What type of study is this?

This is a Retrospective Study study.

September 10, 2025Open Access

Prognostication in patients with idiopathic pulmonary fibrosis using quantitative airway analysis from HRCT: a retrospective study

NYNan YangSouth China University of Technology FFFrancesco FedericoVita-Salute San Raffaele University SHStephen M. HumphriesNational Jewish Health

Key Points

SABRE-based variables effectively predict mortality in patients with idiopathic pulmonary fibrosis, enhancing treatment prioritization.
The study found an AUC of 0.852 for the predictive utility of SABRE at the first year, improving accuracy in identifying high-risk patients.
Utilizing high-resolution CT scans, the analysis adjusted for traditional measures, revealing stronger prognostic signals.
SABRE captures disease progression insights beyond conventional severity scores, paving the way for better patient management.

Abstract

Background Predicting shorter life expectancy is crucial for prioritizing antifibrotic therapy in fibrotic lung diseases, where progression varies widely, from stability to rapid deterioration. This heterogeneity complicates treatment decisions, emphasizing the need for reliable baseline measures. This study focuses on leveraging artificial intelligence model to address heterogeneity in disease outcomes, focusing on mortality as the ultimate measure of disease trajectory. Methods This retrospective study included 1744 anonymised patients who underwent high-resolution CT scanning. The AI model, SABRE (Smart Airway Biomarker Recognition Engine), was developed using data from patients with various lung diseases (n=460, including lung cancer, pneumonia, emphysema, and fibrosis). Then, 1284 high-resolution CT scans with evidence of diffuse FLD from the Australian IPF Registry and OSIC were used for clinical analyses. Airway branches were categorized and quantified by anatomic structures and volumes, followed by multivariable analysis to explore the associations between these categories and patients’ progression and mortality, adjusting for disease severity or traditional measurements. Results Cox regression identified SABRE-based variables as independent predictors of mortality and progression, even adjusting for disease severity (fibrosis extent, traction bronchiectasis extent, and ILD extent), traditional measures (FVC%, DLCO%, and CPI), and previously reported deep learning algorithms for fibrosis quantification and morphological analysis. Combining SABRE with DLCO significantly improved prognosis utility, yielding an AUC of 0.852 at the first year and a C-index of 0.752. Conclusions SABRE-based variables capture prognostic signals beyond that provided by traditional measurements, disease severity scores, and established AI-based methods, reflecting the progressiveness and pathogenesis of the disease.

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