TY - JOUR
T1 - Systemic lupus erythematosus phenotypes formed from machine learning with a specific focus on cognitive impairment
AU - Barraclough, Michelle
AU - Erdman, Lauren
AU - Diaz-Martinez, Juan Pablo
AU - Knight, Andrea
AU - Bingham, Kathleen
AU - Su, Jiandong
AU - Kakvan, Mahta
AU - Grajales, Carolina Muñoz
AU - Tartaglia, Maria Carmela
AU - Ruttan, Lesley
AU - Wither, Joan
AU - Choi, May Y
AU - Bonilla, Dennisse
AU - Appenzeller, Simone
AU - Parker, Ben
AU - Goldenberg, Anna
AU - Katz, Patricia
AU - Beaton, Dorcas
AU - Green, Robin
AU - Bruce, Ian N
AU - Touma, Zahi
PY - 2022/11/17
Y1 - 2022/11/17
N2 - 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
AB - 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
U2 - 10.1093/rheumatology/keac653
DO - 10.1093/rheumatology/keac653
M3 - Article
SN - 1462-0324
JO - Rheumatology
JF - Rheumatology
M1 - keac653
ER -