The comparison of the predictive performance of studied parameters for detecting fluid responsiveness is given in Figure 5 and Table 3. A decrease of ≥11% in Vco2 during the PEEP challenge predicted a ≥15% increase in CI after fluid administration with high sensitivity and specificity. However, a decrement by ≥5% in Petco2 during the PEEP challenge predicted a fluid-induced increase in CI by ≥15% with a poor sensitivity and specificity. PPV had also a poor sensitivity and specificity for detecting fluid responsiveness according to the reference method.
The main finding of this study is that a decrease in Vco2 observed during a brief PEEP challenge was accurate in predicting fluid responsiveness. The clinical implication of our results is that a dynamic approach using CO2 can detect preload dependency at the bedside in a totally noninvasive way by means of a simple ventilator maneuver. Thus, the always difficult diagnosis of preload dependency could be easily established in patients in whom more invasive CI monitoring equipment is not available. This is of particular importance in the operating theater for medical, economical, and ethical reasons, because most of our patients present no clinical indication for invasive and expensive hemodynamic monitoring.
We found that 40% of our patients were fluid responders according to the standard definitions.6–8 This result is in line with the studies by Kim et al.28 and Preisman et al.19 who found that the incidence of fluid responsiveness in patients undergoing cardiac surgery was 38% and 46%, respectively.
Different dynamic ventilatory maneuvers to assess fluid responsiveness in mechanically ventilated patients have been described.29–31 These include cyclical changes during mechanical breaths or step changes in PEEP and expiratory pauses.15–18 Based on the physiologic principles governing heart–lung interactions, these maneuvers stress the hemodynamic state reversibly without the need to administer fluids. The principle tested whether a step PEEP change is a reversible maneuver that can help detect fluid responsiveness as measured by different invasive parameters.15,16 In experimental animals, Lambert et al.18 found that 10 cm H2O of PEEP affected stroke volume in proportion to the deficit in intravascular fluids. Michard et al.16 showed that the variations in pulse pressure induced by 10 cm H2O of PEEP were predictive of fluid responsiveness in patients with acute respiratory distress syndrome. In patients undergoing cardiac surgery, Geerts et al.15 showed that PEEP-induced changes in CVP predicted fluid responsiveness in the same way as the combination of passive leg-rising test and CI.
Monitoring expired CO2 is attractive because it is simple, real time, and noninvasive. The amount of eliminated CO2 depends simultaneously and continuously on the body’s metabolism, pulmonary perfusion, and alveolar ventilation. Therefore, the changes in Vco2 must be interpreted with caution.27 When metabolic production of CO2 and alveolar ventilation are constant during the measuring period, as in our short lasting protocol, a change in CO2 can be explained conclusively by a parallel change in pulmonary blood flow.20,21
Petco2 is the parameter most commonly used in time-based capnography, which has demonstrated a close association with CI in different scenarios.11–13 However, we found a rather poor correlation between absolute and relative values of Petco2 and CI after fluid administration and during the PEEP challenge. The performance of ΔPetco2 in predicting fluid responsiveness was poor in our study (AUC, 0.69; sensitivity, of 0.67; specificity, 0.77) but excellent in the studies by Monnet et al.10 (AUC, 0.93; sensitivity, 0.71; and specificity, 1) and Monge et al.9 (AUC, 0.94; sensitivity, 0.91; and specificity, 0.94). Despite these differences, the cutoff value for ΔPetco2 to detect fluid responsiveness was the same (5%) in all studies.
These differences in the performance of Petco2 could be explained in part by differences in the patient populations studied (intensive care versus cardiac surgery patients) and by the nature of the dynamic maneuvers used to challenge the heart–lung interaction (passive leg-raising test versus PEEP test). We speculate that the degree of change in Petco2 that these opposing maneuvers induce might be rather different, because the value of Petco2 highly depends on the slope of phase III of the capnogram.32–34 Increasing CI by passive leg-raising can increase the slope of phase III, and hence its final CO2 value, the Petco2.21 Conversely, a PEEP challenge will have an opposite effect because the slope of phase III becomes flatter and thus Petco2 becomes lower any time CI decreases. In other words, the positive changes in Petco2 seen during the passive leg-raising test could be larger than the negative changes induced by PEEP.
Our results support the hypothesis that Vco2 is a better capnographic-derived parameter than Petco2 in predicting fluid responsiveness for the following reasons: First, the correlation between ΔCI and ΔVco2 during the PEEP challenge before fluid administration was good, whereas the correlation between ΔCI and ΔPetco2 was poor. Second, the ΔCI induced by fluids at baseline was well correlated with the ΔVco2 induced by the PEEP challenge before fluid administration but not with ΔPetco2 at the same instance. Furthermore, Vco2 is obtained by volume-based, but not by time-based, capnography and thus is measured in the flow domain, the same as for CI.35 In patients undergoing weaning from cardiopulmonary bypass, we demonstrated that Vco2 was directly proportional to the amount of pulmonary blood flow.20,21 The fact that the cutoff values for ΔCI and ΔVco2 to detect fluid responsiveness were similar in our responder patients supports the existence of such a close relationship. The ROC curve confirmed the aforementioned notion, showing a higher sensitivity and specificity for ΔVco2 than for ΔPetco2 to predict fluid responsiveness.
The observed changes in PPV, similar to Vco2 and Petco2, presented a predicted physiologic behavior during fluid and PEEP challenges in both responders and nonresponders. Even though these congruent changes in PPV were significant (Table 2), this variable was poorly correlated with CI and Vco2 and had a limited performance in predicting fluid responsiveness in our patients (Table 3). This poor performance of PPV in defining fluid responsiveness in our study can perhaps be explained by the use of 7 mL/kg of VT. There is a trend toward decreasing intraoperative VT that limits the value of PPV as a clinical tool for monitoring in the operating room.36,37
The impact of lung diseases or pulmonary shunt on our methodology is unknown, because we did not evaluate these clinical conditions separately. Vco2 could increase with the application of 10 cm H2O of PEEP because of a potential recruitment of small airways and atelectasis, thereby mitigating the PEEP-induced decrement in Vco2 in responders. To eliminate this confounding factor, we standardized lung volume by applying 10 deep breaths before starting the protocol.
We can speculate that our method should also be reliable in patients with lung diseases or shunt because (1) patients served as their independent controls regardless of the underlying lung condition and (2) ΔCI, a variable hardly affected by chronic lung diseases and fixed shunt in the short run, during the first PEEP challenge changed in a similar way as ΔVco2 (−12% vs −11%, respectively) while a good correlation between them was found (Fig. 4). This important issue should be properly tested in future studies.
The combination of a reversible hemodynamic challenge by PEEP in conjunction with the response in noninvasive Vco2 may be a simple way to identify those patients undergoing cardiac surgery who could benefit from fluid administration.
Name: Gerardo Tusman, MD.
Contribution: This author helped design the study, conduct the study, collect the data, analyze the data, and prepare the manuscript.
Attestation: Gerardo Tusman has reviewed the original study data and data analysis and is the archival author.
Conflicts of Interest: Gerardo Tusman performs consultant activities for Maquet Critical Care and is the owner of a patent on volumetric capnography.
Name: Iván Groisman, MD.
Contribution: This author helped collect the data and analyze the data.
Attestation: Iván Groisman collected and analyzed the data.
Conflicts of Interest: None.
Name: Gustavo A. Maidana, MD.
Contribution: This author helped collect the data.
Attestation: Gustavo A. Maidana collected the data.
Conflicts of Interest: None.
Name: Adriana Scandurra, PhD.
Contribution: This author helped analyze the data.
Attestation: Adriana Scandurra did the main calculations of studied variables and the statistical analysis.
Conflicts of Interest: None.
Name: Jorge Martinez Arca, Eng.
Contribution: This author helped in statistical analysis.
Attestation: Jorge Martinez Arca did the statistical analysis.
Conflicts of Interest: None.
Name: Stephan H. Bohm, MD.
Contribution: This author helped design the study and prepare the manuscript.
Attestation: Stephan H. Bohm reviewed the original study data and data analysis.
Conflict of Interest: Stephan H. Bohm is the owner of a patent on volumetric capnography.
Name: Fernando Suarez-Sipmann, PhD.
Contribution: This author helped design the study and prepare the manuscript.
Attestation: Fernando Suarez-Sipmann reviewed the original study data and data analysis.
Conflict of Interest: Fernando Suarez-Sipmann received a grant from the “Fondo de Investigación Sanitaria” Instituto de salud Carlos III. FIS-PI070136 and performed consultant activities for Maquet Critical Care.
This manuscript was handled by: Maxime Cannesson, MD, PhD.
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