| Summary: | Abstract Objective To predict the next-year status in patients with rheumatoid arthritis using big data. Methods Joint index (JI) of upper/large (UL), upper/small (US), lower/large (LL), and lower/small (LS) was calculated as the sum of tender and swollen joint counts divided by the number of evaluable joints in each region of interest. Joint index vector V (x, y, z) was defined as x = JIUL + JIUS, y = JILL + JILS, and z = JIUL + JILL − JIUS − JILS. Low disease activity was defined as |Vxy| (= √x2 + y2) ≤ 0.1. Patients with |Vxy| > 0.1 were further classified into three groups: evenly affected (EVN): |z| ≤ 0.2, small joint dominant (SML): z < − 0.2, and large joint dominant (LAR): z > 0.2. To predict the next-year V (x, y, z) of each patient, a transformation matrix was computed from the mean vectors of the EVN, SML, and LAR groups and their translation vectors. Results |Vxy| was correlated with Simplified Disease Activity Index (SDAI) (r = 0.82). Z of mean vector increased as the disability index of the Health Assessment Questionnaire (HAQ-DI) and the Steinbrocker class worsened. The LAR group had the worst HAQ-DI and the second highest SDAI after those in the SML group. Positive predictive value and likelihood ratio in predicting the LAR group were 58.7% and 5.9, respectively. Likelihood ratio was greater with treatment, at 7.2, 7.4, and 8.6 when targeted patients were treated with methotrexate, biologics, and both drugs, respectively. Conclusions Patients with high disease activity and poor functional state were predicted with high probability using joint index vectors.
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