The main finding in this study was that dynamic cardiac power parameters somewhat surprisingly were less able to track hypovolemia than equivalent dynamic flow- and pressure parameters both during spontaneous ventilation and during NPPV. The PWR-integral was the best non-dynamic parameter both during spontaneous breathing and during NPPV. However, SV tracked hypovolemia almost as well as the PWR-integral.
Dynamic parameters
The first hypothesis, that respiratory variations in PWR parameters would be stronger than respiratory variations in flow- and pressure parameters, is not in agreement with our observations. The reason seems to be that respiratory variations in flow and pressure are out of phase, as illustrated in Fig. 7. Respiratory variation in flow seems to be 2–3 cardiac cycles ahead of pressure variation, which would lead to these variations partially cancelling each other out when pressure and flow are combined to calculate the PWR trace. This observation is consistent across individuals and LBNP-levels, and likely explains why Δ PWRmax and Δ PWR-integral did not follow LBNP-levels better.
The finding that respiratory variation in flow lags respiratory variation in pressure by 2–3 cardiac cycles indicates cyclic changes in impedance, the vascular opposition to blood flow. We retrospectively performed an impedance analysis by Fourier transformation [23, 24, 26] using Matlab, to see which part or parts of the vascular system was responsible for these cyclic changes. The systemic vascular resistance (SVR), also described as Z0, and the first impedance harmonic amplitude |Z1| [23] showed signs of cyclic changes following respiration in several of the research subjects, indicating cyclic changes in the peripheral vascular resistance matching respiratory variation in hemodynamic parameters. The characteristic impedance amplitude Zc and f0, the frequency where the impedance phase first crosses 0 [23], did not follow the respiratory cycle, indicating that impedance of the proximal vasculature and reflections did not play a part in the cyclic changes of the total impedance. These findings were however not convincing in all subjects, typically the subjects who cooperated best with the ventilator showed these signs most clearly. In the literature we have found indications of cyclic variation in peripheral resistance with frequencies up to 0.1 Hz [27]. However, cyclic variation in hemodynamic parameters matching respiration will have significantly higher frequencies. We suggest further research of this phenomenon in intubated and paralyzed subjects.
The ability of dynamic parameters to reflect volume status in mechanically ventilated patients and during NPPV has been demonstrated in previous studies [9, 16]. During spontaneous breathing the same parameters are less useful, dynamic flow parameters have however demonstrated an ability to reflect volume status in some studies [7, 28, 29], although only passive leg raise and end-expiratory occlusion test have shown convincing results [30, 31]. Our finding that Δ peak aortic flow was the best dynamic tracker of LBNP supports consideration of respiratory blood flow variation also in spontaneously breathing patients.
Non-dynamic parameters
The observations regarding non-dynamic parameters are in agreement with our second hypothesis, that non-dynamic PWR parameters could track hypovolemia better than equivalent pressure and flow parameters. This is reasonable since the PWR-integral will incorporate both reductions in flow and pressure, but the pressure-reduction was small while the flow-reduction was substantial. One could imagine that oscillatory/pulsatile energy would change in hypovolemia [11], and since the PWR-integral incorporates both mean and oscillatory/pulsatile energy, it would not necessarily track hypovolemia better than SV and pulse pressure. One could also imagine the timing of the peak pressure and peak flow in each cardiac cycle shifting with hypovolemia. This would make the calculation of PWRmax unpredictable with increasing hypovolemia. These speculations are not supported by our findings.
Clinical application
Evaluating hypovolemia and fluid responsiveness during spontaneous breathing with Δ peak aortic flow could be challenging in clinical practice. The cut-off value for hypovolemia seems to be around 12 % respiratory variation in Δ peak aortic flow, which may be difficult to distinguish from the baseline values of approximately 9 % respiratory variation using an ultrasound monitor. Of the non-dynamic parameters the PWR-integral tracks hypovolemia slightly better than SV, however considering the technical complexity in acquiring the PWR-integral today, SV seems adequate based on these results in healthy volunteers. If further research shows better results for power parameters in patient populations, a relatively small technological adaptation of ultrasound scanners would be necessary to make power parameter easily available. For now we would recommend paying attention to aortic flow and SV with ultrasound where hypovolemia is suspected, as the information provided may be valuable combined with other clinical signs of hypovolemia.
Limitations of the study
The subjects in this study were healthy, young volunteers. The usefulness of power parameters could be better in older patients, and in patients with illnesses and conditions affecting the cardiovascular system, where blood pressure is likely to be more affected by hypovolemia. Further studies would be necessary to clarify this.
Blood pressure was measured using Finometer on the left third finger. A proper cardiac power calculation should be based on aortic pressure, which would demand methods too invasive to justify in healthy volunteers, and probably inapplicable in most clinical circumstances. Finometer has in many studies shown good agreement with radial pressure [25, 32]. Although radial pressure produces reliable measures of diastolic and mean aortic pressure, radial systolic pressure is often higher than aortic systolic pressure [33]. We only used time calibration as a transfer function from radial to aortic pressure, in the magnitude of 5–10 milliseconds, so that the systolic upstrokes in pressure and flow were synchronized before calculating the power curve. This probably introduced an error compared to cardiac power based on aortic pressure, as the systolic cardiac power value based on radial pressure often will result in an overestimation. As long as radial blood pressure is the standard for continuous blood pressure measurement in clinical use, it is however most clinically relevant to base cardiac power measurements on radial blood pressure.
The aortic diameter was assumed to be 20 mm in every individual as in previous studies involving this method of cardiac output determination [19]. When considering absolute values of all flow and cardiac power related parameters, this will introduce an error. However, when considering relative changes from baseline values due to increasing LBNP as in this study, an error in aortic diameter will not affect the result assuming a fairly constant diameter [20] in each individual. Similarly, errors introduced by assuming a 20 o angle would also be insignificant when considering relative changes due to LBNP, assuming the probe position is relatively constant in each individual.
This study used the suprasternal window to measure aortic flow. In clinical use an apical window may be more appropriate, but this window was obstructed by the LBNP-chamber in this study. Achieving aortic flow through the apical window is considered a relatively basic echocardiographic skill, achievable in most patients [34].
Using volume controlled ventilation in non-sedated subjects is unusual in clinical practice. All kinds of controlled ventilation in healthy, non-sedated subjects is challenging. One aim of the study was to compare dynamic variables during hypovolemia and positive pressure ventilation. Aortic flow variations and pulse pressure variations are induced by cyclic changes in intrathoracic pressure. These cyclic changes are induced by positive pressure ventilation and disturbed by any spontaneous breathing activity, including triggering. Pressure support would naturally lead to spontaneous breathing efforts. Pressure control mode (BiPAP) led to uneven tidal volumes and excessive spontaneous breathing efforts. Volume control (IPPV) yielded constant tidal volumes and minimized spontaneous breathing efforts. One possible reason could be that while the tidal volumes are kept constant during IPPV, the mode allows for variation in the inspiratory pressures required to obtain these volumes. Some subjects could not comply with controlled ventilation at all, and were not included in the study. After thorough testing before the experiments, we found volume control mode with relatively large tidal volumes (10 ml/kg) to minimize spontaneous breathing activity, which would have interrupted the cyclic intrathoracic pressure we aimed for. The physiological aspect of investigating respiratory variations in positive pressure ventilation with increasing hypovolemia simulated by LBNP was however achieved, allowing us to compare different dynamic parameters to each other.
The sample size is limited, but comparable to other LBNP-studies, and we were able to demonstrate several significant effects indicating sufficient statistical power.