Prediction of Response to Intra-Articular Injections of Hyaluronic Acid for Knee Osteoarthritis

Eva Lee, Eva Lee, Eva Lee, Fan Yuan, Barton Mann, Marlene DeMaio, Marlene DeMaio

2024

Abstract

Osteoarthritis (OA) is a degenerative joint disease, with the knee being the most frequently affected joint. Knee OA is a leading cause of arthritis disability, with 50% of knee OA patients eventually receiving surgical procedures. Specifically, 99% of these knee replacements are performed to address pain and functional limitations. However, it was reported that about one-third of these surgeries are unnecessary. Intra-articular injections of hyaluronic acid (HA) can serve as a non-invasive cost-effective alternative to surgery for knee osteoarthritis. Although research studies have clearly demonstrated that HA improves knee function, the efficacy of this treatment remains controversial. However, many clinicians have observed that effects depend on several patient characteristics such as age, weight, gender, severity of the OA, and technical issues such as injection site and placement. In this study, a multi-stage multi-group machine learning model is utilized to uncover discriminatory features that can predict the response status of knee OA patients to different types of HA treatment. The algorithm can identify certain subgroups of knee OA patients who respond well (or those who don’t) to HA therapy. Specifically, a baseline result including factors such as patients’ weight, smoking status and frequency allows physicians the first step of patient treatment recommendation, steering those patients most suitable for HA injection. The model can achieve over 85% blind prediction accuracy. The data and model derived from this study allows physicians to administer HA products more selectively and effectively, which will increase the percentage of patients who experience a successful HA therapy. Information about predicted responses could also easily be shared with patients to incorporate their values and preferences into treatment selection. In addition, the decision support tools would allow providers to quickly determine whether a patient is exhibiting at least an expected treatment response and if not, to potentially take corrective action. The model is generalizable and can also be used to predict patient responses to other treatments and conditions.

Paper Citation

in Harvard Style

Lee E., Yuan F., Mann B. and DeMaio M. (2024). Prediction of Response to Intra-Articular Injections of Hyaluronic Acid for Knee Osteoarthritis. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-716-0, SciTePress, pages 497-508. DOI: 10.5220/0013071800003838

in Bibtex Style

@conference{kdir24,
author={Eva Lee and Fan Yuan and Barton Mann and Marlene DeMaio},
title={Prediction of Response to Intra-Articular Injections of Hyaluronic Acid for Knee Osteoarthritis},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2024},
pages={497-508},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013071800003838},
isbn={978-989-758-716-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Prediction of Response to Intra-Articular Injections of Hyaluronic Acid for Knee Osteoarthritis
SN - 978-989-758-716-0
AU - Lee E.
AU - Yuan F.
AU - Mann B.
AU - DeMaio M.
PY - 2024
SP - 497
EP - 508
DO - 10.5220/0013071800003838
PB - SciTePress