Comorbidity burden in axial spondyloarthritis: a cluster analysis
Bibliographic record
Abstract
OBJECTIVES: To examine how comorbidities cluster in axial spondyloarthritis (axSpA) and whether these clusters are associated with quality of life, global health and other outcome measures. METHODS: We conducted a cross-sectional study of consecutive patients meeting ASAS criteria for axSpA in Liverpool, UK. Outcome measures included quality of life (EQ5D), global health and disease activity (BASDAI). We used hierarchical cluster analysis to group patients according to 38 pre-specified comorbidities. In multivariable linear models, the associations between distinct comorbidity clusters and each outcome measure were compared, using axSpA patients with no comorbidities as the reference group. Analyses were adjusted for age, gender, symptom duration, BMI, deprivation, NSAID-use and smoking. RESULTS: We studied 419 patients (69% male, mean age 46 years). 255 patients (61%) had at least one comorbidity, among whom the median number was 1 (range 1-6). Common comorbidities were hypertension (19%) and depression (16%). Of 15 clusters identified, the most prevalent clusters were hypertension-coronary heart disease and depression-anxiety. Compared with patients with no comorbidities, the fibromyalgia-irritable bowel syndrome cluster was associated with adverse patient-reported outcome measures; these patients reported 1.5-unit poorer global health (95%CI 0.01, 2.9), reduced quality of life (0.25-unit lower EQ5D; 95%CI -0.37, -0.12) and 1.8-unit higher BASDAI (95% CI 0.4, 3.3). Similar effect estimates were found for patients in the depression-anxiety cluster. CONCLUSION: Comorbidity is common among axSpA patients. The two most common comorbidities were hypertension and depression. Patients in the depression-anxiety and fibromyalgia-IBS clusters reported poorer health and increased axSpA severity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".