Evidence map›Paper›PMID 30693395›Full record

ArticleClinical rheumatology2019

Pattern of sleep dysfunction in systemic lupus erythematosus: a cluster analysis.

Domenico Paolo Emanuele Margiotta, Alice Laudisio, Luca Navarini, Fabio Basta, Carmen Mazzuca, Silvia Angeletti, Massimo Ciccozzi, Raffaele Antonelli Incalzi, Antonella Afeltra

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Article in Clinical rheumatology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.1field-weighted citation impact, top 23% of its field
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

6 citing papers in PubMed, 8 citations in OpenAlex.

  1. Application of Machine Learning Models in Systemic Lupus Erythematosus.International journal of molecular sciences · 2023
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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

9 authors at 1 institution in 1 country.

Domenico Paolo Emanuele MargiottaUnit of Allergy, Clinical Immunology and Rheumatology, Department of Medicine, Campus Bio-Medico University of Rome, via Alvaro del Portillo 21, 00128, Rome, Italy.
Alice LaudisioUnit of Geriatrics, Department of Medicine, Campus Bio-Medico of Rome University, Rome, Italy.
Luca NavariniUnit of Allergy, Clinical Immunology and Rheumatology, Department of Medicine, Campus Bio-Medico University of Rome, via Alvaro del Portillo 21, 00128, Rome, Italy. l.navarini@unicampus.it.
Fabio BastaUnit of Allergy, Clinical Immunology and Rheumatology, Department of Medicine, Campus Bio-Medico University of Rome, via Alvaro del Portillo 21, 00128, Rome, Italy.
Carmen MazzucaUnit of Allergy, Clinical Immunology and Rheumatology, Department of Medicine, Campus Bio-Medico University of Rome, via Alvaro del Portillo 21, 00128, Rome, Italy.
Silvia AngelettiUnit of Clinical Laboratory Science, Department of Medicine, Campus Bio-Medico of Rome University, Rome, Italy.
Massimo CiccozziUnit of Clinical Laboratory Science, Department of Medicine, Campus Bio-Medico of Rome University, Rome, Italy.
Raffaele Antonelli IncalziUnit of Geriatrics, Department of Medicine, Campus Bio-Medico of Rome University, Rome, Italy.
Antonella AfeltraUnit of Allergy, Clinical Immunology and Rheumatology, Department of Medicine, Campus Bio-Medico University of Rome, via Alvaro del Portillo 21, 00128, Rome, Italy.
Università Campus Bio-Medico · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo investigate how the different components of sleep dysfunction described in SLE patients combine together in sleep clusters.

methodsWe conducted a cross-sectional study on a perspective cohort of 79 SLE patients (mean age 8.2 ± 14.3 years). Sleep was evaluated using Pittsburgh Sleep Quality Index (PSQI). Clusters were defined using the single components of PSQI in a hierarchical clustering model. We used Beck Depression Inventory, Hamilton Anxiety Rating Scale, and Medical Outcomes Study Short Form 36 (SF36) to measure depressive symptoms, anxiety, and quality of life, respectively.

resultsThree sleep clusters were identified. The cluster 1 (N = 47) is characterized by the lowest values of PSQI total score. The cluster 2 (N = 21) presents higher values of sleep latency, but sleep duration similar to cluster 1. In cluster 3 (N = 11), we found sleep latency increased as in cluster 2, but the highest values of PSQI total score and reduced sleep duration. Scores of anxiety and sedentary time were higher in clusters 2 and 3 than in cluster 1. Cluster 3 presented the highest scores of depression and reduced mental and physical components of SF36.

conclusionsThe combination of different sleep components in SLE patients allowed us to identify three patterns of dysfunction: a first cluster with better sleep latency and duration, a second with increased sleep latency but conserved duration, and a third with impairment of both latency and duration. The stratification of sleep disorders in clusters might be useful for the personalization of therapy in relation to sleep cluster membership.

Indexed as

AdultAnxietyCluster AnalysisCross-Sectional StudiesDepressionFemaleHumansItalyLogistic ModelsLupus Erythematosus, SystemicMaleMiddle AgedPsychiatric Status Rating ScalesQuality of LifeSleep Initiation and Maintenance DisordersSurveys and QuestionnairesAnxiety (MeSH)Lupus erythematosusQuality of life (MeSH)Sedentary behaviorSleep (MeSH)Systemic (MeSH)

Identifiers

PMID30693395
OpenAlexW2913917683

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Registered trials

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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.