Systemic sclerosis & Raynaud's
Damage Trajectories in Systemic Sclerosis Using Group‐Based Trajectory Modeling
Authors' conclusion
We identified 3 trajectories of damage accrual in a combined incident SSc cohort. Several characteristics increased the odds of belonging to worse trajectories.
Study context
- Population
- Incident systemic sclerosis (SSc) cases (<2 years) in the Australian Scleroderma Interest Group and Canadian Scleroderma Research Group prospective registries
- Participants
- 410 cases
- Exposure
- High vs low damage trajectory groups
- Outcome
- SCTC-DI damage trajectories
- Follow-up
- First 5 annual visits
Methodological detailsgroup-based trajectory modeling, multicentre registries (Australia and Canada), multivariable models
- Group-based trajectory modeling
- Multicentre registries (Australia and Canada)
- Multivariable models
Main finding
As reported in the abstract
Damage trajectory group membership
Observational data: associations, not effects.
Further findings
| Outcome | OR (95% CI) | |
|---|---|---|
| Baseline factors associated with high- vs low-damage trajectoryHighvs LowOlder age | 1.57 (1.18–2.10) | |
| Baseline factors associated with high- vs low-damage trajectoryHighvs LowMale sex | 2.55 (1.10–5.88) | |
| Baseline factors associated with high- vs low-damage trajectoryHighvs LowDiffuse disease | 6.7 (2.57–17.48) | |
| Baseline factors associated with high- vs low-damage trajectoryHighvs LowTendon friction rubs | 5.4 (1.86–15.66) | |
| Baseline factors associated with high- vs low-damage trajectoryHighvs LowElevated CRP | 1.98 (1.49–2.63) | |
| Baseline factors associated with high- vs low-damage trajectoryHighvs LowWhite ethnicity | 0.31 (0.12–0.75) | |
| Baseline factors associated with high- vs low-damage trajectoryHighvs LowAnticentromere antibodies | 0.24 (0.07–0.77) | |
| 0.050.21520Odds ratio (log scale) |
Original publication
Damage Trajectories in Systemic Sclerosis Using Group‐Based Trajectory Modeling. Arthritis Care Res (Hoboken). Published 15 November 2022. doi:10.
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