UCSF

Idiopathic pulmonary fibrosis (IPF) is traditionally staged with terms such as "mild", "severe", "early" and "advanced" based on pulmonary function tests. This approach allows physicians to monitor disease progression and advise patients and their families. However, it is not known if the stages of this model reflect distinct biological or clinical phenotypes and the therapeutic and prognostic value of this system is limited. Novel methods of IPF staging have recently been developed. The GAP model includes four baseline variables that were found to be predictive of outcome, as identified by logistic regression. These factors are: gender (G), age (A) and two lung physiology variables (P) (forced vital capacity and diffusing capacity of the lung for carbon monoxide). The clinical utility and accuracy of staging models may be further improved in the future by the integration of dynamic parameters that can be measured over time, as well as biological data from biomarkers which may be able to directly measure disease activity. The development of an evidence-based, multidimensional IPF staging model that builds on the current staging approaches to IPF is an important objective for improving the management of IPF.