Perioperative estimations of oxygen consumption from LiDCO™plus-derived cardiac output and Ca-cvO2 difference: Relationship with measurements by indirect calorimetry in elderly patients undergoing major abdominal surgery

Julia Jakobsson; Carl Norén; Eva Hagel; Magnus Backheden; Sigridur Kalman; Erzsébet Bartha

doi:10.1371/journal.pone.0272239

Abstract

Background

Feasible estimations of perioperative changes in oxygen consumption (VO₂) could enable larger studies of its role in postoperative outcomes. Current methods, either by reverse Fick calculations using pulmonary artery catheterisation or metabolic by breathing gas analysis, are often deemed too invasive or technically requiring. In addition, reverse Fick calculations report generally lower values of oxygen consumption.

Methods

We investigated the relationship between perioperative estimations of VO₂ (EVO₂), from LiDCO^™plus-derived (LiDCO Ltd, Cambridge, UK) cardiac output and arterial-central venous oxygen content difference (Ca-cvO₂), with indirect calorimetry (GVO₂) by QuarkRMR (COSMED srl. Italy), using data collected 2017–2018 during a prospective observational study on perioperative oxygen transport in 20 patients >65 years during epidural and general anaesthesia for open pancreatic or liver resection surgery. Eighty-five simultaneous intra- and postoperative measurements at different perioperative stages were analysed for prediction, parallelity and by traditional agreement assessment.

Results

Unadjusted bias between GVO₂ and EVO₂ indexed for body surface area was 26 (95% CI 20 to 32) with limits of agreement (1.96SD) of -32 to 85 ml min⁻¹m⁻². Correlation adjusted for the bias was moderate, intraclass coefficient(A,1) 0.51(95% CI 0.34 to 0.65) [F (84,84) = 3.07, P<0.001]. There was an overall association between GVO₂ and EVO₂, in a random coefficient model [GVO₂ = 73(95% CI 62 to 83) + 0.45(95% CI 0.29 to 0.61) EVO₂ ml min⁻¹m⁻², P<0.0001]. GVO₂ and EVO₂ changed in parallel intra- and postoperatively when normalised to their respective overall means.

Conclusion

Based on this data, estimations from LiDCO^™plus-derived cardiac output and Ca-cvO₂ are not reliable as a surrogate for perioperative VO₂. Results were in line with previous studies comparing Fick-based and metabolic measurements but limited by variability of data and possible underpowering. The parallelity at different perioperative stages and the prediction model can provide useful guidance and methodological tools for future studies on similar methods in larger samples.

Citation: Jakobsson J, Norén C, Hagel E, Backheden M, Kalman S, Bartha E (2024) Perioperative estimations of oxygen consumption from LiDCO™plus-derived cardiac output and Ca-cvO2 difference: Relationship with measurements by indirect calorimetry in elderly patients undergoing major abdominal surgery. PLoS ONE 19(7): e0272239. https://doi.org/10.1371/journal.pone.0272239

Editor: Ioannis Savvas, Aristotle University of Thessaloniki School of Veterinary Medicine, GREECE

Received: July 16, 2022; Accepted: June 10, 2024; Published: July 25, 2024

Copyright: © 2024 Jakobsson et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

A postoperative imbalance between oxygen consumption and delivery, leading to increased oxygen extraction, has been associated with increased morbidity and mortality after major surgery [1]. The focus of goal-directed haemodynamic therapy (GDHT) has traditionally been on oxygen delivery, which is often easier to assess and to develop measurable optimisation strategies for [2]. Recently, interest is growing to reassess perioperative oxygen consumption in current surgical populations using modern monitoring and analytic methodologies [3–6]. Feasible estimations could enable larger studies on the role of oxygen consumption in postoperative outcomes. Available techniques, by pulmonary artery catheterisation or indirect calorimetry, are either deemed too invasive or difficult to manage in a clinical study setting during non-cardiac surgery. Using oxygen uptake calculated from fractions of inspiratory and expiratory oxygen in the closed breathing circuit during low-flow anaesthesia [7] has not demonstrated agreement when compared to standard methods [8]. Importantly, it can not be used in awake patients in the postoperative period. Commonly used haemodynamic monitoring in major surgery, such as minimal-invasive cardiac output with arterial and central venous access, could offer a possibility not only to estimate intra- and postoperative oxygen consumption but also to follow changes over time. By substituting mixed with central venous oxygen content and using the cardiac output derived from a minimal-invasive monitor, an estimation of oxygen consumption could theoretically be calculated by the reverse Fick principle [9]. The lack of absolute agreement between calorimetric and Fick-based methods has been reported previously, the latter do not include pulmonary oxygen consumption and global oxygen consumption values are usually reported around 20–40 ml min⁻¹m⁻² lower compared to those obtained from breathing gas analysis [10–12]. Examples of previous studies comparing methods for assessing oxygen consumption by either breathing gas analysis or Fick-based measurements are presented in Table 1. Yet, if this bias remains unchanged in the intra- and postoperative period, such estimations could be studied in larger samples and related to other clinical parameters and outcomes.

Download:

Table 1. Examples of previous studies comparing methods for gas-derived VO2 with Fick-derived VO2.

https://doi.org/10.1371/journal.pone.0272239.t001

We aimed to investigate the relationship and temporal changes between estimations of oxygen consumption (EVO₂), from LiDCO™plus-derived cardiac output and blood gas sampling from arterial and central venous lines, and measured oxygen consumption (GVO₂). We used prospectively collected data from an observational study on perioperative oxygen consumption and delivery in elderly patients undergoing major abdominal surgery [13].

Materials and methods

This was a pre-planned prospective explorative study based on data collected during an observational study in patients > 65 yrs undergoing open liver or pancreatic surgery between Dec 2017 and April 2018 (clinicaltrials.gov NCT03355118). The results on oxygen transport parameters from that study has been published [13]. The Regional Ethics Review Board of the Stockholm Region (ID 2017/291-31/4) approved the study and written informed consent was obtained from all participants.

Patients and settings

A description of selection and enrolment, patient characteristics’ and perioperative management can be found in the previous publication [13]. As stated there, 20 ASA II-IV patients over 65 years undergoing open pancreatic or liver resection surgery in epidural and general anaesthesia were included. The study was conducted at the Karolinska University Hospital in Huddinge, a tertiary referral center for upper abdominal surgery.

Data extraction and time-points

Paired values of oxygen consumption by estimations based on cardiac output monitoring from LiDCO™plus and arterial-central venous blood gas samples (EVO₂) and indirect calorimetry GVO₂) from five perioperative time-points were analysed; T1: during anaesthesia, right before surgical skin incision; T2: early during surgery, directly after skin incision; T3: later during surgery, >2h after skin incision; T4: early postoperatively, <12h after extubation; T5: late postoperatively, on postoperative day 1. The mean values for GVO₂ during the approximate 20-minute measurement periods were compared with simultaneous cardiac output measurements averaged for each minute exported from LiDCOviewPRO (LiDCO Ltd, Cambridge, UK). The blood gas parameters were calculated as means of two simultaneously drawn arterial and central venous samples at 5 and 15 minutes into the measurement period.

Measurements of VO2 by indirect calorimetry (GVO2)

Indirect calorimetry was performed by QuarkRMR (COSMED srl, Italy). This device applies a breath-by-breath technique to measure gas flow and concentrations that are synchronised by data processing algorithms. The Haldane transformation is used to calculate oxygen consumption [14]. During intraoperative measurements, the flow meter (Flow-REE, COSMED srl, Italy), gas sampling line and moist filter were placed between the endotracheal tube and the Y-piece of the ventilator. The ventilator was set to a fresh gas flow of 2 L min^-1 and FiO₂ of 0.5 during measurements to allow for gas sampling. All other ventilation settings were left unchanged. Postoperative measurements were made with a tight-fitting face mask connected to a bidirectional turbine flow meter and a gas sampling line. No supplemental oxygen was administered during the postoperative measurements. The calorimeter was calibrated before start of intraoperative measurements and before each postoperative measurement after a warm-up time of 20 minutes with a standardised gas mixture containing 16% oxygen and 5% carbon dioxide. The gas sampling line, Flow-REE and moist filter were changed before each measurement (except before T2, continuous to T1) and all flowmeters were calibrated with a 3L-syringe.

Estimation of VO2 by minimal-invasive cardiac output and arterial-central venous oxygen content difference (EVO2)

EVO₂ was calculated by the reverse Fick’s principle with central venous instead of pulmonary artery blood using the following formulas: [15] [CO; cardiac output in L min^-1, Ca-cvO₂; oxygen content difference between arterial and central venous blood in ml dl^-1, Hb; haemoglobin in g dl^-1, SaO₂; arterial oxygen saturation, ScvO₂; central venous saturation, PaO₂; partial pressure of oxygen in arterial blood, PcvO₂; partial pressure of oxygen in central venous blood, constants 1.31 and 0.0225, referring to the Hüfner constant and the solubility coefficient of oxygen (ml O₂ dl^-1 kPa^-1), and 10 as a conversion factor from dL to L.].

Cardiac output was obtained from LiDCO^™plus (LiDCO Ltd, Cambridge, UK). The device was calibrated and recalibrated a minimum of three times according to the manufacturer’s instructions. This was done using a transpulmonary lithium bolus indicator dilution technique for an absolute CO value to obtain a calibration factor and to perform autocalibration after which continuous measurement of haemodynamic variables is carried out by the pulse power analyses integrated in the LiDCO^™plus system. Calibration procedures was undertaken at times to avoid interference by non-depolarising muscle relaxants. Missing values from CO measurements (averaged for each minute) were substituted by linear interpolation between the subsequent measurements, making sure not more than three data points were missing, and no major haemodynamic changes occurred. Blood gases were analysed immediately after sampling by ABL800 Flex or ABL90 Flex (Radiometer Medical ApS, Denmark). Cardiac output and measured oxygen consumption were indexed for body surface area using the DuBois formula yielding values of GVO₂ and EVO₂ in ml min⁻¹m⁻² [16].

Statistical analysis

The sample size calculation was performed for the primary study [13], based on a previous meta-analysis [4], from which 20 patients were expected to demonstrate a relevant change in oxygen consumption after induction of anaesthesia. This would yield a maximum of 100 paired measurements of EVO₂ and GVO₂ which was considered sufficient based on sample sizes in previous studies (Table 1) and with a possible >10% data loss. Continuous data was tested for normality distribution and statistical tests applied accordingly. Statistical analyses were performed and constructed in R (version 3.5.3; R Foundation for Statistical Computing, Vienna, Austria, URL; https://www.R-project.org) and SAS (version 9.4; SAS Institute Inc, Cary, NC, U.S.). The statisticians conducting the analyses were not involved in the data collection. Mean difference between EVO₂ and GVO₂ with 95% confidence interval were calculated from the individual paired measurements and grouped by time point (T1-5). These changes over time were analysed by linear mixed models with Holm-adjusted Tukey post-hoc tests. To investigate the overall association between EVO₂ and GVO₂, a random coefficient model was used based on individual slopes and coefficients. Analyses of the perioperative changes over time of GVO₂ compared to EVO₂ and its input variables (CI; cardiac index and Ca-cvO₂) were conducted by random effect mixed models with method or component and time as fixed effects. Adjustment for differences in variances of the methods or components was made. In these models, the relative changes were normalised to the patients’ individual baseline measurements (T1). In the model analysing changes of each method in awake and anaesthetised subjects, the changes were normalised to the respective overall mean.

Traditional agreement assessment was also performed by intraclass correlation and Bland-Altman analysis. Single score intraclass correlation was used, a in a two-way model yielding ICC coefficients with 95% CI. Bias and limits of agreement with 95% CI was visualised in Bland-Altman plots. Both ICC and Bland-Altman analyses were performed separately for each time-point T1–T5. The overall ICC and Bland-Altman analyses were not adjusted for repeated measurements as these were performed under varying intra- and postoperative conditions. Normality and homoscedasticity were assessed in residual plots. An alpha of 0.05 was considered significant.

Results

A total of 85 paired measurements of EVO₂ (LiDCO™plus-derived cardiac output Ca-cvO2) and GVO₂ (measurements by indirect calorimetry) were obtained in 20 subjects; 58 were obtained intraoperatively and 27 in the postoperative period. Four paired intraoperative measurements were not performed due to early termination of surgery (unexpected metastatic spread of malignancy) in two patients. Thirteen paired measurements could not be performed in the postoperative period because of technical or arterial line failure (n = 2), logistical reasons (n = 2), patients’ decline (n = 3), exclusion due to short postoperative stay (n = 4) and need for supplemental oxygen (n = 2). Correct positioning of the CVC was confirmed by postoperative chest x-ray in all patients. As stated above, patients’ characteristics and perioperative data along with enrolment details can be found in the main oxygen transport study [13].

Taking all 85 paired measurements together, EVO₂ was generally lower than GVO₂ with an overall mean difference of oxygen consumption between EVO₂ and GVO₂ of -26 (95% CI -20 to -32; P<0.001) ml min⁻¹m⁻². The difference at the different perioperative stages (anaesthesia, early and late surgery, early and late postoperative) is presented in Fig 1. The changes between these stages were not statistically significant. [F(4, 168) = 1.39, P = 0.241]. The mean overall difference between GVO₂ and EVO₂ unadjusted for body surface area was -50 (95% CI -61 to -39; P<0.001) ml min^-1. Percentage error (PE) for all measurements was 30 (95% CI 26 to 34) % with a coefficient of variation of 61%. Intraoperative measurements had a PE of 34 (95% CI 30 to 38) % and postoperative 21 (95% CI 13 to 29) % with coefficients of variation of 47% and 94%, respectively.

Download:

Fig 1. Difference between oxygen consumption measured indirect calorimetry (GVO₂) and estimated from LiDCOplus™-derived cardiac output and Ca-cvO₂ (EVO₂) at each perioperative time-point; anaesthesia (before skin incision), early surgery (after skin incision), late surgery (>2hrs after skin incision), early postop (<12 hrs after extubation, late postop (postoperative day 1) expressed as mean (95% CI) ml min^-1 m^-2.

https://doi.org/10.1371/journal.pone.0272239.g001

Bland-Altman plots were constructed to illustrate the bias and limits of agreement between EVO₂ and GVO₂ at the different time-points, see Fig 2. The overall unadjusted mean bias was 26 ml min^-1 m^-2 with limits of agreement (1.96SD) of -32 to 85 ml min⁻¹m⁻². Excluding one outlier in the late postoperative period (a patient with a large Ca-cvO₂ difference) changed the unadjusted bias to 28 (LoA -20 to 75) ml min^-1 m^-2. The overall correlation for absolute agreement was poor, with an intraclass coefficient ICC(A,1) of 0.37 (95% CI 0.34 to 0.65) [F(84,10.2) = 3.07, P = 0.0266], and improved to moderate but with large confidence intervals when adjusted for lower overall mean difference of EVO₂, ICC(A,1) = 0.51 (95% CI 0.34 to 0.65) [F(84, 84) = 3.07, P<0.001]. Graphs depicting the correlation between indexed GVO₂ and EVO₂ at the different time-points (T1–5) including the unadjusted overall correlation are presented in S1 File.

Download:

Fig 2. Bland-Altman plots describing mean GVO₂-EVO₂ vs ΔGVO₂-EVO₂ in ml min^-1 m^-2 at the different time-points with bias (continuous lines) and limits of agreement (dotted lines).

Anaesthesia (T1; N = 20); b. Early surgery (T2; N = 20); c. Late surgery (T3; N = 18); d. Early postop (T4; N = 13); e. Late postop (T5; N = 14); f. all time-points in the same plot (N = 85 paired measurements).

https://doi.org/10.1371/journal.pone.0272239.g002

Parallel changes were demonstrated between GVO₂ and EVO₂ when separated to the anaesthetised intraoperative state [F(2, 49.9) = 0.57, P = 0.5669] and the awake postoperative state F(1, 22) = 0.00, P = 0.9604), Fig 3a and 3b. An overall association between GVO₂ and EVO₂ was demonstrated in a random coefficient model for predicting GVO₂ from EVO₂, but with large predictions intervals as illustrated by the model coefficients (Fig 4). The two patients with early termination of surgery were excluded from this analysis. The variances of EVO₂ and its components, oxygen content difference in arterial and central venous blood (Ca-cvO₂) and cardiac index (CI) were larger compared to GVO₂ at all time-points, these analyses are presented in S2 File.

Download:

Fig 3.

a and b. Perioperative changes of GVO₂ (red) and EVO₂ (black) separated for anaesthetised (a) and awake postoperative (a) states. Least square means estimates with 95% CI and normalised to overall means (= 1.0) of each method in anaesthetised intraoperative (T1–T3) and awake postoperative states (T4–T5). (T1) anaesthesia; Early surgery (T2); Late surgery (T3); Early postop (T4); Late postop (T5).

https://doi.org/10.1371/journal.pone.0272239.g003

Download:

Fig 4. A random coefficient model for predicted GVO₂ from EVO₂ based on all perioperative time-points.

GVO₂ = β0 + β1 (EVO₂). Two patients with a total of only 2 paired intraoperative measurements(T1–T2) were excluded from the analysis.

https://doi.org/10.1371/journal.pone.0272239.g004

Discussion

To the best of our knowledge, this is the first study investigating a Fick-based estimation method for perioperative oxygen consumption based on commonly used haemodynamic monitoring (LiDCO^™plus and blood gas sampling from arterial and central venous lines). Bias and agreement with indirect calorimetry were approximate to previous studies using pulmonary artery catheters in which Fick-based methods have demonstrated 20–40 ml min^-1 m^-2 lower values compared to metabolic measurements. As in many of these studies, limits of agreement and coefficients of variation were unacceptably large [12, 17–19]. The relationship between estimated and measured oxygen consumption was investigated by intraclass correlation, parallelity and with a random coefficient model. Parallelity and overall association was demonstrated, but the model had large prediction intervals, probably attributable to the limited number of observations and the variability of collected data. This estimation method cannot be recommended as clinically useful to assess perioperative oxygen consumption. Nevertheless, it can provide important guidance in the design and analytical approaches of future studies involving precise monitoring and larger number of observations.

Most previous studies investigating methods for oxygen consumption monitoring perioperatively or in critically ill patients were performed decades ago using traditional method comparison analytical methods. Examples of the earlier method comparison studies are summarised in Table 1. Newer studies using non-invasive cardiac output monitors have not shown agreement with oxygen consumption measurements from indirect calorimetry [20] or pulmonary artery catheters [21]. However, the monitors used were not calibrated by transpulmonary or indicator dilution such as the PiCCO^™ or LiDCO^™plus systems and did not analyse changes over time. Estimates of increased oxygen extraction, i.e low mixed or central venous saturation, are associated with poor surgical outcomes [22, 23]. However, cut-off levels remain unclear and the quality of evidence is low [24]. In order to further study and distinguish the role of oxygen consumption in the perioperative period, feasible estimations are needed. Oxygen consumption calculated by the reverse Fick equation is consistently reported lower than simultaneous measurements by analysis of respiratory gas exchange [8, 10–12, 17, 25–32]. This difference or bias has been attributed to the pulmonary oxygen consumption [30, 33, 34]. However, variability of Fick-derived measurements [28, 31] and wide limits of agreement [19] has made it difficult to estimate a systematic methodological bias. Many previous studies have either been performed in thoracic or cardiovascular surgery [10–12, 17, 33] or in critically ill patients [26, 29, 31, 35]. Pulmonary oxygen consumption can be expected to increase after thoracic surgery [12] and in intensive care patients with varying degrees of lung injury [36]. Some studies that involve patients undergoing predominately abdominal surgery have shown acceptable agreement between the methods [37, 38]. The age of the studies is also reflected by the frequent use of the Deltatrac Metabolic Monitor^® (Datex Instrumentarium, Helsinki, Finland), a metabolic monitor using a mixing-chamber technique and which is no longer in production. Many metabolic monitors in modern clinical use are based on breath-by-breath technology such as the Es-COVX^® (GE Healthcare, Helsinki, Finland) or the QuarkRMR^® in our study. Although there is supporting evidence for some overestimation of oxygen consumption, the technology has shown clinically acceptable agreement when compared with mixing-chamber methods [39, 40] and it has been validated in the semi-closed circle absorber systems commonly used in anaesthesia [41]. Our results on GVO₂ were comparable with studies using Deltatrac II when corrected for difference in units (Table 1). The estimations of oxygen consumption rely on accurate cardiac output determinations and oxygen content difference measurements. The LiDCO^™plus has shown acceptable performance against the pulmonary artery catheter and other devices in cross-comparisons in cardiac output accuracy studies [42, 43]. During rapidly changing haemodynamic situations, concerns regarding trending ability and underestimation of cardiac output have been raised [44, 45]. The 20-minute data extraction periods in this study were specifically chosen to represent perioperative time points that usually are without considerable circulatory instability. Central and mixed venous oxygen saturation have not shown interchangeability [46–48] but some studies have suggested that trends in ScvO₂ can replace SvO₂ [49–51]. During stable intraoperative conditions, oxygen content difference is not expected to vary to a large extent whereas cardiac output can show considerable in- and between patient variability [21]. In our study, oxygen content difference and cardiac output demonstrated similar coefficients of variation.

The known lack of agreement between gas- and Fick-derived measurements of oxygen consumption and the lack of a clinically available golden standard method led us to apply alternative analytical approaches. Time effects and repeated measurements in the same subject under changing conditions constitute important statistical challenges in studies involving perioperative patients. Previous studies have often used simple linear regression or correlation [10, 11, 20, 29] or Bland-Altman analysis [52] without correction for repeated measurements [12, 20, 26, 37] except for some [19, 32]. Only a few address the relationship between measurements over time [17, 27]. In the present study, we developed a prediction model for EVO₂ and GVO₂ by using a random coefficient model based on individual slopes and intercepts. A significant positive association was demonstrated here, but with large prediction intervals. Such prediction models should obviously be evaluated in larger samples. We also present analyses of relative changes of EVO₂ and its components with GVO₂. The parallelity that was demonstrated could indicate an ability of EVO₂ to track changes in oxygen consumption. To address this further, multiple measurements during shorter periods of time would be required. Analytic models previously used for cardiac output monitors such as polar plot approaches could assess the magnitude and direction of changes [44]. Intraclass correlation (ICC) was used as it better reflects reliability and agreement based on analysis of variance of the pooled data [53]. When adjusted for the consistently lower values of EVO₂, the ICC estimates of the model improved but not so much (ICC coefficient 0.51 vs 0.37). Bland-Altman analysis has since long been the standard method for visualisation of agreement when comparing different methods of oxygen consumption monitoring [19]. Myles and Cui further elaborated the methodological issues related to repeated measurements in the same subject already considered by Bland and Altman [54] and proposed different random effects models to adjust limits of agreement [19]. As measurements were performed under varying perioperative conditions, we present the time-points separately and did not adjust the overall limits of agreement for repeated measurements in the same patient.

This study has several major limitations in addition to those discussed above. Although the number of observations is in the same range as in many previous studies (Table 1), no sample size calculation was performed for the specific analytical approaches that were applied. Consequently, it is possibly underpowered for many of these outcomes. There was a considerable loss of data in the postoperative period limiting the conclusions on the changes at the different perioperative stages.

In summary, this estimation method cannot be regarded as clinically useful, and results were comparable to previous comparisons between Fick-based and metabolic methods. Large variability of data and possible underpowering limited the construction of a prediction model and determination of a precise systematic bias. The results on parallelity and the overall association between the methods should be regarded as indicative. That aside, it can provide useful methodological tools for future studies on oxygen consumption assessment.

Supporting information

S1 File. Intraclass correlation comparing GVO2 and EVO2 at different perioperative stages.

S1. Two-way single score intraclass correlation, ICC (A,1), of GVO2 and EVO2 indexed for body surface area (i) in ml min-1 m-2 at the different time-points with ICC coefficients (95% CI): a. Anaesthesia (T1; N = 20) -0.066 (-0.187,0.273); b. Early surgery (T2; N = 20) -0.131 (-0.285, 0.212); c. Late surgery (T3; N = 18) 0.019 (-0.081, 0.212); d. Early postop (T4; N = 13) 0.214 (-0.164, 0.619) e. Late postop (T5; N = 14) 0.224 (-0.316, 0.660); f. overall (see text for details).

https://doi.org/10.1371/journal.pone.0272239.s001

(TIF)

S2 File. Parallelity analyses between GVO2 and EVO2 and its components.

S2:1 Mixed effect models for parallelity analysis between GVO2 and EVO2 normalised to baseline Anaesthesia (T0) (= 1.0); Early surgery (T2); Late surgery (T3); Early postop (T4); Late postop (T5) pp. 2–9 S2:2. Mixed effect models for parallelity analysis between GVO2 and Ca-cvO2 (arterio-central venous oxygen content difference) normalised to baseline Anaesthesia (T0) (= 1.0); Early surgery (T2); Late surgery (T3); Early postop (T4); Late postop (T5) pp.10–17 S2:3 Mixed effect models for paralellity analysis between GVO2 and CI (cardiac index) normalised to baseline Anaesthesia (T0) (= 1.0); Early surgery (T2); Late surgery (T3); Early postop (T4); Late postop (T5) pp.18–25 S2:4.Mixed effect models for parallelity analysis between GVO2 and EVO2 normalised to overall mean for each method (= 1.0) in anaesthetized subjects Anaesthesia (T0); Early surgery (T2); Late surgery (T3) pp.26–31S2:5. Mixed effect models for paralellity analysis between GVO2 and EVO2 normalised to overall mean for each method (= 1.0) in awake subjects Early postop (T4); Late postop (T5) pp. 32–37.

https://doi.org/10.1371/journal.pone.0272239.s002

(DOCX)

Acknowledgments

The authors would like to thank research assistant nurses at the Clinical Research Unit, Dept. of Upper Abdominal Surgery, Karolinska University Hospital Huddinge, Birgitta Holmberg and Sirje Laur, for their help in recruiting and informing the patients included in this study.

References

1. Shoemaker WC, Appel PL, Kram HB. Hemodynamic and oxygen transport responses in survivors and nonsurvivors of high-risk surgery. Crit Care Med. 1993;21(7):977–90. pmid:8319478
- View Article
- PubMed/NCBI
- Google Scholar
2. Grocott MP, Dushianthan A, Hamilton MA, Mythen MG, Harrison D, Rowan K, et al. Perioperative increase in global blood flow to explicit defined goals and outcomes following surgery. Cochrane Database Syst Rev. 2012;11:CD004082. pmid:23152223
- View Article
- PubMed/NCBI
- Google Scholar
3. Maheshwari K. Principles for minimizing oxygen debt: can they translate to clinical application and improve outcomes? Best Practice & Research Clinical Anaesthesiology. 2020. pmid:34801216
- View Article
- PubMed/NCBI
- Google Scholar
4. Jakobsson J, Vadman S, Hagel E, Kalman S, Bartha E. The effects of general anaesthesia on oxygen consumption: A meta-analysis guiding future studies on perioperative oxygen transport. Acta Anaesthesiol Scand. 2019;63(2):144–53. pmid:30238445
- View Article
- PubMed/NCBI
- Google Scholar
5. Carsetti A, Amici M, Bernacconi T, Brancaleoni P, Cerutti E, Chiarello M, et al. Estimated oxygen extraction versus dynamic parameters of fluid-responsiveness for perioperative hemodynamic optimization of patients undergoing non-cardiac surgery: a non-inferiority randomized controlled trial. BMC anesthesiol. 2020;20(1):87. pmid:32305061
- View Article
- PubMed/NCBI
- Google Scholar
6. Briesenick L, Schaade A, Bergholz A, Hoppe P, Kouz K, Krause L, et al. Energy Expenditure Under General Anesthesia: An Observational Study Using Indirect Calorimetry in Patients Having Noncardiac Surgery. Anesth Analg. 2023;137(1):169–75. pmid:36622833
- View Article
- PubMed/NCBI
- Google Scholar
7. Biro P. A formula to calculate oxygen uptake during low flow anesthesia based on FIO2 measurement. J Clin Monit Comput. 1998;14(2):141–4. pmid:9669452
- View Article
- PubMed/NCBI
- Google Scholar
8. Leonard IE, Weitkamp B, Jones K, Aittomaki J, Myles PS. Measurement of systemic oxygen uptake during low-flow anaesthesia with a standard technique vs. a novel method. Anaesthesia. 2002;57(7):654–8. pmid:12059823
- View Article
- PubMed/NCBI
- Google Scholar
9. McLellan S, Walsh TS. Oxygen delivery and haemoglobin. Cont Ed Anaesth Crit Care Pain 2004;4(4):123–6.
- View Article
- Google Scholar
10. Keinanen O, Takala J. Calculated versus measured oxygen consumption during and after cardiac surgery. Is it possible to estimate lung oxygen consumption? Acta Anaesthesiol Scand. 1997;41(7):803–9. pmid:9265920
- View Article
- PubMed/NCBI
- Google Scholar
11. Peyton PJ, Robinson GJ. Measured pulmonary oxygen consumption: difference between systemic oxygen uptake measured by the reverse Fick method and indirect calorimetry in cardiac surgery. Anaesthesia. 2005;60(2):146–50. pmid:15644011
- View Article
- PubMed/NCBI
- Google Scholar
12. Saito H, Minamiya Y, Kawai H, Motoyama S, Katayose Y, Kimura K, et al. Estimation of pulmonary oxygen consumption in the early postoperative period after thoracic surgery. Anaesthesia. 2007;62(7):648–53. pmid:17567338
- View Article
- PubMed/NCBI
- Google Scholar
13. Jakobsson J, Noren C, Hagel E, Kalman S, Bartha E. Peri-operative oxygen consumption revisited: An observational study in elderly patients undergoing major abdominal surgery. Eur J Anaesthesiol. 2021;38(1):4–12. pmid:32858583
- View Article
- PubMed/NCBI
- Google Scholar
14. Wasserman K. Principles of exercise testing and interpretation including patophysiology and clinical applications. 5th ed. Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins; 2012.
15. Dunn JO MM, Grocott MP. Physiology of oxygen transport. BJA Ed. 2016;16(10):341–8.
- View Article
- Google Scholar
16. Wang Y, Moss J, Thisted R. Predictors of body surface area. J Clin Anesth. 1992;4(1):4–10. pmid:1540367
- View Article
- PubMed/NCBI
- Google Scholar
17. Bizouarn P, Soulard D, Blanloeil Y, Guillet A, Goarin Y. Oxygen consumption after cardiac surgery—a comparison between calculation by Fick’s principle and measurement by indirect calorimetry. Intensive Care Med. 1992;18(4):206–9. pmid:1430583
- View Article
- PubMed/NCBI
- Google Scholar
18. Hofland J, Tenbrinck R, Eggermont AM, Eijck CH, Gommers D, Erdmann W. Effects of simultaneous aortocaval occlusion on oxygen consumption in patients. Clin Physiol Funct Imaging. 2003;23(5):275–81. pmid:12950325
- View Article
- PubMed/NCBI
- Google Scholar
19. Myles PS, Cui J. Using the Bland-Altman method to measure agreement with repeated measures. Br J Anaesth. 2007;99(3):309–11. pmid:17702826
- View Article
- PubMed/NCBI
- Google Scholar
20. Inadomi C, Terao Y, Yamashita K, Fukusaki M, Takada M, Sumikawa K. Comparison of oxygen consumption calculated by Fick’s principle (using a central venous catheter) and measured by indirect calorimetry. J. 2008;22(2):163–6. pmid:18500614
- View Article
- PubMed/NCBI
- Google Scholar
21. Burtman DTM, Stolze A, Genaamd Dengler SEK, Vonk ABA, Boer C. Minimally Invasive Determinations of Oxygen Delivery and Consumption in Cardiac Surgery: An Observational Study. J Cardiothorac Vasc Anesth. 2018;32(3):1266–72. pmid:29422283
- View Article
- PubMed/NCBI
- Google Scholar
22. van Beest P, Wietasch G, Scheeren T, Spronk P, Kuiper M. Clinical review: use of venous oxygen saturations as a goal—a yet unfinished puzzle. Crit Care. 2011;15(5):232. pmid:22047813
- View Article
- PubMed/NCBI
- Google Scholar
23. Pearse R, Dawson D, Fawcett J, Rhodes A, Grounds RM, Bennett ED. Changes in central venous saturation after major surgery, and association with outcome. Crit Care. 2005;9(6):R694–9. pmid:16356220
- View Article
- PubMed/NCBI
- Google Scholar
24. Chemtob RA, Eskesen TG, Moeller-Soerensen H, Perner A, Ravn HB. Systematic review of the association of venous oxygenation and outcome in adult hospitalized patients. Acta Anaesthesiol Scand. 2016;60(10):1367–78. pmid:27620815
- View Article
- PubMed/NCBI
- Google Scholar
25. Bizouarn P, Blanloeil Y, Pinaud M. Comparison between oxygen consumption calculated by Fick’s principle using a continuous thermodilution technique and measured by indirect calorimetry. Br J Anaesth. 1995;75(6):719–23. pmid:8672320
- View Article
- PubMed/NCBI
- Google Scholar
26. Epstein CD, Peerless JR, Martin JE, Malangoni MA. Comparison of methods of measurements of oxygen consumption in mechanically ventilated patients with multiple trauma: the Fick method versus indirect calorimetry. Crit Care Med. 2000;28(5):1363–9. pmid:10834679
- View Article
- PubMed/NCBI
- Google Scholar
27. Myles PS, McRae R, Ryder I, Hunt JO, Buckland MR. Association between oxygen delivery and consumption in patients undergoing cardiac surgery. Is there supply dependence? Anaesth Intensive Care. 1996;24(6):651–7. pmid:8971311
- View Article
- PubMed/NCBI
- Google Scholar
28. Smithies MN, Royston B, Makita K, Konieczko K, Nunn JF. Comparison of oxygen consumption measurements: indirect calorimetry versus the reversed Fick method. Crit Care Med. 1991;19(11):1401–6. pmid:1935161
- View Article
- PubMed/NCBI
- Google Scholar
29. Soussi S, Vallee F, Roquet F, Bevilacqua V, Benyamina M, Ferry A, et al. Measurement of Oxygen Consumption Variations in Critically Ill Burns Patients: Are the Fick Method and Indirect Calorimetry Interchangeable? Shock. 2017;48(5):532–8. pmid:28452908
- View Article
- PubMed/NCBI
- Google Scholar
30. Stuart-Andrews C, Peyton P, Robinson G, Terry D, O’Connor B, Van der Herten C, et al. Non-invasive metabolic monitoring of patients under anaesthesia by continuous indirect calorimetry—an in vivo trial of a new method. Br J Anaesth. 2007;98(1):45–52. pmid:17124187
- View Article
- PubMed/NCBI
- Google Scholar
31. Walsh TS, Hopton P, Lee A. A comparison between the Fick method and indirect calorimetry for determining oxygen consumption in patients with fulminant hepatic failure. Crit Care Med. 1998;26(7):1200–7. pmid:9671369
- View Article
- PubMed/NCBI
- Google Scholar
32. Hofland J, Tenbrinck R, van Eijck CH, Eggermont AM, Gommers D, Erdmann W. Comparison of closed circuit and Fick-derived oxygen consumption in patients undergoing simultaneous aortocaval occlusion. Anaesthesia. 2003;58(4):377–84. pmid:12688276
- View Article
- PubMed/NCBI
- Google Scholar
33. Loer SA, Scheeren TW, Tarnow J. How much oxygen does the human lung consume? Anesthesiology. 1997;86(3):532–7. pmid:9066318
- View Article
- PubMed/NCBI
- Google Scholar
34. Oudemans-van Straaten HM, Scheffer GJ, Eysman L, Wildevuur CR. Oxygen consumption after cardiopulmonary bypass—implications of different measuring methods. Intensive Care Med. 1993;19(2):105–10. pmid:8486864
- View Article
- PubMed/NCBI
- Google Scholar
35. Schaffartzik W, Sanft C, Schaefer JH, Spies C. Different dosages of dobutamine in septic shock patients: determining oxygen consumption with a metabolic monitor integrated in a ventilator. Intensive Care Med. 2000;26(12):1740–6. pmid:11271080
- View Article
- PubMed/NCBI
- Google Scholar
36. Jolliet P, Thorens JB, Nicod L, Pichard C, Kyle U, Chevrolet JC. Relationship between pulmonary oxygen consumption, lung inflammation, and calculated venous admixture in patients with acute lung injury. Intensive Care Med. 1996;22(4):277–85. pmid:8708163
- View Article
- PubMed/NCBI
- Google Scholar
37. Hanique G, Dugernier T, Laterre PF, Dougnac A, Roeseler J, Reynaert MS. Significance of pathologic oxygen supply dependency in critically ill patients: comparison between measured and calculated methods. Intensive Care Med. 1994;20(1):12–8. pmid:8163752
- View Article
- PubMed/NCBI
- Google Scholar
38. Brandi LS, Bertolini R, Pieri M, Giunta F, Calafa M. Comparison between cardiac output measured by thermodilution technique and calculated by O2 and modified CO2 Fick methods using a new metabolic monitor. Intensive Care Med. 1997;23(8):908–15. pmid:9310812
- View Article
- PubMed/NCBI
- Google Scholar
39. Allingstrup MJ, Kondrup J, Perner A, Christensen PL, Jensen TH, Henneberg SW. Indirect Calorimetry in Mechanically Ventilated Patients: A Prospective, Randomized, Clinical Validation of 2 Devices Against a Gold Standard. JPEN J Parenter Enteral Nutr. 2017;41(8):1272–7. pmid:27488830
- View Article
- PubMed/NCBI
- Google Scholar
40. Rehal MS, Fiskaare E, Tjader I, Norberg A, Rooyackers O, Wernerman J. Measuring energy expenditure in the intensive care unit: a comparison of indirect calorimetry by E-sCOVX and Quark RMR with Deltatrac II in mechanically ventilated critically ill patients. Crit Care. 2016;20:54. pmid:26951095
- View Article
- PubMed/NCBI
- Google Scholar
41. Stuart-Andrews CR, Peyton P, Robinson GJ, Terry D, O’Connor B, Van der Herten C, et al. In vivo validation of the M-COVX metabolic monitor in patients under anaesthesia. Anaesth Intensive Care. 2007;35(3):398–405. pmid:17591136
- View Article
- PubMed/NCBI
- Google Scholar
42. Lamia B, Kim HK, Severyn DA, Pinsky MR. Cross-comparisons of trending accuracies of continuous cardiac-output measurements: pulse contour analysis, bioreactance, and pulmonary-artery catheter. J Clin Monit Comput. 2018;32(1):33–43. pmid:28188408
- View Article
- PubMed/NCBI
- Google Scholar
43. Hadian M, Kim HK, Severyn DA, Pinsky MR. Cross-comparison of cardiac output trending accuracy of LiDCO, PiCCO, FloTrac and pulmonary artery catheters. Crit Care. 2010;14(6):R212. pmid:21092290
- View Article
- PubMed/NCBI
- Google Scholar
44. Critchley LA, Lee A, Ho AM. A critical review of the ability of continuous cardiac output monitors to measure trends in cardiac output. Anesth Analg. 2010;111(5):1180–92. pmid:20736431
- View Article
- PubMed/NCBI
- Google Scholar
45. Beattie C, Moores C, Thomson AJ, Nimmo AF. The effect of anaesthesia and aortic clamping on cardiac output measurement using arterial pulse power analysis during aortic aneurysm repair. Anaesthesia. 2010;65(12):1194–9. pmid:21182600
- View Article
- PubMed/NCBI
- Google Scholar
46. Lequeux PY, Bouckaert Y, Sekkat H, Van der Linden P, Stefanidis C, Huynh CH, et al. Continuous mixed venous and central venous oxygen saturation in cardiac surgery with cardiopulmonary bypass. Eur J Anaesthesiol. 2010;27(3):295–9. pmid:19935073
- View Article
- PubMed/NCBI
- Google Scholar
47. van Beest PA, van Ingen J, Boerma EC, Holman ND, Groen H, Koopmans M, et al. No agreement of mixed venous and central venous saturation in sepsis, independent of sepsis origin. Crit Care. 2010;14(6):R219. pmid:21114844
- View Article
- PubMed/NCBI
- Google Scholar
48. Soussi MS, Jebali MA, Le Manach Y, Nasri M, Zouari B, Chenik S, et al. Central venous saturation is not an alternative to mixed venous saturation during cardiopulmonary bypass in coronary artery surgery patients. Perfusion. 2012;27(4):300–6. pmid:22499058
- View Article
- PubMed/NCBI
- Google Scholar
49. Reinhart K, Kuhn HJ, Hartog C, Bredle DL. Continuous central venous and pulmonary artery oxygen saturation monitoring in the critically ill. Intensive Care Med. 2004;30(8):1572–8. pmid:15197435
- View Article
- PubMed/NCBI
- Google Scholar
50. el-Masry A, Mukhtar AM, el-Sherbeny AM, Fathy M, el-Meteini M. Comparison of central venous oxygen saturation and mixed venous oxygen saturation during liver transplantation. Anaesthesia. 2009;64(4):378–82. pmid:19317701
- View Article
- PubMed/NCBI
- Google Scholar
51. Dueck MH, Klimek M, Appenrodt S, Weigand C, Boerner U. Trends but not individual values of central venous oxygen saturation agree with mixed venous oxygen saturation during varying hemodynamic conditions. Anesthesiology. 2005;103(2):249–57. pmid:16052106
- View Article
- PubMed/NCBI
- Google Scholar
52. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1(8476):307–10. pmid:2868172
- View Article
- PubMed/NCBI
- Google Scholar
53. Liljequist D, Elfving B, Skavberg Roaldsen K. Intraclass correlation—A discussion and demonstration of basic features. PLoS ONE. 2019;14(7):e0219854. pmid:31329615
- View Article
- PubMed/NCBI
- Google Scholar
54. Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res. 1999;8(2):135–60. pmid:10501650
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Shoemaker WC, Appel PL, Kram HB. Hemodynamic and oxygen transport responses in survivors and nonsurvivors of high-risk surgery. Crit Care Med. 1993;21(7):977–90. pmid:8319478
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Grocott MP, Dushianthan A, Hamilton MA, Mythen MG, Harrison D, Rowan K, et al. Perioperative increase in global blood flow to explicit defined goals and outcomes following surgery. Cochrane Database Syst Rev. 2012;11:CD004082. pmid:23152223
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Maheshwari K. Principles for minimizing oxygen debt: can they translate to clinical application and improve outcomes? Best Practice & Research Clinical Anaesthesiology. 2020. pmid:34801216
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Jakobsson J, Vadman S, Hagel E, Kalman S, Bartha E. The effects of general anaesthesia on oxygen consumption: A meta-analysis guiding future studies on perioperative oxygen transport. Acta Anaesthesiol Scand. 2019;63(2):144–53. pmid:30238445
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. Carsetti A, Amici M, Bernacconi T, Brancaleoni P, Cerutti E, Chiarello M, et al. Estimated oxygen extraction versus dynamic parameters of fluid-responsiveness for perioperative hemodynamic optimization of patients undergoing non-cardiac surgery: a non-inferiority randomized controlled trial. BMC anesthesiol. 2020;20(1):87. pmid:32305061
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref6] 6. Briesenick L, Schaade A, Bergholz A, Hoppe P, Kouz K, Krause L, et al. Energy Expenditure Under General Anesthesia: An Observational Study Using Indirect Calorimetry in Patients Having Noncardiac Surgery. Anesth Analg. 2023;137(1):169–75. pmid:36622833
View Article
PubMed/NCBI
Google Scholar

[22] View Article

[23] PubMed/NCBI

[24] Google Scholar

[ref7] 7. Biro P. A formula to calculate oxygen uptake during low flow anesthesia based on FIO2 measurement. J Clin Monit Comput. 1998;14(2):141–4. pmid:9669452
View Article
PubMed/NCBI
Google Scholar

[26] View Article

[27] PubMed/NCBI

[28] Google Scholar

[ref8] 8. Leonard IE, Weitkamp B, Jones K, Aittomaki J, Myles PS. Measurement of systemic oxygen uptake during low-flow anaesthesia with a standard technique vs. a novel method. Anaesthesia. 2002;57(7):654–8. pmid:12059823
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref9] 9. McLellan S, Walsh TS. Oxygen delivery and haemoglobin. Cont Ed Anaesth Crit Care Pain 2004;4(4):123–6.
View Article
Google Scholar

[34] View Article

[35] Google Scholar

[ref10] 10. Keinanen O, Takala J. Calculated versus measured oxygen consumption during and after cardiac surgery. Is it possible to estimate lung oxygen consumption? Acta Anaesthesiol Scand. 1997;41(7):803–9. pmid:9265920
View Article
PubMed/NCBI
Google Scholar

[37] View Article

[38] PubMed/NCBI

[39] Google Scholar

[ref11] 11. Peyton PJ, Robinson GJ. Measured pulmonary oxygen consumption: difference between systemic oxygen uptake measured by the reverse Fick method and indirect calorimetry in cardiac surgery. Anaesthesia. 2005;60(2):146–50. pmid:15644011
View Article
PubMed/NCBI
Google Scholar

[41] View Article

[42] PubMed/NCBI

[43] Google Scholar

[ref12] 12. Saito H, Minamiya Y, Kawai H, Motoyama S, Katayose Y, Kimura K, et al. Estimation of pulmonary oxygen consumption in the early postoperative period after thoracic surgery. Anaesthesia. 2007;62(7):648–53. pmid:17567338
View Article
PubMed/NCBI
Google Scholar

[45] View Article

[46] PubMed/NCBI

[47] Google Scholar

[ref13] 13. Jakobsson J, Noren C, Hagel E, Kalman S, Bartha E. Peri-operative oxygen consumption revisited: An observational study in elderly patients undergoing major abdominal surgery. Eur J Anaesthesiol. 2021;38(1):4–12. pmid:32858583
View Article
PubMed/NCBI
Google Scholar

[49] View Article

[50] PubMed/NCBI

[51] Google Scholar

[ref14] 14. Wasserman K. Principles of exercise testing and interpretation including patophysiology and clinical applications. 5th ed. Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins; 2012.

[ref15] 15. Dunn JO MM, Grocott MP. Physiology of oxygen transport. BJA Ed. 2016;16(10):341–8.
View Article
Google Scholar

[54] View Article

[55] Google Scholar

[ref16] 16. Wang Y, Moss J, Thisted R. Predictors of body surface area. J Clin Anesth. 1992;4(1):4–10. pmid:1540367
View Article
PubMed/NCBI
Google Scholar

[57] View Article

[58] PubMed/NCBI

[59] Google Scholar

[ref17] 17. Bizouarn P, Soulard D, Blanloeil Y, Guillet A, Goarin Y. Oxygen consumption after cardiac surgery—a comparison between calculation by Fick’s principle and measurement by indirect calorimetry. Intensive Care Med. 1992;18(4):206–9. pmid:1430583
View Article
PubMed/NCBI
Google Scholar

[61] View Article

[62] PubMed/NCBI

[63] Google Scholar

[ref18] 18. Hofland J, Tenbrinck R, Eggermont AM, Eijck CH, Gommers D, Erdmann W. Effects of simultaneous aortocaval occlusion on oxygen consumption in patients. Clin Physiol Funct Imaging. 2003;23(5):275–81. pmid:12950325
View Article
PubMed/NCBI
Google Scholar

[65] View Article

[66] PubMed/NCBI

[67] Google Scholar

[ref19] 19. Myles PS, Cui J. Using the Bland-Altman method to measure agreement with repeated measures. Br J Anaesth. 2007;99(3):309–11. pmid:17702826
View Article
PubMed/NCBI
Google Scholar

[69] View Article

[70] PubMed/NCBI

[71] Google Scholar

[ref20] 20. Inadomi C, Terao Y, Yamashita K, Fukusaki M, Takada M, Sumikawa K. Comparison of oxygen consumption calculated by Fick’s principle (using a central venous catheter) and measured by indirect calorimetry. J. 2008;22(2):163–6. pmid:18500614
View Article
PubMed/NCBI
Google Scholar

[73] View Article

[74] PubMed/NCBI

[75] Google Scholar

[ref21] 21. Burtman DTM, Stolze A, Genaamd Dengler SEK, Vonk ABA, Boer C. Minimally Invasive Determinations of Oxygen Delivery and Consumption in Cardiac Surgery: An Observational Study. J Cardiothorac Vasc Anesth. 2018;32(3):1266–72. pmid:29422283
View Article
PubMed/NCBI
Google Scholar

[77] View Article

[78] PubMed/NCBI

[79] Google Scholar

[ref22] 22. van Beest P, Wietasch G, Scheeren T, Spronk P, Kuiper M. Clinical review: use of venous oxygen saturations as a goal—a yet unfinished puzzle. Crit Care. 2011;15(5):232. pmid:22047813
View Article
PubMed/NCBI
Google Scholar

[81] View Article

[82] PubMed/NCBI

[83] Google Scholar

[ref23] 23. Pearse R, Dawson D, Fawcett J, Rhodes A, Grounds RM, Bennett ED. Changes in central venous saturation after major surgery, and association with outcome. Crit Care. 2005;9(6):R694–9. pmid:16356220
View Article
PubMed/NCBI
Google Scholar

[85] View Article

[86] PubMed/NCBI

[87] Google Scholar

[ref24] 24. Chemtob RA, Eskesen TG, Moeller-Soerensen H, Perner A, Ravn HB. Systematic review of the association of venous oxygenation and outcome in adult hospitalized patients. Acta Anaesthesiol Scand. 2016;60(10):1367–78. pmid:27620815
View Article
PubMed/NCBI
Google Scholar

[89] View Article

[90] PubMed/NCBI

[91] Google Scholar

[ref25] 25. Bizouarn P, Blanloeil Y, Pinaud M. Comparison between oxygen consumption calculated by Fick’s principle using a continuous thermodilution technique and measured by indirect calorimetry. Br J Anaesth. 1995;75(6):719–23. pmid:8672320
View Article
PubMed/NCBI
Google Scholar

[93] View Article

[94] PubMed/NCBI

[95] Google Scholar

[ref26] 26. Epstein CD, Peerless JR, Martin JE, Malangoni MA. Comparison of methods of measurements of oxygen consumption in mechanically ventilated patients with multiple trauma: the Fick method versus indirect calorimetry. Crit Care Med. 2000;28(5):1363–9. pmid:10834679
View Article
PubMed/NCBI
Google Scholar

[97] View Article

[98] PubMed/NCBI

[99] Google Scholar

[ref27] 27. Myles PS, McRae R, Ryder I, Hunt JO, Buckland MR. Association between oxygen delivery and consumption in patients undergoing cardiac surgery. Is there supply dependence? Anaesth Intensive Care. 1996;24(6):651–7. pmid:8971311
View Article
PubMed/NCBI
Google Scholar

[101] View Article

[102] PubMed/NCBI

[103] Google Scholar

[ref28] 28. Smithies MN, Royston B, Makita K, Konieczko K, Nunn JF. Comparison of oxygen consumption measurements: indirect calorimetry versus the reversed Fick method. Crit Care Med. 1991;19(11):1401–6. pmid:1935161
View Article
PubMed/NCBI
Google Scholar

[105] View Article

[106] PubMed/NCBI

[107] Google Scholar

[ref29] 29. Soussi S, Vallee F, Roquet F, Bevilacqua V, Benyamina M, Ferry A, et al. Measurement of Oxygen Consumption Variations in Critically Ill Burns Patients: Are the Fick Method and Indirect Calorimetry Interchangeable? Shock. 2017;48(5):532–8. pmid:28452908
View Article
PubMed/NCBI
Google Scholar

[109] View Article

[110] PubMed/NCBI

[111] Google Scholar

[ref30] 30. Stuart-Andrews C, Peyton P, Robinson G, Terry D, O’Connor B, Van der Herten C, et al. Non-invasive metabolic monitoring of patients under anaesthesia by continuous indirect calorimetry—an in vivo trial of a new method. Br J Anaesth. 2007;98(1):45–52. pmid:17124187
View Article
PubMed/NCBI
Google Scholar

[113] View Article

[114] PubMed/NCBI

[115] Google Scholar

[ref31] 31. Walsh TS, Hopton P, Lee A. A comparison between the Fick method and indirect calorimetry for determining oxygen consumption in patients with fulminant hepatic failure. Crit Care Med. 1998;26(7):1200–7. pmid:9671369
View Article
PubMed/NCBI
Google Scholar

[117] View Article

[118] PubMed/NCBI

[119] Google Scholar

[ref32] 32. Hofland J, Tenbrinck R, van Eijck CH, Eggermont AM, Gommers D, Erdmann W. Comparison of closed circuit and Fick-derived oxygen consumption in patients undergoing simultaneous aortocaval occlusion. Anaesthesia. 2003;58(4):377–84. pmid:12688276
View Article
PubMed/NCBI
Google Scholar

[121] View Article

[122] PubMed/NCBI

[123] Google Scholar

[ref33] 33. Loer SA, Scheeren TW, Tarnow J. How much oxygen does the human lung consume? Anesthesiology. 1997;86(3):532–7. pmid:9066318
View Article
PubMed/NCBI
Google Scholar

[125] View Article

[126] PubMed/NCBI

[127] Google Scholar

[ref34] 34. Oudemans-van Straaten HM, Scheffer GJ, Eysman L, Wildevuur CR. Oxygen consumption after cardiopulmonary bypass—implications of different measuring methods. Intensive Care Med. 1993;19(2):105–10. pmid:8486864
View Article
PubMed/NCBI
Google Scholar

[129] View Article

[130] PubMed/NCBI

[131] Google Scholar

[ref35] 35. Schaffartzik W, Sanft C, Schaefer JH, Spies C. Different dosages of dobutamine in septic shock patients: determining oxygen consumption with a metabolic monitor integrated in a ventilator. Intensive Care Med. 2000;26(12):1740–6. pmid:11271080
View Article
PubMed/NCBI
Google Scholar

[133] View Article

[134] PubMed/NCBI

[135] Google Scholar

[ref36] 36. Jolliet P, Thorens JB, Nicod L, Pichard C, Kyle U, Chevrolet JC. Relationship between pulmonary oxygen consumption, lung inflammation, and calculated venous admixture in patients with acute lung injury. Intensive Care Med. 1996;22(4):277–85. pmid:8708163
View Article
PubMed/NCBI
Google Scholar

[137] View Article

[138] PubMed/NCBI

[139] Google Scholar

[ref37] 37. Hanique G, Dugernier T, Laterre PF, Dougnac A, Roeseler J, Reynaert MS. Significance of pathologic oxygen supply dependency in critically ill patients: comparison between measured and calculated methods. Intensive Care Med. 1994;20(1):12–8. pmid:8163752
View Article
PubMed/NCBI
Google Scholar

[141] View Article

[142] PubMed/NCBI

[143] Google Scholar

[ref38] 38. Brandi LS, Bertolini R, Pieri M, Giunta F, Calafa M. Comparison between cardiac output measured by thermodilution technique and calculated by O2 and modified CO2 Fick methods using a new metabolic monitor. Intensive Care Med. 1997;23(8):908–15. pmid:9310812
View Article
PubMed/NCBI
Google Scholar

[145] View Article

[146] PubMed/NCBI

[147] Google Scholar

[ref39] 39. Allingstrup MJ, Kondrup J, Perner A, Christensen PL, Jensen TH, Henneberg SW. Indirect Calorimetry in Mechanically Ventilated Patients: A Prospective, Randomized, Clinical Validation of 2 Devices Against a Gold Standard. JPEN J Parenter Enteral Nutr. 2017;41(8):1272–7. pmid:27488830
View Article
PubMed/NCBI
Google Scholar

[149] View Article

[150] PubMed/NCBI

[151] Google Scholar

[ref40] 40. Rehal MS, Fiskaare E, Tjader I, Norberg A, Rooyackers O, Wernerman J. Measuring energy expenditure in the intensive care unit: a comparison of indirect calorimetry by E-sCOVX and Quark RMR with Deltatrac II in mechanically ventilated critically ill patients. Crit Care. 2016;20:54. pmid:26951095
View Article
PubMed/NCBI
Google Scholar

[153] View Article

[154] PubMed/NCBI

[155] Google Scholar

[ref41] 41. Stuart-Andrews CR, Peyton P, Robinson GJ, Terry D, O’Connor B, Van der Herten C, et al. In vivo validation of the M-COVX metabolic monitor in patients under anaesthesia. Anaesth Intensive Care. 2007;35(3):398–405. pmid:17591136
View Article
PubMed/NCBI
Google Scholar

[157] View Article

[158] PubMed/NCBI

[159] Google Scholar

[ref42] 42. Lamia B, Kim HK, Severyn DA, Pinsky MR. Cross-comparisons of trending accuracies of continuous cardiac-output measurements: pulse contour analysis, bioreactance, and pulmonary-artery catheter. J Clin Monit Comput. 2018;32(1):33–43. pmid:28188408
View Article
PubMed/NCBI
Google Scholar

[161] View Article

[162] PubMed/NCBI

[163] Google Scholar

[ref43] 43. Hadian M, Kim HK, Severyn DA, Pinsky MR. Cross-comparison of cardiac output trending accuracy of LiDCO, PiCCO, FloTrac and pulmonary artery catheters. Crit Care. 2010;14(6):R212. pmid:21092290
View Article
PubMed/NCBI
Google Scholar

[165] View Article

[166] PubMed/NCBI

[167] Google Scholar

[ref44] 44. Critchley LA, Lee A, Ho AM. A critical review of the ability of continuous cardiac output monitors to measure trends in cardiac output. Anesth Analg. 2010;111(5):1180–92. pmid:20736431
View Article
PubMed/NCBI
Google Scholar

[169] View Article

[170] PubMed/NCBI

[171] Google Scholar

[ref45] 45. Beattie C, Moores C, Thomson AJ, Nimmo AF. The effect of anaesthesia and aortic clamping on cardiac output measurement using arterial pulse power analysis during aortic aneurysm repair. Anaesthesia. 2010;65(12):1194–9. pmid:21182600
View Article
PubMed/NCBI
Google Scholar

[173] View Article

[174] PubMed/NCBI

[175] Google Scholar

[ref46] 46. Lequeux PY, Bouckaert Y, Sekkat H, Van der Linden P, Stefanidis C, Huynh CH, et al. Continuous mixed venous and central venous oxygen saturation in cardiac surgery with cardiopulmonary bypass. Eur J Anaesthesiol. 2010;27(3):295–9. pmid:19935073
View Article
PubMed/NCBI
Google Scholar

[177] View Article

[178] PubMed/NCBI

[179] Google Scholar

[ref47] 47. van Beest PA, van Ingen J, Boerma EC, Holman ND, Groen H, Koopmans M, et al. No agreement of mixed venous and central venous saturation in sepsis, independent of sepsis origin. Crit Care. 2010;14(6):R219. pmid:21114844
View Article
PubMed/NCBI
Google Scholar

[181] View Article

[182] PubMed/NCBI

[183] Google Scholar

[ref48] 48. Soussi MS, Jebali MA, Le Manach Y, Nasri M, Zouari B, Chenik S, et al. Central venous saturation is not an alternative to mixed venous saturation during cardiopulmonary bypass in coronary artery surgery patients. Perfusion. 2012;27(4):300–6. pmid:22499058
View Article
PubMed/NCBI
Google Scholar

[185] View Article

[186] PubMed/NCBI

[187] Google Scholar

[ref49] 49. Reinhart K, Kuhn HJ, Hartog C, Bredle DL. Continuous central venous and pulmonary artery oxygen saturation monitoring in the critically ill. Intensive Care Med. 2004;30(8):1572–8. pmid:15197435
View Article
PubMed/NCBI
Google Scholar

[189] View Article

[190] PubMed/NCBI

[191] Google Scholar

[ref50] 50. el-Masry A, Mukhtar AM, el-Sherbeny AM, Fathy M, el-Meteini M. Comparison of central venous oxygen saturation and mixed venous oxygen saturation during liver transplantation. Anaesthesia. 2009;64(4):378–82. pmid:19317701
View Article
PubMed/NCBI
Google Scholar

[193] View Article

[194] PubMed/NCBI

[195] Google Scholar

[ref51] 51. Dueck MH, Klimek M, Appenrodt S, Weigand C, Boerner U. Trends but not individual values of central venous oxygen saturation agree with mixed venous oxygen saturation during varying hemodynamic conditions. Anesthesiology. 2005;103(2):249–57. pmid:16052106
View Article
PubMed/NCBI
Google Scholar

[197] View Article

[198] PubMed/NCBI

[199] Google Scholar

[ref52] 52. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1(8476):307–10. pmid:2868172
View Article
PubMed/NCBI
Google Scholar

[201] View Article

[202] PubMed/NCBI

[203] Google Scholar

[ref53] 53. Liljequist D, Elfving B, Skavberg Roaldsen K. Intraclass correlation—A discussion and demonstration of basic features. PLoS ONE. 2019;14(7):e0219854. pmid:31329615
View Article
PubMed/NCBI
Google Scholar

[205] View Article

[206] PubMed/NCBI

[207] Google Scholar

[ref54] 54. Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res. 1999;8(2):135–60. pmid:10501650
View Article
PubMed/NCBI
Google Scholar

[209] View Article

[210] PubMed/NCBI

[211] Google Scholar

Figures

Abstract

Background

Methods

Results

Conclusion

Introduction

Materials and methods

Patients and settings

Data extraction and time-points

Measurements of VO2 by indirect calorimetry (GVO2)

Estimation of VO2 by minimal-invasive cardiac output and arterial-central venous oxygen content difference (EVO2)

Statistical analysis

Results

Discussion

Supporting information

S1 File. Intraclass correlation comparing GVO2 and EVO2 at different perioperative stages.

S2 File. Parallelity analyses between GVO2 and EVO2 and its components.

Acknowledgments

References