ArticleFrontiers in psychology2022
Development of a nomogram prediction model for depression in patients with systemic lupus erythematosus.
Article in Frontiers in psychology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk factors for depression in systemic lupus erythematosus: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Comprehensive analysis of predictive models for disease manifestations and case fatality in systemic lupus erythematosus.NPJ digital medicine · 2026Review
- Development and Validation of a Risk Screening Model for Depressive Symptoms in Older Inpatients: A Cross-Sectional Study.Clinical interventions in aging · 2026Article
- One-Year Outcomes for Depression and Anxiety in SLE Patients.Biomedicines · 2024Article
- Early diagnosis and clinical application of systemic lupus erythematosus based on a nomogram model.Heliyon · 2024Article
- SVM-Based Model Combining Patients' Reported Outcomes and Lymphocyte Phenotypes of Depression in Systemic Lupus Erythematosus.Biomolecules · 2023Article
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Authors and funding
8 authors.
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Abstract
Systemic lupus erythematosus (SLE) is an inflammatory autoimmune disease with depression as one of its most common symptoms. The aim of this study is to establish a nomogram prediction model to assess the occurrence of depression in patients with SLE. Based on the Hospital Anxiety and Depression Scale cutoff of 8, 341 patients with SLE, recruited between June 2017 and December 2019, were divided into depressive and non-depressive groups. Data on socio-demographic characteristics, medical history, sociopsychological factors, and other risk factors were collected. Between-group differences in clinical characteristics were assessed with depression as the dependent variable and the variables selected by logistic multiple regression as predictors. The model was established using R language. Marital status, education, social support, coping, and anxiety predicted depression (
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