ObjectivesTo identify patient- and disease-related characteristics that make it possible to predict higher disease severity in recent-onset PsA.MethodsWe performed a multicenter observational prospective study (2-year follow-up, regular annual visits). The study population comprised patients aged ≥ 18 years who fulfilled the CASPAR criteria and less than 2 years since the onset of symptoms. Severe disease was defined at each visit as fulfillment of at least 1 of the following criteria: need for systemic treatment, Health Assessment Questionnaire (HAQ) > 0.5, polyarthritis. The dataset contained data for the independent variables from the baseline visit and follow-up visit number 1. These were matched with the outcome measures from follow-up visits 1 and 2, respectively. We trained a logistic regression model and random forest–type and XGBoost machine learning algorithms to analyze the association between the outcome measure and the variables selected in the bivariate analysis.ResultsThe sample comprised 158 patients. At the first follow-up visit, 78.2% of the patients who attended the clinic had severe disease. This percentage decreased to 76.4% at the second visit. The variables predicting severe disease were patient global pain, treatment with synthetic DMARDs, clinical form at diagnosis, high CRP, arterial hypertension, and psoriasis affecting the gluteal cleft and/or perianal area. The mean values of the measures of validity of the machine learning algorithms were all ≥ 80%.ConclusionOur prediction model of severe disease advocates rigorous control of pain and inflammation, also addressing cardiometabolic comorbidities, in addition to actively searching for hidden psoriasis.

Background Very few data are available on predictors of minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis (PsA). Such data are crucial, since the therapeutic measures used to change the adverse course of PsA are more likely to succeed if we intervene early. In the present study, we used predictive models based on machine learning to detect variables associated with achieving MDA in patients with recent-onset PsA. Methods We performed a multicenter observational prospective study (2-year follow-up, regular annual visits). The study population comprised patients aged ≥18 years who fulfilled the CASPAR criteria and less than 2 years since the onset of symptoms. The dataset contained data for the independent variables from the baseline visit and from follow-up visit number 1. These were matched with the outcome measures from follow-up visits 1 and 2, respectively. We trained a random forest–type machine learning algorithm to analyze the association between the outcome measure and the variables selected in the bivariate analysis. In order to understand how the model uses the variables to make its predictions, we applied the SHAP technique. We used a confusion matrix to visualize the performance of the model. Results The sample comprised 158 patients. 55.5% and 58.3% of the patients had MDA at the first and second follow-up visit, respectively. In our model, the variables with the greatest predictive ability were global pain, impact of the disease (PsAID), patient global assessment of disease, and physical function (HAQ-Disability Index). The percentage of hits in the confusion matrix was 85.94%. Conclusions A key objective in the management of PsA should be control of pain, which is not always associated with inflammatory burden, and the establishment of measures to better control the various domains of PsA.

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BackgroundBiologic therapy has changed the prognosis of patients with juvenile idiopathic arthritis (JIA). The aim of this study was to examine the pattern of use, drug survival, and adverse events of biologics in patients with JIA during the period from diagnosis to adulthood.MethodsAll patients included in BIOBADASER (Spanish Registry for Adverse Events of Biological Therapy in Rheumatic Diseases), a multicenter prospective registry, diagnosed with JIA between 2000 and 2015 were analyzed. Proportions, means, and SDs were used to describe the population. Incidence rates and 95% CIs were calculated to assess adverse events. Kaplan-Meier analysis was used to compare the drug survival rates.ResultsA total of 469 patients (46.1% women) were included. Their mean age at diagnosis was 9.4 ± 5.3 years. Their mean age at biologic treatment initiation was 23.9 ± 13.9 years. The pattern of use of biologics during their pediatric years showed a linear increase from 24% in 2000 to 65% in 2014. Biologic withdrawal for disease remission was higher in patients who initiated use biologics prior to 16 years of age than in those who were older (25.7% vs 7.9%, p < 0.0001). Serious adverse events had a total incidence rate of 41.4 (35.2–48.7) of 1000 patient-years. Patients younger than 16 years old showed significantly increased infections (p < 0.001).ConclusionsSurvival and suspension by remission of biologics were higher when these compounds were initiated in patients with JIA who had not yet reached 16 years of age. The incidence rate of serious adverse events in pediatric vs adult patients with JIA treated with biologics was similar; however, a significant increase of infection was observed in patients under 16 years old.Electronic supplementary materialThe online version of this article (10.1186/s13075-018-1728-3) contains supplementary material, which is available to authorized users.

BackgroundThe prognosis of patients with undifferentiated arthritis may vary from self-limited to severe destructive rheumatoid arthritis. Early diagnosis is important, specially in seronegative oligoarthritis in order to start a treatment as early as possible.ObjectivesTo describe the evolution of patients older than 16 years diagnosed with negative HLA B27 seronegative oligoarthritis without axial involvement.MethodsWe retrospectively studied 45 patients (23 women, 22 men) with negative HLA B27 seronegative oligoarthritis without axial involvement who debuted between 1985 and 1990 and who did not meet the criteria for any of the rheumatic diseases at the time of debut: rheumatoid arthritis (AR), psoriatic arthropathy (PsA), spondyloarthropathy, enteropathic arthritis, reactive arthritis, microcrystalline arthritis or connective tissue disease.ResultsThe mean age at onset of oligoarthritis was 42.2 years (range 17–66). The mean follow-up time was 13.7 years (range 1–32). In its evolution, a definitive diagnosis was reached in 21 (46.6%) patients, with the mean time between debut and diagnosis being 5.47 years (range 1–25): 8 AR, 4 APs (3 with involvement) peripheral and 1 mixed), 2 undifferentiated spondyloarthritis, enteropathic arthritis, 5 gouty arthropathies and one SLE. In the case of RA, the diagnosis was made on an average of 4.8 years after the debut (range 1–16); the RF was positive in 4 patients a mean of 7.6 years (range 3–11) after the debut, and the anti-CCP were positive in 3 of the patients with positive RF. Within PsA, one developed skin psoriasis, another psoriatic onicopathy at 4 years after debut and 2 continue without skin involvement but with a family history of psoriasis, all met CASPAR criteria. From the other 24 patients (53.3%), only 3 patients (12.5%) continued to be followed up, with an average of 21.3 years (range 18–26) without meeting the criteria that allow us to define diagnosis. With the rest of the patients (40.8%), followed for an average of 4.5 years, a diagnosis was not achieved by resolution of the clinical picture or loss of follow-up.ConclusionsIn our series, 46.6% of the patients with a diagnosis of negative HLA B27 seronegative oligoarthritis began to meet diagnostic criteria for rheumatic disease after a mean time of 5.47 years, with RA being the most frequent diagnosis (38%) after an average of 4.8 years after the arthritis onset.Reference[1] K.N. Verpoort, H. van Dongen, C.F. Allaart, R.E.M. Toes, F.C. Breedveld, T.W.J. Huinzinga. Undifferentiated arthritis-Disease course assessed in several inception cohorts. Clin Exp Rheumatol2004;22 (Suppl.35):S12-S17.Disclosure of InterestNone declared