UCSF

Rationale

Mortality prediction is well studied in idiopathic pulmonary fibrosis (IPF), but little is known about predictors of premortality disease progression. Identification of patients at risk for disease progression would be useful for clinical decision-making and designing clinical trials.

Objectives

To develop prediction models for disease progression in IPF.

Methods

In a large clinical trial cohort of patients with IPF (n = 1,113), we comprehensively screened multivariate models of candidate baseline and past-change predictors for disease progression defined by 48-week worsening of FVC, dyspnea (University of California, San Diego Shortness of Breath Questionnaire [UCSD SOBQ]), 6-minute-walk distance (6MWD), and occurrence of respiratory hospitalization, or death. Progression outcomes were modeled as appropriate, by slope change using linear regression models and time to binary outcomes using Cox proportional hazards models.

Measurements and main results

The overall cohort experienced considerable disease progression. Top-performing prediction models did not meaningfully predict most measures of disease progression. For example, prediction modeling explained less than or equal to 1% of the observed variation in 48-week slope change in FVC, UCSD SOBQ, and 6MWD. Models performed better for binary measures of time to disease progression but were still largely inaccurate (cross-validated C statistic ≤0.63 for ≥10% decline in FVC or death, ≤0.68 for ≥20-U increase in UCSD SOBQ or death, ≤0.70 for ≥100 m decline in 6MWD or death). Models for time to respiratory hospitalization or death (C statistic ≤0.77) or death alone (C statistic ≤0.81) demonstrated acceptable discriminative performance.

Conclusions

Clinical prediction models poorly predicted physiologic and functional disease progression in IPF. This is in contrast to respiratory hospitalization and mortality prediction.