Literature DB >> 34865103

Rheumatoid arthritis is a risk factor for refracture in patients with fragility fractures.

Hotaka Ishizu1, Hirokazu Shimizu1, Tomohiro Shimizu1, Taku Ebata1, Yuki Ogawa1, Masahiro Miyano1, Kosuke Arita1, Yusuke Ohashi1, Norimasa Iwasaki1.   

Abstract

OBJECTIVES: To determine whether patients with rheumatoid arthritis (RA) who have had fragility fractures are at an increased risk of refractures.
METHODS: Patients with fragility fractures who were treated surgically at 10 hospitals from 2008 to 2017 and who underwent follow-up for >24 months were either categorized into a group comprising patients with RA or a group comprising patients without RA (controls). The groups were matched 1:1 by propensity score matching. Accordingly, 240 matched participants were included in this study. The primary outcome was the refracture rate in patients with RA as compared to in the controls. Multivariable analyses were also conducted on patients with RA to evaluate the odds ratios (ORs) for the refracture rates.
RESULTS: Patients with RA were significantly associated with increased rates of refractures during the first 24 months (OR: 2.714, 95% confidence interval [95% CI]: 1.015-7.255; p = 0.040). Multivariable analyses revealed a significant association between increased refracture rates and long-term RA (OR: 6.308, 95% CI: 1.195-33.292; p = 0.030).
CONCLUSIONS: Patients with RA who have experienced fragility fractures are at an increased risk of refractures. Long-term RA is a substantial risk factor for refractures. © Japan College of Rheumatology 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  Fragility fractures; propensity score matching; refractures; rheumatoid arthritis

Mesh:

Year:  2022        PMID: 34865103     DOI: 10.1093/mr/roab109

Source DB:  PubMed          Journal:  Mod Rheumatol        ISSN: 1439-7595            Impact factor:   2.862


  1 in total

1.  Machine Learning Algorithms: Prediction and Feature Selection for Clinical Refracture after Surgically Treated Fragility Fracture.

Authors:  Hirokazu Shimizu; Ken Enda; Tomohiro Shimizu; Yusuke Ishida; Hotaka Ishizu; Koki Ise; Shinya Tanaka; Norimasa Iwasaki
Journal:  J Clin Med       Date:  2022-04-05       Impact factor: 4.241

  1 in total

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