Abstract
ObjectiveTo predict the next-year status in patients with rheumatoid arthritis using big data.MethodsJoint 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.ConclusionsPatients with high disease activity and poor functional state were predicted with high probability using joint index vectors.
Highlights
It is difficult to forecast which joints will be involved or intact in patients with rheumatoid arthritis (RA) in the long term, the ability to predict the next-year distribution of affected joints for individual patients would be useful for choosing the correct therapeutic option.Nishiyama et al J Big Data (2018) 5:37A number of factors affect the development of RA, and several markers including genetic information have been investigated as potential means of distinguishing patients with preferable outcomes from others; confirmed evidence regarding personalized therapy is still limited [1, 2]
A tendency of |Vxy| to increase over time was observed, this increase was nonlinear compared to the increase in z value as physical function deteriorated (Fig. 3a, b). |Vxy| and z stayed close to their original values regardless of disease duration (Fig. 3c). |Vxy| increased as the disease activity rose, while z value did not (Fig. 3d)
Several articular indices have been developed, including the Ritchie articular index, which grades patients according to the severity of pain in tender joints [9], the Lansbury articular index, which assigns a weighted grade according to the joint surface area [10], and the 68/66 and 28 simple inflamed joint count methods [11, 12]
Summary
It is difficult to forecast which joints will be involved or intact in patients with rheumatoid arthritis (RA) in the long term, the ability to predict the next-year distribution of affected joints for individual patients would be useful for choosing the correct therapeutic option.Nishiyama et al J Big Data (2018) 5:37A number of factors affect the development of RA, and several markers including genetic information have been investigated as potential means of distinguishing patients with preferable outcomes from others; confirmed evidence regarding personalized therapy is still limited [1, 2]. Since it is hard to predict single joint involvement separately, we tried to forecast the proportions of affected joints in four joint categories: upper/small, upper/ large, lower/small, and lower/large. We have previously reported that upper/small joints affected activity-related HAQ, whereas large-joint involvement was associated with an increase in both activity-related and damage-related HAQ [4]. These findings indicate that we should discriminate large joints from small joints as well as upper joints from lower joints
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