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贝叶斯预测利用出血信息支持因子 VIII 的剂量个体化。

Bayesian Forecasting Utilizing Bleeding Information to Support Dose Individualization of Factor VIII.

机构信息

Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.

Bayer, Berlin, Germany.

出版信息

CPT Pharmacometrics Syst Pharmacol. 2019 Dec;8(12):894-903. doi: 10.1002/psp4.12464. Epub 2019 Oct 30.

Abstract

Bayesian forecasting for dose individualization of prophylactic factor VIII replacement therapy using pharmacokinetic samples is challenged by large interindividual variability in the bleeding risk. A pharmacokinetic-repeated time-to-event model-based forecasting approach was developed to contrast the ability to predict the future occurrence of bleeds based on individual (i) pharmacokinetic, (ii) bleeding, and (iii) pharmacokinetic, bleeding and covariate information using observed data from the Long-Term Efficacy Open-Label Program in Severe Hemophilia A Disease (LEOPOLD) clinical trials (172 severe hemophilia A patients taking prophylactic treatment). The predictive performance assessed by the area under receiver operating characteristic (ROC) curves was 0.67 (95% confidence interval (CI), 0.65-0.69), 0.78 (95% CI, 0.76-0.80), and 0.79 (95% CI, 0.77-0.81) for patients ≥ 12 years when using pharmacokinetics, bleeds, and all data, respectively, suggesting that individual bleed information adds value to the optimization of prophylactic dosing regimens in severe hemophilia A. Further steps to optimize the proposed tool for factor VIII dose adaptation in the clinic are required.

摘要

贝叶斯预测在预防因子 VIII 替代疗法中的剂量个体化,使用药代动力学样本受到出血风险的个体间变异性大的挑战。开发了一种基于药代动力学重复时间事件模型的预测方法,以对比基于个体(i)药代动力学、(ii)出血和(iii)药代动力学、出血和协变量信息,使用来自严重血友病 A 疾病的长期疗效开放标签计划(LEOPOLD)临床试验(172 名接受预防治疗的严重血友病 A 患者)的观察数据,预测未来出血发生的能力。通过接收者操作特征(ROC)曲线下面积评估的预测性能分别为 0.67(95%置信区间[CI],0.65-0.69)、0.78(95%CI,0.76-0.80)和 0.79(95%CI,0.77-0.81),当分别使用药代动力学、出血和所有数据时,≥12 岁的患者表明,个体出血信息为优化严重血友病 A 的预防剂量方案提供了价值。需要进一步优化用于因子 VIII 剂量适应的临床建议工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5434/6930854/4e51146fcb8c/PSP4-8-894-g001.jpg

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