The lung allocation score and other available models lack predictive accuracy for post-lung transplant survival.

ABSTRACT:

Background

Improved predictive models are needed in lung transplantation in the setting of a proposed allocation system that incorporates longer-term post-transplant survival in the United States. Allocation systems require accurate mortality predictions to justly allocate organs.

Methods

Utilizing the United Network for Organ Sharing database (2005-2017), we fit models to predict 1-year mortality based on the Lung Allocation Score (LAS), the Chan, et al, 2019 model, a novel "clinician" model (a priori clinician selection of pre-transplant covariates), and two machine learning models (Least Absolute Shrinkage and Selection Operator; LASSO and Random Forests) for predicting 1-year and 3-year post-transplant mortality. We compared predictive accuracy among models. We evaluated

SUBMITTER: Brahmbhatt JM 

PROVIDER: S-EPMC9329266 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature