RT Journal Article
SR Electronic
T1 New risk model is able to identify patients with a low risk of progression in systemic sclerosis
JF RMD Open
JO RMD Open
FD EULAR
SP e001524
DO 10.1136/rmdopen-2020-001524
VO 7
IS 2
A1 Nina Marijn van Leeuwen
A1 Marc Maurits
A1 Sophie Liem
A1 Jacopo Ciaffi
A1 Nina Ajmone Marsan
A1 Maarten Ninaber
A1 Cornelia Allaart
A1 Henrike Gillet van Dongen
A1 Robbert Goekoop
A1 Tom Huizinga
A1 Rachel Knevel
A1 Jeska De Vries-Bouwstra
YR 2021
UL http://rmdopen.bmj.com/content/7/2/e001524.abstract
AB Objectives To develop a prediction model to guide annual assessment of systemic sclerosis (SSc) patients tailored in accordance to disease activity.Methods A machine learning approach was used to develop a model that can identify patients without disease progression. SSc patients included in the prospective Leiden SSc cohort and fulfilling the ACR/EULAR 2013 criteria were included. Disease progression was defined as progression in ≥1 organ system, and/or start of immunosuppression or death. Using elastic-net-regularisation, and including 90 independent clinical variables (100% complete), we trained the model on 75% and validated it on 25% of the patients, optimising on negative predictive value (NPV) to minimise the likelihood of missing progression. Probability cutoffs were identified for low and high risk for disease progression by expert assessment.Results Of the 492 SSc patients (follow-up range: 2–10 years), disease progression during follow-up was observed in 52% (median time 4.9 years). Performance of the model in the test set showed an AUC-ROC of 0.66. Probability score cutoffs were defined: low risk for disease progression (<0.197, NPV:1.0; 29% of patients), intermediate risk (0.197–0.223, NPV:0.82; 27%) and high risk (>0.223, NPV:0.78; 44%). The relevant variables for the model were: previous use of cyclophosphamide or corticosteroids, start with immunosuppressive drugs, previous gastrointestinal progression, previous cardiovascular event, pulmonary arterial hypertension, modified Rodnan Skin Score, creatine kinase and diffusing capacity for carbon monoxide.Conclusion Our machine-learning-assisted model for progression enabled us to classify 29% of SSc patients as ‘low risk’. In this group, annual assessment programmes could be less extensive than indicated by international guidelines.Data are available on reasonable request. All data relevant to the study are included in the article or uploaded as online supplemental information.