Prognostic Impact of Quantitative CT Disease Extent and Pattern in Fibrotic Hypersensitivity Pneumonitis

Quantitative CT using multiple instance learning (MIL)-usual interstitial pneumonia (UIP) classifier and data-driven textural analysis (DTA) provides objective and reproducible measurements of disease pattern and extent, respectively. However, the understanding of their individual or combined relationships with outcomes in fibrotic hypersensitivity pneumonitis (fHP) remains limited.

To determine whether visual CT patterns of fHP or UIP, a MIL-UIP classifier, baseline and changes in DTA over 12-months and DTA at 12 months are associated with progression-free survival (PFS) in fHP.

The primary cohort included 134 participants with fHP. Associations between CT variables and subsequent lung function change, and PFS were assessed using linear mixed models and Cox hazards models. Results were validated in a combined dataset from three independent populations (N = 216).

Visual patterns of fHP or UIP were not associated with PFS. A baseline MIL-UIP classification and DTA were significantly associated with 24-month PFS. However, the MIL-UIP did not predict PFS after adjusting for DTA fibrosis extent. Over 12 months, a 1-point increase in DTA across and within patients resulted in a significant decline in FVC% and DLCO%. A 12-month change in DTA (HR = 1.07, 95%CI 1.01-1.13) and DTA at 12-months (HR = 1.03, 95%CI 1.01-1.05) were associated with subsequent 12-month PFS. Similar estimates were observed in the validation cohort.

In fHP, the objectively measured extent of lung fibrosis is more prognostic than either a UIP classifier or visual CT patterns of fHP or UIP. Baseline DTA, DTA at 12 months, and its change over 12-months are associated with PFS.