The willingness of patients to accept an additional mortality risk in order to improve renal graft survival.

BACKGROUND: This study was performed to assess renal transplant patient preferences with respect to the acceptance of an additional mortality risk induced by immunosuppressive therapy in order to prevent graft loss in case of acute rejection. METHODS: The two decision analysis tools standard gamble and time trade-off were used to interview 155 patients with a functioning renal graft, and 11 on dialysis awaiting transplantation. RESULTS: Defining the best possible outcome as being alive with a functioning graft (utility value = 1), and the worst outcome as dying (utility value = 0), median utility values of 0.68 (0.59 +/- 0.32, mean +/- SD) with standard gamble and of 0.65 (0.57 +/- 0.32) with time trade-off were obtained for the intermediate outcome (i.e., staying alive but returning to dialysis). Thirteen percent of the patients attributed a utility value of 0 to this intermediate outcome (i.e., they would rather die than return to dialysis), and 8% a utility value of 1 (i.e., they would take absolutely no risk from additional antirejection therapy). Individual utility values for returning to dialysis correlated with time on dialysis before transplantation (R= 0.76, P < 0.005), but no relationship was found between utility values and age, sex, religion, previous methods of dialysis, time with a functioning graft, number of transplantations, or time on the transplantation waiting list. CONCLUSION: The large interindividual variability of utility values precludes a prediction about the acceptance of a new therapeutic regimen by an individual patient. The assessment of the utility enables, however, a more objective judgment of the general acceptance of any possible risk/benefit ratio induced by a new immunosuppressive regimen in our patient population.

Influence of arterial vs. venous sampling site on nicotine tolerance model selection and parameter estimation.

In this modeling study we utilize previously published nicotine pharmacokinetic (PK) and pharmacodynamic (PD, heart rate) data to investigate the influence of PK sampling site (venous vs. arterial) on the selection of a specific PD tolerance model and estimation of its parameters. We describe a general model for tolerance which includes as special cases feedback (TF), and kinetic based tolerance (TK) models. A TK model has arterial plasma drug concentrations (Ca) driving (hypothetical) effect (Ce) and antagonist (Cm) site concentrations, which drive a non-feedback effect (Enf): tolerance depends on the relative rate of equilibration of Ce and Cm with Ca. The TF model adds feedback which makes tolerance depend on Enf, not just on drug kinetics for nicotine. The arterial-sampling-analysis (PKPDa) has Ca driving Ce and Cm. The venous-sampling-analysis (PKPDv) does the same but estimates Ca from venous data by means of deconvolution. A TF model (with Cm = Ce) was always selected in the PKPDa. According to this model tolerance developed rapidly with a median half-life of 6.6 min, and median decrease of effect due to tolerance of 31%. Different variants of the TF or TK models were selected in the PKPDv. Parameter estimates for PKPDv show higher variability, and, for the TF model, lower rate and extent of tolerance development and threefold increase in EC50. The study shows that (i) TF models are more appropriate than TK models to describe nicotine effect data, (ii) venous sampling may lead to incorrect model selection and inaccurate and imprecise parameter estimation in respect to arterial sampling, and (iii) arterial sampling should be preferred for accurate (non-steady-state) PD modeling.

Non-linear dynamics models characterizing long-term virological data from AIDS clinical trials.

Human immunodeficiency virus (HIV) dynamics represent a complicated variant of the text-book case of non-linear dynamics: predator-prey interaction. The interaction can be described as naturally reproducing T-cells (prey) hunted and killed by virus (predator). Virus reproduce and increase in number as a consequence of successful predation; this is countered by the production of T-cells and the reaction of the immune system. Multi-drug anti-HIV therapy attempts to alter the natural dynamics of the predator-prey interaction by decreasing the reproductive capability of the virus and hence predation. These dynamics are further complicated by varying compliance to treatment and insurgence of resistance to treatment. When following the temporal progression of viral load in plasma during therapy one observes a short-term (1-12 weeks) decrease in viral load. In the long-term (more than 12 weeks from the beginning of therapy) the reduction in viral load is either sustained, or it is followed by a rebound, oscillations and a new (generally lower than at the beginning of therapy) viral load level. Biomathematicians have investigated these dynamics by means of simulations. However the estimation of the parameters associated with the dynamics from real data has been mostly limited to the case of simplified, in particular linearized, models. Linearized model can only describe the short-term changes of viral load during therapy and can only predict (apparent) suppression. In this paper we put forward relatively simple models to characterize long-term virus dynamics which can incorporate different factors associated with resurgence: (Fl) the intrinsic non-linear HIV-1 dynamics, (F2) drug exposure and in particular compliance to treatment, and (F3) insurgence of resistant HIV-1 strains. The main goal is to obtain models which are mathematically identifiable given only measurements of viral load, while retaining the most crucial features of HIV dynamics. For the purpose of illustration we demonstrate an application of the models using real AIDS clinical trial data involving patients treated with a combination of anti-retroviral agents using a model which incorporates compliance data.