ArticleBMC nursing2025
Predicting psychological distress in informal caregivers of older people with dementia: development and interpretation of a nomogram and XGBoost model.
Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Identifying Risk Factors for Caregiver Burden in Neurological Disorders Using Machine Learning.Medical sciences (Basel, Switzerland) · 2026Observational
- Symptoms of anxiety and depression in informal caregivers of persons with dementia: a latent profile analysis and computer-simulated network analysis.BMC geriatrics · 2026Article
- Clinical risk stratification of stroke caregiver distress: an interpretable nonlinear model of caregiving-load categories.Frontiers in psychology · 2026Article
- Towards cost-effective cognitive impairment diagnosis systems by emulating doctors' reasoning with deep reinforcement learning.BMC geriatrics · 2025Article
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8 authors.
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Abstract
backgroundMost informal caregivers of older people with dementia (PwD) experience a high level of psychological distress, which significantly impacts caregivers' psychosomatic health. Effective identification and timely intervention for psychological distress are of great significance for improving the psychosomatic health of informal caregivers. However, there is a lack of predictive models for psychological distress in this population. This study aims to develop and interpret nomogram and eXtreme Gradient Boosting (XGBoost) models to predict the psychological distress of informal caregivers of PwD.
methodsThis cross-sectional study collected 485 pairs of PwD and informal caregivers from October 2023 to September 2024. Participants were randomly divided into training and validation cohorts (7:3 ratio). We constructed nomogram and XGBoost models to predict psychological distress. The nomogram was subsequently developed into an online web calculator, and SHapley Additive exPlanations (SHAP) was employed to enhance the XGBoost model's interpretability.
resultsThe final models included six predictors: caregivers' social support, caregivers' employment situation, caregivers' average daily care duration, caregivers' monthly family income, caregivers' self-efficacy and PwD's chronic disease. The area under the curve (AUC) of nomogram and XGBoost models in the validation cohort were 0.882 (95%CI: 0.825-0.939) and 0.884 (95%CI: 0.829-0.939) respectively. Two models exhibited good calibration and clinical utility. SHAP analysis identified caregivers' social support as the most influential predictor of psychological distress in XGBoost model.
conclusionThe nomogram and XGBoost models developed in this study offer valuable tools for predicting psychological distress in informal caregivers of PwD. The nomogram-based web calculator could be an effective tool for predicting the risk of psychological distress in practice. Our interpretable XGBoost model provides valuable insights into individualized intervention strategies for psychological distress.
trial registrationNot applicable. This study is an observational study without involving any interventions or treatment methods. Therefore, this study does not meet the definition of a clinical trial and does not require registration of a clinical trial number.
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