Machine learning classifier is associated with mortality in interstitial lung disease: a retrospective validation study leveraging registry data.

Journal: BMC pulmonary medicine
PMID: 38783245

Abstract

BACKGROUND: Mortality prediction in interstitial lung disease (ILD) poses a significant challenge to clinicians due to heterogeneity across disease subtypes. Currently, forced vital capacity (FVC) and Gender, Age, and Physiology (GAP) score are the two most utilized metrics in prognostication. Recently, a machine learning classifier system, Fibresolve, designed to identify a variety of computed tomography (CT) patterns associated with idiopathic pulmonary fibrosis (IPF), was demonstrated to have a significant association with mortality across multiple subtypes of ILD. The purpose of this follow-up study was to retrospectively validate these findings in a large, external cohort of patients with ILD.

Authors

  • Kavitha C Selvan
    Section of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Chicago Medicine, 5841 S Maryland Avenue, Chicago, IL, 60637, USA. kavitha.selvan@uchicagomedicine.org.
  • Joshua Reicher
    Department of Radiology, Palo Alto VA Medical Center, Palo Alto, California.
  • Michael Muelly
    Imvaria, Inc, USA.
  • Angad Kalra
    Department of Computer Science, University of Toronto, Toronto, Ontario, Canada. Electronic address: angadk@cs.toronto.edu.
  • Ayodeji Adegunsoye
    Division of Pulmonary and Critical Care Medicine, University of Chicago, Chicago, IL.

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