Psoriatic arthritis (PsA) is a systemic, inflammatory disease associated with the skin disease psoriasis. PsA can be difficult to diagnose in primary care due to its heterogeneous nature and similarity to other forms of arthritis. This study’s objective is to build predictive models to assist with the earlier detection of PsA in primary care. The primary aim is to develop a novel temporal Bayesian network to model patterns of clinical symptoms, blood tests and prescriptions associated with an increased probability of developing PsA in a cohort of patients with incident psoriasis. The model’s performance will be compared to two common approaches for similar tasks in the literature: a Cox proportional hazards model and a neural network. This will be a cohort study consisting of cases of incident psoriasis so that the disease trajectory can be followed prospectively over time in the data.
However, not all PsA cases in the CPRD will have an incident psoriasis diagnosis. This could be due to psoriasis diagnosis concurrently or after their PsA diagnosis, or before the patient’s data entered the CPRD. To include these scenarios in the study, a secondary aim will be to incorporate all incident cases of PsA in a case-control study. Cases (those with incident PsA) and controls (those with no PsA diagnosis) will be required to have at least 5 years of data collection before the matched PsA index date to capture the progressive nature of the prodromal phase of PsA. This methodology will be used to verify that the direction of associations found in the primary study extends to the wider PsA population. Findings from this study could highlight characteristics of the pre-clinical phase of PsA, and predictive models could aid screening procedures by selecting patients at an elevated risk for referral to secondary care rheumatologists.
PsA diagnosis; clinical symptoms, blood tests and prescriptions that could be predictors of PsA.
Theresa Smith - Chief Investigator - University of Bath
Alex Rudge - Corresponding Applicant - University of Bath
Anita McGrogan - Collaborator - University of Bath
Julia Snowball - Collaborator - University of Bath
Neil McHugh - Collaborator - University of Bath
Rachel Charlton - Collaborator - University of Bath
William Tillett - Collaborator - University of Bath