Dynamic lactate indices as predictors of outcome in critically ill patients
© Nichol et al.; licensee BioMed Central Ltd. 2011
Received: 9 May 2011
Accepted: 20 October 2011
Published: 20 October 2011
Dynamic changes in lactate concentrations in the critically ill may predict patient outcome more accurately than static indices. We aimed to compare the predictive value of dynamic indices of lactatemia in the first 24 hours of intensive care unit (ICU) admission with the value of more commonly used static indices.
This was a retrospective observational study of a prospectively obtained intensive care database of 5,041 consecutive critically ill patients from four Australian university hospitals. We assessed the relationship between dynamic lactate values collected in the first 24 hours of ICU admission and both ICU and hospital mortality.
We obtained 36,673 lactate measurements in 5,041 patients in the first 24 hours of ICU admission. Both the time weighted average lactate (LACTW24) and the change in lactate (LACΔ24) over the first 24 hours were independently predictive of hospital mortality with both relationships appearing to be linear in nature. For every one unit increase in LACTW24 and LACΔ24 the risk of hospital death increased by 37% (OR 1.37, 1.29 to 1.45; P < 0.0001) and by 15% (OR 1.15, 1.10 to 1.20; P < 0.0001) respectively. Such dynamic indices, when combined with Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, improved overall outcome prediction (P < 0.0001) achieving almost 90% accuracy. When all lactate measures in the first 24 hours were considered, the combination of LACTW24 and LACΔ24 significantly outperformed (P < 0.0001) static indices of lactate concentration, such as admission lactate, maximum lactate and minimum lactate.
In the first 24 hours following ICU admission, dynamic indices of hyperlactatemia have significant independent predictive value, improve the performance of illness severity score-based outcome predictions and are superior to simple static indices of lactate concentration.
Keywordslactate hyperlactaemia dynamic intensive care unit critical illness mortality
In the critically ill, a higher admission blood lactate concentration is associated with a higher risk of death [1–8]. We recently reported that even within the current 'normal range' (< 2.00 mmol.L-1) a higher admission blood lactate concentration is associated with significantly increased hospital mortality , a finding which suggests that even the subtle perturbations of lactate homeostasis may be important.
An elevated blood lactate concentration (a 'static' index) at any time point must be due to an increase in its production, a decrease in its clearance, or both. Likewise, an increasing blood lactate concentration (a 'dynamic' index) must be due to increasing production, decreasing clearance, or both simultaneously [9–11]. Static derangements in lactate homeostasis during ICU stay have become established as clinically useful markers of increased risk of hospital and ICU mortality [1, 3, 4, 12]. However, dynamic indices of lactate homeostasis, which describe not only magnitude but also duration and trend over time, may be even more useful in predicting outcome. In support of this hypothesis, a number of small single centre observational studies, principally in patients with severe sepsis and septic shock, have suggested that early changes in blood lactate concentration may be useful in identifying those at high risk of death [5, 6, 13–16]. Furthermore, one interventional study (n = 348) has suggested that interventions aimed at targeting a dynamic reduction in lactate (20% per two hours for the first eight hours) in the critically ill with an abnormal admission lactate level may be associated with reduced organ failure and increased survival . However, the association between dynamic changes in blood lactate concentration during the first 24 hours of ICU admission and mortality has not yet been investigated in a very large heterogeneous cohort of critically ill patients. Furthermore, to our knowledge no study has compared the ability of dynamic compared to static indices of lactate homeostasis to predict mortality in the critically ill.
To study this association, we examined the relationship between six indices of lactate homeostasis in the first 24 hours of ICU admission and both hospital and ICU mortality. Three indices were static: i) admission lactate (LACADM), ii) maximum lactate (LACMAX24), iii) minimum lactate (LACMIN24), and three were dynamic: iv) time weighted lactate (LACTW24), v) absolute change in (delta) lactate (LACΔ24) and vi) percentage change in lactate (LAC%Δ24).
Materials and methods
The data collection and data analysis for this study are part of ongoing de-identified data auditing processes across the participating hospitals, which have all waived the need for informed consent. The Austin Hospital Ethics Committee approved studies related to this database.
Study population and data sources
The study population and data sources used in this study are similar to those previously reported by us, in a previous manuscript describing lactate homeostasis in the critically ill . In brief, this four-centre retrospective investigation of a prospectively gathered intensive care database enrolled patients from January 2000 to October 2004. However, in this study a minimum of two lactate values collected over the first 24 hours were necessary for inclusion into the study, with the latter criteria needed to produce both a time weighted lactate (LACTW24) and a change in lactate (LACΔ24) over the first 24 hours.
The blood lactate concentration data used for this study were stored and retrieved electronically. We obtained age, sex, use of mechanical ventilation, reason for ICU admission (surgical and non-surgical, further classified as trauma, cardiac/vascular, gastrointestinal tract, neurological and thoracic/respiratory), and Acute Physiology and Chronic Health Evaluation (APACHE) II score  from the electronic data repositories of each ICU, using data prospectively collected as part of the Australian and New Zealand Intensive Care Society-Centre for Outcome and Resources Evaluation (ANZICS-CORE) quality assurance program .
The timing of lactate measurement (Rapilab, Bayer Australia, Sydney, NSW, Australia) was at the discretion of the managing critical care team. Laboratories in the participating hospitals comply with standards of the National Association of Testing Authorities  and the Royal College of Pathologists of Australia .
The change in lactate over 24 hours (LACΔ24) was calculated by regressing lactate against time for each individual patient, with the regression slope representing the projected change over a 24-hour period. To avoid undue influence from extreme outliers, the maximal and minimal slope values were capped in accordance with Tukey . Values that were found to be more than three times the interquartile range to the left of the 25th percentile or to the right of the 75th percentile were considered to be extreme outliers. This resulted in a maximum increase or decrease in slope over the first 24 hours of 5 mmol.L-1. LAC%Δ24 describes the change in lactate over a 24-hour period as the percentage change from the admission blood lactate concentration.
The primary outcomes for analysis were hospital and ICU mortality. We performed univariate analysis for comparison between groups according to hospital mortality status using chi-square test for proportions, Student t-tests for normally distributed outcomes and, otherwise, used Wilcoxon rank sum tests. We performed multivariate analysis with all available predictors of hospital mortality included in the models (gender, age, APACHE II, mechanical ventilation, surgical admission and diagnosis type). To account for changes in practice over the four-year period of the study, the patient admission date was included along with the specific hospital. To further account for surveillance bias, the number of lactate measurements collected over the first 24 hours was also included. After forcing all of the previous described variables into the multivariate model, both stepwise and backwards elimination procedures were used to determine which lactate indices could independently aid in the prediction of mortality. Six lactate indices specifically relating to the first 24 hours of ICU admission were considered: Three indices were static: i) admission lactate (LACADM), ii) maximum lactate (LACMAX24), iii) minimum lactate (LACMIN24), and three were dynamic: iv) time weighted lactate (LACTW24), v) absolute change in (delta) lactate (LACΔ24) and vi) percentage change in lactate (LAC%Δ24).
To determine if the relationship between lactate and mortality was consistent across patient subgroups and study hospitals, we examined interactions between measures of lactate and other variables in the model. To confirm that the potential relationships between lactate and mortality were linear in nature, measures of lactate were divided into quintiles and analysed as categorical variables, with quintiles chosen to provide a minimum of 1,000 patients per category. Goodness of fit was determined using the Hosmer and Lemeshow statistic. All analyses were performed using SAS version 9.2 (SAS Institute Inc., Cary, NC, USA). A two-sided P-value of 0.05 was considered to be statistically significant.
Having established which lactate variables had the strongest relationships with mortality, to then determine the true predictive capacity of these lactate variables, the data were randomly divided into two groups, with 50% of the data used as a derivation sample and the remaining 50% of the data used as a validation sample. Univariate models and multivariate models (with and without the inclusion of lactate) were constructed from the derivation sample for both hospital and ICU mortality. The prediction equations were then applied to the holdout sample and the improvement in the Area under the Receiver Operating Characteristic Curve (AUC-ROC) was recorded.
Comparison of hospital survivors vs.nonsurvivors
APACHE II score
Mechanical ventilation rate
Diagnosis at admission
Cardiac and vascular
Thoracic and respiratory
Gastrointestinal tract diseases
Hospital stay (days)
10 (4 to 25)
15 (8 to 30)
ICU stay (days)
3.0 (2.0 to 8.0)
3.0 (2.0 to 5.2)
Number of measurements
8 (6 to 10)
7 (5 to 9)
2.20 (1.41 to 3.66)
1.41 (1.02 to 1.97)
-0.21 (-1.66 to 0.73)
-0.30 (-1.18 to 0.24)
-13% (-49% to 42%)
-22% (-55% to 21%)
2.36 (1.45 to 4.29)
1.6 (1.08 to 2.49)
1.34 (0.92 to 2.16)
1.00 (0.74 to 1.31)
3.44 (1.99 to 6.20)
2.02 (1.37 to 3.20)
Change in RAW (AUC-ROC) for indices of lactate homoeostasis in the first 24 hours and mortality
0.648 ± 0.014
0.700 ± 0.017
0.558 ± 0.016
0.572 ± 0.022
0.534 ± 0.016
0.542 ± 0.022
0.694 ± 0.014
0.769 ± 0.016
0.705 ± 0.014
0.769 ± 0.016
0.682 ± 0.014
0.723 ± 0.018
LACTW and LACΔ24
0.710 ± 0.01
0.763 ± 0.01
Multivariate models for the prediction of hospital and ICU mortality
Odds ratio (95% CI)
Odds ratio (95% CI)
APACHE II score
1.13 (1.11 to 1.14)
1.13 (1.11 to 1.15)
1.02 (1.02 to 1.03)
1.01 (1.00 to 1.02)
1.37 (1.29 to 1.45)
1.43 (1.35 to 1.52)
1.15 (1.10 to 1.20)
1.18 (1.13 to 1.24)
1.93 (1.52 to 2.45)
2.81 (1.95 to 4.05)
Admission date (decreased risk per year)
0.91 (0.83 to 0.99)
0.88 (0.78 to 1.00)
0.67 (0.53 to 0.84)
0.80 (0.59 to 1.08)
Number of measurements
0.98 (0.95 to 1.01)
1.01 (0.97 to 1.04)
Female v male
0.96 (0.81 to 1.14)
0.99 (0.79 to 1.23)
Change in adjusted Area under the Receiver Operating Characteristic Curve
(1) Established risk factors
0.820 ± 0.01
0.873 ± 0.01
(1) + LACADM
0.822 ± 0.01
0.875 ± 0.01
(1) + LACΔ24%
0.824 ± 0.01
0.879 ± 0.01
(1) + LACΔ24
0.825 ± 0.01
0.879 ± 0.01
(1) + LACMAX24
0.827 ± 0.01
0.883 ± 0.01
(1) + LACTW24
0.831 ± 0.01
0.887 ± 0.01
(1) + LACMIN24
0.832 ± 0.01
0.886 ± 0.01
(1) + LACTW & LACΔ24
0.838 ± 0.01**
0.895 ± 0.01**
Interestingly, when LACMIN (a static measure) was added to the models of hospital mortality, the AUC was found to increase to 0.832 ± 0.01 (Table 4). However, when we combined LACTW24 and LACΔ24 we found that this resulted in the greatest improvement in the prediction of both hospital and ICU mortality (Table 4), compared to any other combination.
Statement of key findings
In a large multi-centre, heterogeneous cohort of critically ill patients, we examined whether dynamic indices of blood lactate concentration over the first 24 hours of ICU admission were independently associated with increased risk of hospital mortality. We found that LACTW24 and the LACΔ24 were the indices of lactate homeostasis within the first 24 hours that were independently the most predictive of hospital mortality. A rising, compared to a falling, blood lactate concentration over the first 24 hours was associated with a significantly increased risk of mortality. Furthermore, for every one mmol L-1 increase in LACTW24 and in LACΔ24 the risk of hospital death increased by 37% and 15%, respectively. When LACTW24 and LACΔ24 were separately added to outcome prediction models, the AUC-ROC was found to increase significantly achieving close to 90% discrimination for ICU mortality. Furthermore, the maximal increase in AUC-ROC was achieved by the combination of both these dynamic indices (LACTW24 and LACΔ24) achieving close to 90% discrimination for ICU mortality and close to 85% discrimination for hospital mortality
Comparison with previous studies-static versus dynamic
To our knowledge this is the first demonstration that dynamic measures of lactate can be used to significantly improve the prediction of mortality in a heterogeneous cohort of critically ill patients. Moreover, both LACTW24 and LACΔ24 were positively associated with length of stay in survivors suggesting that these indices are robust and identify patients not only at high risk of mortality but also those who are at high risk of significant morbidity (data not shown). While individual dynamic measures do not outperform all the currently used static measures, the combined measure of lactate dynamics (LACTW24 and LACΔ24) could be the optimal early lactate variable to predict mortality as it can out-perform currently used static measures. This improved predictive ability may rest in the ability of this dynamic composite to assess the absolute magnitude, duration and rate of reduction of lactate derangement. Further studies are now required to confirm the clinical utility of this composite dynamic measure.
Implications for clinicians
There is much interest in finding biomarkers that can assist in the early identification of patients who are, or continue to be, at high risk of death . Our findings suggest that dynamic changes in blood lactate concentration over the first 24 hours may prove useful as a widely available biomarker of increased risk of death. Interestingly, in support of this hypothesis, goal directed resuscitation in critically ill patients with septic shock improves blood lactate concentrations over its six hours of therapy . Furthermore, a prospective study which targeted reductions in blood lactate concentration goals was equally effective as a strategy targeting central venous oxygen saturation [25, 27]. While our findings are in broad agreement with this association between 'physiological' normalisation of lactate and improved survival in the critically ill, further study is required before our findings should be used as a therapeutic target in mixed cohorts of patients and over varying time periods. However, clinicians confronted with a persistently abnormal blood lactate should also be aware of the fact that the absolute degree of derangement (duration and magnitude) and the rate increase of this derangement are associated with risk of death and should continue to maintain a high index of suspicion until these parameters have completely normalised.
Limitations of the study
Our study is retrospective in design and thus potentially subject to systematic error and bias. However, all the clinical and electronic data utilised were collected prospectively in a large number of consecutive critically ill patients in four ICUs, are numerical in nature and were measured independently; thus, they were not amenable to selection bias or unintended manipulation. Furthermore, 71% of all ICU patients admitted during the study period (n = 7,155) had at least two blood lactate measurements (n = 5,041) and were included in our analysis, suggesting that our findings are generalisable and can be applied to the majority of ICU patients. A number of common therapeutic interventions, such as epinephrine , metformin , nucleoside analogues in HIV , high-volume hemofiltration (HVHF) with lactate-buffered replacement fluids , can all affect lactate levels and we did not have information on their use. However, the influence of these potential confounding factors is likely to be negligible given the small numbers of patients in which these factors would have been present, in relation to the size of the cohort; although presence of these factors might alter the applicability of these results to individual patients.
While our calculation of LACΔ24 assumes that the kinetics of lactate is linear in nature, it is possible that it may follow an alternative decay profile, that is, exponential. However, we feel this pragmatic assumption does not positively bias the strength of the associations demonstrated. Furthermore, we believe that more complex modelling of lactate kinetics would obscure the take-home message for clinicians at the bedside of the association between rate of change of lactate concentration and mortality
The prognostic relevance of higher LACTW24 and LACΔ24 derives from the statistical examination of a large group of critically ill patients. Our results show that the inclusion of these variables can significantly improve the strength of predictive models of hospital mortality. This suggests that future investigators developing early prognostic models in the critically ill should consider the inclusion of these parameters (and the composite measure of both together). However, it must be noted that they should not be misused as a reliable prognostic sign in the individual patient, but in comparing groups of patients. In individual patients, higher LACTW24 and LacΔ24, however, should be considered a useful indicator pointing to the severity of illness and to superimposed complications.
These dynamic lactate findings are novel and need to be confirmed by similar studies in other countries and large heterogeneous patient populations before they can be considered to reflect a general biological principle. Ideally, these studies should be conducted prospectively with simultaneous collection of information on interventions which may affect lactate. Furthermore, given the lactate indices assessed in this study are not necessarily the first in hospital lactate measurements and may not, therefore, consider the effects of early resuscitative interventions, future prospective studies should collect all lactate measurements (that is, including the emergency department) to determine if the demonstrated relationships are also observed during this earlier period.
In addition, if these studies confirm the value of LACΔ24, then future studies could potentially focus on this marker as a surrogate endpoint to assess the success of early interventions in critically ill patients in the resuscitative period.
Future studies developing new or refining current severity of illness models in the critically ill should consider the inclusion of measures of lactate homeostasis in the first 24 hours. Our results suggest that the use of dynamic measures may result in the greatest improvement in predictive ability. In addition, it is worth noting that our study was potentially limited by having to add lactate measurements to the composite APACHE II score, rather than its component parts, and that a completely re-derived risk prediction equation that includes one or more index of lactate may further increase the predictive ability over that shown in this study.
In conclusion, higher LACTW24 and LACΔ24 blood lactate concentrations are associated with greater hospital mortality. In the first 24 hours following ICU admission, they have significant independent predictive value, improve the performance of illness severity score-based outcome predictions and are superior to simple static measures of lactate concentration. Future studies should be conducted to determine the clinical utility of the composite measure of lactate derangement (magnitude, duration and rate of correction) to predict mortality and to determine potential the utility of LACΔ24 as a future therapeutic target/trigger. While these dynamic measures do not have a routine role in daily clinical practice, clinicians should be especially alert to all patients with a persistently deranged or rapidly rising blood lactate concentration.
Static derangements in lactate homeostasis during ICU stay have become established as clinically useful markers of increased risk of mortality.
Dynamic indices of lactate homeostasis, which describe not only magnitude but also duration and trend over time, may be even more useful in predicting outcome.
We demonstrated that higher lactate averaged over 24 hours (LACTW24) and an increasing trend in lactate concentration over 24 hours (LACΔ24) is associated with greater hospital mortality.
Furthermore, the combination of these markers out-performed all individual 'static' indices of lactate homeostasis in predicting outcome in the critically ill.
Clinicians should be especially alert in all patients with a persistently deranged or rapidly rising blood lactate concentration
- APACHE II:
Acute Physiology and Chronic Health Evaluation II
Australian and New Zealand Intensive Care Society-Centre for Outcome and Resources Evaluation
Area under the Receiver Operating Characteristic Curve
time weighted lactate
percentage change in lactatein the first 24 hours.
No financial support was received for the collation of this article. However, AN receives a Victorian Neurotruama Initiative (VNI) Early Career Practitioner Fellowship and RB receives a National Health and Medical Research Council (NHMRC) Practitioner Fellowship.
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