Inclusion of dynamic clinical data improves the predictive performance of a 30-day readmission risk model in kidney transplantation.

ABSTRACT:

Background

Thirty-day readmissions (30DRA) are a highly scrutinized measure of healthcare quality and relatively frequent among kidney transplants (KTX). Development of predictive risk models is critical to reducing 30DRA and improving outcomes. Current approaches rely on fixed variables derived from administrative data. These models may not capture clinical evolution that is critical to predicting outcomes.

Methods

We directed a retrospective analysis toward: (1) developing parsimonious risk models for 30DRA and (2) comparing efficiency of models based on the use of immutable versus dynamic data. Baseline and in-hospital clinical and outcomes data were collected from adult KTX recipients between 2005 and 2012. Risk models were developed using backward logistic regression an

SUBMITTER: Taber DJ 

PROVIDER: S-EPMC4369585 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature