Etomidate is a derivative of imidazole and a potent short-acting hypnotic drug with the least hemodynamic effects and therefore preferable for use in general anesthesia.1 The favorable hemodynamic profile of etomidate is mediated by the activation of α2b-adrenoreceptors.2 The resulting vasoconstriction will counteract the hypotensive effects of the concomitant anesthetic agents.3 For patients having right-to-left intracardiac shunts, a decrease in peripheral vascular resistance is poorly tolerated. Therefore, this drug is used widely in pediatric patients with congenital heart disease (CHD), especially in those with tetralogy of Fallot (TOF) in whom cyanotic spells is a concern during anesthesia induction.4 Given its excellent pharmacologic characteristics, anesthesiologists tend to reevaluate the clinical application of etomidate, despite concerns about mild adrenal suppression.
The changes of hemodynamic properties and consequently reduced organ functions significantly influence the pharmacokinetic (PK) and/or pharmacodynamic characteristics of some drugs. For example, previous studies5,6 have reported that the PK of fentanyl and aminoglycoside is altered, and therefore, the dosing regimen might need to be altered in pediatric congenital cardiac malformation patients. Gozal et al7 observed that patients with cyanotic CHD had a more rapid onset of rocuronium. Song et al8 confirmed that the etomidate requirement is decreased in adult patients with obstructive jaundice. Our experiences (personal experience) also showed that a 0.2 mg/kg bolus injection seems to be able to achieve the desired anesthetic effect in some patients with complex CHD, for example, children with TOF (commonly used dosage is 0.3 mg/kg), although it is difficult to determine whether this decline is because of PK and/or pharmacodynamic changes.
Thus far, the majority of studies on PK of anesthetics rarely have involved children with CHD before surgery, which is the time when anesthetics are being used. Considering the limited literature describing the PK of etomidate in the pediatric population, especially in those with CHD, and the complexity and diversity of CHD, this study focused on the children with unrepaired TOF. The primary objective was to explore the effects of TOF on the PK of etomidate in children.
MATERIALS AND METHODS
This study was approved by the Institutional Review Board of the Shanghai Children’s Medical Center, China. Written informed consent was obtained from the parents or legal guardians of each child before their inclusion into the study. The enrollment was not randomized. In the first stage of the study, some children older than 1 year were recruited. Then, based on recommendations from reviewers and the importance of age matched between groups, the data from 15 children younger than 1 year were included, and the additional 14 children were recruited again during the second stage. A pilot study was conducted for measuring the systemic clearance (Cl1) in 6 pediatric patients who received etomidate 60 μg/kg/min intravenously (IV) until a bispectral index (BIS) of ≤50 for 5 seconds. The mean and standard deviation of individual post hoc Cl1 were 0.30 and 0.10 L/min, respectively. A 30% difference between 2 pediatric populations was considered to be clinically meaningful. The required sample size was estimated based on the power calculation. With α = 0.05, 2-tailed and power of 75% to 80%, 13 to 15 patients were needed for every group.
Finally, 29 pediatric patients who were younger than 1 year, had American Society of Anesthesiologists physical status I to III, had an indication for an arterial line as part of the anesthetic plan, and had been scheduled to undergo elective inpatient surgery under general anesthesia were recruited from December 2011 to October 2015. Among them, 15 were diagnosed with TOF, and the others had normal cardiac anatomy. Exclusion criteria included the existing neurologic deficit, allergy to any of the trial drugs, and significant hepatic or renal disease.
Conduct of Anesthesia
All patients fasted in accordance with hospital guidelines, and 0.5 mg/kg oral midazolam was administered 30 minutes preoperatively. Then, they were brought to a quiet surgical operating room with a peripheral IV catheter already inserted. After starting monitoring of routine electrocardiogram, oxyhemoglobin saturation, BIS, and noninvasive blood pressure, 2 mg/kg ketamine was injected IV. After of the patient lost consciousness, 1 cannula was inserted into the radial or femoral artery to measure invasive blood pressure and to sample blood. BIS, electrocardiogram, oxyhemoglobin saturation, invasive arterial blood pressure, end-tidal carbon dioxide, and rectal temperature were monitored continuously throughout the study (Philips HP Viridia 24/26 M1205A, Agilent, VT). Normothermia was maintained throughout the surgery period.
Fifteen minutes after the administration of ketamine, anesthesia was induced with continuous IV infusion of etomidate (60 μg/kg/min; JiangSu HWA Pharmaceutical Co. Ltd, Xuzhou, China) with the use of Graseby 3500 syringe pumps (SIMS Graseby Ltd., Herts, UK) until the BIS ≤50 for 5 seconds.9,10 Rocuronium 0.6 mg/kg was administered IV when BIS ≤50 for 5 seconds was achieved. Ninety seconds after paralysis, a direct laryngoscopic endotracheal intubation was performed. Starting a minute after intubation, anesthesia was maintained with inhaled sevoflurane 1.5% to 2% in oxygen adjusted to maintain adequate anesthesia during surgery. In addition, propofol (6 mg/kg/h) and sufentanil 2.5 μg/kg/min for CHD surgery or intermittent bolus injections 0.2 μg/kg for non-CHD surgery as well as rocuronium (0.6 mg/kg/h) were given. Hemodynamic data at the designated time points were recorded during the study period.
Blood Sampling and Assay
To acquire the arterial blood gas values, arterial blood (0.5 mL) was obtained before the infusion of etomidate (a baseline blood sample). Arterial blood samples (0.5 mL) were collected as follows: Three samples were obtained at the beginning of infusion, during the etomidate infusion, and before the time point of the infusion ended, 6 or 7 sampling points were divided into 7 sampling blocks (0–<5, 5–<15, 15–<30, 30–45, 45–60, 60–90, and 90–120 minutes) after the end of the infusion, and each child was randomized to 1 sampling time per block. The last sampling point of the children scheduled to undergo cardiac surgery was limited to the start of cardiopulmonary bypass.
Blood samples (using lithium heparin to anticoagulate) were immediately centrifuged at 3000 rpm (1006.2g) for 10 minutes. Degradation of etomidate was inhibited by mixing the supernatant plasma (200 μL) with 50% sodium citrate (50 μL), adding acetonitrile, spiral vortexing for 2 minutes, and standing for 30 minutes. Then, the samples were centrifuged at 15,000 rpm (25115g) for 10 minutes at 4°C, and the supernatant liquid was drawn and stored at −70°C until analysis. Etomidate plasma concentrations were analyzed with a validated high-performance liquid chromatography (Aglient 1200; Aglient, Santa Clara, CA), as previously described.11 The lower limit of quantitation of the assay was 10 ng/mL.
Nonlinear mixed-effect population model with NONMEM 7, version 7.3 (ICON Development Solutions, Ellicott City, MD) was used to analyze the pharmacokinetics. Models were run with the first-order conditional estimation with interaction method (ie, FOCEI). The etomidate concentration versus time data were first applied to 1-, 2-, and 3-compartment models to determine the best base structure model. The variability of interindividual PK parameters was modeled as follows:
where θi is the estimated parameter value for the individual subject i, θk is the typical population value of PK parameter k, and ηki is the interindividual random effects for individual i and parameter k. Log-transformed concentration data were used in the population pharmacokinetic analysis. Based on graphical evaluation of plasma concentration versus time profiles, an initial structural model was selected and then tested with various modifications. The goodness of fit of the models was evaluated. Symmetry of the individual parameters around the estimated median parameter was assessed graphically. Model diagnostics provided directions for further model modifications and/or refinements. A log-additive residual model was selected based on model diagnostics and used to describe residual variability in the following equation:
where yij describes the measured concentration in the ith individual at time j, ŷij is the predicted concentration value of the ith individual at time j, and εij is the residual error of concentration value for the ith individual at time j, which is from a normal distribution with mean 0 and variance σ2. After we determined the basic structural model, the per-kilogram model was introduced to investigate the impact of body size on all pharmacokinetic variables, Pi = PTV × (Wi/70 kg), where Pi is the PK parameter in subject i, and Wi is the body weight of subject i, PTV represents the typical value of PK parameter, and Pi standardized for an individual with adult body weight of 70 kg.
Then, a full covariate model was constructed to estimate the impact of covariates on etomidate disposition, referring to the method as implemented in the study by Lin et al12 The statistical significance of a covariate was examined using the objective function (OBJ; −2 log likelihood, OBJ). In the forward screening and backward deletion process, for the addition or deletion of 1 parameter to the model, the improvement of OBJ >6.63 was considered statistical significance (χ2 distribution; df = 1; P < .01). Continuous covariates were introduced into the model with participants’ median value. Categorical covariates were coded as 0 and 1 (eg, gender = 1 for male, TOF = 1 for patients with TOF, gender = 0 for female, TOF = 0 for patients without TOF)
In addition, we also explored the PK characteristics using allometric model, Pi = PTV × (Wi/70 kg)PWR, with PWR fixed at a value of 0.75 for clearances and 1 for volumes, which are advocated by some authors based on physiologic consideration of body size have on metabolism.13
Model evaluation was performed by inspection of observations versus predictions, conditional weighted residuals versus time, and post hoc predictions. The prediction bias and precision of our final model also were evaluated with the bootstrap simulations. We used NONMEM 7.3, PsN version: 4.2.0 for performing a bootstrap analysis. Nonparametric bootstrap (1000 replicates) was used to evaluate the stability of the model and to determine the 2.5th and 97.5th percentiles of the bootstrap results around the final parameter estimates. The data sets were replicated by randomly sampling from the actual data (sample subject ID with replacement of up to the total number of subjects in the original data set).
Data were tested for normal distribution using the Shapiro-Wilk normality test. Normally distributed data were expressed as mean and standard deviations, and the equivalence of the variances was evaluated using the Levene test. The Student t test was used if the variances were equal. The skewed data are shown as median and range and analyzed with the Wilcoxon rank sum test. P < .05 was considered statistically significant. S-Plus 8.2 (TIBCO Software Inc, Palo Alto, CA) was applied to data processing, statistical analysis, and graphical displays.
Twenty-nine patients were included in study. The patients’ age, weight, and height were 236 ± 72 days, 7.7 ± 1.2 kg, and 67.7 ± 5.1 cm, respectively. The etomidate dose required to reach BIS = 50 in TOF children (0.44 ± 0.10 mg/kg) was less than that in normal children (0.60 ± 0.15 mg/kg), and the corresponding mean infusion duration was 7.3 and 9.9 minutes, respectively (P = .003). Other demographic data are shown in Table 1. No adverse complications were noted in any of our patients, and all of them were stable throughout the anesthesia period.
Seven concentration versus time data points were found to be below the lower limit of quantification and were not included in the final analysis. The remaining 244 drug assay samples from 29 individuals were used. The etomidate plasma concentrations versus time profiles for patients with normal cardiac anatomy and with TOF are shown in Figure 1.
Table 2 shows the principal model building steps. A 3-compartment model was chosen as the basic structural model because it was found to produce a statistically superior fit compared with a 1- or 2-compartment model (ΔOBJ = −321.64 and −53.90, respectively; P < .0001), and this was also the case when scaling for body size (Table 2). Compared with an unscaled model, the per-kilogram weight model showed a reduction in OBJ by 11.61 points, based on which we further performed covariate analysis.
The effect of available covariates, including age, gender, height, hemoglobin, hematocrit, creatinine, alanine aminotransferase, aspartate aminotransferase, total bilirubin, total protein, and prothrombin time, on pharmacokinetic parameters was explored systemically; however, only TOF was included in the final PK model. The introduction of TOF as a covariate for Cl1 improved the model and resulted in a significant reduction of OBJ (ΔOBJ = −7.33; P = .0068). The typical PK parameter values and percent relative error of model fitting for the final full covariate per-kilogram model are presented in Table 3. Figure 2 shows the PK parameter changes with age, which clearly demonstrates that children with TOF have lower systemic clearance compared with normal children. The population parameter estimate for systemic clearance (Cl1) with per-kilogram model was 2.28 × (WT/70 kg) L/min in children with normal cardiac anatomy and 1.67 × (WT/70 kg) L/min in children with TOF, and central volume of distribution (V1) was 8.05×(WT/70kg) L, intercompartment clearances (Cl2 and Cl3) were 3.35 × (WT/70 kg) L/min and 0.563 × (WT/70 kg) L/min, respectively, and peripheral volumes of distribution (V2 and V3) were 13.7 × (WT/70 kg) L and 41.3 × (WT/70 kg) L, respectively.
The aforementioned analysis process was repeated with the allometric model. Compared with an unscaled model, the allometric model showed a reduction in OBJ by 13.58 points, which was only slightly superior to the per-kilogram weight model (ΔOBJ = −1.97). The further covariate analysis deduced the same conclusion: children with TOF had lower systemic clearance compared with normal children. The typical PK parameter values and percent relative error of model are also presented in Table 3; however, as shown in Figure 3, the individual post hoc systemic clearance (Cl1) values are almost identical between the per-kilogram model and allometric model.
Goodness-of-fit plots of the observed etomidate concentrations versus the final population model predicted concentrations and individual post hoc predicted concentrations are presented in Figure 4 (top), which revealed no systematic bias in the predicted concentrations. No apparent bias was observed in the residuals plots over time and across post hoc predicted concentration in Figure 4 (bottom). The corresponding graphics of allometric model evaluation are shown as in the Supplemental Digital Content (Supplemental Figure,.
The results from the bootstrap analysis are listed in Table 3; the median values and the range of values from bootstrapping were similar to the parameter estimates and range of estimates from the final full covariate model of the original data set, suggesting that the final population pharmacokinetic model was stable with good precision of parameter estimation.
This study has investigated the pharmacokinetics of short-term IV etomidate infusion in pediatric patients. The primary finding is that unrepaired TOF-associated pathophysiologic changes were the significant covariate of the systemic clearance. Subjects with TOF experienced a decrease in Cl1, only 73.3% of that in normal children. After stopped the infusion, the etomidate plasma concentration decay with time. This is consistent with the characteristics of 3-compartment disposition model which been confirmed by the subsequent modeling process and many previous studies.14–16
Etomidate is metabolized by ester hydrolysis in the plasma and liver, and the metabolic products are excreted mostly in urine and to a lesser degree in feces.17 The former studies18–20 reported a range from 0.63 to 1.75 L/min (standardized to a 70-kg person) in etomidate systemic clearance values. To our knowledge, this is the first report showing that the systemic clearance of etomidate is reduced by approximately 27% in children with unrepaired TOF compared with normal children. It is necessary to explore what exact causes lead to the decreased systemic clearance. However, most scholarly articles on the pathophysiolgy of TOF focused on the heart itself, and little attention was paid to secondary lesions of other organs and physiologic significance of lesions.4 TOF is the most frequent cyanotic CHD with 4 characteristic morphologic abnormalities, which result in decreased pulmonary blood flow, leading to hypoxia and cyanosis.
On the basis of TOF-induced pathophysiology, we speculated that the reduced clearance might be related to the following. First, hepatic blood flow, capacity of the liver enzymes, and free fraction in the blood are the major factors that determine hepatic clearance. When metabolic or transporter-mediated disposition is very rapid, just as with etomidate, the hepatic clearance is governed primarily by hepatic blood flow.21 TOF lowers the oxygen content in the blood delivered to the brain, which stimulates a decrease in cerebral–vascular resistance, termed “brain sparing,”22 contributing to redistribution of blood flow, thereby reducing the blood flow to liver.23 De Paepe et al24 found that systemic clearance of etomidate was decreased when liver perfusion diminished in a hypovolemic model of rat. In addition, the same mechanisms also were used to explain the reduced clearance in another study.25 In this study, the liver function of patients was normal or only mildly abnormal; therefore, the liver function might not be major determinant of etomidate PK, just as the results demonstrated in our earlier study.26 Second, the common feature of TOF is decreased pulmonary blood flow, albeit to varying degrees. Many drugs, for example, ketamine, propofol, and sevoflurane, whose systemic clearance exceeds hepatic blood flow, are considered to have extrahepatic metabolism.27–29 The lung has been mentioned as one of the possible sites for it. Although the role of lung in relation to etomidate metabolism has not been determined fully, it is easy to understand decreased etomidate systemic clearance in TOF children.
Interestingly, compared with our earlier study,26 the present PK results have a smaller steady-state distribution volume and slightly lower clearance in normal children. The major discrepancy obviously is related to the experimental design. Etomidate was administered by Lin et al26 as a rapid IV bolus, whereas we administered it through continuous infusion. The earlier study reported that drug-delivery methods lead to the differences in fitted PK parameters. The propofol PK published by Schüttler and Ihmsen30 demonstrated that the fitted values of Cl2, V1, and V2 with bolus data were 3.02, 2.61, and 1.73, respectively, times those values with infusion data. A drawback of classical models is that these compartments lack physiologic reality, for example, models were established on a fictitious assumption of the drug mixing instantaneously in the central compartment after bolus injection, which is impossible in reality. The peak concentration of a single bolus is apparently greater than that of a continuous infusion. Because of the limitation of former simplified models, measured peak concentrations of single bolus might be out of proportion with the “true” central compartment volume. This will lead to errors in central volume estimation31 and peripheral compartments. It also will lead inevitably to differences of the fitted clearances according to the basic theory of compartment model (eg, Cl1 = V1 × K10). Some papers have advocated that the allometric model is superior to the per-kilogram weight model. However, the current analysis of the data did not confirm this. Both models have only shown trivial differences of individual post hoc systemic clearance (Figure 2) and other PK parameters (results not shown). As we know, the optimal model is not necessarily the 1 with the smallest or smaller OBJ. It must also be practical to implement. The clinicians do not use allometric model with calculating weight to three-fourth power; therefore, the per-kilogram weight model is appropriate from the clinical views.
Because the peak concentration of etomidate depends on its central distribution volume and systemic clearance, reduced clearance in TOF children does not make much sense when etomidate is administrated as a bolus for induction. On the basis of this condition, the clinicians rarely pay attention to the increase in drug concentration within a certain range. Etomidate, however, also can be used as a bolus injection for sedation of shorter procedures.32 Although the therapeutic window of etomidate for the pediatric population remains poorly defined, reduced clearance might suggest that TOF children have longer sedation and recovery times for procedural sedation for shorter, simpler procedures. Baxter et al32 reported that etomidate was administered as the sole sedative for 446 CT scans under sedation. The median etomidate dose was 0.33 mg/kg, and the mean sedation duration was 34 minutes. We take the simulated etomidate concentration at the 34th minute (approximately 35 ng/mL) as the awaking concentration. As shown in Figure 5A, TOF children need longer time to recovery after the bolus injection. Etomidate has also been used with continuous (brief) or target-controlled infusions for anesthesia induction or maintenance.33,34 In these cases, the steady-state concentration (infusion rate/systemic clearance) in TOF children will be 1.36 times that in normal children with the same infusion rate, which suggests that infusion rates in patients with TOF should be proportionally less. To acquire the same target concentration, as shown in Figure 6, the infusion rate ratio of the normal children/TOF children was increased with time until the steady concentration. In this study, the mean infusion duration was less for TOF children (7.3 minutes) compared with normal children (9.9 minutes) based on BIS monitoring. However, the recovery time for TOF children remains slow for the reduced systemic clearance (Figure 5B).
Some limitations have to be specified. The quality of estimation of the clearance, calculated by dividing the IV dose by the area under the concentration versus time curve (AUC),35 depends on the number of subjects, blood samples per subject, and the period of time over which blood samples are collected.36 Our sampling time of patients with TOF was limited by the surgical procedure; however, the extrapolated AUC is only a small fraction of total AUC as shown in Figure 1, and the sampling time will have a small impact on the estimation of clearance. Moreover, short sampling period might lead to overestimate the systemic clearance.37 Therefore, patients with TOF may experience a systemic clearance <1.67 × (WT/70 kg) L/min, <73.3% of that in normal children. In addition, given its stable hemodynamic effects in children with right-to-left shunts, ketamine combined with etomidate was selected as anesthesia-induction plan.38 The analgesic properties of ketamine make it suitable for intubation and arterial catheterization. The effects of ketamine on the BIS cannot be ignored, however, which also makes it impossible to model pharmacodynamics.
In conclusion, we have shown that the clearance for etomidate in children with unrepaired TOF is reduced, which might suggest children with TOF have longer sedation and recovery times after bolus injection for procedural sedation for shorter, simpler procedures. On the basis of the theory that infusions are required to maintain a steady-state concentration, which is determined by systemic clearance, we contemplate that infusion rates in patients with TOF should be proportionally less.
The authors are grateful to the attending anesthetists and nurses in the operating room for their support of this study and to all the pediatric patients who participated in the trial. They would also like to thank Yuying Gao, PhD (Quantitative Solutions Inc, Menlo Park, CA) for her advice given regarding the data analysis.
Name: Yang Shen, MD.
Contribution: This author helped conduct the study, collect the data, analyze the data, and prepare the manuscript.
Name: Mei-Hua Cai, MD.
Contribution: This author helped design the study and analyze the data.
Name: Wei Ji, MD.
Contribution: This author helped conduct the study and collect the data.
Name: Jie Bai, BS.
Contribution: This author helped conduct the study and collect the data.
Name: Yue Huang, MD, PhD.
Contribution: This author helped conduct the study and collect the data.
Name: Ying Sun, MD, PhD.
Contribution: This author helped conduct the study and collect the data.
Name: Lin Lin, MD.
Contribution: This author helped design the study.
Name: Jing Niu, MD.
Contribution: This author helped design the study.
Name: Ma-Zhong Zhang, MD, PhD.
Contribution: This author helped design the study, analyze and interpret the data, and critically review the manuscript.
This manuscript was handled by: James A. DiNardo, MD.
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