Confounders contributing to explain the association between sex and disease impact in patients with recent-onset psoriatic arthritis.

OBJECTIVES: To evaluate the effect of potential confounders on the association between sex and disease impact in recent-onset psoriatic arthritis. METHODS: We performed a multicentre 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. Once variables associated with both PsAID ≥4 and sex were selected, those that led to a difference of >10% between the adjusted and crude estimations were identified as potential confounders in the association between sex and PsAID. Lastly, the final multivariate logistic regression model estimating the association between sex and PsAID was defined. RESULTS: The dataset contained 418 observations (158 at baseline, 135 at the first follow-up visit, and 125 at the second visit). The confounders identified in the multivariate model were HAQ, global pain, level of physical activity, and joint pattern at diagnosis. After adjustment for these variables, no statistically significant association was observed between female sex and PsAID ≥4. CONCLUSIONS: The association between female sex and greater disease impact could be explained by the influence of other variables, specifically higher HAQ score, greater intensity of pain, differences in the level of physical activity and in the joint pattern at diagnosis (lower frequency of the spondylitis pattern in women).

Minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis: predictive model based on machine learning.

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.