Recent-onset Psoriatic Arthritis Research Articles

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.

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

ObjectivesTo evaluate which patient and disease characteristics are associated with the perception of high-impact disease (PsAID ≥4) in recent-onset psoriatic arthritis. 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. 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. ResultsThe 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%. ConclusionsDespite 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

BackgroundVery 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.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. 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.ResultsThe 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%.ConclusionsA 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.

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.

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

To evaluate the effect of potential confounders on the association between sex and disease impact in recent-onset psoriatic arthritis. 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. 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. 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).

Spanish Registry of Recent-onset Psoriatic Arthritis (REAPSER study): Aims and methodology.

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. 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. 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.

Spanish Registry of Recent-onset Psoriatic Arthritis (REAPSER study): Aims and methodology

AimsTo 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. MethodsMulticenter, 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. ConclusionsThe 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.

Immune-Mediated Inflammation Promotes Subclinical Atherosclerosis in Recent-Onset Psoriatic Arthritis Patients without Conventional Cardiovascular Risk Factors.

Studies on the inflammatory burden in recent-onset psoriatic arthritis (PsA) patients without conventional cardiovascular risk factors (CVRFs) are not available. This preliminary study focuses on cardiovascular risk in cutaneous psoriasis (CPs) and recent-onset PsA patients. Blood biochemistry (glucose, cholesterol, uric acid, lipid profile and apolipoprotein B) was analyzed using standard kits. Proatherogenic inflammation markers, C-reactive protein (CRP) and interleukin-6 (IL-6), and endothelial activators monocyte chemoattractant protein-1 (MCP-1) and soluble intercellular adhesion molecule-1 (sICAM-1), were determined by enzyme-linked immunosorbent assay. Ultrasound images allowed measuring carotid intima–media thickness (cIMT). Our study first shows an increase in cIMT, and in serum levels of sICAM-1 and CRP in recent-onset PsA patients not presenting conventional CVRFs over the non-medicated time-period, from disease diagnosis to the beginning of pharmacological treatment, compared with healthy subjects. The outcome highlights the importance of monitoring serum level of sICAM1, CRP, and cIMT, and the value of primary prevention in psoriatic patients even with no history of cardiovascular events.

Is There Subclinical Synovitis in Early Psoriatic Arthritis? A Clinical Comparison With Gray‐Scale and Power Doppler Ultrasound

ObjectiveArthritis activity assessments in psoriatic arthritis (PsA) have traditionally relied on tender and swollen joint counts, but in rheumatoid arthritis, multiple studies have demonstrated subclinical inflammation using modern imaging. The aim of this study was to compare clinical examination and ultrasound (US) findings in an early PsA cohort.MethodsForty-nine disease-modifying antirheumatic drug–naive patients with recent-onset PsA (median disease duration 10 months) underwent gray-scale (GS) and power Doppler (PD) US of 40 joints plus tender and swollen joint counts of 68/66 joints. GS and PD were scored on a 0–3 semiquantitative scale for each joint. Clinically active joints were defined as tender and/or swollen and US active joints were defined as a GS score ≥2 and/or a PD score ≥1.ResultsThe most common sites for subclinical synovitis were the wrist (30.6%), knee (21.4%), metatarsophalangeal (MTP) joints (26.5–33.7%), and metacarpophalangeal joints (10.2–19.4%). Excluding MTP joints and ankles, 37 (75.5%) of 49 patients had subclinical synovitis with a median of 3 (interquartile range [IQR] 1–4) joints involved. In contrast, clinical overestimation of synovitis occurred most commonly at the shoulder (38%) and ankle (28.6%). Twelve of 49 patients were classified clinically as having oligoarthritis; of these, subclinical synovitis identified 8 (75%) as having polyarthritis with an increase in their median joint count from 3 (IQR 1–4) to 6 (IQR 5–7).ConclusionThis study has demonstrated that subclinical synovitis, as identified by US, is very common in early PsA and led to the majority of oligoarthritis patients being reclassified as having polyarthritis. Further research is required into the relationship of such subclinical synovitis to structural progression.

Association between environmental factors and onset of psoriatic arthritis in patients with psoriasis

To investigate the association between potential environmental exposures and the development of psoriatic arthritis (PsA) in patients with psoriasis. In this case-control study, the cases were patients with recent-onset PsA. The controls were psoriasis patients without arthritis. The occurrence of the following environmental exposures was recorded through a standardized questionnaire: smoking, alcohol consumption, infections, injuries, physically demanding occupational tasks, stressful life events, vaccinations, and female hormonal exposures. The association between each exposure to environmental events and disease status was assessed through logistic regression after adjustment for age, sex, education level, and duration and severity of psoriasis. There were 159 subjects in each group. The following exposures remained significantly associated with PsA following multivariate logistic regression: lifting cumulative loads of at least 100 pounds/hour (odds ratio [OR] 2.8, 95% confidence interval [95% CI] 1.51-5.05), infections that required antibiotics (OR 1.7, 95% CI 1.00-2.77), smoking (OR 0.6, 95% CI 0.36-0.89), and injuries (OR 2.1, 95% CI 1.11-4.01). The results were not appreciably changed with the inclusion of each of these factors in a single regression model; however, the level of significance for injuries had become borderline. No association was found between PsA and alcohol consumption, vaccination, stressful life events, and female hormonal exposures. Lifting heavy loads and infections that required antibiotics were associated with the occurrence of arthritis among patients with psoriasis. There was an inverse association between smoking and PsA. Further studies are necessary to determine whether these and other environmental factors are moderated by predisposing genetic factors.

Utilidad y aplicación en la práctica clínica de los criterios CASPAR

Several classification criteria for psoriatic arthritis have been proposed in the literature but it is still unclear which one of them best represents the diseases’ ample spectrum. None of these classification criteria have been universally accepted. New classification criteria (CASPAR) have been recently published. Their application is simple, fast and easy to perform. In addition, they show two important qualities. One is that they allow for the diagnosis of psoriatic arthritis even when there is no skin disease present at the moment of diagnosis. The other is is that it enables us to classify a patient as having psoriatic arthritis in spite of a positive rheumatoid factor. The CASPAR criteria have a sensitivity of 91.4% and a specificity of 98.7%. It seems, in contrast, that it is not as high for recent-onset psoriatic arthritis. Therefore establishing the definition of inflammatory arthritis becomes paramount.