Matteson, Eric L

Giant cell arteritis (GCA) is a relapsing large vessel vasculitis with risk of serious ischemic manifestations, including vision loss and vascular damage in the form of large artery stenosis, aneurysms, and dissections. Approximately 50% of patients treated with glucocorticoid (GC) monotherapy and 30% of patients receiving adjunctive therapy with tocilizumab experience disease relapses, often during the first 2 years after diagnosis. Although most relapses in GCA do not involve life‐ or organ‐threatening presentations and can be controlled successfully, frequent relapses may lead to increased prescription of GC and consequent treatment‐related morbidity, in addition to risk of further vascular damage. Emerging data suggest that persistent disease activity may lead to increased vascular morbidity. Additionally, although tocilizumab decreases the frequency of relapses, more than 50% of patients relapse after discontinuation of therapy. Therefore, although interleukin‐6 blockade suppresses disease activity, it does not restore tolerance. In this article, we discuss the practical diagnosis and management of GCA relapses from an expert perspective. Current treatment options for GCA relapses, including those recommended by international guidelines, and novel potential therapies are reviewed.

Abstract Objectives To establish a framework by which experts define disease subsets in systemic sclerosis associated interstitial lung disease (SSc-ILD). Methods A conceptual framework for subclinical, clinical and progressive ILD was provided to 83 experts, asking them to use the framework and classify actual SSc-ILD patients. Each patient profile was designed to be classified by at least four experts in terms of severity and risk of progression at baseline; progression was based on 1-year follow-up data. A consensus was reached if ≥75% of experts agreed. Experts provided information on which items were important in determining classification. Results Forty-four experts (53%) completed the survey. Consensus was achieved on the dimensions of severity (75%, 60 of 80 profiles), risk of progression (71%, 57 of 80 profiles) and progressive ILD (60%, 24 of 40 profiles). For profiles achieving consensus, most were classified as clinical ILD (92%), low risk (54%) and stable (71%). Severity and disease progression overlapped in terms of framework items that were most influential in classifying patients (forced vital capacity, extent of lung involvement on high resolution chest CT [HRCT]); risk of progression was influenced primarily by disease duration. Conclusions Using our proposed conceptual framework, international experts were able to achieve a consensus on classifying SSc-ILD patients along the dimensions of disease severity, risk of progression and progression over time. Experts rely on similar items when classifying disease severity and progression: a combination of spirometry and gas exchange and quantitative HRCT.

Objective To test the ability of machine learning (ML) approaches with clinical and genomic biomarkers to predict methotrexate treatment response in patients with early rheumatoid arthritis (RA). Methods Demographic, clinical, and genomic data from 643 patients of European ancestry with early RA (mean age 54 years; 70% female) subdivided into a training (n = 336) and validation cohort (n = 307) were used. The genomic data comprised 160 single‐nucleotide polymorphisms (SNPs) previously associated with RA or methotrexate metabolism. Response to methotrexate monotherapy was defined as good or moderate by the European Alliance of Associations for Rheumatology (EULAR) response criteria at the 3‐month follow‐up. Supervised ML methods were trained with 5 repeats and 10‐fold cross‐validation using the training cohort. Prediction performance was validated in the independent validation cohort. Results Supervised ML methods combining age, sex, smoking, rheumatoid factor, baseline Disease Activity Score in 28 joints (DAS28) scores and 160 SNPs predicted EULAR response at 3 months with the area under the receiver operating curve of 0.84 ( P = 0.05) in the training cohort and achieved a prediction accuracy of 76% ( P = 0.05) in the validation cohort (sensitivity 72%, specificity 77%). Intergenic SNPs rs12446816, rs13385025, rs113798271, and ATIC (rs2372536) had variable importance above 60.0 and along with baseline DAS28 scores were among the top predictors of methotrexate response. Conclusion Pharmacogenomic biomarkers combined with baseline DAS28 scores can be useful in predicting response to methotrexate in patients with early RA. Applying ML to predict treatment response holds promise for guiding effective RA treatment choices, including timely escalation of RA therapies.