Systemic lupus erythematosus phenotypes formed from machine learning with a specific focus on cognitive impairment

Michelle Barraclough, Lauren Erdman, Juan Pablo Diaz-Martinez, Andrea Knight, Kathleen Bingham, Jiandong Su, Mahta Kakvan, Carolina Muñoz Grajales, Maria Carmela Tartaglia, Lesley Ruttan, Joan Wither, May Y Choi, Dennisse Bonilla, Simone Appenzeller, Ben Parker, Anna Goldenberg, Patricia Katz, Dorcas Beaton, Robin Green, Ian N BruceZahi Touma

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OBJECTIVE To phenotype SLE based on symptom burden (disease damage, system involvement and patient reported outcomes), with a specific focus on objective and subjective cognitive function. METHODS SLE patients aged 18-65 underwent objective cognitive assessment using the ACR Neuropsychological Battery (ACR-NB) and data was collected on demographic and clinical variables, disease burden/activity, health related quality of life (HRQoL), depression, anxiety, fatigue and perceived cognitive deficits. Similarity network fusion (SNF) was used to identify patient subtypes. Differences between the subtypes were evaluated using Kruskal-Wallis and chi-square tests. RESULTS Of the 238 patients, 90% were female, mean age 41 ± 12 and disease duration 14 ± 10 years at the study visit. The SNF analysis defined two subtypes (A and B) with distinct patterns in objective and subjective cognitive function, disease burden/damage, HRQoL, anxiety and depression. Subtype A performed worst on all significantly different tests of objective cognitive function (p 
Original languageUndefined
Article numberkeac653
Early online date17 Nov 2022
Publication statusPublished - 17 Nov 2022

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