Moderate-High Disease Activity in Patients with Recent-Onset Psoriatic Arthritis-Multivariable Prediction Model Based on Machine Learning.

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.

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.

Severe Disease in Patients With Recent-Onset Psoriatic Arthritis. Prediction Model Based on Machine Learning.

Objectives: To identify patient- and disease-related characteristics that make it possible to predict higher disease severity in 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. 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. Results: The 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%. Conclusion: Our prediction model of severe disease advocates rigorous control of pain and inflammation, also addressing cardiometabolic comorbidities, in addition to actively searching for hidden psoriasis.

Association of Platelet Binding to Lymphocytes with B Cell Abnormalities and Clinical Manifestations in Systemic Lupus Erythematosus.

Systemic lupus erythematosus (SLE) is an autoimmune disease associated with the polyclonal activation of B lymphocytes and the production of autoantibodies that cause immune complex-related inflammation. Immunological factors derived from platelets modulate B cell function in SLE disease. However, platelets do not only modify the immune system by soluble factors. The binding of platelets to lymphocytes can modulate immune response. Thus, we speculate that the binding of platelets to lymphocytes in SLE patients may play a role in abnormal B lymphocyte response and the pathogenesis of SLE. We observed that levels of lymphocytes with bound platelets were higher in SLE patients than in healthy donors (HD). In SLE patients, the percentage of B lymphocytes with bound platelets positively correlated with plasmatic levels of IgG, IgA, IL-10, and soluble CD40L and negatively correlated with IgM levels, though not in HD. Preswitched memory B lymphocytes were the subpopulation with more bound platelets. Lymphocytes with bound platelets from both HD and SLE patients had major levels of CD86 and BAFFR and a greater production of IL-10 than lymphocytes without bound platelets. However, only B lymphocytes with bound platelets from SLE patients had increased levels of IgG and IgA on their surface. SLE patients with a suggestive renal manifestation had the highest levels of B and T lymphocytes with bound platelets. These results suggest that the binding of platelets to lymphocytes plays a role in SLE disease and that controlling this binding may be a promising therapeutic approach.

Spanish Registry of Recent-onset Psoriatic Arthritis (REAPSER study): Aims and methodology. / Registro Español de Artritis Psoriásica de Reciente Comienzo (estudio REAPSER). Objetivos y metodología.

AIMS: To describe the methodology of REAPSER (Spanish Registry of Recent-onset Psoriatic Arthritis), its strengths and limitations. The aim of this study is to identify prognostic factors for the clinical and radiographic course in a cohort of patients with psoriatic arthritis (PsA) diagnosed within 2years of symptom evolution. METHODS: Multicenter, observational and prospective study (with 2-year follow-up including annual visits). Baseline visit intended to reflect patient situation before the disease course was modified by treatments prescribed in rheumatology departments. Patients were invited to participate consecutively in one of their routine visits to the rheumatologist. 211 patients were included. Following data were collected: sociodemographic variables; employment situation; family history; personal history and comorbidities; anthropometric data; lifestyle; use of healthcare services; clinical situation at the time of PsA diagnosis; joint involvement and spinal pain; pain and overall assessment; enthesitis, dactylitis and uveitis; skin and nail involvement; functional situation and quality of life; radiographic evaluation; analytical determinations; treatment; axial and peripheral flare-ups. CONCLUSIONS: The REAPSER study includes a cohort of patients with recent-onset PsA, before the disease course was modified by disease-modifying antirheumatic drugs prescribed in rheumatology departments. Exhaustive information collected in each visit is expected to be an important data source for future analysis.