Predicting the outcome of ankylosing spondylitis therapy

Abstract

Objectives: To create a model that provides a potential basis for candidate selection for anti-tumour necrosis factor (TNF) treatment by predicting future outcomes relative to the current disease profile of individual patients with ankylosing spondylitis (AS).

Methods: ASSERT and GO-RAISE trial data (n=635) were analysed to identify baseline predictors for various disease-state and disease-activity outcome instruments in AS. Univariate, multivariate, receiver operator characteristic and correlation analyses were performed to select final predictors. Their associations with outcomes were explored. Matrix and algorithm-based prediction models were created using logistic and linear regression, and their accuracies were compared. Numbers needed to treat were calculated to compare the effect size of anti-TNF therapy between the AS matrix subpopulations. Data from registry populations were applied to study how a daily practice AS population is distributed over the prediction model.

Results: Age, Bath ankylosing spondylitis functional index (BASFI) score, enthesitis, therapy, C-reactive protein (CRP) and HLA-B27 genotype were identified as predictors. Their associations with each outcome instrument varied. However, the combination of these factors enabled adequate prediction of each outcome studied. The matrix model predicted outcomes as well as algorithm-based models and enabled direct comparison of the effect size of anti-TNF treatment outcome in various subpopulations. The trial populations reflected the daily practice AS population.

Conclusion: Age, BASFI, enthesitis, therapy, CRP and HLA-B27 were associated with outcomes in AS. Their combined use enables adequate prediction of outcome resulting from anti-TNF and conventional therapy in various AS subpopulations. This may help guide clinicians in making treatment decisions in daily practice.

Figure 1

Matrix presentation of outcome rates of different patient subpopulations (%) defined by the categorised predictor variables: (A) ankylosing spondylitis disease activity score (ASDAS) clinically important improvement, (B) ASDAS major improvement, (C) assessment of spondyloarthritis (ASAS) partial remission, (D) ASAS 20 response, (E) Bath ankylosing spondylitis disease activity index (BASDAI) 50 response and (F) ASDAS inactive disease. BASFI, Bath ankylosing spondylitis functional index; CRP, C-reactive protein; DMARD, disease-modifying antirheumatic drug; NSAID, non-steroidal anti-inflammatory drug; TNF, tumour necrosis factor.

Figure 2

Matrix presentation of numbers needed to treat for one patient to respond to anti-tumour necrosis factor treatment according to different outcome instruments at 12 or 24 weeks: (A) ankylosing spondylitis disease activity score (ASDAS) clinically important improvement, (B) ASDAS major improvement, (C) assessment of spondyloarthritis (ASAS) partial remission, (D) ASAS 20 response, (E) Bath ankylosing spondylitis disease activity index (BASDAI) 50 response, and (F) ASDAS inactive disease. BASFI, Bath ankylosing spondylitis functional index; CRP, C-reactive protein.

Figure 3

Cross-sectional application of the ASPECT/Regisponser populations over the matrix grid: percentage of (A) the total registry population (including all patients irrespective of Bath ankylosing spondylitis disease activity index (BASDAI) score), and of (B) the active registry population (including only patients with a BASDAI score of ≥4) defined by the categorised predictor variables. BASFI, Bath ankylosing spondylitis functional index; CRP, C-reactive protein.