Complex Machine-Learning Algorithms and Multivariable Logistic Regression on Par in the Prediction of Insufficient Clinical Response to Methotrexate in Rheumatoid Arthritis

The goals of this study were to examine whether machine-learning algorithms outper-form multivariable logistic regression in the prediction of insufficient response to methotrexate (MTX); secondly, to examine which features are essential for correct prediction; and finally, to in-vestigate whether the best performing model specifically identifies insufficient responders to MTX (combination) therapy. The prediction of insufficient response (3-month Disease Activity Score 28-Erythrocyte-sedimentation rate (DAS28-ESR) > 3.2) was assessed using logistic regression, least absolute shrinkage and selection operator (LASSO), random forest, and extreme gradient boosting (XGBoost). The baseline features of 355 rheumatoid arthritis (RA) patients from the “treatment in the Rotterdam Early Arthritis CoHort” (tREACH) and the U-Act-Early trial were combined for analyses. The model performances were compared using area under the curve (AUC) of receiver operating characteristic (ROC) curves, 95% confidence intervals (95% CI), and sensitivity and specificity. Fi-nally, the best performing model following feature selection was tested on 101 RA patients starting tocilizumab (TCZ)-monotherapy. Logistic regression (AUC = 0.77 95% CI: 0.68–0.86) performed as well as LASSO (AUC = 0.76, 95% CI: 0.67–0.85), random forest (AUC = 0.71, 95% CI: 0.61 = 0.81), and XGBoost (AUC = 0.70, 95% CI: 0.61–0.81), yet logistic regression reached the highest sensitivity (81%). The most important features were baseline DAS28 (components). For all algorithms, models with six features performed similarly to those with 16. When applied to the TCZ-monotherapy group, logistic regression’s sensitivity significantly dropped from 83% to 69% (p = 0.03). In the current dataset, logistic regression performed equally well compared to machine-learning algorithms in the prediction of insufficient response to MTX. Models could be reduced to six features, which are more conducive for clinical implementation. Interestingly, the prediction model was specific to MTX (combination) therapy response

CD8+ T cells in human autoimmune arthritis : The unusual suspects

CD8+ T cells are key players in the body's defence against viral infections and cancer. To date, data on the role of CD8+ T cells in autoimmune diseases have been scarce, especially when compared with the wealth of research on CD4+ T cells. However, growing evidence suggests that CD8+ T-cell homeostasis is impaired in human autoimmune diseases. The contribution of CD8+ T cells to autoimmune arthritis is indicated by the close association of MHC class I polymorphisms with disease risk, as well as the correlation between CD8+ T-cell phenotype and disease outcome. The heterogeneous phenotype, resistance to regulation and impaired regulatory function of CD8+ T cells-especially at the target organ-might contribute to the persistence of autoimmune inflammation. Moreover, newly identified populations of tissue-resident CD8+ T cells and their interaction with antigen-presenting cells might have a key role in disease pathology. In this Review, we assess the link between CD8+ T cells, autoimmune arthritis and the basis of their homeostatic changes under inflammatory conditions. Improved insight into CD8+ T cell-specific pathogenicity will be essential for a better understanding of autoimmune arthritis and the identification of new therapeutic targets