A machine learning approach to knee osteoarthritis phenotyping: data from the FNIH Biomarkers Consortium.

ABSTRACT:

Objective

Knee osteoarthritis (KOA) is a heterogeneous condition representing a variety of potentially distinct phenotypes. The purpose of this study was to apply innovative machine learning approaches to KOA phenotyping in order to define progression phenotypes that are potentially more responsive to interventions.

Design

We used publicly available data from the Foundation for the National Institutes of Health (FNIH) osteoarthritis (OA) Biomarkers Consortium, where radiographic (medial joint space narrowing of ≥0.7 mm), and pain progression (increase of ≥9 Western Ontario and McMaster Universities Osteoarthritis Index [WOMAC] points) were defined at 48 months, as four mutually exclusive outcome groups (none, both, pain only, radiographic only), along with an extensive set o

SUBMITTER: Nelson AE 

PROVIDER: S-EPMC6579689 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature