The ideal index of fluid responsiveness for clinical use in the operating room should be sensitive to changes in loading conditions, predictive of response to volume expansion, reproducible, simple to use, obtainable non-invasively and widely available. Many studies have already focused on this subject but, to the best of our knowledge, no index has been shown to utilize all of these characteristics. Static indicators such as central venous pressure (CVP), pulmonary capillary wedge pressure (PCWP) or left ventricular (LV) end diastolic area are invasive or not easily available and have been shown to be poor predictors of fluid responsiveness [1–8]. Dynamic indicators have consistently been demonstrated as the best predictors of fluid responsiveness in patients under mechanical ventilation. During positive pressure ventilation, inspiratory right ventricular stroke volume decrease is proportional to the degree of hypovolemia and is transmitted to the left heart after two or three beats (pulmonary transit time) [6,7]. These respiratory variations in stroke volume or its surrogates have been shown to be superior to static indicators for the prediction of fluid responsiveness [1–4,6,8–11]. However, they are invasive (respiratory variations in arterial pulse pressure [ΔPP] , stroke volume variations [4,11]) with their associated complications [12,13], technically challenging (respiratory variations in pulse Doppler aortic flow velocity , inferior vena cava diameter ) or not widely available (oesophageal Doppler ).
Pulse oximeters are widely used in anaesthesia. The pulse oximeter plethysmography (POP) waveform depends on arterial pulsatility. We have recently shown that the respiratory variations in POP waveform amplitude (ΔPOP) are closely related to ΔPP in the critical care setting [15,16]. However, the ability of ΔPOP to detect changes in loading conditions has never been reported in the operating room.
The aims of this study were to test the hypothesis that in mechanically ventilated patients undergoing general anaesthesia (1) ΔPOP calculation is feasible and (2) ΔPOP can detect changes in preload.
Materials and methods
The protocol was approved by the Institutional Review Board for Human Subjects (Comité Consultatif de Protection des Personnes dans la Recherche Biomédicale Lyon B). After written informed consent, we studied 25 consecutive patients undergoing cardiac (coronary artery bypass grafting [CABG]) or major vascular surgery (abdominal aorta aneurysm surgery). Patients with cardiac arrhythmias, LV dysfunction (preoperative LV ejection fraction <50%), right ventricular dysfunction and intracardiac shunt were excluded. This group consisted of 20 males and five females aged between 34 and 85yr (mean age 68 ± 13yr). Thirteen patients received beta-blockers preoperatively. Induction of anaesthesia was performed with propofol (1–3 mg kg−1) and sufentanil (0.5–1.0 μg kg−1), and orotracheal intubation was facilitated with pancuronium (0.1–0.15 mg kg−1). After induction of anaesthesia, an 8 cm 5Fr tipped catheter (Arrow International Inc., Reading, PA, USA) was inserted in the left or right radial artery and a triple lumen 16 cm 8.5Fr central venous catheter was inserted in the right internal jugular vein (Arrow International Inc., Reading, PA, USA). Pressure transducers (Medex Medical Ltd., Rossendale, Lancashire, UK) were placed on the midaxillary line and fixed to the operation table in order to keep the transducer at the atrial level all along the protocol. All transducers were set to zero to atmospheric pressure before each step of the protocol. A pulse oximeter probe (Oxymax, Tyco Healthcare Group LP, Pleasanton, CA, USA) was attached to the index of either the right or left hand and was wrapped to prevent outside light from interfering with the signal. Anaesthesia was maintained with continuous infusions of propofol (5–8 mg kg−1 h−1) and sufentanil (0.7–1.0 μg kg−1 h−1) in order to keep a bispectral index (BIS, Aspect A1000, Aspect Medical Systems Inc., Natick, MA, USA) between 40 and 50. All patients were ventilated in a volume-controlled mode with a tidal volume of 8–10 mL kg−1 of body weight at a frequency of 12–14 cycles min−1. Positive end-expiratory pressure was set between 0 and 4 cm H2O according to the attending physician.
Data recording and analysis
Arterial pressure and POP waveforms were recorded from a bedside monitor (Intellivue MP70, Philips Medical Systems, Suresnes, France) to a personal computer using data-acquisition software (TrendfaceSolo 1.1, Ixellence GmbH, Wildau, Germany) and were analysed by an observer blinded to the others’ haemodynamic data.
Respiratory variations in pulse pressure analysis
Pulse pressure (PP) was defined as the difference between systolic and diastolic pressure. Maximal (PPmax) and minimal (PPmin) values were determined over the same respiratory cycle. ΔPP was then calculated as described in : ΔPP = (PPmax − PPmin)/[(PPmax + PPmin)/2]. The measurements were repeated on three consecutive respiratory cycles and averaged for statistical analysis.
Respiratory variations in POP waveform amplitude analysis
The plethysmographic gain factor was held constant during POP waveforms recording so that the POP waveform amplitude did not depend on automatic gain adjustment. The signal quality was considered as optimal when the perfusion index displayed by the monitor was >1.0 as recommended by the manufacturer. POP waveform amplitude was measured on a beat-to-beat basis as the vertical distance between peak and preceding valley trough in the waveform and was expressed in pixels. Maximal POP (POPmax) and minimal POP (POPmin) were determined over the same respiratory cycle (Fig. 1). ΔPOP was then calculated as previously described : ΔPOP = (POPmax − POPmin)/[(POPmax + POPmin)/2]. The measurements were repeated on three consecutive respiratory cycles and averaged for statistical analysis.
Other haemodynamic measurements
At each step of the protocol, the following parameters were recorded: systolic arterial pressure (SAP), mean arterial pressure (MAP), diastolic arterial pressure (DAP), heart rate (HR), CVP and pulse oximeter oxygen saturation (SPO2).
All the patients were studied after induction of anaesthesia and before surgery. All haemodynamic parameters were recorded at each step of the protocol, after at least 2 min of stabilization. First, patients were studied in supine position. Then, they were raised in the anti-Trendelenburg position (head-up 30°) to induce relative depletion of central blood volume and, finally, in the Trendelenburg position (head-down 30°) to mimic a volume expansion [4,17].
All data are presented as mean ± standard deviation (SD). Changes in haemodynamic parameters induced by changes in loading conditions were assessed using a non-parametric U-test or a Wilcoxon signed rank sum test when appropriate. Spearman’s rank correlation was used to test linear correlation. Intra- and interobserver variability for the calculation of ΔPP and ΔPOP, and agreement between ΔPP and ΔPOP were assessed using Bland–Altman analysis . A P value <0.05 was considered as statistically significant. All statistical analyses were performed using SPSS 13.0 for Windows (SPSS, Chicago, IL, USA).
Eighteen patients underwent CABG and seven underwent abdominal aorta aneurysm surgery. None of these patients received vasoactive drugs. POP waveform was analysable in all patients and at each step of the protocol. Perfusion index was always >1.0, consistent with an optimal POP waveform signal. PPmax and POPmax appeared during inspiration in all patients. Correlation between ΔPP and ΔPOP over the 75 measurements was strong (r = 0.89; P < 0.01) (Fig. 2). Agreement between ΔPP and ΔPOP (Bland–Altman analysis) was 2.4 ± 4.4%. Bland–Altman graphical analysis showed that the higher the ΔPP and ΔPOP values, the higher the bias between the two methods (Fig. 2).
Changes in ΔPOP during changes in preload
Haemodynamic data at baseline, in anti-Trendelenburg position and in Trendelenburg position are shown in Table 1. As expected, we observed significant decreases in SAP (from 98 ± 13 to 87 ± 16 mmHg, P < 0.01), MAP (from 66 ± 9 to 58 ± 9 mmHg, P < 0.05) and DAP (from 50 ± 9 to 44 ± 8 mmHg, P < 0.01) from baseline to anti-Trendelenburg position. These changes were associated with increases in both ΔPP (from 10 ± 7 to 14 ± 8%, P < 0.01) and ΔPOP (from 11 ± 9 to 17 ± 12%, P < 0.01). From anti-Trendelenburg to Trendelenburg position, we observed significant increases in SAP (from 87 ± 16 mmHg to 99 ± 14 mmHg, P < 0.01), MAP (from 58 ± 9 to 67 ± 10 mmHg, P < 0.01) and DAP (from 44 ± 8 to 51 ± 9 mmHg, P < 0.01). At the same time, we observed significant decreases in both ΔPP (from 14 ± 8 to 7 ± 5%, P < 0.01) and ΔPOP (from 17 ± 12 to 9 ± 5%, P < 0.01) (Fig. 3).
Relationship between ΔPOP and percent increase in arterial pressure during change in preload condition
There was a statistically significant positive linear correlation between ΔPOP in anti-Trendelenburg position and percent changes in MAP between Trendelenburg and anti-Trendelenburg position (ΔMAP) (r = 0.82, P < 0.01) as well as between ΔPP and ΔMAP (r = 0.82, P < 0.01) (Fig. 4), indicating that the higher the ΔPOP and ΔPP before position change, the higher the increase in MAP. Moreover, we observed a statistically significant relationship between changes in ΔPP between anti-Trendelenburg position and Trendelenburg position and ΔMAP (r = 0.43, P = 0.02) and between changes in ΔPOP between anti-Trendelenburg position and Trendelenburg position and ΔMAP (r = 0.61, P < 0.01), indicating that the higher the decrease in ΔPP and ΔPOP after position change, the higher the increase in MAP. We observed no statistically significant relationship between CVP in anti-Trendelenburg position and ΔMAP (r = 0.06, P = 0.91), and between changes in CVP induced by position change and ΔMAP (r = 0.09, P = 0.88).
Intraobserver variability for ΔPP and ΔPOP assessment was 0.4 ± 3.6% and 1.0 ± 2.7%, respectively. Interobserver variability for ΔPP and ΔPOP assessment was 0.6 ± 3.8% and 1.2 ± 2.4%, respectively.
The present study is the first to show that ΔPOP (an index derived from the pulse oximeter: a widely available, non-invasive tool that is used daily in operating rooms) can be calculated in the operating room, is sensitive to changes in preload, can quantify the effects of change in preload condition on arterial pressure and is reproducible.
Fluid responsiveness prediction has been extensively studied in the intensive care unit (ICU) and in the operating room in mechanically ventilated patients [1–11,14]. It is now well accepted that dynamic parameters (relying on the cardiopulmonary interactions in patients under positive pressure ventilation) are better predictors of fluid responsiveness than static indicators (such as CVP, PCWP and LV end diastolic area). In patients under mechanical ventilation, positive pressure induces a decrease in right ventricle preload and, consequently, a decrease in right ventricle stroke volume (as described by the Frank–Starling relationship) and in pulmonary artery blood flow during inspiration. These phenomena are transmitted to the LV (pulmonary transit time) and are responsible for a decrease in LV stroke volume and output during expiration [6,7]. These respiratory variations in LV stroke volume or its surrogates have been shown to be predictive of response to volume expansion . Moreover, it has been shown that the respiratory variations in arterial PP are more predictive of response to volume expansion than the respiratory variations in SAP, since SAP not only depends on respiration-induced changes in stroke volume but also on respiration-induced changes in intrathoracic pressure . The main limitations of these indices are that they are invasive, technically challenging or not widely available. Arterial catheters, which are mandatory for ΔPP calculation and monitoring, have been shown to be responsible for infections and thrombosis [12,13] and their use is limited in the operating room. On the other hand, transoesophageal echocardiography requires a long training  and is not widely available. Thus, these indices are not used daily in the operating rooms.
Pulse oximeters are part of the routine monitoring during anaesthesia and in the ICU . The main interest of the pulse oximeter is to display arterial oxygenation continuously. It has previously been shown that the respiratory variations in the POP waveform peaks are related to the respiratory variations in SAP [21,22]. More recently, our team has demonstrated that the respiratory variations in POP waveform amplitude (ΔPOP) were closely related to the respiratory variations in arterial pulse pressure (ΔPP) in the critical care setting [15,16]. However, ΔPOP had never been studied in the operating room and it had never been shown that ΔPOP is sensitive to changes in preload. The signal displayed by the pulse oximeter is proportional to light absorption between the nail and the anterior face of the finger. During systole, the amount of haemoglobin present in the fingertip is increased and, consequently, light absorption is decreased. An inverse phenomenon is observed during diastole. Thus, the POP waveform depends on the arterial pulse . Apart from this, the POP waveform also depends on outside light absorption, the signal-processing system and finger perfusion. In our study, attention was paid to all these parameters. The pulse oximeter was wrapped in order to prevent outside light from interfering with the signal, gain was maintained constant during POP waveform recording, and the perfusion index was always >1.0. Consequently, feasibility was 100% in our sample of patients and no patient was excluded. On the other hand, none of our patients received vasoactive drugs such as epinephrine or norepinephrine. Feasibility should be evaluated in this population of patients. Another important point is that the POP waveform is unitless. However, as ΔPOP assesses relative changes in POP waveform amplitude any unit can be used. In the present study, we chose to measure the POP waveform amplitude as pixels since this is the unit that the software-acquisition system uses to record the curve.
In the present study, we confirmed that there is a strong relationship between ΔPP and ΔPOP. However, we found that the agreement between the two indices was weak, especially for the highest values, and that ΔPOP overestimates ΔPP in this situation. Consequently, ΔPOP cannot be used to quantify ΔPP (agreement was 2.4 ± 4.5% vs. 0.8 ± 3.5% in our previous study ). We found that both ΔPOP and ΔPP in anti-Trendelenburg position were related to percent increase in MAP between Trendelenburg and anti-Trendelenburg position, suggesting that ΔPOP could be a useful indicator of fluid responsiveness. However, further studies including standardized volume expansion and cardiac output measurements are required to answer this question. As expected, CVP and MAP were not related to percent increase in MAP between Trendelenburg and anti-Trendelenburg position despite their sensitivity to changes in loading conditions (Table 1). ΔPOP could be a useful tool to assess fluid responsiveness in the operating room. It is non-invasive and widely available. Acquiring ΔPOP waveform curve is not commercially available but we can postulate that automated calculation could be obtained in the near future.
We did not assess systemic vascular resistance in our sample of patients. However, POP waveform amplitude relies on this parameter. Consequently, our results cannot be exported to situations where systemic vascular resistances are different, such as patients under vasoactive drugs. We did not measure cardiac output and did not perform standardized volume expansion. Consequently, we cannot conclude concerning the ability of ΔPOP to predict fluid responsiveness. However, the aim of our study was to test the sensitivity of ΔPOP to changes in preload conditions and to describe the technical methodology allowing us to record and analyse the POP waveform in conditions where confounding variables (gain, outside light and signal quality) are controlled. Future studies are planned to assess the ability of ΔPOP to predict fluid responsiveness in the operating room. We used the change from anti-Trendelenburg to Trendelenburg position to mimic volume expansion. These manoeuvres have been shown to be able to mimic volume expansion  and have already been used in studies designed to assess fluid responsiveness in the operating room [4,17]. Leg rising may have been an alternative to this technique. However, if leg rising may be useful in the ICU, it is much more difficult to perform in the operating room, and we believe that anti-Trendelenburg and Trendelenburg positions are closer to the daily practice of the anaesthesiologists. We excluded patients with LV failure and our data cannot be exported to such patients. In the same idea, ΔPOP interpretation in patients with right ventricular failure must be cautious. Since ΔPOP relies on a beat-to-beat analysis, it cannot be used in patients with cardiac arrhythmia. Patients in our study were ventilated under volume-controlled mode, and our data cannot be exported to other situations. Finally, we studied cardiac surgery and major vascular surgery patients only. Whether ΔPOP can be used in other settings is not answered. However, we can postulate that this sample represents most of the patients that undergo major surgery and for whom fluid responsiveness assessment is important.
In conclusion, ΔPOP appears as a non-invasive, widely available and reproducible index of changes in loading conditions, fluid responsiveness and quantification of the effects of volume expansion on MAP. ΔPOP has potential clinical applications.
The authors wish to thank all the physicians and nurse anaesthetists from the Department of Anaesthesiology (Louis Pradel Hospital, Hospices Civils de Lyon, Lyon, France) for their help and support during this study, and Jean-Fran#x00E7;ois Lemaire from Philips Medical System for technical support. No financial support.
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