Calculating percentage prediction error: a user's note

The equations of calculation of percentage prediction error ((percentage prediction error = frac(measured value - predicted value, measured value) 7 100 or; percentage prediction error = frac(predicted value - measured value, measured value) 7 100) and) similar equations have been widely used. However, not much is known about the property of this type of equation and the caution which should be taken into account when using this type of equation. Moreover, little is known about the power of percentage prediction error as statistical inference. In the present study we address these points in the use of this type of equation

Effects of different sampling strategies on predictions of blood cyclosporine concentrations in haematological patients with multidrug resistance by Bayesian and non-linear least squares methods

The Bayesian method (BM) can use previous information for the optimization of dosage regimen. However, Bayes' law remains true when the parameters are obtained from the infinite population. Therefore a bias might exist in the previous information and affect BM predictive performance. To overcome this shortcoming, the blood drug concentration of a patient can be used to individualize his pharmacokinetic parameters. Until now, at least two sampling strategies, i.e. steady-state and non-steady-state sampling strategies, have been developed to individualize and predict blood drug concentration. In the present study we used five sampling strategies: (1) all samples; (2) post-infusion samples; (3) during-infusion samples; (4) samples within 95% confidence interval/interquartile range of a steady-state concentration; (5) the sample of the mean/median at the mid-time-point of a steady-state to individualize and predict blood cyclosporine concentrations in haematological patients with multidrug resistance. We investigated the effects of different sampling strategies on BM and the nonlinear least squared method (NLLSM) predictive performances. The results showed that BM predictive performance was better than NLLSM. But the results did not prove that the steady-state sampling strategies were superior to the non-steady-state ones

Cyclosporin nephrotoxicity in relation to its metabolism in psoriasis

Cyclosporine (CsA) and some of its metabolites (M9, M17, M18, M21) have been determined by means of an LC-MASS method in eight psoriatic patients developing nephrotoxicity. In comparison with a control group (15 psoriatics who after the same period of time, with the same daily dose, did not develop nephrotoxicity) they showed an increase of CsA metabolites, especially M17. Because M17 blood concentrations in the nephrotoxic group tended to be higher than in the control group from the first week of treatment we suggest that M17 might be considered a marker of ongoing nephrotoxicity

Effect of fluctuations of blood cyclosporine concentrations on renal function.

none6noanoneFurlanut, M; Baraldo, M; Pea, F; Albanese, M C; Albertini, A; Puricelli, CFurlanut, M; Baraldo, M; Pea, F; Albanese, M C; Albertini, A; Puricelli,