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Identification and prediction of novel classes of long-term disease trajectories for patients with juvenile dermatomyositis using growth mixture models

Deakin, CT; Papadopoulou, C; McCann, LJ; Martin, N; Al-Obaidi, M; Compeyrot-Lacassagne, S; Pilkington, CA; ... Juvenile Dermatomyositis Research Group, .; + view all (2020) Identification and prediction of novel classes of long-term disease trajectories for patients with juvenile dermatomyositis using growth mixture models. Rheumatology 10.1093/rheumatology/keaa497. (In press). Green open access

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Abstract

OBJECTIVES: 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. METHODS: Data were obtained for n = 519 patients, including baseline demographic and clinical features, baseline and follow-up records of physician's global assessment of disease (PGA), and skin disease activity (modified DAS). Growth mixture models (GMMs) were fitted to identify classes of patients with similar trajectories of these variables. Baseline predictors of class membership were identified using Lasso regression. RESULTS: GMM analysis of PGA identified two classes of patients. Patients in class 1 (89%) tended to improve, while patients in class 2 (11%) had more persistent disease. Lasso regression identified abnormal respiration, lipodystrophy and time since diagnosis as baseline predictors of class 2 membership, with estimated odds ratios, controlling for the other two variables, of 1.91 for presence of abnormal respiration, 1.92 for lipodystrophy and 1.32 for time since diagnosis. GMM analysis of modified DAS identified three classes of patients. Patients in classes 1 (16%) and 2 (12%) had higher levels of modified DAS at diagnosis that improved or remained high, respectively. Patients in class 3 (72%) began with lower DAS levels that improved more quickly. Higher proportions of patients in PGA class 2 were in DAS class 2 (19%, compared with 16 and 10%). CONCLUSION: GMM analysis identified novel JDM phenotypes based on longitudinal PGA and modified DAS.

Type: Article
Title: Identification and prediction of novel classes of long-term disease trajectories for patients with juvenile dermatomyositis using growth mixture models
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1093/rheumatology/keaa497
Publisher version: https://doi.org/10.1093/rheumatology/keaa497
Language: English
Additional information: © 2020 British Society for Rheumatology. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/).
Keywords: JDM, biostatistics, myositis, paediatric rheumatology, patient outcomes
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Infection, Immunity and Inflammation Dept
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Population, Policy and Practice Dept
URI: https://discovery.ucl.ac.uk/id/eprint/10114987
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