Use of the Auto-inflammatory Disease Activity Index to monitor disease activity in patients with colchicine-resistant Familial Mediterranean Fever, Mevalonate Kinase Deficiency, and TRAPS treated with canakinumab - 19/11/22
, Maryam Piram b, Susanne Benseler c, Jasmin B. Kuemmerle-Deschner d, Annette Jansson e, Itzhak Rosner f, Alberto Tommasini g, Sara Murias h, Omer Karadag i, Jeremy Levy j, Suzanne McCreddin k, Marco Migliaccio l, Fabrizio De Benedetti mHighlights |
• | AIDAI is a tool for the assessment of disease activity as reported by patients or parents/guardians across a wide spectrum of auto- inflammatory diseases, it provides information complementary to the physician's clinical assessment and measurements of biomarkers. |
• | This study uses AIDAI to assess evolution of disease activity in patients with crFMF, MKD, or TRAPS treated with canakinumab in a phase III study. The results are compared with clinical and serological assessments of disease activity, and disease-specific cut-off AIDAI values for inactive disease are calculated using a ROC analysis, together with sensitivity and specificity parameter. |
• | This study provides an empirical basis and appropriate cut-off values for using AIDAI to assess disease activity and well-being of patients with crFMF, MKD, and TRAPS. The AIDAI can be used in clinical practice to monitor the evolution of patients and the effect of medications. |
Abstract |
Objectives |
To evaluate the feasibility of the autoinflammatory disease activity index (AIDAI) as a tool to assess disease activity in patients with hereditary recurrent fever syndromes (HRFs) treated with canakinumab.
Methods |
Patients with active colchicine-resistant familial Mediterranean fever (crFMF), mevalonate kinase deficiency (MKD), or tumor necrosis factor receptor-associated periodic syndrome (TRAPS) were enrolled in the phase III CLUSTER study and asked to complete the AIDAI questionnaire daily. All patients included in the analysis were treated with canakinumab, but regimens and periods of treatment varied per study protocol. The AIDAI for each patient was calculated weekly over the first 40 weeks of study, based on the diaries completed over 30 days. Disease-specific cut-off AIDAI values for inactive disease were calculated in a ROC analysis by comparing AIDAI scores with the occurrence of clinically inactive disease, based on the physician global assessments of disease activity and the occurrence of flares.
Results |
Sixty patients with crFMF, 70 with MKD, and 43 with TRAPS were included in the analysis. Median AIDAI scores were high during the first 4 weeks for the three disease cohorts, and decreased afterwards, with some differences between disease cohorts. AIDAI values of 12.0, 9.6 and 15.5 were obtained as the most optimal thresholds to discriminate patients with inactive disease, with sensitivity and specificity values mostly over 75%.
Conclusions |
The AIDAI allows to discriminate between patients with active and inactive HRFs, and can be used in clinical practice to monitor the disease course of patients and the effect of medications.
Le texte complet de cet article est disponible en PDF.Keywords : AIDAI, Canakinumab, IL-1, Recurrent fever syndromes
Plan
Vol 89 - N° 6
Article 105448- novembre 2022 Retour au numéro
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