Multivariable Predictive Models to Identify the Optimal Biologic Therapy for Treatment of Patients With Psoriasis at the Individual Level.

Importance: Identifying the optimal long-term biologic therapy for patients with psoriasis is often done through trial and error. Objective: To identify the optimal biologic therapy for individual patients with psoriasis using predictive statistical and machine learning models. Design, Setting, and Participants: This population-based cohort study used data from Danish nationwide registries, primarily DERMBIO, and included adult patients treated for moderate-to-severe psoriasis with biologics. Data were processed and analyzed between spring 2021 and spring 2022. Main Outcomes and Measures: Patient clusters of clinical relevance were identified and their success rates estimated for each drug. Furthermore, predictive prognostic models to identify optimal biologic treatment at the individual level based on data from nationwide registries were evaluated. Results: Assuming a success criterion of 3 years of sustained treatment, this study included 2034 patients with a total of 3452 treatment series. Most treatment series involved male patients (2147 [62.2%]) originating from Denmark (3190 [92.4%]), and 2414 (69.9%) had finished an education longer than primary school. The average ages were 24.9 years at psoriasis diagnosis and 45.5 years at initiation of biologic therapy. Gradient-boosted decision trees and logistic regression were able to predict a specific cytokine target (eg, interleukin-17 inhibition) associated with a successful treatment with accuracies of 63.6% and 59.2%, and top 2 accuracies of 95.9% and 93.9%. When predicting specific drugs resulting in success, gradient boost and logistic regression had accuracies of 48.5% and 44.4%, top 2 accuracies of 77.6% and 75.9%, and top 3 accuracies of 89.9% and 89.0%. Conclusions and Relevance: Of the treatment prediction models used in this cohort study of patients with psoriasis, gradient-boosted decision trees performed significantly better than logistic regression when predicting specific biologic therapy (by drug as well as target) leading to a treatment duration of at least 3 years without discontinuation. Predicting the optimal biologic could benefit patients and clinicians by minimizing the number of failed treatment attempts.

High adherence and low dropout rate in a virtual clinical study of atopic dermatitis through weekly reward-based personalized genetic lifestyle reports.

INTRODUCTION: Clinical trials often suffer from significant recruitment barriers, poor adherence, and dropouts, which increase costs and negatively affect trial outcomes. The aim of this study was to examine whether making it virtual and reward-based would enable nationwide recruitment, identify patients with variable disease severity, achieve high adherence, and reduce dropouts. METHODS: In a siteless, virtual feasibility study, individuals with atopic dermatitis (AD) were recruited online. During the 8-week study, subjects used their smartphones weekly to photograph target AD lesions, and completed patient-oriented eczema measure (POEM) and treatment use questionnaires. In return, subjects were rewarded every week with personalized lifestyle reports based on their DNA. RESULTS: Over the course of the 11 day recruitment period, 164 (82% women and 18% men) filled in the form to participate, of which 65 fulfilled the inclusion criteria and signed the informed consent. Ten were excluded as they did not complete the mandatory study task of returning the DNA sample. 55 (91% women, 9% men) subjects returned the DNA sample and were enrolled throughout Denmark, the majority outside the Copenhagen capital region in rural areas with relatively low physician coverage. The mean age was 28.5 (SD ±9.5 years, range 18-52 years). The baseline POEM score was 14.5±5.6 (range 6-28). Based on the POEM, 7 individuals had mild, 28 had moderate, 17 had severe, and 3 had very severe eczema. The retention rate was 96% as 53 out of 55 enrolled completed the study. The adherence was very high, and more than 90% of all study tasks were completed. Follow up of 41 subjects showed that 90% would take part again or continue if the study had been longer. CONCLUSION: A virtual trial design enables recruitment with broad geographic reach and throughout the full spectrum of disease severity. Providing personalized genetic reports as a reward seems to contribute to high adherence and retention.

Use of Complementary and Alternative Therapies in Outpatients with Atopic Dermatitis from a Dermatological University Department.

BACKGROUND: Treatment of atopic dermatitis (AD) may be challenging, therefore some patients seek complementary and alternative medications (CAM). We determined prevalence and predictors for CAM use in a hospital cohort of AD patients. MATERIAL AND METHODS: Between January 1, 2012, and December 31, 2017, AD patients referred to the dermatological outpatient clinic at Bispebjerg Hospital were included in the study. Information on CAM use, demographics and disease characteristics were obtained by questionnaire, and associations were determined by χ2 and t test separately for children (< 16 years) and adults (≥16 years). RESULTS: In total 441 filled in the questionnaire on AD, and 433 patients responded to the questions about CAM use: 198 children and 235 adults. A total of 137 (31.6%) had used one or more CAM. CAM use in children was significantly associated with prior use of ≥2 conventional treatments (p = 0.047) and topical calcineurin inhibitors (p = 0.021), a higher number of affected eczema sites (p < 0.001) including more frequent affection of the face and extremities, a higher SCORAD score (p = 0.045), and low mean overall self-rated health (p = 0.003). CAM use in adults was significantly associated with lower age of onset of AD (p = 0.004), comorbid allergic rhinoconjunctivitis (p = 0.039), frequent use of moisturizing cream (p = 0.024), facial and neck eczema (p = 0.005) and high educational level (p = 0.043). CONCLUSION: CAM use is frequent in both children and adult AD patients. CAM users are characterized by long disease duration, a significant disease burden and by having a longer education. The high prevalence of CAM may indicate that patients' expectations regarding treatment of AD are not redeemed in the conventional health care system.