Clustering-aided prediction of outcomes in patients with idiopathic pulmonary fibrosis.

ABSTRACT:

Background

Blood biomarkers predictive of the progression of idiopathic pulmonary fibrosis (IPF) would be of value for research and clinical practice. We used data from the IPF-PRO Registry to investigate whether the addition of "omics" data to risk prediction models based on demographic and clinical characteristics improved prediction of the progression of IPF.

Methods

The IPF-PRO Registry enrolled patients with IPF at 46 sites across the US. Patients were followed prospectively. Median follow-up was 27.2 months. Prediction models for disease progression included omics data (proteins and microRNAs [miRNAs]), demographic factors and clinical factors, all assessed at enrollment. Data on proteins and miRNAs were included in the models either as raw values or based on clusters

SUBMITTER: Wang L 

PROVIDER: S-EPMC11515489 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature