We developed and independently validated a rheumatoid arthritis (RA) mortality prediction model using the machine learning method Random Survival Forests (RSF). Two independent cohorts from Madrid (Spain) were used: the Hospital Clínico San Carlos RA Cohort (HCSC-RAC; training; 1,461 patients), and the Hospital Universitario de La Princesa Early Arthritis Register Longitudinal study (PEARL; validation; 280 patients). Demographic and clinical-related variables collected during the first two years after disease diagnosis were used. 148 and 21 patients from HCSC-RAC and PEARL died during a median follow-up time of 4.3 and 5.0 years, respectively. Age at diagnosis, median erythrocyte sedimentation rate, and number of hospital admissions showed the higher predictive capacity. Prediction errors in the training and validation cohorts were 0.187 and 0.233, respectively. A survival tree identified five mortality risk groups using the predicted ensemble mortality. After 1 and 7 years of follow-up, time-dependent specificity and sensitivity in the validation cohort were 0.79–0.80 and 0.43–0.48, respectively, using the cut-off value dividing the two lower risk categories. Calibration curves showed overestimation of the mortality risk in the validation cohort. In conclusion, we were able to develop a clinical prediction model for RA mortality using RSF, providing evidence for further work on external validation.

The objective of the study was to develop evidence-based and practical recommendations for the detection and management of comorbidity in patients with rheumatoid arthritis (RA) in daily practice. We used a modified RAND/UCLA methodology and systematic review (SR). The process map and specific recommendations, based on the SR, were established in discussion groups. A two round Delphi survey permitted (1) to prioritize the recommendations, (2) to refine them, and (3) to evaluate their agreement by a large group of users. The recommendations cover: (1) which comorbidities should be investigated in clinical practice at the first and following visits (including treatments, risk factors and patient's features that might interfere with RA management); (2) how and when should comorbidities and risk factors be investigated; (3) how to manage specific comorbidities, related or non-related to RA, including major adverse events of RA treatment, and to promote health (general and musculoskeletal health); and (4) specific recommendations to assure an integral care approach for RA patients with any comorbidity, such as health care models for chronic inflammatory patients, early arthritis units, relationships with primary care, specialized nursing care, and self-management. These recommendations are intended to guide rheumatologists, patients, and other stakeholders, on the early diagnosis and management of comorbidity in RA, in order to improve disease outcomes.

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