key: cord-011029-sbds5sda
authors: Portran, Philippe; Jacquet-Lagreze, Matthias; Schweizer, Remi; Fornier, William; Chardonnal, Laurent; Pozzi, Matteo; Fischer, Marc-Olivier; Fellahi, Jean-Luc
title: Improving the prognostic value of ∆PCO(2) following cardiac surgery: a prospective pilot study
date: 2019-07-10
journal: J Clin Monit Comput
DOI: 10.1007/s10877-019-00352-6
sha: 
doc_id: 11029
cord_uid: sbds5sda

Conflicting results have been published on prognostic significance of central venous to arterial PCO(2) difference (∆PCO(2)) after cardiac surgery. We compared the prognostic value of ∆PCO(2) on intensive care unit (ICU) admission to an original algorithm combining ∆PCO(2), ERO(2) and lactate to identify different risk profiles. Additionally, we described the evolution of ∆PCO(2) and its correlations with ERO(2) and lactate during the first postoperative day (POD1). In this monocentre, prospective, and pilot study, 25 patients undergoing conventional cardiac surgery were included. Central venous and arterial blood gases were collected on ICU admission and at 6, 12 and 24 h postoperatively. High ∆PCO(2) (≥ 6 mmHg) on ICU admission was found to be very frequent (64% of patients). Correlations between ∆PCO(2) and ERO(2) or lactate for POD1 values and variations were weak or non-existent. On ICU admission, a high ∆PCO(2) did not predict a prolonged ICU length of stay (LOS). Conversely, a significant increase in both ICU and hospital LOS was observed in high-risk patients identified by the algorithm: 3.5 (3.0–6.3) days versus 7.0 (6.0–8.0) days (p = 0.01) and 12.0 (8.0–15.0) versus 8.0 (8.0–9.0) days (p < 0.01), respectively. An algorithm incorporating ICU admission values of ∆PCO(2), ERO(2) and lactate defined a high-risk profile that predicted prolonged ICU and hospital stays better than ∆PCO(2) alone.

Tissue perfusion after cardiac surgery may become impaired due to multiple factors and in turn induce organ dysfunction, organ failure, prolonged stay in intensive care unit (ICU) and in hospital and increased mortality [1] . Unfortunately, the adequacy of tissue perfusion remains difficult to assess. Surrogate markers like central venous to arterial PCO 2 difference (ΔPCO 2 ), oxygen extraction ratio (ERO 2 ) and lactate are used to evaluate this adequacy [2, 3] . Elevated arterial lactate is a commonly used marker of global anaerobic metabolism and even mild hyperlactatemia has recently found to be correlated both with microcirculatory flow abnormalities and a worse outcome [3] [4] [5] . However, lactate is not a pure marker of anaerobic metabolism and non-hypoxic causes of hyperlactatemia are common in septic shock or after cardiopulmonary bypass [6] [7] [8] . Accordingly, additional markers of tissue perfusion have been explored. ERO 2 can be calculated on basis of oxygen arterial saturation (S a O2) and central venous saturation (S cv O 2 ) using the following formula: ERO 2 = (S a O 2 −S cv O 2 )/S a O 2 . Hemodynamic monitoring guidelines recommend to monitor this ratio after cardiac surgery and a S cv O 2 ≥ 70% is often considered a target for optimal hemodynamic resuscitation [9, 10] . However, 1 3 because of the potential extraction defect some patient might have microcirculatory impairment with a normal or supranormal S cv O 2 .

In this context ΔPCO 2 has been proposed as a global marker of tissue perfusion adequacy [11] [12] [13] . ΔPCO 2 is a marker of the venous blood flow ability to remove the excess CO 2 produced in tissue. An impaired tissue perfusion, due to low cardiac output or microcirculatory alteration, is therefore the main determinant of an elevated ΔPCO 2 [14] .

Yet, the prognostic significance of ΔPCO 2 after cardiac surgery remains unclear and conflicting results have been published [11, 15] . Moreover, limited prospectively reported data on ΔPCO 2 after cardiopulmonary bypass (CPB) are available. Finally, in clinical practice ΔPCO 2 and lactate or ERO 2 values frequently appear contradictive, which makes the interpretation of an elevated ΔPCO 2 difficult. Several authors already suggested that interpreting ΔPCO 2 with S cv O 2 improve the prognostic significance of these markers [11, 12, 16] . De Backer in a recent review on hemodynamic in shock suggests an algorithm combining ΔPCO 2 , S cv O 2 and lactate [17] . This multiparametric approach could better discriminate different cardiovascular profiles and improve our understanding of apparently contradictive patterns. Still, no clinical study has yet evaluated algorithms combining these three markers following cardiac surgery.

In this pilot study, we evaluate the prognostic value of ΔPCO 2 at the time of ICU admission and compare it to an original algorithm combining ΔPCO 2 , ERO 2 and lactate to identify different risk profiles after elective conventional cardiac surgery. Additionally, we describe the evolution of ΔPCO 2 and its correlations with ERO 2 and lactate on the first post-operative day (POD1).

All adult patients scheduled for elective cardiac surgery with CPB were eligible for this monocentric, prospective, observational study, which was approved by the local Ethics Committee (Comité de Protection des Personnes, Reference CPP: A13-D55-VOL. 19 ). According to French law and because data were collected during routine care, authorization was granted to waive written informed consent. However, verbal consent was obtained from all study participants before surgery. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

From April 2013 to June 2013, patients who were scheduled for cardiac surgery at Caen University Hospital, Caen, France, were eligible for participation in the study. Inclusion criteria were as follows:

Patients > 18 years old who were admitted to the surgical ICU following elective conventional cardiac surgery (Coronary artery bypass graft (CABG) and/or aortic or mitral valvular surgery) with CPB.

Exclusion criteria were age < 18 years or patient under tutorship, off pump surgery, cardiac transplantation, ventricular assist device implantation and any emergency situations.

The anesthesia was standardized (target-controlled infusion of propofol and remifentanil) and adjusted to obtain a bispectral index value between 40 and 60. Immediately after the induction of general anesthesia and tracheal intubation in the operating room, a radial artery catheter and a right jugular central venous catheter were inserted. More advanced hemodynamic monitoring was left at the anesthetist's discretion. Patients were ventilated at 6-8 mL kg −1 of ideal body weight, positive end expiratory pressure was set to 4-8 cm H 2 O. The ventilator was switched off during CPB. Anticoagulation was obtained during CPB with an initial bolus of heparin (300 UI kg −1 ) to maintain activated coagulation time more than 450 s. Reversion was systematically per-formed with protamine at the end of CPB. CPB was performed under normothermia and myocardial protection was achieved by intermittent cold blood cardioplegia. Boluses of ephedrine and/or phenylephrine were given intraoperatively to maintain mean arterial pressure between 50 and 80 mm Hg. The heart was defibrillated after aortic unclamping, if sinus rhythm did not resume spontaneously. After the termination of CPB, norepinephrine was used to maintain the mean arterial pressure greater than 65 mm Hg, and the trigger for transfusion of packed erythrocytes was set to a hematocrit of 21% in all patients and complied with routine practice at the study institution.

On arrival in the ICU, all pressure monitors were zeroed at the mid-axillary line upon arrival and the position of the tip of the central venous catheter in the upper part of the right atrium was verified by chest radiography. Discontinuation of invasive ventilation, administration of blood products, management of hemodynamics and fluid balance, ICU and hospital discharge followed institutional standards.

Data elements included demographic variables: age, gender, body mass index (kg/m 2 ), EuroSCORE 2 (%), baseline serum creatinine value (μmol/L); intra operative data: type of surgery, CPB time, Cross clamp time, use of epinephrine, norepinephrine or dobutamine, preoperative. The first blood sample was collected on admission to the ICU (T0) and 6 h (T6), 12 h (T12) and 24 h (T24) after. Venous and arterial blood gas were drawn simultaneously from radial arterial catheter and central venous catheter. Central venous and arterial lactate serum level, CO 2 partial pressure, oxygen saturation and content were obtained from the blood gases. ΔPCO 2 was calculated as P cv CO 2 minus P a CO 2 . The patient's heart rate, mean arterial pressure (MAP), central temperature and oxygen saturation were measured simultaneously to the blood samples. The primary outcome was ICU length of stay. At the ICU discharge, we also collected duration of mechanical ventilation, the need for epinephrine, norepinephrine or dobutamine on POD1 and total postoperative chest tube drainage. Secondary outcomes included Sequential Organ Failure Assessment (SOFA) score on POD1, acute kidney injury (AKI) on first and second postoperative day (POD1 and POD2) and hospital length of stay [18] . AKI was defined according to the Acute Kidney Injury Network criteria as stage 1 or higher (increase in peak postoperative serum creatinine level to > 150% or ≥ 26.5 μmol/L from baseline value) [19] . Hyperlactatemia was defined as an arterial lactate level above 1.5 mmol/L [4, 5] . We choose a ΔPCO 2 > 6 mmHg to define an elevated ΔPCO 2 and a normal ERO 2 as a level of 30% or lower. These cutoff values were chosen according to previous studies [12, 13] .

We constructed an algorithm combining ΔPCO 2 , ERO 2 and lactate. This algorithm helped us identifying a low-risk profile and a high-risk profile (Fig. 1 ). In the algorithm, lactate elevation was considered a marker of global anaerobic metabolism in the presence of altered values of ΔPCO 2 or ERO 2 . If ΔPCO 2 and ERO 2 were both normal, lactate elevation was considered to be non-hypoxic hyperlactatemia. ΔPCO 2 elevation was considered to reflect low cardiac Overall, 13 (52%) patients had high-risk profile whereas 12 (48%) patients experienced a low-risk profile output (CO) in case of high ERO 2 or microcirculatory dysfunction with impaired oxygen extraction if ERO 2 was normal. An elevated ERO 2 in presence of a normal ΔPCO 2 was considered to reflect a decrease in DO 2 non-related to a low CO (anemia, hypoxemia). Patients with global anaerobic metabolism, non-hypoxic hyperlactatemia or microcirculatory dysfunction with impaired oxygen extraction as determined by the algorithm were considered to have a high-risk profile. The ICU length of stay (LOS) of high-risk profile patients was then compared to the rest of the population.

The number of patients included in that pilot study was fixed empirically to 25. All data were tested for normal distribution with the Kolmogorov-Smirnov test. Normally distributed data were displayed as mean ± standard deviation and not normally distributed data were displayed as median with 25th percentile and 75th percentile. Comparisons were performed using Fisher's exact test or Chi squared test for categorical data according to the distribution. Independentsamples T test was used to test the differences in normally distributed variables and Mann-Whithney U test for not normally distributed variables. Trends in the parameters over time in two groups were compared with repeated-measures ANOVA. Pairwise comparisons were corrected for multiple testing with the Bonferroni procedure. Pearson or Spearman correlation coefficients for data normally distributed and not normally distributed, respectively, were used to evaluate the relation between two variables. We analyzed correlation between values of the whole dataset and markers POD1 variations. To calculate markers POD1 variations we identified, among the whole dataset, the successive samples of Lactate, ΔPCO 2 and ERO 2 measured within 6 to 12 h intervals in a given patient and calculated the markers variations as following: Marker variation = 100% × (second marker value−initial marker value)/initial marker value. We used the log-rank test to compare the length of stay in ICU according to the patient risk group on admission. For all tests, a two-tailed P value less than 0.05 was considered significant. Statistical analyses were performed using R software (R Foundation for Statistical Computing 2016).

Twenty-five consecutive adult patients were included in the study (52% CABG, 36% valvular surgery and 12% combined surgery). Baseline characteristics and surgery-related parameters in the whole cohort of patients and in both lowand high-risk groups are shown in Table 1 . No significant difference was found between groups with the exception of the ICU LOS.

At the time of admission, 64% of the patients had elevated ΔPCO 2 (Fig. 1) . On POD1 a ΔPCO 2 ≥ 6 mmHg was reported at least once in all patients. ΔPCO 2 did not significantly decrease on POD1 and patients with high ΔPCO 2 on admission did not have significantly different ΔPCO 2 at T6, T12 and T24 compared with patients with normal ΔPCO 2 (Fig. 2) . Correlations between ΔPCO 2 and lactate or ERO 2 for POD1 values and variations are shown in Table 2 . A weak correlation was found between ΔPCO 2 and ERO 2 both for POD1 absolute values (r = 0.41, p < 0.01) and variations (r = 0.46, p < 0.01). Correlation between S cv O 2 and ERO 2 was excellent (r = − 0.99, p < 0.01). Other correlations between markers were weak or non-significant ( Table 2 ). S cv O 2 was not significantly different in patients with normal or elevated ΔPCO 2 (70 (63-74) % versus 65 (58-70) %, respectively, p = 0.1).

At the time of ICU admission an elevated ΔPCO 2 did not predict prolonged ICU and hospital stays ( Fig. 3a and  b) .

The algorithm combining ΔPCO 2 with ERO 2 and lactate identified 12 patients with a low-risk profile and 13 patients with a high-risk profile at the time of admission. Out of the 16 patients with elevated ΔPCO 2 upon admission, 7 (43%) were classified as low-risk profile while 9 (57%) were identified as high-risk profile. Conversely, out of the 9 patients with low ΔPCO 2 upon admission, 4 (44%) were identified as high-risk profile and 5 (56%) as low-risk profile. The precise incidence of the different hemodynamic patterns is further described in Fig. 1 . Temperature on ICU admission was not different in patients with normal and elevated ΔPCO 2 (36.5±0.5 °C vs. 36.5±0.5 °C respectively, p = 1.0) and between high and low risk groups (36.7±0.4 °C vs. 36.4±0.5 °C respectively, p = 0.1). The high-risk patient group at the time of admission had ICU LOS twice as long as those in the low-risk patient group. (Table 1 and Fig. 3c ) Hospital length of stay was also significantly longer for patient in the high-risk group compared to the low risk group (12.0 (8.0-15.0) versus 8.0 (8.0-9.0) days respectively, p < 0.01). (Figure 3d ) We found significantly more AKI in patients of the high-risk group compared to the low risk group ((4 (30%) vs. 0 (0%), respectively, p = 0.04) and serum creatinine on POD1 and POD2 were significantly higher ( Table 1 ). All AKI were stage 1 according to the Acute Kidney Injury Network. Need for prolonged mechanical ventilation, inotropic support and SOFA on POD1 were not significantly different between groups ( Table 1 ). The ICU LOS was not significantly different across patients with low or elevated lactate (5.0 (3.0-7.0) days versus 7.0 (6.0-8.0) days, respectively, p = 0.09) and/or low or elevated ERO 2 (6.0 (5.0-7.5) days versus 6.5 (3.0-7.0) days, respectively, p = 0.60) at the time of admission.

The main results of this prospective pilot study are as: -High ΔPCO 2 (≥ 6 mmHg) on admission and on POD1 of conventional cardiac surgery was found to be very frequent and did not predict an elevated ΔPCO 2 at T6, T12 or T24. Correlations between POD1 values and POD1 variations for ΔPCO 2 and ERO 2 or lactate were weak or non-existent.

-At the time of admission an elevated ΔPCO 2 alone did not predict a prolonged ICU stay. Conversely, ICU LOS increased by 2-fold in the high-risk patient group identified with the algorithm. High-risk patients also had significantly more postoperative AKI and longer hospital LOS.

Limited prospectively reported data on ΔPCO 2 after CPB are available. As previously reported in retrospective studies, our prospective study demonstrates that a widening in ΔPCO 2 on POD1 after conventional elective cardiac surgery is quite frequent [11, 15] . The reason why ΔPCO 2 remains elevated on ICU admission and on POD1 is unclear. An adequate venous blood flow is the main contributor of ΔPCO 2 and depends on both cardiac output and tissue perfusion [14, 20] . It has been demonstrated that ΔPCO 2 after cardiac surgery is only poorly correlated to cardiac output or regional blood flow [21] . It has been suggested that impaired microcirculation could be responsible for the widening in ΔPCO 2 especially when it is associated with normal S cv O 2 [11, 12] . In these studies the pattern of a high ΔPCO 2 with a normal S cv O 2 was associated to further post-operative complications, impaired splanchnic function or elevated lactate. Yet, it is still uncertain whether a high ΔPCO 2 after cardiac surgery is related to microcirculatory hypoperfusion and further studies are needed. We found no strong correlations between ΔPCO 2 and ERO 2 or lactate, which is concordant with previous studies in the settings of cardiac surgery and septic shock [2, 15, [22] [23] [24] . This particular lack of strong correlation is not surprising, since ΔPCO 2 , ERO 2 and lactate provide information on different hemodynamic mechanisms. For example, high lactate is a marker of global anaerobic metabolism whereas high ΔPCO 2 indicates decreased blood flow that can occur without anaerobic metabolism [14] . Conversely, tissue hypoxia from non-ischemic cause will not be detected by ΔPCO 2. but will induce a rise in lactate value [14] . Concerning ERO 2 , its elevation is a normal adaptation mechanism that can also occur without tissue hypoxia [1] .

We did not find any significant prognostic value of an elevated ΔPCO 2 at the time of admission. Similarly, ERO 2 and lactate level taken alone at the time of admission did not predicted a prolonged ICU stay. It is important to not misinterpret these results. We underline that the prognostic value of hyperlactatemia or abnormal ERO 2 after cardiac surgery has been demonstrated in several studies [3, 9, 22, 23] . Concerning ΔPCO 2 , an elevation ≥ 6 mmHg at admission has also been shown to be associated with poor prognosis in high surgical risk patients but the results are conflicting in cardiac surgery [11, 13, 15] . In a recent retrospective study, high ΔPCO 2 after cardiac surgery was not associated with a worst outcome but the authors analyzed ΔPCO 2 alone [15] . Conversely, Habicher et al. found that in presence of normal S cv O 2 ≥ 70%, a high ΔPCO 2 (ΔPCO 2 ≥ 8 mmHg) was associated to further post-operative complications [11] . The combination of these two markers seem to better predict complications. Similarly, our study suggests that an algorithm combining ΔPCO 2 with ERO 2 and lactate improved prognostic signification of these markers at admission. Indeed, in our study, 43% of patients with high ΔPCO 2 at the time of admission were classified as low-risk group and 44% patients with low ΔPCO 2 were eventually categorized in the high-risk group. Consequently, we think that ΔPCO 2 , ERO 2 and lactate should not be interpreted separately but together using an algorithm.

Our study population was at low risk according to the EuroSCORE 2 evaluation. However, when associating to this preoperative scoring system the ΔPCO 2 , ERO 2 and lactate measures immediately at the ICU admission almost half of our population was eventually classified as highrisk group. Interestingly, although our study was lacking of power for prognosis evaluation, ICU and hospital stays were significantly longer and patients had more acute renal failure in the high-risk group compared to the low-risk group while their EuroSCORE 2 did not differ significantly. We think that our algorithm-based evaluation at the time of ICU admission may have led to the identification of clinically significant intraoperative complications. The design of our interpretation algorithm is quite similar to the algorithm which was recently published by De Backer in a review on hemodynamic in shock [17] . Yet, some differences should be discussed. For example, De Backer considers hyperlactatemia with normal ERO 2 and ΔPCO 2 as a profile of high cardiac output with dysoxia (i.e. sepsis). In our algorithm we consider this pattern as non-hypoxic hyperlactatemia. Indeed, in the setting of cardiac surgery, the occurrence of hyperlactatemia without evidence of inadequate oxygen delivery (DO 2 ) has been reported [7] . Although, the pathogenesis of this disorder remains unclear, according to the authors it should be considered as reflecting a type B lactic acidosis instead of an anaerobic metabolism. These patients with non-hypoxic hyperlactatemia were still considered as high risk. Indeed, an increased lactate level with a normal tension difference/arteriovenous O 2 content difference ratio (ΔPCO 2 /ΔContO 2 ; another anaerobic metabolism marker) was shown to be correlated to poor prognosis in a medical ICU [25] . Another difference of interpretation relates to the pattern of an increased ERO 2 with normal lactatemia and ΔPCO 2 . Both our algorithm and De Backer's findings agree on a decrease in DO 2 but De Backer associates it with dysoxia while we consider a rise in ERO 2 to be a normal adaptation mechanism [1] . Nevertheless, we regard both algorithms as useful tools for clinicians to improve comprehension of the patterns drawn by these routine markers of systemic perfusion. This study is the first to describe the incidence of the different hemodynamic profiles defined by these algorithms.

There are several limitations to our study. First, the circulatory profiles defined by the algorithm were not externally validated by, for example, a measurement of cardiac output and an evaluation of microcirculation by video microscopy. Further studies are needed to assess both macro and microcirculation in the suggested hemodynamic profiles. Another important limitation was the small size of the study population and the moderate severity of disease within it. We used central instead of mixed-venous blood to assess ERO 2 and CO 2 derived variables; our results may have differed if a pulmonary artery catheter (PAC) had been used [26] . Fig. 3 Length of stay in ICU stay according to ΔPCO 2 alone (a) or according to risk group (ΔPCO 2 in combination with ERO 2 and lactate) (b) at the time of admission. ΔPCO 2 central venous to arterial PCO 2 difference, ICU intensive care unit. Patients with global anaerobic metabolism, non-hypoxic hyperlactatemia or microcircula-tory dysfunction with impaired oxygen extraction as determined by the algorithm were considered to have a high-risk profile (Fig. 1) . Patients with normal tissue perfusion or decreased oxygen delivery without anaerobic metabolism were considered to have low-risk profile However, PAC is no longer used in conventional cardiac surgery. Hypothermia is also a potential confounder for ΔPCO 2 measurement. It may decrease cellular respiration and CO 2 generation, especially for very low temperature [20] . Nevertheless none of our patients had profound hypothermia on ICU admission and normothermia was rapidly achieved for all of our patients. Finally, lactate, ΔPCO 2 or ERO 2 surely have different physiological kinetics, with the clearance of lactate probably slower than that of ERO 2 and CO 2 derived variables. This makes it difficult to interpret a snapshot of those markers. That being said, taking the kinetics variations of the markers into account would also be very difficult. We designed our algorithm to provide clinicians with an everyday tool for the interpretation of arterial and central venous blood gases, and it appears to correlate with a clinical reality as it predicts occurrence of post-operative AKI and longer ICU and hospital stays.

In this original pilot study on patients who underwent standard cardiac surgery high ΔPCO 2 (≥ 6 mmHg) on admission and on POD1 of conventional cardiac surgery was found to be very frequent and high ΔPCO 2 on admission did not predict an elevated ΔPCO 2 at T6, T12 or T24. Correlations between POD1 values and POD1 variations for ΔPCO 2 and ERO 2 or lactate were weak or non-existent. An algorithm incorporating the ICU admission values of ΔPCO 2 , ERO 2 and lactate defined a high-risk profile that predicted prolonged ICU and hospital stays better than ΔPCO 2 alone.

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Acknowledgements The proofreading of this article was supported by the Bibliothèque Scientifique de l'Internat de Lyon and the Hospices Civils de Lyon.

Conflict of interest The authors declare that they have no conflict of interest.Ethical approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.Informed consent According to the French law and because data were collected during routine care, authorization was granted to waive written informed consent. However, verbal consent was obtained from all study participants before surgery.