Psoriatic arthritis (PsA) is a long-term condition that causes joint pain, swelling, and skin problems. When PsA is caught early, doctors have a choice: start with a standard step-up approach, gradually increasing treatment if needed, or begin more intensively from the outset, using a combination of drugs or biologic medicines straight away. Previous trials have given mixed answers about which approach works better, with some studies showing intensive treatment offers a clear advantage and others showing only a modest benefit.
To make sense of these differing results, we combined individual-level data from four separate clinical trials, creating a larger and more detailed dataset of 441 patients with early, untreated PsA. Pooling the data allowed us to estimate the overall benefit of intensive treatment more precisely than any single trial could alone. We found that, on average, intensive treatment led to meaningfully better disease control than standard care.
However, an "average" benefit doesn't mean intensive treatment helps everyone equally. Using newer statistical and machine learning methods, we looked beyond the average to ask: which patients benefit most? We found that factors such as age, baseline disease activity, and body weight (BMI) played an important role in how much a patient was likely to gain from intensive treatment. From this, we built simple decision rules to help identify which patients are good candidates for intensive treatment versus those who may do just as well with a standard approach.
These findings are an early but encouraging step towards more personalised treatment in PsA.