Psoriasis/Psoriatic Arthritis Patients’ Long-term Treatment Patterns and Adherence to Systemic Treatments Monitoring Recommendations

Krantz, Alfred; Carrero, Juan Jesús; Yang, Yuanhang; Schalin, Lovisa; Lysell, Josefin; Mazhar, Faizan

doi:10.2340/actadv.v103.6505

Cited by 5 publications

(1 citation statement)

References 27 publications

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“…Specifically for psoriasis, increased disease severity [15] and decreased quality of life [16] are associated with lower adherence. For psoriasis topical treatment in particular, low adherence values have been reported, ranging from 39% to 73% [17][18][19], with varying degrees of adherence across different treatments [20]. In a review comprising 20 studies in relation to assessment methods of psoriasis topical treatment adherence, adherence was highly variable [21].…”

Section: Introductionmentioning

confidence: 99%

Influence of Clinical and Psychosocial Factors on the Adherence to Topical Treatment in Psoriasis

Teixeira,

Gaio

et al. 2024

Healthcare

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(1) Background: Psoriasis is a common chronic inflammatory skin disease with different manifestations, affecting the quality of life at social, emotional, and professional dimensions and requiring long-term treatment. This study aimed to investigate the effect of psychosocial and clinical factors on adherence to topical treatment in psoriasis. (2) Methods: Self-reported measures and weighing the medicines were used to assess adherence. Psychopathological symptoms were measured using the Brief Symptoms Inventory (BSI). Social and clinical factors were assessed by a sociodemographic and clinical questionnaire. Adherence to treatment with topical medication was assessed using a sample of 102 psoriasis patients. (3) Results: The explanatory models of adherence to topical treatment in psoriasis translated into positive associations between adherence and the education level (higher education) (p = 0.03; φ = 0.23), the single-family household (p = 0.01; φ = 0.44), active employment status (p = 0.05; φ = −0.19), familiar history of psoriasis (p = 0.04; φ = −0.21), and the presence of obsessive-compulsive symptoms (p = 0.01; d = 0.29). (4) Conclusions: In patients who present the characteristics identified that influence non-adherence, instructions should be reinforced to increase adherence. The experimental mortality (39.6%) reduced the sample size, representing a limitation of the study.

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Section: Introductionmentioning

confidence: 99%

Influence of Clinical and Psychosocial Factors on the Adherence to Topical Treatment in Psoriasis

Teixeira,

Gaio

et al. 2024

Healthcare

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Treatment patterns and factors associated with discontinuation of monoclonal antibodies

Alkaabi,

Rabbani,

Rao

et al. 2024

SAGE Open Medicine

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Background: Biological agents have revolutionized care in specialties such as oncology, immunology, infectious diseases, and genetic disorders, offering targeted actions on specific molecules or select immune cells. Monoclonal antibodies, known for their high specificity and precision, represent one of the most significant and rapidly expanding categories of these agents. Understanding the drug utilization patterns of monoclonal antibodies is crucial to ensure their optimal use, especially given their high cost and potential adverse effects. Methods: This analytical cross-sectional study was conducted in a secondary hospital in the United Arab Emirates. Patients of either gender receiving monoclonal antibodies at the study site were included. Treatment patterns, utilization, and factors associated with the discontinuation of monoclonal antibodies were assessed. Results: Hyperlipidemia (136, 39.1%) was the most common indication for monoclonal antibodies, followed by prophylaxis of respiratory syncytial virus infection in congenital heart disease (104, 29.9%) and osteoporosis (42, 12.1%). Evolocumab was the most commonly prescribed monoclonal antibody (135, 38.8%), followed by palivizumab (104, 29.9%), and dupilumab (38, 10.9%). The majority of monoclonal antibodies demonstrated a prescribed daily dose to defined daily dose ratio of 1.0, reflecting their appropriate utilization. One hundred twenty-nine patients (37.0%) discontinued their treatment during the study. Patient’s level of education (OR: 0.416, 95% CI: 0.183–0.943, p = 0.036), BMI (OR: 2.358, 95% CI: 1.164–4.777, p = 0.017), number of concomitant medications (OR: 2.457, 95% CI: 1.202–5.025, p = 0.014), and treatment duration (OR: 9.180, 95% CI: 4.909–17.165, p < 0.001) were identified as predictors of discontinuation of monoclonal antibodies. Conclusion: This study represents the first comprehensive investigation in the United Arab Emirates focused on treatment patterns, utilization, and discontinuation of monoclonal antibodies among the local population. Monoclonal antibodies were prescribed for the management of a wide range of clinical conditions. The study reports appropriate utilization of most monoclonal antibodies and identifies factors such as patient education level, BMI, concomitant medications, and treatment duration as independent predictors of monoclonal antibody treatment discontinuation.

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Using Automated Machine Learning to Predict Necessary Upcoming Therapy Changes in Patients With Psoriasis Vulgaris and Psoriatic Arthritis and Uncover New Influences on Disease Progression: Retrospective Study

Schaffert,

Bibi,

Blauth

et al. 2024

JMIR Form Res

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Background Psoriasis vulgaris (PsV) and psoriatic arthritis (PsA) are complex, multifactorial diseases significantly impacting health and quality of life. Predicting treatment response and disease progression is crucial for optimizing therapeutic interventions, yet challenging. Automated machine learning (AutoML) technology shows promise for rapidly creating accurate predictive models based on patient features and treatment data. Objective This study aims to develop highly accurate machine learning (ML) models using AutoML to address key clinical questions for PsV and PsA patients, including predicting therapy changes, identifying reasons for therapy changes, and factors influencing skin lesion progression or an abnormal Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) score. Methods Clinical study data from 309 PsV and PsA patients were extensively prepared and analyzed using AutoML to build and select the most accurate predictive models for each variable of interest. Results Therapy change at 24 weeks follow-up was modeled using the extreme gradient boosted trees classifier with early stopping (area under the receiver operating characteristic curve [AUC] of 0.9078 and logarithmic loss [LogLoss] of 0.3955 for the holdout partition). Key influencing factors included the initial systemic therapeutic agent, the Classification Criteria for Psoriatic Arthritis score at baseline, and changes in quality of life. An average blender incorporating three models (gradient boosted trees classifier, ExtraTrees classifier, and Eureqa generalized additive model classifier) with an AUC of 0.8750 and LogLoss of 0.4603 was used to predict therapy changes for 2 hypothetical patients, highlighting the significance of these factors. Treatments such as methotrexate or specific biologicals showed a lower propensity for change. An average blender of a random forest classifier, an extreme gradient boosted trees classifier, and a Eureqa classifier (AUC of 0.9241 and LogLoss of 0.4498) was used to estimate PASI (Psoriasis Area and Severity Index) change after 24 weeks. Primary predictors included the initial PASI score, change in pruritus levels, and change in therapy. A lower initial PASI score and consistently low pruritus were associated with better outcomes. BASDAI classification at onset was analyzed using an average blender of a Eureqa generalized additive model classifier, an extreme gradient boosted trees classifier with early stopping, and a dropout additive regression trees classifier with an AUC of 0.8274 and LogLoss of 0.5037. Influential factors included initial pain, disease activity, and Hospital Anxiety and Depression Scale scores for depression and anxiety. Increased pain, disease activity, and psychological distress generally led to higher BASDAI scores. Conclusions The practical implications of these models for clinical decision-making in PsV and PsA can guide early investigation and treatment, contributing to improved patient outcomes.

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Psoriasis/Psoriatic Arthritis Patients’ Long-term Treatment Patterns and Adherence to Systemic Treatments Monitoring Recommendations

Cited by 5 publications

References 27 publications

Influence of Clinical and Psychosocial Factors on the Adherence to Topical Treatment in Psoriasis

Influence of Clinical and Psychosocial Factors on the Adherence to Topical Treatment in Psoriasis

Treatment patterns and factors associated with discontinuation of monoclonal antibodies

Using Automated Machine Learning to Predict Necessary Upcoming Therapy Changes in Patients With Psoriasis Vulgaris and Psoriatic Arthritis and Uncover New Influences on Disease Progression: Retrospective Study

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