基于网络的人群药代动力学服务(WAPPS)对剂量选择的影响:一个儿科中心的经验

C. Kwan, M. Bhatt, K. Strike, K. Decker, D. Matino, Anthony K. C. Chan
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引用次数: 0

摘要

背景:目前对严重血友病的治疗包括预防性因子替代以预防出血。凝血因子产品在药代动力学(PK)参数上具有显著的患者间变异性。最佳管理需要根据个人PK参数定制预防措施。基于web的群体药代动力学服务应用程序(WAPPS)是一个使用群体方法估计个体PK值的工具。尽管它越来越多地用于指导剂量选择,但很少有研究调查其临床影响。目的研究严重血友病患儿在因子水平低于1%时,使用WAPPS进行pk前和pk后预防方案和每周用药小时数的变化。方法回顾性分析2013年4月至2018年7月在麦克马斯特儿童医院接受治疗的所有使用WAPPS的严重血友病患儿。提取的数据包括:患者人口统计学、WAPPS生成的PK数据、预防方案前后PK检测、方案变更原因。使用WAPPS计算因子水平在pk前和pk后低于1%的每周小时数。结果纳入31例患者;42% (n=13)的患者在进行PK试验后改变了预防方案。在使用PK数据个性化预防建议后,因子水平低于1%的每周小时数减少(从平均13.1小时/周减少到11.8小时/周),尽管没有统计学意义(p=0.16)。结论WAPPS生成的PK数据对预防建议的改变具有直接影响。这种个性化的方法促进了以患者为中心的护理和患者参与,而不会增加花费的时间,因子水平低于1%。它也证实和验证了临床实践。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Web-based Application for the Population Pharmacokinetic Service (WAPPS)'s impact on dosage selection: a single paediatric centre experience
Abstract Background Current treatment for severe haemophilia includes prophylactic factor replacement to prevent bleeding. Coagulation factor products have significant inter-patient variability in pharmacokinetic (PK) parameters. Optimal management requires tailoring prophylaxis to individual PK parameters. Web-based Application for the Population Pharmacokinetic Service (WAPPS) is a tool that estimates individual PK values using a population approach. Despite its growing use to help guide dosing selection, few studies have investigated its clinical impact. Aim To investigate any change in prophylaxis regimen and hours per week where factor level is under 1%, pre- and post-PK testing using WAPPS, for paediatric patients with severe haemophilia. Methods A retrospective chart review was conducted for all paediatric patients with severe haemophilia receiving care between April 2013 and July 2018 at McMaster Children's Hospital who have used WAPPS. Data extracted included: patient demographics, PK data generated by WAPPS, prophylaxis regimen pre- and post-PK testing, and reason for regimen change. The number of hours per week where factor level was under 1% pre- and post-PK testing was calculated using WAPPS. Results Thirty-one patients were included; 42% (n=13) changed their prophylaxis regimen after PK testing. After using PK data to personalise prophylaxis recommendations, there was a decrease in the number of hours per week where factor level is under 1% (from an average of 13.1 hours/week to 11.8 hours/week), though not statistically significant (p=0.16). Conclusion PK data generated by WAPPS has direct impact by informing changes to prophylaxis recommendations. This individualised approach promotes patient-centred care and patient engagement without increasing the time spent with factor levels below 1%. It also confirms and validates clinical practice.
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