Blood-Based Immune Profiling Combined with Machine Learning Discriminates Psoriatic Arthritis from Psoriasis Patients

Psoriasis (Pso) is a chronic inflammatory skin disease, and up to 30% of Pso patients develop psoriatic arthritis (PsA), which can lead to irreversible joint damage. Early detection of PsA in Pso patients is crucial for timely treatment but difficult for dermatologists to implement. We, therefore, aimed to find disease-specific immune profiles, discriminating Pso from PsA patients, possibly facilitating the correct identification of Pso patients in need of referral to a rheumatology clinic. The phenotypes of peripheral blood immune cells of consecutive Pso and PsA patients were analyzed, and disease-specific immune profiles were identified via a machine learning approach. This approach resulted in a random forest classification model capable of distinguishing PsA from Pso (mean AUC = 0.95). Key PsA-classifying cell subsets selected included increased proportions of differentiated CD4+CD196+CD183-CD194+ and CD4+CD196-CD183-CD194+ T-cells and reduced proportions of CD196+ and CD197+ monocytes, memory CD4+ and CD8+ T-cell subsets and CD4+ regulatory T-cells. Within PsA, joint scores showed an association with memory CD8+CD45RA-CD197- effector T-cells and CD197+ monocytes. To conclude, through the integration of in-depth flow cytometry and machine learning, we identified an immune cell profile discriminating PsA from Pso. This immune profile may aid in timely diagnosing PsA in Pso

Impairment in work and activities of daily life in patients with psoriasis:results of the prospective BioCAPTURE registry

Background: Little is known about the extent of impairments in work and activities of daily life (ADL) in patients with psoriasis, and the influence of contextual factors such as disease-related characteristics and treatment. Therefore, this study aimed to assess these impairments in patients with psoriasis who started using biologicals/small molecule inhibitors. Methods: Using data from the prospective BioCAPTURE registry, we collected patient, disease, and treatment parameters, as well as work/ADL impairments at baseline, 6 and 12 months. Changes in impairment parameters and correlations between impairment and patient/disease characteristics were assessed using generalized estimating equations. Results: We included 194 patients in our analysis. After biological initiation, disease activity decreased significantly (PASI 11.2 at baseline versus 3.9 at 12 months, p &lt; 0.001). Work-for-pay in this cohort was lower than in the Dutch general population (53% versus 67%, p = 0.01). In patients who had work-for-pay, presenteeism improved over time (5% at baseline versus 0% at 12 months, p = 0.04). Up to half of the patients reported impairments in ADL, which did not change over time. Associations between impairments and contextual factors varied, but all impairments were associated with worse mental/physical general functioning. Conclusion: Patients with psoriasis using biologicals are less likely to have work-for-pay. Treatment improves the work productivity of employed patients, but we were unable to detect changes in ADL performance.</p

Impairment in work and activities of daily life in patients with psoriasis: results of the prospective BioCAPTURE registry

Background: Little is known about the extent of impairments in work and activities of daily life (ADL) in patients with psoriasis, and the influence of contextual factors such as disease-related characteristics and treatment. Therefore, this study aimed to assess these impairments in patients with psoriasis who started using biologicals/small molecule inhibitors. Methods: Using data from the prospective BioCAPTURE registry, we collected patient, disease, and treatment parameters, as well as work/ADL impairments at baseline, 6 and 12 months. Changes in impairment parameters and correlations between impairment and patient/disease characteristics were assessed using generalized estimating equations. Results: We included 194 patients in our analysis. After biological initiation, disease activity decreased significantly (PASI 11.2 at baseline versus 3.9 at 12 months, p < 0.001). Work-for-pay in this cohort was lower than in the Dutch general population (53% versus 67%, p = 0.01). In patients who had work-for-pay, presenteeism improved over time (5% at baseline versus 0% at 12 months, p = 0.04). Up to half of the patients reported impairments in ADL, which did not change over time. Associations between impairments and contextual factors varied, but all impairments were associated with worse mental/physical general functioning. Conclusion: Patients with psoriasis using biologicals are less likely to have work-for-pay. Treatment improves the work productivity of employed patients, but we were unable to detect changes in ADL performance

Beliefs About Medicines in Patients with Psoriasis Treated with Methotrexate or Biologics: A Cross-sectional Survey Study

Methotrexate (MTX) and biologics are frequently used treatments for psoriasis. Exploring patients’ beliefs about their treatment may help to elucidate patients’ attitudes towards these therapies. A cross-sectional survey was conducted using the Beliefs about Medicines Questionnaire-Specific (BMQ-Specific) in patients treated with methotrexate or biologics. BMQ-Specific scores (Necessity and Concerns scales) were calculated and patients were classified as “accepting”, “indifferent”, “ambivalent” or “sceptical” towards their treat­ment. Biologics users scored higher on the Necessity scale than did methotrexate users. Both groups had lower Concerns scores than Necessity scores. A high Necessity scale was associated with a low Psoriasis Area and Severity Index score in both groups and long treatment duration in the methotrexate group. Although this study cannot make a direct comparison, it was observed that most patients on biologics could be classified as “accepting” (59%), and most patients on MTX could be classified as “indifferent” (47%). In conclusion, the BMQ-Specific is useful to identify patients with a sceptical, ambivalent or indifferent profile. These profiles may negatively influence patient’s attitude towards their medication

Is Telemedicine Suitable for Patients with Chronic Inflammatory Skin Conditions? A Systematic Review

Telemedicine, the provision of remote healthcare, has gained prominence, accelerated by the COVID-19 pandemic. It has the potential to replace routine in-person follow-up visits for patients with chronic inflammatory skin conditions. However, it remains unclear whether telemedicine can effectively substitute in-person consultations for this patient group. This systematic review assessed the effectiveness and safety of telemedicine compared with traditional in-person care for chronic inflammatory skin diseases. A comprehensive search in various databases identified 11 articles, including 5 randomized controlled trials (RCTs) and 1 clinical controlled trial (CCT). These studies evaluated telemedicine’s impact on patients with psoriasis and atopic dermatitis, with varying methods like video consultations and digital platforms. The findings tentatively suggest that telemedicine does not seem to be inferior compared with in-person care, particularly in terms of condition severity and quality of life for patients with chronic inflammatory skin diseases. However, these results should be interpreted with caution due to the inherent uncertainties in the evidence. There are indications that telemedicine can offer benefits such as cost-effectiveness, time savings, and reduced travel distances, but it is important to recognize these findings as preliminary, necessitating further validation through more extensive research

Blood-Based Immune Profiling Combined with Machine Learning Discriminates Psoriatic Arthritis from Psoriasis Patients

Psoriasis (Pso) is a chronic inflammatory skin disease, and up to 30% of Pso patients develop psoriatic arthritis (PsA), which can lead to irreversible joint damage. Early detection of PsA in Pso patients is crucial for timely treatment but difficult for dermatologists to implement. We, therefore, aimed to find disease-specific immune profiles, discriminating Pso from PsA patients, possibly facilitating the correct identification of Pso patients in need of referral to a rheumatology clinic. The phenotypes of peripheral blood immune cells of consecutive Pso and PsA patients were analyzed, and disease-specific immune profiles were identified via a machine learning approach. This approach resulted in a random forest classification model capable of distinguishing PsA from Pso (mean AUC = 0.95). Key PsA-classifying cell subsets selected included increased proportions of differentiated CD4+CD196+CD183-CD194+ and CD4+CD196-CD183-CD194+ T-cells and reduced proportions of CD196+ and CD197+ monocytes, memory CD4+ and CD8+ T-cell subsets and CD4+ regulatory T-cells. Within PsA, joint scores showed an association with memory CD8+CD45RACD197- effector T-cells and CD197+ monocytes. To conclude, through the integration of in-depth flow cytometry and machine learning, we identified an immune cell profile discriminating PsA from Pso. This immune profile may aid in timely diagnosing PsA in Pso