A new assessment tool for ulnar drift in patients with rheumatoid arthritis using pathophysiological parameters of the metacarpophalangeal joint

Toyama, Shogo; Oda, Ryo; Tokunaga, Daisaku; Taniguchi, Daigo; Nakamura, Satoru; Asada, Maki; Fujiwara, Hiroyoshi; Kubo, Toshikazu

doi:10.1080/14397595.2018.1442171

Cited by 5 publications

(14 citation statements)

References 22 publications

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“…UD of the fingers was assessed based on four parameters of the MP joint (deviation, subluxation, reduction, and bone destruction) and was scored from zero to two for each parameter (Toyama et al., 2014, 2019). The degree of UD was evaluated based on the sum of each parameter score; the sum score increased in tandem with worsening of the UD.…”

Section: Methodsmentioning

confidence: 99%

A modified Terrono classification for Type 1 thumb deformity in rheumatoid arthritis: a cross-sectional analysis

Toyama

Oda

Asada

et al. 2019

J Hand Surg Eur Vol

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The most common thumb deformity in rheumatoid arthritis is Nalebuff Type 1 deformity (boutonniere deformity). Type 1 deformity severely impairs hand function, and this impairment is evaluated by the Terrono classification. In some cases, the Terrono classification incorrectly categorizes advanced thumb deformity into earlier stages. We modified the Terrono classification by assessing the active range of motion of the interphalangeal joint prior to assessing the passive range of movement of the metacarpophalangeal joint. An active range of movement of the interphalangeal joint was strongly correlated with hand function. In 55 hands that we treated between 2004 and 2015, we compared the modified classification with the original Terrono classification. Our modified classification could detect advanced deformity earlier, and was more strongly correlated with hand function. Additionally, correlation analysis showed that advanced Type 1 deformity should be treated first, even in cases with severe ulnar drift. Our results suggest that the modified classification may benefit the treatment of Type 1 deformity, including joint-preserving surgery. Level of evidence: III

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Section: Methodsmentioning

confidence: 99%

A modified Terrono classification for Type 1 thumb deformity in rheumatoid arthritis: a cross-sectional analysis

Toyama

Oda

Asada

et al. 2019

J Hand Surg Eur Vol

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“…The ulnar drift total score was calculated by adding scores for the four parameters for the metacarpophalangeal joints (deviation, subluxation, reduction, and bone destruction) and was scored from 0 to 2 for each parameter. The severity of ulnar drift was evaluated according to the sum of each parameter's score, and scores increased with worsening of the ulnar drift [8].…”

Section: Deformity Evaluationmentioning

confidence: 99%

“…Another study described severity of deformity from a 5-year observation of established rheumatoid arthritis patients and found that overall deformities worsened with time [4]. The authors evaluated thumb deformity using the Nalebuff classification system (type 1-6) [5], finger swan-neck deformity using the Nalebuff classification (type 1-4) [6], finger boutonnière deformity using the Nalebuff classification (stage 1-3), and ulnar drift by the authors' new method [7], which quantified ulnar drift by evaluating joint parameters in an extended cohort [8].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive assessment of rheumatoid hand using cluster analysis involving parameter analysis of three major hand deformities: a cross-sectional and longitudinal study

Toyama¹,

Oda²,

Tokunaga³

et al. 2020

Preprint

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Background Although drug therapy in rheumatoid arthritis has recently improved, treating established rheumatoid hand consisting of three major deformities—thumb deformity, finger deformities, and ulnar drift—remains a challenge. Underlying complex pathophysiology makes it difficult to comprehensively understand these deformities, and comprehensive assessment methods require accumulated skill and long learning curves. We aimed to establish an easier composite method of understanding the pathophysiology using data from our cohort and cluster analysis. Methods We established a rheumatoid hand cohort in 2004, and clinically evaluated 134 hands (67 patients). We repeated the evaluations in 2009 and 2015, which provided data for 297 hands and 43 hands for cross-sectional and longitudinal analyses, respectively. Thumb deformities, finger deformities (swan-neck and boutonnière deformity), and ulnar drift were semi-quantified and entered as parameters into a two-step cluster (cross-sectional) analysis. Parameter distributions were considered to clarify each cluster’s characteristics. Next, hands with cluster change over the study period were reviewed to clarify deformity progression (longitudinal analysis). We also performed a stratified analysis between the clusters and the affected period to clarify whether long affected period plays an important role in deformity progression. Results We identified seven clusters: cluster 1: mild finger deformities; cluster 2: type 1 thumb deformity; cluster 3: type 2 thumb deformity and severe ulnar drift; cluster 4: type 3 or 4 thumb deformity and low or moderate swan-neck deformity; cluster 5: various thumb deformities and severe boutonnière deformity; cluster 6: type 1 thumb deformity and severe swan-neck deformity; and cluster 7: type 6 thumb deformity. The ulnar drift parameters were equally distributed among the clusters except for cluster 3. Larger cluster numbers generally indicated lower function. At the study endpoint, cluster 1 had changed mainly to cluster 2 or 4, cluster 2 changed to cluster 3, and cluster 7 was considered the final morphology with the lowest hand function. Patients affected for > 30 years had increased risk of rapid disability progression. Conclusions Our comprehensive assessment indicated seven deformity patterns and a progressive course in rheumatoid hand. Using patterns may provide rheumatologists with easier information for practical interventions and to determine functional prognosis.

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“…In 2015, we completed a nal follow-up survey of 37 patients with 63 hands (2 men and 35 women) ( Fig. 1) [8].…”

Section: Patientsmentioning

confidence: 99%

“…Another study described the severity of deformity in a 5-year observation of established rheumatoid arthritis patients and found that overall deformities worsened with time [4]. The authors evaluated thumb deformity using the Nalebuff classi cation system (type 1-6) [5], nger swan-neck deformity using the Nalebuff classi cation (type 1-4) [6], nger boutonnière deformity using the Nalebuff classi cation (stage 1-3), and ulnar drift by the authors' own method [7], which quanti ed ulnar drift by evaluating joint parameters in an extended cohort [8].…”

Section: Introductionmentioning

confidence: 99%

Eleven-year alterations in hand deformities in rheumatoid arthritis by comprehensive assessment using cluster analysis and analysis of covariance

Toyama

Tokunaga

Tsuchida

et al. 2020

Preprint

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BackgroundAlthough drug therapy in rheumatoid arthritis has recently improved, treating established rheumatoid hand, consisting of three major deformities—thumb deformity, finger deformities, and ulnar drift— remains a challenge. Underlying complex pathophysiology makes it difficult to comprehensively understand these deformities, and comprehensive assessment methods require accumulated skill and long learning curves. We aimed to establish an easier composite method of understanding the pathophysiology and to elucidate alterations in deformities.MethodsWe established a rheumatoid hand cohort in 2004 and clinically evaluated 134 hands (67 patients). We repeated the evaluations in 2009 (100 hands in 52 patients) and 2015 (63 hands in 37 patients) after case exclusion. Thumb deformities, finger deformities (swan-neck and boutonnière deformity), and ulnar drift were semi-quantified and entered as parameters into a two-step cluster (cross-sectional) analysis for the data in 2004. The parameters in each cluster were plotted at each evaluation point. Two-way analysis of covariance was performed to examine whether differences existed between evaluation points and clusters for the deformity parameters.ResultsFive clusters were most appropriate to clarify each deformity: cluster 1: minimal deformity; cluster 2: type 1 thumb deformity; cluster 3: thumb deformity and severe boutonnière deformity; cluster 4: type 2 or 3 thumb deformity and severe ulnar drift; cluster 5: thumb deformity and severe swan-neck deformity. Clusters 1 and 2 had higher function than cluster 5, and cluster 3 had moderate function. Clusters 1–4 had similar disease duration, and showed different paths of deformity progression from disease onset. We considered clusters 1 and 2 as a conservative subset and clusters 3, 4, and 5 as a progressive subset. Over time, thumb deformity type altered to other types, and swan-neck deformity worsened significantly.ConclusionsOur comprehensive assessment indicated five deformity patterns and a progressive course in rheumatoid hand. Knowledge of the characteristics of the progressive subset may allow rheumatologists to more easily determine practical interventions and functional prognosis.

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A new assessment tool for ulnar drift in patients with rheumatoid arthritis using pathophysiological parameters of the metacarpophalangeal joint

Abstract: Our UD evaluation appeared to be simple and related to function. Additionally, it enables dividing UD hands into five stages. Thus, our assessment should be beneficial compared to the Fearnley classification in considering treatments of UD.

Cited by 5 publications

References 22 publications

A modified Terrono classification for Type 1 thumb deformity in rheumatoid arthritis: a cross-sectional analysis

A modified Terrono classification for Type 1 thumb deformity in rheumatoid arthritis: a cross-sectional analysis

Comprehensive assessment of rheumatoid hand using cluster analysis involving parameter analysis of three major hand deformities: a cross-sectional and longitudinal study

Eleven-year alterations in hand deformities in rheumatoid arthritis by comprehensive assessment using cluster analysis and analysis of covariance

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