1117 SLE phenotypes formed from machine learning and their associations with cognitive impairment

• Published in: Lupus science & medicine 2021-11, Vol.8 (Suppl 2), p.A41-A42
• Main Authors: Barraclough, Michelle, Erdman, Lauren, Knight, Andrea, Diaz-Martinez, Juan Pablo, Bingham, Kathleen, Su, Jiandong, Kakvan, Mahta, Tartaglia, Maria Carmela, Ruttan, Lesley, Wither, Joan, Choi, May Y, Fritzler, Marvin J, Bonilla, Dennisse, Beaton, Dorcas, Parker, Ben, Green, Robin, Katz, Patti, Bruce, Ian N, Touma, Zahi
• Format: Article
• Language: English
• Subjects: 1100 – Clinical research in SLE; Anxiety; Cognitive ability; Executive function; Fibromyalgia; Machine learning; Patients
• ISSN: 2053-8790
• DOI: 10.1136/lupus-2021-lupus21century.60

BackgroundCognitive impairment (CI) in SLE is highly prevalent. Several factors are associated with CI: depression, pain, fatigue, medications, as well as more specific SLE factors such as disease damage, and autoantibodies. We aimed to phenotype CI in SLE using machine learning techniques to enable personalised targeted treatments.MethodsSLE patients aged 18-65 years attending a single completed the ACR Neuropsychological Battery (ACR-NB) cognitive assessment. Z-scores on all 19 tests of ACR-NB. ACR-NB tests were reduced using principal component analysis (PCA) to generate a factor score (CI Factor Score).Demographic, clinical data, and patient reported outcomes including, SF-36, LupusQoL, the PDQ-20 (perceived cognitive deficits), Beck Depression Inventory-II, Beck Anxiety Inventory, and the fatigue severity scale (FSS) were analysed using similarity network fusion (SNF) to identify patient subtypes. Differences between the SNF identified subtypes were evaluated using Kruskal-Wallis tests and chi-square tests.ResultsOf 301 patients, 89% were women, mean age and disease duration at study visit 40.9 ± 12.1 years. The CI Factor score accounted for 28.8% of the variance and was associated predominantly with executive function and verbal memory. The SNF defined three subtypes (1, 2 and 3 with 60, 112, and 129 patients respectively) with distinct patterns in health-related quality of life (HRQoL), depression, anxiety, fatigue, fibromyalgia, medication usage, and damage. The CI Factor Score was significantly different between the subtypes. Examining specific cognitive domains revealed the most significant differences in the language processing and executive function tests. Subtype 3 performed worst on the majority of cognitive domains). Further exploration revealed statistical differences with depression, anxiety, fatigue, and fibromyalgia between the subtypes (figure 1). Differences were also found relating to organ involvement within the last ten years and damage within specific organs. No differences were found for SLE disease activity. Subtype 3 had higher levels of all conditions and disease damage, Subtype 2 had lower levels and Subtype 1 mixed levels.Abstract 1117 Figure 1Variables with significant differences between the three phenotyped subtypes. Box and whisker plots: blue=subtype 1, red =subtype and purple=subtype 3. Bar charts: red=number of participants with variable and blue=number withoutConclusionThe subtype with the greatest psychiatric and dis