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Intra-and inter-observer variability of point of care ultrasound measurements to evaluate hemodynamic parameters in healthy volunteers



Point-of-care ultrasound (POCUS) is a valuable tool for assessing the hemodynamic status of acute patients. Even though POCUS often uses a qualitative approach, quantitative measurements have potential advantages in evaluating hemodynamic status. Several quantitative ultrasound parameters can be used to assess the hemodynamic status and cardiac function. However, only limited data on the feasibility and reliability of the quantitative hemodynamic measurements in the point-of-care setting are available. This study investigated the intra- and inter-observer variability of PoCUS measurements of quantitative hemodynamic parameters in healthy volunteers.


In this prospective observational study, three sonographers performed three repeated measurements of eight different hemodynamic parameters in healthy subjects. An expert panel of two experienced sonographers evaluated the images’ quality. The repeatability (intra-observer variability) was determined by calculating the coefficient of variation (CV) between the separate measurements for each observer. The reproducibility (inter-observer variability) was assessed by determining the intra-class correlation coefficient (ICC).


32 subjects were included in this study, on whom, in total, 1502 images were obtained for analysis. All parameters were in a normal physiological range. Stroke volume (SV), cardiac output (CO), and inferior vena cava diameter (IVC-D) showed high repeatability (CV under 10%) and substantial reproducibility (ICC 0.61–0.80). The other parameters had only moderate repeatability and reproducibility.


We demonstrated good inter-observer reproducibility and good intra-observer repeatability for CO, SV and IVC-D taken in healthy subjects by emergency care physicians.


Assessment of the hemodynamic status is important in emergency medicine to predict which patients may benefit from volume resuscitation adequately or may develop adverse outcomes [1]. History taking and physical examination are often insufficient to determine hemodynamic status, and they suffer from significant limitations [2]. Over the last few years, point-of-care ultrasound (PoCUS) has shown to be a valuable tool for promptly obtaining more detailed information about the hemodynamic status of patients at the emergency department (ED), especially in patients with shock [3, 4]. POCUS uses a qualitative approach to perform hemodynamic measurements, often as part of a pre-defined algorithm such as Rapid Ultrasound for Shock and Hypotension (RUSH) protocol [4] and Echo Guided Life Support (EGLS) protocol [5]. Nevertheless, inexperienced practitioners must be aware of some common misinterpretations related to this approach that may lead to wrong decisions at the bedside [6].

Quantitative measurements, however, have potential advantages over qualitative evaluation of hemodynamic status. Several quantitative ultrasound parameters exist to evaluate the hemodynamic status and cardiac function: Overall cardiac function can be assessed by measuring cardiac output (CO), carotid blood flow (CBF) [1, 11], or stroke volume (SV) [7] or by quantification of individual components of CO such as cardiac contractility. E-Point septal separation (EPSS) [8] and Mitral annulus plane systolic excursion (MAPSE) [9] are commonly used to assess left ventricular systolic function; the Tricuspid annulus plane systolic excursion (TAPSE) [10] is used for the assessment of right ventricular systolic function. Hemodynamic status can be evaluated by measuring Inferior Vena Cava Diameter (IVC-D) and Collapsibility Index (IVC-CI). All aforementioned quantitative POCUS parameters are noninvasive and rapidly repeatable; using these parameters, singularly or in combination, can play a role in patient evaluation and hemodynamic status detection in patients in the emergency setting [11].

So far, only limited data are available on the feasibility of the quantitative hemodynamic measurements in the point-of-care setting [12, 13] and data about the reliability of the various hemodynamic measurements are sparse [14]. Moreover, those studies were focused on ultrasound image reinterpretations by a different observer but not on the operator’s repeatability, and generally, they considered only one or a few ultrasound parameters [12, 14, 15]. Furthermore, it is unknown whether specialists in acute internal medicine can reliably perform quantitative POCUS measurements to assess cardiac function and hemodynamic status.

We have performed a prospective observational study to simultaneously investigate the intra- and inter-observer variability of several POCUS quantitative hemodynamic parameters. POCUS measurements were taken in healthy volunteers by the same sonographers.

Materials and methods

Study design

This prospective observational study was conducted between 8th September 2020 and 25th January 2021 at the University Medical Center Groningen (UMCG) to investigate the intra and inter-observer variability of various quantitative hemodynamic parameters obtained by POCUS in healthy volunteers by specialists in acute internal medicine. This study has been approved by the central ethical commission (CTc UMCG), registration number: 202000917.


The study population consisted of 32 healthy adult volunteers. Subjects were eligible for participation when they were aged > 18 and had no previously known cardiovascular medical conditions. Patients were excluded if they were unable to tolerate a supine position for the duration of the ultrasound examination or when they were pregnant. Written informed consent was obtained from all volunteers.


Three different specialists in acute internal medicine examined the included subjects. All three sonographers have several years of experience in qualitative POCUS ultrasound with at least Entrustable Professional Activity (EPA- level) 4 [16]. This means they have performed at least 25 ultrasound exams for each specific POCUS application, e.g., at least 25 POCUS echocardiography exams and 25 IVC measurements. However, they had little knowledge of the use of doppler, and they never made the quantitative measurements of the parameters used in this study (except for the inferior vena cava (IVC) measurements), making them relatively novices. Before starting the study, each sonographer received instructions about making POCUS measurements involved in the research and a two-hour training session. This consisted of explaining how to make the proper calculations using the ultrasound machine program and in a training moment where the sonographers could practice each ultrasound parameter execution on several volunteers.

For the study, each investigator measured eight different hemodynamic parameters and repeated each measurement three times (starting over and changing the probe position after each measurement). At least two investigators examined all subjects. All measurements in individual subjects were conducted within 1.5 h by the different investigators to ensure similar conditions and avoid changes in hydration status or other hemodynamic factors. The investigators did not see each other performing the measurements.

Data collection

All examinations were performed with the GE Venue R1 (General Electric Healthcare, Chicago, USA), equipped with automatic tools to measure hemodynamic functions, including the cardiac output, the inferior vena cava collapsibility index and diameter, and the carotid blood flow (CBF).

In our study, we measured LVOT-D, EPSS, MAPSE, and TAPSE manually. For the other measurements (LVOT-VTI, IVC-D, rCBF–D, and rCBF–VTI), automatic tools on the ultrasound machine were used. From the eight different measurements described above, additional four parameters were calculated automatically from the GE Venue R1 (SV, CO, IVC-CI and rCBF). A total of twelve ultrasound parameters were registered, as described in Additional file 1: Table S1. To standardize the exams, an ultrasound protocol (Additional file 1: Table S2) based on several ultrasound guidelines was used [4, 17].

The data were stored anonymously on the UMCG research platform; the images were reviewed and analyzed with the MicroDicom DICOM-viewer. A quality review of the images obtained was performed, before analysis took place. Two blinded experienced sonographers evaluated the images following sonographic assessment criteria based on the standard of the American Society of Echocardiography [17]. If more than one of the three images of the same parameter of the same observer were of poor quality, the whole set of three measurements was excluded from further analysis.

Primary endpoint

Evaluation of repeatability (intra-observer variability) and reproducibility (inter-observer variability) of each of the twelve individual quantitative hemodynamic POCUS measurements taken in healthy volunteers by acute physicians experts in qualitative POCUS evaluation underwent training for those specific quantitative POCUS measurements.

Data analysis

Repeatability was determined by calculating the coefficient of variation (CV) between the separate measurements for each observer. We took the average of the CV across all models to measure overall repeatability. Based on Kirkwood et al. we considered a CV of 20% as high dispersion of data, thus a high variability, and a CV of 10% as a low dispersion of data, thus a low variability, whereby the range between the two was considered moderate [18].

Reproducibility was assessed by determining the intra-class correlation coefficient (ICC) [16] using a two-way random-effects model. To facilitate interpretation, we applied the Portney and Watkins classification [19]. The normality of data was tested using the Shapiro–Wilk test before running the model.

Statistical analysis was performed using Microsoft Excel 2010 and SPSS Statistic version 23 (IBM Corp.Armonk, USA).


Ultrasound image acquisition and selection

A total of 32 subjects participated in the study. There were 18 (56%) females and 14 (44%) males, with a mean (SD) age of 23 (2.96) years.

The three sonographers examined 30, 27, and 28 volunteers, respectively. Twenty-one participants received ultrasound evaluations from all three observers. A total of 595 sets of images (1785 in total) were taken. The number of sets of images available for each parameter and observer is given in Additional file 1: Table S3.

Of the total 1785 ultrasound images taken, 31 images were missing, mainly due to storage issues of the GE Venue R1 apparatus, and a total of 252 (14%) images did not fulfill the quality assessment criteria and were excluded from the analysis. We included a total of 1502 images for analysis (Fig. 1).

Fig. 1
figure 1

Flow chart of inclusion and exclusion of all patients

The main reason for excluding IVC-D and IVC-CI images was the presence of artifacts in the calculation time interval (auto measurements). Most MAPSE images were excluded due to the incorrect obliqueness of the image (M Mode and cardiac image); the others were excluded due to the wrong alignment of the M Mode cursor along the mitral lateral annulus and misalignment of the septum. CBF measurement images were excluded mainly due to inadequate tracing of the VTI lines, followed by wrong sample gate placement and the obliqueness of the vessel walls. Moreover, lastly, we excluded LVOT-VTI images because the sample gate cursor placement was incorrect or due to the bad quality of the VTI spectral tracing.

Results of image quality assessment per parameter and observer, missing images, and total images for analysis are summarized in Additional file 1: Table S4.

Hemodynamic parameters

Baseline values (mean ± SD) of the hemodynamic parameters measured with POCUS stratified by observers are represented in Table 1. As expected for healthy volunteers, all parameters were in a normal physiological range.

Table 1 Baseline characteristics (mean ± SD) parameter


The results related to intra-observer variability are summarized in Table 2.

Table 2 Intra-researcher variability for each POCUS parameter

The LVOT-D parameter showed the lowest intra-observer variability (2.54%). The IVC-D, rCBF-D, CO, SV, LVOT-VTI, TAPSE, and MAPSE all had a mean CV under 10% (high repeatability). The rCBF (and rCBF-VTI) and the IVC-CI demonstrated moderate repeatability (intra-observer variability between 10 and 20%). Only the EPSS showed a high variance with a mean CV higher than 20% (low repeatability). The variables in increasing order of CV are shown in Additional file 1: Graph 1.


The inter-observer variability, calculated for those patients for whom full sets of images were available for all three examiners, is represented in Table 3. Data about the ICC based on the comparison between operators are summarized in Additional file 1: Table S5.

Table 3 Inter-researcher variability: ICC based on all three examiners’ full sets of images, with the 95% confidence interval and the correlating sample size number of volunteers

A substantial reproducibility (ICC 0.61–0.80) was found for SV, CO, and IVC-D. A moderate reproducibility (ICC 0.41–0.60) was seen in MAPSE, LVOT-VTI and LVOT-D, IVC-CI, and EPSS. However, rCBF and TAPSE showed fair reproducibility (ICC 0.21–0.40).

Integrating repeatability and reproducibility of hemodynamic parameters

To assess which hemodynamic function parameter showed the most reliable outcome for the repeatability per observer and reproducibility between observers, we compared intra-observer variability (CV) with inter-observer variability (ICC). CO, SV and IVC-D had a low intra-observer variability and substantial reproducibility, implying that of all examined parameters, they have the best balance between CV and ICC.


This study showed good inter-observer reproducibility and good intra-observer repeatability for CO, SV and IVC diameter measurements.

Ultrasound image acquisition and interpretation are operator dependent [20]; only a few studies describe intra- and inter-observer variabilities for hemodynamic ultrasound parameters. They are mainly based on the reinterpretation of the same ultrasound images by a different observer, but not on the same operator's repeatability. They generally focus on only one or a few ultrasound parameters [12, 14, 15]. This is in contrast to our study, which also assesses the repeatability and reproducibility of POCUS measurements. Moreover, to determine the inter-observer reproducibility, we used the intraclass correlation coefficient, which allows the analyses of both the degree of correlation and the level of agreement of the measurements made by the different observers. Furthermore, we have analyzed the feasibility of simultaneously performing both cardiac function and hemodynamic status parameters. Previous feasibility studies mainly focused on only one parameter [12, 13]. Thus, this is the first study where intra- and inter-observer variabilities for hemodynamic ultrasound parameters were studied simultaneously.

Of all parameters regarding cardiac function, stroke volume and cardiac output showed the best test characteristics. This seems to be unexpected because the measurement of these parameters presents certain challenges: any inaccuracies in the measurement of LVOT diameter are taken to the second power in the continuity equation [21], and inconsistent placement of the pulsed Doppler sample volume in the LVOT is a consistent source of error [22]. Previous studies suggested that cardiac output can only be reliably measured by more experienced observers [23]. In our study, the sonographers already had experience performing qualitative cardiac function ultrasound, which may explain why a short introduction training was enough. Also, cardiac output was automatically determined by the GE Venue R1 machine using an automated VTI tracing system. This is in line with other feasibility studies showing the increasing quality of ultrasound images with an increasing level of training [13]. Hence, even though technical difficulties are described in the literature, we obtained reliable CO and SV measurements after a short training program. As already known in the literature, automatic tools correlate closely with manual measurements for LVOT-VTI measurements [24]; the vantage we could have was that the automatic method could allow realizing measurements within a much shorter time than the standard manual tracing method [25]. We think we could obtain the same result by calculating the LVOT-D measurement manually, as we did, and also tracing the LVOT-VTI measurement manually. The automatic tool helped us trace the wave and make calculations (using the equation), but we did not have any advantage in angle alignment. The vantages we had were about saving time, making the process more executable in the emergency environment, and reducing the possibility of calculation errors.

We found a moderate reproducibility and a high exclusion rate of the MAPSE and the TAPSE ultrasound images, which we did not expect as these parameters are measured in only one ultrasound window and thereby allow brief examination. We revealed that obtaining MAPSE and TAPSE images for specialists in acute internal medicine might be more challenging than for more trained cardiac examiners like cardiologists. The differences in reliability between our result and the literature might be due to the difference in the study population. The previous studies analyzed bigger sample sizes and a more diverse study population, with patients with both physiological and pathological values. It can be argued that the diversity in outcomes for the individual patients, in its extremity, led to a higher correlation and agreement of data and, therefore, better reproducibility [26, 27].

We found a high exclusion rate for the CBF measurements due to inadequate tracing of the automatic VTI line functions. The intra-observer variability of the CBF was only moderate, in line with other studies demonstrating a large range of reproducibility [28]. Some explanations for this difference in reproducibility might be the difference in the location of the measurement [29] and the physiological carotid artery diameter changing during systole and diastole [30]. Because the VTI curve and the tracing were frequently not aligned, it is possible that fewer images would have been excluded by manually tracing the VTI.

Identifying the IVC diameter and collapsibility is part of the essential ultrasound examination at the ED [31, 32] to assess the hemodynamic status and guide fluid resuscitation. Previous studies have shown that the IVC-CI and IVC-D ultrasound measurements can easily be performed with minimal training [33]. Although our intra-observer variability and inter-observer reproducibility for the IVC-D were both good, IVC-CI intra-observer variability and inter-observer reliability were only moderate. This corresponds with the findings of previous studies [34]. We think that the reliability of the IVC measurements might have been influenced by the automatic trace of the vessel in M-mode and partially explains the high number of excluded images. We suppose IVC-CI measurements showed more variation than IVC-D measurements due to a slight variation in breathing. On the other hand, the automated tracing of the IVC-CI measurements could be more vulnerable due to variance of interest (because it is calculated from two measurements) than the IVC-D measurements, which are only affected by natural variation (unwanted variance). We don’t think there was a difference between automated and manual tracing of IVC diameter. The automatic tool traced the vessel; manual tracing would give the same result. The vantage was that the automatic tool eliminated variation due to human error when measuring IVC.

There are limitations to our study. First, it has a relatively small sample size. We might have found different results if we had analyzed a larger population. Moreover, it was only sometimes possible for all three observers to examine each participant due to scheduling difficulties, so only some subjects received measurements from all three observers. Second, this is a feasibility study conducted on healthy volunteers, and practicability may differ in real patients in acute care settings: measurements in healthy subjects can be more accessible because they can maintain the asking posture and guarantee optimal preparation. Moreover, the measurements would be in the physiological range, with a low variance rate. Third, a measurement bias due to the observer bias [35] may have occurred if the individual observers tried to match measurements to their previous examinations, considering they were aware that their measurements were being studied [36]. Fourth, we used automated measuring tools, which the ultrasound machine we have in our emergency department was equipped with; but those functions are only readily available in some emergency departments. In addition, using manual measurements (instead of auto tools) could make the measurement acquisition process longer and, therefore, more challenging to measure in the field of an emergency department. Fifth, the three sonographers have several years of experience in qualitative POCUS ultrasound and could perform quantitative measurements after a brief instruction and a two-hour training session. If more limited experience sonographers had performed the same POCUS measurements, the study would have given other results. Sixth, ICC was calculated only when reliable measurements could be obtained. Because the ICC depends on N: the lower the N, the higher the ICC when the mean and standard deviation remain the same. By selecting only patients in whom both base parameters were considered acceptable (e.g., for CO and SV), we had a lower N. This means that these numbers are based on a slightly different data set, which impacts the ICC due to relatively lower numbers and can contribute to an artificially higher ICC.


We demonstrate good inter-observer reproducibility and intra-observer repeatability for CO, SV and IVC-D measurements by those specialists in acute internal medicine with basic ultrasound knowledge that underwent a specific limited training program. Those measurements have relevance for evaluating the hemodynamic status in healthy volunteers. Future studies should validate the observed findings in hemodynamically unstable patients.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.



Angle of correction


Carotid bulb


Common carotid artery


Common carotid artery diameter


Carotid blood flow


Cardiac output


Coefficient of variation


Central venous pressure


Emergency department


E Point septal separation


Focus cardiac ultrasound


Flow volume


Intraclass correlation coefficient


Inferior vena cava


Inferior vena cava collapsibility index


Inferior vena cava diameter


Inferior vena cava measurements


Left ventricular ejection fraction


Left ventricular outflow tract


Left ventricular outflow tract diameter


Left ventricular outflow tract velocity time integral


Mitral annular plane systolic excursion


Motion mode


Pulmonary artery catheter


Point of care ultrasound


Peak systolic velocity


Parasternal long axis


Rapid ultrasound for shock and hypotension


Right carotid blood flow


Right ventricular outflow tract


Stroke volume


Systematic vascular resistance


Time average mean velocity


Time average maximum velocity


Tricuspid annulus plane systolic excursion


Tricuspid valve


Velocity time integral


  1. Pourmand A, Pyle M, Yamane D et al (2019) The utility of point-of-care ultrasound in the assessment of volume status in acute and critically ill patients. World J Emerg Med 10:232.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Frank Peacock W, Soto KM (2010) Current technique of fluid status assessment. Congest Heart Fail 16:45–51.

    Article  Google Scholar 

  3. Berg I, Walpot K, Lamprecht H et al (2022) A systemic review on the diagnostic accuracy of point-of-care ultrasound in patients with undifferentiated shock in the emergency department. Cureus 14:15–22.

    Article  Google Scholar 

  4. Blanco P, Aguiar FM, Blaivas M (2015) Rapid ultrasound in shock (RUSH) velocity-time integral: a proposal to expand the RUSH protocol. J Ultrasound Med 34:1691–1700.

    Article  PubMed  Google Scholar 

  5. Lanctôt JF, Valois M, Beaulieu Y (2011) EGLS: echo-guided life support. Crit Ultrasound J 3:123–129.

    Article  Google Scholar 

  6. Blanco P, Volpicelli G (2016) Common pitfalls in point-of-care ultrasound: a practical guide for emergency and critical care physicians. Crit Ultrasound J.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Mcgregor D, Sharma S, Gupta S et al (2019) Emergency department non-invasive cardiac output study ( EDNICO ): a feasibility and repeatability study. Scand J Trauma Resusc Emerg Med.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Stenberg Y, Wallinder L, Lindberg A et al (2021) Preoperative point-of-care assessment of left ventricular systolic dysfunction with transthoracic echocardiography. Anesth Analg 132:717–725.

    Article  PubMed  Google Scholar 

  9. Matos J, Kronzon I, Panagopoulos G, Perk G (2012) Mitral annular plane systolic excursion as a surrogate for left ventricular ejection fraction. J Am Soc Echocardiogr 25:969–974.

    Article  PubMed  Google Scholar 

  10. Lanspa MJ, Cirulis MM, Wiley BM et al (2021) Right ventricular dysfunction in early sepsis and septic shock. Chest 159:1055–1063.

    Article  PubMed  Google Scholar 

  11. Blanco P (2020) Rationale for using the velocity–time integral and the minute distance for assessing the stroke volume and cardiac output in point-of-care settings. Ultrasound J.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Koster G, Kaufmann T, Hiemstra B et al (2020) Feasibility of cardiac output measurements in critically ill patients by medical students. Ultrasound J 12:1–9.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Betcher J, Majkrzak A, Cranford J et al (2018) Feasibility study of advanced focused cardiac measurements within the emergency department. Crit Ultrasound J.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Moulson N, Jaff Z, Wiltshire V et al (2019) Feasibility and reliability of Nonexpert POCUS for cardiovascular preparticipation screening of varsity athletes: the SHARP protocol. Can J Cardiol 35:35–41.

    Article  PubMed  Google Scholar 

  15. Marta Pinedoa EVCTRAJLARIGEFJASR (2010) Inter- and Intra-Observer Variability in the Echocardiographic Evaluation of Right Ventricular Function. Rev Esp Cardiol.

  16. Olgers TJ, Azizi N, Blans MJ et al (2019) Point-of-care ultrasound (PoCUS) for the internist in acute medicine: a uniform curriculum. Netherlands J Med 77:168–176

    CAS  Google Scholar 

  17. Mitchell C, Rahko PS, Blauwet LA et al (2019) Guidelines for performing a comprehensive transthoracic echocardiographic examination in adults: recommendations from the American Society of echocardiography. J Am Soc Echocardiogr 32:1–64.

    Article  PubMed  Google Scholar 

  18. Betty R. Kirkwood JACS (2003) Essential Medical Statistics, 2nd Edition, 2nd Editio. Blackwell Science Ltd

  19. Portney LG, Watkins MP (2008) Foundations of Clinical Research: Applications to Practice, 3rd Editio

  20. Van Dantzig JM (2006) Echocardiography in the emergency department. Semin Cardiothorac Vasc Anesth 10:79–81.

    Article  PubMed  Google Scholar 

  21. Michelena HI, Margaryan E, Miller FA et al (2013) Inconsistent echocardiographic grading of aortic stenosis: is the left ventricular outflow tract important? Heart 99:921–931.

    Article  PubMed  Google Scholar 

  22. Kebed K, Sun D, Addetia K, Mor-Avi V, Markuzon N, Roberto Lang M (2020) Measurement errors in serial echocardiographic assessments of aortic valve stenosis severity. Int J Cardiovasc Imaging.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Vermeiren GLJ, Malbrain MLNG, Walpot JMJB (2015) Cardiac Ultrasonography in the critical care setting: a practical approach to asses cardiac function and preload for the “non-cardiologist.” Anaesthesiol Intensive Ther 47:s89–s104.

    Article  PubMed  Google Scholar 

  24. Gosling AF, Thalappillil R, Ortoleva J et al (2020) Automated spectral Doppler profile tracing. J Cardiothorac Vasc Anesth 34:72–76.

    Article  PubMed  Google Scholar 

  25. Gaillard E, Kadem L, Clavel MA et al (2010) Optimization of doppler echocardiographic velocity measurements using an automatic contour detection method. Ultrasound Med Biol 36:1513–1524.

    Article  PubMed  Google Scholar 

  26. Baron T, Berglund L, Hedin EM, Flachskampf FA (2019) Test–retest reliability of new and conventional echocardiographic parameters of left ventricular systolic function. Clin Res Cardiol 108:355–365.

    Article  PubMed  Google Scholar 

  27. Olmos-Temois SG, Santos-Martínez LE, Álvarez-Álvarez R et al (2016) Interobserver agreement on the echocardiographic parameters that estimate right ventricular systolic function in the early postoperative period of cardiac surgery. Medicina Intensiva (English Edition) 40:491–498.

    Article  CAS  Google Scholar 

  28. Sinha AK, Cane C, Kempley ST (2006) Blood flow in the common carotid artery in term and preterm infants: reproducibility and relation to cardiac output. Arch Dis Child Fetal Neonatal Ed 91:31–35.

    Article  Google Scholar 

  29. Gassner M, Killu K, Bauman Z et al (2015) Feasibility of common carotid artery point of care ultrasound in cardiac output measurements compared to invasive methods. J Ultrasound 18:127–133.

    Article  PubMed  Google Scholar 

  30. Stolz LA, Mosier JM, Gross AM et al (2015) Can emergency physicians perform common carotid doppler flow measurements to assess volume responsiveness? Western J Emerg Med 16:255–259.

    Article  Google Scholar 

  31. Mackenzie DC, Noble VE (2014) Assessing volume status and fluid responsiveness in the emergency department. Clin Exp Emerg Med 1:67–77.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Oord M, Olgers TJ, Doff-Holman M et al (2017) Ultrasound and NICOM in the assessment of fluid responsiveness in patients with mild sepsis in the emergency department: a pilot study. BMJ Open 7:1–6.

    Article  Google Scholar 

  33. Long E, Oakley E, Duke T, Babl FE (2017) Does respiratory variation in inferior vena cava diameter predict fluid responsiveness: a systematic review and meta-analysis. Shock 47:550–559.

    Article  PubMed  Google Scholar 

  34. Fields JM, Lee PA, Jenq KY et al (2011) The interrater reliability of inferior vena cava ultrasound by bedside clinician sonographers in emergency department patients. Acad Emerg Med 18:98–101.

    Article  PubMed  Google Scholar 

  35. Mahtani K, Spencer EA, Brassey J, Heneghan C (2018) Catalogue of bias: observer bias. BMJ Evid Based Med 23:23–24.

    Article  PubMed  Google Scholar 

  36. Pronin E (2006) Perception and misperception of bias in human judgment. Trends Cogn Sci.

    Article  PubMed  Google Scholar 

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This research study is not financially funded by any organization or individual; it is the authors’ personal, original work.

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Authors and Affiliations



DB: contributed to statistical analysis, results interpretation, and confection of the manuscript. ECS: contributed to the conception and design of the study, patient assessment, and data collection. TJO: contributed to data collection and interpretation as well as the writing and critical review of the manuscript after its completion. EtA: contributed to statistical analysis, results interpretation, and the writing and critical review of the manuscript after its completion. HRB: contributed to the design, strategy, and execution of the research, data collection, and subsequent analysis. NA: contributed to the implementation of the statistical analysis. JCtM: contributed to the design, strategy, and execution of the research, data collection and subsequent analysis, and interpretation of the results, as well as the writing of this document. All authors read and approved the final manuscript.

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Correspondence to Deborah Blanca.

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This study is in accordance with the Good Clinical Practice regulations and has been approved by the central ethical commission (CTc UMCG—Centrale ethische Toetsingscommissie niet WMO plichtig onderzoek van het Universitair Medisch Centrum Groningen), registration number: 202000917. The written informed consent form was signed by each participant.

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The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1: Supplementary Table 1:

Description of the methods used to acquire the parameters of interest and the procedure for measuring and automatically calculating them with GE Venue R1. Supplementary Table 2: Description of the ultrasound protocol used to acquire the parameters, based on the "Guidelines for performing a comprehensive transthoracic echocardiographic examination in adults: recommendations from the American Society of Echocardiography." Supplementary Table 3: Numbers of available image sets divided by each parameter and observer. Supplementary Table 4: Image quality assessment results divided by parameter and observer, missing images, and total images for analysis. Supplementary Table 5: Data about the ICC based on operator comparison.

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Blanca, D., Schwarz, E.C., Olgers, T.J. et al. Intra-and inter-observer variability of point of care ultrasound measurements to evaluate hemodynamic parameters in healthy volunteers. Ultrasound J 15, 22 (2023).

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  • Point of care ultrasound
  • Intra-observer variability
  • Inter-observer reliability
  • Repeatability
  • Reproducibility
  • Acute care