Back to Search Start Over

Prediction of damage trajectories in systemic sclerosis using group-based trajectory modelling.

Authors :
Baron, Murray
Barbacki, Ariane
Man, Ada
Vries-Bouwstra, J K de
Johnson, Dylan
Stevens, Wendy
Osman, Mohammed
Wang, Mianbo
Zhang, Yuqing
Sahhar, Joanne
Ngian, Gene-Siew
Proudman, Susanna
Nikpour, Mandana
Group, the Australian Scleroderma Interest Group and the Canadian Scleroderma Research
Source :
Rheumatology. Sep2023, Vol. 62 Issue 9, p3059-3066. 8p.
Publication Year :
2023

Abstract

Objectives Damage accrual in SSc can be tracked using the Scleroderma Clinical Trials Consortium Damage Index (DI). Our goal was to develop a prediction model for damage accrual in SSc patients with early disease. Methods Using patients with <2 years disease duration from Canada and Australia as a derivation cohort, and from the Netherlands as a validation cohort, we used group-based trajectory modelling (GBTM) to determine 'good' and 'bad' latent damage trajectories. We developed a prediction model from this analysis and applied it to patients from derivation and validation cohorts. We plotted the actual DI trajectories of the patients predicted to be in 'good' or 'bad' groups. Results We found that the actual trajectories of damage accumulation for lcSSc and dcSSc were very different, so we studied each subset separately. GBTM found two distinct trajectories in lcSSc and three in dcSSc. We collapsed the two worse trajectories in the dcSSc into one group and developed a prediction model for inclusion in either 'good' or 'bad' trajectories. The performance of models using only baseline DI and sex was excellent with ROC AUC of 0.9313 for lcSSc and 0.9027 for dcSSc. Using this model, we determined whether patients would fall into 'good' or 'bad' trajectory groups and then plotted their actual trajectories which showed clear differences between the predicted 'good' and 'bad' cases in both derivation and validation cohorts. Conclusions A simple model using only cutaneous subset, baseline DI and sex can predict damage accumulation in early SSc. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14620324
Volume :
62
Issue :
9
Database :
Academic Search Index
Journal :
Rheumatology
Publication Type :
Academic Journal
Accession number :
171352463
Full Text :
https://doi.org/10.1093/rheumatology/kead002