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Background <p>Stroke is a major global health burden, with post-stroke depression and suicidal ideation prevalent yet often underdiagnosed complications. Existing prediction models rely on acute-phase, hospital-based data, limiting their applicability in community settings. This study aimed to develop community-applicable nomograms for predicting risk of depression and suicidal ideation in stroke survivors.</p> Methods <p>Using data from the National Health and Nutrition Examination Survey (NHANES, 2005–2018), predictors included sociodemographic, lifestyle, medical history, functional status, and blood test variables. Lasso regression and the Boruta algorithm were used for predictor selection. Logistic regression models were developed, and nomograms were constructed. Prediction model performance was evaluated through discrimination (area under curve [AUC]), calibration (calibration plot and Brier score), and clinical utility (decision curve analysis). Internal validation used bootstrapping, and external validation was performed on temporally distinct NHANES datasets.</p> Results <p>The prevalence of depression among stroke survivors was 34.8%, with one-third of cases going unrecognized or untreated. The prevalence of suicidal ideation among stroke survivors was 8.2%. The nomograms demonstrated strong discriminative ability (AUC: 0.76 for depression, 0.74 for suicidal ideation) and calibration accuracy (Brier score: 0.11 for depression, 0.07 for suicidal ideation). Key predictors for depression included age, marital status, current smoking, cancer, healthcare utilization, sleep duration, and inability to work. For suicidal ideation, marital status, hypertension, arthritis, antidepressant use, and inability to work were significant. Decision curve analysis demonstrated clinical utility within probability thresholds of 6%–40% for depression and 3%–17% for suicidal ideation. Internal and external validation supported generalizability and reproducibility of the nomograms.</p> Conclusions <p>This study provides validated, community-applicable nomograms for predicting depression and suicidal ideation in stroke survivors, enabling primary care providers to identify high-risk individuals and facilitate timely mental health interventions.</p> Clinical trial number <p>Not applicable.</p>

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Development and validation of nomograms for predicting depression and suicidal ideation in stroke survivors: a community-based study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月7日

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  • Zicheng Cheng,
  • Lili Ma,
  • Meiqi Zhao,
  • Tong Xu,
  • Jianing Wang,
  • Xinxia Zhu,
  • Zhao Han

摘要

Background

Stroke is a major global health burden, with post-stroke depression and suicidal ideation prevalent yet often underdiagnosed complications. Existing prediction models rely on acute-phase, hospital-based data, limiting their applicability in community settings. This study aimed to develop community-applicable nomograms for predicting risk of depression and suicidal ideation in stroke survivors.

Methods

Using data from the National Health and Nutrition Examination Survey (NHANES, 2005–2018), predictors included sociodemographic, lifestyle, medical history, functional status, and blood test variables. Lasso regression and the Boruta algorithm were used for predictor selection. Logistic regression models were developed, and nomograms were constructed. Prediction model performance was evaluated through discrimination (area under curve [AUC]), calibration (calibration plot and Brier score), and clinical utility (decision curve analysis). Internal validation used bootstrapping, and external validation was performed on temporally distinct NHANES datasets.

Results

The prevalence of depression among stroke survivors was 34.8%, with one-third of cases going unrecognized or untreated. The prevalence of suicidal ideation among stroke survivors was 8.2%. The nomograms demonstrated strong discriminative ability (AUC: 0.76 for depression, 0.74 for suicidal ideation) and calibration accuracy (Brier score: 0.11 for depression, 0.07 for suicidal ideation). Key predictors for depression included age, marital status, current smoking, cancer, healthcare utilization, sleep duration, and inability to work. For suicidal ideation, marital status, hypertension, arthritis, antidepressant use, and inability to work were significant. Decision curve analysis demonstrated clinical utility within probability thresholds of 6%–40% for depression and 3%–17% for suicidal ideation. Internal and external validation supported generalizability and reproducibility of the nomograms.

Conclusions

This study provides validated, community-applicable nomograms for predicting depression and suicidal ideation in stroke survivors, enabling primary care providers to identify high-risk individuals and facilitate timely mental health interventions.

Clinical trial number

Not applicable.