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 Table of Contents  
Year : 2019  |  Volume : 3  |  Issue : 4  |  Page : 76-80

Correlation between variation of aortic peak blood flow velocity, inferior vena cava diameter variation and stroke volume variation in children

Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand

Date of Submission10-Dec-2019
Date of Decision24-May-2020
Date of Acceptance04-Jun-2020
Date of Web Publication28-Sep-2020

Correspondence Address:
Wicharn Boonjindasup
1873 Rama 4 Road, Pathumwan, Bangkok 10330
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/prcm.prcm_17_19

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Background: Non-invasive monitoring using ultrasound or Doppler assists in quick haemodynamic assessment and helps to improve outcomes in critical care. Parameters including aortic peak blood flow velocity variation (ΔVpeak), inferior vena cava diameter variation (ΔIVC) and stroke volume variation (SVV) have been commonly used in children. Objectives: The aim of this study was to assess the correlations between ΔVpeak from transthoracic echocardiography, ΔIVC from abdominal ultrasound and SVV from ultrasonic cardiac output monitoring. Settings and Design: A prospective observational cohort study was undertaken in the paediatric intensive care unit in a tertiary university hospital. Methods: ΔVpeak, ΔIVC and SVV were measured in mechanically ventilated children using ultrasound- or Doppler-based monitoring. Statistical Analysis Used: Pearson correlation coefficient was computed to assess the relationship. Results: A convenient sample of 55 patients with a median age of 31 months (range 6 months to 5 years) was enrolled. ΔVpeak, ΔIVC and SVV showed significant positive correlations between the three variables, i.e., ΔVpeak and ΔIVC (r = 0.415 with P = 0.002), ΔVpeak and SVV (r = 0.539 with P < 0.001) and ΔIVC and SVV (r = 0.524 with P < 0.001). Conclusions: In mechanically ventilated children, there is a positive correlation between ΔVpeak, ΔIVC and SVV. ΔVpeak and SVV provided the best, though moderate, correlation.

Keywords: Child, critical care, Doppler, echocardiography, haemodynamic monitoring, ultrasonography

How to cite this article:
Boonjindasup W, Samransamruajkit R. Correlation between variation of aortic peak blood flow velocity, inferior vena cava diameter variation and stroke volume variation in children. Pediatr Respirol Crit Care Med 2019;3:76-80

How to cite this URL:
Boonjindasup W, Samransamruajkit R. Correlation between variation of aortic peak blood flow velocity, inferior vena cava diameter variation and stroke volume variation in children. Pediatr Respirol Crit Care Med [serial online] 2019 [cited 2023 May 29];3:76-80. Available from: https://www.prccm.org/text.asp?2019/3/4/76/296482

  Introduction Top

In paediatric critical care, haemodynamic monitoring is one of the key elements in resuscitation. The goal is to achieve adequate oxygen delivery by maintaining adequate stroke volume or cardiac output.[1],[2] It is generally accepted that the assessment based on signs and symptoms from the physical examination is not accurate enough, especially in children,[3],[4] so other parameters have been frequently used. The standard of haemodynamic assessment for fluid responsiveness is to assess the change in a haemodynamic parameter in response to volume administration. Advanced parameters using dilution method or arterial pulse contour analysis seem to be reliable predictors of fluid responsiveness when taking cardiac contractility into account.[5] However, the advanced parameters are not generally used because of the complexity for both the device and the procedure.

Other parameters frequently studied in both adult and children are dynamic parameters.[1],[2],[3] The dynamic parameters are measured based on the effect of the respiratory cycle on these parameters including flow velocity, stroke volume and pressure variation. Moreover, measurement of these parameters can be done using non-invasive monitoring which is relatively easy, safe and convenient.[6] Among non-invasive monitoring, aortic peak blood flow velocity variation (ΔVpeak) from echocardiography, inferior vena cava diameter variation (ΔIVC) from ultrasonography and stroke volume variation (SVV) from aortic Doppler or ultrasonic cardiac output monitoring (USCOM) are the parameters widely used in paediatric practice.[6],[7],[8],[9],[10] Values higher than the cut-off values for each parameter are predictive for fluid resuscitation responsiveness. These cut-off values in mechanically ventilated patients are ΔVpeak >7%–20%,[6],[7] ΔIVC >8%–21%[9],[10],[11] and SVV from USCOM >9%–19%.[12] ΔVpeak requires developed skills to perform transthoracic or transoesophageal echocardiography correctly which may be a limitation for some paediatricians. In contrast, SVV from USCOM and ΔIVC from ultrasound are easier to perform and require less training.

Given that the choice of non-invasive monitoring depends on expertise and equipment availability, the correlation between these parameters would be important information supporting interchange of monitoring. To the best of our knowledge, there is no previous study demonstrating the relationship between common non-invasive haemodynamic parameters in children. Therefore, this study aims to determine the correlation between ΔVpeak from echocardiography, ΔIVC from ultrasonography and SVV from USCOM.

  Methods Top

Design, setting and patients

After ethical approval was provided by the Institutional Review Board, we conducted a prospective observational study in our paediatric intensive care unit from June 2016 to May 2017. A convenient sample of 69 paediatric patients was potentially eligible by being clinically stable on mechanical ventilation. We included 55 consecutive participants whose parents provided written informed consent from this sample. The inclusion criteria were children aged 3 months to 15 years who were intubated and ventilated. The exclusion criteria were the presence of arrhythmia, congenital heart diseases, severe left ventricular dysfunction below 50% calculated by Teichholz method, use of high-frequency ventilator and the presence of abdominal distension and impediments to measurement such as morbid obesity, short neck and skin wound or infection.


The following haemodynamic parameters were measured from each enrolled participant: ΔVpeak, ΔIVC and SVV were measured by echocardiography, ultrasonography and USCOM, respectively. All the patients were mechanically ventilated (Servo-i, Maquet, Solna, Sweden, or model 840, Puritan Bennett, Pleasanton, CA, USA) via an endotracheal tube in a supine position. To interpret the dynamic parameters accurately, the ventilator was monitored for regular respiration in continuous mandatory ventilation mode without spontaneous breathing activity during the measurements.

Standard two-dimensional echocardiography using portable ultrasound machine (Logiq e, GE Medical Systems) was performed as a transthoracic approach to identify the aortic valve in an apical five-chamber view. In Doppler mode, we demonstrated the velocity–time integral at the aortic valve area. From the integral, we measured two values, the maximum and the minimum, for peak blood flow velocity, which varied in the same respiratory cycle, and calculated ΔVpeak using the following formula.[13]

ΔVpeak (%) = [(Vpeakmax − Vpeakmin)/(Vpeakmax + Vpeakmin)/2] × 100.

Ultrasonography (Logiq e) was performed using a transabdominal approach to identify IVC in a longitudinal view. In M-mode, we demonstrated the change of IVC diameter at 2 cm before the right atrium. From the display, we measured two IVC diameters, the maximum and the minimum, which varied in the same respiratory cycle, and calculated ΔIVC using the following formula.[14]

ΔIVC (%) = [(IVCmax − IVCmin)/(IVCmax + IVCmin)/2] × 100.

USCOM (model 1A, Uscom, Sydney, NSW, Australia) was performed using a suprasternal approach to detect blood flow in the ascending aorta. When an adequate velocity–time integral was displayed (including continuous wave, sharp peak flow and no artefact), the integral was captured. SVV was automatically calculated using data from the integral and demographic data of the patient including age, gender, ethnicity, weight, height, central venous pressure and systolic and diastolic blood pressure.

Each parameter was measured at least three times accepting repeatability within 10% variation and calculated as average value for each patient. All measurements in this study were conducted by a single investigator and quality of all results was approved by a single consultant in the paediatric intensive care unit. Unacceptable measurements, such as inaccurate measuring position and interrupted integral, were discarded from the study.

Statistical analyses

Categorical data were presented as frequency and percentage, and continuous data are presented as mean with standard deviation for normally distributed data or median with interquartile range where data were not normally distributed. Statistical analyses were performed using SPSS Statistics for Windows version 17 (Armonk, NY: IBM Corp.), and P < 0.05 was considered significant for all analyses of statistical inference. Associations among variables were evaluated using Pearson's correlation coefficient, and the strength of the association was gauged using the guidelines provided by Evans.[15]

  Results Top

[Table 1] presents the characteristics of paediatric patients in this study. We enrolled 55 eligible patients, consisted of 26 boys and 29 girls, with median age 31 months (range from 6 months to 5 years, interquartile range: 15–39 months). No patient was excluded by unacceptable measurement. Of the 44 patients who had underlying diseases, almost one-third (30%) had neurological diseases such as epilepsy and almost one-fifth (18%) had malignancies, including hematologic and solid tumour. The main reason for paediatric intensive care unit admission was respiratory failure, followed by post-operative observation (including craniofacial surgery, solid tumour removal and adenotonsillectomy) and neurological diseases (including seizure, increased intracranial pressure and intracranial haemorrhage). The patients were mechanically ventilated using average ventilator setting as shown in [Table 1]. During the measurements, the patients were clinically and haemodynamically stable according to the references.[6],[7],[8],[9],[10],[11],[12] The median of ΔVpeak was 6.7%, ΔIVC was 11.4% and SVV was 18%.
Table 1: Characteristics of patients and the haemodynamic parameters (n=55)

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[Figure 1] presents scatter plots between ΔVpeak and ΔIVC, ΔVpeak and SVV and ΔIVC and SVV. Perusal of the scatter plots suggests a positive correlation between these three variables. Correlation analysis reveals weak correlation of ΔVpeak with ΔIVC (r = 0.415 with P = 0.002) and moderate correlation of ΔVpeak with SVV (r = 0.539 with P < 0.001) and ΔIVC with SVV (r = 0.524 with P < 0.001).
Figure 1: Scatter plots of variation in aortic peak blood flow velocity variation (ΔVpeak), inferior vena cava diameter variation (ΔIVC) and stroke volume variation (SVV). (a) ΔVpeak and ΔIVC (b) ΔVpeak and SVV (c) ΔIVC and SVV.

Click here to view

  Discussion Top

We found weak-to-moderate linear associations between ΔVpeak, ΔIVC and SVV in children who were mechanically ventilated. The correlations of ΔVpeak with SVV and ΔIVC with SVV had closer correlation than ΔVpeak with Δ IVC.

Predictably, ΔVpeak measured by echocardiography and SVV measured by USCOM were correlated given that both are calculated from variations in peak velocity of blood flowing in the aorta. Echocardiography is looking upwards superiorly at the left ventricular outflow tract from the anterior, while USCOM is looking downwards at the ascending aorta from the suprasternal notch.

However, ΔVpeak from echocardiography seems to be more precise than SVV from USCOM because echocardiography can visually display aortic valve area for the measurement of blood flow, while USCOM blindly measures stroke volume assuming correct position from adequate velocity–time integral. Further, SVV from USCOM is calculated depending on the aortic valve area estimated from patient's demographic data. Therefore, the estimated valve area may differ from the actual value and consequently affects the accuracy of SVV.

ΔIVC and ΔVpeak had the lowest correlation among all parameters in this study. This finding may be explained by the characteristic of two measurement locations. ΔIVC reflects the variation of blood volume entering the heart, while ΔVpeak demonstrates velocity of blood flow exiting the heart. Change of blood volume may be disproportionate to change of blood flow because other haemodynamic factors such as driving pressure, resistance and compliance of vessels are different between two points, before and after cardiopulmonary system. Many conditions can affect the accuracy of ΔIVC, including intrathoracic pressure, abdominal pressure, central venous pressure and compliance of the vessel.[16] During critical illness, patients generally have increased abdominal pressure from decreased bowel function, and various central venous pressures depend on fluid status, heart contractility and vascular compliance.

The measurement technique of ΔIVC is another considerable issue. Most studies measured IVC diameter approximately 2 to 4 cm from the right atrium junction in the longitudinal axis. As a matter of fact, the location for obtaining the diameter greatly varies in the literature, ranging from the right atrium junction to the left renal vein junction, and there is no evidence suggesting the best location. Moreover, the diaphragm movement during respiration also frequently displaces the measured location.[17]

The current study's limitations included only establishing the relationship between the haemodynamic parameters but not the predictive ability in fluid responsiveness. We did not administer intravenous volume to evaluate the change of parameters and stroke volume nor did we evaluate the effect of various diseases and treatments on these parameters. For example, congenital heart disease, heart failure, sepsis, thoracic surgery, vasoactive agents, sedatives or neuromuscular blocking agents may differ in aspects of the measured parameters. Further studies to clarify these potential limitations are warranted.

  Conclusions Top

Among the correlation studied, the best was a moderate correlation between ΔVpeak from echocardiography and SVV from USCOM. These two parameters may be a surrogate of each other for haemodynamic monitoring in mechanically ventilated children.


We thank all attending staff of the Pediatric Pulmonology and Critical Care unit for their in-kind support of the present study. None of the authors have any potential conflict of interest to declare.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

  References Top

Marik PE. Techniques for assessment of intravascular volume in critically ill patients. J Intensive Care Med 2009;24:329-37.  Back to cited text no. 1
Gan H, Cannesson M, Chandler JR, Ansermino JM. Predicting fluid responsiveness in children: A systematic review. Anesth Analg 2013;117:1380-92.  Back to cited text no. 2
Michard F, Teboul JL. Predicting fluid responsiveness in ICU patients: A critical analysis of the evidence. Chest 2002;121:2000-8.  Back to cited text no. 3
Tibby SM, Hatherill M, Marsh MJ, Murdoch IA. Clinicians' abilities to estimate cardiac index in ventilated children and infants. Arch Dis Child 1997;77:516-8.  Back to cited text no. 4
Saxena R, Durward A, Steeley S, Murdoch IA, Tibby SM. Predicting fluid responsiveness in 100 critically ill children: The effect of baseline contractility. Intensive Care Med 2015;41:2161-9.  Back to cited text no. 5
Elbarbary M, Ismail S, Shaath G, Jijeh A, Kabbani MS. Critical' ultrasound: The new essential skill in pediatric cardiac intensive care unit (PCICU). Eur Heart J Suppl 2014;16:68-71.  Back to cited text no. 6
Desgranges FP, Desebbe O, Pereira de Souza Neto E, Raphael D, Chassard D. Respiratory variation in aortic blood flow peak velocity to predict fluid responsiveness in mechanically ventilated children: A systematic review and meta-analysis. Paediatr Anaesth 2016;26:37-47.  Back to cited text no. 7
Zhang Z, Lu B, Sheng X, Jin N. Accuracy of stroke volume variation in predicting fluid responsiveness: A systematic review and meta-analysis. J Anesth 2011;25:904-16.  Back to cited text no. 8
Zhang Z, Xu X, Ye S, Xu L. Ultrasongraphic measurement of the respiratory variation in the inferior vena cava diameter is predictive of fluid responsiveness in critically ill patients: Systematic review and meta-analysis. Ultrasound Med Biol 2014;40:845-53.  Back to cited text no. 9
Si X, Xu H, Liu Z, Wu J, Cao D, Chen J, et al. Does respiratory variation in inferior vena cava diameter predict fluid responsiveness in mechanically ventilated patients? A systematic review and meta-analysis. Anesth Analg 2018;127:1157-64.  Back to cited text no. 10
Huang H, Shen Q, Liu Y, Xu H, Fang Y. Value of variation index of inferior vena cava diameter in predicting fluid responsiveness in patients with circulatory shock receiving mechanical ventilation: A systematic review and meta-analysis. Crit Care 2018;22:204.  Back to cited text no. 11
Cattermole GN, Leung PY, Mak PS, Chan SS, Graham CA, Rainer TH. The normal ranges of cardiovascular parameters in children measured using the Ultrasonic Cardiac Output Monitor. Crit Care Med 2010;38:1875-81.  Back to cited text no. 12
Feissel M, Michard F, Mangin I, Ruyer O, Faller JP, Teboul JL. Respiratory changes in aortic blood velocity as an indicator of fluid responsiveness in ventilated patients with septic shock. Chest 2001;119:867-73.  Back to cited text no. 13
Feissel M, Michard F, Faller JP, Teboul JL. The respiratory variation in inferior vena cava diameter as a guide to fluid therapy. Intensive Care Med 2004;30:1834-7.  Back to cited text no. 14
Evans JD. Straightforward Statistics for the Behavioral Sciences. Brooks/Cole Publishing. California: Pacific Grove; 1996.  Back to cited text no. 15
Bodson L, Vieillard-Baron A. Respiratory variation in inferior vena cava diameter: Surrogate of central venous pressure or parameter of fluid responsiveness? Let the physiology reply. Crit Care 2012;16:181.  Back to cited text no. 16
Stone MB, Huang JV. Inferior vena cava assessment: Correlation with CVP and plethora in tamponade. Glob Heart 2013;8:323-7.  Back to cited text no. 17


  [Figure 1]

  [Table 1]

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