Risk prediction and end-points in idiopathic pulmonary fibrosis: one step at a time

Predicting mortality in IPF: a commentary on new data showing the prognostic importance of 6-min walking distance http:// http://ow.ly/tCD0NIdiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with a median survival of approximately 3 years from diagnosis.Accurate prediction of mortality is important in IPF, as it helps determine the urgency of lung transplantation, guides other management and end-of-life decisions, and facilitates enrichment of clinical trial populations.Although most patients with IPF die from progressive respiratory failure [1], predicting outcomes is challenging due to the heterogeneity of disease progression.In this issue of the European Respiratory Journal, DU BOIS et al. [2] use the INSPIRE (International Study of Survival Outcomes in Idiopathic Pulmonary Fibrosis with Interferon-c-1b) dataset to describe a new prediction model that estimates mortality in IPF.The INSPIRE study was a multicentre double-blind randomised trial comparing interferon-c-1b to placebo in 826 patients with mild to moderate IPF [3].This trial did not meet its primary end-point but has generated high-quality data that continue to provide valuable insight into the progression of IPF.DU BOIS et al. [4] have previously used the INSPIRE data to show that age, baseline forced vital capacity (FVC), 24-week change in FVC, and recent respiratory hospitalisation independently predict 1-year mortality in patients with IPF [4].These authors have now extended their findings by showing that mortality prediction is improved by adding both baseline and 24-week change in 6-min walking distance (6MWD) to this multivariate model [2].This new study is a noteworthy addition to the literature, providing important data on the prediction of mortality in IPF and suggesting potential options for clinical trial end-points. Defining mortality risk in IPFThe model developed by DU BOIS et al.[2] accurately estimates 1-year mortality in patients with IPF with a C-statistic of 0.80.The C-statistic, or C-index, is a measure of how well a model predicts the risk of an outcome (e.g.1-year mortality) in any two individuals randomly selected from the cohort of interest.A Cstatistic of 1.0 indicates that the model appropriately identifies the individual at higher risk of the outcome in every paired comparison.A C-statistic above 0.70 is generally considered acceptable, and a C-statistic of 0.50 indicates that the prediction model performs no better than chance.The C-statistic of 0.80 in this study compares favourably with models that are in routine clinical use in other fields and other validated models in IPF [5].It is therefore tempting to use this new model in the clinical setting; however, there are some questions that require further study.

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