Background: Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic lung disease with an overall poor prognosis. A simple-to-use staging system for IPF may improve prognostication, help guide management, and facilitate research.
Objective: To develop a multidimensional prognostic staging system for IPF by using commonly measured clinical and physiologic variables.
Design: A clinical prediction model was developed and validated by using retrospective data from 3 large, geographically distinct cohorts.
Setting: Interstitial lung disease referral centers in California, Minnesota, and Italy.
Patients: 228 patients with IPF at the University of California, San Francisco (derivation cohort), and 330 patients at the Mayo Clinic and Morgagni-Pierantoni Hospital (validation cohort).
Measurements: The primary outcome was mortality, treating transplantation as a competing risk. Model discrimination was assessed by the c-index, and calibration was assessed by comparing predicted and observed cumulative mortality at 1, 2, and 3 years.
Results: Four variables were included in the final model: gender (G), age (A), and 2 lung physiology variables (P) (FVC and Dlco). A model using continuous predictors (GAP calculator) and a simple point-scoring system (GAP index) performed similarly in derivation (c-index of 70.8 and 69.3, respectively) and validation (c-index of 69.1 and 68.7, respectively). Three stages (stages I, II, and III) were identified based on the GAP index with 1-year mortality of 6%, 16%, and 39%, respectively. The GAP models performed similarly in pooled follow-up visits (c-index ≥71.9).
Limitation: Patients were drawn from academic centers and analyzed retrospectively.
Conclusion: The GAP models use commonly measured clinical and physiologic variables to predict mortality in patients with IPF.
Primary Funding Source: University of California, San Francisco Clinical and Translational Science Institute.