Skip to main content
Download PDF

Use of near-infrared spectroscopy during a vascular occlusion test to assess the microcirculatory response during fluid challenge

Critical Care volume 15, Article number: R214 (2011) Cite this article

Abstract

Introduction

Adequate volume expansion (VE) in patients with evidence of hypoperfusion should be aimed not only at achieving an increase in stroke volume (SV) and cardiac index (CI) but also at improved tissue perfusion and oxygenation. Our aim in this study was to assess the dynamic changes in muscle tissue oxygen saturation (StO2) during hypovolaemia and in response to VE.

Methods

We conducted a prospective study of 42 fluid challenges in patients undergoing major abdominal surgery with evidence of hypovolaemia, defined as pulse pressure variation (PPV) >13% and SV variation (SVV) >12%. CI, SV, SVV (FloTrac/Vigileo) and PPV were measured before and after VE. Fluid responsiveness was defined as an increase of SV >15% after a 500-mL colloid infusion over 15 minutes. In all patients, the muscle StO2 and its changes during a standardised vascular occlusion test were analysed using a near-infrared spectroscopy device after anaesthesia induction (which defined the baseline state) and before and after each VE.

Results

No patients were preload-responsive after anaesthesia induction. Twenty-nine of forty-two fluid challenges (69%) were positive for VE, with a statistically significant (P < 0.001) difference in SV changes between positive and negative responses to VE. There was a statistically significant difference in PPV and SVV values before VE in the positive and negative fluid responses [PPV: 16% (15% to 18%) vs. 14% (13% to 15%), P = 0.001; and SVV: 14% (13% to 16%) vs. 16% (15% to 16%), P = 0.03 or positive and negative fluid responses, respectively]. Data are presented as medians and 25th and 75th percentiles Before VE there was no significant difference in StO2 values relative to baseline [86% (78% to 88%) vs. 84% (77% to 91%), P = 0.83], without a significant difference (P = 0.36) between positive and negative fluid challenges. Hypovolaemia was associated with a significant reduction (P = 0.004) in StO2 recovery slope, with a significant difference (P = 0.02) between positive and negative fluid challenges. The VE-induced increase in the StO2 recovery slope was 62 ± 49% (P < 0.001) for positive fluid challenges and 26 ± 34% (P = 0.04) for negative fluid challenges.

Conclusions

Hypovolaemia significantly affects the muscle StO2 recovery slope. Restoring effective intravascular volume with fluid loading significantly improves the StO2 recovery slope, despite apparently ineffective changes in systemic haemodynamics.

Introduction

Fluid loading is a first-line therapy when hypovolaemia is suspected in patients with evidence of hypoperfusion, and it is commonly used in operating rooms and ICUs. The maintenance of adequate oxygen delivery and tissue perfusion is considered a primary goal in volume replacement [1] while avoiding fluid overload, which may lead to interstitial oedema [2]. Several studies have demonstrated the superiority of dynamic preload indices, such as pulse pressure variation (PPV) and stroke volume variation (SVV), rather than static indices for individualised evaluation of patients who are likely to benefit from an increase in preloading [3–5]. In addition, the use of SVV or PPV can reduce organ failure during individualised, goal-directed fluid optimisation [6, 7].

Although a fluid challenge should correct macrohaemodynamics (stroke volume (SV) and cardiac output (CO)), the ideal volume replacement strategy should also improve microcirculation perfusion and tissue oxygenation. Hypovolaemia during major surgery or sepsis leads to inadequate perfusion of the microcirculation and insufficient oxygen availability to meet tissue oxygen needs [8]. However, previous reports have suggested a mismatch between global haemodynamics and microcirculation and a potential independence of macrocirculation and microcirculation during fluid loading [9, 10]. Thus, fluid administration may correct systemic haemodynamic variables but not regional and microcirculatory oxygenation and perfusion [11].

Microcirculatory haemoglobin and oxygen availability can be measured by use of near-infrared spectroscopy (NIRS) [12], a noninvasive technique that can be performed at the bedside. In this method, the differential absorption of infrared light at two specific wavelengths (680 and 800 nm) by deoxyhaemoglobin is used to define the haemoglobin saturation level in vessels located in the tissue volume that is illuminated by the probe [13]. The dynamic response to tissue oxygen saturation (StO2), especially the StO2 recovery slope, during a standardised vascular occlusion test (VOT) is assumed to reflect the recruitment of microvessels in response to a local hypoxic stimulus [14]. Researchers in previous studies found that the StO2 recovery slope was a prognostic factor in septic patients [15] and was useful in evaluating the response to norepinephrine in severely hypotensive septic shock patients [16]. However, there is no information on the StO2 response during fluid resuscitation and in the presence of abnormal vascular reactivity in patients undergoing major surgery. Our aim in this study was to assess thenar muscle StO2 changes during VOT in responses to hypovolaemia and to assess the dynamic responses of the StO2 recovery slope in response to volume expansion (VE).

Materials and methods

This study was approved by the Ethics Committee of our institution. The requirement for written consent was waived, as no interventions were required. The protocol was part of our routine practise in patients undergoing major abdominal surgery. All patients had arterial catheters for invasive blood pressure monitoring. The haemodynamic measurements and fluid loading are routinely used to assess fluid responsiveness.

We studied 24 consecutive Caucasian patients (13 males and 11 females), all with American Society of Anesthesiology Physical Status scores of 2 or 3, who were undergoing major abdominal surgery. Patients with permanent cardiac arrhythmia, aortic regurgitation, body mass index ≥35 kg/m2, those receiving β-blocker therapy and those with contraindications for VOT (arteriovenous shunt) were excluded. The surgical procedures that our 24 patients underwent were duodenopancreatectomy (n = 9), colectomy (n = 10), gastrectomy (n = 3) and hepatectomy (n = 2), and all were scheduled for tumour resection.

Study design

Standardised anaesthetic management was applied for all patients. General anaesthesia was induced with propofol (2 to 3 mg/kg), sufentanil (0.2 to 0.3 μg/kg) and cisatracurium (0.15 mg/kg) to facilitate endotracheal intubation and was maintained with a continuous infusion of propofol and sufentanil (using target-controlled infusion) to target a bispectral index of 40 to 50 (Aspect A-1000; Aspect Medical Systems, Norwood, MA, USA). Anaesthetic concentrations were based on predicted body weight. After tracheal intubation all patients were ventilated in the supine position in controlled volume mode using a tidal volume of 8 to 10 mL/kg of predicted body weight, a respiratory rate adjusted to maintain an end-tidal carbon dioxide tension of 30 to 35 mmHg, an inspiratory/expiratory ratio of 1:2 and a positive end-expiratory pressure of 5 cmH2O. The inspiratory oxygen fraction was set at 0.5 (Datex-Ohmeda Avance; GE Healthcare, Helsinki, Finland). Ventilatory settings were kept constant during the entire study period. Intraoperative fluid intake was maintained using 8 mL/kg/hour of lactated Ringer's solution. Normothermia was maintained during the entire procedure using a convective air warming system (WarmTouch; Tyco Healthcare, Pleasanton, CA, USA).

Measurements

Standard monitoring included measurements with a five-lead continuous electrocardiograph and measurements of heart rate, peripheral oxygen saturation and end-tidal partial carbon dioxide tension. As part of our routine haemodynamic monitoring during major surgery, all patients intubated with a 20-gauge, 8-cm arterial catheter (Arrow International, Reading, PA, USA), which was inserted into the left radial artery. Arterial pressure was measured using a high-fidelity dedicated pressure transducer (FloTrac Sensor; Edwards Lifesciences, Irvine, CA, USA) connected to a Vigileo version 3.01 monitor (Edwards Lifesciences) and a bedside monitor (IntelliVue MP50; Philips Medical Systems, Suresnes, France). The pressure transducer was levelled at the midaxillary line, zeroed at atmospheric pressure and fixed to the operating table so that the transducer was at the level of the atrium during the study protocol. In all patients, automated online PPV and SVV were measured continuously from the algorithm integrated in the monitors as described in detail elsewhere [5, 17, 18]. The following variables were recorded before and after each episode of VE: cardiac index (CI), SV, systolic arterial blood pressure, mean arterial blood pressure (MAP) and diastolic arterial blood pressure.

Near-infrared spectroscopy and vascular occlusion test

The StO2 was continuously and noninvasively measured using the InSpectra™ StO2 System (model 650; Hutchinson Technology Inc., Hutchinson, MN, USA). A 15-mm NIRS sensor probe (model 1615; Hutchinson Technology Inc.) placed on the right thenar eminence allowed us to measure StO2 at a depth of 14 mm. StO2 values were recorded continuously and stored every two seconds by the NIRS monitor. StO2 stability was defined as variation <2% over 30 seconds (pre-VOT StO2) [19]. The values were then transferred to a personal computer and analysed using a dedicated program (InSpectra Analysis Program version 4.0; Hutchinson Technology Inc.).

The VOT was performed by using a sphygmomanometer placed around the upper arm. The sphygmomanometer was rapidly inflated to 50 mmHg more than systolic pressure and was kept inflated until StO2 decreased to 40% [16]. Upon the completion of the ischaemic period, the sphygmomanometer was rapidly deflated and the StO2 response was followed until it returned to the baseline value. For every test, the following VOT-derived StO2 variables were calculated automatically by the InSpectra Analysis Program: the StO2 desaturation slope (desStO2, expressed as percentage per minute), the StO2 recovery slope (recStO2, expressed as percentage per second) and the hyperaemia recovery area (Figure 1).

Figure 1
figure 1

Response to tissue oxygen saturation during a vascular occlusion test. StO2, tissue oxygen saturation. dt = time to reach the StO2 baseline (preVOT) value. The two-way arrow is dt.

Full size image

Experimental protocol

In all patients, fluid responsiveness was evaluated before induction of anaesthesia by use of a passive leg raising test as described previously [20, 21]. In cases of passive leg raising-induced changes in SV >16%, a 250-mL fluid bolus of hydroxyethylstarch (HES 130/0.4 (Voluven); Fresenius-Kabi AG, Bad Homburg, Germany) was delivered over a period of 15 minutes before anaesthesia induction. After anaesthesia induction and a five-minute period of stabilisation (which was defined as the baseline state), the first set of measurements was recorded (systolic arterial pressure (PAS), diastolic arterial pressure (PAD) ultrasound, mean arterial pressure (PAM), CI, SV, baseline StO2, desStO2, recStO2 and hyperaemia recovery area). For each episode of suspected hypovolaemia, defined as PPV >13% and SVV >12% [5], VE was performed using a 500-mL bolus of hydroxyethylstarch delivered over a 15-minute period. Fluid responsiveness was defined as an increase in SV of ≥15% [3]. Before and after each VE, an additional set of measurements was obtained (PAS, PAD, PAM, CI, SV, baseline StO2, desStO2, recStO2 and hyperaemia recovery area).

Statistical analysis

We subdivided the population into two groups based on the percentage increase in SV after intravascular VE: (1) positive response to fluid challenge when SV was ≥15% and (2) negative response to fluid challenge when SV was <15%. The results were tested for normality using the one-sample Kolmogorov-Smirnov goodness-of-fit test. Normally distributed data are presented as means ± standard deviation (SD) or and non-normally distributed data as medians with 25th and 75th percentiles. The χ2 test was used to compare categorical data. Quantitative data were compared using analysis of variance (ANOVA) when the distributions were normal and the variances were equivalent; otherwise, they were compared using the Kruskal-Wallis H-test. The paired Student's t-test was used to compare data at two different time points, with adjustment of the t-statistics whenever indicated, to take into account the presence of several measurements in the patient studied. The within-group effect of fluid loading was analysed using ANOVA or the Kruskal-Wallis H-test as appropriate. To assess the reproducibility of VOT-derived StO2 variables, the coefficient of variability was calculated to obtain the StO2 recovery slopes for both positive and negative responses to fluid loading [22]. Differences between groups were assessed using Student's t-test or a Mann-Whitney U test as appropriate. A mixed model using the restricted maximum likelihood method was used to estimate covariance components, taking into account the random effects of patient and time (before and after fluid loading) and the covariate interaction group × time. The receiver operating characteristic (ROC) curve was also generated for the StO2 recovery slope, and area under the ROC curve, sensitivity, specificity, positive predictive value and negative predictive value were calculated for recStO2. When applicable, correlations were evaluated on the basis of the Spearman's ρ coefficient. Statistical analysis was performed using SEM version 2.0 software [23]. P < 0.05 was considered statistically significant.

Results

Table 1 summarises the baseline demographic clinical characteristics of the 24 patients. On the basis of the passive leg-raising test, no patient was considered preload-dependent before induction of anaesthesia. The duration of the surgical procedures ranged from 75 to 300 minutes (median, 120 minutes). Table 2 shows the baseline macrocirculatory and thenar StO2 curve variables, which were recorded after induction of anaesthesia. No patients required vasopressor therapy during the study period.

Table 1 General characteristics of the study population before induction of anaesthesiaa
Full size table
Table 2 Macrocirculatory and muscle tissue oxygen saturation curve variables at baseline after induction of anaesthesiaa
Full size table

Macrocirculatory variables

Compared to baseline values, hypovolaemia was associated with a significant reduction in SV (70 ± 20 mL vs. 58 ± 12 mL; P = 0.038), but CO was not significantly different (5.0 ± 0.9 L/minute vs. 4.5 ± 0.9 L/minute; P = 0.13). Before VE, PPV was 16% (range, 14% to 18%) and SVV was 15% (range, 14% to 16%). A total of 42 fluid challenges (one to three per patient) were performed. In the whole study population, delivery of a fluid bolus was associated with a 27.7 ± 20% increase in SV (P < 0.001). According to the expected 15% increase in SV, 29 (69%) of 42 fluid challenges were positive in relation to VE (mean change in SV: 36 ± 17%) and 13 fluid challenges were negative (mean change in SV: 10 ± 16%). There was a statistically significant difference (P < 0.001) in SV changes between positive and negative responses to fluid challenge. With regard to the type of fluid response, there was a statistically significant difference in PPV and SVV values before VE for the positive and negative fluid responses [PPV: 16% (15% to 18%) vs. 14% (13% to 15%), P = 0.001, and SVV: 14% (13% to 16%) vs. 16% (15% to 16%), P = 0.03, for positive and negative fluid responses, respectively]. In the positive fluid challenge, VE also induced significant changes in CO and MAP, but there were no evident differences induced by the negative fluid challenge (Table 3). There was no statistically significant difference in haemoglobin levels during VE (12.6 ± 2 g/dL before VE vs. 11.7 ± 2 g/dL after VE; P = 0.10).

Table 3 Changes in macrocirculatory and microcirculatory variables during fluid challengea
Full size table

Near-infrared spectroscopy variables

There was no significant difference in the mean StO2 values before and after induction of anaesthesia [80% (77% to 83%) vs. 86% (78% to 88%); P = 0.15]. Table 3 shows the mean values of the NIRS variables before and after VE. Before VE there was no significant difference in pre-VOT StO2 values during hypovolaemia relative to baseline [86% (78% to 88%) vs. 84% (77% to 91%); P = 0.83] and no significant difference between positive and negative fluid challenges (P = 0.36). There was also no significant difference in desStO2 values before VE relative to baseline (P = 0.53) or between positive and negative fluid challenges (Table 3). Hypovolaemia was associated with a significant reduction in recStO2 values relative to baseline [5.1% (3.89% to 5.53%)/second vs. 3.57% (2.71% to 4.58%)/second; P = 0.004]. There was also a significant difference in recStO2 before VE in the positive and negative fluid challenges (P = 0.02) (Table 3). Before VE there was no significant difference in StO2 hyperaemia recovery area relative to baseline (P = 0.30) or between positive and negative fluid challenges (P = 0.29) (Table 3).

There was no significant change in pre-VOT StO2 after VE (P = 0.22) in the positive and negative fluid challenges (Table 3). VE resulted in an overall increase in recStO2 of 50 ± 47% (P < 0.001) (Figure 2). For positive fluid challenge, the VE-induced increase in recStO2 was 62 ± 49% (P < 0.001); for negative fluid challenge, the VE-induced increase in recStO2 was 26 ± 34% (P = 0.04) (Figure 3). There was a significant difference (P = 0.016) in VE-induced increase in recStO2 between positive and negative responses to VE. The coefficient of variability of recStO2 was 25% and 33% for the negative fluid challenge group and the positive fluid challenge group, respectively, with a statistically significant difference between groups (P = 0.0453). The results obtained with the mixed model showed a significant interaction between covariates (P = 0.039). Variance attributable to each random effect was 45.27% for patient, 49.60% for time and 5.13% for residual variability. The area under the ROC curve of recStO2 was 0.740 (95% confidence interval, 0.56 to 0.91). The optimal cutoff value for recStO2 was 18% (sensitivity 85.7%, specificity 61.5%, positive predictive value 82.8% and negative predictive value 66.7%). There were no significant changes in desStO2 or StO2 hyperaemia area during VE and no differences in positive and negative fluid challenges (Table 3).

Figure 2
figure 2

Changes in the tissue oxygen saturation recovery slope during fluid challenge. Boxplots showing the median (horizontal lines within the boxes) 75th and 25th percentiles (upper and lower edges of the boxes), maximum and minimum values (upper and lower bars), and means (dark diamonds within the boxes). RecStO2, StO2 recovery slope; StO2, tissue oxygen saturation.

Full size image
Figure 3
figure 3

Changes in the thenar tissue oxygen saturation recovery slope before and after volume expansion for positive (A) and negative (B) fluid events. A positive fluid event was characterised by a 15% increase in stroke volume with pulse pressure variation >13% and stroke volume variation >12%. Boxplots show the medians (horizontal lines within the boxes) with 75th and 25th percentiles (upper and lower edges of the boxes), maximum and minimum values (upper and lower bars) and means (dark diamonds within the boxes). RecStO2, tissue oxygen saturation recovery slope.

Full size image

Changes in stroke volume, cardiac output and tissue oxygen saturation recovery slope during volume expansion

No significant relationship was observed between VE-induced changes in SV and changes in the StO2 recovery slope (Spearman's ρ coefficient = 0.15, P = 0.33) or between VE-induced changes in CO and the StO2 recovery slope (Figure 4).

Figure 4
figure 4

Relationship between changes in cardiac output and changes in the tissue oxygen saturation recovery slope in all fluid challenges. x-axis: Difference in cardiac output after and before the fluid challenge (L/minute). y-axis: Difference in the tissue oxygen saturation (StO2) recovery slope after and before the fluid challenge (percentage per second). Closed circles represent positive fluid challenges. Open circles represent negative fluid challenges. Spearman's ρ coefficient = 0.10, P = 0.56.

Full size image

Discussion

The main findings of our study are that (1) hypovolaemia is associated with significant alterations in NIRS variables measured at the thenar eminence and (2) restoring effective intravascular volume with fluid loading significantly improves the StO2 recovery slope. Furthermore, during apparently ineffective fluid loading (that is, without significant changes in systemic haemodynamics), VE also leads to a significant improvement in the StO2 recovery slope.

Monitoring the effects of fluid therapy at the bedside remains a cornerstone in the operating room and ICU. Although there are limitations [24, 25], previous research indicates that dynamic indices (based on flow or preload parameters) are useful for predicting individualised fluid responsiveness [26] and prevention of excessive fluid intake. However, preload responsiveness does not equate to fluid requirement [27]. We used a PPV value >13% and a SVV value >12% to predict fluid responsiveness during mechanical ventilation with a tidal volume >8 mL/kg [5], but 69% of the significant changes in SV were induced by VE and increased CO. Conversely, one-third of the volume challenges were not accompanied by a significant increase in SV, although the patients were supposed to be on the steep portion of the Frank-Starling curve. This was very close to the range that was previously reported (40% to 72%) [28]. We used a fixed cutoff value for SV (≥15% increase) to dichotomise positive and negative responses to fluid loads [3]. Nevertheless, because the response to a given volume load is a continuum ranging from no increase to a large increase in SV, it can be assumed that a lower threshold value (10% instead of 15%) would have led to a different level of significance. In addition, although we used generation software whose accuracy has been well established to estimate CO in surgical patients, concerns have been raised regarding its reliability to track changes in SV or CO in some patients [29, 30]. Taken together, this information could explain, at least in part, the absence of significant changes in some measurements of CO despite reductions in both PPV and SVV values with fluid loading. Nevertheless, fluid resuscitation is designed to restore systemic haemodynamics, so determining whether a patient is preload-dependent provides only part of the answer, because fluid challenge should be performed within the context of known or suspected tissue hypoperfusion [27].

In our study, preload responsiveness was associated with a 25% decrease in the StO2 recovery slope. Previous data have suggested that hypovolaemia leads to inadequate perfusion of the microcirculation, which results in insufficient oxygen availability [31]. The StO2 recovery slope is believed to reflect the microvascular blood flow response to a transient tissue hypoxia-induced oxygen deficit created by the ischaemic stimulus [19]. Vasodilatation of arterioles and recruitment of closed capillaries are responsible for this reactive hyperaemia [16]. We used a fixed StO2 target of 40%, as recommended by other investigators [32, 33], and found that the baseline StO2 recovery slopes were very close to those previously reported in healthy volunteers [15, 33]. Previous studies have suggested that StO2 values can be used to detect changes in peripheral tissue oxygenation resulting from a lower-body negative pressure model of simulated central hypovolaemia [34]. A reduction in muscle oxygen was also found to be an earlier indicator of hypovolaemia than the standard clinical measures (heart rate and blood pressure) [35]. Nevertheless, previous studies have suggested that resting StO2 values are insensitive to the assessment of tissue hypoperfusion [36]. Our results, which indicate similar resting StO2 values in spite of an insufficient flow, support the findings of these previous studies.

We also found a 50% increase in the StO2 recovery slope with fluid loading. This suggests that restoration of intravascular volume in preload-dependent patients improves muscle tissue oxygenation and increases SV and CO. This finding may have important clinical implications, because the ultimate goal of resuscitation should be improvement of tissue oxygenation and perfusion. We hypothesise that this is due to improved microvessel recruitment during the fluid challenge, together with an increase venular blood compartment volume, despite the absence of macrocirculatory changes. It is unlikely that changes in the StO2 recovery slope with VE were due to changes in rheologic factors, because we found no significant differences in haemoglobin levels before and after VE. In addition, Creteur et al. [37] recently performed VOT before and after red blood cell transfusion and reported no differences despite the different haemoglobin levels. It must be stressed that the StO2 recovery slope remained low, or even decreased, in some of our patients after VE (Figure 2), even though CO improved with VE. This is in agreement with the hypothesis that VE can cause apparent improvement in systemic parameters, even though microcirculation and tissue oxygenation remain uncorrected. In addition, a 500-mL fluid infusion might have been insufficient in some patients.

Our study has several limitations that need to be addressed. First, we studied only surgical patients. Although major surgery is associated with significant impairment in both microvascular flow and tissue oxygenation [38], our data should not be extrapolated to other, more specific patient populations (with an increased intersubject variability) until further investigations are carried out. In addition, repeated measurements were performed in some patients. An advantage of analysing the response to repeated fluid loading is that it reproduces daily practice, when fluid responsiveness has to be evaluated in the same patient on different occasions. Second, we did not evaluate patient outcomes. In other words, we did not determine whether higher StO2 recovery slopes were associated reduced organ failure. Third, we placed the NIRS probe on the thenar eminence, a region with very little fat, and therefore there was little interference with the spectroscopic measurements. Although measurement at this site have very low variance [39] and even though we used a standardised VOT, the interpretation of VOT should be viewed with caution. Fourth, in all patients, anaesthesia was maintained by continuous infusion of propofol, which previous research has indicated increases blood flow to the muscles and the vascular bed [40]. Although all the VOTs were performed while patients were under similar conditions of anaesthesia, we cannot exclude the possibility that our results would have been different if different drugs had been used. Fifth, none of the patients were given vasoactive drugs during the study protocol. Although norepinephrine can improve the StO2 recovery slope [16], the effects of VE in such conditions has not been evaluated.

In conclusion, with respect to our study conditions, we found that preload dependence is associated with significant changes in the StO2 recovery slope based on NIRS measurements at the thenar eminence. In addition, our findings suggest that VE can improve tissue oxygenation. Taken together, our results suggest that measurements of StO2 using a standardised VOT could be a useful complementary tool along with the dynamic indices to improve fluid optimisation. Further studies are warranted to validate this hypothesis.

Key messages

  • When hypovolaemia is suspected, fluid loading should restore systemic haemodynamics, but the ultimate goal of fluid resuscitation should be improvement of tissue perfusion and oxygenation.

  • Preload dependence is associated with significant alterations in the StO2 recovery slope based on NIRS measurements, suggesting the coexistence of microcirculatory abnormalities.

  • VE induces positive effects at the level of muscle tissue oxygenation as measured by the elevation of the StO2 recovery slope.

  • The StO2 recovery slope better assesses the effects of hypovolaemia and fluid loading on muscle tissue oxygenation than other NIRS-derived variables.

Abbreviations

CI:

cardiac index

CO:

cardiac output

desStO2:

StO2 desaturation slope

NIRS:

near-infrared spectroscopy

PPV:

pulse pressure variation

recStO2:

StO2 recovery slope

StO2:

tissue oxygen saturation

SV:

stroke volume

SVV:

stroke volume variation

VE:

volume expansion.

References

  1. Vallet B, Futier E: Perioperative oxygen therapy and oxygen utilization. Curr Opin Crit Care 2010, 14: 359-364.

    Article  Google Scholar 

  2. Kimberger O, Arnberger M, Brandt S, Plock J, Sigurdsson GH, Kurz A, Hiltebrand L: Goal-directed colloid administration improves the microcirculation of healthy and perianastomotic colon. Anesthesiology 2009, 110: 496-504. 10.1097/ALN.0b013e31819841f6

    Article  CAS  PubMed  Google Scholar 

  3. Marik PE, Cavallazzi R, Vasu T, Hirani A: Dynamic changes in arterial waveform derived variables and fluid responsiveness in mechanically ventilated patients: a systematic review of the literature. Crit Care Med 2009, 37: 2642-2647. 10.1097/CCM.0b013e3181a590da

    Article  PubMed  Google Scholar 

  4. Cannesson M, Vallet B, Michard F: Pulse pressure variation and stroke volume variation: from flying blind to flying right? Br J Anaesth 2009, 103: 896-899. 10.1093/bja/aep321

    Article  CAS  PubMed  Google Scholar 

  5. Derichard A, Robin E, Tavernier B, Costecalde M, Fleyfel M, Onimus J, Lebuffe G, Chambon JP, Vallet B: Automated pulse pressure and stroke volume variations from radial artery: evaluation during major abdominal surgery. Br J Anaesth 2009, 103: 678-684. 10.1093/bja/aep267

    Article  CAS  PubMed  Google Scholar 

  6. Benes J, Chytra I, Altmann A, Hluchy M, Kasal E, Svitak R, Pradl R, Stepan M: Intraoperative fluid optimization using stroke volume variation in high risk surgical patients: results of prospective randomized study. Crit Care 2010, 14: R118. 10.1186/cc9070

    Article  PubMed Central  PubMed  Google Scholar 

  7. Lopes MR, Oliveira MA, Pereira VO, Lemos IP, Auler JO Jr, Michard F: Goal-directed fluid management based on pulse pressure variation monitoring during high-risk surgery: a pilot randomized controlled trial. Crit Care 2007, 11: R100. 10.1186/cc6117

    Article  PubMed Central  PubMed  Google Scholar 

  8. Edouard AR, Degrémont AC, Duranteau J, Pussard E, Berdeaux A, Samii K: Heterogeneous regional vascular responses to simulated transient hypovolemia in man. Intensive Care Med 1994, 20: 414-420. 10.1007/BF01710651

    Article  CAS  PubMed  Google Scholar 

  9. Vallée F, Mateo J, Dubreuil G, Poussant T, Tachon G, Ouanounou I, Payen D: Cutaneous ear lobe PCO 2 at 37°C to evaluate microperfusion in patients with septic shock. Chest 2010, 138: 1062-1070. 10.1378/chest.09-2690

    Article  PubMed  Google Scholar 

  10. De Backer D, Ortiz JA, Salgado D: Coupling microcirculation to systemic hemodynamics. Curr Opin Crit Care 2010, 16: 250-254. 10.1097/MCC.0b013e3283383621

    Article  PubMed  Google Scholar 

  11. Boldt J, Ince C: The impact of fluid therapy on microcirculation and tissue oxygenation in hypovolemic patients: a review. Intensive Care Med 2010, 36: 1299-1308. 10.1007/s00134-010-1912-7

    Article  PubMed  Google Scholar 

  12. Jobsis FF: Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters. Science 1977, 198: 1264-1267. 10.1126/science.929199

    Article  CAS  PubMed  Google Scholar 

  13. Ward KR, Ivatury RR, Barbee RW, Terner J, Pittman R, Filho IP, Spiess B: Near infrared spectroscopy for evaluation of the trauma patient: a technology review. Resuscitation 2006, 68: 27-44. 10.1016/j.resuscitation.2005.06.022

    Article  PubMed  Google Scholar 

  14. De Blasi RA, Palmisani S, Alampi D, Mercieri M, Romano R, Collini S, Pinto G: Microvascular dysfunction and skeletal muscle oxygenation assessed by phase-modulation near-infrared spectroscopy in patients with septic shock. Intensive Care Med 2005, 31: 1661-1668. 10.1007/s00134-005-2822-y

    Article  PubMed  Google Scholar 

  15. Creteur J, Carollo T, Soldati G, Buchele G, De Backer D, Vincent JL: The prognostic value of muscle StO 2 in septic patients. Intensive Care Med 2007, 33: 1549-1556. 10.1007/s00134-007-0739-3

    Article  PubMed  Google Scholar 

  16. Georger JF, Hamzaoui O, Chaari A, Maizel J, Richard C, Teboul JL: Restoring arterial pressure with norepinephrine improves muscle tissue oxygenation assessed by near-infrared spectroscopy in severely hypotensive septic patients. Intensive Care Med 2010, 36: 1882-1889. 10.1007/s00134-010-2013-3

    Article  CAS  PubMed  Google Scholar 

  17. Aboy M, McNames J, Thong T, Phillips CR, Ellenby MS, Goldstein B: A novel algorithm to estimate the pulse pressure variation index ΔPP. IEEE Trans Biomed Eng 2004, 51: 2198-2203. 10.1109/TBME.2004.834295

    Article  PubMed  Google Scholar 

  18. Manecke GR: Edwards FloTrac™ sensor and Vigileo™ monitor: easy, accurate, reliable cardiac output assessment using the arterial pulse wave. Expert Rev Med Devices 2005, 2: 523-527. 10.1586/17434440.2.5.523

    Article  PubMed  Google Scholar 

  19. Gómez H, Torres A, Polanco P, Kim HK, Zenker S, Puyana JC, Pinsky MR: Use of non-invasive NIRS during a vascular occlusion test to assess dynamic tissue O 2 saturation response. Intensive Care Med 2008, 34: 1600-1607. 10.1007/s00134-008-1145-1

    Article  PubMed  Google Scholar 

  20. Biais M, Vidil L, Sarrabay P, Cottenceau V, Revel P, Sztark F: Changes in stroke volume induced by passive leg raising in spontaneously breathing patients: comparison between echocardiography and Vigileo™/FloTrac™ device. Crit Care 2009, 13: R195. 10.1186/cc8195

    Article  PubMed Central  PubMed  Google Scholar 

  21. Jabot J, Teboul JL, Richard C, Monnet X: Passive leg raising for predicting fluid responsiveness: importance of the postural change. Intensive Care Med 2009, 35: 85-90. 10.1007/s00134-008-1293-3

    Article  PubMed  Google Scholar 

  22. Reed GF, Lynn F, Meade BD: Use of coefficient of variation in assessing variability of quantitative assays. Clin Diagn Lab Immunol 2002, 9: 1235-1239.

    PubMed Central  PubMed  Google Scholar 

  23. Kwiatkowski F, Girard M, Hacene K, Berlie J: [Sem: a suitable statistical software adapted for research in oncology] [in French]. Bull Cancer 2000, 87: 715-721.

    CAS  PubMed  Google Scholar 

  24. Muller L, Louart G, Bousquet PJ, Candela D, Zoric L, de La Coussaye JE, Jaber S, Lefrant JY: The influence of the airway driving pressure on pulsed pressure variation as a predictor of fluid responsiveness. Intensive Care Med 2010, 36: 496-503. 10.1007/s00134-009-1686-y

    Article  PubMed  Google Scholar 

  25. De Backer D, Heenen S, Piagnerelli M, Koch M, Vincent JL: Pulse pressure variations to predict fluid responsiveness: influence of tidal volume. Intensive Care Med 2005, 31: 517-523. 10.1007/s00134-005-2586-4

    Article  PubMed  Google Scholar 

  26. Magder S: Hemodynamic monitoring in the mechanically ventilated patient. Curr Opin Crit Care 2011, 17: 36-42. 10.1097/MCC.0b013e32834272c1

    Article  PubMed  Google Scholar 

  27. Pinsky MR, Payen D: Functional hemodynamic monitoring. Crit Care 2005, 9: 566-572. 10.1186/cc3927

    Article  PubMed Central  PubMed  Google Scholar 

  28. Michard F, Teboul JL: Predicting fluid responsiveness in ICU patients: a critical analysis of the evidence. Chest 2002, 121: 2000-2008. 10.1378/chest.121.6.2000

    Article  PubMed  Google Scholar 

  29. Compton FD, Zukunft B, Hoffmann C, Zidek W, Schaefer JH: Performance of a minimally invasive uncalibrated cardiac output monitoring system (FloTrac™/Vigileo™) in haemodynamically unstable patients. Br J Anaesth 2008, 100: 451-456. 10.1093/bja/aem409

    Article  CAS  PubMed  Google Scholar 

  30. Monnet X, Lahner D: Can the "FloTrac" really track flow in septic patients? Intensive Care Med 2011, 37: 183-185. 10.1007/s00134-010-2109-9

    Article  PubMed  Google Scholar 

  31. Ince C: Microcirculation in distress: a new resuscitation end point? Crit Care Med 2004, 32: 1963-1964. 10.1097/01.CCM.0000139617.88704.B9

    Article  PubMed  Google Scholar 

  32. Gómez H, Mesquida J, Simon P, Kim HK, Puyana JC, Ince C, Pinsky MR: Characterization of tissue oxygen saturation and the vascular occlusion test: influence of measurement sites, probe sizes and deflation thresholds. Crit Care 2009,13(Suppl 5):S3..

    Article  PubMed Central  PubMed  Google Scholar 

  33. Mayeur C, Campard S, Richard C, Teboul JL: Comparison of four different vascular occlusion tests for assessing reactive hyperemia using near-infrared spectroscopy. Crit Care Med 2011, 39: 695-701. 10.1097/CCM.0b013e318206d256

    Article  PubMed  Google Scholar 

  34. Bartels SA, Bezemer R, Wallis de Vries FJ, Milstein DMJ, Lima A, Cherpanath TGV, van den Meiracker AH, van Bommel J, Heger M, Karemaker JM, Ince C: Multi-site and multi-depth near-infrared spectroscopy in a model of simulated (central) hypovolemia: lower body negative pressure. Intensive Care Med 2011, 37: 671-677. 10.1007/s00134-010-2128-6

    Article  PubMed Central  PubMed  Google Scholar 

  35. Soller BR, Yang Y, Soyemi OO, Ryan KL, Rickards CA, Walz JM, Heard SO, Convertino VA: Noninvasively determined muscle oxygen saturation is an early indicator of central hypovolemia in humans. J Appl Physiol 2008, 104: 475-481.

    Article  PubMed  Google Scholar 

  36. Skarda DE, Mulier KE, Myers DE, Taylor JH, Beilman GJ: Dynamic near-infrared spectroscopy measurements in patients with severe sepsis. Shock 2007, 27: 348-353. 10.1097/01.shk.0000239779.25775.e4

    Article  CAS  PubMed  Google Scholar 

  37. Creteur J, Neves AP, Vincent JL: Near-infrared spectroscopy technique to evaluate the effects of red blood cell transfusion on tissue oxygenation. Crit Care 2009,13(Suppl 5):S11. 10.1186/cc8009

    Article  PubMed Central  PubMed  Google Scholar 

  38. Jhanji S, Lee C, Watson D, Hinds C, Pearse RM: Microvascular flow and tissue oxygenation after major abdominal surgery: association with post-operative complications. Intensive Care Med 2009, 35: 671-677. 10.1007/s00134-008-1325-z

    Article  PubMed  Google Scholar 

  39. Poeze M: Tissue-oxygenation assessment using near-infrared spectroscopy during severe sepsis: confounding effects of tissue edema on StO 2 values. Intensive Care Med 2006, 32: 788-789. 10.1007/s00134-006-0121-x

    Article  CAS  PubMed  Google Scholar 

  40. De Blasi RA, Palmisani S, Boezi M, Arcioni R, Collini S, Troisi F, Pinto G: Effects of remifentanil-based general anaesthesia with propofol or sevoflurane on muscle microcirculation as assessed by near-infrared spectroscopy. Br J Anaesth 2008, 101: 171-177. 10.1093/bja/aen136

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This study was supported by the University Hospital of Clermont-Ferrand (Clermont-Ferrand, France). The sponsor of the study had no role in the study design, data collection, data analysis, interpretation of data or writing of this report.

Author information

Authors and Affiliations

  1. Department of Anaesthesiology and Critical Care Medicine, Estaing Hospital, University Hospital of Clermont-Ferrand, 1 place Lucie Aubrac, Clermont-Ferrand, F-63000, France

    Emmanuel Futier, Sebastien Christophe, Antoine Petit & Jean-Etienne Bazin

  2. Department of Anaesthesiology and Critical Care Medicine, University Hospital of Lille, University Nord de France, Rue du Pr. Emile Laine, Lille, F-59037, France

    Emmanuel Robin & Jacques Desbordes

  3. Department of Clinical Research (DRCI), Biostatics Unit, University Hospital of Clermont-Ferrand, place Henri Dunant, Clermont-Ferrand, F-63000, France

    Bruno Pereira

  4. Federation of Anaesthesiology and Critical Care Medicine, University Hospital of Lille, University Nord de France, Rue du Pr. Emile Laine, Lille, F-59037, France

    Benoit Vallet

Authors
  1. Emmanuel Futier
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sebastien Christophe
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Emmanuel Robin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Antoine Petit
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bruno Pereira
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jacques Desbordes
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jean-Etienne Bazin
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Benoit Vallet
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Emmanuel Futier.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

EF and ER conceived and designed the original study. SC and AP were responsible for patient enrolment and participated in data acquisition. EF, ER, BV, JD and JEB drafted the manuscript. All authors read and approved the final manuscript.

Authors’ original submitted files for images

Rights and permissions

Reprints and permissions

About this article

Cite this article

Futier, E., Christophe, S., Robin, E. et al. Use of near-infrared spectroscopy during a vascular occlusion test to assess the microcirculatory response during fluid challenge. Crit Care 15, R214 (2011). https://doi.org/10.1186/cc10449

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/cc10449

Keywords

Critical Care

ISSN: 1364-8535