Abstract
Study Design.
Cross-sectional cohort study.
Objective. The aim of this study was to develop and validate a patient-completed screening questionnaire for axial spondyloarthropathy for use in the United Kingdom.
Summary of Background Data. Axial spondyloarthropathy (axial SpA) can be difficult to diagnose in the early stages of disease, leading to diagnostic delay and morbidity. Existing population screening tools lack sensitivity or have not been validated in the UK population.
Methods. Questionnaires were sent to 295 patients with definite ankylosing spondylitis (meeting modified New York criteria), nonradiographical axial SpA (sacroiliitis on magnetic resonance imaging), or mechanical back pain. Responses from 190 patients were analyzed. Binary logistic regression was used to develop a model differentiating inflammatory from mechanical pain.
Results. The final model (male sex, onset of symptoms by age 33 years, no radiation of pain, pain gets better as day goes on, pain increases with rest, and personal history of iritis) correctly classified 86% of cases with Nagelkerke R2 = 0.486. A numerical score (with 1 point assigned for each feature present) was calculated and receiver operating characteristic curve was constructed, with area under the curve of 0.911 (95% confidence interval: 0.87–0.96). A score of ≥3/6 had sensitivity of 75.6% and specificity of 87.9% for inflammation.
Conclusion. We have developed a model that differentiates patients with ankylosing spondylitis/axial SpA from those with mechanical spinal disease and can be used as a self-completed screening tool.
Cross-sectional cohort study.
Objective. The aim of this study was to develop and validate a patient-completed screening questionnaire for axial spondyloarthropathy for use in the United Kingdom.
Summary of Background Data. Axial spondyloarthropathy (axial SpA) can be difficult to diagnose in the early stages of disease, leading to diagnostic delay and morbidity. Existing population screening tools lack sensitivity or have not been validated in the UK population.
Methods. Questionnaires were sent to 295 patients with definite ankylosing spondylitis (meeting modified New York criteria), nonradiographical axial SpA (sacroiliitis on magnetic resonance imaging), or mechanical back pain. Responses from 190 patients were analyzed. Binary logistic regression was used to develop a model differentiating inflammatory from mechanical pain.
Results. The final model (male sex, onset of symptoms by age 33 years, no radiation of pain, pain gets better as day goes on, pain increases with rest, and personal history of iritis) correctly classified 86% of cases with Nagelkerke R2 = 0.486. A numerical score (with 1 point assigned for each feature present) was calculated and receiver operating characteristic curve was constructed, with area under the curve of 0.911 (95% confidence interval: 0.87–0.96). A score of ≥3/6 had sensitivity of 75.6% and specificity of 87.9% for inflammation.
Conclusion. We have developed a model that differentiates patients with ankylosing spondylitis/axial SpA from those with mechanical spinal disease and can be used as a self-completed screening tool.
| Original language | English |
|---|---|
| Pages (from-to) | 502-6 |
| Number of pages | 5 |
| Journal | Spine |
| Volume | 38 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 15 Mar 2013 |
Keywords
- Adult
- Back Pain
- Cohort Studies
- Cross-Sectional Studies
- Diagnosis, Differential
- Female
- Great Britain
- Humans
- Logistic Models
- Male
- Mass Screening
- Middle Aged
- Population Surveillance
- Questionnaires
- Reproducibility of Results
- Sacroiliitis
- Self Report
- Sensitivity and Specificity
- Spondylarthropathies
- Spondylitis, Ankylosing