Model-Informed Precision Dosing of Tacrolimus in Pediatric Kidney Transplantation: A Hybrid Population Pharmacokinetics-Machine Learning Approach
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 130
- 主要终点
- Development of a novel and applicable dosing algorithm.
研究概览
简要总结
The purpose of this study is to develop a novel and applicable dosing algorithm that helps support the clinical decision to achieve tacrolimus levels within the optimal immunosuppression range despite sparse sampling in routine clinical practice in pediatric kidney transplantation. This clinical decision support tool (CDST) is based on a hybrid population pharmacokinetics-machine learning approach aiming for tacrolimus dose individualization and reducing the risk of acute graft rejection.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- — 至 18 Years（Child, Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Pediatric patients (< 18 years old).
- •First-time kidney transplant recipients who have less than 20% PRA and no DSA.
- •Adherent to tacrolimus for a year post-transplantation.
- •All data, including therapeutic drug monitoring (TDM) data, are available.
排除标准
- •Non-adherent patients to tacrolimus.
- •Unavailable or missing data.
结局指标
主要结局
Development of a novel and applicable dosing algorithm.
时间窗: June 2026 - January 2027
A dosing algorithm that rapidly achieves accurate tacrolimus levels within the optimal immunosuppression range despite sparse sampling in routine clinical practice.
次要结局
- Reduction in the risk of acute graft rejection.(June 2026 - January 2027)
研究者
Sondos Mohamed Salem
Teaching Assistant
Helwan University