Lupus & antiphospholipid syndrome
Systemic lupus erythematosus phenotypes formed from machine learning with a specific focus on cognitive impairment
Authors' conclusion
This study demonstrates the complexity of cognitive impairment (CI) in SLE and that individual, multifactorial phenotypes exist. Those with greater disease burden, from SLE-specific factors or other factors associated with chronic conditions, report poorer cognitive functioning and perform worse on objective cognitive measures.
Study context
- Population
- SLE patients aged 18–65 years with objective cognitive assessment (ACR Neuropsychological Battery (ACR-NB)); 90% female, mean age 41 years (SD 12), disease duration 14 years (SD 10)
- Participants
- 238 patients
- Exposure
- Similarity network fusion (SNF)-derived subtype A vs subtype B
- Outcome
- SLE phenotypes based on symptom burden (disease damage, system involvement, patient-reported outcomes), focusing on objective and subjective cognitive function
Methodological detailssimilarity network fusion (machine learning), Kruskal-Wallis and χ2 tests
- Similarity network fusion (machine learning)
- Kruskal-Wallis and χ2 tests
Main finding
As reported in the abstract
SNF phenotypes
- Two subtypes (A and B) with distinct patterns in objective and subjective cognitive function, disease burden/damage, health-related quality of life (HRQoL), anxiety and depression
- Subtype A vs Subtype B: P < 0.03
- Subtype A vs Subtype B: P < 0.001
- Subtype A vs Subtype B: P < 0.04
- Subtype A vs Subtype B: P < 0.001
- Subtype A vs Subtype B: P < 0.001
Observational data: associations, not effects.
Parts of this Brief are withheld: the automatic checks could not confirm them against the abstract.
Original publication
Systemic lupus erythematosus phenotypes formed from machine learning with a specific focus on cognitive impairment. Rheumatology (Oxford). Published 17 November 2022. doi:10.
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