Open AccessIdentification and prediction of novel classes of long-term disease trajectories for patients with juvenile dermatomyositis using growth mixture models
Author(s) -
Claire T. Deakin,
Charalampia Papadopoulou,
Liza McCann,
Neil Martin,
Muthana Al-Obaidi,
Sandrine Compeyrot-Lacassagne,
Clarissa Pilkington,
Sarah Tansley,
Neil McHugh,
Lucy R. Wedderburn,
Bianca L De Stavola
Publication year - 2020
Publication title -
rheumatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.957
H-Index - 173
eISSN - 1462-0332
pISSN - 1462-0324
DOI - 10.1093/rheumatology/keaa497
Subject(s) - medicine , logistic regression , disease , regression analysis , statistics , mathematics
Uncertainty around clinical heterogeneity and outcomes for patients with JDM represents a major burden of disease and a challenge for clinical management. We sought to identify novel classes of patients having similar temporal patterns in disease activity and relate them to baseline clinical features.