Background
The maintenance of optimal blood volume without the development of a positive fluid balance is a major challenge in the daily care of patients suffering from acute traumatic, subarachnoid hemorrhage or infectious inflammatory disorders [
1‐
10]. Indeed, capillary leak significantly contributes to the development of tissue edema and causes persistent hypovolemia despite fluid resuscitation [
11‐
13]. The consequence is two-fold, with (1) a large volume fluid resuscitation and (2) an increase in tissue edema with impairment of microcirculation architecture and oxygen diffusion. This fluid overload caused by fluid resuscitation and excess sodium can be the source of organ dysfunction, including acute lung injury, abdominal compartment syndrome and acute renal injury [
2‐
5,
14,
15], thereby contributing to higher mortality [
7‐
9]. A 4-kg weight gain, corresponding to an accumulation of 4 L of water and 36 g of sodium chloride, is the limit beyond which morbidity and mortality increase [
3,
16‐
18].
The accurate monitoring of fluid balance is therefore crucial in guiding fluid resuscitation. However, there is no gold standard method for complete measurement and the use of input/output charts in intensive care unit (ICU) patients is notorious for being incomplete and inaccurate. Consequently, accurate and reproducible methods to improve the monitoring of fluid balance are essential.
Several plasmatic biomarkers may contribute to arbitrating conflict between the control of optimal blood volume and the development of fluid overload: these include stress hormones, such as cortisol and catecholamines; hormones involved in volume regulation by sodium chloride or water retention, such as the renin-angiotensin II-aldosterone system [
19]; vasopressin, expressed by (CT)-pro-arginine vasopressin, known as copeptin [
20]; endothelin expressed by pro-endothelin and atrial natriuretic peptide (ANP), measured by pro-ANP; factors involved in the production of red blood cells, such as erythropoietin (EPO) [
21]; and factors that repair the endothelium after injury, such as mid-regional pro-adrenomedullin (MR-proADM) [
22,
23].
The first aim of the Etude des marqueurs iNnovants de la VOLémie (ENVOL study) was to identify a reliable surrogate biomarker capable of predicting blood volumes and/or cumulative sodium and water balance (∆Na+ and ∆H2O). We also tried to evaluate any relationship between extracellular volume and blood volume measurements. For these purposes, we precisely characterized changes in extracellular and blood volumes and changes in several plasmatic biomarkers involved in volume regulation during the first 7 days of ICU stay.
Methods
This prospective, 7-day observational study was conducted between March 2012 and September 2014 in the 38-bed Department of Anesthesiology and Intensive Care at Bicêtre University Hospital, in Le Kremlin-Bicêtre, France. The Institutional Review Board of the hospital (Comité de protection des personnes Ile de France VII) approved the study on December 2011 (reference 11-045). Written informed consent was systematically obtained from all participants included in the study or from a relative, in accordance with French legal ethics.
We studied four groups of ICU patients, including those with severe brain trauma (SBT), aneurysmal subarachnoid hemorrhage (SAH), severe non-cerebral trauma (NCT) or postoperative peritonitis with septic shock (PPS). Patients were included if they required continuous mechanical ventilation on day 2. SBT was defined as brain trauma with a Glasgow coma score of <9. Patients with SAH were included in the presence of a score ≥4 on the World Federation of Neurosurgical Societies (WFNS) scale [
24]. Patients with NCT were included when the Injury Severity Score was ≥25. Patients with PPS were included after abdominal surgery complicated by manifestations of hemodynamic shock, including hypotension and low cardiac output or lactate concentration >4 mmol/L. Patients <18 years, pregnant or presenting with New York Heart Association (NYHA) graded ≥ II were excluded.
Data collection
During the ICU stay, general and demographic data were collected, including age, sex, weight, height, simplified index of illness severity after the first 48 h (Simplified Index of Gravity (IGS) II) and admission date. Sequential Organ Failure Assessment (SOFA) scores were measured upon admission (day 0 (D
0)), and on days 2, 5 and 7 (D
2, D
5 and D
7) [
25]. Mean arterial pressure, doses of norepinephrine and core temperature were recorded daily. Blood laboratory tests included measurements of hemoglobin, proteins, electrolytes, creatinine and urinary concentrations of Na
+, K
+, Cl
-, urea, creatinine and osmolarity. The urinary electrolytes, creatinine, urea and osmolarity were measured in the morning and the total 24-h output was used to calculate the previous day’s loss of Na
+, K
+, urea and the creatinine clearance. Weight was carefully measured using weighing beds and recorded on D
2 and D
7. The baseline weight was that recorded by the patient or a relative.
The daily intravenous fluid administration was based on the monitoring of heart rate, arterial pressure, blood lactate concentration, serial echocardiograms, cardiac filling pressures and output and signs of fluid responsiveness in ventilated patients [
26,
27].
Sodium and fluid balances were calculated daily in order to estimate changes in extracellular space. The previous day’s inputs and outputs of sodium and water were calculated each morning. All other losses were measured, including from ileostomies and external ventricular drainage when present. Sodium losses were measured from all liquids and deducted from sodium intake. The difference between water administration from enteral nutrition and daily crystalloids or colloid infusion and water loss was calculated. Insensitive losses were adjusted for body temperature. The sodium and water gains or losses were calculated daily and added to the previous day’s measurements as cumulative fluid balance (∆Na
+ and ∆H
2O). A >36 g ∆Na
+ or >4 L ∆H
2O was defined as fluid overload [
16‐
18]. The creatinine clearance was calculated daily. All calculations were made by one caregiver and verified by another (PEL, HF and BV).
Blood volume measurements
The red blood cell volume (RBCV) was measured on D
2 and D
7 (±1 day), using 10 mL of the patient’s red blood cells (RBC) labeled with radioactive chromium (
51Cr-RBC). We re-injected a known quantity of radioactive RBC intravenously and collected two arterial samples 10 and 30 minutes later. From the radioactivity of these samples we derived RBCV in mL/kg using the patient’s body weight recorded before admission [
28]. The arterial hematocrit and RBCV defined the total blood volume (TBV), in mL/kg and the plasma volume (PV), in mL/kg. The normal values are 32 ± 6 mL/kg for RBCV, 72 ± 14 mL/kg for TBV, and 40 ± 8 mL/kg for PV. Hypovolemia was noted when TBV was <20 % of normal values [
28]. On D
7, we also measured PV by intravenously injecting a small amount of albumin labeled with radioactive iodine (
125I-albumin), and collected arterial samples at 10 and 30 minutes and at 2 h [
29]. The normal PV measured with
125I is 45 ± 10 mL/kg, slightly larger than that measured with
51Cr-RBC [
28].
Biomarker analysis
Plasma biomarkers were analyzed on D2, D5 and D7. MR-proADM, Pro-ANP, renin, angiotensin II, aldosterone, cortisol, norepinephrine and epinephrine, copeptin, pro-endothelin and EPO were measured for potential interference with extracellular or blood volumes. All biologic biomarkers were analyzed together after inclusion in November 2014.
Statistical analysis
Because our original aim was to assess the correlation of the biomarkers with intravascular volumes, the study sample size was calculated considering that brain natriuretic peptide (BNP) and EPO are good surrogate markers of intravascular volumes. We then used published values of BNP and EPO concentrations in similar patients [
21,
30] to calculate the number of patients needed (three groups of patients with hypervolemia, normovolemia and hypovolemia, respectively, considering the initial protocol with a 50 % between-group difference and power of 80 %). Data were analyzed using R [
31]. The normality of data distribution was verified using quantile-quantile plots and Shapiro’s test. Because most data were measured with error, Deming regression with equal variances was used to calculate the slope of the regression curves.
Because biomarkers were approximately log-normally distributed, comparisons between D
2, D
5 and D
7 values were performed on the log transformed data using the Tukey test after analysis of variance (ANOVA). As our objective was also the measurement of extracellular volume, we also studied fluid overload. For this purpose we considered 4 L and 36 g as ∆H
2O and ∆Na
+ (equivalent to 4 L of saline), respectively, for the cutoff of response variables [
16‐
18].
We also calculated the performance of biomarkers to predict a SOFA score >9. The calculation of ∆H
2O and ∆Na
+ and SOFA score were performed on the same day as the biomarkers were assessed. Because of the large number of variables that were candidates for inclusion in multivariate analysis, we first used random forest regression [
32] to select the most pertinent demographic and biological covariates explaining fluid and sodium overload and SOFA score. This was followed by linear mixed effect regression in which subject and day of measurement were considered as random effects due to correlation between days of measurement.
Receiver operating characteristic (ROC) curves were constructed to calculate the performance of the biomarkers in predicting fluid and sodium overload (∆H2O and ∆Na+) and SOFA score. The optimal sensitivity/specificity cutoff for predictive variables was calculated, using the non-weighted Youden index. The statistical significance was set at P <0.05. Data are reported as means ± standard deviation (SD), medians (25–75 percentiles) or counts and percentages or 95 % confidence interval (95 % CI).
Discussion
This study revealed that MR-proADM, a biomarker of endothelial permeability [
22,
23], may be used as a surrogate for the increase in sodium and water balance in the extracellular space, within the first week after admission of critically ill patients to the ICU. In addition, we found no relationship between the increase in sodium or water balance and direct measurement of blood volumes on D
2 and D
7. We found that MR-proADM thresholds of 0.865 nmol/L for ∆Na
+ and 1.125 nmol/L for ∆H
20 were predictive of fluid and of salt overloads, respectively. Moreover, MR-proADM was related to the concurrent SOFA score.
Excessive sodium and fluid balance is a risk factor for morbidity and mortality in critically ill patients [
7‐
9,
33]. A reliable and easy to measure surrogate biomarker could be very useful in improving the monitoring of fluid balance and identification of patients with a positive interstitial fluid and sodium balance. This biomarker should enable us to better personalize therapy and to guide fluid resuscitation and administration of vasopressors or diuretics.
While MR-proADM seems to be a particularly reliable indicator of sodium and fluid balance, it does not exclusively indicate capillary permeability. Indeed, MR-proADM is a stable fragment of pro-adrenomedullin which reflects levels of the rapidly degraded active peptide adrenomedullin [
22]. In addition to its role in vascular endothelial barrier permeability in blood vessels [
23], adrenomedullin also stabilizes the lymphatic endothelial barrier [
34] and is implicated as an important pleiotropic effector of the host defense mechanism [
35].
In the ICU, fluid balance is evaluated by the use of input/output charts and by the daily measure of weight. However, it is time consuming and difficult to perform accurately. In addition, the present study identified a weak relationship between weight and fluid balance. Measurement of patient’s weight in inflammatory disorders may be biased by practical issues but also by loss of muscle mass, interfering with the estimate of fluid overload. Moreover, we found that the usual markers of extracellular volume, such as plasma proteins and hemoglobin also had a weak relationship with sodium or water balance. Thus, the monitoring of sodium and fluid balance is currently a difficult task using tools that have their own limits. MR-proADM concentration, on the other hand, offers a measure of sodium balance and extracellular space, which could be used as a good surrogate to improve fluid balance monitoring. Possibly, it will be useful in the future to test a score centered on MR-proADM but also taking into account simple values such as basal weight or plasma proteins, to easily obtain an even better measurement.
No marker accurately estimated the TBV, PV or RBCV. Moreover, ∆Na
+ or ∆H
2O are not predictors of blood volume. We found no relationship between fluid balance and plasma volume. This was particularly unexpected, because plasma volume expansion is the main justification for fluid infusion. Yet, plasma was the only volume that was in the normal range on D
2 and D
7. The absence of correlation between PV and fluid balance supports the hypothesis of possible trapping of Na
+ and water in the interstitial volume [
36,
37]. Tighter control of PV would be useful in daily practice, though we did not find relationships with biomarkers, proteins or hemoglobin. This issue warrants further examination with a combination of other biomarkers or predictors.
The good correlation between the measurements of PV deducted from RBCV with 51Cr and directly measured with 125I-albumin at D7 strengthened our results. The distribution volume of albumin may be larger than that obtained with RBC, especially when the capillary permeability is pathologically increased. On D7 the difference was weak, suggesting that the capillary permeability was nearly repaired. While it would have been worthwhile to examine this comparison on D2, this was precluded by the interactions between 125I and some of the measurements of biomarkers.
The observation of a decreased RBCV is common and explains low TBV. The measurement of RBCV with
51Cr is a recognized method but requires time [
28]. As hematocrit, hemoglobin is dependant of the ratio between RBCV and PV, we found, as others, that hemoglobin concentration is a poor surrogate for RBCV in ICU [
38]. EPO is related to RBCV but the ROC curve is not sufficiently discriminative. We found no good biomarker of RBCV to help address this problem.
The present study has some limitations. First, we found no marker that describes blood volume. We need a better understanding of all physiologic determinants, including capillary permeability, hormonal influences and low RBCV and interactions among theses determinants. On the other hand, we identified a biomarker of cumulative salt and water balance, which could be an interesting tool for fluid management in the ICU. Second, we made our study measurements on D2, D5 and D7, long after the admission of patients into the ICU, often after the peak of the disease manifestation. The distribution of extracellular volume may be different at D0 during initial resuscitation. The precise timing of measurements may also be a critical factor. While our study observed the changes that took place on D2 and D7 after an acute event, further studies are needed to fill the gaps.
Our study was observational. Patients who were included reflect the daily practice of our service. However, the group of patients as a covariate did not influence the prediction of fluid balance or blood volume, suggesting that volume abnormalities are independent of the pathology. Other studies will need to confirm our results.
Acknowledgements
Not applicable.