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Prediction of fluid responsiveness using respiratory variations in left ventricular stroke area by transoesophageal echocardiographic automated border detection in mechanically ventilated patients

Abstract

Background

Left ventricular stroke area by transoesophageal echocardiographic automated border detection has been shown to be strongly correlated to left ventricular stroke volume. Respiratory variations in left ventricular stroke volume or its surrogates are good predictors of fluid responsiveness in mechanically ventilated patients. We hypothesised that respiratory variations in left ventricular stroke area (ΔSA) can predict fluid responsiveness.

Methods

Eighteen mechanically ventilated patients undergoing coronary artery bypass grafting were studied immediately after induction of anaesthesia. Stroke area was measured on a beat-to-beat basis using transoesophageal echocardiographic automated border detection. Haemodynamic and echocardiographic data were measured at baseline and after volume expansion induced by a passive leg raising manoeuvre. Responders to passive leg raising manoeuvre were defined as patients presenting a more than 15% increase in cardiac output.

Results

Cardiac output increased significantly in response to volume expansion induced by passive leg raising (from 2.16 ± 0.79 litres per minute to 2.78 ± 1.08 litres per minute; p < 0.01). ΔSA decreased significantly in response to volume expansion (from 17% ± 7% to 8% ± 6%; p < 0.01). ΔSA was higher in responders than in non-responders (20% ± 5% versus 10% ± 5%; p < 0.01). A cutoff ΔSA value of 16% allowed fluid responsiveness prediction with a sensitivity of 92% and a specificity of 83%. ΔSA at baseline was related to the percentage increase in cardiac output in response to volume expansion (r = 0.53, p < 0.01).

Conclusion

ΔSA by transoesophageal echocardiographic automated border detection is sensitive to changes in preload, can predict fluid responsiveness, and can quantify the effects of volume expansion on cardiac output. It has potential clinical applications.

Introduction

Volume expansion is one of the most common manoeuvres to increase cardiac output (CO) in patients with circulatory failure. However, if inappropriate, volume expansion may have deleterious effects such as volume overload, systemic and pulmonary oedema, and increased tissue hypoxia [1]. It is therefore important to obtain reliable information concerning fluid responsiveness in patients presenting with circulatory failure in the operating room or in the intensive care unit.

Static indicators of fluid responsiveness such as central venous pressure (CVP) and pulmonary capillary wedge pressure have been shown to be poor predictors of fluid responsiveness [2–6]. In contrast, indices relying on the cardiopulmonary interactions in mechanically ventilated patients under general anaesthesia have been shown to be good predictors of fluid responsiveness [2–13].

Transoesophageal echocardiography is widely used in the operating room or in the intensive care unit for monitoring left ventricular (LV) systolic function and LV preload [14, 15] using LV end-diastolic area index (LVEDAI). However, LVEDAI is a static indicator and has poor predictive value to assess fluid responsiveness [16]. Automated border detection has been shown to be able to display LV area on a beat-to-beat basis, representing the dynamic variant of the left ventricular end-diastolic area (LVEDA) [17, 18]. Moreover, through the LV area, the LV volume can be assessed in a non-invasive manner and changes in stroke area (SA) and stroke volume have been shown to be closely related [19–21]. The aim of our study was to evaluate the ability of respiratory variations in LV SA (ΔSA) to detect changes in loading conditions and to predict the effects of volume expansion in mechanically ventilated patients.

Materials and methods

This study was approved by the institutional review board committee (Comité Consultatif de Protection des Personnes dans la Recherche Biomédicale Lyon B), and all patients gave written informed consent. Twenty patients (aged 37 to 84 years old; 13 men, 7 women) undergoing coronary artery bypass grafting were studied. Exclusion criteria were cardiac arrhythmia, cardiac shunts, LV dysfunction (preoperative LV ejection fraction < 50%), and any contraindication to transoesophageal echocardiography.

Anaesthesia was induced using propofol (1 to 3 mg/kg) and sufentanil (0.5 to 1.0 μg/kg), and orotracheal intubation was facilitated with pancuronium (0.1 to 0.15 mg/kg). After induction of anaesthesia, an 8-cm 5-French tipped catheter (Arrow International, Inc., Reading, PA, USA) was inserted in the left or right radial artery and a triple-lumen 16-cm 8.5-French central venous catheter was inserted in the right internal jugular vein (Arrow International, Inc.). Pressure transducers (Medex Medical Ltd., Rossendale, Lancashire, UK) were placed on the mid-axillary line and fixed to the operation table to ensure their position at an atrial level during the entire protocol. All transducers were zeroed to atmospheric pressure before each step of the protocol. Thereafter, a 5-MHz transoesophageal multiplane transducer (Philips 5.0–6.4 MHz, 21367A; Philips Medical Systems, Andover, MA, USA) was inserted in the patient's oesophagus. Anaesthesia was maintained with continuous infusions of propofol (5 to 8 mg/kg per hour) and sufentanil (0.7 to 1.0 μg/kg per hour) to keep a bispectral index (Aspect 1000; Aspect Medical Systems, Inc., Norwood, MA, USA) between 40 and 50. Patients were ventilated in a volume-controlled mode with a tidal volume of 10 ml/kg at a frequency of 12 to 14 cycles per minute (average maximum inspiratory pressure was 18 ± 5 cm H2O). Inspiratory-to-expiratory ratio was set to 1:2. Positive end-expiratory pressure was set between 0 and 4 cm H2O according to the attending physician.

Haemodynamic measurements

The following haemodynamic parameters were monitored continuously (Philips Intellivue MP70 Anaesthesia; Philips Medizin Systeme Böblingen GmbH, Böblingen, Germany): heart rate, systolic arterial pressure, diastolic arterial pressure, mean arterial pressure, and CVP.

Echocardiography

Echocardiographic images were recorded using a Hewlett-Packard Sonos 2500 (HP M2406A; Hewlett-Packard Company, Palo Alto, CA, USA) with automated border detection capabilities. The transoesophageal multiplane transducer was positioned to obtain a transgastric, cross-sectional view of the LV at midpapillary muscle level with the transducer positioned to obtain the image that had the most-circular overall geometry [22]. The cross-sectional view of the LV was chosen because of a demonstrated relationship between LV cross-sectional area and LV volume [23–25]. Automated border detection quantifies the cardiac chamber areas instantaneously by detecting the tissue-blood interface, which results in a continuous, beat-to-beat ventricular area and has already been described in great detail elsewhere [17–20, 26]. Briefly, the endocardial border of the LV, including the papillary muscles, was circumscribed manually to define the region of interest (careful attention was paid to circumscribe the LV all along the respiratory cycle). The threshold for the determination of the blood-tissue border inside this region was set manually by adjusting the gain. The LV area was then displayed on a beat-to-beat basis simultaneously with the patient's electrocardiogram and respiratory curve. It was then recorded and analysed off-line by an investigator blinded to the other results (Figure 1).

Figure 1
figure 1

Transoesophageal echocardiographic transgastric, cross-sectional view of the left ventricle at midpapillary muscle level with automated border detection (ABD). Endocardial border of the left ventricle, including the papillary muscles, was circumscribed manually to define the region of interest (blue line). ABD quantifies the cardiac chamber areas instantaneously by detecting the blood-tissue interface (red line), which results in a continuous, beat-to-beat left ventricular area curve (green line). Left ventricular end-diastolic area (LVEDA) was defined as peak of the left ventricular area during diastole. Left ventricular end-systolic area (LVESA) was defined as minimum left ventricular area during systole. Stroke area (SA) was defined as LVEDA – LVESA over the same cardiac cycle.

Data analysis

Respiratory variations in pulse pressure

Pulse pressure (PP) was defined as the difference between systolic and diastolic pressures. Maximal (PPmax) and minimal (PPmin) values of PP were determined over the same respiratory cycle. The respiratory variations in PP, ΔPP, were then calculated as described by Michard and colleagues. [9], ΔPP = [(PPmax - PPmin)/([PPmax + PPmin]/2)] × 100%, and averaged over three consecutive respiratory cycles.

Respiratory variations in stroke area

SA was defined as the difference between the end-diastolic area (LVEDA) and the end-systolic area (Figure 1) [20]. Maximal (SAmax) and minimal (SAmin) values of PP were determined over the same respiratory cycle. ΔSA was then calculated using the same formula described previously to calculate ΔPP: [(SAmax - SAmin)/([SAmax + SAmin]/2)] × 100% (Figure 2). ΔSA and ΔPP were calculated over the same respiratory cycles.

Figure 2
figure 2

Transoesophageal echocardiographic transgastric, cross-sectional views of the left ventricle at midpapillary muscle level with automated border detection at baseline (top panel) and after volume expansion induced by passive leg raising manoeuvre (bottom panel). Left ventricular area curve was displayed with electrocardiogram and respiratory curve. Stroke area (SA) was defined as the difference between the end-diastolic area (LVEDA) and the end-systolic area. Maximal (SAmax) and minimal (SAmin) values of pulse pressure were determined over the same respiratory cycle. Respiratory variations in left ventricular SA (ΔSA) were then calculated using the following formula: ΔSA = [(SAmax - SAmin)/([SAmax + SAmin]/2)] × 100%. Passive leg raising manoeuvre induced a decrease in ΔSA and an increase in LVEDA. Gain was held constant throughout the protocol.

Left ventricular end-diastolic area

LVEDA was defined as peak of the LV area during diastole. LVEDAI was defined as LVEDA/surface body area. For each measurement, an average of three consecutive cardiac beats throughout the respiratory cycle were evaluated.

Cardiac output

CO was used to monitor an increase in stroke volume in response to volume expansion. CO was calculated using the velocity time integral (VTI) obtained by transoesophageal echocardiography from the transgastric long-axis view [27]. VTI was measured by a pulsed-wave Doppler beam at the level of the aortic valve. The mean of three measurements was used. Aortic valve area was measured at baseline and was considered constant throughout the protocol as aortic valve area = π × (aortic diameter/2)2. [4, 28]. The stroke volume was calculated as aortic valve area × VTI. CO was calculated as stroke volume × heart rate.

Protocol

All patients were studied after induction of anaesthesia but before surgery. Haemodynamic and echocardiographic data were recorded during two consecutive steps. (a) The patient was studied in the semirecumbent position (45°) after a two minute period of haemodynamic stability. First, pulsed Doppler aortic flow was recorded from the transgastric long-axis view. Then, automated border detection data were recorded from a transgastric, cross-sectional view of the LV at midpapillary muscle level. (b) Using the automatic operation table, the patient's legs were raised to a 45° angle with the patient's trunk in a supine position. In this position, echocardiographic and haemodynamic data were recorded within two minutes. Automated border detection data were recorded before pulsed Doppler aortic flow. This sequence was chosen in order to keep the automated border detection settings stable between the two measurements because of a known dependency of automated border detection on echocardiographic gain settings. This protocol was chosen because of its demonstrated ability to mimic fluid challenge [29–32]. Mechanical ventilation and anaesthetic drug concentrations were held constant throughout the study protocol.

Statistical analysis

All data are presented as mean ± standard deviation. Changes in haemodynamic parameters induced by changes in loading conditions within the same group were assessed using a non-parametric Wilcoxon test. Spearman rank method was used to test linear correlation. Patients were divided into two groups according to the percentage increase in CO after the passive leg raising manoeuvre: responders were defined as patients presenting an increase in CO of more than or equal to 15% [9] and non-responders as patients presenting an increase in CO of less than 15%. The comparison of haemodynamic parameters before passive leg raising in responder and non-responder patients was assessed using a non-parametric Mann-Whitney U test. Receiver operating characteristic (ROC) curves were generated for CO, CVP, LVEDA, ΔPP, and ΔSA, varying the discriminating threshold of each parameters, and areas under the ROC curves were calculated and compared [33] (MedCalc 8.0.2.0; MedCalc Software, Mariakerke, Belgium). Intra- and interobserver variabilities for the calculation of ΔSA were assessed using Bland-Altman analysis and are expressed as mean percentage error [34]. This analysis comprised visualisation and re-installation of the automated border detection in nine patients at baseline by two different operators. A p value less than 0.05 was considered statistically significant. All statistic analysis was performed using SPSS 13.0 for Windows (SPSS Inc., Chicago, IL, USA).

Results

Two patients (10%) were excluded because of poor echocardiographic images.

Effects of changes in loading conditions

As expected, passive leg raising induced a significant increase in CO, from 2.16 ± 0.79 litres per minute to 2.78 ± 1.08 litres per minute (p < 0.01). All haemodynamic parameters changed significantly in response to the passive leg raising manoeuvre (Table 1). ΔSA and ΔPP decreased significantly in response to passive leg raising (from 17.1% ± 6.8% to 8.1% ± 5.8% and from 9.9% ± 5.5% to 7.9% ± 3.2%, respectively; p < 0.05 for both) (Figure 2). Likewise, LVEDAI increased from 9.2 ± 4.5 cm2/m2 to 10.8 ± 6.3 cm2/m2 (p < 0.05) and CVP increased from 3 ± 4 mm Hg to 18 ± 4 mm Hg (p < 0.01) (Figure 2).

Table 1 Haemodynamic data at baseline and after volume expansion induced by passive leg raising manoeuvre

ΔSA to predict fluid responsiveness

Twelve patients were responders and six patients were non-responders. Their haemodynamic data are shown in Table 2. We observed a significant relationship (r = 0.62, p < 0.05) and an acceptable agreement between ΔSA and ΔPP (3% ± 5%) at baseline. ΔSA and ΔPP at baseline were significantly higher in responders than in non-responders (20.5% ± 4.8% versus 10.0% ± 4.6% and 17% ± 5% versus 8% ± 4%; p < 0.01 for both), whereas neither difference in CVP (6 ± 3 mm Hg in responders versus 9 ± 4 mm Hg in non-responders; p = 0.13) nor difference in LVEDAI (7.9 ± 4.1 cm2/m2 versus 11.9 ± 4.5 cm2/m2; p = 0.06) and CO (2.17 ± 0.94 litres per minute in responders versus 2.14 ± 0.43 litres per minute in non-responders; p = 0.94) reached statistical significance between these two groups. The areas under the ROC curves (± standard error) were 0.910 ± 0.073 for ΔPP, 0.958 ± 0.043 for ΔSA, 0.271 ± 0.125 for CVP, 0.236 ± 0.114 for LVEDAI, and 0.431 ± 0.134 for CO (Figure 3). The area for ΔSA was significantly higher than the area for CVP, LVEDAI, and CO (p < 0.05). Difference in area under the curve between ΔSA and ΔPP did not reach significance (p = 0.83). The threshold ΔPP value of 12% allowed discrimination between responders and non-responders with a sensitivity of 92% and a specificity of 83%. The threshold ΔSA value of 16% allowed discrimination between responders and non-responders with a sensitivity of 92% and a specificity of 83% (Figure 4).

Table 2 Echocardiographic and haemodynamic data in responders and non-responders to volume expansion induced by passive leg raising manoeuvre
Figure 3
figure 3

Receiver operating characteristic curves comparing the ability of respiratory variations in left ventricular stroke area (ΔSA), respiratory variations in pulse pressure (ΔPP), left ventricular end-diastolic area index (LVEDAI), and central venous pressure (CVP) at baseline to predict response to volume expansion induced by passive leg raising manoeuvre.

Figure 4
figure 4

Respiratory variations in stroke area (ΔSA) values at baseline in responders and non-responders to volume expansion induced by passive leg raising manoeuvre. A ΔSA threshold value of 16% allowed discrimination between responders and non-responders with a 93% sensitivity and an 82% specificity.

ΔSA to quantify response to volume expansion

ΔSA before volume expansion was significantly related to changes in CO in response to volume expansion (r = 0.53, p < 0.05). ΔPP before volume expansion also showed a significant correlation to changes in CO (r = 0.73, p < 0.01), confirming previous results. In contrast, static indicators such as LVEDAI and CVP before volume expansion were not related to changes in CO in response to volume expansion (r = -0.42, p = 0.08 and r = -0.23, p = 0.36, respectively) (Figure 5).

Figure 5
figure 5

Relationship between respiratory variations in left ventricular stroke area (ΔSA) (top left panel), respiratory variations in pulse pressure (ΔPP) (top right panel), left ventricular end-diastolic area index (LVEDAI) (bottom left panel), and central venous pressure (CVP) (bottom right panel) at baseline and percentage increase in cardiac output (CO) after volume expansion (VE) induced by passive leg raising manoeuvre.

Reproducibility analysis

Intraobserver variability for ΔSA assessment was 8% ± 12%. Interobserver variability for ΔSA assessment was 10% ± 12%.

Discussion

This is the first study to show that ΔSA can be assessed using automated border detection. ΔSA is sensitive to changes in LV loading conditions, can predict and quantify fluid responsiveness, and is reproducible.

Fluid responsiveness assessment has been widely studied in mechanically ventilated patients during the past 10 years [2–13, 28, 31, 35, 36]. Positive pressure ventilation induces a decrease in right ventricular preload during inspiration followed by a decrease in right ventricular stroke volume (as described by the Frank-Starling relationship). These phenomena are transmitted to the LV (pulmonary transit time) and induce a decrease in LV preload followed by a decrease in LV stroke volume during expiration [2, 37]. These respiratory variations in LV stroke volume or its surrogates are greater when the LV operates on the steep portion of the Frank-Starling curve rather than on the plateau. These phenomena explain how the respiratory variations in LV stroke volume or its surrogates (PP, pulsed Doppler aortic flow) can be predictive of response to volume expansion [2]. Indices derived from these respiratory variations are qualified as dynamic predictors of fluid responsiveness in opposition to static predictors such as CVP, pulmonary capillary wedge pressure, or LVEDAI [2, 37]. Moreover, it is now well established that dynamic indicators have better predictive value for fluid responsiveness assessment than do static indicators alone [2, 37].

Automated border detection allows accurate and reproducible on-line measurements of cross-sectional LV area. It analyses received unprocessed radio frequency data to define the interface between blood and myocardial tissue. Then, the software calculates the blood cavity area within a specified region of interest (LV) and displays the area as a calibrated waveform in real time. Several previous investigations have shown a strong relationship between cross-sectional LV area and LV volume [18, 23]. Moreover, this relationship was demonstrated during a wide range of haemodynamic alterations such as occlusion and release of inferior vena cava, pulmonary artery, and aorta. By displaying LV area continuously, automated border detection allows beat-to-beat determination of LV SA (defined as LV end-diastolic area – LV end-systolic area). LV SA has been shown to be closely related to LV stroke volume [20, 21, 26, 38], and this relationship has been demonstrated in various ventricular loading conditions [20, 21] and in patients with wall motion abnormalities [21]. Coupled to LV pressure, LV cavity area has been proposed to construct pressure-area loops in real time in order to estimate LV contractility from end-systolic relationships of cavity area (as a surrogate for LV volume) and central arterial pressure (as a surrogate for LV pressure) with promising results [39]. Gorcsan and colleagues [20, 21] have shown that changes in LV stroke volume during vena cava occlusion were strongly related to changes in LV SA in patients undergoing coronary artery bypass surgery and in dogs. It must be emphasised that these changes were studied in a beat-to-beat analysis. Thus, changes in preload can be assessed using automated border detection LV SA. Our results are consistent with this previously published data given that changes in preload induced by positive pressure ventilation were quantifiable using respiratory changes in LV SA (ΔSA). Furthermore, these variations were reduced after a volume expansion induced by a passive leg raising manoeuvre and were higher in responders to volume expansion than in non-responders. Of note, in 1978, Brenner and colleagues [40] were the first to describe respiratory changes in LV dimensions using echocardiography. In a study focusing on spontaneously breathing patients with normal ventricular function, they showed respiration-induced changes in LV end-diastolic and end-systolic diameters measured from a parasternal mid-short-axis view using M-mode. It is interesting to note that in this study the authors showed an inspiratory decrease in LV stroke volume. In mechanically ventilated patients, we observed an inspiratory increase in LV SA consistent with the cardiopulmonary interactions in patients under positive pressure ventilation [2]. However, the relationship between mechanical ventilation, arterial pressure, and LV area and volume is still complex and some studies found an inconstant association between respiration-induced changes in systolic arterial pressure and changes in LV area [41].

Recently published studies have shown that the passive leg raising manoeuvre was able to mimic volume expansion and to predict fluid responsiveness in mechanically ventilated patients [31, 32]. These two studies show that patients who significantly increase CO after a passive leg raising manoeuvre are more likely to be responders to volume expansion. The major interest of this manoeuvre is that it can be performed in patients with arrhythmia, even if the patient is triggering on the ventilator in the intensive care unit. However, the passive leg raising manoeuvre may not be easy to perform in the operating room in patients undergoing surgical procedures with surgeons needing exposure and access to the operating field.

Transoesophageal echocardiography is widely used in the intensive care unit and in the operating room. It is now a well-established tool for intensivists and anaesthesiologists. It allows analysis of left and right ventricular functions and provides invaluable information for the management of patients with circulatory failure [15, 27]. In the intensive care unit, echocardiography is a useful tool to assess fluid responsiveness (respiratory variations in pulsed Doppler aortic blood velocity, inferior vena cava diameter, and superior vena cava collapsibility) [42]. In this setting, automated border detection could be used as a new tool to discriminate between responders to volume expansion and non-responders. In the operating room, transoesophageal echocardiography has been proposed for LV systolic function and LV preload monitoring [16, 35, 43, 44]. Monitoring preload is different from assessing fluid responsiveness, and LVEDAI has been shown to poorly predict response to volume expansion. Using automated border detection and ΔSA in this setting may be helpful to monitor both systolic function and fluid responsiveness from a transgastric, cross-sectional view of the LV at midpapillary muscle level.

Study limitations

The patients enrolled in this study underwent coronary artery bypass grafting and may have wall motion abnormalities. However, a study conducted in patients undergoing coronary artery bypass grafting demonstrated that even in this group of patients a linear correlation exists between changes in SA and stroke volume [21]. Thus, we are confident that wall motion abnormalities had no influence on ΔSA. We performed passive leg raising with trunk lowering from 45° to 0° to mimic volume expansion as described by Monnet and colleagues[31]. This manoeuvre has been shown to be a simple method of transiently increasing venous return [29, 30] and has recently been shown to be able to predict fluid responsiveness in mechanically ventilated patients [31]. Moreover, echocardiographic data were obtained within two minutes after passive leg raising because it is known that the fluid challenge induced by passive leg raising does not persist if legs are maintained elevated. This is in accordance with previously published studies [31, 32]. We chose to use a standardised tidal volume of 10 ml/kg because it has been demonstrated that tidal volume influences dynamic parameters [45, 46]. Most of the studies focusing on dynamic parameters in the operating room chose to use tidal volumes between 8 and 10 ml/kg. Thus, we believe that our choice is in accordance with these studies.

A limitation of this study is a possible artifact caused by a respiratory-related motion of the heart relative to a fixed echocardiographic probe. Brenner and colleagues [40] described this hypothesis without being able to reject it. In our study, we cannot exclude such an artifact, which would result in a different LV short axis during the respiratory cycle and influence the respiratory changes in end-diastolic and end-systolic area, especially during the passive leg raising manoeuvre. This limitation may be observed for most of the previously described echocardiographic predictors of fluid responsiveness because respiration may move either the ultrasound beam or the studied structure (inferior [28] or superior [36] vena cava diameter, LV outflow tract for aortic pulsed Doppler flow [4]). However, the respiratory changes in LV SA are consistent with previously published respiratory changes in LV stroke volume or its surrogates in patients under mechanical ventilation and we can postulate that ΔSA accurately reflects respiratory changes in LV stroke volume. In our study, passive leg raising may have induced displacement of the transoesophageal probe. On the other hand, from a prospective point of view, we used ΔSA before passive leg raising to predict fluid responsiveness. Consequently, this index was not influenced by change in body position. Thus, the predictive value of ΔSA is not impacted by this manoeuvre. Moreover, previously published studies focusing on oesophageal Doppler and passive leg raising did not mention this technical problem [31, 32]. An additional limitation is that automated border detection is dependent on the gain setting. However, we held the gain constant throughout the protocol. Whether ΔSA can be assessed using transthoracic echocardiography has to be demonstrated. The results were obtained from 18 patients and the study is underpowered to permit a definitive conclusion regarding the threshold value of 16%. Further studies in other settings will be required to validate this value. Finally, ΔSA cannot be used in spontaneously breathing patients or in patients with cardiac arrhythmia.

Conclusion

ΔSA derived from transoesophageal echocardiographic automated border detection appears to be a non-invasive and reproducible index of changes in loading conditions, fluid responsiveness, and quantification of the effects of volume expansion on CO in mechanically ventilated patients. ΔSA has potential clinical applications.

Key messages

  • ΔSA can be assessed using transoesophageal echocardiographic automated border detection in mechanically ventilated patients.

  • ΔSA is sensitive to changes in LV preload.

  • A ΔSA cutoff value of 16% allows discrimination between responders to volume expansion and non-responders.

  • The higher the ΔSA before volume expansion, the higher the increase in CO induced by volume expansion.

  • ΔSA is a simple tool for fluid responsiveness assessment using transoesophageal echocardiographic LV short-axis view.

Abbreviations

CO:

cardiac output

CVP:

central venous pressure

LV:

left ventricle or left ventricular

LVEDA:

left ventricular end-diastolic area

LVEDAI:

left ventricular end-diastolic area index

PP:

pulse pressure

ΔPP:

respiratory variations in pulse pressure

ROC:

receiver operating characteristic

SA:

stroke area

ΔSA:

respiratory variations in left ventricular stroke area

VTI:

velocity time integral.

References

  1. Practice parameters for hemodynamic support of sepsis in adult patients in sepsis. Task Force of the American College of Critical Care Medicine, Society of Critical Care Medicine Crit Care Med 1999, 27: 639-660. 10.1097/00003246-199903000-00049

  2. Michard F: Changes in arterial pressure during mechanical ventilation. Anesthesiology 2005, 103: 419-428. 10.1097/00000542-200508000-00026

    Article  PubMed  Google Scholar 

  3. Michard F, Teboul JL: Predicting fluid responsiveness in ICU patients. A critical analysis of the evidence. Chest 2002, 121: 2000-2008. 10.1378/chest.121.6.2000

    Article  PubMed  Google Scholar 

  4. 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: 867-873. 10.1378/chest.119.3.867

    Article  CAS  PubMed  Google Scholar 

  5. Tavernier B, Makhotine O, Lebuffe G, Dupont J, Scherpereel P: Systolic pressure variation as a guide to fluid therapy in patients with sepsis-induced hypotension. Anesthesiology 1998, 89: 1313-1321. 10.1097/00000542-199812000-00007

    Article  CAS  PubMed  Google Scholar 

  6. Pinsky MR, Teboul JL: Assessment of indices of preload and volume responsiveness. Curr Opin Crit Care 2005, 11: 235-239. 10.1097/01.ccx.0000158848.56107.b1

    Article  PubMed  Google Scholar 

  7. Bendjelid K, Romand JA: Fluid responsiveness in mechanically ventilated patients: a review of indices used in intensive care. Intensive Care Med 2003, 29: 352-360. 10.1007/s00134-003-1777-0

    Article  PubMed  Google Scholar 

  8. Feissel M, Badie J, Merlani PG, Faller JP, Bendjelid K: Pre-ejection period variations predict the fluid responsiveness of septic ventilated patients. Crit Care Med 2005, 33: 2534-2539. 10.1097/01.CCM.0000186415.43713.2F

    Article  PubMed  Google Scholar 

  9. Michard F, Boussat S, Chemla D, Anguel N, Mercat A, Lecarpentier Y, Richard C, Pinsky MR, Teboul JL: Relation between respiratory changes in arterial pulse pressure and fluid responsiveness in septic patients with acute circulatory failure. Am J Respir Crit Care Med 2000, 162: 134-138.

    Article  CAS  PubMed  Google Scholar 

  10. Reuter DA, Felbinger TW, Schmidt C, Kilger E, Goedje O, Lamm P, Goetz AE: Stroke volume variations for assessment of cardiac responsiveness to volume loading in mechanically ventilated patients after cardiac surgery. Intensive Care Med 2002, 28: 392-398. 10.1007/s00134-002-1211-z

    Article  PubMed  Google Scholar 

  11. Rex S, Brose S, Metzelder S, Huneke R, Schalte G, Autschbach R, Rossaint R, Buhre W: Prediction of fluid responsiveness in patients during cardiac surgery. Br J Anaesth 2004, 93: 782-788. 10.1093/bja/aeh280

    Article  CAS  PubMed  Google Scholar 

  12. Slama M, Masson H, Teboul JL, Arnould ML, Nait-Kaoudjt R, Colas B, Peltier M, Tribouilloy C, Susic D, Frohlich E, Andrejak M: Monitoring of respiratory variations of aortic blood flow velocity using esophageal Doppler. Intensive Care Med 2004, 30: 1182-1187. 10.1007/s00134-004-2190-z

    Article  PubMed  Google Scholar 

  13. Wiesenack C, Fiegl C, Keyser A, Prasser C, Keyl C: Assessment of fluid responsiveness in mechanically ventilated cardiac surgical patients. Eur J Anaesthesiol 2005, 22: 658-665. 10.1017/S0265021505001092

    Article  CAS  PubMed  Google Scholar 

  14. Bergquist BD, Bellows WH, Leung JM: Transesophageal echocardiography in myocardial revascularization: II. Influence on intraoperative decision making. Anesth Analg 1996, 82: 1139-1145. 10.1097/00000539-199606000-00007

    CAS  PubMed  Google Scholar 

  15. Cheitlin MD, Armstrong WF, Aurigemma GP, Beller GA, Bierman FZ, Davis JL, Douglas PS, Faxon DP, Gillam LD, Kimball TR, et al.: ACC/AHA/ASE 2003 guideline update for the clinical application of echocardiography: summary article: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (ACC/AHA/ASE Committee to Update the 1997 Guidelines for the Clinical Application of Echocardiography). Circulation 2003, 108: 1146-1162. 10.1161/01.CIR.0000073597.57414.A9

    Article  PubMed  Google Scholar 

  16. Cheung AT, Savino JS, Weiss SJ, Aukburg SJ, Berlin JA: Echocardiographic and hemodynamic indexes of left ventricular preload in patients with normal and abnormal ventricular function. Anesthesiology 1994, 81: 376-387. 10.1097/00000542-199408000-00016

    Article  CAS  PubMed  Google Scholar 

  17. Perez JE, Waggoner AD, Barzilai B, Melton HE Jr, Miller JG, Sobel BE: On-line assessment of ventricular function by automatic boundary detection and ultrasonic backscatter imaging. J Am Coll Cardiol 1992, 19: 313-320.

    Article  CAS  PubMed  Google Scholar 

  18. Vandenberg BF, Rath LS, Stuhlmuller P, Melton HE Jr, Skorton DJ: Estimation of left ventricular cavity area with an on-line, semiautomated echocardiographic edge detection system. Circulation 1992, 86: 159-166.

    Article  CAS  PubMed  Google Scholar 

  19. Gorcsan J 3rd, Morita S, Mandarino WA, Deneault LG, Kawai A, Kormos RL, Griffith BP, Pinsky MR: Two-dimensional echocardiographic automated border detection accurately reflects changes in left ventricular volume. J Am Soc Echocardiogr 1993, 6: 482-489.

    Article  PubMed  Google Scholar 

  20. Gorcsan J 3rd, Lazar JM, Romand J, Pinsky MR: On-line estimation of stroke volume by means of echocardiographic automated border detection in the canine left ventricle. Am Heart J 1993, 125: 1316-1323. 10.1016/0002-8703(93)91001-U

    Article  PubMed  Google Scholar 

  21. Gorcsan J 3rd, Gasior TA, Mandarino WA, Deneault LG, Hattler BG, Pinsky MR: On-line estimation of changes in left ventricular stroke volume by transesophageal echocardiographic automated border detection in patients undergoing coronary artery bypass grafting. Am J Cardiol 1993, 72: 721-727. 10.1016/0002-9149(93)90892-G

    Article  PubMed  Google Scholar 

  22. Schiller NB, Shah PM, Crawford M, DeMaria A, Devereux R, Feigenbaum H, Gutgesell H, Reichek N, Sahn D, Schnittger I, et al.: Recommendations for quantitation of the left ventricle by two-dimensional echocardiography. American Society of Echocardiography Committee on Standards, Subcommittee on Quantitation of Two-Dimensional Echocardiograms. J Am Soc Echocardiogr 1989, 2: 358-367.

    Article  CAS  PubMed  Google Scholar 

  23. Appleyard RF, Glantz SA: Two dimensions describe left ventricular volume change during hemodynamic transients. Am J Physiol 1990, 258: H277-284.

    CAS  PubMed  Google Scholar 

  24. Eaton LW, Maughan WL, Shoukas AA, Weiss JL: Accurate volume determination in the isolated ejecting canine left ventricle by two-dimensional echocardiography. Circulation 1979, 60: 320-326.

    Article  CAS  PubMed  Google Scholar 

  25. Weiss JL, Eaton LW, Kallman CH, Maughan WL: Accuracy of volume determination by two-dimensional echocardiography: defining requirements under controlled conditions in the ejecting canine left ventricle. Circulation 1983, 67: 889-895.

    Article  CAS  PubMed  Google Scholar 

  26. Gorcsan J III, Denault A, Mandarino WA, Pinsky MR: Left ventricular pressure-volume relations with transesophageal echocardiographic automated border detection: comparison with conductance-catheter technique. Am Heart J 1996, 131: 544-552. 10.1016/S0002-8703(96)90097-6

    Article  PubMed  Google Scholar 

  27. Shanewise JS, Cheung AT, Aronson S, Stewart WJ, Weiss RL, Mark JB, Savage RM, Sears-Rogan P, Mathew JP, Quinoñes MA, et al.: ASE/SCA guidelines for performing a comprehensive intraoperative multiplane transesophageal echocardiography examination: recommendations of the American Society of Echocardiography Council for Intraoperative Echocardiography and the Society of Cardiovascular Anesthesiologists Task Force for Certification in Perioperative Transesophageal Echocardiography. J Am Soc Echocardiogr 1999, 12: 884-900. 10.1016/S0894-7317(99)70199-9

    Article  CAS  PubMed  Google Scholar 

  28. 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: 1834-1837. 10.1007/s00134-004-2233-5

    Article  PubMed  Google Scholar 

  29. Rutlen DL, Wackers FJ, Zaret BL: Radionuclide assessment of peripheral intravascular capacity: a technique to measure intravascular volume changes in the capacitance circulation in man. Circulation 1981, 64: 146-152.

    Article  CAS  PubMed  Google Scholar 

  30. Reich DL, Konstadt SN, Raissi S, Hubbard M, Thys DM: Trendelenburg position and passive leg raising do not significantly improve cardiopulmonary performance in the anesthetized patient with coronary artery disease. Crit Care Med 1989, 17: 313-317. 10.1097/00003246-198904000-00003

    Article  CAS  PubMed  Google Scholar 

  31. Monnet X, Rienzo M, Osman D, Anguel N, Richard C, Pinsky MR, Teboul JL: Passive leg raising predicts fluid responsiveness in the critically ill. Crit Care Med 2006, 34: 1402-1407. 10.1097/01.CCM.0000215453.11735.06

    Article  PubMed  Google Scholar 

  32. Lafanechere A, Pene F, Goulenok C, Delahaye A, Mallet V, Choukroun G, Chiche J, Mira J, Cariou A: Changes in aortic blood flow induced by passive leg raising predict fluid responsiveness in critically ill patients. Crit Care 2006, 10: R132. 10.1186/cc5044

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  33. Hanley JA, McNeil BJ: A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 1983, 148: 839-843.

    Article  CAS  PubMed  Google Scholar 

  34. Bland JM, Altman DG: Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986, 1: 307-310.

    Article  CAS  PubMed  Google Scholar 

  35. Tousignant CP, Walsh F, Mazer CD: The use of transesophageal echocardiography for preload assessment in critically ill patients. Anesth Analg 2000, 90: 351-355. 10.1097/00000539-200002000-00021

    CAS  PubMed  Google Scholar 

  36. Vieillard-Baron A, Chergui K, Rabiller A, Peyrouset O, Page B, Beauchet A, Jardin F: Superior vena caval collapsibility as a gauge of volume status in ventilated septic patients. Intensive Care Med 2004, 30: 1734-1739.

    PubMed  Google Scholar 

  37. Bendjelid K, Romand JA: Fluid responsiveness in mechanically ventilated patients: a review of indices used in intensive care. Intensive Care Med 2003, 29: 352-360. 10.1007/s00134-003-1777-0

    Article  PubMed  Google Scholar 

  38. Katz WE, Gasior TA, Reddy SC, Gorcsan J 3rd: Utility and limitations of biplane transesophageal echocardiographic automated border detection for estimation of left ventricular stroke volume and cardiac output. Am Heart J 1994, 128: 389-396. 10.1016/0002-8703(94)90493-6

    Article  CAS  PubMed  Google Scholar 

  39. Denault AY, Gorcsan J 3rd, Mandarino WA, Kancel MJ, Pinsky MR: Left ventricular performance assessed by echocardiographic automated border detection and arterial pressure. Am J Physiol 1997, 272: H138-147.

    CAS  PubMed  Google Scholar 

  40. Brenner JI, Waugh RA: Effect of phasic respiration on left ventricular dimension and performance in a normal population. An echocardiographic study. Circulation 1978, 57: 122-127.

    Article  CAS  PubMed  Google Scholar 

  41. Denault AY, Gasior TA, Gorcsan J 3rd, Mandarino WA, Deneault LG, Pinsky MR: Determinants of aortic pressure variations during positive pressure ventilation in man. Chest 1999, 116: 176-186. 10.1378/chest.116.1.176

    Article  CAS  PubMed  Google Scholar 

  42. Charron C, Caille V, Jardin F, Vieillard-Baron A: Echocardiographic measurement of fluid responsiveness. Curr Opin Crit Care 2006, 12: 249-254. 10.1097/01.ccx.0000224870.24324.cc

    Article  PubMed  Google Scholar 

  43. Swenson JD, Bull D, Stringham J: Subjective assessment of left ventricular preload using transesophageal echocardiography: corresponding pulmonary artery occlusions pressures. J Cardiothorac Vasc Anesth 2001, 15: 580-583. 10.1053/jcan.2001.26535

    Article  CAS  PubMed  Google Scholar 

  44. Simon P, Mohl W: Intraoperative echocardiographic assessment of global and regional myocardial function. Echocardiography 1990, 7: 333-341.

    Article  CAS  PubMed  Google Scholar 

  45. Reuter DA, Bayerlein J, Goepfert MS, Weis FC, Kilger E, Lamm P, Goetz AE: Influence of tidal volume on left ventricular stroke volume variations measured by pulse contour analysis in mechanically ventilated patients. Intensive Care Med 2003, 29: 476-480.

    PubMed  Google Scholar 

  46. Charron C, Fessenmeyer C, Cosson C, Mazoit JX, Herbert JL, Benhamou D, Edouard AR: The influence of tidal volume on the dynamic variables of fluid responsiveness in critically ill patients. Anesth Analg 2006, 102: 1511-1517. 10.1213/01.ane.0000209015.21418.f4

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors wish to thank Dr. Freek J. Ziljstra and Dr. Jasper van Bommel from Erasmus MC University, Rotterdam, The Netherlands, for their thoughtful comments and expertise during this study.

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Correspondence to Maxime Cannesson.

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The authors declare that they have no competing interests.

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MC conceived of and designed the study, performed analysis and interpretation of data, edited the manuscript, and gave final approval of the manuscript. JS performed analysis and interpretation of data, drafted the manuscript, and gave final approval of the manuscript. OD and FF performed analysis and interpretation of data and gave final approval of the manuscript. OB and J-JL revised the manuscript critically for important intellectual content, edited the manuscript, and gave final approval of the manuscript.

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Cannesson, M., Slieker, J., Desebbe, O. et al. Prediction of fluid responsiveness using respiratory variations in left ventricular stroke area by transoesophageal echocardiographic automated border detection in mechanically ventilated patients. Crit Care 10, R171 (2006). https://doi.org/10.1186/cc5123

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