Evidence map›Paper›PMID 36250087›Full record

ArticleFrontiers in medicine2022

Predictors of stress in patients with Lupus.

Meenakshi Jolly, Patricia Katz

Open access · goldAbstract read
In one paragraph

Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.9field-weighted citation impact, top 26% 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

7 citing papers in PubMed, 6 citations in OpenAlex.

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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

2 authors at 2 institutions in 1 country.

Meenakshi JollyDepartment of Medicine, Rush University Medical Center, Chicago, IL, United States.
Patricia KatzDepartment of Medicine, University of California, San Francisco, San Francisco, CA, United States.
Rush University Medical Center · USUniversity of California, San Francisco · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Stress is common in patients with Systemic Lupus Erythematosus (SLE), and is associated with depression, fatigue, and disease flares. Stress may be modifiable and identifying those at high risk allows clinicians and allied health care professionals to develop a multidisciplinary management plan to direct appropriate resources. This study is aimed at identifying predictors of high stress over time among patients with SLE. Methods: Longitudinal data from two interviews of the Lupus Outcomes Study 2 years apart from 726 patients with SLE were analyzed for stress, measured using the Perceived Stress Scale (PSS; high-stress PSS ≥6). Results: The mean age of the cohort was 50.6 (12.5) years, 92% were women and 68% were Caucasian. The mean (SD) PSS was 5.3 (3.6), and high stress (PSS >6) was noted in 253 participants. Those with high stress were more frequently below the poverty line and less commonly employed. They had a greater prevalence of comorbidities and HCU; and worse disease severity (activity, flare, damage) and QOL. In regression analyses, high stress (baseline) was associated with younger age, married status, worse QOL, and presence of diabetes. Better QOL (PCS, MCS) independently predicted decreased odds of high stress, while high stress (baseline) predicted high stress (OR 3.16, 95% CI 1.85, 5.37, Conclusion: Patients with SLE should be routinely screened for QOL and stress during their clinical care, to identify those at risk for poor health outcomes. This information can facilitate multidisciplinary management for those at risk for worse health outcomes.

Indexed as

biopsychosocial modellongitudinal analysisquality of lifeSLEstress

Identifiers

PMID36250087
PMCPMC9556948
OpenAlexW4298003465

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.