Introduction

Prediction of volume responsiveness is an important issue in critically ill patients because clinicians must find the best compromise between central blood volume depletion and volume overloading (i.e. two opposing conditions potentially associated with poor outcome). There is now much evidence that dynamic indices based on the heart–lung interaction are useful in the decision-making process regarding volume resuscitation in patients receiving mechanical ventilation with a tidal volume above 8 ml/kg and exhibiting neither inspiratory efforts nor arrhythmias.

It is beyond the scope of this commentary to review in detail the complex physiological background that underlies the use of these dynamic indices, which has been extensively covered in previous review articles [1–3]. Schematically, the influence of positive pressure ventilation on hemodynamics is greater when central blood volume is low than when it is normal or high. In this regard, the larger the respiratory stroke volume variation, the larger the degree of volume responsiveness should be. Various indirect measures of stroke volume have been proposed to guide fluid therapy using the heart–lung interaction. By virtue of their ability to display values calculated automatically, real-time monitoring devices should make indices of the heart–lung interaction increasingly popular. Respiratory variations in arterial pulse pressure, in 'pulse contour' stroke volume and in Doppler aortic blood velocity have been shown to predict volume responsiveness far better than static markers of preload such as cardiac filling pressures or dimensions [4–7].

Until now there has been no ideal tool because the devices that provide these indices either are fairly invasive or they require a lengthy training period before the clinician can acquire sufficient skill. A nonivasive and easy-to-use device that can track changes in stroke volume in order to detect volume responsiveness would be particularly attractive. The pulse oximeter used to monitor arterial blood saturation could be a good candidate because the pulse oximetry plethysmographic (POP) signal resembles the peripheral arterial pressure waveform. Indeed, analysis of the respiratory variation in pulse oximeter waveforms has long been proposed as a technique with which to assess blood volume status in mechanically ventilated patients [8, 9]. In some studies [8–10] the degree of respiratory variation in the peak value of the plethysmographic waveform has been correlated with that of systolic arterial pressure.

Respiratory variation in pulse oximetry waveform

In a recent issue of Critical Care, Cannesson and coworkers [11] reported a new finding by demonstrating a reasonably good correlation between respiratory variation in the amplitude of the 'pulse' wave (peak–nadir) calculated from variations in the POP waveform (called ΔPOP by the authors) and the respiratory variation in arterial pulse pressure recorded with an arterial catheter. The strength of the study is that it takes into account the variation in the 'pulse' wave rather than that in the peak of the wave. By reflecting the pulsatile changes in absorption of infrared light between the light source and the photo detector of the pulse oximeter, the 'pulse' wave is assumed to be the result of the beat-to-beat changes in stroke volume transmitted to arterial blood. In this respect, ΔPOP is potentially a marker of respiratory stroke volume variation. By contrast, the respiratory variation in the peak of the plethysmographic wave – previously proposed for assessing volume status [8, 9] – depends not only on local changes in arterial blood volume but also on the slower ventilatory changes in local venous blood volume resulting from the ventilatory changes in venous return that mainly affect the highly compliant venous bed. The two figures presented in the report by Cannesson and coworkers [11] (Figs 1 and 4) clearly illustrate the slow ventilatory variation in the plethsymographic waveform (particularly apparent at the wave 'nadir' level) in addition to the cyclic changes in the 'pulse' wave.

Limitations of pulse oximetry waveform interpretation

The following weaknesses may limit the extent of the conclusions that may be drawn from the study [11]: no volume challenge was performed and Bland–Altman analysis revealed unsatisfactory agreement between ΔPOP and pulse pressure variation. Thus, utility of ΔPOP in detecting fluid responsiveness was not demonstrated.

Furthermore, numerous limitations and pitfalls related to the pulse oximetry method must be highlighted. For technical reasons, the pulse oximetry signal may be of poor quality in the presence of motion, hypothermia or arterial vasoconstriction, although the new generation of pulse oximeters allows one to optimize the recorded signal-to-noise ratio and thus improve the quality of the displayed signal [12]. In this regard, the proprietary software included with pulse oximeter monitors generate plethysmographic signals that are substantially filtered, amplified and smoothed before they are displayed. Any significant signal processing makes the theoretical proportionality between respiratory variation in left ventricular stroke volume and indices such as ΔPOP highly questionable. This may occur under conditions of low peripheral perfusion, in which amplification of the signal-to-noise ratio is maximized. In an attempt to limit the potential influence of the signal processing on the displayed waveform, the automatic gain incorporated into the pulse oximeter was disengaged in the study conducted by Cannesson and coworkers [11]. This allowed the investigators to maintain a constant gain throughout the study, rendering quantitative analysis of the waveform easier.

Conclusion

Additional studies – including volume challenge – are mandatory if we are to determine whether respiratory variation in pulse oximetry really can predict volume responsiveness in mechanically ventilated patients without arrhythmias or inspiratory effort. In such studies it would be important to seek a threshold value of ΔPOP that permits acceptable prediction and to investigate whether this value differs between pulse oximeter models, because their signal processing software may differ. Finally, it would be important to determine the extent to which ΔPOP is able to decrease in parallel with the increase in cardiac output that occurs after volume loading. Achievement of these objectives is mandatory before oximetry waveform variation can be recommended as a guide to fluid therapy in mechanically ventilated patients in intensive care units and operating rooms, in the same way that arterial pulse pressure variation and other heart–lung interaction indices are currently used [1, 13].