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

被引:5
|
作者
Baron, Murray [1 ]
Barbacki, Ariane [1 ]
Man, Ada [2 ]
de Vries-Bouwstra, J. K. [3 ]
Johnson, Dylan [4 ]
Stevens, Wendy [5 ]
Osman, Mohammed [4 ]
Wang, Mianbo [1 ]
Zhang, Yuqing [6 ]
Sahhar, Joanne [7 ]
Ngian, Gene-Siew [7 ]
Proudman, Susanna [8 ,9 ]
Nikpour, Mandana [4 ,10 ]
机构
[1] Lady Davis Inst Med Res, Montreal, PQ, Canada
[2] Univ Manitoba, Fac Med, Rheumatol, Winnipeg, MB, Canada
[3] Leiden Univ, Dept Rheumatol, Leiden, Netherlands
[4] Univ Alberta, Dept Med, Edmonton, AB, Canada
[5] St Vincents Hosp Melbourne, Div Rheumatol, Fitzroy, Vic, Australia
[6] Harvard Med Sch, Massachusetts Gen Hosp, Div Rheumatol Allergy & Immunol, Boston, MA USA
[7] Monash Hlth, Clayton, Vic, Australia
[8] Royal Adelaide Hosp, Adelaide, SA, Australia
[9] Univ Adelaide, Discipline Med, Adelaide, SA, Australia
[10] McGill Univ, Jewish Gen Hosp, Div Rheumatol, Suite A 710,3755 Cote St Catherine Rd, Montreal, PQ H3T 1E2, Australia
基金
加拿大健康研究院;
关键词
SSc; damage; prediction; 'group-based trajectory modelling'; subset; mortality; trajectory; COMPOSITE RESPONSE INDEX; AMERICAN-COLLEGE; SAS PROCEDURE;
D O I
10.1093/rheumatology/kead002
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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.
引用
收藏
页码:3059 / 3066
页数:8
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