Alonso-Blanco C, Fernández-de-las-Peñas C, Cleland JA: Preliminary clinical prediction rule for identifying patients with ankylosing spondylitis who are likely to respond to an exercise program: A pilot study.
The aim of this study was to develop a preliminary clinical prediction rule to identify the potential predictors for identifying patients presenting with ankylosing spondylitis who are likely to respond to a specific exercise program.
Consecutive patients with ankylosing spondylitis underwent a standardized examination and then received eight physical therapy sessions during a 2-mo period, which included an exercise program based on the treatment of the shortened muscle chains, following the guideline described by the global posture re-education method. Patients were classified as having experienced a successful outcome at 1 mo after discharge based on a 20% reduction on Bath Ankylosing Spondylitis Functional Index and self-report perceived recovery. Potential predictor variables were entered into a stepwise logistic regression model to determine the most accurate set of variables for identifying treatment success.
Data from 35 patients were included, of which 16 (46%) experienced a successful outcome. A clinical prediction rule with three variables (physical role >37, bodily pain >27, and Bath Ankylosing Spondylitis Disease Activity Index >31) was identified. The most accurate predictor of success was if the patient exhibited two of the three variables, and the positive likelihood ratio was 11.2 (95% confidence interval, 1.7–76.0) and the posttest probability of success increased to 91%. The accuracy of prediction declined if either 1/3 (+likelihood ratio = 7.7; 95% confidence interval, 0.52–113.5) or 3/3 (+likelihood ratio = 2.6, 95% confidence interval, 1.6–4.0) variables were present.
The present preliminary clinical prediction rule provides the potential to identify patients with ankylosing spondylitis who are likely to experience short-term follow-up success with a specific exercise program. Future studies are necessary to validate the clinical prediction rule.