Deriving common comorbidity indices from the MedDRA classification and exploring their performance on key outcomes in patients with rheumatoid arthritis.

OBJECTIVE: To develop algorithms for calculating the Rheumatic Diseases Comorbidity Index (RDCI), Charlson-Deyo Index (CDI) and Functional Comorbidity Index (FCI) from the Medical Dictionary for Regulatory Activities (MedDRA), and to assess how these MedDRA-derived indices predict clinical outcomes, utility and health resource utilization (HRU). METHODS: Two independent researchers linked the preferred terms of the MedDRA classification into the conditions included in the RDCI, the CDI and the FCI. Next, using data from the Norwegian Register-DMARD study (a register of patients with inflammatory joint diseases treated with DMARDs), the explanatory value of these indices was studied in models adjusted for age, gender and DAS28. Model fit statistics were compared in generalized estimating equation (prediction of outcome over time) models using as outcomes: modified HAQ, HAQ, physical and mental component summary of SF-36, SF6D and non-RA related HRU. RESULTS: Among 4126 patients with RA [72% female, mean (s.d.) age 56 (14) years], median (interquartile range) of RDCI at baseline was 0.0 (1.0) [range 0-6], CDI 0.0 (0.0) [0-7] and FCI 0.0 (1.0) [0-6]. All the comorbidity indices were associated with each outcome, and differences in their performance were moderate. The RDCI and FCI performed better on clinical outcomes: modified HAQ and HAQ, hospitalization, physical and mental component summary, and SF6D. Any non-RA related HRU was best predicted by RDCI followed by CDI. CONCLUSION: An algorithm is now available to compute three commonly used comorbidity indices from MedDRA classification. Indices performed comparably well in predicting a variety of outcomes, with the CDI performing slightly worse when predicting outcomes reflecting functioning and health.

Cost-effectiveness of TNF inhibitors vs synthetic disease-modifying antirheumatic drugs in patients with rheumatoid arthritis: a Markov model study based on two longitudinal observational studies.

OBJECTIVE: The objective of this study was to estimate the additional costs and health benefits of adding a TNF inhibitor (TNFi) (adalimumab, certolizumab, etanercept, golimumab, infliximab) to a synthetic DMARD (sDMARD), e.g. MTX, in patients with RA. METHODS: We developed the Norwegian RA model as a Markov model simulating 10 years of treatment with either TNFi plus sDMARDs (TNFi strategy) or sDMARDs alone (synthetic strategy). Patients in both strategies started in one of seven health states, based on the Short Form-6 Dimensions (SF-6D). The patients could move to better or worse health states according to transition probabilities. In the TNFi strategy, patients could stay on TNFi (including switch of TNFi), or switch to non-TNFi-biologics (abatacept, rituximab, tocilizumab), sDMARDs or no DMARD. In the synthetic strategy, patients remained on sDMARDs. Data from two observational studies were used for the assessment of resource use and utilities in the health states. Health benefits were evaluated using the EuroQol-5 Dimensions (EQ-5D) and SF-6D. RESULTS: The Norwegian RA model predicted that 10-year discounted health care costs totalled €124,942 (€475,266 including production losses) for the TNFi strategy and €65,584 (€436,517) for the synthetic strategy. The cost per additionally gained quality-adjusted life-year of adding a TNFi was €92,557 (€60,227 including production losses) using SF-6D and €61,285 (€39,841) using EQ-5D. Including health care costs only, the probability that TNFi treatment was cost-effective was 90% when using EQ-5D, assuming a Norwegian willingness-to-pay level of €67,300. CONCLUSION: TNFi treatment for RA is cost-effective when accounting for production losses. Excluding production losses, TNFi treatment is cost-effective using EQ-5D, but not SF-6D.

Two-year direct and indirect costs for patients with inflammatory rheumatic joint diseases: data from real-life follow-up of patients in the NOR-DMARD registry.

OBJECTIVE: The overall aim of this study was to estimate the total costs for patients with RA, AS and psoriatic arthritis (PsA) treated with DMARDs. Specific aims were to compare the costs across diagnoses and over time. METHODS: The main data source was the Norwegian DMARD register (NOR-DMARD) that captures outcomes and resource use among patients starting therapy with synthetic and biologic DMARDs. Costs were estimated for four 6-month periods from the start of a DMARD regimen. We included RA (n=1152), AS (n=186) and PsA (n=374) patients with available 2-year data. Direct costs included pharmaceuticals, imaging examinations, in-hospital and out-hospital care, stays in rehabilitation units and visits to general practitioners, private rheumatologists and physiotherapists. Indirect cost included patients' work absenteeism. Differences in costs across diagnoses were tested by Kruskal-Wallis equality-of-populations rank test and changes in costs between first and fourth 6-month periods were tested by paired t-tests. RESULTS: Total 2-year costs were similar across diagnoses for patients on synthetic treatment (RA/AS/PsA €64,300/63,200/64,500) and on biologic treatment (€121,900/115,319/111,200). The largest cost component was productivity loss. Total costs decreased significantly from the first to the fourth 6-month periods for all diagnoses, and this decrease was influenced by reductions both in direct and indirect costs. CONCLUSION: Total costs were similar across the main inflammatory rheumatic diseases. Biologic DMARD treatment entails considerable drug cost, but the total costs decline during the first 2 years on treatment in both RA, AS and PsA.