Maxime Verhoeven

71 Validation of a prediction model for insufficient response to MTX of proinflammatory adipokines (e.g., leptin, interleukin-6 and or TNF-alpha) from the adipose tissue. 20 Also the predictive power of smoking was is in accordance with previous studies. 6,7,20,22 So far, several prediction models for MTX non-response have been proposed which resulted in AUCs ranging from 0.65 – 0.85. 6–8,22,23 Different outcome measures at different time points and combination therapies complicate comparison between models and their validation. However, the best-performing models all included clinical parameters and laboratory parameters, which is in line with our findings. 6,8 We also showed that clinical predictors (DAS28 >5.1 and HAQ >0.6) alone classified fewer insufficient responders correctly (67%) compared to the model combining clinical, life style (BMI and smoking) and laboratory predictors (erythrocyte folate), which classified 73% of patients correctly. Most clinical predictors and life style predictors are easy to assess. Erythrocyte folate may not be available in every laboratory, however the assay is relatively easy to assess. 24 Strengths of this study are that both derivation and validation studies were prospectively designed and that patients in the external validation cohort were included from different districts in the Netherlands. Limitations are that the size of the external validation cohort was limited, however the number of cases in both the internal (tREACH) and external (U-Act-Early) cohort were similar. In addition, the model was validated in an MTX monotherapy group, while it was designed in a combination (GC and csDMARD) therapy group. Commonly, however MTX is co-prescribed with a short course of GCs (prednisone) as MTX’s optimal effect ensues after 8-12 weeks. 1 Despite differences in co-medication between the cohorts, the prediction model had similar predictive value and OR for predictors were in the same direction in both cohorts, indicating that co- medication did not affect the prediction of response to MTX. Another limitation is that the smoking status was assessed using questionnaires; possibly biasing the results as smoking behavior could be underreported or underestimated. In future studies, cotinine, the degradation product of nicotine, could be quantified as an objective measure for smoking status which can easily be determined in serum. 25 Furthermore, we showed that the online platform Evidencio provides an easy tool for implementation of the prediction model in clinical practice. Evidencio is freely available so that the data can be uploaded to automatically validate the model in specific cohorts. In addition, using the Evidencio platform clinicians can directly use the model in their practice. When a new patient is diagnosed with RA, patient’s information on DAS28, HAQ, erythrocyte-folate, BMI and smoking status can be provided to Evidencio. Subsequently, a probability of insufficient response to MTX with corresponding specificity, positive predictive value (PPV), sensitivity and negative predictive value (NPV) are provided by the tool and may help clinicians and patients in shared-decision making on step-up treatment with bDMARDs or tsDMARDs. The choice of a cut-off depends on 4