Visualizing hemodynamics: innovative graphical displays and imaging techniques in anesthesia and critical care

Abstract

The advancements in cardiovascular imaging over the past two decades have been significant. The miniaturization of ultrasound devices has greatly contributed to their widespread adoption in operating rooms and intensive care units. The integration of AI-enabled tools has further transformed the field by simplifying echocardiographic evaluations and enhancing the reproducibility of hemodynamic measurements, even for less experienced operators. Speckle tracking echocardiography offers a direct, visual, and quantitative assessment of myocardial shortening, serving as a compelling alternative to traditional methods for evaluating right and left ventricular systolic function. In critically ill patients, sublingual microcirculation imaging has revealed a high prevalence of microvascular alterations, which are markers of disease severity. The use of handheld vital microscopes enables the quantification of several key parameters, including vessel density, perfusion, red blood cell velocity, and the perfused vascular density. Such metrics are useful for evaluating microcirculatory health. The development of automated software marks a significant advance toward real-time bedside microvascular assessment. These advancements could eventually allow shock resuscitation to be tailored based on microvascular responses. In parallel with imaging advances, cardiac output monitors have evolved significantly. Once cumbersome devices displaying basic numerical data in tabular form, they now feature sleek, touch-screen interfaces integrated with visual decision-support tools. These tools synthesize hemodynamic data into intuitive graphical formats, allowing clinicians to quickly grasp the determinants of circulatory shock. This visual clarity supports more efficient and accurate decision-making, which may ultimately lead to improved patient care and outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-024-05239-w.

Introduction

The advancements in cardiovascular imaging over the past two decades have been impressive. The cinematic quality of cardiac magnetic resonance imaging (MRI), which provides vivid 3D visuals of blood flow through the heart and great vessels, underscores the adage “A picture is worth a thousand words.” Magnetic resonance imaging is not available at the bedside. However, other imaging techniques have benefited from significant improvements and are increasingly used in anesthesia and critical care [1]. Echocardiography, for example, has undergone substantial hardware and software advancements. Pending accessibility to ultrasound innovations and appropriate training, echocardiography might become a cornerstone of hemodynamic assessment, enabling timely and precise interventions. Meanwhile, newer imaging techniques, such as sublingual videomicroscopy, are in their developmental stages. This technology provides a detailed view of the microcirculation, offering insights into the dynamics of tissue perfusion that could pave the way for more individualized treatments.

In parallel with imaging advances, cardiac output monitors have evolved significantly. Once cumbersome devices displaying basic numerical data in tabular form, they now feature sleek, touch-screen interfaces integrated with visual decision-support tools. These tools synthesize hemodynamic data into intuitive graphical formats, allowing clinicians to quickly grasp the determinants of circulatory shock.

In this narrative review on the visualization of hemodynamics, we successively discuss the evolution of graphical displays on hemodynamic monitors, AI-enabled ultrasound tools, speckle-tracking echocardiography, and sublingual video microscopy.

Graphical displays

Graphical displays translate hemodynamic data into intuitive visual formats, potentially facilitating detection of physiological changes, improving diagnostic accuracy, enabling longer maintenance within therapeutic targets, and reducing clinicians’ cognitive workload [2, 3]. All cardiac output monitors now feature trend lines adjacent to numeric values, offering a dynamic view of patients’ trajectories and helping to identify gradual shifts in hemodynamic variables that single-point measurements might overlook.

Modern interactive trend screens enhance this functionality by enabling the automatic quantification of changes over a specified time frame. This feature proves particularly useful during diagnostic tests, such as a passive leg-raising maneuver, or therapeutic interventions, such as administering a fluid bolus.

At-a-glance monitoring

The human brain is remarkably adept at processing visual information, interpreting images up to 60,000 times faster than text or numbers. This innate capability underlines one of the major benefits of graphical displays in hemodynamic monitoring: they make complex information immediately understandable. The concept of “at-a-glance monitoring” was introduced by Ford et al. [4] in a seminal study that analyzed how anesthesiologists interacted with monitoring systems during surgeries. By video-recording anesthesiologists at the beginning, middle, and end of procedures, the study revealed that clinicians devoted minimal time to observing monitors—approximately 30 s per 10-min segment. Their observations were characterized by frequent, brief glances lasting an average of 1–2 s, occurring 15–20 times within each 10-min window. Remarkably, this behavior remained consistent across all surgical phases. Graphical displays capitalize on this behavioral pattern by consolidating a vast array of hemodynamic data into a single, visually intuitive object. This integration allows clinicians to grasp a patient’s overall status within seconds, streamlining decision-making during critical moments. Such displays might be particularly beneficial in high-stakes environments, where rapid situational awareness can significantly impact patient outcomes.

The concept of at-a-glance monitoring also supports the integration of all monitoring data into a single, unified display [5]. Currently, advanced hemodynamic variables are typically presented on dedicated, bulky stand-alone monitors positioned alongside standard multiparametric monitors that display vital signs. Systems based on pulse contour or pulse wave analysis are essentially algorithms that calculate stroke volume and derived parameters (e.g., cardiac output and vascular resistance) from the arterial pressure waveform [6]. From a technical perspective, it is somewhat surprising that such algorithms have not yet been integrated into standard bedside monitors. Doing so would free up valuable space around the patient and reduce the overall cost of hemodynamic monitoring [7]. Moreover, consolidating these variables into a single, thoughtfully designed interface could enhance clinicians’ ability to quickly identify abnormal trends or situations while reducing cognitive load [5].

Turning data into knowledge

Until recently, hemodynamic variables were often displayed as isolated metrics, differentiated by color but without standardization or a clear prioritization of their clinical importance. This approach left clinicians to mentally integrate and interpret data, which can be time-consuming and prone to error during critical situations [8]. A more intuitive strategy for presenting hemodynamic data involves visual aids like dials or color-coding to indicate when variables fall outside normal ranges or pre-defined therapeutic targets. Since 2007, there has been a growing adoption of “airplane cockpit-like” screens on hemodynamic monitors (Fig. 1). A further advancement would be to hierarchize hemodynamic variables based on their physiological relationships. For example, mean arterial pressure is determined by cardiac output and systemic vascular resistance, while cardiac output itself depends on stroke volume and heart rate. Displaying these interrelationships visually, as illustrated in Fig. 2, could make hemodynamic profiles immediately apparent to the clinician. Although machine learning (ML) algorithms have recently been developed to automatically identify hemodynamic phenotypes or profiles [9], visual profiling might provide an equally effective and simpler alternative [10]. Visual tools capitalize on clinicians’ ability to quickly process graphical data and can enhance understanding of the underlying mechanisms of circulatory failure without the need for complex algorithms. To validate this approach, comparative studies evaluating the efficacy of visual profiling against machine learning algorithms in clinical settings would be highly valuable.

Fig. 1.

Example of cockpit screen. This screen was designed by F. Michard and U.J. Pfeiffer in 2007 for the Intellegio monitor from UP-MED (Munich, Germany). Since then, several companies have adopted this concept for their monitors. With permission from U.J. Pfeiffer, founder of UP-MED

Fig. 2.

The evolution of graphical displays: from data to knowledge. From data (numerical table with meaningless color coding) to information (hemodynamic variables out of normal range become highly visible) and knowledge (hemodynamic variables are presented according to their physiological relationship, enabling the identification of hemodynamic profiles at a glance—hypovolemic shock in this example)

Abstract displays

Several studies have explored the clinical utility of abstract graphical displays—such as histograms, polygons, or spider web-like targets—compared to traditional tabular numerical displays. These investigations have consistently demonstrated the potential of graphical formats to enhance the interpretation of physiological data. In a controlled laboratory setting, Gurushanthaiah et al. [11] assessed the performance of anesthesia residents using either a standard numerical display or graphical formats (histograms and polygons) to monitor vital variables. Their findings revealed that graphical displays significantly reduced response times and improved the accuracy of detecting acute physiological changes. Blike et al. [12] examined whether a graphical object—a visual representation of data with meaningful shapes—could enhance anesthesiologists’ ability to accurately and rapidly diagnose five common etiologies of shock: anaphylaxis, bradycardia, myocardial ischemia, hypovolemia, and pulmonary embolism. Utilizing datasets that included heart rate, blood pressure, pulmonary artery pressure, central venous pressure, and cardiac output, the study compared alpha-numeric data presentations with graphical displays. Anesthesiologists using the graphical format made fewer diagnostic errors and completed both shock recognition and diagnosis more rapidly. Vallée et al. [13] introduced a spider web-like visual tool designed to assist clinicians in adhering to the Surviving Sepsis Campaign guidelines. This innovative target screen simultaneously displayed variables such as central venous oxygen saturation, lactate levels, mean arterial pressure, arterial oxygen saturation, and cardiac index. The study found that the graphical tool improved compliance with sepsis guidelines, as evidenced by increased measurement rates of central venous oxygen saturation and lactate levels. While these studies have not demonstrated direct improvements in patient outcomes, the potential downstream benefits are compelling. Enhanced compliance with established guidelines and faster, more accurate diagnoses are likely to translate into improved clinical results over time.

Anatomical or metaphorical displays

Anatomical or metaphorical displays provide visual representations of organs or the human body, aiming to help clinicians—particularly those with less experience—quickly identify and better understand hemodynamic abnormalities [14]. Agutter et al. [15] evaluated anesthesiologists’ performance while managing simulated complex surgical cases using either traditional bedside monitors or a graphical representation of the cardiovascular system designed to visualize hemodynamic variables measured by pulmonary arterial catheter. The study revealed that the use of the graphic display led to earlier treatment initiation, smaller deviations in systolic blood pressure and central venous pressure from baseline, and higher arterial oxygen saturation levels by the end of the simulation. In a subsequent study [16], the same metaphorical display demonstrated its utility in reducing both the time required to detect adverse events and the time to initiate treatment during simulated scenarios. Recent investigations have explored the value of animated patient "avatars" in clinical settings [17]. These avatars dynamically represent a patient’s physiological state by visually modifying elements such as the shape, color, and animations of the vena cava, heart, and aorta to reflect hemodynamic variables (e.g., heart rate, blood pressure, central venous pressure, and cardiac index). Research indicates that avatar-based monitoring could improve caregivers’ situational awareness and reduce cognitive workload [17–19]. These findings suggest that metaphorical displays, whether static or animated, hold significant promise for advancing hemodynamic monitoring.

The (r)evolution of point of care echocardiography

Echocardiography is a cornerstone in the assessment of cardiac function, offering real-time imaging and hemodynamic insights essential for evaluating and managing critically ill patients [20–22]. It is widely recommended as a first-line diagnostic and management tool for patients presenting with signs of circulatory shock [23]. Recent advancements in echocardiography technology have improved its accessibility and utility. One of the most notable innovations is the advent of pocket-sized imaging devices (Fig. 3). These portable tools have made echocardiography more accessible, enabling clinicians to perform point-of-care ultrasound (POCUS) assessments efficiently and conveniently, even in resource-limited settings. Despite their compact form, many modern devices provide high-resolution imaging and incorporate features once exclusive to larger, more complex systems [24–26].

Fig. 3.

The evolution of echocardiographic devices. In three decades, ultrasound devices evolved from bulky and expensive cart-based machines to affordable wireless pocket transducers connectable to a smartphone or an electronic tablet

However, the effectiveness of ultrasound evaluations remains highly operator-dependent. Achieving precise quantitative assessments of cardiac function can be particularly challenging for less experienced clinicians or trainees. To address these limitations, recent ultrasound devices often include advanced software innovations designed to enhance the quality and consistency of bedside echocardiographic assessments. These tools aim to simplify image acquisition, optimize measurements, and reduce operator variability, thereby improving diagnostic accuracy.

AI-enabled ultrasound tools

The integration of artificial intelligence (AI) into ultrasound imaging has opened new avenues for hemodynamic assessment. Machine learning algorithms, trained on vast datasets of echocardiographic images, are now capable of recognizing multiple ultrasound views, guiding users to optimize image quality, and automatically measuring key echocardiographic variables. These advancements reduce intra-operator variability, a common limitation of traditional ultrasound assessments, thereby enhancing diagnostic reliability and consistency [27–29]. AI-enabled ultrasound tools bring sophisticated echocardiographic analysis within reach of clinicians with varying levels of expertise. These systems can provide critical information and parameters that may have previously been inaccessible without extensive training. By democratizing access to advanced hemodynamic assessments, AI-driven innovations hold the potential to improve patient care.

Most ML algorithms for echocardiography have been developed to estimate left ventricular ejection fraction (LVEF) in real-time and are primarily validated in cardiology patients [30–32]. In critically ill patients, Varudo et al. [33] recently used a neural network algorithm that automatically identifies the apical 4-chamber view, detects the endocardial borders of the left ventricle, and determines the timing of end-diastole and end-systole based on mitral valve motion. Using these data, it calculates left ventricular volumes and LVEF. This study reported an excellent specificity (> 95%) for detecting left ventricular systolic dysfunction. Notably, novice users achieved more consistent LVEF measurements using the ML algorithm compared to manual measurements performed by expert echocardiographers. This highlights the potential of AI to improve reliability, particularly for less experienced operators. Of note, some ML algorithms can estimate LVEF directly without calculating left ventricular volumes [34].

Machine learning algorithms are now capable of automatically assessing a variety of hemodynamic variables, including the subaortic velocity time integral (VTI). The subaortic VTI, a surrogate for left ventricular stroke volume, plays a critical role in hemodynamic assessments. It can help determine fluid responsiveness by observing significant increases in VTI during a passive leg-raising maneuver or fluid challenge. Additionally, VTI measurements are used to calculate cardiac output [35]. Machine learning algorithms can autonomously identify the apical 5-chamber view and locate the left ventricular outflow tract. They then position the pulsed wave Doppler sampling box optimally in the outflow tract to capture a high-quality Doppler signal and calculate the average VTI over a few seconds (Fig. 4) [36, 37]. This process provides rapid and reliable VTI estimations, even when performed by trainees [37]. Furthermore, ML algorithms extend their capabilities to other hemodynamic markers. They can automatically quantify respiratory variations in the inferior vena cava diameter, a recognized indicator of fluid responsiveness in mechanically ventilated patients [38]. Machine learning tools also measure the mitral annular plane systolic excursion (MAPSE), a marker of left ventricular systolic function [39, 40].

Fig. 4.

Example of AI-enabled tool for the automatic measurement of the subaortic velocity time integral (VTI)

Speckle tracking echocardiography

Speckle tracking echocardiography (STE) is a sophisticated technique that enhances myocardial function assessment and is now more accessible to clinicians, even those without extensive prior experience in echocardiography. Unlike traditional methods, STE tracks the natural acoustic markers, or “speckles,” to visualize and quantify myocardial shortening [41, 42]. One of the key advancements in STE is its availability for real-time application, as opposed to its earlier reliance on offline, post-imaging analysis. This real-time capability provides clinicians with actionable insights during bedside evaluations, making it a valuable tool in guiding patient management. By offering precise data on myocardial strain, STE can complement conventional echocardiographic techniques, helping to detect subtle changes in cardiac function that might be missed otherwise.

Global longitudinal strain (GLS) is a direct and quantitative measure of myocardial shortening during systole, offering a more precise assessment of cardiac function compared to right or left ventricular ejection fraction [41, 42]. Unlike LVEF, which is derived from end-systolic and end-diastolic volumes and relies on geometric assumptions such as those used in Simpson’s disc method, GLS measures myocardial deformation without such constraints, making it potentially more reliable [41]. GLS values are expressed as a negative percentage, reflecting myocardial shortening, with normal values typically ranging from − 20 to − 30% for both ventricles. Less negative values (e.g., − 15%) indicate impaired myocardial shortening. Beyond numerical values, STE paired with color-coding generates dynamic visualizations of myocardial strain, enhancing the clinician’s ability to assess cardiac function. An example of left ventricular systolic function assessment using STE is shown in Video S1 (Supplementary file 1). The GLS has proven to be a highly sensitive marker for detecting subclinical cardiac dysfunction. A meta-analysis highlighted its superior sensitivity compared to traditional echocardiographic parameters, particularly in the early identification of left ventricular dysfunction [43]. Moreover, GLS measurement demonstrated reduced operator dependency, offering greater reproducibility compared to conventional assessments of left ventricular function [44, 45].

Speckle tracking echocardiography is also a valuable tool for assessing right ventricular function, providing insights beyond traditional echocardiographic parameters [42, 46]. In patients with chronic heart failure and those undergoing cardiac surgery, right ventricular GLS has demonstrated superior sensitivity in detecting right ventricular dysfunction compared to tricuspid annular plane systolic excursion (TAPSE) [47]. Furthermore, right ventricular GLS has been found to be a more accurate predictor of clinical outcomes in these populations [48]. The limitations of TAPSE may stem from its narrow focus on a small region of the right ventricle, offering only a regional assessment of systolic function. Additionally, TAPSE is angle-dependent, which can introduce variability and reduce its sensitivity to detect global systolic dysfunction [49, 50]. Similarly, fractional area contraction (FAC), another commonly used parameter for evaluating right ventricular systolic function, shares the indirect nature of LVEF and is subject to geometric assumptions. In contrast, right ventricular GLS provides a direct measure of myocardial shortening. However, it should be noted that it is influenced by interventricular septal shortening, which can be influenced by left ventricular systolic function, potentially confounding its interpretation [51]. Despite this limitation, studies suggest that right ventricular GLS might outperform FAC [48] and even three-dimensional ejection fraction in identifying right ventricular systolic dysfunction [42]. These advantages underscore the growing importance of right ventricular GLS in the evaluation and management of patients with compromised right ventricular function.

These hardware and software innovations mark just the beginning of a transformative era in ultrasound technology. Indeed, wearable Doppler sensors are now available to monitor dynamic changes in carotid blood flow [52], and innovative ultrasound adhesive patches are on the horizon [53]. On the other hand, accessibility to existing echocardiographic solutions and adequate training for potential users remain a challenge in many hospitals, especially in middle-income countries. In settings with reliable internet access, remote interpretation of images captured by non-physicians could offer a practical solution, ensuring more patients benefit from ultrasound innovations.

Sublingual videomicroscopy

Microcirculation imaging in sepsis and shock states has revealed a high prevalence of microvascular alterations, which are markers of disease severity [54]. When resuscitation efforts aimed at normalizing blood pressure and cardiac output fail to restore adequate tissue perfusion (a condition referred to as hemodynamic incoherence) occult shock is likely present [55]. In this context, microcirculatory shock is characterized by persistent tissue hypoperfusion despite the apparent normalization of systemic and regional blood flow (Video S2 Supplementary file 2). This underscores the potential value of monitoring the microcirculation during resuscitation to ensure effective restoration of tissue perfusion and oxygenation [56].

Handheld vital microscopes

Twenty-five years ago, the advent of handheld vital microscopes enabled bedside visualization of the microcirculation [57]. The first innovation came with the development of Orthogonal Polarization Spectral (OPS) imaging, which allowed non-invasive observation of human microcirculation in exposed tissues and organs [58]. This technique utilized linearly polarized light at a wavelength corresponding to hemoglobin absorption, rendering red blood cells visible as dark structures flowing through microvessels [58]. The OPS technique was subsequently surpassed by the Sidestream Dark Field (SDF) imaging technique, which offered enhanced contrast and improved visualization of capillaries [59]. The SDF technique employs six pulsed green LED light sources synchronized with a camera’s frame rate, enabling intravital stroboscopy for superior imaging. The latest generation of handheld vital microscopes, using Incident Dark Field (IDF) illumination, has further advanced the field with higher-resolution optics and autofocusing capabilities. These innovations provide a threefold increase in the field of view, allowing more comprehensive and detailed microcirculatory assessments [60] (Fig. 5).

Fig. 5.

The evolution of sublingual microcirculation images. From Orthogonal Polarization Spectral (OPS) to Sidestream Dark Field (SDF) and Indirect Dark Field (IDF) imaging techniques

Because it is relatively easy to access, the sublingual mucosa is the most studied area for microcirculatory assessment in critically ill patients. This vascular-rich tissue is directly supplied by the lingual artery, a branch of the external carotid artery, and drains into the internal jugular vein [61]. Discrepancies have been reported between the microcirculatory states of various organs, highlighting that a reduction in sublingual microcirculation does not always equate to hypoperfusion of the gut or kidneys. The use of handheld vital microscopes to assess the sublingual microcirculation enables the quantification of several key parameters, including vessel density, perfusion, red blood cell velocity, and heterogeneity. Among these, the perfused vascular density (PVD) stands out as a key measurement. The PVD reflects the number of capillaries with continuous blood flow, providing a direct assessment of perfusion and an indirect estimation of oxygen delivery to tissues [62, 63]. Such metrics are useful for evaluating microcirculatory health. Whether and how they may also help guide therapeutic interventions in critically ill patients remains to be explored.

Practical challenges

Capturing high-quality videos of the microcirculation requires significant training to achieve proper illumination, accurate focus, stable imaging sequences, and the avoidance of pressure artifacts. Vessel compression during imaging, which can disrupt normal flow patterns, is the most frequent issue encountered and is often misinterpreted as microcirculatory dysfunction [62, 64]. To differentiate true microcirculatory alterations from imaging artifacts, certain signs should be carefully assessed. For example, bidirectional flow or the sudden restoration of flow in previously stopped vessels typically suggests external compression rather than pathological changes. In physiological conditions, microvascular flow is continuous and unidirectional [64]. Awareness of these nuances is essential to avoid misdiagnosis and ensure the reliable assessment of microcirculation.

Another challenge in integrating microcirculatory assessment into routine ICU practice is the absence of automated, instant results, necessitating offline and time-consuming analysis. Attempts to develop qualitative, point-of-care evaluation methods have consistently fallen short due to issues with accuracy and precision [63, 65]. Existing software for analyzing SDF and IDF imaging demands significant manual input, making the process labor-intensive and less suited for real-time decision-making [66].

Next frontiers in the microvascular assessment and analysis

The development of automated software is needed to envision real-time bedside microvascular assessment. Using advanced computer vision algorithms, new software analyzes image sequences autonomously, eliminating the need for manual intervention [67]. In addition to standard variables, it introduces novel metrics like capillary hematocrit (the ratio of red blood cell volume to microvascular volume) and tissue red blood cell perfusion, which quantifies red blood cell displacement within vessels. Nevertheless, these metrics vary substantially between individuals [68] and whether they could redefine targets for tissue perfusion in the future remains to be explored [69]. Another innovation involves integrating two-wavelength oximetry with SDF imaging, enabling the measurement of oxy- and deoxyhemoglobin extinction coefficients to determine hemoglobin saturation [70]. This technology ambitions to simultaneously measure tissue perfusion, oxygenation, inflammation, and tissue edema by assessing depth of focus. Additionally, it tracks leukocyte kinetics, and might offer insights into glycocalyx degradation and microvascular inflammation.

Conclusion

The miniaturization and reduced costs of ultrasound devices have played a pivotal role in their increasing adoption in operating rooms and intensive care units. Coupled with AI-enabled tools, these devices now facilitate simplified echocardiographic evaluations, delivering reproducible hemodynamic measurements even for less experienced clinicians. Speckle tracking echocardiography has emerged, providing direct, visual, and quantitative assessments of myocardial shortening. This technique offers a compelling alternative to traditional methods for evaluating right and left ventricular systolic function.

Meanwhile, imaging of sublingual microcirculation has highlighted a high prevalence of microvascular alterations in critically ill patients. Handheld vital microscopes now allow clinicians to quantify parameters such as vessel density, perfusion, red blood cell velocity, and PVD, offering new perspectives on microcirculatory health. However, the role of these metrics in guiding therapeutic interventions remains an area of ongoing research.

In parallel, cardiac output monitors have undergone substantial changes, incorporating advanced graphical displays. These tools provide visual clarity in detecting abnormal hemodynamic states and trajectories, helping clinicians understand the underlying mechanisms of hemodynamic instability.

Abbreviations

AI

Artificial intelligence

FAC

Fractional area of contraction

GLS

Global longitudinal strain

IDF

Incident dark field

LVEF

Left ventricular ejection fraction

MAPSE

Mitral annular plane systolic excursion

ML

Machine learning

OPS

Orthogonal polarization spectral

POCUS

Point of care ultrasound

PVD

Perfused vascular density

SDF

Sidestream dark field

STE

Speckle tracking echocardiography

TAPSE

Tricuspid annular plane systolic excursion

Author contributions

FM wrote the first draft of the paragraph on Graphical displays, AW wrote the first draft on Echocardiography and VKE wrote the first draft on Videomicroscopy. All authors significantly improved the manuscript’s content and approved the final version.

Funding

None.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

FM is the founder of MiCo (michardconsulting.com), a consulting and research firm based in Switzerland. MiCo does not sell any medical device, and FM does not own shares from any medical device company. AW has received honoraria from GE Healthcare and Mindray for the delivery of educational material. VKE has no conflict of interest to declare.