Open AccessShifting Clinical Trial Endpoints in Kidney Transplantation: The Rise of Composite Endpoints and Machine Learning to Refine Prognostication
Author(s) -
Imran J. Anwar,
Titte R. Srinivas,
Qimeng Gao,
Stuart J. Knechtle
Publication year - 2022
Publication title -
transplantation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.45
H-Index - 204
eISSN - 1534-6080
pISSN - 0041-1337
DOI - 10.1097/tp.0000000000004107
Subject(s) - immunosuppression , medicine , kidney transplantation , clinical trial , surrogate endpoint , transplantation , clinical endpoint , intensive care medicine , medical physics , artificial intelligence , surgery , computer science
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.