Personalized dose reduction strategies for biologic disease-modifying antirheumatic drugs for treating ankylosing spondylitis: a clinical and economic evaluation with predictive modeling

Background Ankylosing spondylitis (AS) is a chronic inflammatory disease that significantly affects quality of life and imposes a high economic burden on patients due to the cost of biologic disease-modifying antirheumatic drugs (bDMARDs). Dose reduction strategies for bDMARDs may offer a feasible approach to maintaining clinical efficacy while reducing costs. This study aimed to evaluate the clinical effectiveness and cost-efficiency of bDMARD dose reduction in patients with AS and apply predictive modeling to identify key factors influencing disease control. Methods This 12-month prospective study included 368 patients with AS who were divided into two groups: those who received dose reduction and those with full-dose therapy. Clinical outcomes such as C-reactive protein (CRP) levels, the Bath ankylosing spondylitis disease activity index (BASDAI) and ankylosing spondylitis disease activity score (ASDAS) were assessed, along with cost effectiveness using incremental cost effectiveness ratios (ICER). Random forest models were developed to predict the achievement of inactive disease (ASDAS < 1.3) and to identify key predictors. Results The ICER to achieve an ASDAS < 1.3 was highly favorable (-$16,772.62). Patients in the dose reduction group demonstrated significant improvements in CRP levels (-4.65 vs. -1.32 mg/L, p < 0.001), BASDAI (-3.00 vs. -0.42, p < 0.001), and ASDAS (-1.72 vs. -0.15, p < 0.001), compared with the full dose group. Predictive modeling identified baseline CRP level, baseline ASDAS, and dose adjustment as key factors influencing outcomes, with the medium feature model achieving an area under the receiver operating characteristic curve of 81.86%. Conclusions The reduction in bDMARD doses maintained clinical efficacy and achieved significant cost savings, offering a viable strategy for the management of AS. Predictive modeling provided actionable insights to optimize personalized treatment strategies, balancing efficacy and economic sustainability. These findings support the integration of dose reduction strategies into routine practice, particularly in resource-limited settings.