Characteristics associated with the perception of high-impact disease (PsAID ≥4) in patients with recent-onset psoriatic arthritis. Machine learning-based model.

OBJECTIVES: To evaluate which patient and disease characteristics are associated with the perception of high-impact disease (PsAID ≥4) in recent-onset psoriatic arthritis. 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 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. RESULTS: 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%. CONCLUSIONS: Despite the higher weight given to pain when scoring PsAID, we observed a greater influence of physical function on disease impact.

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.

Responses to Ixekizumab in Male and Female Patients with Psoriatic Arthritis: Results from Two Randomized, Phase 3 Clinical Trials.

INTRODUCTION: Differences in psoriatic arthritis (PsA) treatment response between sexes for ixekizumab, an interleukin-17A antagonist, are largely unexplored. This analysis used data from randomized clinical trials (RCTs) evaluating ixekizumab to study differences in treatment response between male and female patients with PsA. METHODS: We used pooled data from patients enrolled in SPIRIT-P1 and SPIRIT-P2 (NCT01695239 and NCT02349295, respectively), phase 3 RCTs evaluating ixekizumab every 4 and 2 weeks in patients with active PsA. Subgroups of patients were defined by sex (male, female). Efficacy was measured by the proportion of male and female patients achieving American College of Rheumatology 20%/50%/70% improvement criteria (ACR20/50/70), minimal disease activity or very low disease activity (MDA/VLDA), and Disease Activity Index for Psoriatic Arthritis (DAPSA) scores representing low disease activity (LDA) or remission through week 156. Changes from baseline in components of the above measures were also assessed through week 156. RESULTS: Compared to male patients at baseline, female patients were older, had higher body mass index and lower C-reactive protein levels, and had worse tender joint count, Health Assessment Questionnaire Disability Index, and Leeds Enthesitis Index scores. Through week 156, female patients in all treatment arms had lower response rates than male patients in all analyzed composite measures (ACR20/50/70; MDA/VLDA; DAPSA LDA/remission), with significant differences observed at multiple timepoints in both ixekizumab treatment arms. Female patients also had smaller numeric changes from baseline in the composite measures' individual components. CONCLUSION: Compared to female patients, male patients had greater response rates in ACR20/50/70, MDA/VLDA, and DAPSA LDA/remission and numerically larger improvements in these measures' individual components, although clinical significance is unclear. Continued efforts to understand sex differences in treatment response may provide insights that can help optimize clinical decision making. TRIAL REGISTRATION: ClinicalTrials.gov identifiers, NCT01695239 and NCT02349295.