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Anesthesia & Analgesia:
doi: 10.1213/ANE.0b013e31825e703e
Critical Care, Trauma, and Resuscitation: Research Reports

Are Central Venous Lactate and Arterial Lactate Interchangeable? A Human Retrospective Study

Réminiac, François MD; Saint-Etienne, Christophe MD; Runge, Isabelle MD; Ayé, Denis Ykpé MD; Benzekri-Lefevre, Dalila MD; Mathonnet, Armelle MD; Boulain, Thierry MD

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From the Medical-Surgical ICU, Centre Hospitalier Régional d'Orléans, Orléans, France.

The authors declare no conflicts of interest.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.anesthesia-analgesia.org).

Reprints will not be available from the authors.

Address correspondence to Thierry Boulain, MD, Medical-Surgical ICU, Centre Hospitalier Régional d'Orléans, 14 Ave. de l'Hôpital, BP 6709, 45067, Orléans, France. Address e-mail to thierry.boulain@chr-orleans.fr.

Accepted April 25, 2012

Published ahead of print June 28, 2012

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Abstract

BACKGROUND: In critically ill patients, arterial blood lactate concentration (Lacta) and Lacta clearance are used for the diagnosis of shock, for prognosis assessment, and to guide therapy. In recent years, central venous oxygen saturation (ScvO2), a surrogate for mixed venous blood saturation, either measured by fiberoptic catheters or from central venous blood samples, was used in shock to estimate the global balance between oxygen delivery and consumption. When central venous blood is drawn for ScvO2 measurement, it also could be used to measure central venous lactate concentration (Lactcv). In this study, we evaluated the utility of Lactcv and Lactcv clearance as predictors of Lacta and Lacta clearance, respectively, in critically ill patients.

METHODS: This retrospective study was performed in an intensive care unit of a regional and teaching hospital. Using the electronic registry of our blood gas analyzer from March 2007 to December 2009, we identified patients with circulatory or respiratory failure who had pairs of Lactcv and Lacta obtained within a 30-minute interval. To assess the utility of Lactcv as a predictor of Lacta above 2 and 4 mmol/L, we calculated the area under receiver operating characteristic curves (AUCs) for these thresholds. We also calculated AUC of Lactcv clearance to detect a Lacta clearance <10% or >10%.

RESULTS: Six hundred seventy-three Lactcv/Lacta pairs in 188 patients were analyzed. AUC of Lactcv to predict a Lacta above 2 and 4 mmol/L was 0.98 (95% confidence interval: 0.97–0.99) and 0.98 (95% confidence interval: 0.96–0.99), respectively. Lactcv with the cutoff value of 2 mmol/L can predict a Lacta above 2 mmol/L with sensitivity >92% and specificity >90%. AUC for Lactcv clearance to detect a Lacta clearance <10% or >10% was 0.93 or 0.94, respectively.

CONCLUSION: Lactcv and Lacta collected within a 30-minute range are interchangeable for clinical practice.

Arterial blood lactate concentration (Lacta) is correlated with the development of multiple organ failure and death in patients with septic shock1 or trauma.2 It is recommended that intensivists track the course of Lacta in patients with shock,3 to assess and guide therapy.4,5 Lactate concentration in the mixed venous blood reflects the balance between lactate production and clearance in the whole body. Because mixed venous blood is not easy to sample in routine practice, Lacta is considered a surrogate for lactate concentration in the mixed venous blood. In 1987, Weil et al.6 demonstrated good correlation between Lacta and lactate levels in venous blood, sampled either from a pulmonary artery or from a central venous catheter. Subsequent studies have reported significant lactate production by the lungs in patients with sepsis7 or with acute respiratory distress syndrome (ARDS),8 resulting in significant differences between mixed venous and arterial lactate levels. Moreover, among those who recently studied the correlation or agreement between central venous lactate concentration (Lactcv) and Lacta,913 none have assessed the performance of Lactcv and Lactcv clearance to predict Lacta and Lacta clearance, respectively.

In our medical-surgical intensive care unit (ICU), for several years, we routinely measured central venous saturation (ScvO2) by frequent blood sampling from the superior vena cava in patients with acute circulatory failure and/or respiratory failure. Many central venous blood samples were drawn simultaneously with arterial samples, providing an opportunity to examine the hypothesis that Lactcv accurately predicted Lacta. The aim of the present retrospective study was to evaluate the performance of Lactcv and Lactcv clearance to predict Lacta level and Lacta clearance, respectively, in critically ill patients.

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METHODS

The Ethics Committee of the Société de Réanimation de Langue Française approved the study design and waived informed consent. Nevertheless, all patients and their families admitted to our ICU were routinely informed that observational studies could be done using data related to their hospitalization and that they had the right to refuse to be included in such studies.

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Patients

We routinely measure ScvO2 in blood sampled from the superior vena cava in patients with acute circulatory failure and/or respiratory failure. Arterial and venous (via central venous catheter) samples are routinely analyzed on a blood gas analyzer/oximeter Rapidlab 865 (BAYER Healthcare Diagnostics Division, Loos, France), which displays pH, blood gas, plasma sodium, measured hemoglobin oxygen saturation, and lactate concentration. The range for lactate measurements with the Rapidlab 865 analyzer extends from 0 to 30 mmol/L with a standard deviation of 0.019 to 0.046 and an accuracy of 98% to 101.7%. Although the lactate concentration is automatically displayed by the blood gas analyzer for venous blood, we have not included Lactcv in our therapeutic decisions.

When blood samples are drawn in our ICU, per protocol the nurses perform the analysis within 3 minutes. Using the electronic registry of the blood gas analyzer, we retrospectively identified the pairs of Lactcv and Lacta performed within a 30-minute interval for ICU patients hospitalized from March 2007 to December 2009. The clinical, laboratory, and demographic characteristics of the corresponding patients were retrieved from the hospital electronic registry.

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Data Collection

For each patient, we recorded age, gender, cause of ICU admission, Simplified Acute Physiology Score II,14 cause of shock, the presence of pulmonary infection or sepsis from other origin, the presence of ARDS,15 liver failure, and ICU outcome. For each Lactcv/Lacta pair, we collected respiratory status (invasive, noninvasive, or spontaneous breathing), body temperature, heart rate, respiratory rate, mean arterial blood pressure, arterial and venous pH, PCO2, PO2, oxygen saturation, lactate level, and plasma sodium. We excluded Lactcv/Lacta pairs with a difference between venous and arterial sodium >10%, because this difference suggested an imperfect blood sampling technique.

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Objective

We assessed the utility of Lactcv as a predictor of Lacta above a predetermined threshold and Lactcv clearance to predict Lacta clearance <10% or >10%. We chose the thresholds of 2 and 4 mmol/L because Lacta higher than 2 mmol/L is an indicator of hypoperfusion, and Lacta higher than 4 mmol/L suggests severe circulatory failure.16,17

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Data Analysis

We tested the correlation between Lactcv and Lacta by single linear regression and calculated the mean bias, limits of agreement, and their 95% confidence interval (CI). We assessed the discriminatory power of Lactcv to predict Lacta by the area under the receiver operating characteristic curve (AUC).18 Sensitivity and specificity were computed for the best Lactcv thresholds. We compared the Lactcv/ Lacta bias (by unpaired 2-tailed t test) and the AUCs19 between subgroups of patients (with or without ARDS), and patients with or without pneumonia.

To perform the above analyses on the whole dataset, and to consider the intrapatient correlation, we calculated mean bias, limits of agreement, and mean AUCs from 1000 replicated samples using bootstrapping20 with patient as the unit of resampling, and defined the 95% CI as the interval comprising 95% of the 1000 bootstrap samples (from the 2.5 to the 97.5 percentile). This is further discussed in the Supplemental Digital Content 1 (http://links.lww.com/AA/A424).

Finally, among the whole dataset, we identified the successive couples of Lactcv/Lacta pairs measured within 4- to 6-hour intervals in a given patient and calculated the Lactcv and Lacta clearances as previously reported21,22 (lactate clearance = 100% × [initial lactate − second lactate]/initial lactate). We calculated the AUC of Lactcv clearance to detect a Lacta clearance <10% or >10%.

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Sample Size Calculation

We hypothesized that the AUC of Lactcv to predict any predetermined Lacta was between 0.90 and 1. For a ratio of negative/positive cases ranging from 1 to 5, we calculated that a number of 180 included patients would be sufficient to estimate the AUC with a CI narrower than ±5% if only 1 pair of Lactcv/Lacta pairs is analyzed for each patient.19 Additionally, we aimed at including a sufficient number of Lactcv/Lacta pairs in ARDS patients to give the comparison of AUC between ARDS and non-ARDS patients a power of at least 80% to detect a difference of 10%. With the hypothesis that half of the ARDS patients will have Lacta >2 mmol/L, we calculated that at least 162 Lactcv/Lacta pairs in ARDS patients were needed.20 Data were processed using MedCalc® version 10.3.2.0 (Mariakerke, Belgium) and SAS (version 9.2; SAS Institute Inc., Cary, NC). A P value <0.05 was considered significant.

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RESULTS

Among the 696 Lactcv/Lacta pairs measured within a 30-minute interval, 23 pairs were discarded because of differences between venous and arterial sodium above 10%. Our final dataset was composed of 673 Lactcv/Lacta pairs in 188 patients (Tables 1 and 2).

Table 1
Table 1
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Table 2
Table 2
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For the first pair of Lactcv/Lacta for each of the 188 patients, the bias between Lactcv and Lacta was −0.07 mmol/L ± 0.68 and limits of agreement were −1.4 (95% CI: −1.6 to −1.3) and 1.3 (95% CI: 1.1–1.4). The AUC of Lactcv for predicting Lacta above 2 mmol/L was 0.98 (95% CI: 0.95–0.99) with a best cutoff value of 2.1 mmol/L, yielding a sensitivity of 98% (95% CI: 94–100) and a specificity of 89% (95% CI: 80–95). For predicting Lacta above 4 mmol/L, the AUC was 0.97 (95% CI: 0.94–0.99) with a best cutoff value of 3.8 mmol/L, yielding a sensitivity of 90% (95% CI: 80–96) and a specificity of 98% (95% CI: 93–99).

In the whole dataset, Lacta ranged from 0.6 to 26.6 mmol/L and Lactcv from 0.6 to 22.8 mmol/L; the mean Lactcv/Lacta bias was −0.043 mmol/L (95% CI: −0.11 to 0.003), and limits of agreement were −1.2 mmol/L (95% CI: −1.7 to −1.0) and 1.2 mmol/L (95% CI: 0.98–1.5) (Fig. 1). Bias expressed in percentage of the average of Lactcv and Lacta was 1.5% (95% CI: −29% to 32%). In the whole dataset, AUCs of Lactcv to predict a Lacta above 2 and above 4 mmol/L were 0.98 (95% CI: 0.97–0.99) and 0.98 (95% CI: 0.96–0.99), respectively. The best cutoff value of Lactcv to predict a Lacta above 2 mmol/L was 2.1 associated with sensitivity of 95% (95% CI: 92%–97%) and specificity of 93% (95% CI: 90%–96%), whereas the cutoff value of 2.0 mmol/L was associated with sensitivity of 97% and specificity of 90%. To predict a Lacta above 4 mmol/L, the best Lactcv threshold was 3.9, with sensitivity of 92% (95% CI: 90%–95%) and specificity of 98% (95% CI: 97%–99%), whereas the cutoff value of 4.0 mmol/L was associated with sensitivity of 90% and specificity of 99%.

Figure 1
Figure 1
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There was a trend toward a higher bias for the highest values of Lacta (Fig. 1), an effect mainly attributable to patients with acute-on-chronic liver failure. In these patients (116 pairs sampled in 34 patients), however, to detect Lacta above 2 or 4 mmol/L, Lactcv still performed well: AUC = 0.97 (95% CI: 0.92–0.99) or 0.99 (95% CI: 0.97–1), respectively.

The Lactcv/Lacta bias and the performance of Lactcv to predict Lacta were very similar in ARDS (185 pairs of Lactcv/Lacta) and non-ARDS patients (488 pairs of Lactcv/Lacta): The Lactcv/Lacta bias was 0.021 and −0.065 mmol/L, in ARDS and non-ARDS patients, respectively (P = 0.13); the AUC of Lactcv to detect Lacta above 2 mmol/L was 0.99 (95% CI: 0.98–1) and 0.98 (95% CI: 0.97–0.99), respectively (P = 0.39), and was 0.99 (95% CI: 0.98–1) and 0.98 (95% CI: 0.95–0.99), respectively, to detect Lacta above 4 mmol/L (P = 0.32). The results were similar in a comparison of patients with and without pneumonia (see Supplemental Digital Content 2, http://links.lww.com/AA/A425).

As shown on the regression graph (Fig. 2), Lactcv was highly correlated to Lacta (r2 = 0.97; P < 0.0001) and still had very good discriminatory power to detect Lacta above threshold values other than 2 and 4 mmol/L. All results were similar when examining only patients with septic shock, cardiogenic shock, shock of other origin, or with no shock (see Supplemental Digital Content 3, http://links.lww.com/AA/A426), or when examining Lactcv/Lacta pairs measured under invasive ventilation or spontaneous breathing (data not shown). Additionally, within the interval of 30 minutes, the Lactcv/Lacta bias was not influenced by the time elapsed between venous and arterial sampling (see Supplemental Digital Content 4, http://links.lww.com/AA/A427).

Figure 2
Figure 2
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As displayed in Figure 1, in our whole dataset, there were 19 pairs (among 673)with bias outside the prediction interval. Among these pairs, 6 corresponded to measurements made in 4 patients with hepatic failure. The remaining 13 points corresponded to measurements made in 14 patients (i.e., 1 patient with 2 pairs). A majority of these 14 patients were in septic shock.

Among the whole dataset, we identified 171 couples of Lactcv/Lacta pairs measured within a 4- to 6-hour interval in a given patient. The AUC for Lactcv clearance to detect a Lacta clearance less than −10% or >10% was 0.93 (95% CI: 0.87–0.98) or 0.94 (95% CI: 0.89–0.98), respectively. In patients with acute-on-chronic liver failure, 22 couples of Lactcv/Lacta pairs were measured and yielded an AUC of 1 (95% CI: 0.84–1) for Lactcv clearance to detect a Lacta clearance less than −10% or >10%.

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DISCUSSION

The results of this retrospective study are similar to those of Middleton et al.9 who showed good agreement between Lactcv and Lacta in 168 sampled pairs in 110 critically ill patients. Additionally, we demonstrated that Lactcv may be used in place of Lacta to detect blood lactate levels above 2 mmol/L or above 4 mmol/L. We also demonstrated that Lacta clearance can be reliably estimated from Lactcv clearance.

Several studies have shown that the lungs can produce a significant amount of lactate during ARDS or acute lung injury7,8 so that Lacta can be slightly different from the mixed venous blood lactate concentration. However, this difference was too small to affect clinical decisions. The cause of lung lactate overproduction in case of ARDS (i.e., lung or systemic inflammation) is still debated.23 Regardless of the cause, our results show that the bias between Lactcv and Lacta is not significantly influenced by the presence of ARDS or pneumonia.

In our study, the bias between Lactcv and Lacta was very close to zero as already reported for animals11 and humans.9,10 However, it should be emphasized that the Bland and Altman method18 applies to the comparison of different methods of measuring the same physiological variable, which was not the case here. Our results show that the bias of 1.5% (with 95% CI ranging from −29% to 32%) between Lactcv and Lacta cannot be fully explained by the intrinsic variability of lactate measurement with our Rapidlab analyzer. Indeed, Lactcv and Lacta cannot be the same because they represent the balance between lactate production and clearance from different parts of the body. Lactcv is not equal to the lactate concentration in the inferior vena cava. The mixing of lactate in the mixed venous blood is not exactly equal to Lacta.13

Similarly, our results show that in some pairs, Lactcv could underestimate Lacta so that it could be seen as clinically irrelevant (Fig. 2). As shown in Figure 1, this was mainly attributable to patients with acute-on-chronic liver failure, and more pronounced for the highest blood lactate values. This could be explained either by a lactate overproduction by the lungs24 or by a lactate overproduction from the hepato-splanchnic circulation not detected by sampling from the superior vena cava. However, our results show that this potential difference between Lactvc and Lacta in such patients did not reduce the relevance of Lactcv measurements to estimate the whole-body lactate clearance. Based on the good AUCs for Lactcv to predict Lacta above specific thresholds and arterial lactate clearance, Lactcv and Lacta are clinically interchangeable in critically ill patients.

As recently shown in animals,11 Lactcv may poorly predict Lacta in uncontrolled hemorrhage. Because our dataset comprises data from only 4 patients with hemorrhagic shock, this could not be examined in our study. We did not correct measured lactate for body temperature. However, because this was the case for both central and arterial measurements, it seems unlikely that it could have biased our results.

The major limitation of our work was its retrospective nature. We think, however, that it is unlikely that this biased our results because (1) patients and measurements were selected according to only 1 criterion, i.e., the presence of Lactcv and Lacta measured within a 30-minute delay; because in our unit central venous blood is sampled in patients with acute circulatory or respiratory failure, the only selection bias it could have brought is the selection of the more severe patients (i.e., those who need frequent lactate dosages and ScvO2 measurements); (2) we showed that within the time window of 30 minutes, the delay between venous and arterial dosages did not influence the bias between Lactcv and Lacta (Supplemental Digital Content 4, http://links.lww.com/AA/A427); (3) multiple measurements were made in unstable patients with different types of shock; (4) the utility of Lactcv as a predictor of Lacta was also found when selecting the first pair of measurements for each patient; (5) in a previous study comparing Lactcv and Lacta in critically ill patients, the correlation between Lactcv and Lacta was as good as the one we found6; and (6) the limits of agreement in the sole prospective study performed to date in critically ill patients were narrower than those reported here.9 Thus, it seems to us improbable that a prospective study would yield different results.

Arterial blood gas is the most frequently ordered laboratory test in the ICU and may account for nearly 40% of blood phlebotomized.25 Intensivists routinely place arterial catheters to provide convenient access to arterial blood samples, allowing continuous arterial blood pressure measurement to guide resuscitation.26 Frequent sampling from arterial catheters may result in anemia and need for transfusion, which is an independent predictor of worse clinical outcome.27,28 Indwelling arterial catheters have a 1% risk of serious complication, resulting in considerable morbidity among the 8 million and 2.5 million arterial catheters placed yearly in American and European ICUs, respectively.29

The measurement of ScvO2 is useful in the management of circulatory failure, at least in sepsis, and recent recommendations suggest that continuous or intermittent ScvO2 measurements could be used interchangeably.30 If one chooses to measure ScvO2 intermittently by blood sampling rather than with a fiberoptic catheter, our results and other studies suggest that central venous blood analysis could replace arterial blood sampling and analysis: (1) we demonstrated that Lactcv can replace Lacta for clinical purposes; (2) Middleton et al.9 showed that central venous values of pH, bicarbonates, and base excess might be acceptable substitutes for arterial measurements; (3) pulse oximetry monitoring can be used to detect hypoxemia31 or for management of patients with acute lung injury and ARDS32; and (4) Malinoski et al.33 showed that central venous carbon dioxide tension can be used to exclude the existence of respiratory acidosis.

Additionally, Lakhal et al.34 demonstrated that oscillometric noninvasive arterial blood pressure measurements have a good discriminative power for identifying hypotensive patients and tracking blood pressure changes in critically ill patients, a power that might be even better with recent noninvasive blood pressure measurement devices.35 Therefore, from the above results taken together, one could speculate that many critically ill patients could be managed without systematic arterial cannulation. This is however premature and deserves further prospective and comparative studies. For the moment, because ScvO2 and lactate clearance are probably 2 complementary helpful physiological variables for the management of shock,36,37 our study suggests that both can be simultaneously tracked by superior vena cava blood sampling.

In conclusion, our results suggest that Lactcv is similar to Lacta collected within a 30-minute range, and can be used to detect hyperlactatemia and to estimate lactate clearance in critically ill patients with circulatory and/or respiratory failure.

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DISCLOSURES

Name: François Réminiac, MD.

Contribution: This author helped design the study, conduct the study, analyze the data, and prepare the manuscript.

Attestation: François Réminiac attests to the integrity of the original data and analyses reported in the present article.

Name: Christophe Saint-Etienne, MD.

Contribution: This author helped design the study, conduct the study, analyze the data, and prepare the manuscript.

Name: Isabelle Runge, MD.

Contribution: This author helped design the study, conduct the study, analyze the data, and prepare the manuscript.

Name: Denis Ykpé Ayé, MD.

Contribution: This author helped conduct the study and analyze the data.

Name: Dalila Benzekri-Lefevre, MD.

Contribution: This author participated in the conduct of study and the data analysis.

Name: Armelle Mathonnet, MD.

Contribution: This author participated in the conduct of study and the data analysis.

Name: Thierry Boulain, MD.

Contribution: This author helped design the study, conduct the study, analyze the data, and prepare the manuscript.

Attestation: Thierry Boulain attests to the integrity of the original data and analyses reported in the present article.

This manuscript was handled by: Steven L. Shafer, MD.

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ACKNOWLEDGMENTS

We thank Chantal Brossard, laboratory technician, for her invaluable help in data acquisition. We are very grateful to Amélie Le Gouge and Bruno Giraudeau for their advice and help in statistical calculations.

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