RMD open · 2021
New risk model is able to identify patients with a low risk of progression in systemic sclerosis.
СтатьяНазвание и аннотация: Перевод в очереди
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van Leeuwen Nina Marijn, Maurits Marc, Liem Sophie, Ciaffi Jacopo, Ajmone Marsan Nina, Ninaber Maarten, Allaart Cornelia, Gillet van Dongen Henrike, Goekoop Robbert, Huizinga Tom, Knevel Rachel, De Vries-Bouwstra Jeska
Аннотация
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.
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.