Influence of renal function estimation on pharmacokinetic modeling of vancomycin in elderly patients.

Vancomycin is a renally excreted drug, and its body clearance correlates with creatinine clearance. However, the renal function estimation equation that best predicts vancomycin clearance has not been established yet. The objective of this study was to compare the abilities of different renal function estimation equations to describe vancomycin pharmacokinetics in elderly patients. The NPAG algorithm was used to perform population pharmacokinetic analysis of vancomycin concentrations in 78 elderly patients. Six pharmacokinetic models of vancomycin clearance were built, based on the following equations: Cockcroft-Gault (CG), Jelliffe (JEL), Modification of Diet in Renal Disease (MDRD), Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) (both in milliliters per minute per 1.73 m(2)), and modified MDRD and CKD-EPI equations (both in milliliters per minute). Goodness-of-fit and predictive performances of the six PK models were compared in a learning set (58 subjects) and a validation set (20 patients). Final analysis was performed to estimate population parameters in the entire population. In the learning step, the MDRD-based model best described the data, but the CG- and JEL-based models were the least biased. The mean weighted errors of prediction were significantly different between the six models (P = 0.0071). In the validation group, predictive performances were not significantly different. However, the use of a renal function estimation equation different from that used in the model building could significantly alter predictive performance. The final analysis showed important differences in parameter distributions and AUC estimation across the six models. This study shows that methods used to estimate renal function should not be considered interchangeable for pharmacokinetic modeling and model-based estimation of vancomycin concentrations in elderly patients.

Describing Assay Precision-Reciprocal of Variance Is Correct, Not CV Percent: Its Use Should Significantly Improve Laboratory Performance.

BACKGROUND: Describing assay error as percent coefficient of variation (CV%) fails as measurements approach zero. Results are censored if below some arbitrarily chosen lower limit of quantification (LLOQ). CV% gives incorrect weighting to data obtained by therapeutic drug monitoring, with incorrect parameter values in the resulting pharmacokinetic models, and incorrect dosage regimens for patient care. METHODS: CV% was compared with the reciprocal of the variance (1/var) of each assay measurement. This method has not been considered by the laboratory community. A simple description of assay standard deviation (SD) as a polynomial function of the assay measurement over its working range was developed, the reciprocal of the assay variance determined, and its results compared with CV%. RESULTS: CV% does not provide correct weighting of measured serum concentrations as required for optimal therapeutic drug monitoring. It does not permit optimally individualized models of the behavior of a drug in a patient, resulting in incorrect dosage regimens. The assay error polynomial described here, using 1/var, provides correct weighting of such data, all the way down to and including zero. There is no need to censor low results, and no need to set any arbitrary LLOQ. CONCLUSIONS: Reciprocal of variance is the correct measure of assay precision and should replace CV%. The information is easily stored as an assay error polynomial. The laboratory can serve the medical community better. There is no longer any need for LLOQ, a significant improvement. Regulatory agencies should implement this more informed policy.

Are vancomycin trough concentrations adequate for optimal dosing?

The current vancomycin therapeutic guidelines recommend the use of only trough concentrations to manage the dosing of adults with Staphylococcus aureus infections. Both vancomycin efficacy and toxicity are likely to be related to the area under the plasma concentration-time curve (AUC). We assembled richly sampled vancomycin pharmacokinetic data from three studies comprising 47 adults with various levels of renal function. With Pmetrics, the nonparametric population modeling package for R, we compared AUCs estimated from models derived from trough-only and peak-trough depleted versions of the full data set and characterized the relationship between the vancomycin trough concentration and AUC. The trough-only and peak-trough depleted data sets underestimated the true AUCs compared to the full model by a mean (95% confidence interval) of 23% (11 to 33%; P = 0.0001) and 14% (7 to 19%; P < 0.0001), respectively. In contrast, using the full model as a Bayesian prior with trough-only data allowed 97% (93 to 102%; P = 0.23) accurate AUC estimation. On the basis of 5,000 profiles simulated from the full model, among adults with normal renal function and a therapeutic AUC of ≥400 mg · h/liter for an organism for which the vancomycin MIC is 1 mg/liter, approximately 60% are expected to have a trough concentration below the suggested minimum target of 15 mg/liter for serious infections, which could result in needlessly increased doses and a risk of toxicity. Our data indicate that adjustment of vancomycin doses on the basis of trough concentrations without a Bayesian tool results in poor achievement of maximally safe and effective drug exposures in plasma and that many adults can have an adequate vancomycin AUC with a trough concentration of <15 mg/liter.

A two-compartment population pharmacokinetic-pharmacodynamic model of digoxin in adults, with implications for dosage.

A population pharmacokinetic/pharmacodynamic model of digoxin in adult subjects was originally developed by Reuning et al in 1973. They clearly described the 2-compartment behavior of digoxin, the lack of correlation of effect with serum concentrations, and the close correlation of the observed inotropic effect of digoxin with the calculated amount of drug present in the peripheral nonserum compartment. Their model seemed most attractive for clinical use. However, to make it more applicable for maximally precise dosage, its model parameter values (means and SD's) were converted into discrete model parameter distributions using a computer program developed especially for this purpose using the method of maximum entropy. In this way, the parameter distributions became discrete rather than continuous, suitable for use in developing maximally precise digoxin dosage regimens, individualized to an adult patient's age, gender, body weight, and renal function, to achieve desired specific target goals either in the central (serum) compartment or in the peripheral (effect) compartment using the method of multiple model dosage design. Some illustrative clinical applications of this model are presented and discussed. This model with a peripheral compartment reflecting clinical effect has contributed significantly to an improved understanding of the clinical behavior of digoxin in patients than is possible with models having only a single compartment, and to the improved management of digoxin therapy for more than 20 years.

Software for dosage individualization of voriconazole for immunocompromised patients.

The efficacy of voriconazole is potentially compromised by considerable pharmacokinetic variability. There are increasing insights into voriconazole concentrations that are safe and effective for treatment of invasive fungal infections. Therapeutic drug monitoring is increasingly advocated. Software to aid in the individualization of dosing would be an extremely useful clinical tool. We developed software to enable the individualization of voriconazole dosing to attain predefined serum concentration targets. The process of individualized voriconazole therapy was based on concepts of Bayesian stochastic adaptive control. Multiple-model dosage design with feedback control was used to calculate dosages that achieved desired concentration targets with maximum precision. The performance of the software program was assessed using the data from 10 recipients of an allogeneic hematopoietic stem cell transplant (HSCT) receiving intravenous (i.v.) voriconazole. The program was able to model the plasma concentrations with a high level of precision, despite the wide range of concentration trajectories and interindividual pharmacokinetic variability. The voriconazole concentrations predicted after the last dosages were largely concordant with those actually measured. Simulations provided an illustration of the way in which the software can be used to adjust dosages of patients falling outside desired concentration targets. This software appears to be an extremely useful tool to further optimize voriconazole therapy and aid in therapeutic drug monitoring. Further prospective studies are now required to define the utility of the controller in daily clinical practice.

Comparison of four renal function estimation equations for pharmacokinetic modeling of gentamicin in geriatric patients.

Most aminoglycoside pharmacokinetic models include an index of renal function, such as creatinine clearance, to describe drug clearance. However, the best clinical descriptor of renal function for the pharmacokinetic modeling of aminoglycosides has not been established. This analysis was based on 412 gentamicin concentrations from 92 geriatric patients who received intravenous gentamicin for various infectious diseases. Four two-compartment population models were fitted to gentamicin concentrations in a learning set of 64 patients using the nonparametric adaptive grid (NPAG) algorithm. Each model included an index of renal function, namely, the Cockcroft-Gault (CG), Jelliffe (JEL), modification of diet in renal disease (MDRD), or modified MDRD (MDRDm; adjusted to individual body surface area) equation as a covariate influencing gentamicin serum clearance. Goodness of fit and predictive performance of the four models were compared using standard criteria in both the learning set and in a validation set of 28 patients. A final analysis was performed to estimate the population pharmacokinetic parameter values of the entire 92-patient group. In the learning set, the CG-based model best fit the data, followed by JEL-, MDRD-, and MDRDm-based models, with relative reductions of the Akaike information criterion of 29.4, 20.2, 14.2, and 4.2, respectively. Bias and precision of population predictions were significantly different among the four models. In the validation set, individual predictions from the four models showed marginally different biases. The final estimation confirmed the previous results. Specifically, the CG-based model showed predictive performance that was comparable to or better than that of the MDRD-based model at each stage of the analysis. This study shows that methods used to estimate renal function should not be considered interchangeable for the model-based estimation of gentamicin concentrations.

Some comments and suggestions concerning population pharmacokinetic modeling, especially of digoxin, and its relation to clinical therapy.

Population pharmacokinetic and dynamic modeling is often employed to analyze data of steady-state trough serum digoxin concentrations in the course of what is frequently regarded as routine therapeutic drug monitoring (TDM). Such a monitoring protocol is extremely uninformative. It permits only the estimation of a single parameter of a 1-compartment model, such as clearance. The use of D-optimal design strategies permits much more information to be obtained, employing models having a really meaningful structure. Strategies and protocols for routine TDM policies greatly need to be improved, incorporating these principles of optimal design. Software for population pharmacokinetic modeling has been dominated by NONMEM. However, because NONMEM is a parametric method, it must assume a shape for the model parameter distributions. If the assumption is not correct, the model will be in error, and the most likely results given the raw data will not be obtained. In addition, the likelihood as computed by NONMEM is only approximate, not exact. This impairs statistical consistency and reduces statistical efficiency and the resulting precision of model parameter estimates. Other parametric methods are superior, as they provide exact likelihoods. However, they still suffer from the constraints of assuming the shape of the model parameter distributions. Nonparametric methods are more flexible. One need not make any assumptions about the shape of the parameter distributions. Nonparametric methods also provide exact likelihoods and are statistically consistent, efficient, and precise. They also permit maximally precise dosage regimens to be developed for patients using multiple model dosage design, something parametric modeling methods cannot do. Laboratory assay errors are better described by the reciprocal of the assay variance of each measurement rather than by coefficient of variation. This is easy to do and permits more precise models to be made. This also permits estimation of assay error separately from the other sources of uncertainty in the clinical environment. This is most useful scientifically. Digoxin has at least 2-compartment behavior. Its pharmacologic and clinical effects correlate not with serum digoxin concentrations but with those in the peripheral nonserum compartment. Some illustrative clinical examples are discussed. It seems that digitalis therapy, guided by TDM and our 2 compartment models based on that of Reuning et al, can convert at least some patients with atrial fibrillation and flutter to regular sinus rhythm. Investigators have often used steady-state trough concentrations only to make a 1-compartment model and have sought only to predict future steady-state trough concentrations. Much more than this can be done, and clinical care can be much improved. Further work along these lines is greatly to be desired.

Accurate detection of outliers and subpopulations with Pmetrics, a nonparametric and parametric pharmacometric modeling and simulation package for R.

INTRODUCTION: Nonparametric population modeling algorithms have a theoretical superiority over parametric methods to detect pharmacokinetic and pharmacodynamic subgroups and outliers within a study population. METHODS: The authors created "Pmetrics," a new Windows and Unix R software package that updates the older MM-USCPACK software for nonparametric and parametric population modeling and simulation of pharmacokinetic and pharmacodynamic systems. The parametric iterative 2-stage Bayesian and the nonparametric adaptive grid (NPAG) approaches in Pmetrics were used to fit a simulated population with bimodal elimination (Kel) and unimodal volume of distribution (Vd), plus an extreme outlier, for a 1-compartment model of an intravenous drug. RESULTS: The true means (SD) for Kel and Vd in the population sample were 0.19 (0.17) and 102 (22.3), respectively. Those found by NPAG were 0.19 (0.16) and 104 (22.6). The iterative 2-stage Bayesian estimated them to be 0.18 (0.16) and 104 (24.4). However, given the bimodality of Kel, no subject had a value near the mean for the population. Only NPAG was able to accurately detect the bimodal distribution for Kel and to find the outlier in both the population model and in the Bayesian posterior parameter estimates. CONCLUSIONS: Built on over 3 decades of work, Pmetrics adopts a robust, reliable, and mature nonparametric approach to population modeling, which was better than the parametric method at discovering true pharmacokinetic subgroups and an outlier.

Mathematical modeling of pulmonary tuberculosis therapy: Insights from a prototype model with rifampin.

There is a critical need for improved and shorter tuberculosis (TB) treatment. Current in vitro models of TB, while valuable, are poor predictors of the antibacterial effect of drugs in vivo. Mathematical models may be useful to overcome the limitations of traditional approaches in TB research. The objective of this study was to set up a prototype mathematical model of TB treatment by rifampin, based on pharmacokinetic, pharmacodynamic and disease submodels. The full mathematical model can simulate the time-course of tuberculous disease from the first day of infection to the last day of therapy. Therapeutic simulations were performed with the full model to study the antibacterial effect of various dosage regimens of rifampin in lungs. The model reproduced some qualitative and quantitative properties of the bactericidal activity of rifampin observed in clinical data. The kill curves simulated with the model showed a typical biphasic decline in the number of extracellular bacteria consistent with observations in TB patients. Simulations performed with more simple pharmacokinetic/pharmacodynamic models indicated a possible role of a protected intracellular bacterial compartment in such a biphasic decline. This modeling effort strongly suggests that current dosage regimens of RIF may be further optimized. In addition, it suggests a new hypothesis for bacterial persistence during TB treatment.

Intrapulmonary pharmacokinetics and pharmacodynamics of micafungin in adult lung transplant patients.

Invasive pulmonary aspergillosis is a life-threatening infection in lung transplant recipients; however, no studies of the pharmacokinetics and pharmacodynamics (PKPD) of echinocandins in transplanted lungs have been reported. We conducted a single-dose prospective study of the intrapulmonary and plasma PKPD of 150 mg of micafungin administered intravenously in 20 adult lung transplant recipients. Epithelial lining fluid (ELF) and alveolar cell (AC) samples were obtained via bronchoalveolar lavage performed 3, 5, 8, 18, or 24 h after initiation of infusion. Micafungin concentrations in plasma, ELF, and ACs were determined using high-pressure liquid chromatography. Noncompartmental methods, population analysis, and multiple-dose simulations were used to calculate PKPD parameters. Cmax in plasma, ELF, and ACs was 4.93, 1.38, and 17.41 microg/ml, respectively. The elimination half-life in plasma was 12.1 h. Elevated concentrations in ELF and ACs were sustained during the 24-h sampling period, indicating prolonged compartmental half-lives. The mean micafungin concentration exceeded the MIC90 of Aspergillus fumigatus (0.0156 microg/ml) in plasma (total and free), ELF, and ACs throughout the dosing interval. The area under the time-concentration curve from 0 to 24 h (AUC0-24)/MIC90 ratios in plasma, ELF, and ACs were 5,077, 923.1, and 13,340, respectively. Multiple-dose simulations demonstrated that ELF and AC concentrations of micafungin would continue to increase during 14 days of administration. We conclude that a single 150-mg intravenous dose of micafungin resulted in plasma, ELF, and AC concentrations that exceeded the MIC90 of A. fumigatus for 24 h and that these concentrations would continue to increase during 14 days of administration, supporting its potential activity for prevention and early treatment of pulmonary aspergillosis.

Expert Discussion of the Role of Rate Constant Versus Clearance Approaches to Define Drug Pharmacokinetics: Theoretical and Clinical Considerations.

This article provides a dialogue covering an ongoing controversy on the use of clearance versus rate constant approaches for model parameterization when assessing pharmacokinetic (PK) data. It reflects the differences in opinions that can exist among PK experts. Importantly, this discussion extends beyond theoretical arguments to demonstrate how these different approaches impact the analysis and interpretation of data acquired in clinical situations. By not shying away from such dialogues, this article showcases how dissimilarity in well-grounded perspectives can influence how one applies PK and mathematical principles.

Development of a methodology to make individual estimates of the precision of liquid chromatography-tandem mass spectrometry drug assay results for use in population pharmacokinetic modeling and the optimization of dosage regimens.

BACKGROUND: The clinical value of therapeutic drug monitoring can be increased most significantly by integrating assay results into clinical pharmacokinetic models for optimal dosing. The correct weighting in the modeling process is 1/variance, therefore, knowledge of the standard deviations (SD) of each measured concentration is important. Because bioanalytical methods are heteroscedastic, the concentration-SD relationship must be modeled using assay error equations (AEE). We describe a methodology of establishing AEE's for liquid chromatography-tandem mass spectrometry (LC-MS/MS) drug assays using carbamazepine, fluconazole, lamotrigine and levetiracetam as model analytes. METHODS: Following method validation, three independent experiments were conducted to develop AEE's using various least squares linear or nonlinear, and median-based linear regression techniques. SD's were determined from zero concentration to the high end of the assayed range. In each experiment, precision profiles of 6 ("small" sample sets) or 20 ("large" sample sets) out of 24 independent, spiked specimens were evaluated. Combinatorial calculations were performed to attain the most suitable regression approach. The final AEE's were developed by combining the SD's of the assay results, established in 24 specimens/spiking level and using all spiking levels, into a single precision profile. The effects of gross hyperbilirubinemia, hemolysis and lipemia as laboratory interferences were investigated. RESULTS: Precision profiles were best characterized by linear regression when 20 spiking levels, each having 24 specimens and obtained by performing 3 independent experiments, were combined. Theil's regression with the Siegel estimator was the most consistent and robust in providing acceptable agreement between measured and predicted SD's, including SD's below the lower limit of quantification. CONCLUSIONS: In the framework of precision pharmacotherapy, establishing the AEE of assayed drugs is the responsibility of the therapeutic drug monitoring service. This permits optimal dosages by providing the correct weighting factor of assay results in the development of population and individual pharmacokinetic models.

An Algorithm for Nonparametric Estimation of a Multivariate Mixing Distribution with Applications to Population Pharmacokinetics.

Population pharmacokinetic (PK) modeling has become a cornerstone of drug development and optimal patient dosing. This approach offers great benefits for datasets with sparse sampling, such as in pediatric patients, and can describe between-patient variability. While most current algorithms assume normal or log-normal distributions for PK parameters, we present a mathematically consistent nonparametric maximum likelihood (NPML) method for estimating multivariate mixing distributions without any assumption about the shape of the distribution. This approach can handle distributions with any shape for all PK parameters. It is shown in convexity theory that the NPML estimator is discrete, meaning that it has finite number of points with nonzero probability. In fact, there are at most N points where N is the number of observed subjects. The original infinite NPML problem then becomes the finite dimensional problem of finding the location and probability of the support points. In the simplest case, each point essentially represents the set of PK parameters for one patient. The probability of the points is found by a primal-dual interior-point method; the location of the support points is found by an adaptive grid method. Our method is able to handle high-dimensional and complex multivariate mixture models. An important application is discussed for the problem of population pharmacokinetics and a nontrivial example is treated. Our algorithm has been successfully applied in hundreds of published pharmacometric studies. In addition to population pharmacokinetics, this research also applies to empirical Bayes estimation and many other areas of applied mathematics. Thereby, this approach presents an important addition to the pharmacometric toolbox for drug development and optimal patient dosing.

Population modeling and Monte Carlo simulation study of the pharmacokinetics and antituberculosis pharmacodynamics of rifampin in lungs.

Little information exists on the pulmonary pharmacology of antituberculosis drugs. We used population pharmacokinetic modeling and Monte Carlo simulation to describe and explore the pulmonary pharmacokinetics and pharmacodynamics of rifampin (RIF; rifampicin). A population pharmacokinetic model that adequately described the plasma, epithelial lining fluid (ELF), and alveolar cell (AC) concentrations of RIF in a population of 34 human volunteers was made by use of the nonparametric adaptive grid (NPAG) algorithm. The estimated concentrations correlated well with the measured concentrations, and there was little bias and good precision. The results obtained with the NPAG algorithm were then imported into Matlab software to perform a 10,000-subject Monte Carlo simulation. The ability of RIF to suppress the development of drug resistance and to induce a sufficient bactericidal effect against Mycobacterium tuberculosis was evaluated by calculating the proportion of subjects achieving specific target values for the maximum concentration of drug (C(max))/MIC ratio and the area under the concentration-time curve from time zero to 24 h (AUC(0-24))/MIC ratio, respectively. At the lowest MIC (0.01 mg/liter), after the administration of one 600-mg oral dose, the rates of target attainment for C(max)/MIC (> or =175) were 95% in ACs, 48.8% in plasma, and 35.9% in ELF. Under the same conditions, the target attainment results for the killing effect were 100% in plasma (AUC(0-24)/MIC > or = 271) but only 54.5% in ELF (AUC(0-24)/MIC > or = 665). The use of a 1,200-mg RIF dose was associated with better results for target attainment. The overall results suggest that the pulmonary concentrations obtained with the standard RIF dose are too low in most subjects. This work supports the need to evaluate higher doses of RIF for the treatment of patients with tuberculosis.

Visual estimation of patients' body weight in hospital: the more observers, the better?

OBJECTIVE: Patients are not always weighed in hospitals. A visual estimate of patients' body weight is often used. Little information exists about the validity of this practice. We assessed the visual estimation of body weight in a population of elderly hospitalised patients. METHOD: Three observers performed a visual estimation of weight in 71 geriatric patients. Estimated body weights from each observer were compared to measured body weights. Various panels--three panels of two observers and two panels of three observers--were also evaluated. RESULTS: Overall results showed that a three observer panel gave better weight estimates than one or two individuals. CONCLUSION: While further clinical studies are necessary to confirm these findings, using the mean or the median of several visual estimates may be a practical solution for body weight estimation when weighing patients is not possible.

Stochastic model for outcome prediction in acute illness.

The aims were to apply a stochastic model to predict outcome early in acute emergencies and to evaluate the effectiveness of various therapies in a consecutively monitored series of severely injured patients with noninvasive hemodynamic monitoring. The survival probabilities were calculated beginning shortly after admission to the emergency department (ED) and at subsequent intervals during their hospitalization. Cardiac function was evaluated by cardiac output (CI), heart rate (HR), and mean arterial blood pressure (MAP), pulmonary function by pulse oximetry (SapO(2)), and tissue perfusion function by transcutaneous oxygen indexed to FiO(2),(PtcO(2)/FiO(2)), and carbon dioxide (PtcCO(2)) tension. The survival probability (SP) of survivors averaged 81.5+/-1.1% (SEM) and for nonsurvivors 57.7+/-2.3% (p<0.001) in the first 24-hour period of resuscitation and subsequent management. The CI, SapO(2),PtcO(2)/FiO(2) and MAP were significantly higher in survivors than in nonsurvivors during the initial resuscitation, while HR and PtcCO(2) tensions were higher in the nonsurvivors. Predictions made during the initial resuscitation period in the first 24-hours after admission were compared with the actual outcome at hospital discharge, which were usually several weeks later; misclassifications were 9.6% (16/167). The therapeutic decision support system objectively evaluated the responses of alternative therapies based on responses of patients with similar clinical-hemodynamic states.

Noninvasive hemodynamic monitoring for combat casualties.

The aims of this study were to develop and to test a noninvasive hemodynamic monitoring system that could be applied to combat casualties to supplement conventional vital signs, to use an advanced information system to predict outcomes, and to evaluate the relative effectiveness of various therapies with instant feedback information during acute emergency conditions. In a university-run inner city public hospital, we evaluated 1,000 consecutively monitored trauma patients in the initial resuscitation period, beginning shortly after admission to the emergency department. In addition to conventional vital signs, we used noninvasive monitoring devices (cardiac index by bioimpedance with blood pressure and heart rate to measure cardiac function, arterial hemoglobin oxygen saturation by pulse oximetry to reflect changes in pulmonary function, and tissue oxygenation by transcutaneous oxygen tension indexed to fractional inspired oxygen concentration and carbon dioxide tension to evaluate tissue perfusion). The cardiac index, mean arterial pressure, pulse oximetry (arterial hemoglobin oxygen saturation), and transcutaneous oxygen tension/fractional inspired oxygen concentration were significantly higher in survivors, whereas the heart rate and carbon dioxide tension were higher in nonsurvivors. The calculated survival probability was a useful outcome predictor that also served as a measure of severity of illness. The rate of misclassification of survival probability was 13.5% in the series as a whole but only 6% for patients without severe head injuries and brain death. Application of noninvasive hemodynamic monitoring to acute emergency trauma patients in the emergency department is feasible, safe, and inexpensive and provides accurate hemodynamic patterns in continuous, on-line, real-time, graphical displays of the status of cardiac, pulmonary, and tissue perfusion functions. Combined with an information system, this approach provided an early outcome predictor and evaluated, with an objective individualized method, the relative efficacy of alternative therapies for specific patients.

Outcome prediction in chest injury by a mathematical search and display program.

OBJECTIVE: This study applies a stochastic or probability search and display model to prospectively predict outcome and to evaluate therapeutic effects in a consecutively monitored series of 396 patients with severe thoracic and thoracoabdominal injuries. STUDY DESIGN: Prospective observational study of outcome prediction using noninvasive hemodynamic monitoring by previously designed protocols and tested against actual outcome at hospital discharge in a level 1 trauma service of a university-run, inner-city public hospital. METHODS: Cardiac index (CI), heart rate (HR), mean arterial pressure (MAP), arterial oxygen saturation measured by pulse oximetry (Sp(O2)), transcutaneous oxygen tension (PtC(O2)), and transcutaneous carbon dioxide tension (Ptc(CO2)) were monitored beginning shortly after admission to the emergency department. The stochastic search and display model with a decision support program based on noninvasive hemodynamic monitoring was applied to 396 severely ill patients with major thoracic and thoracoabdominal trauma. The survival probability (SP) was calculated during initial resuscitation continuously until patients were stabilized, and compared with the actual outcome when the patient was discharged from the hospital usually a week or more later. RESULTS: The CI, Sp(O2), Ptc(O2), and MAP were appreciably higher in survivors than in nonsurvivors. HR and Ptc(CO2) were higher in the nonsurvivors. The calculated SP in the first 24-h observation period of survivors of chest wounds averaged 83 +/- 18% (+/- SD) and 62 +/- 19% for nonsurvivors. Misclassifications were 9.6%. The relative effects of alternative therapies were evaluated before and after therapy, using hemodynamic monitoring and SP as criteria. CONCLUSIONS: Noninvasive hemodynamic monitoring with an information system provided a feasible approach to early outcome predictions and therapeutic decision support.

Recombinant human erythropoietin for the treatment of renal anaemia in children: no justification for bodyweight-adjusted dosage.

BACKGROUND: Drug doses for children are usually calculated by reducing adult doses in proportion to bodyweight. The clinically effective dose of recombinant human erythropoietin (epoetin) in children, however, seems to be higher than predicted by this calculation. OBJECTIVE: To determine the quantitative relationship between epoetin dose, bodyweight and response in children with end-stage renal disease. PATIENTS AND METHODS: The time-course of haemoglobin in 52 children during long-term treatment with epoetin beta was analysed by population pharmacodynamic modelling. Patients were 5-20 years old and weighed 16-53kg at the beginning of treatment. Epoetin beta was given intravenously three times per week after haemodialysis. Doses ranged from 110 to 7500IU (3-205 IU/kg). Haemoglobin versus time was described by assuming that the haemoglobin level rises after each dose due to the formation of new red blood cells, which then survive according to a logistic function. The initial rise after each dose was modelled in terms of absolute dose (not dose/kg). A parametric analysis was done with NONMEM, followed by a nonparametric analysis with NPAG. RESULTS: Dose-response was best described by a sigmoid maximum-effect (E(max)) model with median E(max) = 0.29 g/dL, median 50% effective dose (ED(50)) = 2400IU and shape parameter gamma = 2. The estimated median survival time of the epoetin-induced red blood cells, tau, was 76 days. Neither of the dose-response parameters E(max) and ED(50) showed dependence on bodyweight. The median haemoglobin response to a standard dose, 0.042 g/dL for 1000IU, was similar to that reported for adults with intravenous administration. CONCLUSIONS: Doses for children in this age range should be specified as absolute amounts rather than amounts per unit bodyweight. Initial doses can be calculated individually, based on haemoglobin level before treatment, the desired haemoglobin at steady state and the median population parameters E(max), ED(50) and tau.

The role of digitalis pharmacokinetics in converting atrial fibrillation and flutter to regular sinus rhythm.

This report examined the role of digitalis pharmacokinetics in helping to guide therapy with digitalis glycosides with regard to converting atrial fibrillation (AF) or flutter to regular sinus rhythm (RSR). Pharmacokinetic models of digitoxin and digoxin, containing a peripheral non-serum effect compartment, were used to analyze outcomes in a non-systematic literature review of five clinical studies, using the computed concentrations of digitoxin and digoxin in the effect compartment of these models in an analysis of their outcomes. Four cases treated by the author were similarly examined. Three literature studies showed results no different from placebo. Dosage regimens achieved ≤11 ng/g in the model's peripheral compartment. However, two other studies achieved significant conversion to RSR. Their peripheral concentrations were 9-14 ng/g. In the four patients treated by the author, three converted using classical clinical titration with incremental doses, plus therapeutic drug monitoring and pharmacokinetic guidance from the models for maintenance dosage. They converted at peripheral concentrations of 9-18 ng/g, similar to the two studies above. No toxicity was seen. Successful maintenance was achieved, using the models and their pharmacokinetic guidance, by giving somewhat larger than average recommended dosage regimens in order to maintain peripheral concentrations present at conversion. The fourth patient did not convert, but only reached peripheral concentrations of 6-7 ng/g, similar to the studies in which conversion was no better than placebo. Pharmacokinetic analysis and guidance play a highly significant role in converting AF to RSR. To the author's knowledge, this has not been specifically described before. In my experience, conversion of AF or flutter to RSR does not occur until peripheral concentrations of 9-18 ng/g are reached. Results in the four cases correlated well with the literature findings. More work is needed to further evaluate these provocative findings.