Aims A major concern with any antithrombotic therapy is an increase

Aims A major concern with any antithrombotic therapy is an increase in the risk of haemorrhage. 0.698, 0.738], a distribution volume of 5.24 l (95% CI 4.20, 6.28) and an elimination half-life of 5.0 h. Enoxaparin clearance was significantly related to patient weight and creatinine clearance, and was the only impartial predictor of experiencing both all (10.7%, = 0.0013) and major (2.2%, = 0.0004) haemorrhagic events. A creatinine clearance of 30 ml min?1 was associated with a decrease in enoxaparin clearance of 27% compared with that in a patient with a median creatinine clearance of 88 ml min?1, and was related to a 1.5- and 3.8-fold increase in the risk of all and major haemorrhagic episodes, respectively. Conclusions Enoxaparin clearance depends on body weight, and, therefore, weight-adjusted dosing is recommended to minimize interpatient variability in drug exposure and the risk of haemorrhage. The importance of an increased risk of 24003-67-6 haemorrhage with decreasing renal function must be weighed against the benefit of treatment with enoxaparin in patients with UA and NSTEMI. < 0.05 ( > 3.84), and retained if significant at < 0.005 ( > 7.88) in the presence of all other included covariates (multivariate analysis). Empirical Bayes estimates of individual clearance values were obtained at various actions in the analysis. Formal validation is not universal and should depend around the intended use of the model [5, 6]. We used validation approaches that tested the appropriateness of covariate effects retained in the final model. Two different approaches were used: Validation through parameters[6]. A learning set of patients was used for model building and a test set for model validation. This approach is based on both qualitative and quantitative evaluations of the learning-set populace model prediction of CL in 24003-67-6 the test-set patients [7]. The learning set populace model predictions of test set patients CL (based on covariate values TIE1 of the test patients) were compared with individual CL estimates, based on observed concentration and Bayesian estimation after fitting a base model (without covariates) to the test set. The evaluation was based on the prediction error that was plotted against covariates to assess any 24003-67-6 residual dependence of CL on covariates in the test set (qualitative) and evaluated for bias (median prediction error) and precision (median absolute prediction error) in subpopulation of at-risk patients (e.g. patients with low CRCL). The bootstrap approach [8]. This validation was performed around the refined final model based on the whole populace (index plus validation sets). It consisted of repeatedly fitting the final model to 500 bootstrap replicates of the data. The percentage of runs in which the covariate effects were significant was calculated (95% CI of cov for each run not including 0), as were the 95% CI of bootstrapped parameters estimates (taken as the 2 2.5th to 97.5th interpercentile range of the 500 replicates). Prediction of haemorrhage risk (PK/PD analysis) This assessment was limited to the incidence of major haemorrhage (primary endpoint of the study) and the incidence of all haemorrhages. The most severe event score was assigned to patients experiencing several events during treatment so that there was only one event per patient. No attempt was made to account for the possible repetition of bleeding events. In addition, the analysis did not take account of the role of treatment duration around the incidence of events. A final set of individual clearance and AUC (over a dosage interval of 0C12 h at constant state) data was generated on each occasion by Bayesian estimation, using parameter estimates from the final populace model as prior information. When multiple estimates of clearance and AUC were available for an individual patient, mean values were calculated across the study period and used.

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