Current state of clinical end-points assessment in transplant: Key points

Abstract

Solid organ transplantation is the treatment of choice for patients with end-stage organ disease. However, organ transplantation can stress the cardiovascular system and decrease immune surveillance, leading to early mortality and graft loss due to multiple underlying comorbidities. Clinical end-points in transplant include death and graft failure. Thus, generating accurate predictive models through regression models is crucial to test for definitive clinical post-transplantation end-points. Survival predictive models should assemble efficient surrogate markers or prognostic factors to generate a minimal set of variables derived from a proper modeling strategy through regression models. However, a few critical points should be considered when reporting survival analyses and regression models to achieve proper discrimination and calibration of the predictive models. Additionally, population-based risk scores may underestimate risk prediction in transplant. The application of predictive models in these patients should therefore incorporate both classical and non-classical risk factors, as well as community-based health indicators and transplant-specific factors to quantify the outcomes in terms of survival properly. This review focuses on assessment of clinical end-points in transplant through regression models by combining predictive and surrogate variables, and considering key points in these analyses to accurately predict definitive end-points, which could aid clinicians in decision making.