Individual dynamic prediction and prognostic analysis for long-term allograft survival after kidney transplantation.

ABSTRACT:

Background

Predicting allograft survival is vital for efficient transplant success. With dynamic changes in patient conditions, clinical indicators may change longitudinally, and doctors' judgments may be highly variable. It is necessary to establish a dynamic model to precisely predict the individual risk/survival of new allografts.

Methods

The follow-up data of 407 patients were obtained from a renal allograft failure study. We introduced a landmarking-based dynamic Cox model that incorporated baseline values (age at transplantation, sex, weight) and longitudinal changes (glomerular filtration rate, proteinuria, hematocrit). Model performance was evaluated using Harrell's C-index and the Brier score.

Results

Six predictors were included in our analysis. The Kaplan-M

SUBMITTER: Huang B 

PROVIDER: S-EPMC9641958 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature