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.
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.
Non-adherent patients to tacrolimus.
Unavailable or missing data.
Pediatric kidney recipients receiving tacrolimus
Cohort
Prospective
Not listed
Development of a novel and applicable dosing algorithm.
A dosing algorithm that rapidly achieves accurate tacrolimus levels within the optimal immunosuppression range despite sparse sampling in routine clinical practice.
Reduction in the risk of acute graft rejection.
Reduction in the risk of acute graft rejection by developing a clinical decision support tool (CDST) to guide the dose individualization of tacrolimus following pediatric kidney transplantation.
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Helwan University
Lead sponsor
Cairo University
Collaborator
This trial is not recruiting at the moment. You can still explore other options:
