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Meeting Abstracts  |   February 2001
Influence of Hemorrhagic Shock on Remifentanil: A Pharmacokinetic and Pharmacodynamic Analysis
Author Affiliations & Notes
  • Ken B. Johnson, M.D.
    *
  • Steven E. Kern, Ph.D.
  • Elizabeth A. Hamber, M.D.
  • Scott W. McJames, M.S.
    §
  • Katelijne M. Kohnstamm, B.S.
    ‖‖
  • Talmage D. Egan, M.D.
    #
  • *Interim Instructor, †Research Assistant Professor, §Research Associate, ‖‖Research Fellow, #Associate Professor, Department of Anesthesiology, University of Utah School of Medicine. ‡Anesthesiologist, St. Mary’s Health Services, Grand Rapids, Michigan.
  • Received from the Department of Anesthesiology, University of Utah School of Medicine, Salt Lake City, Utah.
Article Information
Meeting Abstracts   |   February 2001
Influence of Hemorrhagic Shock on Remifentanil: A Pharmacokinetic and Pharmacodynamic Analysis
Anesthesiology 2 2001, Vol.94, 322-332. doi:
Anesthesiology 2 2001, Vol.94, 322-332. doi:
ANESTHESIOLOGISTS have long recognized the need to moderate the dose of opioids for patients who have significant blood loss before or during surgery. However, the scientific rationale for moderating doses of opioids during hemorrhagic shock is not well established.
Prior work in our laboratory investigated the influence of severe blood loss on the pharmacokinetics of fentanyl in a porcine isobaric hemorrhagic shock model. We found that after a 5-min fentanyl infusion, plasma levels in shocked swine were twofold higher than in controls. 1 Using a compartmental model to estimate pharmacokinetic parameters, we found that hemorrhagic shock reduced the central compartment volume and clearance. As described by Hughes et al.  , 2 we performed computer simulations of the context-sensitive half-time (the time required to achieve a 50% decrease in plasma concentration after termination of an infusion) as a means of understanding the clinical impact of shock on the pharmacokinetics of fentanyl. Our kinetic simulations revealed a prolonged context-sensitive half-time in shocked animals for fentanyl infusions longer than 100 min. We concluded that the shock-induced changes in pharmacokinetic parameters were primarily caused by alterations in the rapid distribution of fentanyl during and shortly after the infusion and a reduction in fentanyl clearance. Based on the known decrease in hepatic perfusion and function in isobaric shock models, 3–8 we speculated that fentanyl metabolism in the liver was compromised, resulting in the prolonged context-sensitive half-time.
In view of these results for fentanyl, we investigated the influence of hemorrhagic shock on the pharmacokinetics and pharmacodynamics of remifentanil, an esterase metabolized opioid that does not require delivery to a metabolic organ for metabolism. Our hypothesis was that the distribution characteristics of remifentanil would be changed in a fashion similar to what we observed with fentanyl (a reduction in the central compartment volume and steady state distribution volume). Furthermore, we hypothesized that because the metabolism of remifentanil is rapid and independent of hepatic perfusion, the shock-induced changes in clearance would be less marked and would not produce as great an effect on the context-sensitive half-times as observed with fentanyl. Finally, based on our clinical observation that reduced doses of opioid appear to be adequate despite the pharmacokinetic changes we have documented, we hypothesized that the pharmacodynamics of remifentanil would not be substantially altered by hemorrhagic shock.
Methods and Materials
Experimental Design
Experiments were performed on commercial farm-bred pigs of either sex. The study was approved by the Institutional Animal Care and Use Committee at the University of Utah. Animals were randomly assigned to either an isobaric hemorrhage or a control group (n = 8 for each group). In the shock group, animals were first bled to a shock state and then administered the remifentanil infusion. Animals in the control group were instrumented in an identical fashion to the shock group and maintained in an anesthetized ventilated state for 60 min before receiving the remifentanil infusion. This was performed to ensure that both groups would receive the remifentanil infusion after near equivalent times under anesthesia.
Animal Preparation
Swine weighing between 21 and 31 kg were commercially obtained and quarantined for 6 days in a temperature- and light-controlled environment. Animals had access to food and water ad libitum  . Anesthesia was induced with an intramuscular injection of 1.7 mg/kg tiletamine HCl and 1.7 mg/kg zolazepam. Intravascular access was obtained from an ear vein. Muscle relaxation was achieved with an intravenous injection of succinylcholine (1.5 mg/kg).
The animal’s tracheas were then intubated and mechanically ventilated. Initial ventilator settings were a tidal volume of 8–10 ml/kg, a respiratory rate of 20 breaths/min, a fraction of inspired oxygen of 100%, and no positive end-expiratory pressure. Tissue oxygenation was monitored using continuous pulse oximetry placed on the tongue or ear. Ventilation was monitored using an inspired–expired gas analyzer that measured oxygen, carbon dioxide, and potent inhalation agent concentrations. Ventilator settings were adjusted as needed to keep the oxygen saturation by pulse oximetry above 95% and the end-tidal carbon dioxide at 38 ± 2 mmHg. Once satisfactory ventilator settings were established, a baseline arterial blood gas was obtained. Ventilator settings were adjusted further if needed to maintain the arterial carbon dioxide partial pressure at 40 ± 2 mmHg.
A continuous level of anesthesia was achieved with isoflurane and intermittent bolus doses of pancuronium (0.1 mg/kg). Expired isoflurane levels were monitored and kept at 1.0 minimum alveolar concentration equivalent for swine. 9 Subcutaneous electrocardiograph electrodes were placed, and the electrocardiograph was monitored throughout the study.
The left femoral artery was cannulated with a 16-gauge arterial sheath using sterile technique to monitor arterial blood pressure and heart rate continuously. The right femoral artery was cannulated with a 16-gauge arterial sheath for blood removal and subsequent reinfusion. An internal jugular vein was cannulated with a pulmonary artery catheter for thermodilution estimates of cardiac output. Colonic temperatures were monitored and maintained at 37°C throughout the study with a heating blanket and heating lamps as needed. Once access to the vascular compartment was obtained, each animal was anticoagulated with an intravenous bolus injection of heparin (100 U/kg body weight) and allowed to stabilize for 10 min before starting the protocol.
Instrumentation for electroencephalographic monitoring was accomplished by preparing the skin over the fronto-occipital regions bilaterally and placing four cutaneous electrodes (Aspect Medical, Framingham, MA). Four channels of the electroencephalogram were amplified and digitally recorded using an Aspect A1000 electroencephalographic machine. Digitized raw electroencephalographic waveform data and processed electroencephalographic values were collected electronically.
Remifentanil (10 μg · kg−1· min−1) was administered intravenously at a constant-rate infusion using a syringe pump (model number AS40a; Baxter, Deerfield, IL) for 10 min. During and after the infusion, 3-ml arterial blood samples were obtained at preset intervals, with more rapid sampling during the infusion and immediately after termination of the infusion. During the infusion, samples were collected every 2 min. After the infusion was stopped, samples were collected at 0.5, 1, 1.5, 2, 2.5, 3, 4, 5, 7.5, 10, 15, 20, 30, 40, 50, and 65 min.
Hemorrhage Protocol
The hemorrhage protocol was designed to ensure that each animal was at an equivalent degree of metabolic compromise from hemorrhagic shock before initiating the remifentanil infusion. This was accomplished by using an isobaric hemorrhage model and tracking the progression of the lactic acidemia before drug infusion.
Animals in the hemorrhage group were bled in an isobaric fashion using a computer-controlled algorithm based on a modified Wiggers’ isobaric hemorrhage model. 10 The arterial blood pressures were measured with a pressure transducer (Utah Medical, Midvale, UT). Blood was removed and stored in a citrated bag at a rate required to achieve a mean arterial blood pressure (MABP) of 40 mmHg over 20 min. Blood was then removed or reinfused by a servo-controlled peristaltic pump to maintain the target pressure as shown in figure 1. With the blood reservoir bag on a scale, shed blood volume was determined from the weight.
Fig. 1. Typical time course of mean arterial blood pressure (solid line) and shed blood volume (dotted line) changes during an isobaric hemorrhage to 40 mmHg.
Fig. 1. Typical time course of mean arterial blood pressure (solid line) and shed blood volume (dotted line) changes during an isobaric hemorrhage to 40 mmHg.
Fig. 1. Typical time course of mean arterial blood pressure (solid line) and shed blood volume (dotted line) changes during an isobaric hemorrhage to 40 mmHg.
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The compensatory phase of hemorrhagic shock was defined as the time during which blood had to be removed to maintain the MABP at 40 mmHg. The decompensatory phase of hemorrhagic shock was defined as the time period during which blood had to be reinfused to maintain the MABP at 40 mmHg. A computerized data acquisition system recorded the MABP, systolic and diastolic arterial pressures, heart rate, and shed blood volume every 5 s. The peak shed blood volume (PSBV) was defined as the maximum amount of blood removed during the isobaric hemorrhage process.
Arterial blood samples for p  H, oxygen partial pressure, carbon dioxide partial pressure, bicarbonate, glucose, potassium, hematocrit, glucose, and lactate were measured using blood gas and chemistry analyzers (Stat Profile 1 Analyzer, Nova Biomedical, Waltham, MA; and YSI Model 2700 Select Biochemistry Analyzer, Yellow Springs Instrument Company, Yellow Springs, OH) at 25% intervals of the estimated PSBV and at the actual PSBV. Sampling times were based on an estimated PSBV of 45 ml/kg body weight.
The remifentanil infusion was initiated once the animals reached the actual PSBV manifested by (1) the need to reinfuse shed blood to maintain the MABP at 40 mmHg for at least 5 min, and (2) a plasma lactate level greater than 2 mm. Metabolic (plasma lactate, plasma glucose, and arterial p  H) and hemodynamic parameters (heart rate, MABP, and cardiac output) were recorded at the time of infusion. Metabolic and hemodynamic parameters for each group were compared at the time of blood reinfusion using an unpaired two-tailed Student t  test. P  values less than 0.05 were considered significant.
Blood Sampling Processing and Concentration Assay
Because of the metabolic pathway of remifentanil, special processing was necessary to prevent continued metabolism of remifentanil after sample collection. The process consisted of inhibition of remifentanil hydrolysis in whole blood by the addition of 20 μl of 50% citric acid per milliliter of blood immediately after collection. Samples were agitated for 1 min and then immediately stored at −20°C until the time of assay. Remifentanil blood concentrations were measured by a high-pressure liquid chromatography assay with ultraviolet detection with a quantitation limit of 1 ng/ml. 11 
Pharmacokinetic Analysis
The concentration-versus  -time data for both groups was analyzed using several techniques. First, estimates of the individual pharmacokinetic parameters for a three-compartment model were made using a two-stage approach using pharmacokinetic modeling software (WinNonLin; Pharsight Corporation, Mountain View, CA). Second, an exploration of pharmacokinetic parameter–covariate relations was made. Third, the control and shock groups were combined to build a population model using nonlinear mixed-effect modeling software (NONMEM; University of California–San Francisco, San Francisco, CA). Covariates demonstrating a strong correlation with pharmacokinetic parameters were introduced into the population model in an effort to improve the model’s ability to predict remifentanil blood concentrations. Finally, computer simulations based on the mean of individual pharmacokinetic parameters for each group were performed to provide more clinically relevant meaning to the analysis. Linear pharmacokinetics were assumed for the purposes of these analyses.
Two-stage Analysis.
A two-stage approach implemented in WinNonLin was performed to estimate the mean pharmacokinetic parameters for each group. The first stage involved fitting a three-compartment mamillary model to the remifentanil concentration-versus  -time data to estimate the pharmacokinetic parameters for each animal. The triexponential equation for each animal was parameterized in terms of clearances and apparent distribution volumes. The second stage was to calculate the average of the pharmacokinetic parameters to obtain mean population estimates for each group. The shock and control groups were then compared with an unpaired two-tailed Student t  test. P  values less than 0.05 were considered significant.
Exploration of Parameter–Covariate Relations.
The feasibility of using hemodynamic and metabolic covariates to improve the overall model was studied. The individual pharmacokinetic parameter estimates from the two-stage analysis were regressed independently on each covariate as advocated by Maitre et al.  12 Covariate parameters included a mean of the following parameters during the drug infusion: shed blood volume, heart rate, MABP, cardiac index, plasma lactate levels, arterial p  H, and plasma glucose levels. This step was intended to identify useful relations and to characterize the shape of these relations between model parameters and covariates.
Nonlinear Mixed-effects Model Analysis.
In contrast to the two-stage approach, remifentanil concentration-versus  -time data for both the shock and control groups were combined and used to construct a three-compartment population pharmacokinetic model using NONMEM. NONMEM simultaneously analyzed the entire population’s data and provided an estimate of typical values for the parameters along with an estimate of the parameter’s interindividual variability. Interindividual variability on each parameter was modeled using a log-normal error model:MATHwhere θindividualis the true value in the individual, θtypicalis the population mean estimate, and ηindividualis a random variable whose distribution is estimated by NONMEM with a mean of zero and a variance of ω2. The estimates of ω obtained with NONMEM are similar to the coefficient of variation often used in standard descriptive statistics. Residual error was modeled assuming a log-normal distribution.
The performance of this population model was evaluated in terms of its ability to predict individual animal blood concentrations in both groups. The model was quantitatively assessed in terms of weighted residuals (WRs), the difference between a measured blood concentration (Cm) and the model-predicted concentration (Cp) in terms of Cp. Thus, WR was defined as: WR = (Cm− Cp)/Cp. Using this definition, the WRs for the NONMEM population model were computed at every measured data point for all animals in the combined shock and control group.
Using the WR data, the overall accuracy of the model was determined by computing the median absolute WR (MDAWR), defined as MDAWR = median {|WR1|, |WR2|,...,|WRn|}, where n is the total number of samples in the study population. Using this formula, the MDAWR for the population models constructed by NONMEM were computed. The median WRs, a measure of model bias, were also computed. The performance of each model was also visually assessed by plotting the Cm/Cpversus  time and examining the plots for accuracy and bias.
Model Expansion with Covariate Effects.
After obtaining the best NONMEM model without covariates, the influence of shed blood volume, heart rate, MABP, cardiac index, plasma lactate, arterial p  H, and plasma glucose were evaluated. Guided by the initial regression analysis exploring the relations between model parameters and subject covariate, an improved population model was built in a stepwise fashion in which the individual covariate effects on each model parameter were incorporated into the model, and the resulting expanded model was examined for significant improvement. The objective function was defined as negative two times the log likelihood. A change in the objective function of at least 4 was viewed as sufficient justification to include an additional parameter in the model (in the form of a covariate or covariate plus a constant that represented the addition of two model parameters). A total of 63 different models were tested. The various models were tested both forward (starting with no covariates) and backward (starting with all covariates) to confirm that the observed improvement was not a result of covariate correlation. A series of MDAWRs, median WRs, and Cm/Cp-versus  -time plots were generated for each model to assess the extent of model improvement. Those covariate combinations with the largest reduction in the MDAWR are presented in tabular and graphical form.
Computer Simulations.
Computer simulations using the pharmacokinetic parameters obtained from the two-stage approach provided an illustration of the predicted decline in blood concentration in the shock and control groups when remifentanil is administered by infusion. These simulations were used to predict the time necessary to achieve a 50 or 80% decrease in drug concentration after termination of a variable-length infusion targeted to a constant drug concentration in blood as described by Hughes et al.  2 The simulations are based on Euler’s solution to the three-compartment model with a step size of 1 s. Four simulations were performed for a 30-kg pig: a 50% and 80% decrement time for both the shock and control groups.
Pharmacodynamic Analysis
Spectral Edge Analysis.
The pharmacologic effect of remifentanil was characterized by examining the influence of remifentanil on the spectral edge frequency as described by Scott et al.  , 13,14 who used spectral edge as a surrogate measure of opioid effect with fentanyl, sufentanil, and alfentanil. The spectral edge was calculated by the Aspect A1000 machine. The spectral edge was determined by calculating the area under the power-versus  -frequency histogram and identifying the frequency below which 95% of the total area is found. 15 
Because we were concerned that shock itself in the absence of remifentanil might alter the electroencephalographic waveform, pilot studies were conducted to exclude this possibility. The isobaric hemorrhagic shock protocol elicited no significant change in the spectral edge when compared with normotensive animals under identical anesthetic conditions.
Parametric Modeling of the Concentration–Effect Relation.
Because plots of the concentration–effect relation were sigmoid in shape, an inhibitory sigmoid Emaxequation (i.e.,  Hill equation) was used to model the relation parametrically. 16 Using WinNonLin, the equation MATHwhere E is the predicted effect, E0is the baseline effect level, Emaxis the maximal effect, Cbis the blood concentration, γ is a measure of curve steepness, and EC50is the blood concentration that produces 50% of maximal effect, was fit to the effect–versus  –plasma concentration data.
Computer Simulations.
Computer simulations were performed using the full pharmacokinetic–pharmacodynamic model from the study to illustrate the clinical application of the estimated parameters. The first simulation was intended to illustrate the time to peak concentration and magnitude and duration of effect for a bolus and infusion of remifentanil using typical human dosages of remifentanil. The first simulation estimated remifentanil blood levels after bolus administration (1 μg/kg body weight) followed by an infusion (0.5 μg · kg−1· min−1) using parameters derived from the control and shock groups. The second simulation, intended to illustrate dosing requirements of an equipotent infusion, was a simulation of the total dose required to deliver a 1-h computer-controlled infusion targeted to the blood EC50for both control and shock groups. Simulations were performed using drug infusion simulation software (Stanpump; Stanford University, Palo Alto, CA).
Results
Effect of Hemorrhagic Shock on Metabolic Parameters
Animals subjected to the isobaric hemorrhagic shock protocol developed a lactic acidemia once the PSBV was achieved when compared with control animals. A comparison of mean heart rate, cardiac index, MABP, plasma lactate levels, plasma glucose levels, and arterial p  H is presented in table 1. Cardiac index data were not available in two of the shock animals because of technical problems. An example of the typical changes in blood pressure and shed blood volume during the isobaric hemorrhage is presented in figure 1. The average shed blood volume at the time of drug administration was 48 ± 2.2 ml/kg body weight. The average shed blood volume returned during the 10-min infusion of remifentanil to maintain the MABP at 40 mmHg was 4.1 ± 1.0 ml/kg body weight.
Table 1. Hemodynamic and Metabolic Parameters in Control and Hemorrhaged Animals Just before Remifentanil Infusion
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Table 1. Hemodynamic and Metabolic Parameters in Control and Hemorrhaged Animals Just before Remifentanil Infusion
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Pharmacokinetic Analysis
The infusion of remifentanil administered in this protocol resulted in time-versus  -concentration curves characteristic of brief intravenous infusions. The mean remifentanil concentrations in the shock and control groups are contrasted in figure 2. The shock group reached higher peak concentrations and exhibited higher levels throughout the experiment. The shock group also exhibited greater pharmacokinetic variability.
Fig. 2. Mean remifentanil blood concentration-versus  -time data. The open circles represent the mean blood level for normotensive animals, and the solid circles represent the mean blood level for shocked animals.
Fig. 2. Mean remifentanil blood concentration-versus 
	-time data. The open circles represent the mean blood level for normotensive animals, and the solid circles represent the mean blood level for shocked animals.
Fig. 2. Mean remifentanil blood concentration-versus  -time data. The open circles represent the mean blood level for normotensive animals, and the solid circles represent the mean blood level for shocked animals.
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Two-stage Analysis.
The raw concentration-versus  -time data were adequately described by a three-compartment model. The two-stage compartmental analysis revealed a smaller central compartment volume (V1), a smaller peripheral compartment (V2), and a slower rate of clearance from the central compartment (Cl1 ) in the shocked animals. All other pharmacokinetic parameters for the shocked and control groups were not different. The two-stage three-compartment model parameters for each group are shown in table 2.
Table 2. Mean and Standard Error of Individual Parameter Estimates (Two-stage Approach)
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Table 2. Mean and Standard Error of Individual Parameter Estimates (Two-stage Approach)
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Exploration of Parameter–Covariate Relations.
Plots of the individual parameter estimates versus  the covariates revealed several potentially useful relations. The most pronounced relations were found between the central compartment clearance and shed blood volume, MABP, or cardiac index. The results of these linear regressions, including the coefficients of determination (i.e.  , r2) are presented in table 3. Selected parameter–covariate relations are presented in figure 3.
Table 3. Linear Regression Analysis of Selected Covariates versus Individual Pharmacokinetic Parameter Estimates
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Table 3. Linear Regression Analysis of Selected Covariates versus Individual Pharmacokinetic Parameter Estimates
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Fig. 3. (A  ) A plot of the central compartment clearance versus  shed blood volume. (B  ) A plot of central compartment clearance versus  mean arterial blood pressure. (C  ) A plot of the central compartment clearance versus  cardiac index.
Fig. 3. (A 
	) A plot of the central compartment clearance versus 
	shed blood volume. (B 
	) A plot of central compartment clearance versus 
	mean arterial blood pressure. (C 
	) A plot of the central compartment clearance versus 
	cardiac index.
Fig. 3. (A  ) A plot of the central compartment clearance versus  shed blood volume. (B  ) A plot of central compartment clearance versus  mean arterial blood pressure. (C  ) A plot of the central compartment clearance versus  cardiac index.
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Nonlinear Mixed-effects Model Population Analysis.
The simple (i.e.  , no covariates) population model parameters are presented in table 4. The performance of the NONMEM population model with no covariates (i.e.  , simple model) is presented graphically in figure 4. Measured blood remifentanil levels are plotted along with the population model estimates for the control group (fig. 4A) and the shock group (fig. 4B). The simple population model underestimated measured blood concentrations in the shocked animals and overestimated them in the control animals. In figure 4C, a Cm/Cp-versus  -time plot demonstrates a wide range of variability in the population model’s overestimate of remifentanil concentrations in shocked animals and, by contrast, and a more narrow range of variability in the population model’s underestimate of remifentanil concentrations in control animals.
Table 4. Simple and Final Covariate Expanded NONMEM Population Models
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Table 4. Simple and Final Covariate Expanded NONMEM Population Models
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Fig. 4. Measured (dashed lines) and population model estimates (solid lines) of remifentanil blood levels for the control group (A  ) and the shock group (B  ) using the simple NONMEM model. (C  ) The measured over predicted plot for the population model using all animals from both the control and shock groups. Each line represents the performance of the simple NONMEM population model when applied to an individual pig. A pig whose blood concentrations were perfectly predicted by the model would be represented by a straight line at 1. Solid lines represent shock animals, and dashed lines represent control animals.
Fig. 4. Measured (dashed lines) and population model estimates (solid lines) of remifentanil blood levels for the control group (A 
	) and the shock group (B 
	) using the simple NONMEM model. (C 
	) The measured over predicted plot for the population model using all animals from both the control and shock groups. Each line represents the performance of the simple NONMEM population model when applied to an individual pig. A pig whose blood concentrations were perfectly predicted by the model would be represented by a straight line at 1. Solid lines represent shock animals, and dashed lines represent control animals.
Fig. 4. Measured (dashed lines) and population model estimates (solid lines) of remifentanil blood levels for the control group (A  ) and the shock group (B  ) using the simple NONMEM model. (C  ) The measured over predicted plot for the population model using all animals from both the control and shock groups. Each line represents the performance of the simple NONMEM population model when applied to an individual pig. A pig whose blood concentrations were perfectly predicted by the model would be represented by a straight line at 1. Solid lines represent shock animals, and dashed lines represent control animals.
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Model Expansion with Covariate Effects.
Sixty-three evaluations were made exploring the influence of (1) individual covariates, (2) combinations of covariates, and (3) intercept terms from the parameter–covariate analysis on improving the accuracy of the population model. Optimal population model performance was achieved by scaling the central compartment clearance and volume to the shed blood volume covariate, as was suggested by the initial exploration of the parameter-versus  -covariate relations. An intercept term for both the central compartment clearance and volume improved the model. Combinations of other covariates did not improve the model. Although scaling clearance to the cardiac index covariate yielded a lower objective function value, it did not improve the model bias or MDAWR as great as the shed blood volume covariate. The parameter values for the shed blood volume–scaled NONMEM model are presented in table 4.
Scaling the central compartment clearance and volume to shed blood volume resulted in an improvement in the objective function from 1,512 to 1,305 and an improvement in the MDAWR and median WR. These results, including the MDAWR 10th and 90th percentile values, are shown in table 5. The performance of the best covariate enhanced model is presented graphically in figure 5. Measured remifentanil levels are plotted along with the population model estimates of blood levels for the shock group (fig. 5A) and the control group (fig. 5B). Here, the population model demonstrated improved estimates of measured remifentanil levels in both the shocked and control animals as manifest by an improvement in the bias and MDAWR. In figure 5C, a Cm/Cp-versus  -time plot demonstrates a narrowing in the range of variability in the population model’s estimate of remifentanil in all animals. The results of some other covariate models that were evaluated merit attention. Although heart rate, cardiac index, and plasma lactate demonstrated favorable relations to pharmacokinetic parameters in the regression analysis, these covariates did not improve the population model.
Table 5. Median Absolute Weighted Residuals (MDAWR), 10th and 90th MDAWR Percentiles, Median Weighted Residuals (MDWR), and NONMEM Objective Function Values for Selected NONMEM Population Models
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Table 5. Median Absolute Weighted Residuals (MDAWR), 10th and 90th MDAWR Percentiles, Median Weighted Residuals (MDWR), and NONMEM Objective Function Values for Selected NONMEM Population Models
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Fig. 5. Measured (dashed lines) and covariate (shed blood volume) adjusted population model estimates (solid lines) of remifentanil blood levels for the control group (A  ) and the shock group (B  ). (C  ) The measured over predicted plot for the shed blood volume adjusted population model using all animals from both the control and shock groups. Each line represents the performance of the shed blood volume adjusted population model when applied to an individual pig. A pig whose blood concentrations were perfectly predicted by the model would be represented by a straight line at 1. Solid lines represent shock animals, and dashed lines represent control animals.
Fig. 5. Measured (dashed lines) and covariate (shed blood volume) adjusted population model estimates (solid lines) of remifentanil blood levels for the control group (A 
	) and the shock group (B 
	). (C 
	) The measured over predicted plot for the shed blood volume adjusted population model using all animals from both the control and shock groups. Each line represents the performance of the shed blood volume adjusted population model when applied to an individual pig. A pig whose blood concentrations were perfectly predicted by the model would be represented by a straight line at 1. Solid lines represent shock animals, and dashed lines represent control animals.
Fig. 5. Measured (dashed lines) and covariate (shed blood volume) adjusted population model estimates (solid lines) of remifentanil blood levels for the control group (A  ) and the shock group (B  ). (C  ) The measured over predicted plot for the shed blood volume adjusted population model using all animals from both the control and shock groups. Each line represents the performance of the shed blood volume adjusted population model when applied to an individual pig. A pig whose blood concentrations were perfectly predicted by the model would be represented by a straight line at 1. Solid lines represent shock animals, and dashed lines represent control animals.
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Computer Simulations.
The context sensitive half-time simulations demonstrated that hemorrhagic shock produced only minimal changes in the pharmacokinetic behavior of remifentanil. The time required to achieve a 50% decrease in the remifentanil blood level after termination of an infusion was nearly identical for both groups and was independent of infusion duration (fig. 6). The time required to achieve an 80% decrease in remifentanil blood levels was longer for the shock group (6.6 min) when compared with the control group (3.3 min).
Fig. 6. A simulation of the time necessary to achieve a 50 or 80% decrease in remifentanil blood concentration after variable-length intravenous infusions of remifentanil. These simulations were performed using the two-stage three-compartment model parameters.
Fig. 6. A simulation of the time necessary to achieve a 50 or 80% decrease in remifentanil blood concentration after variable-length intravenous infusions of remifentanil. These simulations were performed using the two-stage three-compartment model parameters.
Fig. 6. A simulation of the time necessary to achieve a 50 or 80% decrease in remifentanil blood concentration after variable-length intravenous infusions of remifentanil. These simulations were performed using the two-stage three-compartment model parameters.
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Pharmacodynamic Analysis
Spectral Edge Analysis.
The remifentanil infusion produced a large decrease in the spectral edge that returned to baseline within 30 min of the infusion for both the control and shock groups (fig. 7). The timing and magnitude of the remifentanil-induced decrease in spectral edge was not significantly different between control and shock groups. The differences in these parameters between these groups were not significant.
Fig. 7. Mean spectral edge changes versus  time during the remifentanil infusion. The open circles represent the mean spectral edge measurements for the control animals, and the solid circles represent the mean spectral edge measurements for the shocked animals.
Fig. 7. Mean spectral edge changes versus 
	time during the remifentanil infusion. The open circles represent the mean spectral edge measurements for the control animals, and the solid circles represent the mean spectral edge measurements for the shocked animals.
Fig. 7. Mean spectral edge changes versus  time during the remifentanil infusion. The open circles represent the mean spectral edge measurements for the control animals, and the solid circles represent the mean spectral edge measurements for the shocked animals.
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Parametric Modeling of the Concentration–Effect Relation.
Similar to the timing and magnitude of the decrease in spectral edge during the remifentanil infusion, there was no significant difference between EC50values for the shock and the control groups, respectively. The pharmacodynamic parameters for each group are presented in table 6. The concentration–effect relation for each animal as characterized by the pharmacodynamic model is shown in figure 8.
Table 6. Pharmacodynamic Parameters
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Table 6. Pharmacodynamic Parameters
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Fig. 8. The concentration–effect relation for each animal as characterized by the pharmacodynamic model. The solid lines represent the control animals over a remifentanil blood concentration range of 1–1,000 ng/ml. The dotted lines represent the shocked animals over the same range. The bold lines portray the mean pharmacodynamic model for each group. The horizontal axis is on the log scale.
Fig. 8. The concentration–effect relation for each animal as characterized by the pharmacodynamic model. The solid lines represent the control animals over a remifentanil blood concentration range of 1–1,000 ng/ml. The dotted lines represent the shocked animals over the same range. The bold lines portray the mean pharmacodynamic model for each group. The horizontal axis is on the log scale.
Fig. 8. The concentration–effect relation for each animal as characterized by the pharmacodynamic model. The solid lines represent the control animals over a remifentanil blood concentration range of 1–1,000 ng/ml. The dotted lines represent the shocked animals over the same range. The bold lines portray the mean pharmacodynamic model for each group. The horizontal axis is on the log scale.
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Computer Simulations.
The computer simulations revealed differences in the clinical pharmacology of remifentanil between the control and shock groups. The simulation of a typical remifentanil bolus dose (1 μg/kg) followed by a remifentanil infusion (0.5 μg · kg−1· min−1) for both study groups is presented in figure 9. The simulation of the bolus dose yielded a peak remifentanil blood concentration of 28 and 15 ng/ml for the shock and control groups, respectively. The simulation of the infusion yielded a peak remifentanil blood concentration of 9 and 6 ng/ml for the shock and control groups, respectively.
Fig. 9. (Top  ) Simulation of plasma concentrations (Cp) that result from equipotent bolus and infusion of remifentanil using the pharmacokinetic model. (Bottom  ) The plasma concentration that results from a 1-h computer-controlled infusion targeted to an EC50of 24 ng/ml for normotensive (dotted line) and shocked (solid line) animals.
Fig. 9. (Top 
	) Simulation of plasma concentrations (Cp) that result from equipotent bolus and infusion of remifentanil using the pharmacokinetic model. (Bottom 
	) The plasma concentration that results from a 1-h computer-controlled infusion targeted to an EC50of 24 ng/ml for normotensive (dotted line) and shocked (solid line) animals.
Fig. 9. (Top  ) Simulation of plasma concentrations (Cp) that result from equipotent bolus and infusion of remifentanil using the pharmacokinetic model. (Bottom  ) The plasma concentration that results from a 1-h computer-controlled infusion targeted to an EC50of 24 ng/ml for normotensive (dotted line) and shocked (solid line) animals.
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These results suggested that during shocked conditions, conventional dosing would result in elevated remifentanil blood concentrations, leading to a more pronounced opioid effect. Further simulations were performed examining the difference between the total dose required to maintain the EC50with shock group pharmacokinetic parameters and the total dose required to maintain the EC50with control group pharmacokinetic parameters for 60 min. Because there was no difference in the EC50between the shock and control groups, the target EC50was the average of all EC50measurements (24 ng/ml). The total dose required to maintain the EC50with the control group parameters was larger than the dose required to maintain the EC50with the shock group parameters (114 vs. 74 μg/kg, respectively).
Discussion
We examined the effect of hemorrhagic shock on the pharmacokinetics and pharmacodynamics of remifentanil. We hypothesized that severe blood loss would alter the pharmacokinetics but not the pharmacodynamics of remifentanil. Our hypothesis was confirmed. The most important findings of this study were threefold. First, severe hemorrhage decreased the central compartment volume and clearance, resulting in elevated remifentanil blood concentrations. Second, when these kinetic differences were examined for clinical relevance through the use of a context-sensitive half-time computer simulation, minimal impact was observed. Finally, severe hemorrhage did not significantly change the pharmacodynamics (i.e.  , potency) of remifentanil.
Influence of Hemorrhagic Shock on Remifentanil Pharmacokinetics
Based on our study of fentanyl pharmacokinetics during shock, we anticipated remifentanil blood levels would be higher in shock. Inspection of the raw data confirmed our hypothesis. In shocked animals, peak remifentanil blood levels were more than twofold higher and remained higher throughout the study period when compared with control animals.
The pharmacokinetic modeling techniques also confirmed our hypothesis. The two-stage compartmental analyses demonstrated a decrease in the central compartment volume and clearance in shocked animals. As expected, the non–covariate-adjusted mixed-effects population model performed poorly. In keeping with our findings regarding fentanyl, incorporating shed blood volume, MABP, or cardiac index improved the mixed-effects population model accuracy substantially.
Kinetic simulations of the context-sensitive half-time were also consistent with our hypothesis. Unlike our findings with fentanyl, where hemorrhagic shock prolonged the context-sensitive half-time with long infusions, the context-sensitive half-time of remifentanil was only minimally effected by hemorrhagic shock. Youngs and Shafer, 17 in their analysis of how pharmacokinetic parameters influence a range of decrement times, identified a subset of key pharmacokinetic parameters that impact decrement times for fentanyl, sufentanil, and alfentanil. As one would intuitively expect, they found that an increase in the central compartment volume or a decrease in central compartment clearance prolonged the context-sensitive half-time. We hypothesized that hemorrhagic shock would decrease the central compartment volume and decrease the central compartment clearance, and our pharmacokinetic analysis confirmed this hypothesis. Thus, based on the work of Youngs and Shafer, 17 we speculated that hemorrhagic shock might introduce two opposing influences on the context-sensitive half-time: a reduced central compartment volume that would shorten the context-sensitive half-time and a decreased central compartment clearance that would lengthen the context-sensitive half-time.
Influence of Hemorrhagic Shock on Remifentanil Pharmacodynamics
Before beginning our study, we examined the effect of severe blood loss on the electroencephalogram spectral edge in three pilot animals that did not receive remifentanil. We anticipated that hemorrhagic shock would not significantly change the electroencephalogram waveform because compensatory mechanisms such as cerebral blood flow autoregulation are at work to preserve brain function. 18 We found no change in the spectral edge throughout our hemorrhage protocol, and, specifically, no change was noted during the phase in isobaric hemorrhage, where we planned to administer remifentanil. In addition, we found that during the 10 min just after reaching the PSBV, the time that would have corresponded to the remifentanil infusion, no shed blood was returned to maintain the MABP at 40 mmHg.
The dose of remifentanil we used produced a large decrease in the spectral edge in both the shocked and control animals, suggesting that sufficient drug had been administered to elicit maximum drug effect. Plots of the spectral edge–versus  –plasma concentration revealed minimal hysteresis, and thus modeling of an “effect compartment” was unnecessary.
The simulations presented in figure 9Awere consistent with the pharmacokinetic profile observed in our raw data. Remifentanil blood levels were consistently higher in the bolus and infusion simulations using parameters derived from the shock group when compared with the simulations using parameters derived from the control group. In these simulations, we administered doses of remifentanil typical of clinical practice in humans. However, the blood levels reached were well below the EC50for remifentanil except for the bolus dose of remifentanil using the shock kinetics. It is important to point out that analgesic effects are typically observed at concentrations well below those required for spectral edge changes. For example, with fentanyl, analgesic effects are achieved with plasma concentrations between 1 and 3 ng/ml, but the EC50for electroencephalographic changes does not occur until plasma concentrations of 6.9 ng/ml are reached. 19 
Possibly the most unique finding of our pharmacodynamic analysis was that hemorrhagic shock did not significantly change the potency of remifentanil. No prior data are available in pigs examining the EC50of remifentanil, but our results were remarkably similar to what has been reported for humans (19.5 ng/ml). 20 Shocked animals demonstrated no significant shift in the EC50, suggesting that a near equivalent remifentanil concentration would be required to achieve the same degree of spectral edge suppression. However, as demonstrated by the simulation of the dose necessary to target the EC50for the two study groups, the pharmacokinetic differences result in a much lower dosage requirement during shock. This simulation examined kinetics and economic issues, not pharmacodynamic ones.
Limitations of the Study
A shortcoming of our study relates to the use of an isobaric hemorrhage model in swine to mimic what a patient in hemorrhagic shock might experience. Several issues that relate to species and type of hemorrhage model must be considered when interpreting our data. Isobaric hemorrhage models are often criticized as being far removed from a typical clinical scenario, in part because severe blood loss associated with trauma or surgery does not occur as a controlled hemorrhage over time with a target MABP, but rather as an uncontrolled hemorrhage with a highly variable MABP. 21 Hemorrhagic shock is a dynamic process involving dramatic changes in cardiovascular and metabolic states that vary with time, species, and laboratory, and we felt it was important to examine the effects of remifentanil at an equivalent stage of physiologic compensation. This was best achieved with an isobaric model. This model permitted us to consistently administer remifentanil at the onset of cardiovascular decompensation as manifest by the transition from removal to reinfusion of blood to maintain the target MABP. Achieving a consistent pathophysiologic end point using hemorrhage models that better simulate hemorrhage associated with trauma or surgery (i.e.  , isovolemic or uncontrolled hemorrhage models) is difficult.
The difference in how species exhibit their response to shock should be considered. Although, in general, pigs are thought to be pharmacologically and cardiovascularly similar to humans, they do have some splenic erythrocyte reserve that can introduce variability into the progression of the shock-induced metabolic demise. A splenectomy performed several days before the experiment has been shown to produce a more stable shock model for some experimental purposes. 22 Swine also have a dissimilar hemoglobin P50for oxygen that is 50% higher that that for human hemoglobin (e.g.  , 36 vs.  26 mmHg). 23 This becomes an issue when the oxygen delivery plays a critical role in maintaining the metabolic integrity required for drug biotransformation. It should also be noted that patients suffering from severe blood loss who require surgery are usually undergoing some form of fluid resuscitation before receiving an anesthetic. Hence, future studies investigating the influence of resuscitation after severe hemorrhage on the pharmacodynamics and pharmacokinetics of anesthetics are warranted, as well as the examination of other drug classes such as the sedative hypnotics.
We presented an approach using a population model with and without hemodynamic covariates to explore covariate effects on model performance. We introduced these model covariates as physiologic state variables. The rationale for choosing these parameters was that during severe hemorrhagic shock, one or more of these parameters would change in a similar fashion to shock-induced changes in pharmacokinetic parameters. However, there is no question that the hemodynamic variables we used as covariates can be influenced by other physiologic states. For example, we acknowledge that in physiologic states other than hemorrhagic shock, changes in MABP may not represent the same magnitude of change in pharmacokinetic parameters for remifentanil that they did in hemorrhagic shock.
References
Egan TD, Kuramkote S, Gong G, Zhang J, McJames SW, Bailey PL: Fentanyl pharmacokinetics in hemorrhagic shock: A porcine model. A nesthesiology 1999; 91: 156–66Egan, TD Kuramkote, S Gong, G Zhang, J McJames, SW Bailey, PL
Hughes MA, Glass PSA, Jacobs JR: Context-sensitive half-times in multicompartment pharmacokinetics models for intravenous anesthetic drugs. A nesthesiology 1992; 76: 334–41Hughes, MA Glass, PSA Jacobs, JR
Lindberg B: Liver circulation and metabolism in haemorrhagic shock: An experimental study with special reference to the effect of glucagon. Acta Chir Suppl 1977; 476: 1–18Lindberg, B
Wang P, Ba ZF, Burkhardt J, Chaudry IH: Measurement of hepatic blood flow after severe hemorrhage: Lack of restoration despite adequate resuscitation. Am J Physiol 1992; 262: G92–8Wang, P Ba, ZF Burkhardt, J Chaudry, IH
Frank ED, Frank HA, Jacob SW, Fine J: Hepatic blood flow in experimental hemorrhagic shock. Am J Physiol 1962; 202: 7–11Frank, ED Frank, HA Jacob, SW Fine, J
Slater G, Vladeck BC, Bassin R, Shoemaker WC: Sequential changes in hepatic blood flows during hemorrhagic shock. Am J Physiol 1972; 223: 1428–32Slater, G Vladeck, BC Bassin, R Shoemaker, WC
Wang P, Hauptman JG, Chaudry IH: Hepatocellular dysfunction occurs early after hemorrhage and persists despite fluid resuscitation. J Surg Res 1980; 48: 464–70Wang, P Hauptman, JG Chaudry, IH
DiPiro JT, Hooker KD, Sherman JC, Gaines MG, Wynn JJ: Effect of experimental hemorrhagic shock on hepatic drug elimination. Crit Care Med 1992; 20: 810–5DiPiro, JT Hooker, KD Sherman, JC Gaines, MG Wynn, JJ
Lundeen G, Manohar M, Parks C: Systemic distribution of blood flow in swine while awake and during 1.0 and 1.5 MAC isoflurane anesthesia with or without 50% nitrous oxide. Anesth Analg 1983; 62: 499–512Lundeen, G Manohar, M Parks, C
Wiggers CJ: Physiology of Shock. New York, Oxford University Press, 1950
Haidar SH, Liang Z, Selinger K, Hamlett L, Edington ND: Determination of remifentanil, an ultra-short-acting opioid anesthetic, in rat blood by high performance liquid chromotography with ultraviolet detection. J Pharm Biomed Anal 1996; 14: 1727–32Haidar, SH Liang, Z Selinger, K Hamlett, L Edington, ND
Maitre PO, Buhrer M, Thomson D, Stanski DR: A three step approach combining bayesian regression and NONMEM population analysis: application to midazolam. J Pharmacokinet Biopharm 1991; 19: 377–84Maitre, PO Buhrer, M Thomson, D Stanski, DR
Scott JC, Cooke JE, Stanski DR: Electroencephalographic quantitation of opioid effect: Comparative pharmacodynamics of fentanyl and sufentanil. A nesthesiology 1991; 74: 34–42Scott, JC Cooke, JE Stanski, DR
Scott JC, Ponganis KV, Stanski DR: EEG quantification of narcotic effect: The comparative pharmacodynamics of fentanyl and alfentanil. A nesthesiology 1985; 62: 234–41Scott, JC Ponganis, KV Stanski, DR
Levy WJ, Shapiro HM, Maruchak G, Meathe E: Automated EEG processing for intraoperative monitoring: A comparison of techniques. A nesthesiology 1980; 53: 223–36Levy, WJ Shapiro, HM Maruchak, G Meathe, E
Holford NHG, Sheiner LB: Understanding the dose-effect relationship: Clinical application of pharmacokinetic-pharmacodynamic models. Clin Pharmacokinet 1981; 6: 429–53Holford, NHG Sheiner, LB
Youngs EJ, Shafer SL: Pharmacokinetic parameters relevant to recovery from opioids. A nesthesiology 1994; 81: 833–42Youngs, EJ Shafer, SL
Laughlin MH: Cerebral, coronary, and renal blood flows during hemorrhagic hypotension in anesthetized miniature swine. Adv Shock Res 1983; 9: 189–201Laughlin, MH
Ebling WF, Lee EN, Stanski DR: Understanding pharmacokinetics and pharmacodynamics through computer simulation: 1. The comparative clinical profiles of fentanyl and alfentanil. A nesthesiology 1990; 72: 650–8Ebling, WF Lee, EN Stanski, DR
Egan TD, Minto CF, Herman DJM: Remifentanil versus alfentanil, comparative pharmacokinetics and pharmacodynamics in healthy adult male volunteers. A nesthesiology 1996; 84: 821–9Egan, TD Minto, CF Herman, DJM
Bellamy RF, Maningas PA, Wenger BA: Current shock models and clinical correlations. Ann Emerg Med 1986; 15: 1392–5Bellamy, RF Maningas, PA Wenger, BA
Lewis RN, Werle JM, Wiggers CJ: The behavior of the spleen in hemorrhagic hypotension and shock. Am J Physiol 1942; 138: 205–11Lewis, RN Werle, JM Wiggers, CJ
Willford DC, Hill EP: Modest effect of temperature on the porcine oxygen dissociation curve. Respir Physiol 1986; 62: 113–23Willford, DC Hill, EP
Fig. 1. Typical time course of mean arterial blood pressure (solid line) and shed blood volume (dotted line) changes during an isobaric hemorrhage to 40 mmHg.
Fig. 1. Typical time course of mean arterial blood pressure (solid line) and shed blood volume (dotted line) changes during an isobaric hemorrhage to 40 mmHg.
Fig. 1. Typical time course of mean arterial blood pressure (solid line) and shed blood volume (dotted line) changes during an isobaric hemorrhage to 40 mmHg.
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Fig. 2. Mean remifentanil blood concentration-versus  -time data. The open circles represent the mean blood level for normotensive animals, and the solid circles represent the mean blood level for shocked animals.
Fig. 2. Mean remifentanil blood concentration-versus 
	-time data. The open circles represent the mean blood level for normotensive animals, and the solid circles represent the mean blood level for shocked animals.
Fig. 2. Mean remifentanil blood concentration-versus  -time data. The open circles represent the mean blood level for normotensive animals, and the solid circles represent the mean blood level for shocked animals.
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Fig. 3. (A  ) A plot of the central compartment clearance versus  shed blood volume. (B  ) A plot of central compartment clearance versus  mean arterial blood pressure. (C  ) A plot of the central compartment clearance versus  cardiac index.
Fig. 3. (A 
	) A plot of the central compartment clearance versus 
	shed blood volume. (B 
	) A plot of central compartment clearance versus 
	mean arterial blood pressure. (C 
	) A plot of the central compartment clearance versus 
	cardiac index.
Fig. 3. (A  ) A plot of the central compartment clearance versus  shed blood volume. (B  ) A plot of central compartment clearance versus  mean arterial blood pressure. (C  ) A plot of the central compartment clearance versus  cardiac index.
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Fig. 4. Measured (dashed lines) and population model estimates (solid lines) of remifentanil blood levels for the control group (A  ) and the shock group (B  ) using the simple NONMEM model. (C  ) The measured over predicted plot for the population model using all animals from both the control and shock groups. Each line represents the performance of the simple NONMEM population model when applied to an individual pig. A pig whose blood concentrations were perfectly predicted by the model would be represented by a straight line at 1. Solid lines represent shock animals, and dashed lines represent control animals.
Fig. 4. Measured (dashed lines) and population model estimates (solid lines) of remifentanil blood levels for the control group (A 
	) and the shock group (B 
	) using the simple NONMEM model. (C 
	) The measured over predicted plot for the population model using all animals from both the control and shock groups. Each line represents the performance of the simple NONMEM population model when applied to an individual pig. A pig whose blood concentrations were perfectly predicted by the model would be represented by a straight line at 1. Solid lines represent shock animals, and dashed lines represent control animals.
Fig. 4. Measured (dashed lines) and population model estimates (solid lines) of remifentanil blood levels for the control group (A  ) and the shock group (B  ) using the simple NONMEM model. (C  ) The measured over predicted plot for the population model using all animals from both the control and shock groups. Each line represents the performance of the simple NONMEM population model when applied to an individual pig. A pig whose blood concentrations were perfectly predicted by the model would be represented by a straight line at 1. Solid lines represent shock animals, and dashed lines represent control animals.
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Fig. 5. Measured (dashed lines) and covariate (shed blood volume) adjusted population model estimates (solid lines) of remifentanil blood levels for the control group (A  ) and the shock group (B  ). (C  ) The measured over predicted plot for the shed blood volume adjusted population model using all animals from both the control and shock groups. Each line represents the performance of the shed blood volume adjusted population model when applied to an individual pig. A pig whose blood concentrations were perfectly predicted by the model would be represented by a straight line at 1. Solid lines represent shock animals, and dashed lines represent control animals.
Fig. 5. Measured (dashed lines) and covariate (shed blood volume) adjusted population model estimates (solid lines) of remifentanil blood levels for the control group (A 
	) and the shock group (B 
	). (C 
	) The measured over predicted plot for the shed blood volume adjusted population model using all animals from both the control and shock groups. Each line represents the performance of the shed blood volume adjusted population model when applied to an individual pig. A pig whose blood concentrations were perfectly predicted by the model would be represented by a straight line at 1. Solid lines represent shock animals, and dashed lines represent control animals.
Fig. 5. Measured (dashed lines) and covariate (shed blood volume) adjusted population model estimates (solid lines) of remifentanil blood levels for the control group (A  ) and the shock group (B  ). (C  ) The measured over predicted plot for the shed blood volume adjusted population model using all animals from both the control and shock groups. Each line represents the performance of the shed blood volume adjusted population model when applied to an individual pig. A pig whose blood concentrations were perfectly predicted by the model would be represented by a straight line at 1. Solid lines represent shock animals, and dashed lines represent control animals.
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Fig. 6. A simulation of the time necessary to achieve a 50 or 80% decrease in remifentanil blood concentration after variable-length intravenous infusions of remifentanil. These simulations were performed using the two-stage three-compartment model parameters.
Fig. 6. A simulation of the time necessary to achieve a 50 or 80% decrease in remifentanil blood concentration after variable-length intravenous infusions of remifentanil. These simulations were performed using the two-stage three-compartment model parameters.
Fig. 6. A simulation of the time necessary to achieve a 50 or 80% decrease in remifentanil blood concentration after variable-length intravenous infusions of remifentanil. These simulations were performed using the two-stage three-compartment model parameters.
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Fig. 7. Mean spectral edge changes versus  time during the remifentanil infusion. The open circles represent the mean spectral edge measurements for the control animals, and the solid circles represent the mean spectral edge measurements for the shocked animals.
Fig. 7. Mean spectral edge changes versus 
	time during the remifentanil infusion. The open circles represent the mean spectral edge measurements for the control animals, and the solid circles represent the mean spectral edge measurements for the shocked animals.
Fig. 7. Mean spectral edge changes versus  time during the remifentanil infusion. The open circles represent the mean spectral edge measurements for the control animals, and the solid circles represent the mean spectral edge measurements for the shocked animals.
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Fig. 8. The concentration–effect relation for each animal as characterized by the pharmacodynamic model. The solid lines represent the control animals over a remifentanil blood concentration range of 1–1,000 ng/ml. The dotted lines represent the shocked animals over the same range. The bold lines portray the mean pharmacodynamic model for each group. The horizontal axis is on the log scale.
Fig. 8. The concentration–effect relation for each animal as characterized by the pharmacodynamic model. The solid lines represent the control animals over a remifentanil blood concentration range of 1–1,000 ng/ml. The dotted lines represent the shocked animals over the same range. The bold lines portray the mean pharmacodynamic model for each group. The horizontal axis is on the log scale.
Fig. 8. The concentration–effect relation for each animal as characterized by the pharmacodynamic model. The solid lines represent the control animals over a remifentanil blood concentration range of 1–1,000 ng/ml. The dotted lines represent the shocked animals over the same range. The bold lines portray the mean pharmacodynamic model for each group. The horizontal axis is on the log scale.
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Fig. 9. (Top  ) Simulation of plasma concentrations (Cp) that result from equipotent bolus and infusion of remifentanil using the pharmacokinetic model. (Bottom  ) The plasma concentration that results from a 1-h computer-controlled infusion targeted to an EC50of 24 ng/ml for normotensive (dotted line) and shocked (solid line) animals.
Fig. 9. (Top 
	) Simulation of plasma concentrations (Cp) that result from equipotent bolus and infusion of remifentanil using the pharmacokinetic model. (Bottom 
	) The plasma concentration that results from a 1-h computer-controlled infusion targeted to an EC50of 24 ng/ml for normotensive (dotted line) and shocked (solid line) animals.
Fig. 9. (Top  ) Simulation of plasma concentrations (Cp) that result from equipotent bolus and infusion of remifentanil using the pharmacokinetic model. (Bottom  ) The plasma concentration that results from a 1-h computer-controlled infusion targeted to an EC50of 24 ng/ml for normotensive (dotted line) and shocked (solid line) animals.
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Table 1. Hemodynamic and Metabolic Parameters in Control and Hemorrhaged Animals Just before Remifentanil Infusion
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Table 1. Hemodynamic and Metabolic Parameters in Control and Hemorrhaged Animals Just before Remifentanil Infusion
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Table 2. Mean and Standard Error of Individual Parameter Estimates (Two-stage Approach)
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Table 2. Mean and Standard Error of Individual Parameter Estimates (Two-stage Approach)
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Table 3. Linear Regression Analysis of Selected Covariates versus Individual Pharmacokinetic Parameter Estimates
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Table 3. Linear Regression Analysis of Selected Covariates versus Individual Pharmacokinetic Parameter Estimates
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Table 4. Simple and Final Covariate Expanded NONMEM Population Models
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Table 4. Simple and Final Covariate Expanded NONMEM Population Models
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Table 5. Median Absolute Weighted Residuals (MDAWR), 10th and 90th MDAWR Percentiles, Median Weighted Residuals (MDWR), and NONMEM Objective Function Values for Selected NONMEM Population Models
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Table 5. Median Absolute Weighted Residuals (MDAWR), 10th and 90th MDAWR Percentiles, Median Weighted Residuals (MDWR), and NONMEM Objective Function Values for Selected NONMEM Population Models
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Table 6. Pharmacodynamic Parameters
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Table 6. Pharmacodynamic Parameters
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