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A Beta-binominal Regression Model to Predict Doppler Score on Ultrasound Examination in Patients With Inflammatory Hand Joint Pain

Ciurtin, C; Wyszynski, K; Manson, J; Marra, G; (2015) A Beta-binominal Regression Model to Predict Doppler Score on Ultrasound Examination in Patients With Inflammatory Hand Joint Pain. Presented at: Annual Meeting of the British Society for Rheumatology and British Health Professionals in Rheumatology, Manchester, UK. Green open access

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Abstract

BACKGROUND: US assessment of small joints is essential for the diagnosis of polyarticular inflammatory arthritis and guidance of the therapeutic decisions in patients with established RA. The access to US varies among hospitals and rheumatology services. Even if a considerable proportion of patients with hand joint pain or established RA might have subclinical inflammation, it is not cost-effective to screen them all. The aim of our study was to build a statistical model to assess the influence of several outcome measures (such as number of tender joints (TJC) and swollen joints (SJC) out of 28, global visual analogue scale (GVAS), CRP, ESR, presence of RF and anti-CCP antibodies, disease duration and medication) on the presence of a power Doppler signal at the US examination of hand joints. METHODS: We proposed a regression model to assess the contribution of every outcome measure to the risk of having active joint inflammation as well as predict the power Doppler signal. We excluded patients with a power Doppler signal present in more than 10/22 joints to ensure homogeneity in the data. We conducted a real-life study including 276 patients referred for the suspicion of active joint inflammation (new referrals for the suspicion of inflammatory arthritis, RA patients and patients with other inflammatory rheumatic conditions). We assessed 22 hand joints in every patient, irrespective of their hand symptoms, using the OMERACT scoring system for power Doppler signal. All patients had clinical assessments and laboratory tests within 2 weeks from the US scan. The proposed regression model was based on a beta-binomial distribution (1, disease present; 0, disease absent) for the power Doppler score variable and a mix of main interaction effects for the outcome measures stated above. Negative interaction effects showed that the respective outcome was associated with a lower number of joints with power Doppler signal. RESULTS:Table 1 summarizes the marginal effects of different variables on the number of joints with Doppler signal at the US examination in newly referred patients for the suspicion of inflammatory arthritis or previous diagnosis of RA, and patients with other inflammatory conditions.

Type: Conference item (Presentation)
Title: A Beta-binominal Regression Model to Predict Doppler Score on Ultrasound Examination in Patients With Inflammatory Hand Joint Pain
Event: Annual Meeting of the British Society for Rheumatology and British Health Professionals in Rheumatology
Location: Manchester, UK
Dates: 28 - 30 April 2015
Open access status: An open access version is available from UCL Discovery
DOI: 10.1093/rheumatology/kev090.025
Publisher version: https://doi.org/10.1093/rheumatology/kev090.025
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: ultrasonography, pain, hand joint
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Inflammation
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/1510640
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