The performance of matrices in daily clinical practice to predict rapid radiologic progression in patients with early RA
Introduction
One of the most important targets in the management of rheumatoid arthritis (RA) is preventing structural damage and disability in the long term. Many treatment possibilities exist to avert such excessive radiologic progression [1], [2], [3], [4], [5]. However, a physician must decide for each individual patient separately which treatment is optimal. Rapid radiologic progression (RRP) occurs only in a minority of patients with early RA and it is of key importance to detect these patients.
In the last decade, many predicting factors for radiographic progression and RRP were identified, but individually these predictors have only a limited prognostic value [1], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19]. Therefore, composite predicting models, arranged in so-called matrices, were constructed to help the treating physician to detect patients at risk for RRP. Six prediction matrices were identified: the ASPIRE CRP/ESR [20], the BEST [21], the SWEFOT, which has two submodels [22], [23], and the ESPOIR [24] matrices. The ESPOIR matrix was the only model developed in an observational cohort while the others originated from clinical trials. Durnez et al. [25] tested the predictive value of the ASPIRE matrix in a daily-practice early RA cohort and found that it yielded a strong negative predictive value but lacked a positive predictive value. Furthermore, the ASPIRE, the BEST, and the SWEFOT matrices were tested in a cohort of established RA patients and were found to have a limited ability to predict RRP [26]. Fautrel et al. [27] tested the performance of these matrices on the French ESPOIR cohort and concluded that the BEST matrix had the greatest validity to detect RRP in their population.
Our aim was to compare the performance of the existing matrices to reliably predict RRP in an early RA cohort in daily practice in the first year, in the second year, and over a period of 2 years.
Section snippets
Methods
The patient population for this trial was part of an observational cohort at the Department of Rheumatology of the University Hospitals of Leuven. This cohort consisted of consecutive DMARD-naïve, early RA patients, enrolled between 2001 and 2007 [28]. Only patients enrolled in parallel randomized clinical trials (RCT) were excluded. Overall, 74 patients were selected for this study because they had X-rays of hands and feet at baseline, year one, and year two. Patients received initial
Results
Table 2 shows the descriptive statistics at baseline of the total population and of the rapid progressors in the first year, in the second year, and over 2 years. Four patients developed RRP in the first year. Five other patients developed RRP in the second year. Four patients in total had a TSS progression of more than ten points over 2 years. Remarkably, three patients out of these four were common between groups with RRP in the first year and the one with RRP over the two-year study period.
Discussion
In this study, the performance of six matrices to predict RRP in daily clinical practice was analyzed at three time points. The ASPIRE CRP matrix showed the highest discriminating power to predict RRP in our observational cohort of early RA patients. This is in contrast to the findings in the ESPOIR cohort in which the BEST matrix yielded the best predictive value [27]. The overall performance of all matrices was however disappointing in patients with early RA as already demonstrated in
Conclusion
The predicting performance of the six matrices (ASPIRE CRP/ESR, BEST, SWEFOT 1/2, and ESPOIR matrices) to detect risk of RRP was modest at best. The ambiguity in results points to the need to improve the existing matrices or even to build new predicting matrices for use in daily clinical practice. A collaboration to unite several early RA cohorts with various patient profiles would be beneficial to create a prediction matrix in which rheumatologists could trust.
Acknowledgments
We would like to thank Luc Lateur, MD, for analyzing the X-rays of all the patients.
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