Machine Learning-Derived Integer-Based Score and Prediction of Tertiary Hyperparathyroidism among Kidney Transplant Recipients: An Integer-Based Score to Predict Tertiary Hyperparathyroidism.

ABSTRACT:

Background and objectives

Tertiary hyperparathyroidism in kidney allograft recipients is associated with bone loss, allograft dysfunction, and cardiovascular mortality. Accurate pretransplant risk prediction of tertiary hyperparathyroidism may support individualized treatment decisions. We aimed to develop an integer score system that predicts the risk of tertiary hyperparathyroidism using machine learning algorithms.

Design, setting, participants, & measurements

We used two separate cohorts: a derivation cohort with the data of kidney allograft recipients (n=669) who underwent kidney transplantation at Severance Hospital, Seoul, Korea between January 2009 and December 2015 and a multicenter registry dataset (the Korean Cohort Study for Outcome in Patients with Kidney

SUBMITTER: Hong N 

PROVIDER: S-EPMC9269627 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature