Predicting long-term outcomes in people with psoriasis who achieve disease control on systemic therapy: a mixed methods study

Background Psoriasis is a common, incurable inflammatory skin disease associated with high psychosocial morbidity and economic impact.

Systemic therapy is often required for individuals with moderate-to-severe psoriasis to achieve disease control (i.e. clear or almost clear skin).

Systemic therapy encompasses conventional systemic immunosuppressive therapy, as well as a growing list of newer, highly effective biologics.

While the effectiveness of biologics has led to a rapidly expanding cohort who achieve disease control, there still exists no means of predicting whether this will persist longer-term at the individual level.

Uncertainty over the longevity of disease control not only causes significant anxiety for people with psoriasis, but also results in the continuation of a one-size-fits-all healthcare model of regular follow-ups regardless of clinical need.

This leads to poorer treatment experiences, inefficient resource allocation, and a worsening supply and demand mismatch in the NHS.

Aims and Objectives This project aims to develop a risk stratification tool to predict the likelihood of sustaining psoriasis control on systemic therapy.

To do this, I will: Evaluate published evidence on the proportion of individuals sustaining disease control over time and the factors associated with this. Model the likelihood of sustaining disease control on systemic therapy using real-world datasets. Optimise the predictive models for use as a risk stratification tool, with patient and clinician input.

Methods and Timelines Phase 1 (Months 0-9): Conduct a systematic review to determine the proportion of individuals sustaining psoriasis control on systemic therapy over time, and the clinical and genetic factors associated with this.

Phase 2 (Months 7-24): Develop and validate predictive models of sustained disease control on systemic therapy using two large-scale real-world datasets from the UK and Europe to ensure generalisability. I will also explore whether including genetic data improves model performance. Phase 3 (Months 19-36): Refine the predictive models into a risk stratification tool.

Patient and clinician focus groups will determine clinically acceptable levels of model performance, influence the definition of risk categories, and guide user interface design. The tool will be beta tested via a clinician workshop. PPI feedback has been integral to developing this proposal.

PPI collaborators will be closely involved throughout, including assisting with recruitment strategies, co-facilitating focus groups, and helping with qualitative analysis. They will also ensure the research is inclusive and relevant to diverse communities.

Impact Successful delivery of the risk stratification tool could reduce patient anxiety, empower individuals to address modifiable risk factors, and personalise long-term management strategies.

For example, individuals likely to sustain disease control will benefit from increased autonomy and flexibility and may be suited to remote monitoring or patient-initiated follow-up pathways.

Higher-risk individuals may be appropriate for adjunct therapies with closer monitoring, facilitating earlier intervention for flares to reduce disease impact.

Dissemination My findings will be disseminated widely through high-impact peer-reviewed publications and presentations at national and international conferences.

I will co-develop Plain English Summaries and infographics with my PPI collaborators, which will be shared via UK and European psoriasis patient associations, newsletters, and social media platforms to maximise public engagement.