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Hindi A Novel Tool to Evaluate the Accuracy of Predicting Survival and Guiding Lung Transplantation in Cystic Fibrosis
*Corresponding Author:
Copyright: © 2020 . This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Effective transplantation recommendations in cystic
fibrosis (CF) require accurate survival predictions, so
that high-risk patients may be prioritized for
transplantation. In practice, decisions about
transplantation are made dynamically, using routinely
updated assessments. We present a novel tool for
evaluating risk prediction models that, unlike traditional
methods, captures classification accuracy in identifying
high-risk patients in a dynamic fashion.
