The relationship between patient-reported and clinician-assessed outcome measures in Inclusion body myositis - insights from a retrospective cohort study.

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Tác giả: Kelly Beer, Anna Brusch, Ian Cooper, Althea Doverty, Kathryn Hird, Merrilee Needham, Annik Panicker, Madeline Schopp, Katie Schütze

Ngôn ngữ: eng

Ký hiệu phân loại: 346.04344 Private law

Thông tin xuất bản: England : Neuromuscular disorders : NMD , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 709740

 Inclusion body myositis (IBM) is an inflammatory myopathy, characterised by slow progression of weakness, skeletal muscle atrophy, and heterogeneous clinical presentation. This variability in disease progression and presentation complicates tracking of clinical progress and intervention response in clinical trials, presenting challenges in identifying reliable outcome measures. We aimed to identify the most useful suite of clinician-assessed and patient-reported outcome measures (PROMs) for use in clinical practice and trials from a selection of the most commonly used outcome measures in IBM. We retrospectively analysed clinician-assessed outcome measures (manual muscle testing (MMT8, MMT12)), right- and left-handed grip strength, modified timed up and go (mTUG), two-minute walk test (2MWT)
  a clinician-administered patient-reported tool (IBM Functional Rating Scale (IBMFRS))
  and PROMs including the eating assessment tool (EAT-10), and neuromuscular symptom score (NSS) from 20 participants attending a single specialist myositis clinic in Perth, Australia. Correlation analysis revealed significant correlations between the IBMFRS, MMT8, MMT12, mTUG and 2MWT (p <
  0.05). The NSS strongly correlated with the MMT8, MMT12 and 2MWT (p <
  0.05). Univariate regression analyses revealed that 2MWT, MMT12 and mTUG were significant predictors of the IBMFRS and NSS, and backward stepwise linear regression highlighted that the 2MWT was a significant positive predictor for the IBMFRS (p <
  0.001). Overall, we concluded that the IBMFRS, NSS, 2MWT and mTUG models were the best predictors of patient-perceived physical function in IBM.
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