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Assessing Fluid Responsiveness in Clinical Practice: So Many Tools Available, So Many Questions to Answer

Lefrant, Jean Yves MD, PhD; Muller, Laurent MD, PhD

doi: 10.1213/ANE.0000000000001243
Editorials: Editorial

From the Division of Anaesthesia, Intensive Care, Pain and Emergency Medicine, University Hospital of Nîmes, Nîmes, France.

Accepted for publication January 28, 2016.

Funding: None.

The authors declare no conflicts of interest.

Reprints will not be available from the authors.

Address correspondence to Jean Yves Lefrant, MD, PhD, Division of Anaesthesia, Intensive Care, Pain and Emergency Medicine, University Hospital of Nîmes, Place du Pr Robert Debré, 30029 Nîmes, France. Address e-mail to

For the past 2 decades, optimizing cardiac preload in the operative setting has been shown to decrease mortality in major surgery and to decrease morbidity in minor surgery.1–3 Perioperative hemodynamic monitoring relies on 2 main goals: preventing hypovolemia and avoiding fluid overload. Thus, the development of a parameter that could perfectly detect hypovolemia and hypervolemia has long been considered the holy grail of hemodynamic monitoring. In the past 20 years, static and dynamic parameters of fluid responsiveness have been extensively compared.4 Dynamic parameters aim at altering venous return through a transient and reversible maneuver and assessing the impact of this alteration on stroke volume (SV) or on 1 of its surrogates. The transient action can be passive leg raising or mechanical ventilation (tidal volume or manipulation on positive end-expiratory pressure [PEEP]). Because SV measurement is not always available in clinical practice, numerous surrogates have been used such as pulse pressure (the difference between systolic pressure and diastolic pressure) or EtCO2. All these interventions induce a transient increase in venous return and are used to mimic a fluid challenge. If dynamic parameters have been shown to predict fluid responsiveness better than static parameters, their use is associated with an important gray zone that leads to an uncertainty in decision-making in 25% to 60% of patients.5,6 Therefore, developing new indices of fluid responsiveness remains a key issue in perioperative medicine.

In the present issue of Anesthesia & Analgesia, Tusman et al.7 report a trial for assessing the influence of PEEP increase on pulmonary elimination of carbon dioxide (VCO2) production to predict fluid responsiveness. Using VCO2 is particularly relevant because this parameter is correlated with cardiac output (and SV). Over a short period of time, variations in VCO2 could reflect variations in SV. The main difference with previously published studies on the topic is that the described maneuver aims at increasing the backpressure of venous return to move a patient’s heart from the flat portion of the Frank-Starling curve to the steep portion of the Frank-Starling curve. This technique precipitates the patient into a more severe hypovolemia or, to be more precise, to a more preload-dependent state. In this study, it is likely that none of the included patients was on the portion of the Frank-Starling curve where there is a shift from the zone of preload dependence to preload independence. However, this finding suggests that this tool almost perfectly predicts fluid responsiveness (area under the receiver operating characteristic curve = 0.99). Because volumetric capnography is noninvasive, it could be used during minor surgery, when SV cannot be easily monitored.

Therefore, the present study describes an elegant and innovative approach to assess fluid responsiveness by correlating VCO2 with PEEP increase. In 52 patients undergoing cardiovascular surgery under mechanical ventilation with a constant flow, the authors increased the PEEP from 5 to 10 cm H2O and analyzed the impact on the beat-to-beat cardiac index. The area under the receiver operating characteristic curve of VCO2 for predicting fluid responsiveness was 0.99 (95 confidence interval, 0.97–1), a value similar to that of the decrease in the cardiac index but more than those of the PETCO2 and the pulse pressure variation. During PEEP challenge, a decrease in VCO2 by 11% predicted fluid responsiveness with a sensitivity of 0.87 (0.66–0.97) and a specificity of 0.97 (0.82–0.99).

However, many issues remain to be answered before this test can be widely used in daily clinical practice.

When taking care of a patient in the perioperative setting, the questions to answer before using this new test are as follows:

  1. For this patient, should fluid responsiveness be assessed?
  2. It is now well documented that optimizing cardiac preload can reduce mortality in major surgery and morbidity in minor surgery. Therefore, such a strategy should be applied when the duration of surgery is >1 hour and when large fluid volume shifts are expected. Thus, this parameter could be useful in minor-risk patients.
  3. For this patient, which parameters are available for assessing fluid responsiveness?
  4. Dynamic parameters of fluid responsiveness (such as pulse pressure variation) are not always available. In the operating room, Maguire et al.8 reported that pulse pressure variation could be used in only 23% to 39% of patients. In the intensive care unit setting, pulse pressure variation was applicable in only 3% to 40% of patients.6,9,10 Passive leg raising test11 cannot be applied in the operating room or in patients with pelvic, inferior limb or vertebral fractures. EtCO2 cannot be reliably used in patients with chronic obstructive pulmonary disease. VCO2 could be only used when metabolic demand is constant. For physicians, the main issue is to elucidate whether a given parameter is valid in a given setting.
  5. In this patient, what is the clinical impact of assessing fluid responsiveness?
  6. Most studies reporting the clinical validity of parameters assessing fluid responsiveness did not aim to evaluate the clinical impact of this approach on patients’ outcomes.4 Moreover, a recent study challenged the clinical utility of such strategy, even if it is now widely recommended, especially in Europe.12 More interestingly, most previously published studies report a single best threshold for dynamic parameters based on small populations leading to a wide 95% confidence interval for sensitivity, specificity, and positive and negative predictive values. In the future, authors should focus their efforts on studying the impact of predictive parameters on the decision-making process. A methodology based on the “gray zone” approach isolating two special values could be better for answering the major following issue5:
    • - Under the first value, can I attempt a fluid challenge with a risk of error < 10%?
    • - Above another value, can I cancel a fluid challenge with a risk of error < 10%?
    • - More importantly, between the 2 previous values (the gray zone or inconclusive zone), what tool (parameter) or strategy should I rely on to make the decision to administer fluids?

This methodology requires more patients and more multicenter studies. When such studies are performed and these questions are answered, large multicenter studies assessing the impact of such parameters on patient outcome could be planned.

Therefore, the present study by Tusman et al. brought us a new tool for volume status assessment. The physician should keep this tool in mind but must always be thinking: Is this tool valuable in the global decision-making process for fluid responsiveness?

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Name: Jean Yves Lefrant, MD, PhD.

Contribution: This author helped write the manuscript.

Attestation: Jean Yves Lefrant approved the final version of the manuscript.

Name: Laurent Muller, MD, PhD.

Contribution: This author helped write the manuscript.

Attestation: Laurent Muller approved the final version of the manuscript.

This manuscript was handled by: Maxime Cannesson, MD.

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