The prevalence of psoriatic arthritis (PsA) is very heterogeneous. There are no data on its frequency in the general population in Spain. The aim of EPISER2016 study was to estimate the prevalence of PsA in people aged ≥20 years in Spain. Cross-sectional multicenter population-based study. Subjects from all the autonomous communities in Spain were randomly selected using multistage stratified cluster sampling. Participants in each of the municipalities randomly selected for the study were administered a telephone-based questionnaire to screen for the study diseases. If the participant reported being previously diagnosed, rheumatologists from the participant’s reference hospital confirmed the diagnosis based on a review of the clinical history. Subjects not previously diagnosed but whose screening result was positive based on symptoms received a second telephone call from the investigating rheumatologist in order to evaluate the suspicion. If the suspicion remained, an appointment was made at the reference hospital to complete the diagnostic confirmation process according to CASPAR criteria. To calculate the prevalence and its 95% confidence interval (CI), the sample design was taken into account and weighing was calculated considering age, sex and geographic origin. The sample comprised 4916 subjects. The prevalence of PsA was 0.58% (95%CI: 0.38–0.87). All but 1 of the 27 cases (96.30%) had been diagnosed prior to EPISER2016. The prevalence of PsA in Spain was among the highest reported to date, only below that reported in Norway (0.67%) and slightly higher than that reported in Italy (0.42%).

To estimate the prevalence of medical visits due to osteoarticular problems by the adult general population in Spain and its association with sociodemographic, anthropometric and lifestyle variables. Cross-sectional population-based study. Sample with 4916 subjects aged 20 years and over. A telephone questionnaire, with a question about medical visits due to osteoarticular problems, was used. To estimate the prevalence and its 95% confidence interval (95%CI), weights were calculated according to the probability of selection in each of the sampling stages. 28.9% (95%CI: 27.6-30.2) of the people reported having consulted a doctor in the last year because of osteoarticular problems. We observed an association with age, sex, level of education, and body mass index.

The aim was to identify patient- and disease-related characteristics predicting moderate-to-high disease activity in recent-onset psoriatic arthritis (PsA). We performed a multicenter observational prospective study (2-year follow-up, regular annual visits) in patients aged ≥18 years who fulfilled the CASPAR criteria and had less than 2 years since the onset of symptoms. The moderate-to-high activity of PsA was defined as DAPSA > 14. 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. The sample comprised 158 patients. At the first follow-up visit, 20.8% of the patients who attended the clinic had a moderate-to-severe disease. This percentage rose to 21.2% on the second visit. The variables predicting moderate-high activity were the PsAID score, tender joint count, level of physical activity, and sex. The mean values of the measures of validity of the machine learning algorithms were all high, especially sensitivity (98%; 95% CI: 86.89–100.00). PsAID was the most important variable in the prediction algorithms, reinforcing the convenience of its inclusion in daily clinical practice. Strategies that focus on the needs of women with PsA should be considered.

To evaluate which patient and disease characteristics are associated with the perception of high-impact disease (PsAID ≥4) in recent-onset psoriatic arthritis. 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 was generated using data for each patient at the 3 visits (baseline, first year, and second year of follow-up) matched with the PsAID values at each of the 3 visits. PsAID was categorized into two groups (<4 and ≥4). 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. A k-fold cross-validation with k = 5 was performed. The sample comprised 158 patients. Of the patients who attended the clinic, 45.8% scored PsAID ≥4 at baseline; 27.1%, at the first follow-up visit, and in 23.0%, at the second follow-up visit. The variables associated with PsAID ≥4 were, in decreasing order of importance: HAQ, pain, educational level, and physical activity. Higher HAQ (logistic regression coefficient 10.394; IC95% 7.777,13.011), higher pain (5.668; 4.016, 7.320), lower educational level (-2.064; -3.515, -0.613) and high level of physical activity (1.221; 0.158, 2.283) were associated with a higher frequency of PsAID ≥4. The mean values of the measures of validity of the algorithms were all ≥85%. Despite the higher weight given to pain when scoring PsAID, we observed a greater influence of physical function on disease impact.