Shifting Clinical Trial Endpoints in Kidney Transplantation: The Rise of Composite Endpoints and Machine Learning to Refine Prognostication.
The measurement of outcomes in kidney transplantation has been more accurately documented than almost any other surgical procedure result in recent decades. With significant improvements in short- and long-term outcomes related to optimized immunosuppression, outcomes have gradually shifted away from conventional clinical endpoints (ie, patient and graft survival) to surrogate and composite endpoints. This article reviews how outcomes measurements have evolved in the past 2 decades in the setting of increased data collection and summarizes recent advances in outcomes measurements pertaining to clinical, histopathological, and immune outcomes. Finally, we discuss the use of composite endpoints and Bayesian concepts, specifically focusing on the integrative box risk prediction score, in conjunction with machine learning to refine prognostication.
Duke Scholars
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Related Subject Headings
- Surgery
- Machine Learning
- Kidney Transplantation
- Immunosuppression Therapy
- Humans
- Graft Survival
- Bayes Theorem
- 3204 Immunology
- 3202 Clinical sciences
- 11 Medical and Health Sciences
Citation
Published In
DOI
EISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Surgery
- Machine Learning
- Kidney Transplantation
- Immunosuppression Therapy
- Humans
- Graft Survival
- Bayes Theorem
- 3204 Immunology
- 3202 Clinical sciences
- 11 Medical and Health Sciences