Calculating the probability of random sampling for continuous variables in submitted or published randomised controlled trials.

In a previous paper, one of the authors (JBC) used a chi-squared method to analyse the means (SD) of baseline variables, such as height or weight, from randomised controlled trials by Fujii et al., concluding that the probabilities that the reported distributions arose by chance were infinitesimally small. Subsequent testing of that chi-squared method, using simulation, suggested that the method was incorrect. This paper corrects the chi-squared method and tests its performance and the performance of Monte Carlo simulations and ANOVA to analyse the probability of random sampling. The corrected chi-squared method and ANOVA method became inaccurate when applied to means that were reported imprecisely. Monte Carlo simulations confirmed that baseline data from 158 randomised controlled trials by Fujii et al. were different to those from 329 trials published by other authors and that the distribution of Fujii et al.'s data were different to the expected distribution, both p < 10(-16) . The number of Fujii randomised controlled trials with unlikely distributions was less with Monte Carlo simulation than with the 2012 chi-squared method: 102 vs 117 trials with p < 0.05; 60 vs 86 for p < 0.01; 30 vs 56 for p < 0.001; and 12 vs 24 for p < 0.00001, respectively. The Monte Carlo analysis nevertheless confirmed the original conclusion that the distribution of the data presented by Fujii et al. was extremely unlikely to have arisen from observed data. The Monte Carlo analysis may be an appropriate screening tool to check for non-random (i.e. unreliable) data in randomised controlled trials submitted to journals.

From d-tubocurarine to sugammadex: the contributions of T. Cecil Gray to modern anaesthetic practice.

One hundred years after Morton's demonstration of the anaesthetic effects of ether, T. Cecil Gray revolutionized anaesthesia with his introduction of balanced general anaesthesia. Gray's technique involved i.v. induction, administration of a neuromuscular blocking agent (curare), tracheal intubation, controlled ventilation, maintenance of unconsciousness with a light inhaled anaesthetic (supplemented with opioids if necessary), and reversal of neuromuscular blocking agent at the conclusion of the anaesthetic. In the 65 yr since his seminal papers, our drugs have changed, and i.v. anaesthetics suitable for maintenance of anaesthesia have been introduced, but the basic principles of general anaesthesia today are those set forward by Gray 65 yr ago.

Defining depth of anesthesia.

In this chapter, drawn largely from the synthesis of material that we first presented in the sixth edition of Miller's Anesthesia, Chap 31 (Stanski and Shafer 2005; used by permission of the publisher), we have defined anesthetic depth as the probability of non-response to stimulation, calibrated against the strength of the stimulus, the difficulty of suppressing the response, and the drug-induced probability of non-responsiveness at defined effect site concentrations. This definition requires measurement of multiple different stimuli and responses at well-defined drug concentrations. There is no one stimulus and response measurement that will capture depth of anesthesia in a clinically or scientifically meaningful manner. The "clinical art" of anesthesia requires calibration of these observations of stimuli and responses (verbal responses, movement, tachycardia) against the dose and concentration of anesthetic drugs used to reduce the probability of response, constantly adjusting the administered dose to achieve the desired anesthetic depth. In our definition of "depth of anesthesia" we define the need for two components to create the anesthetic state: hypnosis created with drugs such as propofol or the inhalational anesthetics and analgesia created with the opioids or nitrous oxide. We demonstrate the scientific evidence that profound degrees of hypnosis in the absence of analgesia will not prevent the hemodynamic responses to profoundly noxious stimuli. Also, profound degrees of analgesia do not guarantee unconsciousness. However, the combination of hypnosis and analgesia suppresses hemodynamic response to noxious stimuli and guarantees unconsciousness.

A double-blind, randomized comparison of i.v. lorazepam versus midazolam for sedation of ICU patients via a pharmacologic model.

BACKGROUND: Benzodiazepines, such as lorazepam and midazolam, are frequently administered to surgical intensive care unit (ICU) patients for postoperative sedation. To date, the pharmacology of lorazepam in critically ill patients has not been described. The aim of the current study was to characterize and compare the pharmacokinetics and pharmacodynamics of lorazepam and midazolam administered as continuous intravenous infusions for postoperative sedation of surgical ICU patients. METHODS: With Institutional Review Board approval, 24 consenting adult surgical patients were given either lorazepam or midazolam in a double-blind fashion (together with either intravenous fentanyl or epidural morphine for analgesia) through target-controlled intravenous infusions titrated to maintain a moderate level of sedation for 12-72 h postoperatively. Moderate sedation was defined as a Ramsay Sedation Scale score of 3 or 4. Sedation scores were measured, together with benzodiazepine plasma concentrations. Population pharmacokinetic and pharmacodynamic parameters were estimated using nonlinear mixed-effects modeling. RESULTS: A two-compartment model best described the pharmacokinetics of both lorazepam and midazolam. The pharmacodynamic model predicted depth of sedation for both midazolam and lorazepam with 76% accuracy. The estimated sedative potency of lorazepam was twice that of midazolam. The predicted C50,ss (plasma benzodiazepine concentrations where P(Sedation > or = ss) = 50%) values for midazolam (sedation score [SS] > or = n, where n = a Ramsay Sedation Score of 2, 3, ... 6) were 68, 101, 208, 304, and 375 ng/ml. The corresponding predicted C50,ss values for lorazepam were 34, 51, 104, 152, and 188 ng/ml, respectively. Age, fentanyl administration, and the resolving effects of surgery and anesthesia were significant covariates of benzodiazepine sedation. The relative amnestic potency of lorazepam to midazolam was 4 (observed). The predicted emergence times from sedation after a 72-h benzodiazepine infusion for light (SS = 3) and deep (SS = 5) sedation in a typical patient were 3.6 and 14.9 h for midazolam infusions and 11.9 and 31.1 h for lorazepam infusions, respectively. CONCLUSIONS: The pharmacology of intravenous infusions of lorazepam differs significantly from that of midazolam in critically ill patients. This results in significant delays in emergence from sedation with lorazepam as compared with midazolam when administered for ICU sedation.

Propofol dosing regimens for ICU sedation based upon an integrated pharmacokinetic-pharmacodynamic model.

BACKGROUND: The pharmacology of propofol infusions administered for long-term sedation of intensive care unit (ICU) patients has not been fully characterized. The aim of the study was to develop propofol dosing guidelines for ICU sedation based on an integrated pharmacokinetic-pharmacodynamic model of propofol infusions in ICU patients. METHODS: With Institutional Review Board approval, 30 adult male medical and surgical ICU patients were given target-controlled infusions of propofol for sedation, adjusted to maintain a Ramsay sedation scale score of 2-5. Propofol administration in the first 20 subjects was based on a previously derived pharmacokinetic model for propofol. The last 10 subjects were given propofol based on a pharmacokinetic model derived from the first 20 subjects. Plasma propofol concentrations were measured, together with sedation score. Population pharmacokinetic and pharmacodynamic parameters were estimated by means of nonlinear regression analysis in the first 20 subjects, then prospectively tested in the last 10 subjects. An integrated pharmacokinetic-pharmacodynamic model was used to construct dosing regimens for light and deep sedation with propofol in ICU patients. RESULTS: The pharmacokinetics of propofol were described by a three-compartment model with lean body mass and fat body mass as covariates. The pharmacodynamics of propofol were described by a sigmoid model, relating the probability of sedation to plasma propofol concentration. The pharmacodynamic model for propofol predicted light and deep levels of sedation with 73% accuracy. Plasma propofol concentrations corresponding to the probability modes for sedation scores of 2, 3, 4, and 5 were 0.25, 0.6, 1.0, and 2.0 microg/ml. Predicted emergence times in a typical subject after 24 h, 72 h, 7 days, and 14 days of light sedation (sedation score = 3 --> 2) with propofol were 13, 34, 198, and 203 min, respectively. Corresponding emergence times from deep sedation (sedation score = 5 --> 2) with propofol were 25, 59, 71, and 74 h. CONCLUSIONS: Emergence time from sedation with propofol in ICU patients varies with the depth of sedation, the duration of sedation, and the patient's body habitus. Maintaining a light level of sedation ensures a rapid emergence from sedation with long-term propofol administration.

Onset of propofol-induced burst suppression may be correctly detected as deepening of anaesthesia by approximate entropy but not by bispectral index.

The bispectral index (BIS) is a complex EEG variable that combines several disparate descriptors of the EEG into a single value. Approximate entropy is a novel EEG measure that quantifies the regularity of a data time series such as EEG. We report two patients in which the EEG effect of propofol was quantified very similarly by BIS and approximate entropy. However, at the beginning of burst suppression of the EEG, BIS did not indicate an increased anaesthetic drug effect, while approximate entropy did.

Characterization of benzodiazepine receptors in the cerebellum.

1. The goals of the work reported here were to further characterize benzodiazepine/GABA(A) (BDZR) receptor heterogeneity in the cerebellum and to measure the affinities and selectivities of structurally diverse benzodiazepines at each site identified. 2. Five chemical families were included in these studies. These were 1,4-benzodiazepines (flunitrazepam), imidazobenzodiazepines (RO15-1788 and RO15-4513 and RO16-6028), beta-carbolines (Abecarnil) and pyrazoloquinolines (CGS 8216, CGS 9895 and CGS 9896). 3. Saturation and competition binding assays were combined with powerful data analysis software developed in our laboratory. Among the capabilities of this software is the identification of multiple binding sites for a cold ligand using a non-selective labeled ligand that binds with equal, but high, affinity to all the binding sites 4. Saturation binding assays using either [3H]-RO15-1788 or [3H]-RO15-4513 revealed only one apparent binding site, with a higher affinity for RO15-4513 than for RO15-1788. However, using [3H]-RO15-4513 for the competition binding studies in the cerebellum, together with our data analysis software, led to the identification of two distinct binding sites with equal densities for the diverse benzodiazepines studied. 5. In rat cerebellum one of the sites identified corresponds to GABA(A) receptors exhibiting alpha1 subunit pharmacology and the other to GABA(A) receptors exhibiting alpha6 subunit pharmacology. In general, the diverse families of BDZR ligands studied had much lower affinities for the alpha6 containing receptors.

Unraveling the identity of benzodiazepine binding sites in rat hipppocampus and olfactory bulb.

The goals of the work reported here were (i) to identify distinct GABA(A)/benzodiazepine receptors in the rat hippocampus and olfactory bulb using receptor binding assays, and (ii) to determine the affinities and selectivities of benzodiazepine receptor ligands from structurally diverse chemical families at each site identified. These studies were aided by the use of software AFFINITY ANALYSIS SYSTEM, developed in our laboratory for analysis of receptor binding data that allows the determination of receptor heterogeneity using non-selective radioligands. Saturation binding assays using [3H]RO15-4513 (ethyl 8-azido-6-dihydro-5-methyl-6-oxo-4H-imidazo[1, 5-a]-[1,4]benzodiazepine-3-carboxylate) revealed two binding sites in each of these two tissues. The higher affinity site corresponds to alpha(5) subunit-containing GABA(A) receptor and the lower affinity site to a combination of alpha(1), alpha(2), and alpha(3) subunit-containing receptors. These results should be useful in the challenging task of identifying the various functional GABA(A) receptors in the central nervous system, and in providing a link between receptor affinities and in vivo activities of the GABA(A)/benzodiazepine receptor ligands studied.

Pharmacokinetics and pharmacodynamics of inhaled versus intravenous morphine in healthy volunteers.

BACKGROUND: A new pulmonary drug delivery system produces aerosols from disposable packets of medication. This study compared the pharmacokinetics and pharmacodynamics of morphine delivered by an AERx prototype with intravenous morphine. METHODS: Fifteen healthy volunteers were enrolled. Two subjects were administered four inhalations of 2.2 mg morphine each at 1-min intervals or 4.4 mg over 3 min by intravenous infusion. Thirteen subjects were given twice the above doses, i.e., eight inhalations or 8.8 mg intravenously over 7 min. Arterial blood sampling was performed every minute during administration and at 2, 5, 7, 10, 15, 20, 45, 60, 90, 120, 150, 180, and 240 min after administration. The effect of morphine was assessed by measuring pupil diameter and ventilatory response to a hypercapnic challenge. Pharmacokinetic and pharmacodynamic analyses were performed simultaneously using mixed-effect models. RESULTS: The pharmacokinetic data after intravenous administration were described by a three-exponent decay model preceded by a lag time. The pharmacokinetic model for administration by inhalation consisted of the three-exponent intravenous pharmacokinetic model preceded by a two-exponent absorption model. The authors found that, with administration by inhalation, the total bioavailability was 59%, of which 43% was absorbed almost instantaneously and 57% was absorbed with a half-life of 18 min. The median times to the half-maximal miotic effects of morphine were 10 and 5.5 min after inhalation and intravenous administration, respectively (P < 0.01). The pharmacodynamic parameter ke0 was approximately 0.003 min-1. CONCLUSIONS: The onset and duration of the effects of morphine are similar after intravenous administration or inhalation via this new pulmonary drug delivery system. Morphine bioavailability after such administration is 59% of the dose loaded into the dosage form.

The pharmacology of anesthetic drugs in elderly patients.

Elderly patients are more sensitive to anesthetic drugs than younger patients. This increased sensitivity has a pharmacokinetic basis if the dose produces a higher drug concentration in an elderly patient than in a younger patient. The increased sensitivity has a pharmacodynamic basis if the same concentration produces a more profound drug effect in elderly patients. This article reviews the mechanisms of increased sensitivity of elderly patients to opioids, hypnotics, amnestica, and muscle relaxants.

Response surface model for anesthetic drug interactions.

BACKGROUND: Anesthetic drug interactions traditionally have been characterized using isobolographic analysis or multiple logistic regression. Both approaches have significant limitations. The authors propose a model based on response-surface methodology. This model can characterize the entire dose-response relation between combinations of anesthetic drugs and is mathematically consistent with models of the concentration-response relation of single drugs. METHODS: The authors defined a parameter, theta, that describes the concentration ratio of two potentially interacting drugs. The classic sigmoid Emax model was extended by making the model parameters dependent on theta. A computer program was used to estimate response surfaces for the hypnotic interaction between midazolam, propofol, and alfentanil, based on previously published data. The predicted time course of effect was simulated after maximally synergistic bolus dose combinations. RESULTS: The parameters of the response surface were identifiable. With the test data, each of the paired combinations showed significant synergy. Computer simulations based on interactions at the effect site predicted that the maximally synergistic three-drug combination tripled the duration of effect compared with propofol alone. CONCLUSIONS: Response surfaces can describe anesthetic interactions, even those between agonists, partial agonists, competitive antagonists, and inverse agonists. Application of response-surface methodology permits characterization of the full concentration-response relation and therefore can be used to develop practical guidelines for optimal drug dosing.

Population pharmacokinetics of long-term oral amiodarone therapy.

BACKGROUND: Amiodarone is an increasingly popular and uniquely effective antiarrhythmic agent for which population pharmacokinetic parameters in patients receiving long-term oral therapy have not been defined previously. METHODS: We collected 605 observations of serum amiodarone and desethylamiodarone metabolite concentrations from 77 patients (mean follow-up, 2 years). Mixed-effects modeling (NONMEM) was used to determine the typical population pharmacokinetic parameters, their respective variabilities, and a simple oral dosing regimen to rapidly achieve and maintain a target concentration of 1.5 mg/L. Individual serum concentration versus time curves were simulated for the study population based on regimens outlined in the product monograph and were compared with those for the proposed dosing regimen. The relationship between the duration of amiodarone therapy and the rate of decrement in serum concentration after discontinuation was explored. RESULTS: Amiodarone concentrations were best described by a two-compartment model with the typical parameters +/- interindividual coefficients of variation (where applicable) as follows: volumes of distribution/bioavailability (V1/F = 882 L; V2/F = 12,700 L +/- 58%) and clearances/bioavailability (CL1/F = 229 L/day +/- 31%; and CL2/F = 599 L/day +/- 56%). Rapid distribution half-life was 17 hours, and terminal half-life was 55 days. A practical dosing regimen of 1600 mg/d for 2 days, 1,200 mg/d for 5 days, 1,000 mg/d for 7 days, 800 mg/d for 7 days, 600 mg/d for 7 days, and 400 mg/d for 62 days followed by a maintenance dose of 343 mg/d (400 mg/d for 6 of 7 days) is proposed. After steady state is reached, cessation of dosing produces a 25% serum concentration decrement in 3 days and 50% in 36 days. CONCLUSIONS: Population pharmacokinetics confirm that amiodarone has an extraordinarily long half-life. The slow elimination rate makes anticipating the timing of adjustments in amiodarone therapy to avoid toxicity unusually perplexing. However, based on the estimated variability, the proposed dosing regimen would produce steady-state concentrations within the therapeutic window for 90% of patients.

Comparison of plasma compartment versus two methods for effect compartment--controlled target-controlled infusion for propofol.

BACKGROUND: Target-controlled infusion (TCI) systems can control the concentration in the plasma or at the site of drug effect. A TCI system that targets the effect site should be able to accurately predict the time course of drug effect. The authors tested this by comparing the performance of three control algorithms: plasmacontrol TCI versus two algorithms for effect-site control TCI. METHODS: One-hundred twenty healthy women patients received propofol via TCI for 12-min at a target concentration of 5.4 microg/ml. In all three groups, the plasma concentrations were computed using pharmacokinetics previously reported. In group I, the TCI device controlled the plasma concentration. In groups II and III, the TCI device controlled the effect-site concentration. In group II, the effect site was computed using a half-life for plasma effect-site equilibration (t1/2k(eo)) of 3.5 min. In group III, plasma effect-site equilibration rate constant (k(eo)) was computed to yield a time to peak effect of 1.6 min after bolus injection, yielding a t1/2keo of 34 s. the time course of propofol was measured using the bispectral index. Blood pressure, ventilation, and time of loss of consciousness were measured. RESULTS: The time course of propofol drug effect, as measured by the bispectral index, was best predicted in group III. Targeting the effect-site concentration shortened the time to loss of consciousness compared with the targeting plasma concentration without causing hypotension. The incidence of apnea was less in group III than in group II. CONCLUSION: Effect compartment-controlled TCI can be safely applied in clinical practice. A biophase model combining the Marsh kinetics and a time to peak effect of 1.6 min accurately predicted the time course of propofol drug effect.

Bispectral index (BIS) and burst suppression: revealing a part of the BIS algorithm.

OBJECTIVE: The bispectral index (BIS) is a complex EEG parameter which integrates several disparate descriptors of the EEG into a single variable. One of the subparameters incorporated in the BIS is the suppression ratio, quantifying the percentage of suppression during burst suppression pattern. The exact algorithm used to synthetize the information to the BIS value is unpublished and still unknown. This study provides insight into the integration of the suppression ratio into the BIS algorithm. METHODS: EEG data of 10 healthy volunteers during propofol infusion were analyzed. Propofol concentrations were ramped up to 4 predetermined concentrations (1, 2, 3, 4, 6, 8, 9, or 12 microg/ml) using a computer controlled infusion pump (STANPUMP). EEG recordings were performed with an Aspect A-1000 EEG monitor (Version 3.22). The relationship of the processed EEG variables bispectral index and suppression ratio, calculated by the Aspect A-1000 monitor, was analyzed. RESULTS: Up to 40% suppression ratio the average BIS values remained constant regardless of suppression ratios (r = 0.13). Beyond a suppression ratio of 40%, BIS and suppression ratio were invariably linearly correlated (r = -1). At a suppression ratio > or = 40% the BIS value could be calculated as BIS = 50 - suppression ratio/2. CONCLUSIONS: Suppression ratio values > 40% are linearly correlated with BIS values from 30 to 0. An increasing anesthetic drug effect resulting in an increase of the duration of suppression to a suppression ratio up to 40% is not adequately reflected by the BIS value.