Advances in Prognostic Assessment of Idiopathic Pulmonary Fibrosis: From Clinical-Physiological Parameters and Molecular Biomarkers to Multimodal Models.

Abstract

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive fibrotic lung disease with an extremely poor prognosis, and the rate of disease progression varies significantly among patients. Therefore, accurate prognostic assessment is crucial for clinical management. Traditional pulmonary function parameters, such as the absolute value of forced vital capacity (FVC) and the rate of decline in FVC as a percentage of predicted value (FVC%), serve as important bases for predicting disease progression and mortality. In recent years, prognostic prediction models have evolved from single indicators to composite scoring systems that integrate physiological, imaging, clinical manifestations, and even biomarkers. Furthermore, emerging indicators such as quantitative assessment of fibrosis extent on high-resolution computed tomography (HRCT), serum biomarkers, and artificial intelligence-based radiomics analysis provide powerful tools for earlier and more accurate identification of patients at risk of rapid disease progression. However, how to effectively incorporate these emerging indicators into clinical practice and clinical trials remains a current challenge. Future research should focus on validating and optimizing multi-omics prediction models, exploring their application value in individualized risk stratification and guiding treatment decisions for patients, with the ultimate goal of improving the long-term prognosis of IPF patients.