Critical Care Research and Practice

Volume 2013 (2013), Article ID 583598, 7 pages

http://dx.doi.org/10.1155/2013/583598

Research Article

Central Venous-to-Arterial Gap Is a Useful Parameter in Monitoring Hypovolemia-Caused Altered Oxygen Balance: Animal Study

Szilvia Kocsi,1,2 Gabor Demeter,¹ Daniel Erces,³ Eniko Nagy,³ Jozsef Kaszaki,³ and Zsolt Molnar¹

¹Department of Anaesthesiology and Intensive Therapy, University of Szeged, Semmelweis Utca 6., Szeged 6725, Hungary

²Department of Anaesthesiology and Intensive Therapy, MH Honved Hospital, Róbert Károly Körút 44., Budapest 1134, Hungary

³Institute of Surgical Research, University of Szeged, Pécsi Utca 6., Szeged 6720, Hungary

Received 9 February 2013; Revised 10 July 2013; Accepted 19 July 2013

Academic Editor: Samuel A. Tisherman

Copyright © 2013 Szilvia Kocsi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Monitoring hypovolemia is an everyday challenge in critical care, with no consensus on the best indicator or what is the clinically relevant level of hypovolemia. The aim of this experiment was to determine how central venous oxygen saturation (ScvO₂) and central venous-to-arterial carbon dioxide difference (CO₂ gap) reflect hypovolemia-caused changes in the balance of oxygen delivery and consumption. Anesthetized, ventilated Vietnamese minipigs () were given a bolus followed by a continuous infusion of furosemide. At baseline and then in five stages hemodynamic, microcirculatory measurements and blood gas analysis were performed. Oxygen extraction increased significantly, which was accompanied by a significant drop in ScvO₂ and a significant increase in CO₂ gap. There was a significant negative correlation between oxygen extraction and ScvO₂ and significant positive correlation between oxygen extraction and CO₂ gap. Taking % and CO₂ gap >6 mmHg values together to predict an oxygen extraction >30%, the positive predictive value is 100%; negative predicted value is 72%. Microcirculatory parameters, capillary perfusion rate and red blood cell velocity, decreased significantly over time. Similar changes were not observed in the sham group. Our data suggest that % and CO₂ gap >6 mmHg can be complementary tools in detecting hypovolemia-caused imbalance of oxygen extraction.

1. Introduction

Diagnosing hypovolemia is an everyday challenge in critical care. Clinicians utilize a large array of tools from simple clinical signs to invasive hemodynamic measurements, but a universally accepted gold standard remains elusive [1]. Although diagnosis may prove difficult, early recognition of hypovolemia is of utmost importance. By the time macrohemodynamic changes manifest, the microcirculation may already be damaged [2]. Furthermore, fluid therapy is a double-edged sword: on the one hand fluid resuscitation can save lives, but on the other hand a cumulative positive fluid balance is an independent factor for mortality [3, 4]. Deciding on the level of monitoring (noninvasive, “less” invasive, invasive) and which parameter to monitor in order to keep the critically ill patient normovolemic remains uncertain.

Central venous oxygen saturation (ScvO₂), an easily obtained parameter via the central venous catheter already in situ in most critically ill patients, is often used as a marker of the balance between oxygen delivery (DO₂) and consumption (VO₂). The main factors, which influence ScvO₂, are hemoglobin (Hb), arterial oxygen saturation (SaO₂), cardiac output (CO), and VO₂. Theoretically if Hb, SaO₂, and VO₂ are kept constant, the value of ScvO₂ should reflect the change in CO. Recent studies have translated theory into practice and demonstrated that ScvO₂ may be a good marker for assessing fluid responsiveness [5, 6].

The normal value of ScvO₂ varies between 73 and 82%. It is slightly higher than mixed-venous oxygen saturation (SvO₂) and is considered a reasonable surrogate marker in the clinical setting [7].

Changes in ScvO₂ reflect systemic oxygen uptake but may be falsely positive (>70%) in regional hypoxia [8]. Under these conditions the central venous-to-arterial CO₂ difference (CO₂ gap) has been proposed as an alternative [8–10]. The physiological value of CO₂ gap is <5 mmHg, but this may be higher in low-flow states [8, 9]. However, it remains unclear how and whether the CO₂ gap changes in hypovolemia.

Therefore, the aim of our hypovolemic animal model was to investigate the association between ScvO₂, CO₂ gap, microcirculatory blood flow and hypovolemia-caused altered VO₂/DO₂.

2. Methods

The study protocol was approved by the local Ethics Committee and the Institutional Animal Care and Use Committee at the University of Szeged, and the study was conducted in the research laboratory of the Institute of Surgical Research in a manner that does not inflict unnecessary pain or discomfort upon the animal.

2.1. Animals and Instrumentation

Vietnamese minipigs () weighing  kg underwent a 24 hr fast preoperatively but with free access to water. Anesthesia was induced by intramuscular injection of a mixture of ketamine (20 mg/kg) and xylazine (2 mg/kg) and maintained with a continuous infusion of propofol (6 mg/kg/hr i.v.), while analgesia was maintained with nalbuphine (0.1 mg/kg). A tracheal tube was inserted and the animals’ lungs were ventilated mechanically. The tidal volume was set at 10 mL/kg, and the respiratory rate was adjusted to maintain the end-tidal carbon dioxide and partial pressure of arterial carbon dioxide in the range of 35–45 mmHg and the arterial pH between 7.35 and 7.45. The adequacy of the depth of anesthesia was assessed by monitoring the jaw tone. After induction of anesthesia, the right jugular vein and the right femoral artery and vein were dissected and catheterized. Tonometric probes and catheters were placed simultaneously into the stomach and the small bowel. A suprapubic urinary catheter was also inserted to monitor urine output. Animals were kept warm (°C) by an external warming device.

For invasive hemodynamic monitoring, a transpulmonary thermodilution catheter (PiCCO, PULSION Medical Systems SE, Munich, Germany) was placed in the femoral artery, and a pulmonary artery catheter (PV2057 VoLEF Catheter, PULSION Medical Systems SE, Munich, Germany) was placed in the femoral vein. The latter was also used to draw mixed venous blood gas samples from which the VO₂ was calculated. The femoral artery served as the site for arterial blood gas sampling and the central venous line was used for taking central venous blood gas samples and for the injection of cold saline boluses for the thermodilution measurements.

For continuous noninvasive visualization of the microcirculation in the sublingual region an intravital orthogonal polarization spectral (OPS) imaging technique (Cytoscan A/R, Cytometrics, Philadelphia, PA, USA) was used [2, 11]. A 10x objective was introduced onto the sublingual serosa, and microscopic images were recorded with an S-VHS video recorder (Panasonic AG-TL 700, Osaka, Japan).

For the tonometry special probes (Tonosoft Medical-Technical and R&G Ltd.) were used and monitoring was performed with a Sidestream Microcap Handheld Capnograph (Oridion Medical Ltd., Jerusalem, Israel) instrument [12].

To assess further biochemical changes in the microcirculation, plasma big-endothelin-1 (BigET) levels were determined. BigET is a 38 amino acid containing protein, the precursor of endothelin-1, which becomes elevated in tissue hypoxia [13].

2.2. Hemodynamic Measurements

Cardiac output (CO), global end-diastolic volume index (GEDI), stroke volume (SV), heart rate (HR), and mean arterial pressure (MAP) were measured by transpulmonary thermodilution and pulse contour analysis at baseline and at the end of each interval. Detailed description of transpulmonary thermodilution and pulse contour analysis is provided elsewhere [14, 15]. All hemodynamic parameters were indexed for body surface area or bodyweight. The average of three measurements following 10 mL bolus injections of ice-cold 0.9% saline was recorded. Central venous pressure (CVP) was measured via the central venous catheter at the same times as the other hemodynamic variables.

Arterial, central venous, and mixed venous blood gas samples (Cobas b 221, Roche Ltd., Basel, Switzerland) were drawn and analyzed by cooximetry simultaneously at baseline and at the end of each cycle.

2.3. Monitoring the Microcirculation

Microcirculatory evaluation of the sublingual region was performed offline by frame-to-frame analysis of the videotaped images. Capillary red blood cell velocity (RBCV) and capillary perfusion rate (CPR) were determined in three separate fields using a computer-assisted image analysis system (IVM Pictron, Budapest, Hungary). All OPS measurements were performed by one investigator.

Gastric and small bowel changes in partial pressure of carbon dioxide (PCO₂) were calculated by subtracting tonometric PCO₂ from arterial PCO₂ (gastric-arterial: Ga-PCO₂; bowel-arterial: Ba-PCO₂) [12].

For measurements of BigET, blood samples of 2 mL were drawn from the jugular vein into chilled polypropylene tubes containing EDTA (1 mg/mL). The samples were centrifuged at 1200 g for 10 min at 4°C. The plasma samples were then collected and stored at −70°C until assay.

2.4. Experimental Protocol

At baseline () hemodynamic, microcirculatory and blood gas parameters were recorded. Hypovolemia was induced via a bolus followed by a continuous infusion of furosemide (5 mg/kg and 5 mg/kg/2 h, resp.) in a group of 10 animals—hypovolemic group (HG). After the administration of bolus furosemide measurements were recorded in five stages with 20-minute interval between each measurement (–). When the preload parameter (GEDI) decreased by >20% its baseline value, OPS imaging and BigET sampling were performed, which were repeated only at the end of the experiment. There were 5 anaesthetised, ventilated animals in the sham group (SG), who did not receive any furosemide, but maintenance infusion of lactated Ringer (4 mL/kg/h) and hemodynamic, microcirculatory and blood gas parameters were recorded in the same fashion as described previously. At the end of the experiment all animals were humanely euthanized.

2.5. Data Analysis and Statistics

Data are reported as means ± standard deviations unless indicated otherwise. For testing normal distribution the Kolmogorov-Smirnov test was used. Changes in all parameters throughout the experiment were tested by repeated measures analysis of variance (RM ANOVA), and the number of degrees of freedom was adjusted to Greenhouse-Geisser epsilon when needed. Mann-Whitney U-test with Bonferroni correction was used for between-groups analysis. For pairwise comparisons Pearson’s correlation was used. To evaluate the performance of ScvO₂, CO₂ gap and microcirculatory parameters in detecting altered oxygen extraction with a threshold of 30%, receiver operating characteristics (ROC) curve analysis was performed, and sensitivity, specificity, positive predictive (PPV), and negative predictive values (NPV) were also determined. Post hoc calculation showed a power of 83% with an effect of 36% decrease in GEDI for a sample size of 10 and . For statistical analysis SPSS version 18.0 for Windows (SPSS, Chicago, IL, USA) was used and was considered statistically significant.

3. Results

3.1. Hemodynamic Effects of Hypovolemia

Urine output in the hypovolemic group following the bolus and the onset of infusion was  mL at , which increased to  mL at . In contrast, in the sham group, urine output was  mL at and had increased to  mL by . All other hemodynamic data are summarized in Table 1. Preload, as indicated by GEDI, decreased significantly after each phase in the hypovolemic group compared to baseline and dropped by 36% of its baseline value by the end of the experiment. The change of the other macrohemodynamic variables followed a similar pattern. When comparing the sham versus hypovolemic animals, variables differed between the two groups from , but significant differences over time continued only for GEDI, CVP and SVI. In the sham group there were no significant changes over time throughout the experiment.

Table 1: Hemodynamic changes.

GEDI (mL/m²)
HG
349 ± 51
293 ± 53*‡#
257 ± 47*‡#
246 ± 53*#
233 ± 42*#
223 ± 31*#

SG
350 ± 26
387 ± 43
360 ± 22
365 ± 76
356 ± 51
349 ± 46

CVP (mmHg)
HG
7 ± 4
4 ± 3*‡#
4 ± 3*#
4 ± 3*‡#
3 ± 3*#
4 ± 3*#

SG
8 ± 1
7 ± 1
7 ± 1
7 ± 2
7 ± 1
7 ± 2

MAP (mmHg)
HG
121 ± 15
107 ± 17*‡
94 ± 19*‡
86 ± 15*‡
85 ± 15*
83 ± 17*

SG
120 ± 13
121 ± 14
117 ± 15
113 ± 16
121 ± 24
111 ± 25

HR (1/beats)
HG
78 ± 14
83 ± 15*‡
93 ± 17*
102 ± 19*
130 ± 28*‡
142 ± 28*‡#

SG
74 ± 12
75 ± 12
74 ± 9
78 ± 10
83 ± 16
80 ± 9

CI (L/min/m²)
HG
2.30 ± 0.35
1.78 ± 0.31*‡
1.54 ± 0.32*‡
1.46 ± 0.35*‡
1.52 ± 0.41*
1.58 ± 0.36*

SG
2.38 ± 0.50
2.65 ± 0.65
2.42 ± 0.48
2.38 ± 0.76
2.32 ± 0.50
2.27 ± 0.27

SVI (mL/m²)
HG
29 ± 5
19 ± 2*‡#
15 ± 4*#
14 ± 4*#
12 ± 4*#
12 ± 3*#

SG
33 ± 4
34 ± 6
33 ± 3
32 ± 6
31 ± 8
28 ± 5

GEDI: global end-diastolic volume index; CVP: central venous pressure; MAP: mean arterial pressure; HR: heart rate; CI: cardiac index; SVI: stroke volume index. : baseline measurement; : five intervals. * as compared to ; ‡ as compared to the previous value; RM ANOVA; # HG versus SG; Mann-Whitney -test with Bonferroni correction.

3.2. Effects on Oxygen Balance

Variables related to oxygen balance are listed in Table 2. In the hypovolemic group DO₂ fell significantly from and remained so for the rest of the experiment. The VO₂/DO₂ increased significantly over 30% from , while ScvO₂ and CO₂ gap followed this change only after . Lactate changed significantly from . There were no significant changes in the sham group throughout the experiment.

Table 2: Changes of oxygen balance.

SaO₂ (%)
HG
94 ± 6
95 ± 5
95 ± 2
96 ± 2
94 ± 3
94 ± 2

SG
95 ± 5
94 ± 8
96 ± 5
94 ± 8
95 ± 5
94 ± 7

Hb (g/L)
HG
122 ± 9
129 ± 13
132 ± 7*
134 ± 7*‡
137 ± 7*‡
138 ± 8*

SG
119 ± 10
115 ± 9
110 ± 6
108 ± 11
111 ± 9
102 ± 10

DO₂I (mL/min/m²)
HG
361 ± 39
302 ± 52*‡
268 ± 47*‡
258 ± 55*
269 ± 65*
284 ± 55*

SG
365 ± 78
396 ± 88
346 ± 73
337 ± 67
324 ± 55
298 ± 36

VO₂/DO₂ (%)
HG
28 ± 5
31 ± 6*‡
34 ± 7*‡
39 ± 9*‡
41 ± 9*
40 ± 10*

SG
25 ± 4
25 ± 6
26 ± 7
29 ± 5
27 ± 6
30 ± 7

ScvO₂ (%)
HG
74 ± 10
71 ± 10
67 ± 11*‡
64 ± 14*
59 ± 13*‡
57 ± 14*

SG
77 ± 8
76 ± 10
76 ± 9
75 ± 11
73 ± 14
73 ± 12

CO₂-gap (mmHg)
HG
4.3 ± 2.3
7.5 ± 3.3
7.1 ± 2.6*
8.3 ± 2.8*
7.3 ± 2.9*
10.1 ± 5.5*#

SG
4.1 ± 2.4
3.5 ± 1.9
4.5 ± 1.3
3.9 ± 2.9
4.6 ± 1.9
4.4 ± 1.9

Lactate (mmol/L)
HG
3.8 ± 1.4
3.9 ± 1.3
4.3 ± 0.9
4.7 ± 0.9*‡
5.1 ± 1.2*‡
5.3 ± 1.5*

SG
3.8 ± 0.9
3.9 ± 1.2
4.2 ± 1.8
4.6 ± 2.1
4.7 ± 2.7
4.9 ± 3.2

VO₂I (mL/min/m²)
HG
98 ± 11
93 ± 16
88 ± 9
96 ± 7‡
104 ± 10‡
109 ± 16

SG
88 ± 16
96 ± 6
85 ± 12
96 ± 8
85 ± 12
88 ± 14

SaO₂: arterial oxygen saturation; Hb: hemoglobin; DO₂: oxygen delivery; VO₂/DO₂: oxygen extraction ratio; ScvO₂: central venous oxygen saturation; CO₂ gap: venous-to-arterial carbon dioxide difference; VO₂: oxygen consumption. : baseline measurement; : five intervals. * as compared to ; ‡ as compared to the previous value; RM ANOVA; # HG versus SG; Mann-Whitney -test with Bonferroni correction.

In the hypovolemic group there was a significant correlation between VO₂/DO₂ and ScvO₂ and CO₂ gap (Figures 1 and 2). Lactate also showed a significant, but weak correlation with VO₂/DO₂ (, ; ).

Figure 1: Correlation between VO₂/DO₂ and ScvO₂. Data are presented as scatter with a linear regression line and its mean 95% confidence interval. VO₂/DO₂: oxygen extraction, ScvO₂: central venous oxygen saturation.

Figure 2: Correlation between VO₂/DO₂ and CO₂ gap. Data are presented as scatter with a linear regression line and its mean 95% confidence interval. VO₂/DO₂: oxygen extraction; CO₂ gap: central venous-to-arterial carbon dioxide difference.

With receiver-operator characteristic (ROC) curves for ScvO₂, CO₂ gap and lactate to detect a %, the area under the curves (AUC) was significant for ScvO₂, CO₂ gap (AUC SE ; ; , resp.) while lactate did not reach statistical significance. The cut-off values to give the best sensitivity and specificity for ScvO₂ and CO₂ gap were 73% and 6.5 mmHg, respectively. Sensitivity, specificity, positive predictive, and negative predictive values for ScvO₂ and CO₂ gap are summarized in Table 3. Taking ScvO₂ and CO₂ gap values together to predict a %, the false positive and false negative values were reduced.

Table 3: Complementation of ScvO₂ with CO₂ gap.

Sensitivity (%)
Specificity (%)
PPV (%)
NPV (%)

ScvO2 ≤ 73%
78
83
91
63

CO₂ gap > 6 mm Hg
71
72
85
52

ScvO₂ + CO₂ gap (≤73%) (>6 mm Hg)
58
100
100
72

ScvO₂: central venous oxygen saturation; CO₂ gap: venous-to-arterial carbon dioxide difference; PPV: positive redictive value; NPV: negative predictive value.

3.3. Effects on Microcirculation

Variables related to microcirculation are listed in Table 4. In the hypovolemic group tonometry increased significantly only when measured in the intestines. Capillary perfusion rate and red blood cell velocity gradually and significantly decreased over time. This change was accompanied by a significant increase in BigET levels. In contrast, the sham group BigET decreased significantly by , while the other parameters remained unchanged. There was a significant difference in capillary perfusion rate, red blood cell velocity and BigET between the two groups.

Table 4: Changes in microcirculation.

Ba-PCO₂ (mmHg)
HG
24 ± 8
35 ± 16*‡
35 ± 17
36 ± 13*
33 ± 13*
37 ± 16*

SG
19 ± 5
22 ± 9
20 ± 6
22 ± 11
22 ± 10
20 ± 8

Ga-PCO₂ (mmHg)
HG
40 ± 12
43 ± 14
42 ± 14
39 ± 14
36 ± 10
37 ± 11

SG
36 ± 22
37 ± 24
34 ± 21
32 ± 21
34 ± 20
32 ± 18

CPR (%)
HG
82 ± 15
—
53 ± 11*‡#
—
—
45 ± 16*‡#

SG
91 ± 50

81 ± 80

83 ± 70

RBCV (μm/s)
HG
887 ± 141
—
509 ± 120*‡#
—
—
463 ± 209*#

SG
1054 ± 141

848 ± 194

963 ± 51

BigET (fmol/mL)
HG
1.44 ± 0.53
—
1.97 ± 0.84*‡
—
—
2.29 ± 0.89*#

SG
1.36 ± 0.93

1.49 ± 1.27

0.98 ± 0.92*

Ba-PCO₂: small bowel-to-arterial carbon dioxide difference; Ga-PCO₂: gastric-to-arterial carbon dioxide difference; CPR: capillary perfusion rate; RBCV: red blood cell velocity; BigET: big endothelin. : baseline measurement; : five intervals. * as compared to , ‡ as compared to the previous value, GLM repeated measures ANOVA; # HG versus SG, Mann-Whitney -test with Bonferroni correction.

The ROC curves for predicting VO₂/DO₂>30% proved to be significant for capillary perfusion rate and red blood cell velocity in the hypovolemic group. The area under curve did not differ significantly between these two parameters (AUC SE ; ; , resp.).

The correlation between ScvO₂ and the microcirculatory parameters proved to be significant apart from Ga-PCO₂ (ScvO₂-Ba-PCO₂, -CPR, -RBCV, -BigET: , ; , ; , ; , ; , resp.). CO₂ gap showed significant correlations with Ba-PCO₂ and CPR (, ; , ; , resp.).

4. Discussion

The main finding of our study is that it provides further evidence that low or decreasing ScvO₂, as well as high or increasing CO₂-gap can reflect changes and may be complementary in global oxygen balance and altered microcirculatory blood flow in hypovolemia.

4.1. Hemodynamic Changes

Our goal of achieving hypovolemia was reached as global end diastolic index, central venous pressure and stroke volume decreased significantly throughout the experiment, which resulted in a significant drop in cardiac index in the hypovolemia group. This decrease was notable until . Due to this change VO₂/DO₂ increased significantly from .

4.2. CO₂ Gap and ScvO₂ in Hypovolemia

Up until now, there has been consensus neither on the most accurate hemodynamic marker of hypovolemia nor on the endpoints for optimal fluid therapy [1, 16, 17]. Many recent studies have suggested that fluid therapy should be based on dynamic (such as cardiac output, pulse pressure variation and stroke volume variation) rather than static hemodynamic variables (such as CVP, pulmonary artery occlusion pressure), because they are better predictors of fluid responsiveness in ICU patients. However, pulse pressure variation and stroke volume variation are limited to patients who are fully ventilated and have no arrhythmias [18, 19]. Although it is not strictly a hemodynamic variable, in certain clinical conditions an ScvO₂ value of ~70% has been used as a therapeutic endpoint to improve oxygen delivery [3, 7, 20, 21]. In a recent study it was found that a change in ScvO₂ is a reliable parameter to define fluid responsiveness at the bedside in critically ill patients [5]. Similar results have been reported in two other studies demonstrating a close relationship between ScvO₂ and cardiac index [6, 22]. However, one has to bear in mind that fluid resuscitation reduces hemoglobin levels, which may result in no change or decrease in ScvO₂ therefore, the above may only hold true for hypovolemic patients with low ScvO₂.

In our experiment the change in VO₂/DO₂, which increased significantly from , was accompanied by a fall in ScvO₂, which is in accordance with previous findings. The change in VO₂/DO₂ was also accompanied by an increase in CO₂ gap from . There is some evidence that CO₂ gap increases in certain low flow states [8, 9]. The pathophysiology of increased CO₂ gap may be due to the CO₂ stagnation phenomenon. When cardiac output decreases, blood flow is slow and the washout is impaired; therefore, more CO₂ is accumulated in the tissues, and as CO₂ diffuses easily and quickly the CO₂ gap increases [23].

ScvO₂ showed very good sensitivity and specificity with a threshold of 73% for determining %, which was further improved when CO₂ gap >6.5 mmHg was added, leading to less false negative and false positive results. The ScvO₂ and CO₂ gap showed a significant and strong negative correlation. It is also important to note that lactate showed a significant but substantially weaker correlation to VO₂/DO₂ as compared to ScvO₂ or CO₂ gap. This highlights the limitation of lactate levels as therapeutic endpoint for resuscitation. This finding is in accordance with previously published data showing the limitation of lactate levels as therapeutic endpoint for resuscitation [24].

In cases when due to microcirculatory and/or mitochondrial defects oxygen uptake is insufficient, ScvO₂ may be elevated (i.e., false negative). Previous studies have suggested that under such circumstances the increased value of CO₂ gap (>5 mmHg) may help the clinician in detecting inadequate DO₂ to tissues [8–10]. Our results lend further support to this theory. Furthermore, adding the CO₂ gap to ScvO₂ for identifying %, there was an improvement in specificity, positive predictive, and negative predictive values.

We are not aware of any studies that have tailored hemodynamic support based on or supported by changes in CO₂ gap; therefore, its clinical relevance remains unclear. However, our data clearly shows, and to our knowledge this is the first experiment to show that, an altered VO₂/DO₂ caused by hypovolemia is reflected by an increase in CO₂ gap. Therefore its value may be an important alarm signal for the clinician and would help decision making at the bedside, especially when considering a fluid challenge or where commencing advanced hemodynamic monitoring is concerned.

4.3. Microcirculation

In any shock like states the microcirculation plays a vital role, as the most devastating effects of oxygen debt occur here in the cells [2, 22]. It has been demonstrated that microcirculatory disturbances can occur not only in cases of severe hypovolemic shock, but also in cases of a moderate hypovolemia without severe hypotension in human patients [25]. Recently, Bartels et al. evaluated the alteration of the sublingual microcirculation in response to controlled, central hypovolemia using sidestream dark field imaging in human subjects with intact autoregulation. They confirmed that despite adequate compensation of hypovolemia it can still be associated with decreased microcirculatory response, consequently with decreased oxygen delivery to the tissues [26].

In a prospective observational study in patients with septic shock it was found that the capillary perfusion rate was different in survivors (in whom it was increased) as compared to nonsurvivors. Moreover, it was the only factor to differentiate survivors and patients dying of multiple-system organ failure after the shock had resolved [2]. In accordance with these results we saw a gradual and significant decrease in both capillary perfusion rate and red blood cell velocity over time. There was also a very good area under curve when defining % and good correlation with ScvO₂ and CO₂ gap. All these changes were observed only in the hypovolemic, but not in the sham group.

According to the microcirculatory parameters measured in this experiment, the inflicted hypovolemia resulted in significant changes to the microcirculation. Tonometry showed a significant increase in Ba-PCO₂ due to hypovolemia, indicating decreased blood flow in the intestines. This is in accordance with previously published reports [27]. In contrast, there was no significant change in the Ga-PCO₂. Regarding the importance and the value of gastric mucosal pH is controversial and its routine use has declined in intensive care over the last decades [28–30]. The difference between Ga-PCO₂ and Ba-PCO₂ is an interesting observation and also difficult to explain. However monitoring both has already been suggested, in order to give a small additional value in the diagnosis of possible mismatch in splanchnic perfusion [31, 32].

There was also a significant correlation between Ba-PCO₂ both with ScvO₂ and CO₂ gap.

Little is known about BigET, but there is some evidence that ET-1 reflects tissue hypoxia [33]. However, in contrast to the insignificant change in BigET found in healthy volunteers suffering from acute hypoxia, our results showed a significant difference of BigET levels between the hypovolemic and the sham groups, which indicates that there is an effect of hypovolemia-induced tissue hypoxia on BigET levels [34].

4.4. Limitations of the Study

One of the possible limitations of our study is the long preparation period which might have resulted in the slightly elevated lactate levels although this was observed in both groups. Its clinical impact may be limited as a decreased ScvO₂ and elevated CO₂ gap may be influenced by several factors other than hypovolemia, including heart failure, severe sepsis/septic shock, and multiple trauma; thus, these results can only be applied when these conditions are unlikely to be present, for example, in postoperative critical care. Furthermore, these data were obtained in anesthetized animals and may not be the same in conscious human subjects. Finally, as there are no gold standards for hypovolemic animal experiments, therefore one cannot exclude that the choice of furosemide-caused hypovolemia may not be the most appropriate model. The disadvantage of this model is that it does not replicate real life clinical diseases. Traumatic hypovolemia and hypovolemia associated with sepsis are associated with profound microcirculatory changes which become superimposed on the changes following hypovolemia. This is particularly important in patients with sepsis, where ScvO₂ is known to be a poor marker of tissue oxygenation. Indeed, in patients with sepsis, a high rather than a low ScvO₂ is predictive of mortality.

5. Conclusion

Our results have shown that in addition to central venous oxygen saturation (ScvO₂), central venous-to-arterial CO₂ difference (CO₂ gap) may also be used as a simple, but valuable indicator of hypovolemia-caused imbalance of oxygen extraction (VO₂/DO₂). Further clinical studies have to validate its clinical merits in indicating and tailoring hemodynamic support.

Conflict of Interests

The authors declare no conflict of interests.

Acknowledgment

The authors would like to acknowledge the help of the staff in the Institute of Surgical Research without which this work could not have been completed.

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