Evidence map›Paper›PMID 36394258›Full record

ArticleRheumatology (Oxford, England)2023

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 and 11 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Rheumatology (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Application of Machine Learning Models in Systemic Lupus Erythematosus.International journal of molecular sciences · 2023
    Review
  11. Article
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Michelle BarracloughSchroeder Arthritis Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada.ORCID 0000-0002-9698-0917
Lauren ErdmanGenetics and Genome Biology, Department of Computer Science, Hospital for Sick Children, Peter Gilgan Centre for Research and Learning, University of Toronto, Toronto, ON, Canada.
Juan Pablo Diaz-MartinezSchroeder Arthritis Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada.
Andrea KnightDivision of Rheumatology, Hospital for Sick Children, Toronto, ON, Canada.
Kathleen BinghamCentre for Mental Health, University Health Network, Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
Jiandong SuSchroeder Arthritis Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada.
Mahta KakvanSchroeder Arthritis Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada.
Carolina Muñoz GrajalesSchroeder Arthritis Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada.
Maria Carmela TartagliaUniversity of Toronto Krembil Neurosciences Centre, Toronto, ON, Canada.
Lesley RuttanUniversity Health Network-Toronto Rehabilitation Institute, Toronto, ON, Canada.
Joan WitherSchroeder Arthritis Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada.
May Y ChoiCumming School of Medicine, University of Calgary, Calgary, AB, Canada.ORCID 0000-0003-3760-2737
Dennisse BonillaSchroeder Arthritis Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada.
Simone AppenzellerUniversity of Campinas, São Paulo, Brazil.
Ben ParkerCentre for Epidemiology Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
Anna GoldenbergGenetics and Genome Biology, Department of Computer Science, Hospital for Sick Children, Peter Gilgan Centre for Research and Learning, University of Toronto, Toronto, ON, Canada.
Patricia KatzUniversity of California, San Francisco, Novato, CA, USA.ORCID 0000-0002-8146-2519
Dorcas BeatonInstitute for Work and Health, University of Toronto, Toronto, ON, Canada.
Robin GreenUniversity Health Network-Toronto Rehabilitation Institute, Toronto, ON, Canada.
Ian N BruceCentre for Epidemiology Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.ORCID 0000-0003-3047-500X
Zahi ToumaSchroeder Arthritis Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada.ORCID 0000-0001-5177-2076

Funding

CIHR
6 · The paper itself

Abstract

objectiveTo phenotype SLE based on symptom burden (disease damage, system involvement and patient reported outcomes), with a specific focus on objective and subjective cognitive function.

methodsSLE patients ages 18-65 years underwent objective cognitive assessment using the ACR Neuropsychological Battery (ACR-NB) and data were 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 χ2 tests.

resultsOf the 238 patients, 90% were female, with a mean age of 41 years (s.d. 12) and a disease duration of 14 years (s.d. 10) 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 < 0.03) compared with subtype B. Subtype A also had greater levels of subjective cognitive function (P < 0.001), disease burden/damage (P < 0.04), HRQoL (P < 0.001) and psychiatric measures (P < 0.001) compared with subtype B.

conclusionThis 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. By exploring different ways of phenotyping SLE we may better define CI in SLE. Ultimately this will aid our understanding of personalized CI trajectories and identification of appropriate treatments.

Indexed as

Cognitive DysfunctionLupus Erythematosus, SystemicAdultAnxietyFemaleHumansMachine LearningMaleQuality of Lifecognitionmachine learningSLE phenotypes

Identifiers

PMID36394258
PMCPMC10629781

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.