Karam et al. BMC Anesthesiology          (2022) 22:211  
https://doi.org/10.1186/s12871-022-01757-8
RESEARCH
Assessing the discriminative ability 
of the respiratory exchange ratio to detect 
hyperlactatemia during intermediate-to-high 
risk abdominal surgery
Lydia Karam1, Olivier Desebbe2, Sean Coeckelenbergh1,3, Brenton Alexander4, Nicolas Colombo3, 
Edita Laukaityte1, Hung Pham1, Marc Lanteri Minet1, Leila Toubal1, Maya Moussa1, Salima Naili1, 
Jacques Duranteau1, Jean‑Louis Vincent5, Philippe Van der Linden6 and Alexandre Joosten1,3* 
Abstract 
Background: A mismatch between oxygen delivery  (DO2) and consumption  (VO2) is associated with increased 
perioperative morbidity and mortality. Hyperlactatemia is often used as an early screening tool, but this non‑continu‑
ous measurement requires intermittent arterial line sampling. Having a non‑invasive tool to rapidly detect inadequate 
 DO2 is of great clinical relevance. The respiratory exchange ratio (RER) can be easily measured in all intubated patients 
and has been shown to predict postoperative complications. We therefore aimed to assess the discriminative ability 
of the RER to detect an inadequate  DO2 as reflected by hyperlactatemia in patients having intermediate‑to‑high risk 
abdominal surgery.
Methods: This historical cohort study included all consecutive patients who underwent intermediate‑to‑high risk 
surgery from January 1st, 2014, to April 30th, 2019 except those who did not have RER and/or arterial lactate meas‑
ured. Blood lactate levels were measured routinely at the beginning and end of surgery and RER was calculated at the 
same moment as the blood gas sampling. The present study tested the hypothesis that RER measured at the end of 
surgery could detect hyperlactatemia at that time. A receiver operating characteristic (ROC) curve was constructed 
to assess if RER calculated at the end of the surgery could detect hyperlactatemia. The chosen RER threshold corre‑
sponded to the highest value of the sum of the specificity and the sensitivity (Youden Index).
Results: Among the 996 patients available in our study cohort, 941 were included and analyzed. The area under the 
ROC curve was 0.73 (95% CI: 0.70 to 0.76; p < 0.001), with a RER threshold of 0.75, allowing to discriminate a lac‑
tate > 1.5 mmol/L with a sensitivity of 87.5% and a specificity of 49.5%.
Conclusion: In mechanically ventilated patients undergoing intermediate to high‑risk abdominal surgery, the RER 
had moderate discriminative abilities to detect hyperlactatemia. Increased values should prompt clinicians to inves‑
tigate for the presence of hyperlactatemia and treat any potential causes of  DO2/VO2 mismatch as suggested by the 
subsequent presence of hyperlactatemia.
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Open Access
*Correspondence:  joosten‑alexandre@hotmail.com
3 Department of Anesthesiology, Paul Brousse Hospital, 12 Avenue Paul 
Vaillant Couturier, 94800 Villejuif, France
Full list of author information is available at the end of the article
Page 2 of 8
Karam et al. BMC Anesthesiology          (2022) 22:211 
Background
Patients undergoing abdominal surgery are at risk of an 
oxygen delivery  (DO2) and oxygen consumption  (VO2) 
mismatch, which can lead to postoperative complications 
[1]. Many studies have attempted to identify  DO2/VO2 
mismatch using various methods  [2].  Although meas-
urement of arterial lactate is considered standard of care 
for this purpose, this approach is intermittent and opti-
mally requires an arterial catheter [3]. Although other 
measures, including the central venous oxygen saturation 
 (ScvO2) [4], the veno-arterial  CO2 gradient, [5] and the 
ratio of veno-arterial  CO2 to arterio-venous oxygen dif-
ference [6] have shown promise, they share the same lim-
itations as lactate measurements (i.e., intermittent and 
invasive blood sampling) [7].
Monitoring of the respiratory exchange ratio (RER), 
can reflect the presence of anaerobic metabolism in the 
mechanically ventilated patient [1, 8]. This ratio increases 
in the presence of inadequate  DO2 for a given  VO2 and is 
computed using a standard anesthesia machine gas ana-
lyzer that continuously measures inspiratory and expira-
tory concentrations of  O2 and  CO2. The RER is calculated 
by dividing the difference in inspiratory and expiratory 
 CO2 by that of inspiratory and expiratory  O2 values (i.e., 
 (FeCO2—FiCO2) /  (FiO2—FeO2)), and has been shown to 
predict postoperative complications in abdominal sur-
gery patients. In a swine model and in abdominal surgery 
patients, increased RER has been associated with intra-
operative hyperlactatemia [1, 9]. However, the sensitivity 
and specificity of the RER to detect hyperlactatemia in 
humans remains to be determined. If an abnormal RER 
can detect hyperlactatemia, it could be used as a non-
invasive indicator for more aggressive hemodynamic 
optimization (e.g., fluid, vasopressors, and blood admin-
istration) and additional monitoring, especially as this 
variable can be easily calculated at the bedside during 
surgery. The present study tested the hypothesis that RER 
calculated at the end of surgery could help detect hyper-
lactatemia in patients undergoing intermediate-to-high 
risk abdominal surgery.
Methods
The Academic Erasme University Hospital (Université 
Libre de Bruxelles) ethical committee approved this study 
December  4th, 2020 (SRB2020_654) and waived the need 
for informed consent. All methods were performed in 
accordance with the relevant guidelines and regulations. 
We report this study using STROBE guidelines. Patients 
were identified using TrackPro (UltraGenda, Belgium), 
their medical records checked with MediView (IMMJ 
Systems, United Kingdom), and intraoperative data 
retrieved from the anesthetic electronic records software 
(Innovian, Drager, Germany). All patients from our insti-
tution aged 18 years or older were included if they:
1) Underwent elective intermediate-to-high-risk open 
abdominal surgery (including hepatobiliary surgery, 
pancreatectomy, gastrectomy, oesophagectomy, can-
cer debulking, cystectomy, colectomy, nephrectomy, 
aorto-bifemoral bypass, abdominal aortic aneurysm 
surgery or other major abdominal surgery requir-
ing a laparotomy) under general anesthesia between 
January 1st, 2014, and April 30th, 2019. Patients 
who had major vascular surgery were also included 
if the surgery involved an abdominal incision (e.g., 
aortobifemoral bypass and abdominal aortic aneu-
rysm surgery). All of these surgeries were classified 
as intermediate- or high-risk surgeries according to 
Kristensen et al. [10]
2) Had routinely arterial blood gas, arterial lactate,  FiO2, 
 FiCO2,  FeO2, and  FeCO2 measurements done simul-
taneously at the start and end of surgery. Impor-
tantly, RER values are not displayed in real time on 
our intraoperative monitors but were calculated ret-
rospectively using inhaled and exhaled  O2 and  CO2 
values (electronic medical records).
Exclusion criteria included pregnancy, lack of arterial 
blood gas analysis during surgery, emergency surgery, 
and laparoscopy.
As arterial lactate values above 1.5  meq/L have been 
associated with increased mortality in multiple stud-
ies examining surgical and critically ill patients and we 
wanted to establish a clinical cutoff value for optimal util-
ity by most practicing physicians, [11–13] patients were 
split into two groups depending on the arterial lactate 
value recorded at the end of surgery: above 1.5  mEq/l 
(i.e., high lactate) or less than or equal to 1.5meEq/l (i.e., 
low lactate). While splitting patients into two groups 
is not ideal for a continuous variable, we felt this was 
important for increasing clinical utility and was therefore 
done for exploratory purposes.
Anaesthesia protocol
All patients had at least one large peripheral venous 
catheter and were monitored with standard American 
Society of Anesthesiology (ASA) monitoring (i.e., pulse 
Keywords: Tissue hypoxia, Anaerobic metabolism, Shock, Goal‑directed hemodynamic therapy
Page 3 of 8
Karam et al. BMC Anesthesiology          (2022) 22:211 
 
oximetry, non-invasive blood pressure, 3 or 5 lead EKG, 
inhaled and expired gases, and temperature monitor-
ing), and invasive blood pressure monitoring through a 
radial, femoral, or brachial artery catheter. Frontal elec-
troencephalogram monitoring with the Bispectral index, 
hemodynamic pulse-contour analysis, central venous 
pressure, and other supplemental monitoring tools were 
used at the discretion of the attending anesthetist.
Anesthesia administration was not standardized. 
Induction agents included propofol, etomidate, and 
ketamine. Opioids consisted of either remifentanil or 
sufentanil administration. Neuromuscular blockade was 
administered with succinylcholine, rocuronium, or cisa-
tracurium. Maintenance of anesthesia was done with 
either sevoflurane, desflurane, or propofol. Adjuvant 
antinociception with spinal morphine, locoregional local 
anesthetics (e.g., epidural, transverse abdominal plane 
block, etc.), and opioid sparing agents (e.g., ketamine, 
dexmedetomidine, dexamethasone, lidocaine, paraceta-
mol, diclofenac) were administered at the discretion of 
the attending anesthetist.
Although no strict hemodynamic protocol was applied 
during surgical cases, anesthesiologists from our insti-
tution traditionally use vasopressors to maintain mean 
arterial blood pressure (MAP) above 65  mmHg. The 
vasopressor of choice was norepinephrine, but both phe-
nylephrine and ephedrine were less commonly used.
Data collection and outcomes
Patient baseline characteristics, intraoperative variables, 
postoperative major and minor complications, 30-day 
mortality, and 1-year mortality were collected.  FiO2, 
 FiCO2,  FeO2, and  FeCO2 were collected within the first 
hour and during the last hour of surgery at a moment 
of ventilator stability (i.e., no modifications greater than 
50  ml in tidal volume) that coincided with an arterial 
blood gas analysis. The primary objective was to test 
the hypothesis that RER measured at the end of surgery 
could detect hyperlactatemia at that time. As there was 
also an interest to explore other possible associations 
between patients having low vs a high lactate values, 
we presented these data but they should be considered 
purely exploratory.
Statistical analysis
The Kolmogorov Smirnov test determined that distribu-
tion was not normal (skewness of the different variables) 
and continuous variables were thus reported as median 
with quartiles  [25th -75th percentile]. Discrete variables 
were reported as frequencies (with proportions). Our 
primary analysis was the estimation of the area under 
the receiver operating characteristics (AUROC) curve 
to establish discriminative properties of the RER to 
detect hyperlactatemia. We first fit a logistic regression 
model to estimate the ROC curve. We then estimate the 
AUROC according to the Delong et al. methodology and 
its 95% confidence intervals with the calculation of an 
exact Binomial Confidence Interval [14].  From the ROC 
curves, the optimal cut-off value yielding the greatest 
combined sensitivity and specificity was measured. We 
defined values within the 95% CI of the obtained thresh-
old  value as an inconclusive (gray zone)  according 
to  Cannesson  et al. [15]. This gray zone approach is a 
zone of uncertainty which explores the clinical usefulness 
of the RER to detect hyperlactatemia. Statistical analysis 
was conducted with MedCalc® Statistical Software ver-
sion 19.6.4 (MedCalc Software Ltd, Ostend, Belgium; 
https:// www. medca lc. org; 2021).
A total of 996 patients undergoing intermediate- to-
high risk abdominal surgery were eligible, of whom 55 
were excluded due to missing data. Consequently, 941 
patients were included, 622 being in the low lactate group 
and 319 in the high lactate group (Fig. 1).
A RER of > 0.75 (Youden index) at the end of surgery 
detected a lactate value above 1.5  mEq/L with a sensi-
tivity of 87.5% and a specificity of 49.5%. The area under 
the receiver operating characteristic curve was 0.730 
(95% CI: 0.70 to 0.76; p < 0.001) (Fig. 2). Using a sensitivity 
of 90% and a specificity of 90%, four hundred and seven 
patients (43%) were in the grey zone defined from a RER 
of 0.72 to a RER of 0.98 (283 patients had a RER < 0.72 
and 251 patients a RER > 0.98, thus having respectively 
a false negative rate and a false positive rate of 10% or 
below (relative high certainty)).
RER was significantly higher in the high lactate group at 
the beginning and at the end of surgery (Table 2). Over-
all, baseline characteristics were not different between 
groups except for body mass index, which was higher in 
the high lactate group; preoperative hemoglobin, which 
was lower in the high lactate group; preoperative aspirin 
use, which was more frequent in the low lactate group; 
and the type of surgery (Table 1). Intraoperative variables 
were significantly different between the two groups with 
respect to anesthesia and surgery times, fluids infused, 
blood products administered, net fluid balance, vasopres-
sors, lactate, and RER, indicating more challenging intra-
operative conditions in the high lactate group (Table 2). 
While these differences may indicate potential confound-
ing factors, our intention was simply to determine if 
postoperative RER values could be useful to help detect 
increased arterial lactate levels, irrespective of differences 
in patients’ baseline clinical characteristics.
Exploratory secondary objectives were not different 
between groups (Table 3).
In mechanically ventilated patients undergoing inter-
mediate to high-risk abdominal surgery, the RER had 
Page 4 of 8
Karam et al. BMC Anesthesiology          (2022) 22:211 
moderate discriminative properties to detect hyper-
lactatemia. Based on a grey zone approach, 43% of the 
patients lied in an uncertainty zone with limited clinical 
usefulness. However, a RER value above 0.75 can detect 
hyperlactatemia with a relatively high sensitivity (88%). 
Hence, a normal RER makes hyperlactatemia relatively 
unlikely. Since its specificity is rather poor (50%) at this 
level, an increased RER cannot definitely rule in the 
presence of hyperlactatemia.
Bar and colleagues recently demonstrated the poten-
tial of RER to predict postoperative complications 
following both open abdominal high-risk and laparo-
scopic surgeries [1, 8]. During high-risk open abdomi-
nal surgery, both lactate and RER were found to predict 
postoperative complications, with an AUC of 0.77 and 
0.67, respectively [1]. This confirms the importance of 
these measurements. Although this team did demon-
strate an association between these two measurements, 
the sensitivity of specificity of RER to detect hyperlac-
tatemia was not investigated [1].
Fig. 1 Flow Chart
Fig. 2 Receiver operating characteristic curve to examine if the RER 
at the end of the surgery can detect hyperlactatemia
Page 5 of 8
Karam et al. BMC Anesthesiology          (2022) 22:211 
 
In our study, patients in the high lactate group had 
more challenging intraoperative conditions as surgery 
time, fluid requirements, vasopressor infusions, and 
blood loss were all greater than in the low lactate group. 
The major postoperative complications of sepsis, peri-
tonitis, and renal injury confirm that adverse postop-
erative outcomes are associated with hyperlactatemia. 
Other variables have been related to tissue hypoperfu-
sion, such as  ScvO2, the veno-arterial  CO2 gradient, 
the ratio of veno-arterial  CO2 to arterio-venous oxy-
gen. Unfortunately, these measures, similar to arterial 
lactate, require access to arterial, central venous, or even 
pulmonary artery blood sampling. RER has the advan-
tage of being easy to calculate, non-invasive, free and 
its components continuously displayed on all modern 
anesthesia machines. It is easy to imagine a clinical sce-
nario in which an elevated RER following surgery results 
in changes in post-operative clinical management. For 
example, the patient with an elevated RER may require 
a higher level of care, additional monitoring, supple-
mentary laboratory tests or any other treatment that can 
correct  DO2/VO2 mismatch. Such interventions could 
Table 1 Baseline characteristics
Values are presented as medians [interquartiles ranges] or numbers (percentages %)
a Included other laparotomies such as surrenalectomy or prostatectomy
Variables
Lactate ≤ 1.5 mEq/l
(N = 622)
Lactate > 1.5 mEq/l
(N = 319)
P-value
  Age (years)
66 [56–73]
64 [55—72]
0.078
  Sex, Female (%)
220 (35.4%)
125 (39.2%)
0.258
  BMI (kg/m2)
25.17 [22.5–28.86]
25.8 [23.31–29.4]
0.031
  ASA score (1–2 / 3–4)
361 / 261
193 / 126
0.125
  Preoperative   hemoglobin (g/dL)
13.1 [11.8–14.3]
13.5 [12.1–14.5]
0.013
  Preoperative creatinine   (mg/dL)
0.9 [0.7–1.1]
0.9 [0.71–1]
0.850
Comorbidities; N (%)
  Ischemic heart disease
  Coronary artery   bypass graft
  Hypertension
  Hyperlipidemia
  Stroke
  Atrial fibrillation
  Heart failure
  Diabetes mellitus 1
  Diabetes mellitus 2
  COPD
  Cirrhosis
  Asthma
63 (10.1%)
34 (5.5%)
319 (51.3%)
179 (28.8%)
25 (4.0%)
49 (7.9%)
10 (1.6%)
2 (0.3%)
138 (22.2%)
84 (13.5%)
43 (6.9%)
30 (4.8%)
24 (7.5%)
11(3.5%)
144 (45.1%)
86 (27.0%)
15 (4.7%)
20 (6.3%)
4 (1.3%)
2 (0.6%)
66 (20.7%)
30 (9.4%)
30 (9.4%)
19 (6.0%)
0.192
0.170
0.074
0.557
0.623
0.370
0.669
0.495
0.598
0.068
0.176
0.459
Medications; N (%)
  Aspirin
  Clopidogrel
  ẞ blocker
  ACEI
  ARB
  Calcium channel   blocker
  Diuretics
  Statin
  Oral hypoglycaemic   drugs
  Insulin
  Oral anticoagulation
224 (36.0%)
30 (4.8%)
172 (27.7%)
136 (21.9%)
50 (8.0%)
119 (19.1%)
65 (10.5%)
181 (29.1%)
94 (15.1%)
49 (7.9%)
64 (10.3%)
85 (26.7%)
8 (2.51%)
81 (25.4%)
57 (17.9%)
22 (6.9%)
49 (15.4%)
25 (7.8%)
91 (28.5%)
47 (14.7%)
27 (8.5%)
26 (8.2%)
0.004
0.088
0.459
0.151
0.533
0.153
0.197
0.854
0.877
0.755
0.291
Type of Surgery (N)
 < 0.001
  Pancreatectomy
  Hepatobiliary
  Oesophagectomy
  Cystectomy
  Cancer debulking
  Major vascular surgery
  Gastrectomy
  Colectomy
  Nephrectomy
  Other surgical    procedurea
104 (16.7%)
161 (25.9%)
82 (13.2%)
72 (11.6%)
25 (4.0%)
141 (22.7%)
7 (1.1%)
17 (2.7%)
7 (1.1%)
6 (0.1%)
58 (18.2%)
152 (47.7%)
37 (11.6%)
18 (5.6%)
10 (3.1%)
32 (10.0%)
1 (0.3%)
6 (1.9%)
2 (0.6%)
3 (0.9%)
Page 6 of 8
Karam et al. BMC Anesthesiology          (2022) 22:211 
Table 2 Intraoperative Variables
Values are presented as medians [interquartiles ranges] or numbers (percentages %)
a use of any vasopressor (ephedrine, phenylephrine, noradrenaline)
b total colloid included 3% gelatin and 6% tetrastarch
Variables
Lactate ≤ 1.5 mEq/l
(N = 622)
Lactate > 1.5 mEq/l
(N = 319)
P-value
Anaesthesia duration ( min)
347 [254–450]
372 [287–472]
0.001
Surgery duration (min)
262 [180–360]
289 [209–381]
0.002
Total crystalloid (ml)
2000 [1200–3000]
2400 [1500–3500]
 < 0.001
Total colloid (ml)b
500 [500–1000]
1000 [500–1500]
 < 0.001
Total blood product (ml)
498 [261–580]
540 [288–1475]
0.009
Total IN (ml)
2500 [1500–3500]
3000 [2000–4500]
 < 0.001
Estimated blood loss (ml)
400 [200–900]
700 [300–1500]
 < 0.001
Diuresis (ml)
300 [150–500]
300 [150–510]
0.258
Total out (ml)
870 [500–1400]
1110 [680–2000]
 < 0.001
Net fluid balance (ml)
1505 [788–2350]
1750 [1000–2800]
 < 0.001
Use of vasopressors, N (%)a
484 (77.9%)
269 (84.3%)
0.02
Lactate beginning of surgery (mEq/L)
0.7 [0.6–0.9]
0.9 [0.7–1.2]
 < 0.001
RER beginning of surgery
0.80 [0.67–0.80]
0.80 [0.71–0.83]
 < 0.001
Lactate end of surgery (mEq/L)
0.9 [0.7–1.1]
2.3 [1.8–3.0]
 < 0.001
RER end of surgery
0.80 [0.67–0.80]
0.83 [0.80–1.0]
 < 0.001
Table 3 Postoperative outcome
Values are presented as medians [interquartiles ranges] or numbers (percentages %)
Variables
Lactate ≤ 1.5 mEq/l
(N = 622)
Lactate > 1.5 mEq/l
(N = 319)
P-value
Secondary outcomes
1) Length of stay in hospital (days)
9 [6—14]
9 [9—28]
0.426
2) Minor complications; N (%)
147 (23.6%)
66 (20.7%)
0.307
➢ Superficial wound infection
22 (3.5%)
7 (2.2%)
0.259
➢ Urinary infection
37 (5.9%)
13 (4.1%)
0.225
➢ Paralytic ileus
22 (3.5%)
12 (3.8%)
0.861
➢ Pneumonia
19 (3.1%)
8 (2.5%)
0.634
➢ Postoperative confusion
23 (3.7%)
10 (3.1%)
0.657
➢ Other infection
65 (10.5%)
42 (13.2%)
0.214
3) Major complications; N (%)
123 (19.8%)
72 (22.6%)
0.317
➢ Anastomotic leakage
19 (3.1%)
14 (4.4%)
0.292
➢ Peritonitis
3 (0.5%)
6 (1.9%)
0.037
➢ Sepsis
32 (5.1%)
28 (8.8%)
0.031
➢ Necrosis stoma
10 (1.6%)
2 (0.6%)
0.206
➢ Wound dehiscence
10 (1.6%)
5 (1.6%)
0.963
➢ Bleeding requiring a redo surgery
19 (3.1%)
17 (5.3%)
0.085
➢ Pulmonary embolism
5 (0.8%)
2 (0.6%)
0.765
➢ Pulmonary edema
9 (1.5%)
5 (1.6%)
0.881
➢ Acute coronary syndrome
2 (0.3%)
0 (0%)
0.678
➢ Atrial fibrillation / arrhythmia
15 (2.4%)
8 (2.5%)
0.928
➢ Acute kidney injury
34 (5.5%)
29 (9.1%)
0.035
➢ Reoperation
49 (7.9%)
17(5.3%)
0.147
➢ 30‑day mortality
6 (0.1%)
4 (1.3%)
0.682
Page 7 of 8
Karam et al. BMC Anesthesiology          (2022) 22:211 
 
include a fluid challenge, administration of vasoactive 
agents to increase cardiac output or to target a higher 
blood pressure, or a red blood cell transfusion to increase 
the hemoglobin level. Conversely, a normal RER value 
could potentially accelerate the postoperative care and 
limit unnecessary tests and excessive length of stay in a 
high dependency unit. In addition, in patients equipped 
with an arterial line, RER calculation could justify less 
frequent arterial blood gas measurements to check lac-
tate levels. Further clinical exploration and the eventual 
implementation into goal-directed protocols may help 
further clarify the  DO2/VO2 relationship and establish 
the best clinical use for RER. Additional data should be 
soon available as a French team completed recently a 
large randomized controlled trial (N = 350) comparing 
an individualized hemodynamic optimization strategy 
guided by indirect measurement of the RER to a routine 
care in major surgery [16]. 
This retrospective study had both strengths and limi-
tations. It was not possible to couple lactate and RER 
measurements more frequently intraoperatively due to 
the lack of a protocolized approach to sampling blood. 
Consequently, only the first and last arterial lactate val-
ues were compared to concomitant RER values. Patients 
without any arterial blood gas values were excluded 
which represent 5.5% of the study collective. Imputa-
tion methods for missing data were not performed as it is 
never recommended to impute a missing outcome since 
it would only improve predictive properties. Moreover, 
missingness around 5% is usually considered as only 
creating limited bias [17, 18]. Anesthesia practice was 
not standardized, but this reflects typical clinical prac-
tice. Likewise, the population heterogeneity reflects real 
life practices. Types of surgeries (e.g., one-lung venti-
lation or hepatic resection) and patient comorbidities 
(e.g., chronic obstructive pulmonary disease), could have 
effects on either  DO2 or lactate metabolism and may 
alter the relation between RER and lactate. Future studies 
should investigate the impact of these conditions on the 
relationship between RER and hyperlactatemia.
In conclusion, the RER had moderate discriminative 
abilities to detect hyperlactatemia. A RER value above 
0.75 can detect hyperlactatemia with a moderately high 
sensitivity but with a poor specificity. Increased values 
should prompt clinicians to investigate for the pres-
ence of hyperlactatemia and treat any potential causes 
of DO2/VO2 mismatch as suggested by the subsequent 
presence of hyperlactatemia.
Abbreviations
RER: Respiratory exchange ratio; DO2: Oxygen delivery; VO2: Oxygen consump‑
tion; ScvO2: Central venous oxygen saturation; AUC : Area under curve; ROC: 
Receiver operatingcharacteristics.
Acknowledgements
All the clinicians who helped in data collection from the current abdominal 
surgery database. More specifically, we want to thank Dr. Francois Martin Car‑
rier from the University of Montreal for the help in the statistics of the paper.
Authors’ contributions
L.K: Analyzed the data and drafted the manuscript. O.D.: Analyzed the data and 
edited the manuscript. B.A: Analyzed the data and edited the manuscript. B.A: 
Analyzed the data and edited the manuscript. N.C: Collected the data and 
edited the final manuscript. E.L: Collected the data and edited the final manu‑
script. H.P: Collected the data and edited the final manuscript. M.LM: Collected 
the data and edited the final manuscript. L.T: Collected the data and edited 
the final manuscript. M.M: Collected the data and edited the final manuscript. 
S.N: Analyzed the data and edited the final manuscript. J.D: Analyzed the data 
and edited the final manuscript. JLV: Analyzed the data and edited the final 
manuscript. P.VdL: Statistical analysis of the data and edited the final manu‑
script. A.J: Designed the study, collected and analyzed the data and drafted 
the manuscript. All authors read and approved the final manuscript.
Funding
The authors received no funding for this work.
Availability of data and materials
The database is closed and there is no public access. However, permission to 
access and use the database can be obtained if necessary by request to the 
corresponding author.
Declarations
Ethics approval and consent for publication
The Erasme University Hospital (Université Libre de Bruxelles) ethical commit‑
tee approved this study December  4th, 2020 (SRB2020_654) and waived the 
need for informed consent. All methods were performed in accordance with 
the relevant guidelines and regulations. This study adheres to the STROBE 
guidelines.
Consent for publication
Not applicable.
Competing interests
AJ is a consultant for Edwards Lifesciences (Irvine, California, USA), Aguettant 
Laboratoire (Lyon, France) and Fresenius Kabi (Bad Homburg, Germany) OD is 
consultant for Medtronic (Trévoux, FRANCE) and received honoraria for giving 
lectures for Medtronic (Trévoux, FRANCE) and Livanova (Châtillon, France). 
Jean‑Louis Vincent is Editor‑in‑Chief of Critical Care. He has no other conflicts 
related to this article. The other authors have no conflicts of interest related to 
this article.
Author details
1 Department of Anesthesiology and Intensive Care, Université Paris‑Saclay, 
Paul Brousse Hospital, Assistance Publique ‑ Hôpitaux de Paris (APHP), Villejuif, 
France. 2 Department of Anesthesiology and Perioperative Medicine Sauve‑
garde Clinic, Ramsay Santé, Lyon, France. 3 Department of Anesthesiology, 
Paul Brousse Hospital, 12 Avenue Paul Vaillant Couturier, 94800 Villejuif, France. 
4 Department of Anesthesiology, University of California San Diego, La Jolla, 
CA, USA. 5 Department of Intensive Care, Erasme Hospital, Université Libre de 
Bruxelles, Brussels, Belgium. 6 Department of Anesthesiology, Brugmann Hospi‑
tal, Université Libre de Bruxelles, Brussels, Belgium. 
Received: 13 February 2022   Accepted: 23 June 2022
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Karam et al. BMC Anesthesiology          (2022) 22:211 
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Assessing the discriminative ability of the respiratory exchange ratio to detect hyperlactatemia during intermediate-to-high risk abdominal surgery.
Karam, Lydia,Desebbe, Olivier,Coeckelenbergh, Sean,Alexander, Brenton,Colombo, Nicolas,Laukaityte, Edita,Pham, Hung,Lanteri Minet, Marc,Toubal, Leila,Moussa, Maya,Naili, Salima,Duranteau, Jacques,Vincent, Jean-Louis,Van der Linden, Philippe,Joosten, Alexandre