Skip to main content
Download PDF

Ability of pleth variability index to detect hemodynamic changes induced by passive leg raising in spontaneously breathing volunteers

Critical Care volume 12, Article number: R37 (2008) Cite this article

Abstract

Introduction

Pleth Variability Index (PVI) is a new algorithm that allows continuous and automatic estimation of respiratory variations in the pulse oximeter waveform amplitude. Our aim was to test its ability to detect changes in preload induced by passive leg raising (PLR) in spontaneously breathing volunteers.

Methods

We conducted a prospective observational study. Twenty-five spontaneously breathing volunteers were enrolled. PVI, heart rate and noninvasive arterial pressure were recorded. Cardiac output was assessed using transthoracic echocardiography. Volunteers were studied in three successive positions: baseline (semirecumbent position); after PLR of 45° with the trunk lowered in the supine position; and back in the semirecubent position.

Results

We observed significant changes in cardiac output and PVI during changes in body position. In particular, PVI decreased significantly from baseline to PLR (from 21.5 ± 8.0% to 18.3 ± 9.4%; P < 0.05) and increased significantly from PLR to the semirecumbent position (from 18.3 ± 9.4% to 25.4 ± 10.6 %; P < 0.05). A threshold PVI value above 19% was a weak but significant predictor of response to PLR (sensitivity 82%, specificity 57%, area under the receiver operating characteristic curve 0.734 ± 0.101).

Conclusion

PVI can detect haemodynamic changes induced by PLR in spontaneously breathing volunteers. However, we found that PVI was a weak predictor of fluid responsiveness in this setting.

Introduction

Hypovolaemia is among the most frequent causes of circulatory failure in the emergency medicine setting. Fluid loading is often the first therapy to be applied to optimize cardiac output (CO) in this situation. Static and the usual clinical variables (central venous pressure, pulmonary capillary wedge pressure, left ventricular end-diastolic area, mean arterial pressure [MAP] and/or tachycardia) are known to be of little value in discriminating between patients who will and those who will not respond to volume expansion [1–5].

On the other hand, dynamic indices that rely on cardiopulmonary interactions (variation in arterial pulse pressure (ΔPP) [3], inferior vena cava diameter [6], superior vena cava diameter [7], stroke volume [8] and aortic blood flow [4]), which are based on variation in left ventricular stroke volume, have been shown to be more accurate predictors of fluid responsiveness in mechanically ventilated patients [2, 3, 6, 8]. However, these indices are invasive, not universally available, or operator dependent.

Respiratory variation in pulse oximeter waveform amplitude (ΔPOP) has been shown to be strongly related to ΔPP [9], to be sensitive to changes in ventricular preload [10] and to be accurate predictors of fluid responsiveness [2]. Recently, a study conducted in spontaneously ventilated volunteers [11] showed that ΔPOP can reflect changes in ventricular preload in spontaneously breathing volunteers. However, ΔPOP cannot easily be calculated and continuously monitored at the bedside

Pleth Variability Index (PVI; Masimo Corp., Irvine, CA, USA) is new software that allows automatic and continuous monitoring of respiratory variations in the pulse oximeter waveform amplitude. This device has already been tested in our institution in mechanically ventilated patients [12]. The aim of the present study was to test the ability of PVI to detect changes in ventricular preload in spontaneously breathing volunteers.

Materials and methods

This study was conducted in accordance with the ethical standards of our institution and with the Helsinki Declaration of 1975 and revised in 1983. After written informed consent had been obtained, we studied 25 volunteers with no previous arterial hypertension or known cardiac disease, active sepsis and/or cardiac arrhythmias at the time of the study. Each patient was equipped with a pulse oximeter probe (LNOP® Adt; Masimo Corp.) attached at the index of the left hand and wrapped to prevent outside light from interfering with the signal. This pulse oximeter was connected to a Masimo Radical 7 monitor (Masimo SET; Masimo Corp.) with PVI software (version 7.0.3.3). Pulse oximeter plethysmographic waveforms were recorded from this monitor on a personal computer using PhysioLog software (PhysioLog V1.0.1.1; Protolink Inc., Richardson, TX, USA) and were analyzed by an observer who was blinded to other haemodynamic data. An arterial pressure cuff was positioned at the right arm in volunteers in order to measure systolic arterial pressure (SAP), diastolic arterial pressure (DAP) and MAP, as well as heart rate (HR; Solar 6000; General Electric, Milwaukee, WI, USA). Breathing rate was measured clinically by one of the investigators (OD).

Cardiac output

Cardiac output was assessed using echocardiography (CV 70; Acuson-Siemens Corp., Mountain View, CA, USA). Aortic blood flow was measured using a pulsed wave Doppler beam directed at the level of the aortic valve such that the click of the aortic closure could be observed. The aortic valve area was calculated from the diameter of the aortic orifice (which was considered as constant in all patients [2 cm]) as aortic valve area = π × (aortic diameter/2)2. The stroke volume was calculated as stroke volume = aortic valve area × the velocity time integral of aortic blood flow. The CO was calculated as CO = stroke volume × heart rate. The mean of five measurements performed at the end of the expiratory period were used for statistical analysis.

Pleth Variability Index calculation

PVI is a measure of the dynamic change in perfusion index that occurs during a complete respiratory cycle and has previously been detailed [12]. For the measurement of pulse oximeter oxygen saturation, red and infrared lights are used. A constant amount of light (termed DC) from the pulse oximeter is absorbed by skin, other tissues and nonpulsatile blood, whereas a variable amount of blood (termed AC) is absorbed by the pulsating arterial inflow. For Perfusion Index (PI) calculation, the infrared pulsatile signal is indexed against the nonpulsatile infrared signal and expressed as a percentage (PI = [AC/DC] × 100), reflecting the amplitude of the pulse oximeter waveform. Then, PVI is calculated by measuring changes in PI over a time interval sufficient to include one or more complete respiratory cycles as follows: PVI = ([PImax - Pimin]/PImax) × 100.

Other haemodynamic measurements

At each step of the protocol the following parameters were recorded: SAP, MAP, DAP, HR, breathing rate, CO, and pulse oximeter oxygen saturation.

Study protocol

The study protocol is summarized in Figure 1. A first set of measurements was taken with volunteers in the semirecumbent position (45°; baseline1 position), when volunteers were quietly and spontaneously breathing after 5 minutes of rest. Then, the lower limbs were lifted while straight (45°) with the trunk lowered in the supine position (passive leg raising [PLR] position) and volunteers were left in this position for 5 minutes. A second set of measurements was obtained 3 minutes after leg elevation. We chose not to record data after 1 minute after PLR because we observed significant artefacts in the pulse oximeter waveforms that cast doubt on any interpretation. A third set of measurements was recorded after 5 minutes of rest in the semirecumbent position, as in the baseline1 position (baseline2 position). Responders to volume expansion induced by PLR were defined as volunteers presenting more than 12.5% [13] increase in CO after PLR.

Figure 1
figure 1

Study protocol. A first set of measurements was taken with volunteers in the semirecumbent position (45°; baseline1 position), when volunteers were quietly and spontaneously breathing after 5 minutes of rest. Then, the lower limbs were lifted straight (45°) with the trunk lowered in the supine position (passive leg raising [PLR] position), and volunteers were left in this position for 5 minutes. A second set of measurements was obtained 3 minutes after leg elevation. We chose not to record data after 1 minute after PLR because we observed significant artefacts in the pulse oximeter waveforms that cast doubt on any interpretation. A third set of measurements was recorded after 5 minutes rest in the semirecumbent position, as in the baseline1 position (baseline2 position). Responders to volume expansion induced by PLR were defined as those volunteers exhibited more than 12.5% [13] increase in cardiac output after PLR.

Full size image

Statistical analysis

All data are presented as mean ± standard deviation. Changes in haemodynamic parameters induced by changes in loading conditions were assessed using a nonparametric Mann-Whitney U-test or Wilcoxon rank sum test when appropriate. Spearman rank method was used to test linear correlations. Volunteers were divided into two groups according to the percentage increase in CO after PLR: responders were defined as volunteers exhibiting at least a 12.5% [13] increase in CO, and nonresponders were volunteers who exhibited under 12.5% increase in CO. Receiver operating characteristic (ROC) curves was generated for PVI, varying the discriminating threshold of this parameter. P < 0.05 was deemed to represent statistical significance. All statistic analysis was performed using SPSS 13.0 for Windows (SPSS, Chicago, IL, USA).

Results

Twenty-five volunteers were included. This group consisted of 12 females and 13 males aged between 21 and 55 years (mean age 30 ± 9 years).

Effects of changes in body position on haemodynamic data

Data at baseline, in PLR position and back at the baseline position are shown in Table 1. We observed no significant changes in SAP, DAP, MAP, HR, and breathing rate during changes in body position. In contrast, we observed significant changes in CO, PI and PVI during changes in body position. Specifically, CO was significantly increased from baseline1 to the PLR position (from 4.2 ± 1.1 l/minute to 4.6 ± 1.3 l/minute; P < 0.05) and was significantly decreased from PLR position to baseline2 (from 4.6 ± 1.3 l/minute to 3.9 ± 1.1 l/minute; P < 0.05). At the same time, we observed a significant increase in PI (from 3.5 ± 2.4% to 4.9 ± 3.2%; P < 0.05) and a significant decrease in PVI (from 21.5 ± 8.0% to 18.3 ± 9.4%; P < 0.05) from baseline1 to the PLR position, and a significant decrease in PI (from 4.9 ± 3.2% to 2.3 ± 1.7%; P < 0.05) and a significant increase in PVI (fom 18.3 ± 9.4% to 25.4 ± 10.6%; P < 0.05) from PLR position to baseline2 (Figures 2 to 4).

Table 1 Haemodynamic data at baseline, after PLR and back at baseline
Full size table
Figure 2
figure 2

Changes in perfusion index during changes in body position. PLR, passive leg raising.

Full size image
Figure 3
figure 3

Changes in PVI after changes in body position. PLR, passive leg raising; PVI, Pleth Variability Index.

Full size image
Figure 4
figure 4

Evolution in PI and PVI. Shown is the volution in Perfusion Index (PI) and Pleth Variability Index (PVI) during changes in body position over a 15-minute period in an illustrative volunteer. Also shown (at the bottom of the figure) are the raw plethysmographic waveforms at baseline1, passive leg raising (PLR), and baseline2. We observed an increase in PI after PLR and a decrease in PI as the volunteer was positioned in the semirecumbent position (baseline 2; see arrows). At the same time, we observed inverse changes in PVI. Specifically, PVI exhibited a slight increase during PLR that was related to a signal artefact in PI. Raw plethysmographic waveforms corroborate PVI values.

Full size image

Ability of PVI to predict fluid responsiveness in spontaneously breathing patients

Of the 25 studied volunteers, 11 (44 %) were responders to PLR. Responders exhibited significantly higher PVI values at baseline1 compared with nonresponders (25.5 ± 7.9 versus 18.3 ± 6.9; P < 0.05). A threshold PVI value of >19% was a weak but significant predictor of response to PLR (sensitivity 82%, specificity 57%, area under the ROC curve 0.734 ± 0.101). The relationship between PVI value at baseline and percentage increase in CO after PLR was close to but did not reach statistical significance (r = 0.385; P = 0.058; Figure 5).

Figure 5
figure 5

Relationship between PVI at baseline1 and percentage change in CO after PLR. There was non significant relationship between Pleth Variability Index (PVI) at baseline and percentage change in cardiac output (CO) after passive leg raising (PLR). Horizontal dashed line shows increase in CO of 12.5%. Vertical dashed line shows PVI value of 19%, which allowed discrimination between responders and nonresponders to PLR with a sensitivity of 82% and a specificity of 57%.

Full size image

Discussion

This study shows that PVI, an index that allows automatic and continuous calculation of respiratory variations in the pulse oximeter plethysmographic waveform amplitude, can detect haemodynamic changes induced by passive leg raising in spontaneously breathing volunteers. However, we found that PVI was a weak predictor of fluid responsiveness in this setting, as are most of the dynamic indicators of fluid responsiveness in spontaneously breathing patients.

Assessment of fluid responsiveness in mechanically ventilated patients has now been extensively studied, and it is known that dynamic indicators that rely on cardiopulmonary interactions are the best predictors in this setting [2, 3, 14]. Recently, ΔPOP has been shown to be a noninvasive and reliable predictor of fluid responsiveness in the operating room [2, 15, 16] and in the intensive care unit [17] in mechanically ventilated patients. Moreover, it has been demonstrated to decrease significantly after PLR in spontaneously breathing volunteers, suggesting that this parameter may be of value in assessment of fluid responsiveness in this population [11]. However, this index is difficult to measure at the bedside and cannot be visually estimated from the monitor screen because of the gain processing that is used by most of the monitors [2]. We recently demonstrated that PVI could continuously and automatically monitor ΔPOP in mechanically ventilated patients [12]. In that study we found that a PVI value above 11.5% could discriminate between ΔPOP above 13% and ΔPOP of 13% or less with a sensitivity of 93% and a specificity of 97%. Area under the curve for PVI to predict ΔPOP above 13% was 0.990 ± 0.07 in this study. However, although we found PVI to be of value in mechanically ventilated patients, its utility in spontaneously breathing patients had never been investigated [12].

The pulse oximeter waveform relies on light absorption. Briefly, light absorption includes two components. The first component is said to be constant and is due to light absorption by bone, tissue, pigments, nonpulsatile blood and skin. Venous blood is also responsible for some constant absorption, but this is still under investigation [18, 19]. The second component is said to be pulsatile absorption, which is due primarily to arterialized blood. The PI is defined as the ratio between constant absorption (AC) and pulsatile absorption (DC), reflecting the amplitude of the plethysmographic waveform. PVI can automatically detect the maximal and minimal PI value over a period of time sufficient to include at least one complete respiratory cycle. PVI is then automatically and continuously calculated as (PImax - PImin)/PImax, reflecting respiratory variations in PI. This algorithm allows continuous monitoring of the respiratory variations in the pulse oximeter waveform amplitude.

Assessment of fluid responsiveness in spontaneously breathing patients is difficult, and cardiopulmonary interactions in this setting differ greatly from those observed in mechanically ventilated patients [20–22]. Moreover, in this setting, frequency and tidal volumes may vary from breath to breath. However, further studies are required to explore this topic, as suggested by recent published experimentations conducted in this setting and focusing on ΔPOP [11]. PLR mimics a 'rapid and transient' fluid loading of 300 ml by transferring a volume of blood to the central compartment. In association with rapid measurements of changes in aortic blood flow, it provides a useful tool with which to evaluate fluid responsivness in mechanically ventilated but also in spontaneously breathing patients who are suspected of being hypovolaemic [13]. In normotensive individuals, this manoeuvre not only increases preload but also decreases peripheral vascular resistance [13]. Our data, showing that PI significantly increases after PLR (Figure 4), may support this hypothesis because PI is related to vasomotor tone. In the present study, we applied a modified form of PLR associated with trunk lowering, which has previously been used and should amplify the transient haemodynamic changes [13]. These changes occur maximally during the first minute and disappear after a few minutes. We performed measurements only during the third minute in order to obtain a stable and reliable plethysmographic signal that was not disturbed by changes in vasomotor tone.

Recently, Soubrier and coworkers [21] found ΔPP to be a weak predictor of fluid responsiveness in spontaneously breathing patients. In that study they showed that ΔPP above 12% was able to discriminate responders from nonresponders to volume expansion with 92% sensitivity and 63% specificity. These data indicate slightly better performance than suggested by our data obtained with PVI. In particular, area under the ROC curve for ΔPP was 0.81 ± 0.08 in their study as compared with 0.734 ± 0.101 in ours. This difference may be related to the sensitivity of the pulse oximeter waveform to changes in vasomotor tone observed in spontaneously breathing volunteers and to the fact that PVI is unable to discriminate between respiratory changes in PI from other changes in PI. We can postulate that these changes are less frequent and less important in mechanically ventilated patients, as was suggested by a previous study conducted in this setting in our institution and showing that PVI was an accurate monitoring of ΔPOP [12]. Further studies investigating the ability of PVI to predict fluid responsiveness in mechanically ventilated and spontaneously breathing patients are warranted.

Study limitations

We did not conduct real volume expansion in this study. Rather, we chose to use the previously published threshold value of 12.5% increase in CO after PLR as a predictor of fluid responsiveness in spontaneously breathing volunteers [13]. Future studies are planned to assess the ability of PVI to predict fluid responsiveness in the operating room and in spontaneously breathing patients. Moreover, the aim of our study was to describe the changes in PVI after PLR, as was previously done with ΔPOP [11].

As with any other dynamic indicators of fluid responsiveness, PVI cannot be used in patients with cardiac arrhythmias.

We did not assess systemic vascular resistance in our sample of volunteers. However, POP waveform amplitude relies upon this parameter. Consequently, our results cannot be exptrapolated to situations in which systemic vascular resistances are different, such as patients receiving vasoactive drugs.

Conclusion

PVI, a new parameter that allows automatic and continuous monitoring of the respiratory variations in the pulse oximeter plethysmographic waveform amplitude, can detect haemodynamic changes induced by PLR in spontaneously breathing volunteers. However, its ability to predict fluid responsiveness in spontaneously breathing patients is weak, and consequently whether it should be used to guide volume expansion in this setting is uncertain.

Key messages

  • PVI is a novel parameter that allows automatic and continuous calculation of the respiratory variations in the pulse oximeter waveform amplitude.

  • PVI can automatically and noninvasively detect changes in ventricular preload induced by PLR in spontaneously breathing volunteers.

  • Changes in preload induced by PLR also induced changes in PI. However, PI was unable to predict increase in CO induced by PLR.

  • PVI, as does any other dynamic indicator, appears to be a significant but weak predictor of fluid responsiveness in spontaneously breathing individuals.

  • Acute changes in vasomotor tone may influence PVI; hence, this parameter should be interpreted with caution in this setting.

Abbreviations

AC:

alternating current

CO:

cardiac output

CVP:

mean arterial pressure

DAP:

diastolic arterial pressure

DC:

direct current

ΔPOP:

variation in pulse oximeter waveform amplitude

ΔPP:

variation in arterial pulse pressure

HR:

heart rate

PI:

Perfusion Index

PLR:

passive leg raising

PVI:

Pleth Variability Index

ROC:

receiver operating characteristic

SAP:

systolic arterial pressure.

References

  1. 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 

  2. Cannesson M, Attof Y, Rosamel P, Desebbe O, Joseph P, Metton O, Bastien O, Lehot JJ: Respiratory variations in pulse oximetry plethysmographic waveform amplitude to predict fluid responsiveness in the operating room. Anesthesiology 2007, 106:1105-1111. 10.1097/01.anes.0000267593.72744.20

    Article  PubMed  Google Scholar 

  3. 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  PubMed  CAS  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 septick shock. Chest 2001, 119:867-873. 10.1378/chest.119.3.867

    Article  PubMed  CAS  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  PubMed  CAS  Google Scholar 

  6. 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 

  7. 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 

  8. 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  PubMed  CAS  Google Scholar 

  9. Cannesson M, Besnard C, Durand PG, Bohe J, Jacques D: Relation between respiratory variations in pulse oximetry plethysmographic waveform amplitude and arterial pulse pressure in ventilated patients. Crit Care 2005, 9:R562-R568. 10.1186/cc3799

    Article  PubMed  PubMed Central  Google Scholar 

  10. Cannesson M, Desebbe O, Hachemi M, Jacques D, Bastien O, Lehot JJ: Respiratory variations in pulse oximeter waveform amplitude are influenced by venous return in mechanically ventilated patients under general anaesthesia. Eur J Anaesthesiol 2007, 24:245-251. 10.1017/S026502150600161X

    Article  PubMed  CAS  Google Scholar 

  11. Delerme S, Renault R, Le Manach Y, Lvovschi V, Bendahou M, Riou B, Ray P: Variations in pulse oximetry plethysmographic waveform amplitude induced by passive leg raising in spontaneoulsy breathing volunteers. Am J Emerg Med 2007, 25:637-642. 10.1016/j.ajem.2006.11.035

    Article  PubMed  Google Scholar 

  12. Cannesson M, Delannoy B, Morand A, Attof Y, Bastien O, Lehot JJ: Does the Pleth Variability Index indicate the respiratory induced variation in the plethysmogram and arterial pressure waveforms? Anesth Analg 2008, in press.

    Google Scholar 

  13. 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 

  14. Solus-Biguenet H, Fleyfel M, Tavernier B, Kipnis E, Onimus J, Robin E, Lebuffe G, Decoene C, Pruvot FR, Vallet B: Non-invasive prediction of fluid responsiveness during major hepatic surgery. Br J Anaesth 2006, 97:808-816. 10.1093/bja/ael250

    Article  PubMed  CAS  Google Scholar 

  15. Natalini G, Rosano A, Taranto M, Faggian B, Vittorielli E, Bernardini A: Arterial versus plethysmographic dynamic indices to test responsiveness for testing fluid administration in hypotensive patients: a clinical trial. Anesth Analg 2006, 103:1478-1484. 10.1213/01.ane.0000246811.88524.75

    Article  PubMed  Google Scholar 

  16. Wyffels PAH, Durnez PJ, Heldeweirt J, Stockman WMA, De Kegel D: Ventilation-induced plethysmographic variations predict fluid responsiveness in ventilated postoperative cardiac surgery patients. Anesth Analg 2007, 105:448-452. 10.1213/01.ane.0000267520.16003.17

    Article  PubMed  Google Scholar 

  17. Feissel M, Teboul JL, Merlani P, Badie J, Faller JP, Bendjelid K: Plethysmographic dynamic indices predict fluid responsiveness in septic ventilated patients. Intensive Care Med 2007, in press.

    Google Scholar 

  18. Shelley KH, Dickstein M, Shulman SM: The detection of peripheral venous pulsation using the pulse oximeter as a plethysmograph. J Clin Monit 1993, 9:283-287. 10.1007/BF02886699

    Article  PubMed  CAS  Google Scholar 

  19. Shelley KH, Tamai D, Jablonka D, Gesquiere M, Stout RG, Silverman DG: The effect of venous pulsation on the forehead pulse oximeter wave form as a possible source of error in Spo2 calculation. Anesth Analg 2005, 100:743-747. 10.1213/01.ANE.0000145063.01043.4B

    Article  PubMed  Google Scholar 

  20. Lamia B, Ochagavia A, Monnet X, Chemla D, Richard C, Teboul JL: Echocardiographic prediction of volume responsiveness in critically ill patients with spontaneously breathing activity. Intensive Care Med 2007, 33:1125-1132. 10.1007/s00134-007-0646-7

    Article  PubMed  Google Scholar 

  21. Soubrier S, Saulnier F, Hubert H, Delour P, Lenci H, Onimus T, Nseir S, Durocher A: Can dynamic indicators help the prediction of fluid responsiveness in spontaneously breathing critically ill patients? Intensive Care Med 2007, 33:1117-1124. 10.1007/s00134-007-0644-9

    Article  PubMed  Google Scholar 

  22. De Backer D, Pinsky MR: Can one predict fluid responsiveness in spontaneously breathing patients? Intensive Care Med 2007, 33:1111-1113. 10.1007/s00134-007-0645-8

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors wish to thank all the physicians and nurses from the department of anaesthesiology (Louis Pradel Hospital, Hospices Civils de Lyon, Lyon, France) for their help and support during this study; Masimo Corp. for creating and donating hardware and software; and Angela Grunhagen and John Graybeal from Masimo Corp. for technical support.

Author information

Authors and Affiliations

  1. Hospices Civils de Lyon, Groupement Hospitalier Est, Department of Anesthesiology and Intensive Care, Louis Pradel Hospital and Claude Bernard Lyon 1 University, INSERM ERI 22, 28 avenue du doyen Lépine, 69500, Bron-Lyon, France

    Geoffray Keller, Olivier Desebbe, Jean-Jacques Lehot & Maxime Cannesson

  2. Hospices Civils de Lyon, Groupement Hospitalier Est, Department of Cardiology, Louis Pradel Hospital and Claude Bernard Lyon 1 University, 28 avenue du doyen Lépine, 69500, Bron-Lyon, France

    Emmanuel Cassar

Authors
  1. Geoffray Keller
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emmanuel Cassar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Olivier Desebbe
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jean-Jacques Lehot
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Maxime Cannesson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Geoffray Keller, Emmanuel Cassar, Olivier Desebbe, Jean-Jacques Lehot or Maxime Cannesson.

Additional information

Competing interests

Software and hardware were provided by Masimo Corp.

Authors' contributions

GK was responsible for analysis and interpretation of data, and drafting of the manuscript. EC interpreted data and drafted the manuscript. OD interpreted of data and drafted the manuscript. J-JL revising the manuscript critically for important intellectual content and edited the manuscript. MC conceived and designed the study, analyzed and interpreted the data, and edited the manuscript. All authors read and approved the final manuscript.

Authors’ original submitted files for images

Rights and permissions

Reprints and permissions

About this article

Cite this article

Keller, G., Cassar, E., Desebbe, O. et al. Ability of pleth variability index to detect hemodynamic changes induced by passive leg raising in spontaneously breathing volunteers. Crit Care 12, R37 (2008). https://doi.org/10.1186/cc6822

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/cc6822

Keywords

Critical Care

ISSN: 1364-8535