Modelling Treatment Sequences in Immunology: Optimizing Patient Outcomes.

Abstract

For some immune-mediated disorders, despite the range of therapies available there is limited evidence on which treatment sequences are best for patients and healthcare systems. We investigated how their selection can impact outcomes in an Italian setting. A 3-year state-transition treatment-sequencing model calculated potential effectiveness improvements and budget reallocation considerations associated with implementing optimal sequences in ankylosing spondylitis (AS), Crohn's disease (CD), non-radiographic axial spondyloarthritis (NR-AxSpA), plaque psoriasis (PsO), psoriatic arthritis (PsA), rheumatoid arthritis (RA), and ulcerative colitis (UC). Sequences included three biological or disease-modifying treatments, followed by best supportive care. Disease-specific response measures were selected on the basis of clinical relevance, data availability, and data quality. Efficacy was differentiated between biologic-naïve and experienced populations, where possible, using published network meta-analyses and real-world data. All possible treatment sequences, based on reimbursement as of December 2022 in Italy (analyses' base country), were simulated. Sequences with the best outcomes consistently employed the most efficacious therapies earlier in the treatment pathway. Improvements to prescribing practice are possible in all diseases; however, most notable was UC, where the per-patient 3-year average treatment failure was 37.3% higher than optimal. The results focused on the three most crowded and prevalent immunological sub-condition diseases in dermatology, rheumatology, and gastroenterology: PsO, RA, and UC, respectively. By prescribing from within the top 20% of the most efficacious sequences, the model found a 15.1% reduction in treatment failures, with a 1.59% increase in drug costs. Prescribing more efficacious treatments earlier provides a greater opportunity to improve patient outcomes and minimizes treatment failures.