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All superheroes have origin stories. The Washing Well Wenches are superheroes, after a fashion, (the fashion of wearing a towel as a cape!) so here goes our story, as well [...]

Electronic health records in hospitals contribute to improving the quality of care by enabling better management of clinical information. The databases thus constituted facilitate the exchange of health information with healthcare providers and optimize multidisciplinary coordination for better therapeutic results. The EHR4CR (Electronic Health Records for Clinical Research) European project has developed an innovative pilot platform enabling the reuse of this digital information for clinical research. By enhancing and speeding up clinical research procedures, this innovative approach makes it possible to conduct clinical trials more efficiently, faster, and more economically., (© 2018 médecine/sciences – Inserm.)

Objectives: The treatment of active rheumatoid arthritis (RA) usually requires different therapeutic options used sequentially in case of an insufficient response (IR) to previous agents. Since there is a lack of clinical trials comparing biologic treatment sequences, simulation models might add to the understanding of optimal treatment sequences and their cost-effectiveness. The objective of this study was to assess the cost-effectiveness of different biologic treatment strategies in patients with an IR to anti-TNF agents, based on levels of disease activity from the German public payer's perspective., Methods: A cost-effectiveness sequential model was developed in accordance with local RA treatment strategies, using DAS28 scores as dichotomous effectiveness endpoints: achieving remission/no remission (RS/no RS) or a state of low disease activity (LDAS/no LDAS). Costs were estimated using resource utilisation data obtained from a large observational German cohort. Advanced simulations were conducted to assess the cost-effectiveness over 2 years of four sequential biologic strategies composed of up to 3 biologic agents, namely anti-TNF agents, abatacept or rituximab, in patients with moderate-to-severe active RA and an IR to at least one anti-TNF agent., Results: Over two years, the biological sequence including abatacept after an IR to one anti-TNF agent appeared the most effective and cost-effective versus (vs.) use after two anti-TNF agents (€633 vs. €1,067/day in LDAS and €1,222 vs. €3,592/day in remission), and vs a similar sequence using rituximab (€633 vs. €728/day in LDAS and €1,222 vs. €1,812/day in remission). The sequence using a 3rd anti-TNF agent was less effective and cost-effective than the same sequence using abatacept (€2,000 vs. €1,067/day in LDAS and €6,623 vs. €3,592/day in remission). All differences were statistically significant (p<0.01)., Conclusions: The results suggest that in patients with an IR to at least one anti-TNF agent, biologic sequences including abatacept appear more efficacious and cost-effective than similar sequences including rituximab or only cycled anti-TNF agents.

Such as prospective studies can provide evidence-based information for clinicians and regulatory agencies, modelling studies provide useful information when experimental studies are to complex, too long, or too expensive to carry out. If modelling has been widely used in pharmacokinetics, it is in the field of pharmacoeconomics that numerous models have been published in recent years, including models relevant to the management of rheumatoid arthritis (RA). The most common modelling techniques published in RA are decision trees and Markov models which are used to perform cost-effectiveness and cost-utility analyses using real or simulated populations. This paper reviews the main types of modelling techniques used in pharmacoeconomic studies with the aim of clarifying their interest and limitations for the clinicians. Generating such evidence is highly relevant to assisting clinical recommendations and reimbursement decisions towards enabling the optimal management of RA and reducing its overall clinical and economic burden, for the benefits of patients and health systems.