THU0549 COST-EFFECTIVENESS OF DIAGNOSING AXIAL SPONDYLOARTHRITIS IN THE NETHERLANDS
Authors: C. Webers, S. Grimm, A. Van Tubergen, F. A. Van Gaalen, D. Van der Heijde, M. Joore, A. Boonen
Journal: Annals of the Rheumatic Diseases (2020)

Background: In order to facilitate diagnosis of axial spondyloarthritis (axSpA) in clinical practice, three diagnostic algorithms have been published: the original Berlin algorithm (BER), and modifications 1 (M1) and 2 (M2). These mainly differed mainly in the position and definition of inflammatory back pain (Figure). BER was the most specific of these algorithms, while M2 was most sensitive, and the latter was accepted as the algorithm of choice. However, it is unknown to what extent it is acceptable, in terms of costs and effects, to increase sensitivity (i.e. reducing underdiagnosis and undertreatment) at the expense of specificity (i.e. increasing overdiagnosis and overtreatment). Figure. Flowchart of the Berlin algorithm (A), and Modifications 1 (B) and 2 (C) Objectives: To compare lifetime cost-effectiveness of currently available diagnostic algorithms for axSpA in the Netherlands. Methods: A cost-effectiveness model was developed to estimate costs, quality adjusted life years (QALYs) and net monetary benefit (NMB) of different diagnostic algorithms for axSpA among patients with chronic low back pain (CLBP) referred to a rheumatologist, over a 60-year time horizon. The model combined a decision-tree (diagnostic process) with a state-transition model (long-term treatment). For the diagnostic process, patient-level data from an observational cohort of referred CLBP patients (SPondyloArthritis Caught Early, SPACE) were used. In the state-transition model, both axSpA and CLBP were modelled using three health states each, based on disease activity and treatment (including up to three sequential biologicals) (for axSpA), or disease severity (for CLBP). Three available diagnostic algorithms (BER, M1, M2) were compared. To estimate the impact of uncertainty on results, probabilistic sensitivity analyses were conducted. For all analyses, both a societal perspective (including all healthcare, informal and work-related costs) and a healthcare perspective (only including healthcare costs) were adopted. Results were expressed as incremental cost-utility ratio (ICUR) and incremental NMB (iNMB). Results: From a societal perspective, algorithm M2 resulted in more QALYs and lowest costs per patient (24.23 QALY; €157,274), thus dominating BER and M1 (Table). From a healthcare perspective, BER had lowest costs, but M2 was still considered cost-effective at €3,250/QALY (Table). At a willingness-to-pay (WTP) threshold of €20,000 per one QALY gained, the probability of M2 being most cost-effective was 94%, compared to 5% for M1 and 1% for BER. Compared to the most cost-effective algorithm (M2), an additional €7,500 could be spent per patient to achieve perfect diagnosis while remaining cost-effective. Table. Base-case deterministic cost-utility results of comparisons between diagnostic algorithms Perspective Algorithm SE/SP Cost, € QALY iCost, € iQALY ICUR, €/QALY iNMB, €* Societal M2 51% / 88% 157,274 24.23 - - - - M1 73% / 82% 158,236 24.15 962 -0.08 Dominated by M2 -2,583 Berlin 80% / 85% 159,295 23.96 2,021 -0.27 Dominated by M2 -7,479 Healthcare Berlin 51% / 88% 91,755 23.96 - - - - M2 73% / 82% 92,642 24.23 887 0.27 3,250 4,571 M1 80% / 85% 92,710 24.15 67 -0.08 Dominated by M2 -1,688 *Calculated using a WTP threshold of €20,000/QALY. iCost, incremental cost; iQALY, incremental QALY; SE, sensitivity; SP, specificity. Conclusion: The relative increase in sensitivity of M2 at the expense of specificity when compared to the original BER algorithm is acceptable in terms of costs and effects from both societal and healthcare perspectives. A considerably more expensive diagnostic algorithm with better accuracy than M2 would still be considered good value for money. It is worthy to invest in more accurate diagnosis in axSpA. Disclosure of Interests: Casper Webers: None declared, Sabine Grimm: None declared, Astrid van Tubergen Consultant of: Novartis, Floris A. van Gaalen: None declared, Désirée van der Heijde Consultant of: AbbVie, Amgen, Astellas, AstraZeneca, BMS, Boehringer Ingelheim, Celgene, Cyxone, Daiichi, Eisai, Eli-Lilly, Galapagos, Gilead Sciences, Inc., Glaxo-Smith-Kline, Janssen, Merck, Novartis, Pfizer, Regeneron, Roche, Sanofi, Takeda, UCB Pharma; Director of Imaging Rheumatology BV, Manuela Joore: None declared, Annelies Boonen Grant/research support from: AbbVie, Consultant of: Galapagos, Lilly (all paid to the department)