Identification of Symptom Phenotypes of Hand Osteoarthritis Using Hierarchical Clustering: Results From the Digital Cohort Design (DIGICOD) Cohort
Authors' conclusion
The identification of distinct clinical clusters based on HOA cardinal symptoms suggests previously undescribed subtypes of this condition
Study context
- Population
- Patients with hand OA in the Digital Cohort Design (DIGICOD)
- Participants
- 389 patients
- Exposure
- Cluster 1 vs Cluster 2 vs Cluster 3 vs Cluster 4 vs Cluster 5
- Cluster 1n = 88
- Cluster 2n = 91
- Cluster 3n = 80
- Cluster 4n = 42
- Cluster 5n = 88
- Outcome
- Symptom phenotypes (clusters) based on AUSCAN pain, physical function and stiffness subscores and VAS for aesthetic discomfort
Methodological detailshierarchical cluster analysis, hierarchical agglomerative clustering, Kruskal-Wallis and post hoc analyses
- Hierarchical cluster analysis
- Hierarchical agglomerative clustering
- Kruskal-Wallis and post hoc analyses
Main finding
As reported in the abstract
Symptom phenotypes of hand OA
- 5 clusters identified
- Clusters 4 and 5: AUSCAN pain score >41 of 100, only one-third of patients
Observational data: associations, not effects.
Further findings
Associations of clusters with clinical characteristics
- Aesthetic discomfort (clusters 3 and 5) significantly associated with erosive hand OA and a higher number of nodes
- Cluster 5 associated but not significantly with metabolic syndrome
- BMI and CRP level did not differ among clusters
- Symptom intensity significantly associated with joint destruction and with physical and psychological burden
- Patients' main expectations differed among clusters; function improvement the most frequent expectation overall
Original publication
Identification of Symptom Phenotypes of Hand Osteoarthritis Using Hierarchical Clustering: Results From the DIGICOD Cohort. Arthritis Care Res (Hoboken). Published 25 January 2023. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →