What is axial spondyloarthritis? A latent class and transition analysis in the SPACE and DESIR cohorts.

OBJECTIVES: To gain expert-judgement-free insight into the Gestalt of axial spondyloarthritis (axSpA), by investigating its 'latent constructs' and to test how well these latent constructs fit the Assessment of SpondyloArthritis international Society (ASAS) classification criteria. METHODS: Two independent cohorts of patients with early onset chronic back pain (SPondyloArthritis Caught Early (SPACE)) or inflammatory back pain (IBP) (DEvenir des Spondylarthopathies Indifférenciées Récentes (DESIR)) were analysed. Latent class analysis (LCA) was used to estimate the (unobserved) potential classes underlying axSpA. The best LCA model groups patients into clinically meaningful classes with best fit. Each class was labelled based on most prominent features. Percentage fulfilment of ASAS axSpA, peripheral SpA (pSpA) (ignoring IBP) or both classification criteria was calculated. Five-year data from DESIR were used to perform latent transition analysis (LTA) to examine if patients change classes over time. RESULTS: SPACE (n=465) yielded four discernible classes: 'axial' with highest likelihood of abnormal imaging and HLA-B27 positivity; 'IBP+peripheral' with 100% IBP and dominant peripheral symptoms; 'at risk' with positive family history and HLA-B27 and 'no SpA' with low likelihood for each SpA feature. LCA in DESIR (n=576) yielded similar classes, except for the 'no-SpA'. The ASAS axSpA criteria captured almost all (SPACE: 98%; DESIR: 93%) 'axial' patients, but the 'IBP+peripheral' class was only captured well by combining the axSpA and pSpA criteria (SPACE: 78%; DESIR: 89%). Only 4% of 'no SpA' patients fulfilled the axSpA criteria in SPACE. LTA suggested that 5-year transitions across classes were unlikely (11%). CONCLUSION: The Gestalt of axSpA comprises three discernible entities, only appropriately captured by combining the ASAS axSpA and pSpA classification criteria. It is questionable whether some patients with 'axSpA at risk' will ever develop axSpA.

Waiting lists, waiting times and admissions: an empirical analysis at hospital and general practice level.

We report an empirical analysis of the responses of the supply and demand for secondary care to waiting list size and waiting times. Whereas previous empirical analyses have used data aggregated to area level, our analysis focuses on the supply responses of a single hospital and the demand responses of the GP practices it serves, and distinguishes between first outpatient visits, inpatient admissions, day-case treatment and emergency admissions. The results are plausible and in line with the theoretical model. For example: the demand from practices for first outpatient visits is negatively affected by waiting times and distance to the hospital. Increases in waiting times and waiting lists lead to increases in supply; the supply of elective inpatient admissions is affected negatively by current emergency admissions and positively by lagged waiting list and waiting time. We use the empirical results to investigate the dynamic responses to one off policy measures to reduce waiting times and lists by increasing supply.