RESUMEN
OBJECTIVES: Many study groups have developed scores to reflect disease activity. The result of this fragmented process is a multitude of disease activity scores, even for a single disease. We aimed to identify and standardise disease activity scores in rheumatologyMETHODS: We conducted a literature review on disease activity criteria using both a manual approach and in-house computer software (BIBOT) that applies natural language processing to automatically identify and interpret important words in abstracts published in English between 1.1.1975 and 31.12.2018. We selected activity scores with cut-off values divided into four classes (remission and low, moderate and high disease activity). We used a linear interpolation to map disease activity scores to our new score, the AS135, and developed a smartphone application to perform the conversion. RESULTS: A total of 108 activity criteria from various fields were identified, but it was in rheumatology that we found the most pronounced separation into four classes. We built the AS135 score modification for each selected score using a linear interpolation of the existing criteria. The score modification was defined on the interval [0,10], and values of 1, 3 and 5 were used as thresholds. These arbitrary thresholds were then associated with the thresholds of the existing criteria, and an interpolation was calculated, allowing conversion of the existing criteria into the AS135 criterion. Finally, we created a mobile application. CONCLUSIONS: We developed an application for clinicians that enables the use of a single disease activity score for different inflammatory rheumatic diseases using an intuitive scale.
RESUMEN
Objective: The PMR activity score (PMR-AS) includes the CRP value, which may be lacking or invalid owing to anti-IL-6 therapy. Our objective was to develop alternatives to PMR-AS that do not require CRP. Methods: We used the Club Rhumatisme et Inflammation (CRI; 89 patients with PMR) and the Tolerance and Efficacy of tocilizumab iN pOlymyalgia Rheumatica (TENOR; 20 patients with recent-onset PMR naive to glucocorticoid who received three tocilizumab infusions, at weeks 0, 4 and 8, followed by prednisone from weeks 12 to 24) cohorts. In the CRI cohort, we evaluated correlations between PMR-AS items to select the best item for imputing CRP. Then we calculated the PMR-AS with (PMR-AS) and without (clin-PMR-AS) CRP and we used the linear regression between PMR-AS and clin-PMR-AS to obtain CRP-imputed (CRP-imp) PMR-AS. Finally, we evaluated agreement between clin-PMR-AS, CRP-imp PMR-AS, PMR-AS and ESR-PMR-AS in the TENOR cohort during tocilizumab therapy. Results: In the CRI cohort, agreement between PMR-AS and clin-PMR-AS was excellent (κ = 0.90). Linear regression between PMR-AS and clin-PMR-AS [CRP-imp PMR-AS = 1.12(clin-PMR-AS)+0.26] allowed us to build the CRP-imp PMR-AS. Mean (s.d.) values were as follows: 8.40 (9.76) for PMR-AS, 7.24 (8.58) for clin-PMR-AS and 7.84 (9.61) for CRP-imp PMR-AS. CRP-imp PMR-AS agreed more closely with PMR-AS than did clin-PMR-AS. The results in the TENOR cohort confirmed that CRP-imp PMR-AS or ESR-PMR-AS could be used. Conclusion: Alternatives to the PMR-AS obtained without CRP can be used to monitor PMR activity in everyday practice in patients without available CRP values and in those receiving IL-6 antagonist therapy.
