography: a simultaneous catheterization and echocardiographic study. Mayo Clin Proc 75:24–29

    CAS  Article  Google Scholar 

  22. 22.

    Taniguchi T, Ohtani T, Nakatani S et al (2015) Impact of body size on inferior vena cava parameters for estimating right atrial pressure: a need for standardization? J Am Soc Echocardiogr 28:1420–1427

    Article  Google Scholar 

  23. 23.

    Goldhammer E, Mesnick N, Abinader EG, Sagiv M (1999) Dilated inferior vena cava: a common echocardiographic finding in highly trained elite athletes. J Am Soc Echocardiogr 12:988–993

    CAS  Article  Google Scholar 

  24. 24.

    Kimura BJ, Dalugdugan R, Gilcrease GW et al (2011) The effect of breathing manner on inferior vena caval diameter. Eur J Echocardiogr 12:120–123

    Article  Google Scholar 

  25. 25.

    Blehar DJ, Resop D, Chin B et al (2012) Inferior vena cava displacement during respirophasic ultrasound imaging. Crit Ultrasound J 4:18

    Article  Google Scholar 

  26. 26.

    Seo Y, Iida N, Yamamoto M (2017) Estimation of central venous pressure using the ratio of short to long diameter from cross-sectional images of the inferior vena cava. J Am Soc Echocardiogr 30:461–467

    Article  Google Scholar 

  27. 27.

    Huguet R, Fard D, d’Humieres T et al (2018) Three-dimensional inferior vena cava for assessing central venous pressure in patients with cardiogenic shock. J Am Soc Echocardiogr 31:1034–1043

    Article  Google Scholar 

  28. 28.

    Pellicori P, Shah P, Cuthbert J et al (2019) Prevalence, pattern and clinical relevance of ultrasound indices of congestion in outpatients with heart failure. Eur J Heart Fail 21(7):904–916

    CAS  Article  Google Scholar 

  29. 29.

    Lee W-TN, Ling Y, Sheares KK et al (2012) Predicting survival in pulmonary arterial hypertension in the UK. Eur Respir J 40:604–611

    Article  Google Scholar 

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Author information

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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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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Keywords

  • Right atrial pressure
  • Inferior vena cava
  • Edge-tracking
  • Caval Index
  • Cardiac Caval Index
  • Respiratory Caval Index