OBJECTIVE: To explore all the common clinical and biological variables that are characteristic of Systemic onset Juvenile Chronic Arthritis (SoJCA) in order to determine which of them are suitable as predictors of a bad articular outcome (persistence of inflammatory symptoms and/or established limitation of the range of motion (ROM). MATERIAL AND METHODS: Clinical charts for 124 SoJCA patients were retrospectively reviewed. From them, 91 were finally included in the study because they had all of the clinical and biological data at disease onset properly recorded. All have been followed for at least 3 years since the beginning of the disease. Data collected at onset, and after 3 and 6 months of the disease included: 1) systemic symptoms; 2) joint involvement, using both the usual articular count and the value of an articular index (Helsinki Index = HI) which intentionally excludes those joints that are not uniformly recorded in clinical charts; and 3) biological data. HI was used to separate the patients into two groups. When applied 3 years after the disease onset, HI > or = 10 represented a bad articular outcome while HI < 10 meant a good prognosis. SPSS for Windows 6.1 was used for both the univariate and multivariate analyses. RESULTS: From the multivariate logistic regression analysis, two different "clusters" of clinical data were found to be the best predictors of a bad articular outcome. A bad prognosis was linked at onset with the presence of generalized lymphadenopathies, age < 8 years and an HI > 6; at six months a bad outcome was linked with the presence of a polyarticular pattern plus hip involvement. CONCLUSION: Clinical parameters at the beginning of the disease were shown to be extremely useful in predicting the articular outcome of SoJCA. Therefore, they could constitute a good instrument to help clinicians tailor the best therapy for their patients.
OBJECTIVE: To explore all the common clinical and biological variables that are characteristic of Systemic onset Juvenile Chronic Arthritis (SoJCA) in order to determine which of them are suitable as predictors of a bad articular outcome (persistence of inflammatory symptoms and/or established limitation of the range of motion (ROM). MATERIAL AND METHODS: Clinical charts for 124 SoJCA patients were retrospectively reviewed. From them, 91 were finally included in the study because they had all of the clinical and biological data at disease onset properly recorded. All have been followed for at least 3 years since the beginning of the disease. Data collected at onset, and after 3 and 6 months of the disease included: 1) systemic symptoms; 2) joint involvement, using both the usual articular count and the value of an articular index (Helsinki Index = HI) which intentionally excludes those joints that are not uniformly recorded in clinical charts; and 3) biological data. HI was used to separate the patients into two groups. When applied 3 years after the disease onset, HI > or = 10 represented a bad articular outcome while HI < 10 meant a good prognosis. SPSS for Windows 6.1 was used for both the univariate and multivariate analyses. RESULTS: From the multivariate logistic regression analysis, two different "clusters" of clinical data were found to be the best predictors of a bad articular outcome. A bad prognosis was linked at onset with the presence of generalized lymphadenopathies, age < 8 years and an HI > 6; at six months a bad outcome was linked with the presence of a polyarticular pattern plus hip involvement. CONCLUSION: Clinical parameters at the beginning of the disease were shown to be extremely useful in predicting the articular outcome of SoJCA. Therefore, they could constitute a good instrument to help clinicians tailor the best therapy for their patients.
Authors: Timothy Beukelman; Nivedita M Patkar; Kenneth G Saag; Sue Tolleson-Rinehart; Randy Q Cron; Esi Morgan DeWitt; Norman T Ilowite; Yukiko Kimura; Ronald M Laxer; Daniel J Lovell; Alberto Martini; C Egla Rabinovich; Nicolino Ruperto Journal: Arthritis Care Res (Hoboken) Date: 2011-04 Impact factor: 4.794
Authors: Xuefeng B Ling; Kenneth Lau; Chetan Deshpande; Jane L Park; Diana Milojevic; Claudia Macaubas; Chris Xiao; Viorica Lopez-Avila; John Kanegaye; Jane C Burns; Harvey Cohen; James Schilling; Elizabeth D Mellins Journal: Clin Proteomics Date: 2010-09-30 Impact factor: 3.988